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Guo-Xian Yu

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Aug 2026

When Federated Prompt Learning Fails: Understanding and Mitigating Cross-Domain Prompt Interference.

Federated prompt learning (FPL) offers a parameter-efficient way to adapt large-scale vision-language models by collaboratively optimizing lightweight prompts across distributed clients. Existing approaches typically assume that prompts learned on heterogeneous domains can be safely aggregated. We show that this assump...

Yun-Feng Zhao, Jun Wang, Dong Wu et al. · 0 citations
Aug 2026

Cell Type-Specific Isoform Function Prediction by Multiplex Heterogeneous Network

Sequence-importance analysis highlights critical amino-acid regions and shows that domains annotated with the same function can exhibit distinct importance profiles across spliced isoforms, providing new insights into cell-type-specific isoform functionality and establishing cIsoFun as a practical tool for single-cell...

Tong-Hui Gu, Han-Wen Luo, Yue-Qun Wang et al. · 0 citations

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