Traffic forecasting has been regarded as the basis for many intelligent transportation system
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …

The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …

In recent years, the use of convolutional neural networks (CNNs) and graph convolutional
networks (GCNs) has significantly advanced hyperspectral image classification (HSIC) …

The detection of organised disinformation campaigns that spread fake news, by first
camouflaging them as real ones is crucial in the battle against misinformation and …

Human trajectory anomaly detection is critical for applications such as security surveillance
and public health, yet most existing methods focus on vehicle-level traffic, with limited …

Multi-agent reinforcement learning (MARL) has become a fundamental component of next-
generation wireless communication systems. Theoretically, although MARL has the …

Offline reinforcement learning (RL) is a challenging task, whose objective is to learn policies
from static trajectory data without interacting with the environment. Recently, offline RL has …

Root Cause Analysis (RCA) is becoming increasingly crucial for ensuring the reliability of
microservice systems. However, performing RCA on modern microservice systems can be …

Hypergraph neural networks (HyperGNNs) are a family of deep neural networks designed to
perform inference on hypergraphs. HyperGNNs follow either a spectral or a spatial …

In the field of environmental science, traditional methods for predicting PM2. 5
concentrations primarily focus on singular temporal or spatial dimensions. This approach …