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By injecting basic physics, this method achieves up to 9% accuracy gains in human activity recognition, proving that inductive biases still matter for real-world sensor data.
By decoupling patch details from semantics, Cheers achieves state-of-the-art multimodal performance at 20% of the training cost of comparable models.