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AI model predicts robberies across US cities with 86.3% accuracy

Researchers have developed an artificial intelligence model that predicts crime more accurately than several existing ...
Frontiers

scCCVGBen for benchmarking of single-cell representation learning anchored on a centroid-coupled variational graph attention autoencoder across scRNA-seq and scATAC-seq

Six additional scRNA-seq datasets are reserved for the biological case studies (Sections 3.8–3.10) and excluded from every benchmark statistic: sleep-deprivation bone marrow (GSE280145), a TPO-induced ...
IEEE

Improved Variational Graph Autoencoder for Link Prediction on Real-World Telecom Graphs

Mobility management in emerging O-RAN deployments increasingly leverages predictive intelligence to reduce handover failures and improve radio resource utilization. Recent studies have shown that ...
IEEE

A Spatial–Temporal Variational Graph Attention Autoencoder Using Interactive Information for Fault Detection in Complex Industrial Processes

Abstract: Modern industry processes are typically composed of multiple operating units with reaction interaction and energy–mass coupling, which result in a mixed time-varying and spatial–temporal ...