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International Conference on P2P, Parallel, Grid, Cloud and Internet computing (3PGCIC)

作者： Christian Esposito Massimo Ficco Francesco Palmieri Vincenzo Loia Institute of High Performance Computing and Networking (ICAR) Italian National Research Council (CNR) Naples Italy Department of Industrial and Information Engineering Second University of Naples Aversa CE Italy Department of Computer Science University of Salerno Fisciano SA Italy

Proper knowledge management and advanced semantic reasoning are of pivotal importance in promoting innovation and productivity in a cooperative team and opening new opportunities in commerce, science, and computing applications. To address such a requirements a series of solutions for distributed knowledge management have been proposed over the last decade, among which the most promising is the so-called knowledge grid. Such infrastructures have been traditionally designed for science and educational applications, but they are starting to be progressively used in more critical scenarios, such as disaster management and recovery, or financial services. This imposes the need of providing reliability and security within the context of knowledge grids. The goal of this paper is to define such needs and to describe the correspondence of the available solutions to these needs. Last, we highlight possible space for research opportunities within the topics of reliability and security in knowledge grids.

关键词： Reliability Security Knowledge engineering Semantics Routing Context Distributed databases

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Consistency and Diversity induced Human Motion Segmentation

arXiv

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arXiv 2022年

作者： Zhou, Tao Fu, Huazhu Gong, Chen Shao, Ling Porikli, Fatih Ling, Haibin Shen, Jianbing The PCA Lab The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of High Performance Computing Agency for Science Technology and Research Singapore Riyadh Saudi Arabia The Research School of Engineering The Australian National University Australia The Department of Computer Science Stony Brook University Strony Brook NY United States The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University United States

Subspace clustering is a classical technique that has been widely used for human motion segmentation and other related tasks. However, existing segmentation methods often cluster data without guidance from prior knowledge, resulting in unsatisfactory segmentation results. To this end, we propose a novel Consistency and Diversity induced human Motion Segmentation (CDMS) algorithm. Specifically, our model factorizes the source and target data into distinct multi-layer feature spaces, in which transfer subspace learning is conducted on different layers to capture multi-level information. A multi-mutual consistency learning strategy is carried out to reduce the domain gap between the source and target data. In this way, the domain-specific knowledge and domain-invariant properties can be explored simultaneously. Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion (HSIC) is introduced to ensure the diversity of multi-level subspace representations, which enables the complementarity of multi-level representations to be explored to boost the transfer learning performance. Moreover, to preserve the temporal correlations, an enhanced graph regularizer is imposed on the learned representation coefficients and the multi-level representations of the source data. The proposed model can be efficiently solved using the Alternating Direction Method of Multipliers (ADMM) algorithm. Extensive experimental results on public human motion datasets demonstrate the effectiveness of our method against several state-of-the-art approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion analysis

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Lane marking detection algorithm based on high-precision map and multisensor fusion

Lane marking detection algorithm based on high-precision map...

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作者： Yao, Haichang Chen, Chen Liu, Shangdong Li, Kui Ji, Yimu Huang, Guangyan Wang, Ruchuan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer and Software Nanjing Institute of Industry Technology Nanjing China Industrial Software Engineering Technology Research and Development Center of Jiangsu Education Department Nanjing Institute of Industry Technology Nanjing China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China Institute of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Nanjing China School of Information Technology Deakin University Melbourne Australia

In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high-precision map and multisensor fusion is proposed. The basic principle of the algorithm is to use the centimeter-level high-precision positioning combined with high-precision map data to complete the detection of lane markings. In the process of generating high-precision maps or in the uncovered areas of high-precision maps, LIDAR (LIght Detection And Ranging) is used to estimate the curvature of the road to assist in lane marking detection. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust. © 2020 John Wiley & Sons, Ltd.

关键词： Roads and streets

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SpaceCube GHOST: A Resilient Processor for Low-Power, high-Reliability Space computing

SpaceCube GHOST: A Resilient Processor for Low-Power, High-R...

