Researchers at Duke University have created a new artificial intelligence framework designed to uncover straightforward, easy ...

Occam’s razor is the principle that, all else being equal, simpler explanations should be preferred over more complex ones. This principle is thought to guide human decision-making, but the nature of ...

The implications from the design of the clinical GLP-1 agonist trials and studies of caloric restriction are that exercise is ...

Pennywise seems to have a specific interest in Will Hanlon on It: Welcome to Derry, and the time-bending answer is found in the movies.

The findings revealed four critical topological turning points in brain development: around the ages of nine, 32, 66, and 83. Each of these ages marks the transition into distinct epochs of ...

Abstract: We consider the problem of online sparse linear approximation, where a learner sequentially predicts the best sparse linear approximations of an as yet unobserved sequence of measurements in ...

Veterinary ECGs face added challenges, as muscle tremors, shivering and motion artifacts can easily dominate the signal. Smaller animals such as cats and exotic species produce lower QRS amplitudes, ...

Abstract: In this paper, we consider a general problem setup for a wide class of convex and robust distributed optimization problems in peer-to-peer networks. In this setup, convex constraint sets are ...

A research team at Duke University has developed a new AI framework that can uncover simple, understandable rules that govern some of the most complex dynamics found in nature and technology. The AI ...

This article is authored by Dr Rajendra Pratap Gupta, chairman, Academy of Digital Health Sciences and former advisor to the Union health minister.

Thermodynamics was built to describe steam engines and stars, yet the same equations now sit at the heart of quantum computers and nanoscale devices. As physicists push into regimes where individual ...

Multi-step temporal-difference (TD) learning, where the update targets contain information from multiple time steps ahead, is one of the most popular forms of TD learning for linear function ...