Researchers in the Department of Theoretical Physics at Tata Institute of Fundamental Research (TIFR), Mumbai, have discovered that instead of manipulating every component or modifying interactions in a many-body system, ...
Aspens and standing dead trees, which are important to forest biodiversity, can be reliably identified from openly available aerial imagery using methods developed by researchers from the University of Helsinki and the University ...
When ice melts into water, it happens quickly, with the transition from solid to liquid being immediate. However, very thin materials do not adhere to these rules. Instead, an unusual state between solid and liquid arises: ...
A new tool developed by Helmholtz Munich and the German Center for Diabetes Research and the University of Bonn makes spatial proteomics and lipidomics easier to use—no coding required. C-COMPASS allows scientists to profile ...
Wildlife poaching remains a major conservation concern. Technological advancements have enabled webs of acoustic sensors to be deployed throughout rainforests, creating the possibility of real-time alerts to the sounds of ...
Past psychology and behavioral science studies have identified various ways in which people's acquisition of new knowledge can be disrupted. One of these, known as interference, occurs when humans are learning new information ...
Machine learning models called convolutional neural networks (CNNs) power technologies like image recognition and language translation. A quantum counterpart—known as a quantum convolutional neural network (QCNN)—could process ...
A fast and accurate surrogate model screens over 10,000 possible metal-oxide supports for a platinum nanocatalyst to prevent sintering under high temperatures.
Like other sectors of society, artificial intelligence is fundamentally changing how investors, traders and companies make decisions in financial markets. AI models have the ability to analyze massive amounts of data while ...
Researchers at the University of Illinois Urbana-Champaign have developed a new theoretical framework that could dramatically reduce the cost and complexity of predicting chemical reaction energetics without sacrificing accuracy. ...
