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检索条件"任意字段=Proceedings - 24th International Conference on Distributed Computing Systems Workshops"

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DeLiBA-K: Speeding-up Hardware-Accelerated distributed Storage Access by Tighter Linux Kernel Integration and Use of a Modern API 24

DeLiBA-K: Speeding-up Hardware-Accelerated Distributed Stora...

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proceedings of the SC '24 workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Babar Khan Andreas Koch Embedded Systems and Applications Group TU Darmstadt Germany

ISBN: (纸本)9798350355543

We present DeLiBA-K, an improved version of the Development of Linux Block I/O Accelerators (DeLiBA) framework. DeLiBA-K operates at the Linux kernel level, bypassing the user-space interactions of DeLiBA-1 and -2 to interact with the block and network I/O kernel stack directly. Another critical feature of DeLiBA-K is implementing and benchmarking the modern io_uring Asynchronous I/O (AIO) API within a 16nm AMD Alveo U280 FPGA I/O framework. this allows for better parallelism and reduced latency in I/O operations. Our results show significant performance gains, up to a 3.2x improvement in I/O operations per second (IOPS) and 3.45x increased throughput for synthetic workloads. Real-world applications see a 30% reduction in execution time for data-intensive tasks. DeLiBA-K has been successfully tested in an industrial environment using real workloads, demonstrating its effectiveness in large-scale enterprise environments.

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Recurrent Neural Network for M/EEG Source Localization

Recurrent Neural Network for M/EEG Source Localization

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Rodion R. Vakhitov Artem D. Khlopkov Alexander S. Lukin Vladislav V. Toma Alexander E. Snytkin Roman S. Kulikov Computing machines systems and networks Moscow Power Engineering Institute Moscow Russia Biotechnical and medical devices and systems Moscow Power Engineering Institute Moscow Russia Radio systems Moscow Power Engineering Institute Moscow Russia

this paper presents the solution of the inverse M/EEG problem using machine learning algorithms. the data on which the algorithm was trained were obtained as a result of solving the forward problem. the data are generated using a recurrent model of neuronal brain activity, where the consecutive excitation of sources in time of closely spaced sources is based on a stochastic Markov chain. the dipole activation model is expected to increase the accuracy of the M/EEG inverse problem solution by reducing its indeterminacy by replacing uncorrelated excitations of sources whose magnetic field is statically distributed on the brain surface with their spatially related time series. Machine learning algorithms in this case can recognize the correlation of source excitations and solve the problem in real time. the application of a method for localization of neuronal activity of the brain that takes into account the spatiotemporal character of the excitations can presumably provide a more complete picture of the brain functioning.

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Enabling Low-Overhead HT-HPC Workflows at Extreme Scale using GNU Parallel 24

Enabling Low-Overhead HT-HPC Workflows at Extreme Scale usin...

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proceedings of the SC '24 workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Ketan Maheshwari William Arndt Ahmad Maroof Karimi Junqi Yin Frédéric Suter Seth Johnson Rafael Ferreira da Silva Oak Ridge National Laboratory Oak Ridge TN USA Lawrence Berkeley National Laboratory Berkeley CA USA

ISBN: (纸本)9798350355543

GNU Parallel is a versatile and powerful tool for process parallelizatlon widely used in scientific computing. this paper demonstrates its effective application in high-performance computing (HPC) environments, particularly focusing on its scalability and efficiency in executing large-scale high-throughput high-performance computing (HT-HPC) workflows. through real-world examples, we highlight GNU Parallel's performance across various HPC workloads, including GPU computing, container-based workloads, and node-local NVMe storage. Our results on two leading supercomputers, OLCF's Frontier and NERSC's Perlmutter, showcase GNU Parallel's rapid process dispatching ability and its capacity to maintain low overhead even at extreme scales. We explore GNU Parallel's application in massive parallel file transfers using a scheduled Data Transfer Node (DTN) cluster, emphasizing its broad utility in diverse scientific workflows. Beyond its direct application as a viable workflow manager, GNU Parallel can be employed in conjunction with other workflow systems as a "last-mile" parallelizing driver and as a quick prototyping tool to design and extract parallel profiles from application executions. We then argue that the potential for GNU Parallel to transform workflow management at extreme scales is substantial, paving the way for more efficient and effective scientific discoveries.

关键词： distributed computing

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A Novel Low-Complexity Angle-Assisted Delay and Doppler Estimation for MIMO-OTFS

A Novel Low-Complexity Angle-Assisted Delay and Doppler Esti...

