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It is found that LoRA fine-tuned open-source SLMs outperform commercial baselines on triage level prediction and specialist referral recommendation, while diagnosis prediction remains challenging for open-source SLMs.
A random-walk-based LoRA fine-tuning scheme that eliminates the need for global synchronization, substantially reduces communication and computation costs, and avoids aggregation errors is proposed and provides rigorous convergence guarantees for non-convex objectives under standard assumptions.
Quantum networks are expected to support a wide range of emerging applications by enabling entanglement routing among remote nodes. Existing studies on quantum entanglement routing primarily focus on either ground-based networks or satellite-assisted networks. Moreover, bipartite and multipartite entanglement demands a...
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