ABSTRACT: The purpose of this study was to establish the mediating role of job satisfaction (JS) in the relationship between job involvement (JI) and psychological well-being (PWB). A cross-sectional ...

The published wheels are currently not built with LAMMPS. Thus, running multiscale simulations with molecular dynamics is not possible with this quick installation ...

Researchers in Japan have developed an adaptive motion reproduction system that allows robots to generate human-like movements using surprisingly small amounts of training data. Despite rapid advances ...

Abstract: This paper presents a novel approach that integrates Gaussian Process Regression (GPR) with C-Mixup, aiming to explore the synergistic potential of these two techniques in regression tasks.

Implement Logistic Regression in Python from Scratch ! In this video, we will implement Logistic Regression in Python from Scratch. We will not use any build in models, but we will understand the code ...

This important work introduces a family of interpretable Gaussian process models that allows us to learn and model sequence-function relationships in biomolecules. These models are applied to three ...

Gaussian Splatting is a cutting-edge 3D representation technique that models a scene as a set of learnable 3D Gaussian primitives. Each Gaussian defines a point in space with position, color, opacity, ...

School of Materials Science and Engineering, Beihang University, Beijing 100191, China State Key Laboratory of Artificial Intelligence for Materials Science, Beihang University, Beijing 100091, China ...

Abstract: Gaussian Process Regression (GPR) is a machine learning technique that, besides predicting certain target values, also quantifies their uncertainty. With that, GPR is increasingly gaining ...