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Deep learning-based large-scale named entity recognition for anatomical region of mammalian brain

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Quantitative Biology 2022年第3期10卷 253-263页

作者： Xiaokang Chai Yachao Di Zhao Feng Yue Guan Guoqing Zhang Anan Li Qingming Luo Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for OptoelectronicsMoE Key Laboratory for Biomedical PhotonicsHuazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Biomedical Engineering of Hainan Province School of Biomedical EngineeringHainan UniversityHaikou 570228China CAS Key Laboratory of Computational Biology Bio-Med Big Data CenterShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesShanghai 200031China Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging Chinese Academy of Medical SciencesHUST-Suzhou Institute for BrainsmaticsJITRISuzhou 215123China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China

Background:Images of anatomical regions and neuron type distribution,as well as their related literature are valuable assets for neuroscience *** are vital evidence and vehicles in discovering new phenomena and knowledge refinement through image and text big *** knowledge acquired from image data generally echoes with the literature accumulated over the *** knowledge within the literature can provide a comprehensive context for a deeper understanding of the image ***,it is quite a challenge to manually identify the related literature and summarize the neuroscience knowledge in the large-scale ***,neuroscientists are in dire need of an automated method to extract neuroscience knowledge from large-scale ***:A proposed deep learning model named BioBERT-CRF extracts brain region entities from the WhiteText *** model takes advantage of BioBERT and CRF to predict entity labels while ***:The proposed deep learning model demonstrated comparable performance against or even outperforms the previous models on the WhiteText *** BioBERT-CRF model has achieved the best average precision,recall,and F1 score of 81.3%,84.0%,and 82.6%,*** used the BioBERT-CRF model to predict brain region entities in a large-scale PubMed abstract dataset and used a rule-based method to normalize all brain region entities to three neuroscience ***:Our work shows that the BioBERT-CRF model can be well-suited for brain region entity *** rankings of different brain region entities by their appearance in the large-scale corpus indicate the anatomical regions that researchers are most concerned about.

关键词： brain region entity extraction literature mining WhiteText deep learning

WiOpen: A Robust Wi-Fi-based Open-set Gesture Recognition Framework

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arXiv 2024年

作者： Zhang, Xiang Huang, Jingyang Yan, Huan Zhao, Peng Zhuang, Guohang Liu, Zhi Liu, Bin CAS Key Laboratory of Electromagnetic Space Information University of Science and Technology of China Hefei230026 China School of Computer and Information Hefei University of Technology Hefei230601 China School of Big Data and Computer Science Guizhou Normal University Guiyang550025 China Department of Computer and Network Engineering The University of Electro-Communications Tokyo1828585 Japan

Recent years have witnessed a growing interest in Wi-Fi-based gesture recognition. However, existing works have predominantly focused on closed-set paradigms, where all testing gestures are predefined during training. This poses a significant challenge in real-world applications, as unseen gestures might be misclassified as known classes during testing. To address this issue, we propose WiOpen, a robust Wi-Fi-based Open-Set Gesture Recognition (OSGR) framework. Implementing OSGR requires addressing challenges caused by the unique uncertainty in Wi-Fi sensing. This uncertainty, resulting from noise and domains, leads to widely scattered and irregular data distributions in collected Wi-Fi sensing data. Consequently, data ambiguity between classes and challenges in defining appropriate decision boundaries to identify unknowns arise. To tackle these challenges, WiOpen adopts a two-fold approach to eliminate uncertainty and define precise decision boundaries. Initially, it addresses uncertainty induced by noise during data preprocessing by utilizing the CSI ratio. Next, it designs the OSGR network based on an uncertainty quantification method. Throughout the learning process, this network effectively mitigates uncertainty stemming from domains. Ultimately, the network leverages relationships among samples’ neighbors to dynamically define open-set decision boundaries, successfully realizing OSGR. Comprehensive experiments on publicly accessible datasets confirm WiOpen’s effectiveness. Notably, WiOpen also demonstrates superiority in cross-domain tasks when compared to state-of-the-art approaches. © 2024, CC BY-SA.

关键词： Learning systems

Vertex-Critical (P5, chair)-Free Graphs

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arXiv 2023年

作者： Huang, Shenwei Li, Zeyu College of Computer Science Nankai University Tianjin300350 China Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300071 China

Given two graphs H1 and H2, a graph G is (H1, H2)-free if it contains no induced subgraph isomorphic to H1 or H2. A Pt is the path on t vertices. A chair is a P4 with an additional vertex adjacent to one of the middle vertices of the P4. A graph G is k-vertex-critical if G has chromatic number k but every proper induced subgraph of G has chromatic number less than k. In this paper, we prove that there are finitely many 5-vertex-critical (P5, chair)-free graphs. © 2023, CC BY.

