Quantum Algorithm Performing True Calculation Developed

A team of researchers have demonstrated a working quantum algorithm that performs a true calculation for the first time.

An algorithm is any well defined computational procedures that takes some value or set of values as input and produces some value or set of values as output. A mathematical functions are a kind of algorithm where it performs a procedure to come out with a value based on a problem with a defined set of input value or values.

There are also non-computational algorithms, such as directions to calling a person on the phone. The steps are sequential from picking up the phone, getting the number, dialing the number, etc. etc.

But procedures should cover all possibilities and the subsequent action it must take. Going back to the phone call example, the algorithm should include steps for situations where the phone gets a busy signal or that the phone number cannot be found.

In short, algorithms must take all situations that could arise into consideration.

Classical Computers and Quantum Computers

There are three ways to make a computer work faster. One is to make more computers (using multiple computers for one activity). Another is to make new computers faster. And the third is to make algorithms that lets computer do things faster.

Without an algorithm behind a program or application, computers won't be able to perform as it should.

Simulating Molecules Through Nonequilibrium Statistical Mechanics

Dynamic computer simulations of molecular systems depend on finite time steps, but these introduce apparent extra work that pushes the molecules around. Using models of water molecules in a box, researchers have learned to separate this "shadow work" from the protocol work explicitly modeled in the simulations.
Credit: Lawrence Berkeley National Laboratory

Scientists have devised a way using nonequilibrium statistical mechanics to study molecular simulations without the accompanying errors in data gathering.

Scientists look at nonequilibrium statistical mechanics to simulate molecular behavior in a much more natural way. Most models are always at flux. They are continuously in motion and changing. This method tries to interpret real world mechanics into a computer simulation.

The Cell

The smallest form of life is a single cell. The cell is made up of molecules. Science tries to understand how these molecules interact with each other through microscopes.

Microscopes have limitations on the size of the objects it can observe. On a molecular level where microscopes cannot clearly observe, science looks at computer simulations.

A computational microscope doesn't use lenses or glass, it uses a computer to simulate molecules and how they act and interact. An example would be a simulation on how a virus enters and infects a healthy host. By studying its interactions on a simulation, scientists can understand its mechanics.

Simulations and Algorithms

At the heart of a computer simulation is its algorithm. It is a series of instructions on how a particular model or any of the model's components would behave given a particular scenario. There are many factors to take into account when creating an algorithm that would simulate the behavior of a molecule. Some factors to consider are the temperature, environment, time (duration), amount of light, and motion and direction of the molecule.

MIT News: Speeding Up GPS Algorithms Through Data Compression, Line Simplification, and Signal Clustering

MIT Researchers have devised an algorithm that is fast and efficient by compressing the data and processing them into smaller data coresets. This approach which they applied to a GPS program can also be utilized by other algorithms.

Computers process data based on a series of sequential procedures in performing its calculations. This is called an algorithm. Algorithms help computers decide how to treat data without consulting the user.

But how does an algorithm work? Here is a sample algorithm to see if the user is old enough to register in a website. First the program asks the user to input his birthday. The computer checks the present age based on the birthday. If the result is below the allowed age, then the computer informs the user he is too young to register for the sit.

Biomimetic Soft Robots Mimics Nature To Dynamically Change Color

A soft-bodied robot navigating, top to bottom, an obstacle course. Unlike rigid robots, soft robots can be used to squeeze into tight spaces.
Credit: AP Photo/Harvard University, Robert Shepherd

Soft Robots are a new type of robotic structure that combines organic chemistry, soft materials science and robotics. These type of robots differ from the typical hard bodied industrial type robots used today.

Soft robots have more elasticity, are flexible and move very differently from hard bodied robots that use gears and motors for movement. The movements of soft robots are based on organisms such as squid, starfish and worms. Soft robots are outfitted with rubber tentacles or arms that move and grip objects through pneumatic networks using compressed air.

Soft robots, in color

Harvard researchers explore systems that would give 'soft robots' the ability to camouflage themselves or stand out from their environment

A team of researchers led by George Whitesides, the Woodford L. and Ann A. Flowers University Professor, has already broken new engineering ground with the development of soft, silicone-based robots inspired by creatures like starfish and squid.

Now, they're working to give those robots the ability to disguise themselves.

