A HIP-Compatible Accelerator Backend for Fourier-Bessel Particle-in-Cell Simulations on CPU/DCU Heterogeneous Clusters
为解决FBPIC在HIP环境下的部署问题，开发了兼容HIP的加速后端，实现了在CPU/DCU异构集群上的高效运行，并提升了模拟任务的速度。
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ICH-Qwen: A Large Language Model Towards Chinese Intangible Cultural Heritage
China’s intangible cultural heritage (ICH) faces severe challenges—including transmission discontinuity and skill attrition—amid rapid modernization. Existing large language models (LLMs) lack domain-specific adaptation for ICH, limiting their applicability in digital humanities and heritage preservation. To address this, we introduce the first Chinese LLM dedicated to Chinese ICH: built upon the Qwen architecture, it integrates domain-specific pretraining on ICH corpora, synthetic data augmentation tailored to ICH knowledge, supervised fine-tuning, and explicit knowledge alignment. This model achieves the first systematic deep semantic modeling of ICH within LLMs. Empirical evaluation demonstrates substantial improvements over general-purpose baselines across key tasks—including ICH question answering, generative description of traditional craftsmanship, and simulated dialogues with heritage bearers. The work provides a deployable, scalable technical framework and methodological paradigm for intelligent ICH preservation and digital humanities research.
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A HIP-Compatible Accelerator Backend for Fourier-Bessel Particle-in-Cell Simulations on CPU/DCU Heterogeneous Clusters
为解决FBPIC在HIP环境下的部署问题，开发了兼容HIP的加速后端，实现了在CPU/DCU异构集群上的高效运行，并提升了模拟任务的速度。
0 citationsRead paper
ICH-Qwen: A Large Language Model Towards Chinese Intangible Cultural Heritage
China’s intangible cultural heritage (ICH) faces severe challenges—including transmission discontinuity and skill attrition—amid rapid modernization. Existing large language models (LLMs) lack domain-specific adaptation for ICH, limiting their applicability in digital humanities and heritage preservation. To address this, we introduce the first Chinese LLM dedicated to Chinese ICH: built upon the Qwen architecture, it integrates domain-specific pretraining on ICH corpora, synthetic data augmentation tailored to ICH knowledge, supervised fine-tuning, and explicit knowledge alignment. This model achieves the first systematic deep semantic modeling of ICH within LLMs. Empirical evaluation demonstrates substantial improvements over general-purpose baselines across key tasks—including ICH question answering, generative description of traditional craftsmanship, and simulated dialogues with heritage bearers. The work provides a deployable, scalable technical framework and methodological paradigm for intelligent ICH preservation and digital humanities research.
0 citationsRead paper