Abstract: The performance of distributed applications has long been hindered by network communication, which has emerged as a significant bottleneck. At the core of this issue, the many-to-one incast ...
Abstract: Utilizing messages from teammates can improve coordination in cooperative multiagent reinforcement learning (MARL). Previous works typically combine raw messages of teammates with local ...
Abstract: Deep learning has shown remarkable success in remote sensing change detection (CD), aiming to identify semantic change regions between co-registered satellite image pairs acquired at ...
Abstract: Towards building online analytical services on big heterogeneous graphs, we study the problem of the multithreading graph aggregation. The purpose is to exploit the thread-level parallelism ...
Abstract: The unstructured, unordered and inherent irregular sampling properties presents difficulties for accurate and efficient realizing semantic segmentation of large-scale 3D point cloud. The ...
Abstract: Accurately mappingtree stems is essentialfor the analysis and estimation of tree parameters derived from terrestrial laser scanning (TLS) point clouds, including critical measurements such ...
The aggregation pipeline is a powerful tool that allows developers to perform advanced data analysis and manipulation on their collections. The pipeline is a sequence of data processing operations, ...
Abstract: Federated learning (FL), as a promising machine learning paradigm for large-scale distributed data, faces two security challenges of privacy and robustness: the transmitted model updates ...
Abstract: Existing methods for learning 3D point cloud representation often use a single dataset-specific training and testing approach, leading to performance drops due to significant domain shifts ...
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