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arXiv 2021年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

In this paper, we consider a stochastic distributed nonconvex optimization problem with the cost function being distributed over n agents having access only to zeroth-order (ZO) information of the cost. This problem has various machine learning applications. As a solution, we propose two distributed ZO algorithms, in which at each iteration each agent samples the local stochastic ZO oracle at two points with a time-varying smoothing parameter. We show that the proposed algorithms achieve the linear speedup convergence rate O(√p=(nT)) for smooth cost functions under the state-dependent variance assumptions which are more general than the commonly used bounded variance and Lipschitz assumptions, and O(p=(nT)) convergence rate when the global cost function additionally satisfies the Polyak-Lojasiewicz (P-L) condition in addition, where p and T are the dimension of the decision variable and the total number of iterations, respectively. To the best of our knowledge, this is the first linear speedup result for distributed ZO algorithms, which enables systematic processing performance improvements by adding more agents. We also show that the proposed algorithms converge linearly under the relative bounded second moment assumptions and the P-L condition. We demonstrate through numerical experiments the efficiency of our algorithms on generating adversarial examples from deep neural networks in comparison with baseline and recently proposed centralized and distributed ZO algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Cost functions

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Timed data plane update in data center network 25

Timed data plane update in data center network

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25th IEEE International Conference on Parallel and distributed systems, ICPADS 2019

作者： Liu, Yuxiang Gao, Xiaofeng Zheng, Jiaqi Chen, Guihai Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9781728125831

Software-Defined Networks make it more principled and formal to operate and verify networks, which decouple the control plane and the data plane and introduce flexibility and programmability into the network. The SDN control plane needs to frequently update the data plane. However, the decoupled control and data planes face new challenges. The network is still an asynchronous distributed system in rule updates. If the update operations are not carefully scheduled, it may result in blackhole, congestions, and violate the consistency properties. Motivated by the tightly synchronized SDNs, we proposed TIDERS Algorithm for consistent network updates in 'timed SDNs'-where one node update can be scheduled at specific time. In TIDERS, we use synchronized time technique to achieve fast network updates while guaranteeing consistency properties. We focus on congestion-free in scheduling the update operations. To achieve this, we first set up a flow-based dependency graph and transform it into switch-operation level dependency graph. Based on operation dependency graph, we generate an update schedule and gradually optimize it to finally get a fast update schedule. After that, we conduct a series of simulations comparing our approach with several other methods to evaluate it. © 2019 IEEE.

关键词： Flow graphs

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sCompile: Critical Path Identification and Analysis for Smart Contracts 21st

sCompile: Critical Path Identification and Analysis for Smar...

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21st International Conference on Formal Engineering Methods, ICFEM 2019

作者： Chang, Jialiang Gao, Bo Xiao, Hao Sun, Jun Cai, Yan Yang, Zijiang Department of Computer Science Western Michigan University KalamazooMI49009 United States Pillar of Information System Technology and Design Singapore University of Technology and Design Singapore Singapore School of Information Systems Singapore Management University Singapore Singapore State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030324087

Ethereum smart contracts are an innovation built on top of the blockchain technology, which provides a platform for automatically executing contracts in an anonymous, distributed, and trusted way. The problem is magnified by the fact that smart contracts, unlike ordinary programs, cannot be patched easily once deployed. It is important for smart contracts to be checked against potential vulnerabilities. In this work, we propose an alternative approach to automatically identify critical program paths (with multiple function calls including inter-contract function calls) in a smart contract, rank the paths according to their criticalness, discard them if they are infeasible or otherwise present them with user friendly warnings for user inspection. We identify paths which involve monetary transaction as critical paths, and prioritize those which potentially violate important properties. For scalability, symbolic execution techniques are only applied to top ranked critical paths. Our approach has been implemented in a tool called sCompile, which has been applied to 36,099 smart contracts. The experiment results show that sCompile is efficient, i.e., 5 s on average for one smart contract. Furthermore, we show that many known vulnerabilities can be captured if user inspects as few as 10 program paths generated by sCompile. Lastly, sCompile discovered 224 unknown vulnerabilities with a false positive rate of 15.4% before user inspection. © 2019, Springer Nature Switzerland AG.

关键词： Smart contract

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EXPPO: EXecution Performance Profiling and Optimization for CPS Co-simulation-as-a-Service

EXPPO: EXecution Performance Profiling and Optimization for ...