As demonstrated in an August 16 paper published in Science, researchers have developed a system – again, inspired by nature – that allows the soft robots to either camouflage themselves against a background, or to make bold color displays. Such a "dynamic coloration" system could one day have a host of uses, ranging from helping doctors plan complex surgeries to acting as a visual marker to help search crews following a disaster, said Stephen Morin, a Post-Doctoral Fellow in Chemistry and Chemical Biology and first author of the paper.

"When we began working on soft robots, we were inspired by soft organisms, including octopi and squid," Morin said. "One of the fascinating characteristics of these animals is their ability to control their appearance, and that inspired us to take this idea further and explore dynamic coloration. I think the important thing we've shown in this paper is that even when using simple systems – in this case we have simple, open-ended micro-channels – you can achieve a great deal in terms of your ability to camouflage an object, or to display where an object is."

The Stock Market - High Frequency Trading, the Algorithms and the Science Behind It

In Wall Street and other trading environments, some investors use sophisticated technological tools to trade securities like stocks or options. This is called High Frequency Trading (HFT).

HFT utilizes super computers and algorithms to generate automatic trades. One major factor for high frequency trading is that information and actual stock trends are picked up by these super computers in real time and based on the algorithm, react accordingly.

An algorithm is a an order of sequential procedures for performing calculations. It is a step-by-step series of procedures used for calculation, data processing, and automated decision making or reasoning.

Distinguishing Characteristics of High Frequency Trading

Since trading behavior is based on the information coming in, the algorithm programmed into the system, and proprietary (built in) trading strategies, HFT is highly quantitative. The computer just reacts to the data and is highly objective.

Due to the dynamic movement of the market, stock and investment positions are considered temporary and can change in a manner of seconds. HFT systems may trade into a stock and trade out of it almost immediately.

In terms of a net investment position, HFT systems have none. High-frequency trading firms do not employ significant leverage, do not accumulate positions, and typically liquidate their entire portfolios on a daily basis.

Algorithm Developed To Trace Source of Internet Rumor, Epidemic, or Terrorist Attack Within A Network

An algorithm is a an order of sequential procedures for performing calculations. It is a step-by-step series of procedures used for calculation, data processing, and automated decision making or reasoning.

Algorithms contain a finite list of well defined instructions that is used to calculate a function. Starting with the first step or state, the instructions describe a computation that, when executed, will proceed through a finite number of well-defined successive states, eventually producing "output" and terminating at a final ending state.

The transition from one state to the next is not always fixed. Some algorithms incorporate random data or input; randomized algorithms.

Algorithms are used in computers to reach a decision (final ending state) based on available data. An example of a simple algorithm would be a "flip a coin" algorithm. Based on the outcome of a coin flip (heads or tails), the computer will perform a certain instruction.

Rooting out rumors, epidemics, and crime -- with math

Investigators are well aware of how difficult it is to trace an unlawful act to its source. The job was arguably easier with old, Mafia-style criminal organizations, as their hierarchical structures more or less resembled predictable family trees.

In the Internet age, however, the networks used by organized criminals have changed. Innumerable nodes and connections escalate the complexity of these networks, making it ever more difficult to root out the guilty party. EPFL researcher Pedro Pinto of the Audiovisual Communications Laboratory and his colleagues have developed an algorithm that could become a valuable ally for investigators, criminal or otherwise, as long as a network is involved. The team's research was published August 10, 2012, in the journal Physical Review Letters.

Finding the source of a Facebook rumor

"Using our method, we can find the source of all kinds of things circulating in a network just by 'listening' to a limited number of members of that network," explains Pinto. Suppose you come across a rumor about yourself that has spread on Facebook and been sent to 500 people – your friends, or even friends of your friends. How do you find the person who started the rumor? "By looking at the messages received by just 15 of your friends, and taking into account the time factor, our algorithm can trace the path of that information back and find the source," Pinto adds. This method can also be used to identify the origin of a spam message or a computer virus using only a limited number of sensors within the network.

Trace the propagation of an epidemic

Out in the real world, the algorithm can be employed to find the primary source of an infectious disease, such as cholera. "We tested our method with data on an epidemic in South Africa provided by EPFL professor Andrea Rinaldo's Ecohydrology Laboratory," says Pinto. "By modeling water networks, river networks, and human transport networks, we were able to find the spot where the first cases of infection appeared by monitoring only a small fraction of the villages."

