Scientific Research Publishing

Geo-Refined Point Transformer: Coordinate-Aware Excitation and Positional Upsampling for 3D Scene Segmentation ()

The proposed Coordinate-Aware Feature Excitation (CAFE) module and Position-Aware Upsampling (Pos-Up) module both adhere to ...

Temporal anti-parity–time symmetry offers new way to steer energy through systems

The movement of waves, patterns that carry sound, light or heat, through materials has been widely studied by physicists, as ...
IEEE

A Spatial-Temporal Graph Convolutional Network With Self-Attention for City-Level Cellular Network Traffic Prediction

Abstract: Accurate and efficient cellular traffic prediction is crucial for enhancing the user quality of experience in mobile networks. However, this task faces significant challenges due to the ...
GitHub

Dynamic Spatial-Temporal Model for Carbon Emission Forecasting

This repository contains the implementation of the model Dynamic Spatial-Temporal Graph Convolutional Recurrent Network (DSTGCRN) presented in the manuscript "Dynamic Spatial-Temporal Model for Carbon ...
IEEE

Dynamic Spatial-Temporal Graph Convolutional Neural Networks Approach for Active Mode Traffic Prediction

Abstract: Accurate short-term predictions of active mode traffic are crucial for effective urban traffic control and management, helping to reduce delays, stops, and improve travel time reliability, ...
GitHub

Spatio-Temporal Graph Convolutional Networks:

Network Structure Fig. 2 Architecture of spatio-temporal graph convolutional networks. The framework STGCN consists of two spatio-temporal convolutional blocks (ST-Conv blocks) and a fully-connected ...
Scientific Research Publishing

Li, Y. and Luo, Y. (2024) STdGCN: Spatial Transcriptomic Cell-Type Deconvolution Using Graph Convolutional Networks. Genome Biology, 25, Article No. 206.

ABSTRACT: Spatial transcriptomics is undergoing rapid advancements and iterations. It is a beneficial tool to significantly enhance our understanding of tissue organization and relationships between ...
Frontiers

A multi-graph convolutional network method for Alzheimer’s disease diagnosis based on multi-frequency EEG data with dual-mode connectivity

Objective: Alzheimer’s disease (AD) is mainly identified by cognitive function deterioration. Diagnosing AD at early stages poses significant challenges for both researchers and healthcare ...
Frontiers

Insights into gait performance in Parkinson's disease via latent features of deep graph neural networks

Introdcution: Parkinson's Disease (PD) is a progressive neurodegenerative disorder that primarily impacts motor function and is prevalent among older adults worldwide. Gait performance (such as speed, ...