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Titlebook: Database Systems for Advanced Applications; 29th International C Makoto Onizuka,Jae-Gil Lee,Kejing Lu Conference proceedings 2024 The Edito

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51#
發(fā)表于 2025-3-30 08:37:22 | 只看該作者
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發(fā)表于 2025-3-30 14:06:38 | 只看該作者
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發(fā)表于 2025-3-30 17:37:03 | 只看該作者
CrimeAlarm: Towards Intensive Intent Dynamics in?Fine-Grained Crime PredictionMeanwhile, the output probability distributions are reciprocally learned between prediction networks to model unobserved criminal intents. Extensive experiments show that CrimeAlarm outperforms state-of-the-art methods in terms of NDCG@5, with improvements of 4.51% for the NYC16 and 7.73% for the CH
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發(fā)表于 2025-3-30 22:09:16 | 只看該作者
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發(fā)表于 2025-3-31 04:49:18 | 只看該作者
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發(fā)表于 2025-3-31 07:29:51 | 只看該作者
scCDCG: Efficient Deep Structural Clustering for?Single-Cell RNA-Seq via?Deep Cut-Informed Graph Embptures intercellular high-order structural information, overcoming the over-smoothing and inefficiency issues prevalent in prior graph neural network methods. (ii) ., tailored to accommodate the unique complexities of scRNA-seq data, specifically its high-dimension and high-sparsity. (iii) . that si
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發(fā)表于 2025-3-31 11:28:00 | 只看該作者
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發(fā)表于 2025-3-31 18:45:59 | 只看該作者
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發(fā)表于 2025-4-1 01:17:28 | 只看該作者
Hierarchical Cross-Level Graph Contrastive Learning for?Drug-Drug Interaction Predictions. Finally, a cross-level contrastive learning module is introduced to align multi-view information. Extensive evaluation on real-world datasets demonstrates that our method outperforms existing competitors.
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