eLife

Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images

Neural and computational evidence reveals that real-world size is a temporally late, semantically grounded, and hierarchically stable dimension of object representation in both human brains and ...
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, ...
News-Medical.Net

MEA-driven graph modeling identifies early connectivity changes linked to autism

Multi-electrode arrays (MEAs) provide a noninvasive interface with sub-millisecond temporal resolution and long-term, ...
IEEE

RNGCNs: Robust Norm Graph Convolutional Networks in the Presence of Missing Data and Large Noises

Abstract: Graph Convolutional Networks (GCNs) have been widely studied for attribute graph data learning. In many applications, graph node attributes/features may contain various kinds of noises, such ...