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Study of the shear-rate dependence of granular friction based on community detection

science China(Physics,Mechanics & Astronomy) 2019年第4期62卷 38-44页

作者： YongWen Zhang GaoKe Hu XiaoSong Chen Wei Chen WenQi Liu Data Science Research Center Faculty of Science Kunming University of Science and Technology CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences School of Physical Sciences University of Chinese Academy of Sciences State Key Laboratory of Multiphase Complex System Institute of Process Engineering Chinese Academy of Sciences

In this study, computer simulations are performed on three-dimensional granular systems under shear conditions. The system comprises granular particles that are confined between two rigid plates. The top plate is subjected to a normal force and driven by a shearing velocity. A positive shear-rate dependence of granular friction, known as velocity-strengthening, exists between the granular and shearing plate. To understand the origin of the dependence of frictional sliding, we treat the granular system as a complex network, where granular particles are nodes and normal contact forces are weighted edges used to obtain insight into the interiors of granular matter. Community structures within granular property networks are detected under different shearing velocities in the steady state. Community parameters, such as the size of the largest cluster and average size of clusters, show significant monotonous trends in shearing velocity associated with the shear-rate dependence of granular friction. Then, we apply an instantaneous change in shearing velocity. A dramatic increase in friction is observed with a change in shearing velocity in the non-steady state. The community structures in the non-steady state are different from those in the steady state. Results indicate that the largest cluster is a key factor affecting the friction between the granular and shearing plate.

关键词： granular shear-rate friction community detection

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Adversarial Learning data Augmentation for Graph Contrastive Learning in Recommendation 28th

Adversarial Learning Data Augmentation for Graph Contrastiv...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Huang, Junjie Cao, Qi Xie, Ruobing Zhang, Shaoliang Xia, Feng Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China WeChat Tencent Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031306716

Recently, Graph Neural networks (GNNs) achieve remarkable success in Recommendation. To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-based CF methods for enhancing performance. Most GCL methods consist of data augmentation and contrastive loss (e.g., InfoNCE). GCL methods construct the contrastive pairs by hand-crafted graph augmentations and maximize the agreement between different views of the same node compared to that of other nodes, which is known as the InfoMax principle. However, improper data augmentation will hinder the performance of GCL. InfoMin principle, that the good set of views shares minimal information and gives guidelines to design better data augmentation. In this paper, we first propose a new data augmentation (i.e., edge-operating including edge-adding and edge-dropping). Then, guided by InfoMin principle, we propose a novel theoretical guiding contrastive learning framework, named Learnable data Augmentation for Graph Contrastive Learning (LDA-GCL). Our methods include data augmentation learning and graph contrastive learning, which follow the InfoMin and InfoMax principles, respectively. In implementation, our methods optimize the adversarial loss function to learn data augmentation and effective representations of users and items. Extensive experiments on four public benchmark datasets demonstrate the effectiveness of LDA-GCL. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph neural networks

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International Conference on networking and Distributed computing

作者： Shan, Yi Liu, Taoying Liu, Hong Key Laboratory of Network Science Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9780769542072

It is generally believed that Zipf's law is well applied for Web objects. However, as OSNs prevail and Internet begins to concentrate more and more on individuals, many researchers report that request frequency of Web objects deviates from Zipf's law notably, especially for UGC (User Generated Content) sites. In this paper, we take the first step to explain it from the perspective of relations between content provider and consumer. Our contribution is twofold: (1) We propose a new metric named social locality to quantify relations between content providers and consumers of a site. (2) We find that social locality has strong influence on the access patterns of UGC sites. Sites with high social locality may deviate from Zipf's law more significantly than the ones with lower social locality. © 2010 IEEE.

