Determining the least expensive path for a new subway line underneath a metropolis like New York City is a colossal planning challenge—involving thousands of potential routes through hundreds of city ...

Structured data turns the dark funnel of AI search into actionable visibility, bridging what marketers can’t yet measure.

Abstract: Graph Neural Networks (GNNs) have recently achieved remarkable success in various learning tasks involving graph-structured data. However, their application to multi-relational graph anomaly ...

Frontiers in Neurorobotics has published a new paper by Xiaofei Han and Xin Dou introducing a next-generation artificial intelligence framework that combines graph neural networks and multimodal ...

The research aim is to develop an intelligent agent for cybersecurity systems capable of detecting abnormal user behavior ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Protein function prediction is essential for elucidating biological processes and ...

Welcome to the Data Structures and Algorithms Repository! My aim for this project is to serve as a comprehensive collection of problems and solutions implemented in Python, aimed at mastering ...

Introduction: Adolescent suicide is a critical public health concern worldwide, necessitating effective methods for early detection of high suicidal ideation. Traditional detection methods, such as ...

Abstract: This paper presents a novel approach to graph learning, GL-AR, which leverages estimated autoregressive coefficients to recover undirected graph structures from time-series graph signals ...

Thank you for developing HamGNN. I encountered an issue during the usage process. Could you guide me on how to convert the structure and Hamiltonian data from first-principles calculations (openmx) ...