This repository contains the official PyTorch implementation and the UMC4/12 Dataset for the paper: [UrbanGraph: Physics-Informed Spatio-Temporal Dynamic Heterogeneous Graphs for Urban Microclimate ...

Abstract: Graph neural networks (GNNs) have demonstrated significant success in solving real-world problems using both static and dynamic graph data. While static graphs remain constant, dynamic ...

AI tools are frequently used in data visualization — this article describes how they can make data preparation more efficient ...

Explore the power of interactive physics visualizations with animated graphs using VPython and GlowScript for dynamic simulations! This guide demonstrates how to create real-time animated graphs that ...

Explore core physics concepts and graphing techniques in Python Physics Lesson 3! In this tutorial, we show you how to use Python to visualize physical phenomena, analyze data, and better understand ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

The Biden administration grappled with research suggesting natural immunity was more effective than COVID-19 vaccination shortly before federal vaccine mandates in 2021, admitting the rigor of the ...

Wolves are looking to back Rob Edwards in the January window, and we understand one player they are looking at is Lille forward Matias Fernandez-Pardo, and they lead three rivals in the race for his ...

A Federal Reserve split over where its priorities should lie cut its key interest rate Wednesday in a 9-3 vote, but signaled a tougher road ahead for further reductions. The FOMC's "dot plot" ...

Abstract: This paper focuses on representation learning for dynamic graphs with temporal interactions. A fundamental issue is that both the graph structure and the nodes own their own dynamics, and ...