关键词： Computational linguistics

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Optimizing Multi-Dimensional Packet Classification for Multi-Core Systems

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Journal of Computer science & technology 2018年第5期33卷 1056-1071页

作者： Tong Shen Da-Fang Zhang Gao-Gang Xie Xin-Yi Zhang College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China Network Technology Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

Packet classification has been studied for decades; it classifies packets into specific flows based on a given rule set. As software-defined network was proposed, a recent trend of packet classification is to scale the five-tuple model to multi-tuple. In general, packet classification on multiple fields is a complex problem. Although most existing software-based algorithms have been proved extraordinary in practice, they are only suitable for the classic five-tuple model and difficult to be scaled up. Meanwhile, hardware-specific solutions are inflexible and expensive, and some of them are power consuming. In this paper, we propose a universal multi-dimensional packet classification approach for multi-core systems. In our approach, novel data structures and four decomposition-based algorithms are designed to optimize the classification and updating of rules. For multi-field rules, a rule set is cut into several parts according to the number of fields. Each part works independently. In this way, the fields are searched in parallel and all the partial results are merged together at last. To demonstrate the feasibility of our approach, we implement a prototype and evaluate its throughput and latency. Experimental results show that our approach achieves a 40% higher throughput than that of other decomposed-based algorithms and a 43% lower latency of rule incremental update than that of the other algorithms on average. Furthermore, our approach saves 39% memory consumption on average and has a good scalability.

关键词： multi-dimensional multi-core packet classification

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The Quantum Internet (Technical Version)

arXiv

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arXiv 2025年

作者： Rohde, Peter P. Huang, Zixin Ouyang, Yingkai Huang, He-Liang Su, Zu-En Devitt, Simon Ramakrishnan, Rohit Mantri, Atul Tan, Si-Hui Liu, Nana Harrison, Scott Radhakrishnan, Chandrashekar Brennen, Gavin K. Baragiola, Ben Q. Dowling, Jonathan P. Byrnes, Tim Munro, William J. Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia Hearne Institute for Theoretical Physics Louisiana State University Baton Rouge United States School of Mathematical and Physical Sciences Macquarie University NSW2109 Australia School of Mathematical and Physical Sciences University of Sheffield SheffieldS3 7RH United Kingdom Henan Key Laboratory of Quantum Information and Cryptography Henan Zhengzhou450000 China Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science & Technology of China Hefei China CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information & Quantum Physics University of Science & Technology of China Hefei China CAS-Alibaba Quantum Computing Laboratory Shanghai China InstituteQ Aalto University Espoo02150 Finland Indian Institute of Science Bangalore India Singapore University of Technology & Design Singapore Centre for Quantum Technologies National University of Singapore Singapore Horizon Quantum Ireland 24 Fitzwilliam Place Dublin 2 D02 T296 Ireland Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China School of Mathematical Sciences Shanghai Jiao Tong University Shanghai200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China Shanghai Artificial Intelligence Laboratory Shanghai China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China Leibniz Institute for Research & Information in Education Frankfurt am Main Germany Department of Computer Science and Engineering NYU Shanghai 567 West Yangsi Road Shanghai200124 China Centre of Excellence in Engineered Quantum Systems Macquarie University NSW Australia Centre for Quantum Computation and Communication Technology School of Science RMIT University VIC

The desire to share and unite remote digital assets motivated the development of the classical internet, the enabler of the entire 21st century economy and our modern way of life. As we enter the quantum era, it is to be expected there will be a similar demand for networking quantum assets, motivating a global quantum internet for bringing together the world's quantum resources, leveraging off their exponential trajectory in capability. We present models for quantum networking, how they might be applied in the future, and the implications they will have. Socially, economically, politically and geostrategically, the upcoming era of quantum supremacy will be as significant for the 21st century as the transistor was for the 20th. The inherently different scaling in the computational power of quantum computers fundamentally changes the dynamics of how they will operate in the future. Given their high expected initial cost, a client/server model for outsourcing computation will be essential for enabling the accessibility and proliferation of this technology, and ensuring its economic viability. We therefore anticipate the emergence of cloud quantum computing, a model for outsourcing quantum computations to the network. We argue that economic efficiency will mandate that all future quantum computers be united into a single global virtual quantum computer, offering exponentially more power to all network participants than if they were to keep their resources to themselves. This model for the allocation of computational resources is uniquely quantum, with no classical analogue, completely altering the economic landscape for the future of computation. Given the sensitivity of much of the data to which future quantum computers are going to foreseeably be applied, protocols for encrypted quantum computation will be essential - the outsourcing of computations that neither an eavesdropper nor even the server performing the computation can spy upon. This will enable new models fo

关键词： Outsourcing

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An Extractive Automatic Summarization Method for Chinese Long Text 9th

An Extractive Automatic Summarization Method for Chinese Lon...

