Government procurement contracts can be complicated, with extensive risk analysis and compliance reviews. The traditional ...

Abstract: Edge vision systems combining sensing and embedded processing promise low-latency, decentralized, and energy-efficient solutions that forgo reliance on the cloud. As opposed to conventional ...

Recently, Tianjin Yingjie Technology Development Co., Ltd. announced the application for a patent titled "An Optimization Method for Multi-Robot Collaborative Task Scheduling Based on Graph Neural ...

Shenzhen Xister New Energy Technology Co., Ltd. recently announced that it has applied for a patent titled "An AI-Based Circuit Wiring System, Method, Medium, and Device," with publication number ...

Neo4j also trumpeted the value of graphs as vector databases used in generative artificial intelligence. AI training requires ...

Sethupathy, U. (2025) Risk-Aware AI Models for Financial Fraud Detection: Scalable Inference from Big Transactional Data.

We aim to build a pre-trained Graph Neural Network (GNN) model on molecules without human annotations or prior knowledge. Although various attempts have been proposed to overcome limitations in ...

This important work sets out to identify the neural substrates of associative fear responses in adult zebrafish. Through a compelling and innovative paradigm and analysis, the authors suggest brain ...

In the era of digital transformation, few professionals are able to bridge large-scale enterprise technology with frontier ...

Summary: Researchers have developed a new tool, bimodularity, that adds directionality to community detection in networks. Unlike traditional methods that only cluster nodes, this approach groups ...

Abstract: Existing message passing-based and transformer-based graph neural networks (GNNs) cannot satisfy requirements for learning representative graph embeddings due to restricted receptive fields, ...