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international conference on Communication Technology (ICCT)

作者： Weixin Deng Hui Gao Ruohan Cao Xinzhou Cheng Key Laboratory of Trustworthy Distributed Computing and Service MOE Beijing University of Posts and Telecommunications Beijing China China Unicom Research Institute Beijing China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

Multiple-input multiple-output orthogonal time frequency space (MIMO-OTFS) is considered one of the potential technologies for high-speed mobile scenarios. However, in such systems, fractional delay and Doppler estimation is a major challenge due to the limitation of delay-Doppler (DD) grid resolution. In this paper, we propose a low-complexity angle-assisted delay and Doppler estimation (AADDE) algorithm for MIMO-OTFS systems. AADDE reduces computational complexity by estimating delay and Doppler parameters separately, and the angle information is used to improve estimation performance. In addition, we propose a two-dimensional (2D) grid segmentation scheme to estimate fractional DD with AADDE. Specifically, an iterative approach is developed to identify the true fractional DD index using a variable search step size, achieving lower complexity than existing off-grid search methods. Complexity analysis and simulation results demonstrate the effectiveness of the proposed AADDE algorithm, which also approaches the Cramér-Rao lower Bound.

关键词： Time-frequency analysis Lower bound Simulation Two-dimensional displays Search methods Estimation MIMO Delays Doppler effect Iterative methods

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workshops on CIFMA, CoSim-CPS, OpenCERT, and ASYDE 2021, collocated with the 19th international conference on Software Engineering and Formal Methods, SEFM 2021

Workshops on CIFMA, CoSim-CPS, OpenCERT, and ASYDE 2021, col...

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workshops on CIFMA, CoSim-CPS, OpenCERT, and ASYDE 2021, collocated with the 19th international conference on Software Engineering and Formal Methods, SEFM 2021

ISBN: (纸本)9783031124280

the proceedings contain 24 papers. the special focus in this conference is on CIFMA, CoSim-CPS, OpenCERT, and ASYDE. the topics include: Under What Conditions Does a Digital Shadow Track a Periodic Linear Physical System?;convergence Properties of Hierarchical Co-simulation Approaches;co-simulation-Based Pre-training of a Ship Trajectory Predictor;effect of Ship Propulsion Retrofit on Maneuverability Research Based on Co-simulation;co-simulation of a Model Predictive Control System for Automotive Applications;Running Large-Scale and Hybrid Real-Time Aircraft Simulations in an HLA Framework;Comparison Between the HUBCAP and DIGITBrain Platforms for Model-Based Design and Evaluation of Digital Twins;a Life-Long Learning Education Passport Powered by Blockchain Technology and Verifiable Digital Credentials: the BlockAdemiC Project;open Source Discovery, Adoption, and Use: An Informal Perspective;regret from Cognition to Code;DrPython–WEB: A Tool to Help Teaching Well-Written Python Programs;formal Methods Communities of Practice: A Survey of Personal Experience;learning from Mistakes in an Open Source Software Course;a Probabilistic Model Checking Approach to Self-adapting Machine Learning systems;Integration of COTS Processing Architectures in Small Satellites for Onboard computing Using Fault Injection Testing Methodology;In Silico Simulations and Analysis of Human Phonological Working Memory Maintenance and Learning Mechanisms with Behavior and Reasoning Description Language (BRDL);fostering Safe Behaviors via Metaphor-Based Nudging Technologies;developing the Semantic Web via the Resolution of Meaning Ambiguities;original or Fake? How to Understand the Digital Artworks’ Value in the Blockchain;grounding Psychological Shape Space in Convolutional Neural Networks;unexpectedness and Bayes’ Rule;can Reinforcement Learning Learn Itself? A Reply to ‘Reward is Enough’.

