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The paper proposes TriDim, a reusable block for EEG decoding that preserves spatial and temporal structures, achieving state-of-the-art performance across multiple datasets and improving existing models.
This paper proposes PhenMol, a structure-preserving framework for phenotype-aware molecular representation learning that integrates cellular phenotype information while preserving chemical structure neighborhood organization, improving molecular property prediction and drug discovery tasks.