Recently, pipeline parallelism for large-scale Deep Neural Network (DNN) training has been
developed, which partitions the DNN model across multiple devices (eg, GPUs) and …

Satellite cloud imagery is pivotal for meteorologists in characterizing weather patterns,
detecting climate anomaly regions, and predicting rain effects. The task of satellite cloud …

Diagnosing plant diseases is a vital issue in maintaining and develo** of agricultural
products. These diseases occur with changes in the tissue of different parts of plants. While …

Radar reflectivity plays a crucial role in detecting heavy rainfall and is an important tool for
meteorological analysis. However, the coverage of a single radar is limited, leading to the …

Recent years have witnessed the great success of graph neural networks (GNNs) in various
graph data mining tasks. However, studies demonstrate that GNNs are vulnerable to …

Tropical cyclones (TC) are dynamic atmospheric phenomena featuring extreme low-
pressure systems and powerful winds, known for their devastating impacts on weather and …

Innovations in computer vision and satellite imaging technologies have made fully automatic
weather forecasting a reality. The cyclone prediction system is developed with the goal of …

To address the challenges associated with insufficient feature extraction and gradient
degradation encountered when dealing with deepening network structures in image …

Realistic network environments have difficulties collecting malicious traffic data, and training
network models with virtually generated traffic data are inevitably disconnected from the real …

The rapid development of satellite technology has resulted in an exponential growth in the
availability of high-resolution satellite images, which presents both opportunities and …