MIT News: Algorithm Developed For Determining Trajectory For Robot Planes Without GPS

A small, autonomous helicopter, programmed by MIT students under the direction of Professor Nick Roy, passes through a simulated window as part of a competition held over the summer. (2009 Pic)
Credit: Nicholas Roy

Autonomous robotic plane flies indoors

For decades, academic and industry researchers have been working on control algorithms for autonomous helicopters — robotic helicopters that pilot themselves, rather than requiring remote human guidance. Dozens of research teams have competed in a series of autonomous-helicopter challenges posed by the Association for Unmanned Vehicle Systems International (AUVSI); progress has been so rapid that the last two challenges have involved indoor navigation without the use of GPS.

But MIT’s Robust Robotics Group — which fielded the team that won the last AUVSI contest — has set itself an even tougher challenge: developing autonomous-control algorithms for the indoor flight of GPS-denied airplanes. At the 2011 International Conference on Robotics and Automation (ICRA), a team of researchers from the group described an algorithm for calculating a plane’s trajectory; in 2012, at the same conference, they presented an algorithm for determining its “state” — its location, physical orientation, velocity and acceleration. Now, the MIT researchers have completed a series of flight tests in which an autonomous robotic plane running their state-estimation algorithm successfully threaded its way among pillars in the parking garage under MIT’s Stata Center.

“The reason that we switched from the helicopter to the fixed-wing vehicle is that the fixed-wing vehicle is a more complicated and interesting problem, but also that it has a much longer flight time,” says Nick Roy, an associate professor of aeronautics and astronautics and head of the Robust Robotics Group. “The helicopter is working very hard just to keep itself in the air, and we wanted to be able to fly longer distances for longer periods of time.”

With the plane, the problem is more complicated because “it’s going much faster, and it can’t do arbitrary motions,” Roy says. “They can’t go sideways, they can’t hover, they have a stall speed.”

Found in translation

To buy a little extra time for their algorithms to execute, and to ensure maneuverability in close quarters, the MIT researchers built their own plane from scratch. Adam Bry, a graduate student in the Department of Aeronautics and Astronautics and lead author on both ICRA papers, consulted with AeroAstro professor Mark Drela about the plane’s design. “He’s a guy who can design you a complete airplane in 10 minutes,” Bry says. “He probably doesn’t remember that he did it.” The plane that resulted has unusually short and broad wings, which allow it to fly at relatively low speeds and make tight turns but still afford it the cargo capacity to carry the electronics that run the researchers’ algorithms.

MIT News: Algorithm Developed To Allow Cars To Connect To Wi-Fi Network

WiFi is a computer network that uses radio technology to connect to other computers or Wi-Fi enabled devices. This is done without the use of cables.

The wireless technology called 802.11 provides the same function as a wired network which works on Ethernet technology. Wi-Fi networks operate in the 2.4 and 5 GHz radio bands, with some products containing both bands (dual band).

Mobile phones, tablet computers, and laptops use Wi-Fi to connect to the internet. Business establishments and public areas also provide a Wi-Fi connection, these are called Wi-Fi hotspots. While some of these places provide Wi-Fi access for free, others charge a fee to connect to their wireless network.

Sharing data links in networks of cars

Wi-Fi is coming to our cars. Ford Motor Co. has been equipping cars with Wi-Fi transmitters since 2010; according to an Agence France-Presse story last year, the company expects that by 2015, 80 percent of the cars it sells in North America will have Wi-Fi built in. The same article cites a host of other manufacturers worldwide that either offer Wi-Fi in some high-end vehicles or belong to standards organizations that are trying to develop recommendations for automotive Wi-Fi.

Two Wi-Fi-equipped cars sitting at a stoplight could exchange information free of charge, but if they wanted to send that information to the Internet, they’d probably have to use a paid service such as the cell network or a satellite system. At the ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, taking place this month in Portugal, researchers from MIT, Georgetown University and the National University of Singapore (NUS) will present a new algorithm that would allow Wi-Fi-connected cars to share their Internet connections. “In this setting, we’re assuming that Wi-Fi is cheap, but 3G is expensive,” says Alejandro Cornejo, a graduate student in electrical engineering and computer science at MIT and lead author on the paper.

The general approach behind the algorithm is to aggregate data from hundreds of cars in just a small handful, which then upload it to the Internet. The problem, of course, is that the layout of a network of cars is constantly changing in unpredictable ways. Ideally, the aggregators would be those cars that come into contact with the largest number of other cars, but they can’t be identified in advance.

MIT News: Researchers Develop Algorithm That Allows Robots To Learn And Understand

Professor Julie Shah observes while grad students Ron Wilcox (left), and Matthew Gombolay coordinate human-robotic interaction.
Photo: William Litant/MIT

An algorithim is a an order of sequential procedures for performing calculations. It is a step-by-step series of procedures used for calculation, data processing, and automated decision making or reasoning.

