In a new study published in the Journal of the Royal Society Interface, researchers Albert Kao (Harvard University), Andrew Berdahl (Santa Fe Institute), and their colleagues examined just how accurate our collective intelligence is and how individual bias and information sharing skew aggregate estimates. Using their findings, they developed a mathematical correction that takes into account bias and social information to generate an improved crowd estimate.
Machine learning algorithms excel at finding complex patterns within big data, so researchers often use them to make predictions. Researchers are pushing the technology beyond finding correlations to help uncover hidden cause-effect relationships and drive scientific discoveries. At the University of South Florida, researchers are integrating machine learning techniques into their work studying proteins. One of their challenges has been a lack of methods to identify cause-effect relationships in data obtained from molecular dynamics simulations.
US Army-funded researchers at the University of California in Los Angles have found a proverbial smoking gun signature of the long sought-after Majorana particle, and the find, they say, could block intruders on sensitive communication networks.
Professor William Lee shows how the science of math can aid the profits of industry.
Scientists at Tokyo Institute of Technology (Tokyo Tech) used synthetic-aperture radar data from four different satellites, combined with statistical methods, to determine the structural deformation patterns of the largest bridge in Iran.
Computer simulations of peregrine falcon attacks show that the extreme speeds reached during dives from high altitudes enhance the raptors' ability to execute maneuvers needed to nab agile prey that would otherwise escape. Robin Mills and colleagues of the University of Groningen, Netherlands, and Oxford University, UK, report this discovery in PLOS Computational Biology.
Information processing requires a lot of energy. Energy-saving computer systems could make computing more efficient, but the efficiency of these systems can't be increased indefinitely, as ETH physicists show.
Datasets play a crucial role in the training and testing of the computer vision systems. Using manually labeled training datasets, a computer vision system compares its current situation to known situations and takes the best action it can 'think' of -- whatever that happens to be. Scientists have developed a new way to improve how computers 'see' and 'understand' objects in the real world by training the computers' vision systems in a virtual environment.
Scientists from Moscow Institute of Physics and Technology and Skoltech have discovered a general principle for calculating the superconductivity of hydrides based on the periodic table alone. Turned out that certain elements capable of forming superconducting compounds are arranged in a specific pattern in the periodic table.
Researchers proposed implementing the residential energy scheduling algorithm by training three action dependent heuristic dynamic programming (ADHDP) networks, each one based on a weather type of sunny, partly cloudy, or cloudy. ADHDP networks are considered 'smart,' as their response can change based on different conditions.