Scientific Research Publishing

Hayes, A. F. (2022). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (3rd ed.). The Guilford Press.

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 ...
Scientific Research Publishing

Hayes, A. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2nd ed.). Guilford Press.

ABSTRACT: Despite the critical role teachers play in shaping educational outcomes, increasing workload and burnout remain pervasive challenges that threaten their wellbeing and job satisfaction, ...
GitHub

Gap-averaged flow simulations with Gaussian Process regression.

A serial build of LAMMPS is provided for most platforms which allows testing all of GaPFlow's functionality. For production simulations it is however recommended to ...
Interesting Engineering

New adaptive system lets robots replicate human touch with far less training data

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 ...
IEEE

GDM-Net++: Multi-robot 2D and 3D Gas Distribution Mapping Via Deep Q-Learning and Gaussian Process Regression

Abstract: Gas distribution mapping (GDM) refers to the task of mapping the gas concentrations of an airborne chemical over a region of interest. A mobile robot equipped with a gas sensor can be used ...
Hosted on MSN

Build logistic regression in Python from scratch easily

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 ...
eLife

Learning sequence-function relationships with scalable, interpretable Gaussian processes

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 ...
GitHub

A minimal, portable Gaussian Splatting sandbox for rapid small-scale experimentation and performance insights in pure Python.

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, ...
C&EN

Data-Driven Design of High-Temperature-Resistant Polyimides Using Hierarchical Gaussian Process Regression

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 ...