Frontiers

DEG-BRIN-GCN: interpretable graph convolutional framework with differentially expressed genes brain region interaction network prior for AD diagnosis

Due to the intricate dynamic coupling between molecular networks and brain regions, early diagnosis and pathological mechanism analysis of Alzheimer's disease (AD) remain highly challenging. To ...
Frontiers

RS-STGCN: Regional-Synergy Spatio-Temporal Graph Convolutional Network for emotion recognition

Decoding emotional states from electroencephalography (EEG) signals is a fundamental goal in affective neuroscience. This endeavor requires accurately modeling the complex spatio-temporal dynamics of ...
EDN

Understand quadrature encoders with a quick technical recap

An unexpected revisit to my earlier post on mouse encoder hacking sparked a timely opportunity to reexamine quadrature encoders, this time with a clearer lens and a more targeted focus on their signal ...
IEEE

A Novel Low-resource Graph Convolutional Neural Network Approach for Entity Recognition

Abstract: With the rise of large language models in natural language processing, the use of various generative large language models for entity recognition has become a cutting-edge research approach.
IEEE

Triple Dynamic Graph Convolutional Recurrent Network for Traffic Prediction

Abstract: Effective traffic prediction is a critical component of traffic management, especially long-term traffic prediction, as it holds significance for urban traffic planning, traffic warning, ...
Microsoft

xGCN An Extreme Graph Convolutional Network for Large-scale Social Link Prediction

\textit{Graph neural networks} (GNNs) have seen widespread usage across multiple real-world applications, yet in transductive learning, they still face challenges in accuracy, efficiency, and ...