MPCFormer explicitly models multi-vehicle social interaction dynamics via physics-informed discrete state-space and Transformer-learned coefficients, yielding 0.86m ADE over 5s and 94.67% planning success with near-zero collisions in closed-loop tests.
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HeartBERT applies self-supervised pretraining on a RoBERTa architecture to ECG signals, producing embeddings that enable strong performance on sleep staging and heartbeat classification with smaller labeled datasets and fewer parameters than baselines.
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MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
MPCFormer explicitly models multi-vehicle social interaction dynamics via physics-informed discrete state-space and Transformer-learned coefficients, yielding 0.86m ADE over 5s and 94.67% planning success with near-zero collisions in closed-loop tests.
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HeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal Analysis
HeartBERT applies self-supervised pretraining on a RoBERTa architecture to ECG signals, producing embeddings that enable strong performance on sleep staging and heartbeat classification with smaller labeled datasets and fewer parameters than baselines.