A multi-target regression framework uses LLM-derived continuous sentiment profiles from narratives and dynamic functional connectivity from fMRI to track naturalistic emotional trajectories, outperforming static ROI measures.
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Color2Struct is a deep learning framework for inverse design of structural colors that improves on tandem networks by 65% in color difference and 48% in short-wave near-infrared reflectivity through sampling bias correction, adaptive loss weighting, and physics-guided inference.
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Decoding Naturalistic Emotion Dynamics from the Brain: An LLM-Enhanced Regression Framework
A multi-target regression framework uses LLM-derived continuous sentiment profiles from narratives and dynamic functional connectivity from fMRI to track naturalistic emotional trajectories, outperforming static ROI measures.
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Color2Struct: efficient and accurate deep-learning inverse design of structural color with controllable inference
Color2Struct is a deep learning framework for inverse design of structural colors that improves on tandem networks by 65% in color difference and 48% in short-wave near-infrared reflectivity through sampling bias correction, adaptive loss weighting, and physics-guided inference.