Abstract: Change point detection is crucial for identifying state transitions and anomalies in dynamic systems, with applications in network security, health care, and social network analysis. Dynamic ...

A suspected Hamas terrorist, reportedly granted asylum a year from the Gaza war, was arrested by Greek police for allegedly plotting an attack on an Israeli cruise line. The Gaza man, 37, was arrested ...

Accurate prediction of Significant Wave Height (SWH) is vital for marine engineering safety, yet balancing computational efficiency with physical consistency in long-sequence modeling remains a ...

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 ...

To some, METR’s “time horizon plot” indicates that AI utopia—or apocalypse—is close at hand. The truth is more complicated. MIT Technology Review Explains: Let our writers untangle the complex, messy ...

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 ...

A software engineer and book author with many years of experience, I have dedicated my career to the art of automation. A software engineer and book author with many years of experience, I have ...

Dynamic Graph Neural Networks (Dynamic GNNs) have emerged as powerful tools for modeling real-world networks with evolving topologies and node attributes over time. A survey by Professors Zhewei Wei, ...

In our world of today, spreadsheets function mainly as a means to provide quick and easy plotting methods for numerical data in various kinds of graphical charts. Graphs provide us with an effective ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...