Multi-Functional Embedding Models for Funder Name Disambiguation in Scientific Publication Records
为解决科研资助者名称消歧问题，本文提出一种多语言多功能框架，利用多任务学习和对比损失等方法训练嵌入模型，提高匹配准确性。
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Selected work
Representative Papers
Toppling the Hierarchy in Byte-level Language Modeling
本文探讨了字节级模型在字符处理上的不足，指出分层结构限制了字符级理解，并通过实验表明纯字节级模型表现更优。
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From Specialization to Generalization: Instruction-tuned LLMs for Robust Harmful Content Mitigation
本文通过指令调优方法改进大型语言模型，专门针对有害内容（如仇恨言论）的检测和缓解，展示了在领域内及跨领域、跨语言方面的显著性能提升。
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International Transfer of Stochastic Cortical Self-Reconstruction
This study addresses the limitations of traditional cortical atrophy modeling—coarse regional granularity, reliance on covariates, and poor generalizability—by leveraging the Stochastic Cortical Self-Reconstruction (SCSR) framework. It presents the first validation of SCSR’s robust cross-population transferability, successfully generalizing from the UK Biobank cohort to a Chinese population. The approach employs vertex-level personalized healthy references, integrating a spherical U-Net (SUNet) with a multilayer perceptron for reconstruction, and incorporates fine-tuning strategies to enhance performance. The fine-tuned SUNet achieves state-of-the-art results with an average pairwise AUC of 0.848 and exhibits low reconstruction error across the full lifespan, significantly improving the detection of Alzheimer’s disease–related cortical atrophy in diverse populations.
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Multi-Functional Embedding Models for Funder Name Disambiguation in Scientific Publication Records
为解决科研资助者名称消歧问题，本文提出一种多语言多功能框架，利用多任务学习和对比损失等方法训练嵌入模型，提高匹配准确性。
0 citationsRead paper
Toppling the Hierarchy in Byte-level Language Modeling
本文探讨了字节级模型在字符处理上的不足，指出分层结构限制了字符级理解，并通过实验表明纯字节级模型表现更优。
0 citationsRead paper
From Specialization to Generalization: Instruction-tuned LLMs for Robust Harmful Content Mitigation
本文通过指令调优方法改进大型语言模型，专门针对有害内容（如仇恨言论）的检测和缓解，展示了在领域内及跨领域、跨语言方面的显著性能提升。
0 citationsRead paper
International Transfer of Stochastic Cortical Self-Reconstruction
This study addresses the limitations of traditional cortical atrophy modeling—coarse regional granularity, reliance on covariates, and poor generalizability—by leveraging the Stochastic Cortical Self-Reconstruction (SCSR) framework. It presents the first validation of SCSR’s robust cross-population transferability, successfully generalizing from the UK Biobank cohort to a Chinese population. The approach employs vertex-level personalized healthy references, integrating a spherical U-Net (SUNet) with a multilayer perceptron for reconstruction, and incorporates fine-tuning strategies to enhance performance. The fine-tuned SUNet achieves state-of-the-art results with an average pairwise AUC of 0.848 and exhibits low reconstruction error across the full lifespan, significantly improving the detection of Alzheimer’s disease–related cortical atrophy in diverse populations.
0 citationsRead paper