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In mathematics, computer science, and related subjects, an 'algorithm' is an effective method for solving a problem expressed as a finite sequence of instructions. Algorithms are used for calculation, data processing, and many other fields. (In more advanced or abstract settings, the instructions do not necessarily constitute a finite sequence, or even a sequence; see, for example, "nondeterministic algorithm".) Each algorithm is a list of well-defined instructions for completing a task. Starting from an initial state, the instructions describe a computation that proceeds through a well-defined series of successive states, eventually terminating in a final ending state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate randomness. [source = Bing Reference]

UC3M's ASIROV Robotic Satellite Chaser Prototype ASIROV, the Acoplamiento y Agarre de Satélites mediante Sistemas Robóticos basado en Visión (Docking and Capture of Satellites through computer vision) would use computer vision tech to autonomously chase down satellites in orbit for repair or removal. Image courtesy of Universidad Carlos III de Madrid Spanish robotics engineers have devised a new weapon in the battle against zombie-sats and space junk: an automated robotics system that employs computer vision technology and algorithmic wizardry to allow unmanned space vehicles to autonomously chase down, capture, and even repair satellites in orbit. Scientists at the Universidad Carlos III de Madrid (UC3M) created the system to allow for the removal of rogue satellites from low earth orbit or the maintenance of satellites that are nearing the ends of their lives, prolonging their service (and extending the value of large investments in satellite tech). Through a complex set of algorithms, space vehicles known as “chasers” could be placed into orbit with the mission of policing LEO, chasing down satellites that are damaged or have gone “zombie” and dealing with them appropriately.

The first textbook of its kind, Programming Massively Parallel Processors: A Hands-on Approach launches today, authored by Dr. David B. Kirk, NVIDIA Fellow and former chief scientist, and Dr. Wen-mei Hwu, who serves at the University of Illinois at Urbana-Champaign as Chair of Electrical and Computer Engineering in the Coordinated Science Laboratory, co-director of the Universal Parallel Computing Research Center and principal investigator of the CUDA Center of Excellence. The textbook, which is 256 pages, is the first aimed at teaching advanced students and professionals the basic concepts of parallel programming and GPU architectures. Published by Morgan Kaufmann, it explores various techniques for constructing parallel programs and reviews numerous case studies. With conventional CPU-based computing no longer scaling in performance and the world’s computational challenges increasing in complexity, the need for massively parallel processing has never been greater. GPUs have hundreds of cores capable of delivering transformative performance increases across a wide range of computational challenges. The rise of these multi-core architectures has raised the need to teach advanced programmers a new and essential skill: how to program massively parallel processors.

An all-optical integrator, or lightwave capacitor, is a fundamental building block equivalent to those used in multi-functional electronic circuits. Associate Professor David Moss, a senior researcher within the Institute for Photonic and Optical Science (IPOS), leads an international team which has developed the optical integrator on a CMOS compatible silicon chip. The device, a photonic chip compatible with electronic technology (CMOS), will be a key enabler of next generation fully-integrated ultrafast optical data processing technologies for many applications including ultra-fast optical information-processing, optical memory, measurement, computing systems, and real-time differential equation computing units.

Nothing is ever simple in IT, and nanotechnology is no different. For a start, the term nanotechnology can mean different things to different people. For purists, it refers to a microscopic structure equal to or less than one nanometre (nm) in size – about a billionth of a metre. But many vendors and regulators (see How EC rules affect nanotechnology, page 2) believe the term nanotechnology can be applied to any structure between 1nm and 100nm in size, which means various nanoscale silicon components and microchips already inhabit many of the computers and other electrical and electronic devices we use today.

The MATLAB team at MathWorks tested performance scaling of the backslash ("\") matrix division operator to solve for x in the equation A*x = b. In their testing, matrix A occupies far more memory (290 GB) than is available in a single high-end desktop machine—typically a quad core processor with 4-8 GB of RAM, supplying approximately 20 Gigaflops. Therefore, they spread the calculation across machines. In order to solve linear systems of equations they need to be able to access all of the elements of the array even when the array is spread across multiple machines. This problem requires significant amounts of network communication, memory access, and CPU power. They scaled up to a cluster in EC2, giving them the ability to work with larger arrays and to perform calculations at up to 1.3 Teraflops, a 60X improvement. They were able to do this without making any changes to the application code. Here's a graph showing the near-linear scalability of an EC2 cluster across a range of matrix sizes with corresponding increases in cluster size for MATLAB's parallel backslash operator:

Robot motion planning has traditionally been used to avoid collisions when moving a robot arm. Avoiding collisions is important, but many other desirable criteria are often ignored. For example, motions that minimize energy will let the robot extend its battery life. Smoother trajectories may cause less wear on motors and can be more aesthetically appealing. There may be even more useful criteria, like keeping a glass of water upright when moving it around. This summer, Mrinal Kalakrishnan from the Computational Learning and Motor Control Lab at USC worked on a new motion planner called STOMP, which stands for "Stochastic Trajectory Optimization for Motion Planning". This planner can plan paths for high-dimensional robotic systems that are collision-free, smooth, and can simultaneously satisfy task constraints, minimize energy consumption, or optimize other arbitrary criteria. STOMP is derived from gradient-free optimization and path integral reinforcement learning techniques (Policy Improvement with Path Integrals, Theodorou et al, 2010).