关键词： Graphic methods

TOWARDS GENERALIZABLE GRAPH CONTRASTIVE LEARNING: AN INFORMATION THEORY PERSPECTIVE

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arXiv 2022年

作者： Yuan, Yige Xu, Bingbing Shen, Huawei Cao, Qi Cen, Keting Zheng, Wen Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Com- puting Technology Chinese Academy of Sciences Beijing China

Graph contrastive learning (GCL) emerges as the most representative approach for graph representation learning, which leverages the principle of maximizing mutual information (InfoMax) to learn node representations applied in downstream tasks. To explore better generalization from GCL to downstream tasks, previous methods heuristically define data augmentation or pretext tasks. However, the generalization ability of GCL and its theoretical principle are still less reported. In this paper, we first propose a metric named GCL-GE for GCL generalization ability. Considering the intractability of the metric due to the agnostic downstream task, we theoretically prove a mutual information upper bound for it from an information-theoretic perspective. Guided by the bound, we design a GCL framework named InfoAdv with enhanced generalization ability, which jointly optimizes the generalization metric and InfoMax to strike the right balance between pretext task fitting and the generalization ability on downstream tasks. We empirically validate our theoretical findings on a number of representative benchmarks, and experimental results demonstrate that our model achieves state-of-the-art performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Information theory

Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learning

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arXiv 2022年

作者： Xu, Shicheng Pang, Liang Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology CAS University of Chinese Academy of Sciences Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology CAS University of Chinese Academy of Sciences Beijing China

Text matching is a fundamental technique in both information retrieval and natural language processing. Text matching tasks share the same paradigm that determines the relationship between two given texts. The relationships vary from task to task, e.g. relevance in document retrieval, semantic alignment in paraphrase identification and answerable judgment in question answering. However, the essential signals for text matching remain in a finite scope, i.e. exact matching, semantic matching, and inference matching. Ideally, a good text matching model can learn to capture and aggregate these signals for different matching tasks to achieve competitive performance, while recent state-of-the-art text matching models, e.g. Pre-trained Language Models (PLMs), are hard to generalize. It is because the end-to-end supervised learning on task-specific dataset makes model overemphasize the data sample bias and task-specific signals instead of the essential matching signals, which ruins the generalization of model to different tasks. To overcome this problem, we adopt a specialization-generalization training strategy and refer to it as Match-Prompt. In specialization stage, descriptions of different matching tasks are mapped to only a few prompt tokens. In generalization stage, text matching model explores the essential matching signals by being trained on diverse multiple matching tasks. High diverse matching tasks avoid model fitting the data sample bias on a specific task, so that model can focus on learning the essential matching signals. Meanwhile, the prompt tokens obtained in the first step are added to the corresponding tasks to help the model distinguish different task-specific matching signals, as well as to form the basis prompt tokens for a new matching task. In this paper, we consider five common text matching tasks including document retrieval, open-domain question answering, retrieval-based dialogue, paraphrase identification, and natural language inference. Exper

关键词： Information retrieval

A Load Balancing Strategy Of "Container Virtual Machine" Cloud Microservice Based On Deadline Limit 14

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A Load Balancing Strategy Of "Container Virtual Machine" Clo...

14th International Conference on Measuring technology and Mechatronics Automation, ICMTMA 2022

作者： Xia, Hong Liu, MengDi Chen, YanPing Jin, XiaoMin Wang, ZhongMin Wang, FengWei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China ZTE Corporation No.55 Hi-tech Road South Shenzhen518057 China

ISBN: (纸本)9781665499781

Aiming at the problem of workflow scheduling under the "container virtual machine" two-tier structure in cloud microservice workflow application, a "container virtual machine" load balancing strategy based on deadline limit (container virtual machine load) is proposed. Balance strategy), CV LBMs strategy first obtains the optimal economic configuration of the corresponding instance of the micro service according to historical data, and calculated the list of remaining resources in the leased virtual machine;Determined whether the virtual machine is on the virtual machine according to the container image list in the virtual machine. Whether a corresponding container image exists, and create a new container on the virtual machine that already has a container image to reduce the time to extract the container image. The workflow simulation results showed that compared with the existing algorithms, the C-V LBMs strategy had the ability to meet the deadline success rate and reduced the cost. © 2022 IEEE

关键词： Containers

mRNA vaccines expressing homo-prototype/Omicron and hetero-chimeric RBD-dimers against SARS-CoV-2

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细胞研究（英文版） 2022年第11期32卷 1022-1025页

