Dynamic Graph Neural Networks (Dynamic GNNs) have emerged as powerful tools for modeling real-world networks with evolving topologies and node attributes over time. A survey by Professors Zhewei Wei, ...

Graph neural networks (GNNs) have rapidly emerged as a central methodology for analysing complex datasets presented as graphs, where entities are interconnected through diverse relationships. By ...

BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...

The demand for immersive, realistic graphics in mobile gaming and AR or VR is pushing the limits of mobile hardware. Achieving lifelike simulations of fluids, cloth, and other materials historically ...

Accurate stock trend forecasting is a central challenge in financial economics due to the highly nonlinear and interdependent nature of market dynamics. Traditional statistical and machine learning ...

Urban congestion is a big problem in our cities. It leads to commuter delays and economic inefficiency. More tragically, ...

Scientists in Spain have used genetic algorithms to optimize a feedforward artificial neural network for the prediction of energy generation of PV systems. Genetic algorithms use “parents” and ...