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Zhu, Jizhao Duan, Wenyu Yu, Naitong Pan, Xinlong Fan, Chunlong Shenyang Aerospace University Shenyang China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Beijing China Naval Aviation University Yantai China

ISBN: (纸本)9783031466632

The extractive automatic summarization method is capable of quickly and efficiently generating summaries through the steps of scoring, extracting and eliminating redundant sentences. Currently, most extractive methods utilize deep learning technology to treat automatic summarization as a binary classification task. However, the effectiveness of automatic summarization for Chinese long text is limited by the maximum input length of the model, and it requires a large amount of training data. This paper proposes an unsupervised extractive automatic summarization method which solves the long text encoding problem by incorporating contextual semantics into sentence-level encoding. Firstly, we obtain the semantic representation of sentences by using the RoBERTa model. Secondly, we propose an improved k-Means algorithm to cluster sentence representations. By defining sparse and dense clusters, we improve the accuracy of summary sentence selection while preserving maximum semantic information from the original text. Experimental results on the CAIL2020 dataset show that our method outperforms baselines by 6.64/7.68/7.14% respectively on ROUGE-1/2/L. Moreover, we further enhance the automatic summarization results by 4.5/5.36/3.24% by adding domain rules tailored to the dataset’s characteristics. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： K-means clustering

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Quantum Lovász local lemma: Shearer’s bound is tight 2019

Quantum Lovász local lemma: Shearer’s bound is tight

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51st Annual ACM SIGACT Symposium on Theory of computing, STOC 2019

作者： He, Kun Li, Qian Sun, Xiaoming Zhang, Jiapeng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Shenzhen Institute of Computing Sciences Shenzhen University China University of California San DiegoCA United States

ISBN: (纸本)9781450367059

Lovász Local Lemma (LLL) is a very powerful tool in combinatorics and probability theory to show the possibility of avoiding all "bad" events under some "weakly dependent" condition. Over the last decades, the algorithmic aspect of LLL has also attracted lots of attention in theoretical computer science. A tight criterion under which the abstract version LLL (ALLL) holds was given by Shearer. It turns out that Shearer’s bound is generally not tight for variable version LLL (VLLL). Recently, Ambainis et al. introduced a quantum version LLL (QLLL), which was then shown to be powerful for the quantum satisfiability problem. In this paper, we prove that Shearer’s bound is tight for QLLL, i.e., the relative dimension of the smallest satisfying subspace is completely characterized by the independent set polynomial, affirming a conjecture proposed by Sattath et al. Our result also shows the tightness of Gilyén and Sattath’s algorithm, and implies that the lattice gas partition function fully characterizes quantum satisfiability for almost all Hamiltonians with large enough qudits. Commuting LLL (CLLL), LLL for commuting local Hamiltonians which are widely studied in the literature, is also investigated here. We prove that the tight regions of CLLL and QLLL are different in general. This result might imply that it is possible to design an algorithm for CLLL which is still efficient beyond Shearer’s bound. In applications of LLLs, the symmetric cases are most common, i.e., the events are with the same probability and the Hamiltonians are with the same relative dimension. We give the first lower bound on the gap between the symmetric VLLL and Shearer’s bound. Our result can be viewed as a quantitative study on the separation between quantum and classical constraint satisfaction problems. Additionally, we obtain similar results for the symmetric CLLL. As an application, we give lower bounds on the critical thresholds of VLLL and CLLL for several of the most common lattices. ©

关键词： Hamiltonians

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What makes a successful rebuttal in computer science conferences?: A perspective on social interaction

What makes a successful rebuttal in computer science confere...

