twitter.jpgHundreds of millions of daily posts on the social networking service Twitter are providing a new window into bullying — a tough nut to crack for researchers.

“Kids are pretty savvy about keeping bullying outside of adult supervision, and bullying victims are very reluctant to tell adults about it happening to them for a host of reasons,” says Amy Bellmore, a UW–Madison educational psychology professor. “They don’t want to look like a tattletale, or they think an adult might not do anything about it.”

Yet typical bullying research methods rely on the kids — victims and bullies alike — to describe their experiences in self-reporting surveys.

bilinear spatiotemporal basis model.jpgPITTSBURGH—Computer graphic artists who produce computer-animated movies and games spend much time creating subtle movements such as expressions on faces, gesticulations on bodies and the draping of clothes. A new way of modeling these dynamic objects, developed by researchers at Carnegie Mellon University, Disney Research, Pittsburgh, and the LUMS School of Science and Engineering in Pakistan, could greatly simplify this editing process.

 

braincap.jpgCollege Park, Md. - "Brain cap" technology being developed at the University of Maryland allows users to turn their thoughts into motion. 

 Associate Professor of Kinesiology José 'Pepe' L. Contreras-Vidal and his team have created a non-invasive, sensor-lined cap with neural interface software that soon could be used to control computers, robotic prosthetic limbs, motorized wheelchairs and even digital avatars.

MIT research uses information about how frequently objects are seen together to refine the conclusions of object recognition systems.

by Larry Hardesty

Today, computers can’t reliably identify the objects in digital images. But if they could, they could comb through hours of video for the two or three minutes that a viewer might be interested in, or perform web searches where the search term was an image, not a sequence of words. And of course, object recognition is a prerequisite for the kind of home assistance robot that could execute an order like “Bring me the stapler.” Now, MIT researchers have found a way to improve object recognition systems by using information about context. If the MIT system thinks it’s identified a chair, for instance, it becomes more confident that the rectangular thing nearby is a table.

Much scientific research across a range of disciplines tries to find linear approximations of nonlinear behaviors. But what does that mean?

by Larry Hardesty

Spend some time browsing around the web site of MIT’s Computer Science and Artificial Intelligence Laboratory, and you’ll find hundreds if not thousands of documents with titles like “On Modeling Nonlinear Shape-and-Texture Appearance Manifolds” and “Non-linear Drawing systems,” or, on the contrary, titles like “Packrat Parsing: Simple, Powerful, Lazy, Linear Time” and “Linear-Time-Encodable and List-Decodable Codes.”

by Larry Hardesty

MIT researchers helped develop a theory that promised much more efficient data networks; then they were the first to put it into practice.

Today, data traveling over the Internet are much like crates of oranges traveling the interstates in the back of a truck. The data are loaded in at one end, unloaded at the other, and nothing much happens to them in between.

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