A Multimodal Autonomic Sensing Framework for Objective Assessment of Patient Responses to Dental Pulp Stimulation
A multimodal model using ECG-derived and electrodermal signals classifies dental pulp-test responses in 49 patients.
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A multimodal model using ECG-derived and electrodermal signals classifies dental pulp-test responses in 49 patients.
TraceGuard filters poisoned image-text training data via rank agreement among six corpus-level features, removing 98.4% of poison and 5.4% of clean examples.
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