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REFIT introduces a label-free input calibration method for frozen activity-recognition models that corrects wearable sensor placement shifts (e.g., switching wrists or reattaching straps) by fitting axis-transform families to match training statistics, outperforming existing test-time adaptation approaches without retraining.
This paper proposes a novel offline reinforcement learning framework for active flow control that uses a sensor position-conditioned architecture with Point Attention layers to handle varying sensor configurations, enabling data-driven policy extraction without costly online interactions.