Light does not “think” in any human sense. Still, under the right conditions, it can behave in a way that looks uncannily ...

Abstract: This article proposes a data-driven linear parameter variation model predictive control (DDLPVMPC) method for unknown nonlinear (NL) systems. The approach eliminates reliance on prior ...

In microbiome studies, addressing the unique characteristics of sequence data—such as compositionality, zero inflation, overdispersion, high dimensionality, and non-normality—is crucial for accurate ...

Electrostatic interactions are fundamental to the structure, dynamics, and function of biomolecules, with broad applications in protein–ligand binding, enzymatic catalysis, and nucleic acid regulation ...

Reliance on fossil fuels is almost unavoidable — at least for now. By Evan Gorelick It’s been a big week for A.I. data centers. That means it’s also been a big week for coal and natural gas. Nvidia ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the linear support vector regression (linear SVR) technique, where the goal is to predict a single numeric ...

Attention-based architectures are a powerful force in modern AI. In particular, the emergence of in-context learning abilities enables task generalization far beyond the original next-token prediction ...

This study introduces an XGBoost-MICE (Multiple Imputation by Chained Equations) method for addressing missing data in mine ventilation parameters. Using historical ventilation system data from ...

Abstract: Conventional data-driven dynamic process monitoring methods usually rely on data collected at a single sampling rate. The effectiveness of these approaches typically diminishes when ...

In this tutorial, we will learn how to build an interactive health data monitoring tool using Hugging Face’s transformer models, Google Colab, and ipywidgets. We walk you through setting up your Colab ...