Deep learning reveals nanoparticle shape from routine tracking analysis without new hardware

Researchers at the University of Tokyo and the Innovation Center of NanoMedicine (iCONM) have developed an artificial ...
GitHub

STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

AI deciphers long-range DNA signals behind RNA splicing

Accurate RNA splicing is essential for gene expression and human health, yet predicting how DNA sequence variations affect ...
IEEE

DAHBM-GCN: A Flexible Graph Convolution Network Accelerator With Multiple Dataflows and HBM

Abstract: Graph-structured data has been widely applied in transportation, molecular, and e-commerce networks, etc. Graph Convolutional Network (GCN) has emerged as an efficient approach to processing ...
IEEE

BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction

Abstract: Trajectory prediction allows better decision-making in applications of autonomous vehicles (AVs) or surveillance by predicting the short-term future movement of traffic agents. It is ...