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HA-DLC reveals that effectively modeling intra-tumoural heterogeneity can dramatically improve tumour classification accuracy from multiparametric MRI.
SeFoRA enables efficient federated fine-tuning of large models by overcoming the challenges of heterogeneous client ranks, achieving superior performance on benchmark tasks.
EvolveNav achieves a 10.1% boost in success rates for zero-shot navigation by enabling agents to learn and adapt in real-time from their experiences.