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IEEE Conference on Aerospace

作者： Noah Perryman Nicholas Franconi Gary Crum Christopher Wilson Alan D. George Department of Electrical and Computer Engineering NSF Center for Space High-Performance and Resilient Computing (SHREC) University of Pittsburgh Pittsburgh PA Science Data Processing Branch Code 587 Software Engineering Division NASA Goddard Space Flight Center Greenbelt MD

There is a direct need for high-visibility NASA missions that provide significant scientific impact or have a high mission class using completely radiation-hardened (rad-hard) electronics solutions, to enable artificial intelligence (AI) applications in harsh environments despite severe size, weight, and power (SWaP) constraints. For these missions, where current state-of-the-art solutions are too power-demanding or are incapable of surviving the intended radiation environment, an alternative rad-hard processing architecture that can leverage the control-flow capabilities of scalar processors while also incorporating the hardware-acceleration capabilities of an FPGA is of significant value. Therefore, in this research, we propose a miniaturized (3.5 in. × 3.5 in. form factor) processor card featuring the GR740 quad-core rad-hard processor and the CertusPro-NX-RT radiation-tolerant FPGA, called the SpaceCube GR740 Host for Onboard science and Telemetry (GHOST) architecture. The card will be designed to conform to the NASA Goddard Space Flight Center (GSFC) CubeSat Card Specification (CS2), which provides a common template to build new 1U CubeSat-sized cards compatible with a variety of other avionics designs. The GR740 features a fault-tolerant quad- processor LEON4FT SPARC V8 integer unit with a 7-stage pipeline and 4×4 KiB instruction and data caches. To extend the capabilities of the GR740, the CertusPro-NX-RT, a low-power radiation-tolerant FPGA, was combined to create a hybrid system architecture, providing the benefits of a programmable logic fabric. The GHOST architecture supports high-reliability, general-purpose processing and system monitoring via the GR740 while simultaneously increasing the AI-based application performance via a combination of the GR740 and CertusPro-NX-RT.

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A parallel approximation algorithm for maximizing submodular b-matching

arXiv

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arXiv 2021年

作者： Ferdous, S.M. Pothen, Alex Khan, Arif Panyala, Ajay Halappanavar, Mahantesh Computer Science Department Purdue University West LafayetteIN47907 United States Data Science and Machine Intelligence Pacific Northwest National Lab RichlandWA99352 United States High Performance Computing Pacific Northwest National Lab RichlandWA99352 United States

We design new serial and parallel approximation algorithms for computing a maximum weight b-matching in an edge-weighted graph with a submodular objective function. This problem is NP-hard;the new algorithms have approximation ratio 1/3, and are relaxations of the Greedy algorithm that rely only on local information in the graph, making them parallelizable. We have designed and implemented Local Lazy Greedy algorithms for both serial and parallel computers. We have applied the approximate submodular b-matching algorithm to assign tasks to processors in the computation of Fock matrices in quantum chemistry on parallel computers. The assignment seeks to reduce the run time by balancing the computational load on the processors and bounding the number of messages that each processor sends. We show that the new assignment of tasks to processors provides a four fold speedup over the currently used assignment in the NWChemEx software on 8000 processors on the Summit supercomputer at Oak Ridge National lab. © 2021, CC BY.

关键词： Supercomputers

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Uncertainty Quantification of Signal Integrity Analysis for Neuromorphic Chips

IEEE Transactions on Signal and Power Integrity

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IEEE Transactions on Signal and Power Integrity 2022年 1卷 160-169页

作者： Hanzhi Ma Da Li Tuomin Tao Xingjian Shangguan En-Xiao Liu Jose Schutt-Aine Andreas C. Cangellaris Er-Ping Li College of Information Science and Electronic Engineering ZJU-UIUC Institute Zhejiang University Hangzhou China Key Lab of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang University Hangzhou China Zhejiang-Singapore Innovation and AI Joint Research Lab China Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA A*STAR Institute of High Performance Computing Singapore

A dimensionality reduction based neural network framework is introduced for uncertainty quantification of time-domain response based on system uncertain design parameters for neuromorphic chips. The proposed method firstly makes use of the singular value decomposition (SVD) method to find the basis functions and corresponding coefficients of time-domain response, of which coefficients are used as a lower dimensional target outputs in neural network model compared with time sampling points prediction. This newly proposed method then develops an integrated neural network structure to simultaneously find the mean and variance of target coefficients with a combined definition of loss function, which can be utilized together with basis functions to construct the prediction interval of time-domain response. A memrisor-based crossbar array is applied in this work to verify the performance of the proposed method with the comparison of Monte Carlo method.

