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SPINET is introduced, which predicts sequences from molecular dynamics trajectories that uses cellular sheaves to represent residue interactions within each frame and recurrent units to integrate information across frames, then predicts all amino acids in a single pass.
The Temporal Simplicial Neural Network is introduced, which represents EEG recordings as sequences of evolving simplicial complexes, and jointly learns higher-order interactions and their temporal evolution by combining simplicial convolutions with recurrent updates.
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