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Neural motion trackers routinely accumulate drift over repetitive biological cycles; pairing persistent cross-window memory tokens with cyclic teacher-student supervision forces trajectories to close naturally without sacrificing frame-to-frame precision.
TSPFN outperforms both traditional tabular models and specialized deep learning approaches in classifying physiological time series, showcasing its superior generalization capabilities.
Synthesizing more realistic echocardiographic data lets deep learning models beat the clinical standard for myocardial strain estimation, a key biomarker for early diagnosis.
