Single-trial neural states at the movement onset formed staggered orbital geometries, suggesting that target motion modulates peri-movement activity in an orthogonal manner. This neural geometry was .

In a recent advance, a multi-disciplinary team of researchers developed a machine learning framework that adapts to changes in the geometry of the physical settings of PDEs. Called DIMON, the new ...

It is a fictional representation created for illustrative purposes only. Starlink's foray into the Indian market has seen a series of pivotal events shaping its trajectory. As early as November 2021, ...

This book gives the basic notions of differential geometry, such as the metric tensor, the Riemann curvature tensor, the fundamental forms of a surface, covariant derivatives, and the fundamental ...

Introduction to Riemannian geometry. Riemannian connections, curvature and geodesics. Applications to submanifolds and to the connection between topology and curvature. The course aims to give a ...

"In a class populated by students who already have some exposure to the concept of a manifold, the presence of chapter 3 in this text may make for an unusual and interesting course. The primary ...

Employing generalizations of differential geometry and calculus variations, the team successfully derived nonlinear field equations from optimized variations on the spacetime metric, which gave them ...

Department of Physics & Astronomy, University of Southern California, Los Angeles, California 90089, United States Center for Quantum Information Science and Technology, University of Southern ...

Abstract: This paper presents a novel reinforcement learning framework for trajectory tracking of autonomous aerial vehicles in cluttered environments using a dual-agent architecture. Traditional ...