Graph Convolutional Networks (GCNs) are widely applied for spatial domain identification in spatial transcriptomics (ST), where node representations are learned by aggregating information from ...

Welcome to MythBusters, a Golf Digest+ series where we explore answers to some of golf’s most common questions through a series of tests with golfers and robots. Sometimes definitive, other times less ...

This video captures a rare moment as a Boeing 787-9 takes off with no passengers or cargo on board. The reduced weight allows for rapid acceleration, a steep climb, and noticeably shorter take-off ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Graph neural networks for crystal property prediction typically require precise atomic ...

This is a repository of the code used for the experimental work in my Bachelor thesis on Approximation Algorithms for Graph Edit Distance (GED). It includes implementations, benchmarking scripts, and ...

Abstract: In this paper, a Mahalanobis Distance-based Graph Attention Network for graph classification, is proposed. In contrast to traditional Graph Attention Networks, the proposed approach learns ...

Federated learning is a classic of privacy-preserving learning, which enables collaborative learning without sharing data. Structured data has become the mainstream of current applications, where ...

Precise and consistent automatic control of pitch angle will reduce takeoff distance and improve safety. Credit: Embraer Embraer is developing a pilot training program for the industry’s first ...