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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

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

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

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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

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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

arXiv

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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

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Graph Representation Learning for Microarchitecture Design Space Exploration 23

Graph Representation Learning for Microarchitecture Design S...

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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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A Universal Inflow Turbulence Generator for the Large Eddy Simulation of a Wide-Speed Compressible Turbulence Boundary Layer with Minimal Transitional Length

SSRN

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SSRN 2023年

作者： Zhu, Guowang Wu, Han Huang, Shenghong Zhang, Yundong CAS Key Laboratory of Mechanical Behavior and Design of Materials Department of Modern Mechanics University of Science and Technology of China Hefei230027 China Network and Information Center University of Science and Technology of China Hefei230027 China

Inflow turbulence generation with physical spatiotemporal characteristics and the requirement of a specific turbulence state is one of the most challenging scientific problems for engineering applications of large eddy simulation (LES)/direct numerical simulation (DNS) methods. The existing state-of-the-art methods require either precursor simulations or relatively long transitional sections plus a priori parameter tuning, which seriously limits their physical realizability and applicability in applications. In this paper, we propose a universal inflow turbulence generator for the large eddy simulation of a wide-speed compressible turbulence boundary called universal discretizing and synthesizing random flow generation (UDSRFG), which is developed based on a synthetic inflow turbulence generation method proposed by one of the authors for incompressible turbulence flows. Unlike the existing methods, the proposed method can theoretically guarantee divergence free of mass flux fluctuations, which fully eliminates unexpected instability noise sources for both low-speed incompressible and high-speed compressible flows. Large eddy simulations of compressible subsonic flat turbulent boundary layer flow and supersonic turbulent boundary layer flow are conducted to demonstrate the effectiveness and superiority of the proposed method. The synthetic inlet turbulence generated by UDSRFG rapidly develops into realistic turbulence and evolves the expected propertied of the turbulent boundary layer with a reduction in the transitional length in boundary layer flows of 50%~85% compared with that of the existing methods. The detailed turbulence quantity statistics, including the velocity fluctuations, mean velocity, friction coefficient, spectral distribution, and spatial correlation, indicate that the generated boundary layer satisfies all of the expected necessary properties provided in the literature and behaves more consistently for most of the validation datasets than the exist

关键词： Large eddy simulation

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MEFold: Memory-Efficient Optimization for Protein Language Models via Chunk and Quantization

MEFold: Memory-Efficient Optimization for Protein Language M...

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International Joint Conference on Neural networks (IJCNN)

作者： Yanfeng Jiang Ning Sun Zhengxian Lu Shuang Peng Yi Zhang Fei Yang Tao Li College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Zhejiang Lab Hangzhou China Haihe Lab of ITAI Tianjin China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Protein language models are currently experiencing a surge in demand owing to their remarkable accuracy in protein structure prediction. Nevertheless, their applications are hindered by the significant computation and memory requirements. The existing optimization strategies primarily focus on computational efficiency while often neglecting memory optimization, thereby restricting their suitability for devices with limited resources. In this paper, we propose MEFold, a novel memory-efficient optimization framework for protein language models that enables efficient inference on resource-constrained devices. MEFold consists of Look-up Table Chunk and Fine-grained Quantization. Look-up Table Chunk reduces the memory of intermediate activations by chunk and avoids the overhead of obtaining the optimal chunk size configuration through pre-computing. For the memory of model parameters, Fine-grained Quantization, delicately controls the scope of quantization to ensure that memory reduction is achieved while preventing declines in accuracy and computational speed. Experimental results show that, compared to the original model, for protein sequences ranging from 74 to 1024 in length, our method significantly reduces the peak memory during inference from 14.7-54.2GB to 6.0-14.4GB, while minimizing the impact on inference latency. On casP14 and CAMEO datasets, the accuracy loss compared to the original model is below 1%. Moreover, our optimization provides various memory-saving alternatives. Our code is available at https://***/llwx593/MEFold.

