Pressing a button appears easy, but the brain needs a probabilistic internal model to control a press. A new theory exposes significant improvements to button design that help gamers and musicians.
a research team led by associate professor Eiji Watanabe of the National Institute for Basic Biology successfully reproduced illusory motion by deep neural networks trained for prediction.
A pair of autonomous robots developed by Carnegie Mellon University's Robotics Institute will soon be driving through miles of pipes at the US Department of Energy's former uranium enrichment plant in Piketon, Ohio, to identify uranium deposits on pipe walls.
Researchers have taken a key step toward helping wildlife coexist more safely with wind power generation by demonstrating the success of an impact detection system that uses vibration sensors mounted to turbine blades.
An international consortium of cosmology researchers are releasing initial findings from IllustrisTNG, their follow-up to the 2015 record-breaking Illustris simulation -- the largest-ever hydrological simulation of galaxy formation.
A new study by researchers Nikolaos Gatsis, David Akopian and Ahmad F. Taha from the UTSA Department of Electrical and Computer Engineering describes a computer algorithm that mitigates the effects of spoofed GPS attacks on electrical grids and other GPS-reliant technologies. This new algorithm has the potential to help cybersecurity professionals to better detect and prevent cyber attacks in real time.
US Army-funded researchers at Brandeis University have discovered a process for engineering next-generation soft materials with embedded chemical networks that mimic the behavior of neural tissue. The breakthrough material may lead to autonomous soft robotics, dual sensors and actuators for soft exoskeletons, or artificial skins.
The online advertising business, led by companies like Google or Facebook, generated over $200 billion revenue in 2017, with an interanual growth over 15 percent. This online advertising explosion is raising serious data privacy concerns.
Chinese scientists and clinicians have developed a learning artificial intelligence system which can diagnose and identify cancerous prostate samples as accurately as any pathologist. This holds out the possibility of streamlining and eliminating variation in the process of cancer diagnosis. It may also help overcome any local shortage of trained pathologists. In the longer term it may lead to automated or partially automated prostate cancer diagnosis.
In a paper published online March 5 in Nature Communications, University of Washington researchers unveiled an open-access browser to display, analyze and share neurological data collected through a type of magnetic resonance imaging study known as diffusion-weighted MRI.