Algorithms contain a finite list of well defined instructions that is used to calculate a function. Starting with the first step or state, the instructions describe a computation that, when executed, will proceed through a finite number of well-defined successive states, eventually producing "output" and terminating at a final ending state.

The transition from one state to the next is not always fixed. Some algorithms incorporate random data or input; randomized algorithms.

Algorithms are used in computers to reach a decision (final ending state) based on available data. An example of a simple algorithm would be a "flip a coin" algorithm. Based on the outcome of a coin flip (heads or tails), the computer will perform a certain instruction.

Robotic assistants may adapt to humans in the factory

In today’s manufacturing plants, the division of labor between humans and robots is quite clear: Large, automated robots are typically cordoned off in metal cages, manipulating heavy machinery and performing repetitive tasks, while humans work in less hazardous areas on jobs requiring finer detail.

But according to Julie Shah, the Boeing Career Development Assistant Professor of Aeronautics and Astronautics at MIT, the factory floor of the future may host humans and robots working side by side, each helping the other in common tasks. Shah envisions robotic assistants performing tasks that would otherwise hinder a human’s efficiency, particularly in airplane manufacturing.

“If the robot can provide tools and materials so the person doesn’t have to walk over to pick up parts and walk back to the plane, you can significantly reduce the idle time of the person,” says Shah, who leads the Interactive Robotics Group in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). “It’s really hard to make robots do careful refinishing tasks that people do really well. But providing robotic assistants to do the non-value-added work can actually increase the productivity of the overall factory.”

MIT News: Faster and More Accurate Computer Automated Surveillance System Being Developed

The field and study of computer vision is more than image processing. It includes acquiring, analyzing, and letting the computer understand the image being viewed. An algorithm is usually employed for the computer to process the information for it to reach a decision.

Images are usually acquired using video cameras but there are other sources that can be used such as scanners, sound detectors, and heat sensors.

Applications where computer vision is applied are varied. In the industrial sector, it can be used to inspect products as it passes by in the production line. The technology can also be used for autonomous robots to see, perceive, and process its surrounding environment. It can also be used in the medical field such as inspecting a tissue region or searching for organ or nerve damage.

A practical application is using it on security cameras and surveillance systems where it can vigilantly monitor an area 24/7.

System improves automated monitoring of security cameras

Police and security teams guarding airports, docks and border crossings from terrorist attack or illegal entry need to know immediately when someone enters a prohibited area, and who they are. A network of surveillance cameras is typically used to monitor these at-risk locations 24 hours a day, but these can generate too many images for human eyes to analyze.

Now, a system being developed by Christopher Amato, a postdoc at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), can perform this analysis more accurately and in a fraction of the time it would take a human camera operator. “You can’t have a person staring at every single screen, and even if you did the person might not know exactly what to look for,” Amato says. “For example, a person is not going to be very good at searching through pages and pages of faces to try to match [an intruder] with a known criminal or terrorist.”

MIT NEWS: The faster-than-fast Fourier transform

CAMBRIDGE, Mass. -- The Fourier transform is one of the most fundamental concepts in the information sciences. It’s a method for representing an irregular signal — like the voltage fluctuations in the wire that connects an MP3 player to a loudspeaker — as a combination of pure frequencies. It’s universal in signal processing, but it can also be used to compress image and audio files, solve differential equations, and price stock options, among other things.

The reason the Fourier transform is so prevalent is an algorithm called the fast Fourier transform (FFT), devised in the mid-1960s, which made it practical to calculate Fourier transforms on the fly. Ever since the FFT was proposed, however, people have wondered whether an even faster algorithm could be found.

At the 2012 Association for Computing Machinery’s Symposium on Discrete Algorithms (SODA), a group of MIT researchers will present a new algorithm that, in a large range of practically important cases, improves on the fast Fourier transform. Under some circumstances, the improvement can be dramatic — a tenfold increase in speed. The new algorithm could be particularly useful for image compression, enabling, say, smart phones to wirelessly transmit large video files without draining their batteries or consuming their monthly bandwidth allotments.

Like the FFT, the new algorithm works on digital signals. A digital signal is just a series of numbers — discrete samples of an analog signal, such as the sound of a musical instrument. The FFT takes a digital signal containing a certain number of samples and expresses it as the weighted sum of an equivalent number of frequencies.