ChatLaw: Open-Source Legal Large Language Model with Integrated External Knowledge Bases

Large Language Models (LLMs) have shown the potential to revolutionize natural language processing tasks in various domains, sparking great interest in vertical-specific large models. However, unlike proprietary models such as BloombergGPT and FinGPT, which have leveraged their unique data accumulations to make strides in the finance domain, there hasn't not many similar large language models in the Chinese legal domain to facilitate its digital transformation. In this paper, we propose an open-source legal large language model named ChatLaw. Due to the importance of data quality, we carefully designed a legal domain fine-tuning dataset. Additionally, to overcome the problem of model hallucinations in legal data screening during reference data retrieval, we introduce a method that combines vector database retrieval with keyword retrieval to effectively reduce the inaccuracy of relying solely on vector database retrieval. Furthermore, we propose a self-attention method to enhance the ability of large models to overcome errors present in reference data, further optimizing the issue of model hallucinations at the model level and improving the problem-solving capabilities of large models. We also open-sourced our model and part of the data at https://github.com/PKU-YuanGroup/ChatLaw

LibreFace: An Open-Source Toolkit for Deep Facial Expression Analysis

Facial expression analysis is an important tool for human-computer interaction. In this paper, we introduce LibreFace, an open-source toolkit for facial expression analysis. This open-source toolbox offers real-time and offline analysis of facial behavior through deep learning models, including facial action unit (AU) detection, AU intensity estimation, and facial expression recognition. To accomplish this, we employ several techniques, including the utilization of a large-scale pre-trained network, feature-wise knowledge distillation, and task-specific fine-tuning. These approaches are designed to effectively and accurately analyze facial expressions by leveraging visual information, thereby facilitating the implementation of real-time interactive applications. In terms of Action Unit (AU) intensity estimation, we achieve a Pearson Correlation Coefficient (PCC) of 0.63 on DISFA, which is 7% higher than the performance of OpenFace 2.0 while maintaining highly-efficient inference that runs two times faster than OpenFace 2.0. Despite being compact, our model also demonstrates competitive performance to state-of-the-art facial expression analysis methods on AffecNet, FFHQ, and RAFDB. Our code will be released at https://github.com/ihp-lab/LibreFaceComment: 10 pages, 5 figures. Accepted by WACV 2024 Round 1. (Application Track

Build Your Own Robot Friend: An Open-Source Learning Module for Accessible and Engaging AI Education

As artificial intelligence (AI) is playing an increasingly important role in our society and global economy, AI education and literacy have become necessary components in college and K-12 education to prepare students for an AI-powered society. However, current AI curricula have not yet been made accessible and engaging enough for students and schools from all socio-economic backgrounds with different educational goals. In this work, we developed an open-source learning module for college and high school students, which allows students to build their own robot companion from the ground up. This open platform can be used to provide hands-on experience and introductory knowledge about various aspects of AI, including robotics, machine learning (ML), software engineering, and mechanical engineering. Because of the social and personal nature of a socially assistive robot companion, this module also puts a special emphasis on human-centered AI, enabling students to develop a better understanding of human-AI interaction and AI ethics through hands-on learning activities. With open-source documentation, assembling manuals and affordable materials, students from different socio-economic backgrounds can personalize their learning experience based on their individual educational goals. To evaluate the student-perceived quality of our module, we conducted a usability testing workshop with 15 college students recruited from a minority-serving institution. Our results indicate that our AI module is effective, easy-to-follow, and engaging, and it increases student interest in studying AI/ML and robotics in the future. We hope that this work will contribute toward accessible and engaging AI education in human-AI interaction for college and high school students.Comment: Accepted to the Proceedings of the AAAI Conference on Artificial Intelligence (2024

lowdensityextracorporealshockwaveandlowdoseintermittentrecombinanthumanparathyroidhormone134influenceproliferationanddifferentiationofosteoblasts