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International Symposium on Object-Oriented Real-Time distributed Computing

作者： Yogesh D. Barve Himanshu Neema Zhuangwei Kang Hongyang Sun Aniruddha Gokhale Thomas Roth Department of Electrical Engineering and Computer Science Vanderbilt University Nashville TN USA Smart Grid and Cyber-Physical Systems Program Office Engineering Laboratory National Institute of Standards and Technology Gaithersburg MD USA

ISBN: (数字)9781728169583

ISBN: (纸本)9781728169590

A co-simulation may comprise several heterogeneous federates with diverse spatial and temporal execution characteristics. In an iterative time-stepped simulation, a federation exhibits the Bulk Synchronous Parallel (BSP) computation paradigm in which all federates perform local operations and synchronize with their peers before proceeding to the next round of computation. In this context, the lowest performing (i.e., slowest) federate dictates the progression of the federation logical time. One challenge in co-simulation is performance profiling for individual federates and entire federations. The computational resource assignment to the federates can have a large impact on federation performance. Furthermore, a federation may comprise federates located on different physical machines as is the case for cloud and edge computing environments. As such, distributed profiling and resource assignment to the federation is a major challenge for operationalizing the co-simulation execution at scale. This paper presents the Execution Performance Profiling and Optimization (EXPPO) methodology, which addresses these challenges by using execution performance profiling at each simulation execution step and for every federate in a federation. EXPPO uses profiling to learn performance models for each federate, and uses these models in its federation resource recommendation tool to solve an optimization problem that improves the execution performance of the co-simulation. Using an experimental testbed, the efficacy of EXPPO is validated to show the benefits of performance profiling and resource assignment in improving the execution runtimes of co-simulations while also minimizing the execution cost.

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Vector Symbolic Architectures as a Computing Framework for Emerging Hardware

arXiv

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arXiv 2021年

作者： Kleyko, Denis Davies, Mike Frady, E. Paxon Kanerva, Pentti Kent, Spencer J. Olshausen, Bruno A. Osipov, Evgeny Rabaey, Jan M. Rachkovskij, Dmitri A. Rahimi, Abbas Sommer, Friedrich T. The Redwood Center for Theoretical Neuroscience The University of California BerkeleyCA94720 United States The Intelligent Systems Lab Research Institutes of Sweden Kista16440 Sweden The Neuromorphic Computing Laboratory Intel Labs Santa ClaraCA95054 United States The Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå97187 Sweden The Department of Electrical Engineering and Computer Sciences The University of California BerkeleyCA94720 United States International Research and Training Center for Information Technologies and Systems Kyiv03680 Ukraine IBM Research – Zurich Rüschlikon8803 Switzerland

This article reviews recent progress in the development of the computing framework Vector Symbolic Architectures (also known as Hyperdimensional Computing). This framework is well suited for implementation in stochastic, emerging hardware and it naturally expresses the types of cognitive operations required for Artificial Intelligence (AI). We demonstrate in this article that the field-like algebraic structure of Vector Symbolic Architectures offers simple but powerful operations on high-dimensional vectors that can support all data structures and manipulations relevant to modern computing. In addition, we illustrate the distinguishing feature of Vector Symbolic Architectures, "computing in superposition," which sets it apart from conventional computing. It also opens the door to efficient solutions to the difficult combinatorial search problems inherent in AI applications. We sketch ways of demonstrating that Vector Symbolic Architectures are computationally universal. We see them acting as a framework for computing with distributed representations that can play a role of an abstraction layer for emerging computing hardware. This article serves as a reference for computer architects by illustrating the philosophy behind Vector Symbolic Architectures, techniques of distributed computing with them, and their relevance to emerging computing hardware, such as neuromorphic computing. Copyright © 2021, The Authors. All rights reserved.

关键词： Vectors

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Probabilistic verification of hierarchical leader election protocol in dynamic systems

引用

Frontiers of computer science 2018年第4期12卷 763-776页

作者： Yu ZHOU Nvqi ZHOU Tingting HAN Jiayi GU Weigang WU College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 210016 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Department of Computer Science and Information Systems Birkbeck University of London London WC1E 7HX UK Department of Computer Science Sun Yat-sen University Guangzhou 510006 China

Leader election protocols are fundamental for coordination problems--such as consensus--in distributed computing. Recently, hierarchical leader election protocols have been proposed for dynamic systems where processes can dynamically join and leave, and no process has global information. However, quantitative analysis of such protocols is generally lacking. In this paper, we present a probabilistic model checking based approach to verify quantitative properties of these protocols. Particularly, we employ the compositional technique in the style of assume-guarantee reasoning such that the sub-protocols for each of the two layers are verified separately and the correctness of the whole protocol is guaranteed by the assume-guarantee rules. Moreover, within this framework we also augment the proposed model with additional features such as rewards. This allows the analysis of time or energy consumption of the protocol. Experiments have been conducted to demonstrate the effectiveness of our approach.

