Alzheimer's disease (AD) is a prevalent neurode generative disorder that demands more comprehensive and precise modeling to unravel its complex pathological mechanisms and multidimensional brain network abnormalities. However, current brain network analysis approaches often suffer from limitations such as single-modal data processing and shallow fusion strategies, which hinder their ability to capture the multi scale connectivity patterns associated with AD effectively. To alleviate these limitations, a novel multi-modal fusion framework is proposed in this paper, namely a cross-modal self-distillation graph convolutional network (CSD-GCN). The framework can deeply integrate fMRI and DTI data to facilitate the systematic extraction of hierarchical features from multimodal brain net works. The architecture of CSD-GCN comprises three progressive layers: edge-to-edge (E2E), edge-to-node (E2N), and node-to graph (N2G). These layers are designed to perform multi-scale feature extraction, capturing neurodegenerative patterns ranging from local connection strengths to global topological structures. Moreover, the model incorporates a cross-modal self-knowledge distillation strategy and a cross-attention mechanism to enhance inter-modal feature alignment and fusion. These components collectively improve the generalization ability and discriminative capability of the learned representations. Experimental evaluations show that CSD-GCN outperforms existing methods in binary and ternary classification tasks for AD, with ablation studies confirming the individual effect of each component within the proposed framework.
Junchang Xin, Jinying Tao, Qi Chen et al.· IEEE transactions on computa...· 0 citations
Due to their increasingly large volumes, outsourcing of trajectory storage and querying to third-party service providers has become attractive. However, in such outsourced environments, service providers may return incorrect, e.g., incomplete, tampered, or invalid query results, making verifiability of query results an important consideration. Existing hybrid-storage blockchains offer limited support for trajectory data, lacking authenticated data structures (ADS) that enable efficient verification. For example, ADSs designed for queries on one-dimensional data are unsuitable for queries on multidimensional trajectory data, while ADSs tailored for discrete data may yield incomplete results when applied to continuous trajectory data. We propose the first framework for verifiable trajectory range queries in hybrid-storage blockchains, called VTRQ. It features two efficient ADSs: (i) a spatial ADS for road networks that leverages hierarchical organization to aggregate trajectory, edge, and node hashes, thus reducing redundant computations and improving spatial verification efficiency; and (ii) a temporal ADS based on interval trees, which indexes only the start and end times of trajectories, thereby enabling pruning and efficient temporal verification. By separating spatial and temporal indexing, the method reduces the need for data comparison, enhancing both query and verification efficiency. To aggregate spatial and temporal query results, VTRQ provides a spatio-temporal edge aggregation mechanism that combines temporal verification of spatial nodes, spatial intersection computation, and temporal intersection analysis to achieve spatio-temporal filtering.
Zhongming Yao, Junchang Xin, Yumeng Song et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