背景：体外冲击波等应力刺激可促进成骨，甲状旁腺激素激素也参与调控骨代谢。目的：实验探讨低剂量间歇人重组甲状旁腺素1-34和低能体外冲击波对体外培养大鼠成骨细胞增殖及成骨分化的作用。方法：采用改良胶原酶消化法培养大鼠乳鼠颅骨来源成骨细胞备用。分别用60-150次0.18 mJ/mm2低能体外冲击波刺激体外培养大鼠成骨细胞，不同浓度（10-12 mol/L-10-10 mol/L）及作用方式的人重组甲状旁腺素1-34刺激，以及低能体外冲击波和间歇低剂量（10-11 mol/L）间歇人重组甲状旁腺素1-34刺激共同作用后，用锥虫蓝法进行细胞计数、MTT和流式细胞术分析检测大鼠成骨细胞的增殖情况；用酶标仪检测碱性磷酸酶活性，用免疫组化检测Ⅰ型胶原表达来观察大鼠成骨细胞的成骨分化。结果与结论：60-150次0.18 mJ/mm 2低能体外冲击波刺激、间歇人重组甲状旁腺素1-34（10-11和10-10 mol/L）刺激以及低能体外冲击波＋间歇人重组甲状旁腺素1-34（10-11 mol/L）刺激均可显著促进体外培养大鼠成骨细胞增殖和成骨分化（P＜0.05），其中60-150次低能体外冲击波刺激＋间歇人重组甲状旁腺素1-34刺激各组作用最强（P＜0.05）。结果证实，适当的低能体外冲击波应力刺激和低剂量间歇人重组甲状旁腺素1-34刺激联合应用可显著促进体外培养大鼠成骨细胞的增殖和成骨分化

lowdensityextracorporealshockwaveandlowdoseintermittentrecombinanthumanparathyroidhormone134influenceproliferationanddifferentiationofosteoblasts

背景：体外冲击波等应力刺激可促进成骨，甲状旁腺激素激素也参与调控骨代谢。目的：实验探讨低剂量间歇人重组甲状旁腺素1-34和低能体外冲击波对体外培养大鼠成骨细胞增殖及成骨分化的作用。方法：采用改良胶原酶消化法培养大鼠乳鼠颅骨来源成骨细胞备用。分别用60-150次0.18 mJ/mm2低能体外冲击波刺激体外培养大鼠成骨细胞，不同浓度（10-12 mol/L-10-10 mol/L）及作用方式的人重组甲状旁腺素1-34刺激，以及低能体外冲击波和间歇低剂量（10-11 mol/L）间歇人重组甲状旁腺素1-34刺激共同作用后，用锥虫蓝法进行细胞计数、MTT和流式细胞术分析检测大鼠成骨细胞的增殖情况；用酶标仪检测碱性磷酸酶活性，用免疫组化检测Ⅰ型胶原表达来观察大鼠成骨细胞的成骨分化。结果与结论：60-150次0.18 mJ/mm 2低能体外冲击波刺激、间歇人重组甲状旁腺素1-34（10-11和10-10 mol/L）刺激以及低能体外冲击波＋间歇人重组甲状旁腺素1-34（10-11 mol/L）刺激均可显著促进体外培养大鼠成骨细胞增殖和成骨分化（P＜0.05），其中60-150次低能体外冲击波刺激＋间歇人重组甲状旁腺素1-34刺激各组作用最强（P＜0.05）。结果证实，适当的低能体外冲击波应力刺激和低剂量间歇人重组甲状旁腺素1-34刺激联合应用可显著促进体外培养大鼠成骨细胞的增殖和成骨分化

Enhancing the charging performance of lithium-ion batteries by reducing SEI and charge transfer resistances

To enable the mass adoption of electric vehicles, the charging performance of Li-ion batteries needs to be significantly enhanced. The development of novel electrolytes with enhanced transport properties and faster interfacial reaction is one critical approach to realize fast charging within 10 minutes. Most current electrolyte studies are focusing on ester-based electrolytes. In this work, an ether-based electrolyte is reported, which shows remarkably better-charging performance than commercial carbonate electrolytes and other reported ester-based electrolytes in both half and full cells. Electrochemical and spectroscopic characterization shows the superior charging performance of the reported electrolyte is due to significantly reduced SEI resistance and charge transfer resistance. Cycling tests show remarkable stability in Li||graphite(gr) half cells, suggesting the potential of the electrolytes to enhance battery charging performance. LiFePO4 (LFP) ||gr full cells were further tested, and it is found that the resistance of cells builds up during cycling due to gelation of the electrolyte, which limits the cycling performance of full cells. Potential strategies to address this limitation are discussed