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作者： Huang, Junjie Huang, Win-bin Bu, Yi Cao, Qi Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Information Management Peking University Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

With an exponential increase in submissionsto top-tier Computer science (CS) conferences, more and more conferences have introduced a rebuttal stage to the conference peer review process. The rebuttal stage can be modeled as social interactions between authors and reviewers. A successful rebuttal often results in an increased review score after the rebuttal stage. In this paper, we conduct an empirical study to determine the factors contributing to a successful rebuttal using over 3000 papers and 13,000 reviews from ICLR2022, one of the most prestigious computer science conferences. First, we observe a significant difference in review scores before and after the rebuttal stage, which is crucial for paper acceptance. Furthermore, we investigate factors from the reviewer's perspective using signed social network analysis. A notable finding is the increase in balanced network structure after the rebuttal stage. Subsequently, we evaluate several quantifiable author rebuttal strategies and their effects on review scores. These strategies can help authors in improving their review scores. Finally, we used machine learning models to predict rebuttal success and validated the impact of potential factors analyzed in this paper. Our experiments demonstrate that the utilization of all features proposed in this study can aid in predicting the success of the rebuttal. In summary, this work presents a study on the impact factors of successful rebuttals from both reviewers’ and authors’ perspectives and lays the foundation for analyzing rebuttals with social network analysis. © 2023 Elsevier Ltd

关键词： Forecasting

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Context-adaptive matrix factorization for multi-context recommendation 15

Context-adaptive matrix factorization for multi-context reco...

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24th ACM International Conference on Information and Knowledge Management, CIKM 2015

作者： Man, Tong Shen, Huawei Huang, Junming Cheng, Xueqi CAS Key Lab. of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China CompleX Lab. Web Sciences Center University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9781450337946

data sparsity is a long-standing challenge for recommender systems based on collaborative filtering. A promising solution for this problem is multi-context recommendation, i.e., leveraging users' explicit or implicit feedback from multiple contexts. In multi-context recommendation, various types of interactions between entities (users and items) are combined to alleviate data sparsity of a single context in a collective manner. Two issues are crucial for multi-context recommendation: (1) How to differentiate context-specific factors from entity-intrinsic factors shared across contexts? (2) How to capture the salient phenomenon that some entities are insensitive to contexts while others are remarkably context-dependent? Previous methods either do not consider context-specific factors, or assume that a context imposes equal influence on different entities, limiting their capability of combating data sparsity problem by taking full advantage of multiple contexts. In this paper, we propose a context-adaptive matrix factorization method for multi-context recommendation by simultaneously modeling context-specific factors and entity-intrinsic factors in a unified model. We learn an entity-intrinsic latent factor for every entity, and a context-specific latent factor for every entity in each context. Meanwhile, using a context-entity mixture parameter matrix we explicitly model the extent to which each context imposes influence on each entity. Experiments on two real scenarios demonstrate that our method consistently outperforms previous multi-context recommendation methods on all different sparsity levels. Such a consistent performance promotion forms the unique superiority of our method, enabling it to be a reliable model for multi-context recommendation. © 2015 ACM.

关键词： Collaborative filtering

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DSAFF-Net:A Backbone network Based on Mask R-CNN for Small Object Detection

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Computers, Materials & Continua 2023年第2期74卷 3405-3419页

作者： Jian Peng Yifang Zhao Dengyong Zhang Feng Li Arun Kumar Sangaiah Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computing Science and Engineering Vellore Institute of Technology(VIT)Vellore632014India

Recently,object detection based on convolutional neural networks(CNNs)has developed *** backbone networks for basic feature extraction are an important component of the whole detection ***,we present a new feature extraction strategy in this paper,which name is *** this strategy,we design:1)a sandwich attention feature fusion module(SAFF module).Its purpose is to enhance the semantic information of shallow features and resolution of deep features,which is beneficial to small object detection after feature fusion.2)to add a new stage called D-block to alleviate the disadvantages of decreasing spatial resolution when the pooling layer increases the receptive *** method proposed in the new stage replaces the original method of obtaining the P6 feature map and uses the result as the input of the regional proposal network(RPN).In the experimental phase,we use the new strategy to extract *** experiment takes the public dataset of Microsoft Common Objects in Context(MS COCO)object detection and the dataset of Corona Virus Disease 2019(COVID-19)image classification as the experimental object *** results show that the average recognition accuracy of COVID-19 in the classification dataset is improved to 98.163%,and small object detection in object detection tasks is improved by 4.0%.

关键词： Small object detection classification RPN MS COCO COVID-19

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