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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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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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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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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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DAPO-QAOA: An algorithm for solving combinatorial optimization problems by dynamically constructing phase operators

arXiv

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arXiv 2025年

作者： Wang, Yukun Li, ZeYang Wan, Linchun Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum Beijing102249 China State Key Lab of Processors Institute of Computing Technology CAS Beijing100190 China School of Computer and Information Science Southwest University Chongqing400715 China

The Quantum Approximate Optimization Algorithm (QAOA) is a well-known hybrid quantum-classical algorithm for combinatorial optimization problems. Improving QAOA involves enhancing its approximation ratio while addressing practical constraints of Noisy Intermediate Scale Quantum (NISQ) devices, such as minimizing the number of two-qubit gates and reducing circuit depth. Although existing research has optimized designs for phase and mixer operators to improve performance, challenges remain, particularly concerning the excessive use of two-qubit gates in the construction of phase operators. To address these issues, we introduce a Dynamic Adaptive Phase Operator (DAPO) algorithm, which dynamically constructs phase operators based on the output of previous layers and neighborhood search approach, optimizing the problem Hamiltonian more efficiently. By using solutions generated by QAOA itself to simplify the problem Hamiltonian at each layer, the algorithm captures the problem’s structural properties more effectively, progressively steering the solution closer to the optimal target. Experimental results on MaxCut and NAE3SAT problems show that DAPO achieves higher approximation ratios and significantly reduces two-qubit RZZ gates, especially in dense graphs. Compared to vanilla QAOA, DAPO uses only 66% of RZZ gates at the same depth while delivering better results, demonstrating its potential for efficient combinatorial optimization in the NISQ era. © 2025, CC BY.

关键词： Hamiltonians

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RRx-001 ameliorates inflammatory diseases by acting as a potent covalent NLRP3 inhibitor

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Cellular & Molecular Immunology 2021年第6期18卷 1425-1436页

作者： Yun Chen Hongbin He Bolong Lin Yun Chen Xianming Deng Wei Jiang Rongbin Zhou Hefei National Laboratory for Physical Sciences at Microscale the CAS Key Laboratory of Innate Immunity and Chronic DiseaseSchool of Basic Medical SciencesDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiChina Department of Geriatrics Gerontology Institute of Anhui Provincethe First Affiliated Hospital of USTCDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiChina CAS Centre for Excellence in Cell and Molecular Biology University of Science and Technology of ChinaHefeiChina State Key Laboratory of Cellular Stress Biology Innovation Center for Cell Signaling NetworkSchool of Life SciencesXiamen UniversityXiamenFujianChina

The NLRP3 inflammasome plays a crucial role in innate immune-mediated inflammation and contributes to the pathogenesis of multiple autoinflammatory,metabolic and neurodegenerative diseases,but medications targeting the NLRP3 inflammasome are not available for clinical ***-001 is a well-tolerated anticancer agent currently being investigated in phase III clinical trials,but its effects on inflammatory diseases are not ***,we show that RRx-001 is a highly selective and potent NLRP3 inhibitor that has strong beneficial effects on NLRP3-driven inflammatory ***-001 inhibits the activation of the canonical,noncanonical,and alternative NLRP3 inflammasomes but not the AIM2,NLRC4 or Pyrin ***,RRx-001 covalently binds to cysteine 409 of NLRP3 via its bromoacetyl group and therefore blocks the NLRP3-NEK7 interaction,which is critical for the assembly and activation of the NLRP3 *** importantly,RRx-001 treatment attenuates the symptoms of lipopolysaccharide(LPS)-induced systemic inflammation,dextran sulfate sodium(DSS)-induced colitis and experimental autoimmune encephalomyelitis(EAE)in ***,our study identifies RRx-001 as a new potential therapeutic agent for NLRP3-driven diseases.

关键词： RRx-001 NLRP3 inflammasome inflammatory diseases

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Image recognition and detection based on fast area convolutional neural network

Image recognition and detection based on fast area convoluti...

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2021 International Conference on Computer, Communication, Control, Automation and Robotics, CCCAR 2021

作者： Zhang, Hanwen Qin, Zhen Xie, Hua School of Automation Engineering UESTC Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China China

Nowadays, image recognition and detection technologies based on traditional artificial neural networks and convolutional neural networks are slightly inadequate in terms of training and recognition time and accuracy, and are difficult to deploy on devices with limited hardware resources. Therefore, this article proposes a recognition and detection technology based on fast regional convolutional neural networks. We use RPN (Region Proposal network) instead of Selective Search method to rebuild the network, and add a new ROI pooling layer before the fully connected layer of CNN. Determine the category. The average detection accuracy on our data set can reach 83.8%, and the training time is only 0.34 hours. © Published under licence by IOP Publishing Ltd.

关键词： Image recognition

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LSketch: A Label-Enabled Graph Stream Sketch Toward Time-Sensitive Queries

arXiv

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arXiv 2023年

作者： Zeng, Yiling Song, Chunyao Li, Yuhan Ge, Tingjian Tianjin Key Laboratory of Network and Data Security Technology College of Computer Science Nankai University Tianjin300350 China Department of Computer Science University of Massachusetts Lowell United States

Graph streams represent data interactions in real applications. The mining of graph streams plays an important role in network security, social network analysis, and traffic control, among others. However, the sheer volume and high dynamics cause great challenges for efficient storage and subsequent query analysis on them. Current studies apply sketches to summarize graph streams. We propose LSketch that works for heterogeneous graph streams, which effectively preserves the label information carried by the streams in real scenes, thereby enriching the expressive ability of sketches. In addition, as graph streams continue to evolve over time, edges too old may lose their practical significance. Therefore, we introduce the sliding window model into LSketch to eliminate the expired edges automatically. LSketch uses sub-linear storage space and can support structure based queries and time-sensitive queries with high accuracy. We perform extensive experiments over four real datasets, demonstrating the superiority of the proposed method over state-of-the-art methods, in aspects of query accuracy and time efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： network security

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion

Multi-Label Text Classification for Judicial Texts via Dual ...

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Smart World Congress (SWC), IEEE

作者： Qiliang Gu Qin Lu Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to ${2. 2 8 \%}$ and a minimum of ${1. 2 9 \%}$ across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model.

关键词： Attention mechanisms Correlation Law Graph convolutional networks Fuses Text categorization Semantics Predictive models Multitasking Internet

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