Abstract: This paper investigates a GraphRAG framework that integrates knowledge graphs into the Retrieval-Augmented Generation (RAG) architecture to enhance networking applications. While RAG has ...

Abstract: Although deep networks have succeeded in various signal classification tasks, the time sequence samples used to train the deep models are usually required to reach a certain length.

Abstract: Federated Learning is an approach that enables multiple devices to collectively train a shared model without sharing raw data, thereby preserving data privacy. However, federated learning ...

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Abstract: Space-air-ground integrated networks (SAGINs) face unprecedented security challenges due to their inherent characteristics, such as multidimensional heterogeneity and dynamic topologies.

Abstract: Aiming at the problem of poor edge effect segmentation in point cloud segmentation, which fails to fully utilize the correlation between the local geometric and semantic features of point ...

Abstract: Yield volume estimation is the integral part of modern farming. While classical model-based approaches are already well-developed, in the recent years neural network-based end-to-end methods ...

Abstract: We present CO-Net++, a cohesive framework that optimizes multiple point cloud tasks collectively across heterogeneous dataset domains with a two-stage feature rectification strategy. The ...

“Point Break” is smacking the lip once more, as AMC Networks is developing a TV adaptation of the 1991 surf action flick, Variety has confirmed. The series is set in 2026, 35 years after the events of ...

Abstract: This article proposes a method to detect change points in dynamic social networks using Fréchet statistics. We address two main questions: 1) what metric can quantify the distances between ...

Abstract: Point cloud registration, which estimates a rigid transformation matrix between two point clouds, is a fundamental process in numerous applications. While existing detector-free techniques ...