Object recognition systems that deconstruct images into ever smaller elements should be much more efficient and may yield insights on brain behavior, and underlying such systems are new methods developed by researchers at the Massachusetts Institute of Technology (MIT) and the University of California, Los Angeles (UCLA). The researchers have developed a system that learns to recognize new objects by being “trained” with digital images of labeled objects. For each labeled item, the system first identifies the smallest elements, and then seeks instances in which these elements are interconnected into slightly more complex configurations. The system continues to search for instances in which shapes of ever increasing sophistication are linked together until it has put together a hierarchical catalog of increasingly complex components whose top layer is a model of the entire object. The system then sifts through its catalog from the top down, weeding out all redundancies. Memory is saved because different objects can have shapes in common, requiring only once instance of memory storage.
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German researchers have developed a prototype safety system that would reduce the injuries of humans working alongside robots using household tools. The collision detection system uses torque sensors to determine when a kitchen knife, screwdriver, or scissors hits a different substance, and halts the movements of the sharp tool. The team from the Institute of Robotics and Mechatronics conducted strike tests on a silicon lump, a leg from a dead pig, and the arm of a human volunteer, using a robot arm. When the safety system was turned off, the robot produced deep cuts that could prove to be lethal to a living subject.
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Microsoft’s joint venture with a supercomputing center in Barcelona aims to develop a processor that will scale from a data center server to a smartphone, which would save energy and require less space. The researchers hope to apply vector processing technology to commercial applications such as making data centers and mobile handsets run more efficiently. The goal of the energy-efficient, composable vector processor project is to build a device that uses grid computing techniques to analyze multiple streams of data in parallel, and for the device to reconfigure itself on the fly in response to the workload it receives, say Microsoft researchers Timothy Hayes and Oscar Palomar. The technique uses some of the concepts of reduced instruction set computing, as well as new programming so that a single instruction can initiate an array of complex processes. The researchers also are working on scheduling algorithms to allocate work efficiently and to accurately recombine results from processes.
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Yale University researchers have described why computers tend to malfunction more than living organisms by analyzing the control networks in both an E-coli bacterium and the Linux operating system. Both systems are arranged in hierarchies, but with some key differences in how they achieve operational efficiencies. The molecular networks in the bacteria are arranged in a pyramid, with a limited number of master regulator genes at the top that control a wide base of specialized functions. The Linux operating system is set up more like an inverted pyramid, with many different top-level routines controlling a few generic functions at the bottom. This organization arises because software engineers tend to save money and time by building on existing routines rather than starting systems from scratch, says Yale professor Mark Gerstein. “But it also means the operating system is more vulnerable to breakdowns because even simple updates to a generic routine can be very disruptive,” Gerstein says.
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D.E. Shaw Research will house its new Anton supercomputer at the Pittsburgh Supercomputing Center beginning next fall. Anton is a massively parallel, 512-node supercomputer that reportedly offers ground-breaking performance capabilities. “This computer does work that really wasn’t even possible until now,” says Pittsburgh Supercomputing Center biomedical scientist Markus Dittrich. Anton features a series of algorithms that can project how all the thousands of parts of a protein interact. “This computer has the potential to be a great accelerator in the development of drugs, how drugs work, and how systems work,” says Jeremy Berg, director of the National Institute of General Medical Sciences, which provided a $2.7 million grant to pay for Anton’s use at the supercomputing center. Anton took more than 10 years to create at D.E. Shaw Research, a private laboratory founded by David E. Shaw. “It’s a pretty amazing machine [and] now people would like to get their hands on a machine to see if it can do what he says,” notes University of Utah professor Thomas Cheatham.
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