作者： Yuxuan Han Yaling An Qian Chen Kun Xu Xueyuan Liu Senyu Xu Huixin Duan Annette B.Vogel Ugur ?ahin Qihui Wang Lianpan Dai George F.Gao Savaid Medical School University of Chinese Academy of SciencesBeijingChina Institutes of Physical Science and Information Technology Anhui UniversityHefeiAnhuiChina CAS Key Laboratory of Pathogen Microbiology and Immunology Institute of MicrobiologyChinese Academy of SciencesBeijingChina Research Network of Immunity and Health(RNIH) Beijing Institutes of Life ScienceChinese Academy of SciencesBeijingChina Key Laboratory of Tropical Translational Medicine of Ministry of Education School of Tropical Medicine and Laboratory MedicineThe First Affiliated HospitalHainan Medical UniversityHaikouHainanChina School of Public Health Cheeloo College of MedicineShandong UniversityJinanShandongChina Research Network of Immunity and Health(RNIH) Beijing Institutes of Life ScienceChinese Academy of SciencesBeijingChina BioNTech SE MainzGermany Savaid Medical School University of Chinese Academy of SciencesBeijingChina CAS Key Laboratory of Pathogen Microbiology and Immunology Institute of MicrobiologyChinese Academy of SciencesBeijingChina Savaid Medical School University of Chinese Academy of SciencesBeijingChina CAS Key Laboratory of Pathogen Microbiology and Immunology Institute of MicrobiologyChinese Academy of SciencesBeijingChina Key Laboratory of Tropical Translational Medicine of Ministry of Education School of Tropical Medicine and Laboratory MedicineThe First Affiliated HospitalHainan Medical UniversityHaikouHainanChina Savaid Medical School University of Chinese Academy of SciencesBeijingChina CAS Key Laboratory of Pathogen Microbiology and Immunology Institute of MicrobiologyChinese Academy of SciencesBeijingChina Research Network of Immunity and Health(RNIH) Beijing Institutes of Life ScienceChinese Academy of SciencesBeijingChina

Dear Editor,Since emerging in 2019,the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)has caused an ongoing human pandemic of coronavirus disease 2019(COVID-19).1 Vaccines against COVID-19 were developed a...

Dear Editor, Since emerging in 2019,the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)has caused an ongoing human pandemic of coronavirus disease 2019(COVID-19).1 Vaccines against COVID-19 were developed and rolled out for large-scale vaccination.

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GERE: Generative Evidence Retrieval for Fact Verification

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arXiv 2022年

作者： Chen, Jiangui Zhang, Ruqing Guo, Jiafeng Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

Fact verification (FV) is a challenging task which aims to verify a claim using multiple evidential sentences from trustworthy corpora, e.g., Wikipedia. Most existing approaches follow a three-step pipeline framework, including document retrieval, sentence retrieval and claim verification. High-quality evidences provided by the first two steps are the foundation of the effective reasoning in the last step. Despite being important, high-quality evidences are rarely studied by existing works for FV, which often adopt the off-the-shelf models to retrieve relevant documents and sentences in an "index-retrieve-then-rank" fashion. This classical approach has clear drawbacks as follows: i) a large document index as well as a complicated search process is required, leading to considerable memory and computational overhead;ii) independent scoring paradigms fail to capture the interactions among documents and sentences in ranking;iii) a fixed number of sentences are selected to form the final evidence set. In this work, we propose GERE, the first system that retrieves evidences in a generative fashion, i.e., generating the document titles as well as evidence sentence identifiers. This enables us to mitigate the aforementioned technical issues since: i) the memory and computational cost is greatly reduced because the document index is eliminated and the heavy ranking process is replaced by a light generative process;ii) the dependency between documents and that between sentences could be captured via sequential generation process;iii) the generative formulation allows us to dynamically select a precise set of relevant evidences for each claim. The experimental results on the FEVER dataset show that GERE achieves significant improvements over the state-of-the-art baselines, with both time-efficiency and memory-efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Efficiency

Graph Representation Learning for Microarchitecture Design Space Exploration 23

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Graph Representation Learning for Microarchitecture Design S...

Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Xiaoling Yi Jialin Lu Xiankui Xiong Dong Xu Li Shang Fan Yang State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China and School of Microelectronics Fudan University Shanghai China ZTE Corporation China and State Key Laboratory of Mobile Network and Mobile Multimedia Technology China State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China and China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798350323481

Design optimization of modern microprocessors is a complex task due to the exponential growth of the design space. This work presents GRL-DSE, an automatic microarchitecture search framework based on graph embeddings. GRL-DSE uses graph representation learning to build a compact and continuous embedding space. Multi-objective Bayesian optimization using an ensemble surrogate model conducts microarchitecture design space exploration in the graph embedding space to efficiently and holistically optimize performance-power-area (PPA) objectives. Experimental studies on RISC-V BOOM show that GRL-DSE outperforms previous techniques by 74.59% on Pareto front quality and outperforms manual designs in terms of PPA.

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