Integrating adversarial training into the graph autoencoder (GAE) framework imposes regularization on the latent space, improving the distinction between normal and abnormal data representations ...

By integrating graph theory with deep learning ... graph-guided neural networks for anomaly detection in chemical engineering. Topics of interest include, but are not limited to: • Graph construction ...

A physics-based theory sheds new light on how AI's Attention mechanism works—and why it fails with hallucinations and bias.

The precise measurement of states in atomic and molecular systems can help to validate fundamental physics theories and their ...

A histogram is a bar graph representation of data that buckets ... earlier buy and sell signals than the accompanying MACD and signal lines. How Histograms Work Histograms are commonly used ...

High-entropy alloys (HEAs) offer tunable compositions and surface structures, presenting significant potential for creating novel active sites to enhance CO2 reduction (CO2RR) catalysis, a key process ...

Abstract: Recently, provenance-based Intrusion Detection Systems (IDSes ... We present Flash, a scalable IDS that leverages graph representation learning through Graph Neural Networks (GNNs) on data ...

B CUBE – Center for Molecular Bioengineering, TUD Dresden University of Technology, 01307 Dresden, Germany B CUBE – Center for Molecular Bioengineering, TUD Dresden University of Technology, 01307 ...

Our data indicate that POm-striatal inputs provide a behaviorally relevant arousal-related signal, which may prime striatal circuitry for efficient integration of subsequent choice-related inputs. All ...