AI captures particle accelerator behavior to optimize machine performance

Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...
Morning Overview on MSN

Radical new computer could ditch electricity for light and supercharge processing

Computing is hitting a physical and economic wall just as generative artificial intelligence explodes in complexity and cost.

A Radical New Computer Could Replace Electricity With Light—and Make Processing Unstoppable

A team of researchers developed “parallel optical matrix-matrix multiplication” (POMMM), which could revolutionize tensor ...
IEEE

Conic Hull Fitting-Based Dictionary Matrix Learning for Nonnegative Matrix Factorization

Abstract: Nonnegative matrix factorization (NMF) is a powerful tool for signal processing and machine learning. Geometrically, it can be interpreted as the problem of finding a conic hull, which ...
Interesting Engineering on MSN

MIT’s new heat-powered silicon chips achieve 99% accuracy in math calculations

Scientists in the US have created a tiny silicon chip that can perform mathematical ...
GitHub

Transfer-learning-model-of-Mechanical-Properties-for-Particle-Reinforced-Aluminum-Matrix-Composites

1.Core code for the PAMCs-MP model (Pre-traIn,Fine-tuning). 2.Machine learning and grid-search code, including implementations for SVR, Random Forest (RF), Gradient Boosting (GB), and BPNN. 3.Code for ...
Education Week

These Students are Learning the Math That Makes AI Tick

When most educators teach about artificial intelligence, the goal is to help students use an existing technology, like ChatGPT or a bot integrated into an online program. But Clayton Dagler wants his ...
National Academies of Sciences%2c Engineering%2c and Medicine

Making Machine Learning Safer in High-Stakes Settings

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...
Frontiers

Evaluating machine learning models for stroke prediction based on clinical variables

Introduction: Stroke remains one of the leading causes of global mortality and long-term disability, driving the urgent need for accurate and early risk prediction tools. Traditional models such as ...
IEEE

Non-Negative Matrix Factorization and Latent Semantic Analysis for Hybrid Feature Selection: A Proposed Machine Learning System for the Detection of Malicious Executable Files

Abstract: During a typical cyber-attack lifecycle, several key phases are involved, including footprinting and reconnaissance, scanning, exploitation, and covering tracks. The successful delivery of a ...