关键词： distributed computing hierarchical leader election protocol dynamic systems probabilistic model checking

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A reconfigurable quantum local area network over deployed fiber

arXiv

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arXiv 2021年

作者： Alshowkan, Muneer Williams, Brian P. Evans, Philip G. Rao, Nageswara S.V. Simmerman, Emma M. Lu, Hsuan-Hao Lingaraju, Navin B. Weiner, Andrew M. Marvinney, Claire E. Pai, Yun-Yi Lawrie, Benjamin J. Peters, Nicholas A. Lukens, Joseph M. Quantum Information Science Group Oak Ridge National Laboratory Oak RidgeTN37831 United States Autonomous and Complex Systems Group Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Applied Physics Stanford University StanfordCA94305 United States School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

Practical quantum networking architectures are crucial for scaling the connection of quantum resources. Yet quantum network testbeds have thus far underutilized the full capabilities of modern lightwave communications, such as flexible-grid bandwidth allocation. In this work, we implement flex-grid entanglement distribution in a deployed network for the first time, connecting nodes in three distinct campus buildings time-synchronized via the Global Positioning System (GPS). We quantify the quality of the distributed polarization entanglement via log-negativity, which offers a generic metric of link performance in entangled bits per second. After demonstrating successful entanglement distribution for two allocations of our eight dynamically reconfigurable channels, we demonstrate remote state preparation—the first realization on deployed fiber—showcasing one possible quantum protocol enabled by the distributed entanglement network. Our results realize an advanced paradigm for managing entanglement resources in quantum networks of ever-increasing complexity and service demands. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum entanglement

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Regret and Cumulative Constraint Violation Analysis for distributed Online Constrained Convex Optimization

arXiv

引用

arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden The Digital Futures Stockholm10044 Sweden The Department of Control Science and Engineering College of Electronics and Information Engineering The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

This paper considers the distributed online convex optimization problem with time-varying constraints over a network of agents. This is a sequential decision making problem with two sequences of arbitrarily varying convex loss and constraint functions. At each round, each agent selects a decision from the decision set, and then only a portion of the loss function and a coordinate block of the constraint function at this round are privately revealed to this agent. The goal of the network is to minimize the network-wide loss accumulated over time. Two distributed online algorithms with full-information and bandit feedback are proposed. Both dynamic and static network regret bounds are analyzed for the proposed algorithms, and network cumulative constraint violation is used to measure constraint violation, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. In particular, we show that the proposed algorithms achieve O(T max{κ,1-κ}) static network regret and O(T 1-κ/2) network cumulative constraint violation, where T is the time horizon and κ ∈ (0, 1) is a user-defined trade-off parameter. Moreover, if the loss functions are strongly convex, then the static network regret bound can be reduced to O(T κ). Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Economic and social effects

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A dynamic level interaction method based on trusted value

引用

Journal of computers (Taiwan) 2019年第1期30卷 142-152页

作者： Zhang, Yong-sheng Fan, Yue-qin Cui, Ran-ran Wei, Yu Department of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

With the rapid development of the cloud computing, there is a large amount of service providers with low quality in the marketplace. The mendacious service providers cause a serious loss for the users. In order to select an efficient cloud service, it can be trust and meet the user’s privacy protection requirements. In this paper, a dynamic level interaction method based on credible value is indicated. The cloud service providers and cloud users are divided into different dynamic levels according to their historical records. The security level of privacy data is decided according to the users’ trust level. This model introduces the center degree factor to filter the mendacious service and it can get a more accurate service evaluation by limiting the malicious users to make false evaluation for the service and it can control the providers’ access rights to protect the users’ information. The results of simulation experiment show that this model can improve the interaction successful rate and strengthen the safety of user privacy data. It demonstrates the efficiency and effectiveness of our approach in trusted interaction with other counterparts. 2019 © computer Society of the Republic of China. All Rights Reserved.

关键词： Grading

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