关键词： Proteins Quantization (signal) Accuracy Computational modeling Memory management Neural networks Predictive models

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CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks

arXiv

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arXiv 2022年

作者： Chen, Jiangui Zhang, Ruqing Guo, Jiafeng Liu, Yiqun Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China BNRist DCST Tsinghua University Beijing China

Knowledge-intensive language tasks (KILT) usually require a large body of information to provide correct answers. A popular paradigm to solve this problem is to combine a search system with a machine reader, where the former retrieves supporting evidences and the latter examines them to produce answers. Recently, the reader component has witnessed significant advances with the help of large-scale pre-trained generative models. Meanwhile most existing solutions in the search component rely on the traditional "index-retrieve-then-rank" pipeline, which suffers from large memory footprint and difficulty in end-to-end optimization. Inspired by recent efforts in constructing model-based IR models, we propose to replace the traditional multi-step search pipeline with a novel single-step generative model, which can dramatically simplify the search process and be optimized in an end-to-end manner. We show that a strong generative retrieval model can be learned with a set of adequately designed pre-training tasks, and be adopted to improve a variety of downstream KILT tasks with further fine-tuning. We name the pre-trained generative retrieval model as CorpusBrain as all information about the corpus is encoded in its parameters without the need of constructing additional index. Empirical results show that CorpusBrain can significantly outperform strong baselines for the retrieval task on the KILT benchmark and establish new state-of-the-art downstream performances. We also show that CorpusBrain works well under zero- and low-resource settings. Copyright © 2022, The Authors. All rights reserved.

关键词： Pipelines

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Crowdsense Roadside Parking Spaces with Dynamic Gap Reduction Algorithm

arXiv

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arXiv 2024年

作者： Zheng, Wenjun Shi, Zhan Ou, Qianyu Liao, Ruizhi School of Data Science The Chinese University of Hong Kong Shenzhen China Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology School of Humanities and Social Science The Chinese University of Hong Kong Shenzhen China

In the context of smart city development, mobile sensing emerges as a cost-effective alternative to fixed sensing for on-street parking detection. However, its practicality is often challenged by the inherent accuracy limitations arising from detection intervals. This paper introduces a novel Dynamic Gap Reduction Algorithm (DGRA), which is a crowdsensing-based approach aimed at addressing this question through parking detection data collected by sensors on moving vehicles. The algorithm's efficacy is validated through real drive tests and simulations. We also present a Driver-Side and Traffic-Based Model (DSTBM), which incorporates drivers' parking decisions and traffic conditions to evaluate DGRA's performance. Results highlight DGRA's significant potential in reducing the mobile sensing accuracy gap, marking a step forward in efficient urban parking management. © 2024, CC BY.

关键词： Parking

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Chirality-2 fermion induced anti-Klein tunneling in a two-dimensional checkerboard lattice

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Physical Review B 2024年第11期109卷 115429-115429页

作者： Jiannan Hua Z. F. Wang W. Zhu Weiwei Chen Department of Physics School of Science Westlake University Hangzhou Zhejiang 310024 China Institute of Natural Sciences Westlake Institute for Advanced Study Hangzhou Zhejiang 310024 China Hefei National Laboratory for Physical Sciences at the Microscale CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics Department of Physics University of Science and Technology of China Hefei Anhui 230026 China Hefei National Laboratory University of Science and Technology of China Hefei Anhui 230088 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province College of Science China Jiliang University Hangzhou 310018 China

The quantum tunneling effect in a two-dimensional (2D) checkerboard lattice is investigated. By analyzing the pseudospin texture of the states in a 2D checkerboard lattice based on the low-energy effective Hamiltonian, we find that this system has a chiral symmetry with chirality equal to 2, although compared with regular chiral fermions, its pseudospin orientation does not vary uniformly. This suggests that the perfect reflection chiral tunneling, also known as the anti-Klein tunneling, is expected to appear in a checkerboard lattice as well. To verify the conjecture, we calculate the transmission probability and find that normally incident electron states can be perfectly reflected by the barrier with hole states inside and vice versa. Furthermore, we numerically calculate the tunneling conductance of the checkerboard nanotube using the recursive Green's function method. The results show that a perfect on-off ratio can be achieved by confining the energy of the incident states within a certain range. It also suggests that, by tuning the barrier, the checkerboard nanotube can work as a perfect band filter or tunneling field effect transistor, which transmits electrons selectively with respect to the pseudospin of the incident electrons.

关键词： Transport phenomena Nanomechanical devices Transistors Tunnel junctions Landauer formula S-matrix method in transport

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