Teaching AI the laws of navigation: New physics guided detector catches GNSS spoofing even in never before seen attacks

While machine learning has improved detection, most models fail when confronted with attack scenarios they have never seen before, because they learn data patterns rather than the underlying physics ...
IEEE

Hyperspectral Image Classification via Dynamic Adaptive Graph Convolutional Network

Abstract: A dynamic graph convolutional network (DGCN) can represent temporal evolutionary features. Its compatibility with the spectral-dimensional characteristics of hyperspectral images (HSIs), ...
IEEE

Multiscale Spatiotemporal Graph Convolutional Networks With Dynamic Delay Awareness for Traffic Forecasting

The spatiotemporal dynamics of traffic forecasting make it a challenging task. In recent years, by adapting to the topology of traffic networks where road segments serve as nodes, graph convolutional ...
Technology Networks

Deep Learning for Variant Calling: How DeepVariant and Neural Networks Are Improving Genomic Accuracy

Deep learning variant calling has transformed genomic accuracy. Discover how DeepVariant works, outperforms classical tools, ...
technologynetworks

Multiomics Data Integration With Machine Learning: Methods, Tools, and Biological Applications

Multiomics data integration with machine learning has become the standard approach for combining genomic, transcriptomic, proteomic, and metabolomic measurements collected from the same biological ...
AlphaGalileo

AdapGNN: A New Framework Enhances Explainability of Graph Neural Networks for Molecular Property Prediction

Researchers have developed AdapGNN, a novel model-agnostic framework that addresses the oversmoothing problem in graph neural ...
AlphaGalileo

Crossing Virtual Boundaries: Rethinking Social Network Alignment via Graph Learning

Sub-headline: HUST researchers systematize SNA methods, building an evolutionary taxonomy based on graph representation ...