As I noted last week, US Federal Bureau of Investigation (FBI) Director Robert Mueller told the US House Appropriations Committee’s Subcommittee on Commerce, Justice, Science, and Related Agencies that the follow-on to the notoriously failed $170 million Virtual Case File program called Sentinel would once again slip its schedule and cost targets. The latest cost estimate is in excess of $481 million with a total program slip now of some two years and counting.    Director Mueller told the House Committee that the FBI decided to suspend work on Sentinel in early March to correct some "minor" technical issues and make some design changes. These issues included, according to the New York Times, "slow response times, awkward display pages and screen print that was too small."

Their new research is published in the online edition of the Proceedings of the National Academy of Sciences (PNAS), and will soon be published in the print edition of the journal. The findings may enable new applications for semiconductors --materials that are the foundation of today's information technology. In particular, they may help identify alternative materials to use for building a potential quantum computer.

Robots might one day trace the origin of their consciousness to recent experiments aimed at instilling them with the ability to reflect on their own thinking. Although granting machines self-awareness might seem more like the stuff of science fiction than science, there are solid practical reasons for doing so, explains roboticist Hod Lipson at Cornell University's Computational Synthesis Laboratory.

Complex computer models can involve thousands of variables. But paradoxically, adding more variables can sometimes make them easier to work with.

In short, Joe argues, "Throughout, it became abundantly clear that TM, much like generics, was a systemic and platform-wide technology shift. It didn’t require type theory, but the road ahead sure wasn’t going to be easy." The whole blog post deals with how many implementation challenges platform-wide support for STM would be in .NET, including what options were considered. He does not mention Maurice Herlihy's SXM library approach, but refers to Tim Harris's work several times.

A class of molecules whose size, structure and chemical composition have been optimized for photonic use could provide the demanding combination of properties needed to serve as the foundation for low-power, high-speed all-optical signal processing.

Scientists have great expectations that nanotechnologies will bring them closer to the goal of creating computer systems that can simulate and emulate the brain's abilities for sensation, perception, action, interaction and cognition while rivaling its low power consumption and compact size. DARPA for instance, the U.S. military's research outfit known for projects that are pushing the envelope on what is technologically possible, has a program called SyNAPSE that is trying to develop electronic neuromorphic machine technology that scales to biological levels. Started in late 2008 and funded with $4.9 million, the goal of the initial phase of the SyNAPSE project is to "develop nanometer scale electronic synaptic components capable of adapting the connection strength between two neurons in a manner analogous to that seen in biological systems, as well as, simulate the utility of these synaptic components in core microcircuits that support the overall system architecture."

Researchers at the NanoRobotics Laboratory of the École Polytechnique de Montréal, in Canada, are putting swarms of bacteria to work, using them to perform micro-manipulations and even propel microrobots. Led by Professor Sylvain Martel, the researchers want to use flagellated bacteria to carry drugs into tumors, act as sensing agents for detecting pathogens, and operate micro-factories that could perform pharmacological and genetic tests. They also want to use the bacteria as micro-workers for building things. Things like a tiny step pyramid. The video below shows some 5000 bacteria moving like a swarm of little fish, working together to transport tiny epoxy bricks and assemble a pyramidal structure -- all in 15 minutes. The video was presented at IROS last year, along with a wonderfully titled paper, "A Robotic Micro-Assembly Process Inspired By the Construction of the Ancient Pyramids and Relying on Several Thousands of Flagellated Bacteria Acting as Workers."

In this demo we showcase efforts in MSR to collaborate with external researchers to explore the application of new technologies, specifically Dryad and DryadLINQ, to big data research problems in science. We also highlight our efforts to provide software and services to academics across the world, through the release of Dryad and DryadLINQ free of charge to the research community, along with associated programming guides, user documentation, and code libraries. Dryad is a general-purpose distributed computing engine, more flexible than MapReduce or Hadoop!, that was designed to simplify the task of implementing distributed applications on clusters of Windows computers. DryadLINQ is an abstraction layer which simplifies the process of implementing Dryad-based applications. Microsoft Research is acutely aware of the ubiquity of big data and the challenges this presents. We are offering researchers the tools, resources and collaboration to explore this new area.

The e-reader market took the company E-Ink and its low-power, easy-on-the-eyes digital paper technology mainstream. But no one says E-Ink is perfect; the displays, to date, don’t do flexibility or full color well. And they aren’t cheap enough to move into budget-conscious applications, like the long-dreamed of grocery store shelf tags that could be updated remotely to display new prices. E-Ink and its brethren continue to advance down their technology development paths. But a startup company based in Saratoga, Calif., says they’re heading in the wrong direction.

Later this month, Carnegie Mellon's CMDragons small-size robotic soccer team will be competing again at RoboCup, to be held in Singapore. CMDragons has tended to find their edge in their software as opposed to their hardware. Their latest software advantage will be their new "physics-based planning", using physics to decide how to move and turn with the ball in order to maintain control. Previous control strategies simply planned where the robot should move to and shoot from, assuming a ball placed at the front center of the dribbler bar would stay there. The goal of Robocup is to create a humanoid robotic soccer team to compete against human players in 2050. Manuela Veloso, the professor who leads the Carnegie Mellon robotic soccer lab, "believe[s] that the physics-based planning algorithm is a particularly noteworthy accomplishment" that will take the effort one step closer to the collective goal.