关键词： Biological neural networks Uncertainty Memristors Time-domain analysis Integrated circuit modeling Neuromorphics Signal integrity Neural networks Neuromorphics

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On matching, and even rectifying, dynamical systems through koopman operator eigenfunctions

arXiv

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arXiv 2017年

作者： Bollt, Erik M. Li, Qianxiao Dietrich, Felix Kevrekidis, Ioannis Department of Mathematics Department of Electrical and Computer Engineering Department of Physics Clarkson University PotsdamNY13699 United States Institute of High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore Department of Chemical and Biomolecular Engineering Department of Applied Mathematics and Statistics Johns Hopkins University and JHMI

Matching dynamical systems, through different forms of conjugacies and equivalences, has long been a fundamental concept, and a powerful tool, in the study and classification of nonlinear dynamic behavior (e.g. through normal forms). In this paper we will argue that the use of the Koopman operator and its spectrum is particularly well suited for this endeavor, both in theory, but also especially in view of recent data-driven algorithm developments. We believe, and document through illustrative examples, that this can nontrivially extend the use and applicability of the Koopman spectral theoretical and computational machinery beyond modeling and prediction, towards what can be considered as a systematic discovery of "Cole-Hopf-type" transformations for dynamics. Copyright © 2017, The Authors. All rights reserved.

关键词： Dynamical systems

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Editorial: Dependability and security

引用

IEEE Transactions on Dependable and Secure computing 2008年第1期5卷 1-3页

作者： Iyer, Ravishankar K. Gligor, Virgil D. Kalbarczyk, Zbigniew T. Kanoun, Karama Katzenbeisser, Stefan Mukherjee, Shubu Shieh, Shiuhpyng Zorian, Yervant Department of Electrical and Computer Engineering Carnegie Mellon University Microsoft's Trusted Computing Academic Advisory Board Center for Reliable and High-Performance Computing Coordinated Science Laboratory University of Illinois Urbana-Champaign United States IEEE IEEE Computer Society LAAS CNRS IFIP Working Group 10.4 on Dependable Computing and Fault Tolerance Special Interest Group on Dependability Benchmarking IFIP Working Group 10.4 French SEE Technical Committee on Trustworthy Computer Systems SEE Working Group Design and Validation for Dependability Technical University Munich Germany Darmstadt University of Technology ACM IACR Intel's SER Tech Council Intel's Architecture Patent Committee Taiwan Information Security Center NCTU IEEE Reliability Society Taipei and Tainan IACR United States Virage Logic Corp. AT and T Bell Laboratories Logic Vision Inc. University of British Columbia IEEE Test Technology Technical Council

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Sparsification and Optimization for Energy-Efficient Federated Learning in Wireless Edge Networks

Sparsification and Optimization for Energy-Efficient Federat...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Lei Lei Yaxiong Yuan Yang Yang Yu Luo Lina Pu Symeon Chatzinotas School of Information and Communications Engineering Xi’ an Jiaotong University China Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg Competence Center for High Performance Computing Fraunhofer ITWM Germany Department of Electrical and Computer Engineering Mississippi State University USA Department of Computer Science University of Alabama USA

ISBN: (纸本)9781665435413

Federated Learning (FL), as an effective decentral-ized approach, has attracted considerable attention in privacy-preserving applications for wireless edge networks. In practice, edge devices are typically limited by energy, memory, and computation capabilities. In addition, the communications be-tween the central server and edge devices are with constrained resources, e.g., power or bandwidth. In this paper, we propose a joint sparsification and optimization scheme to reduce the energy consumption in local training and data transmission. On the one hand, we introduce sparsification, leading to a large number of zero weights in sparse neural networks, to alleviate devices' computational burden and mitigate the data volume to be uploaded. To handle the non-smoothness incurred by sparsification, we develop an enhanced stochastic gradient descent algorithm to improve the learning performance. On the other hand, we optimize power, bandwidth, and learning parameters to avoid communication congestion and enable an energy-efficient transmission between the central server and edge devices. By collaboratively deploying the above two components, the numerical results show that the overall energy consumption in FL can be significantly reduced, compared to benchmark FL with fully-connected neural networks.

关键词： Wireless communication Training Energy consumption Federated learning Neural networks Bandwidth Energy efficiency

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An integrated computing environment for bio-molecular networks

引用

Journal of Convergence Information Technology 2010年第4期5卷

作者： Xie, Jiang Mao, Guoyong Zhang, Shilin Zhang, Wu School of Computer Engineering and Science Shanghai University Shanghai 200072 China Institute of Systems Biology Shanghai University Shanghai 200444 China Changzhou Key lab for Research and Application of Software Technology Changzhou 213002 China High Performance Computing Center Shanghai University Shanghai 200072 China

With the development of systems biology, more and more researchers focus on the study of bio-molecular networks. In recent years, researchers in different fields have accumulated a large number of biological experimental data and algorithms for analysis and calculation of bio-molecular networks, but these data and methods are relatively independent, difficult to be utilized by biologists. Based on PSE-Bio, a problem solving environment for bioinformatics, this paper describes an integrated computing environment for bio-molecular networks in order to achieve molecular homology analysis, bio-molecular network building, querying, statistics and visualization.

关键词： Bioinformatics

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