CU Boulder researchers have designed microscopic “racetracks” that trap and amplify light with exceptional efficiency. By using smooth curves inspired by highway engineering, they reduced energy loss ...

Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...

Thick cloud cover can completely obscure the surface of the Earth from satellite view, while thinner haze and shadows distort ...

Isomorphic Lab’s proprietary drug-discovery model is a major advance, but scientists developing open-source tools are left guessing how to achieve similar results ...

A University of Hawaiʻi at Mānoa student-led team has developed a new algorithm to help scientists determine direction in ...

Machine learning algorithms that output human-readable equations and design rules are transforming how electrocatalysts for ...

ABSTRACT: Treatment response prediction remains one of the most pressing challenges in precision psychiatry, where patient heterogeneity and complex biomarker interactions limit the reliability of ...

Deep Learning (DL) has emerged as a transformative approach in artificial intelligence, demonstrating remarkable capabilities in solving complex problems once considered unattainable. Its ability to ...

Abstract: Deep learning performs feature extraction through a series of data transformations. Convolutional neural networks (CNNs) are among the most representative methods in deep learning. CNNs ...

Traffic prediction is the core of intelligent transportation system, and accurate traffic speed prediction is the key to optimize traffic management. Currently, the traffic speed prediction model ...

Electric Vehicle (EV) cost prediction involves analyzing complex, high-dimensional data that often contains noise, multicollinearity, and irrelevant features. Traditional regression models struggle to ...