Abstract: In the graph signal processing (GSP) literature, graph Laplacian regularizer (GLR) was used for signal restoration to promote piecewise smooth / constant reconstruction with respect to an ...

Abstract: Graph Neural Networks (GNNs) are powerful tools for processing graph data, but training them is typically time-consuming and expensive in terms of GPU memory. Recently, graph reduction ...

GraGR: Gradient-Guided Graph Reasoner GraGR is a comprehensive gradient-guided graph reasoning framework that addresses gradient conflicts in graph neural networks through systematic conflict ...

Learn what residual standard deviation is, how to calculate it in regression analysis, and why it's crucial for measuring predictability and goodness-of-fit in data modeling.

Two-dimensional liquid chromatography (2D-LC) improves chromatographic performance, with LCxLC providing extensive ...

The AgentCore MCP server offers built-in support for runtime, gateway integration, identity management, and agent memory.

This class to generate force and tension graphs was made for a school assignment The code in question has been written quite fast and is in no way optimized I took shortcuts and also i am to ...

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...

Better rainfall has meant more feed on much of the Prairies, but creep feeding beef calves still pays well in 2025.

Should public company pay be more like private equity fund pay? This analysis compares incentive structures, retention, and ...