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Near-Linear Time Leader Election in Multiagent Networks 25

Near-Linear Time Leader Election in Multiagent Networks

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proceedings of the 24th international conference on Autonomous Agents and Multiagent systems

作者： Ajay D. Kshemkalyani Manish Kumar Anisur Rahaman Molla Gokarna Sharma University of Illinois at Chicago Chicago IL USA Indian Institute of Technology Madras Chennai India Indian Statistical Institute Kolkata Kolkata India Kent State University Kent OH USA

ISBN: (纸本)9798400714269

Leader election is a fundamental and widely studied problem in distributed computing, traditionally explored in the message-passing model, where each node in a distributed network graph represents a static computational device that communicates with others by exchanging messages. this paper studies leader election in the agent-based network, in which the computational devices are modeled as relocatable or mobile agents that explore a graph and perform computations. Each node in the graph serves as a container or host for these mobile agents, and communication occurs between agents when they move to the same node. We consider the scenario where n agents are arbitrarily placed on the nodes of an anonymous, arbitrary n-node, m-edge graph G. the goal is for the agents to elect a leader such that one agent is designated as the leader, knowing it is the leader, while all other agents recognize they are not the leader. the objective is to minimize the time to elect the leader and memory usage per agent. Following the literature, we consider the synchronous setting where each agent performs its operations synchronously with others and hence the time complexity is measured in rounds. there exists a deterministic solution that elects a leader in O(m) rounds with O(nłog n) bits of memory at each agent in the agent-based network. In this paper, we present a deterministic algorithm that elects a leader in O(n łog2 n) rounds with each agent using only O(łog n) bits. this is a significant improvement, as the memory usage is optimal and the time complexity is almost linear in n (up to O(łog2 n) factor). Additionally, leveraging this leader election result, we provide improved time and/or memory bounds for four core distributed graph problems: minimum spanning tree, gathering, maximal independent set, and minimal dominating set.

关键词： distributed algorithms

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12th international conference on Broadband Communications, Networks, and systems, BROADNETS 2021

12th International Conference on Broadband Communications, N...

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12th international conference on Broadband Communications, Networks, and systems, BROADNETS 2021

ISBN: (纸本)9783030934781

the proceedings contain 24 papers. the special focus in this conference is on Broadband Communications, Networks, and systems. the topics include: Road Rage Recognition System Based on Face Detection Emotion;a Drip Irrigation Remote Control System Using 5G-IoT Technology;Multipath QUIC – Directions of the Improvements;ARTI: One New Adaptive Elliptical Weighting Model Combining with the Tikhonov-p-norm for Image Reconstruction;Calculation and Numerical Simulation of Building Integrated Photovoltaic System Based on BIM Technology;connected Autonomous Vehicle Platoon Control through Multi-agent Deep Reinforcement Learning;accurate Estimation on the State-of-Charge of Lithium-Ion Battery Packs;Fire Simulation and Optimal Evacuation Based on BIM Technology;Discrete Sliding Mode Control of PMSM with Network Transmission;end-to-End Dynamic Pipelining Tuning Strategy for Small Files Transfer;smart Medical and Nursing Platform Based on 5G Technology;time-Domain Predictable Trajectory Planning for Autonomous Driving Based on Internet of Vehicles;Rate-Compatible Shortened Polar Codes Based on RM Code-Aided;research on Wheat Impurity Image Recognition Based on Convolutional Neural Network;based on Energy Router Energy Management Control Strategy in Micro-grid;containers’ Privacy and Data Protection via Runtime Scanning Methods;digital Twin for Cybersecurity: Towards Enhancing Cyber Resilience;differential Privacy-Based Permissioned Blockchain for Private Data Sharing in Industrial IoT;Efficient Privacy-Preserving User Matching with Intel SGX;developing an Online Examination Timetabling System Using Artificial Bee Colony Algorithm in Higher Education;a Topology-Aware Scheduling Strategy for distributed Stream computing System;a Data Stream Prediction Strategy for Elastic Stream computing systems.

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Establishing a High-Performance and Productive Ecosystem for distributed Execution of Python Functions Using Globus Compute 24

Establishing a High-Performance and Productive Ecosystem for...

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proceedings of the SC '24 workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Rachana Ananthakrishnan Yadu Babuji Josh Bryan Kyle Chard Ryan Chard Ben Clifford Ian Foster Lev Gorenstein Kevin Hunter Kesling Chris Janidlo Daniel S. Katz Reid Mello J. Gregory Pauloski Lei Wang University of Chicago University of Chicago and Argonne National Laboratory Argonne National Laboratory Argonne National Laboratory and University of Chicago University of Illinois Urbana-Champaign

ISBN: (纸本)9798350355543

the research computing ecosystem is increasingly heterogeneous and diverse. Democratizing access to these essential resources is critical for accelerating research progress. However, the gap between a high-level workload, such as Python in a Jupyter notebook, and the resources and interfaces exposed by HPC systems is significant. Users must securely authenticate, manage network connections, deploy and manage software, provision and configure nodes, and manage workload execution. Globus Compute reduces these barriers by providing a managed, fire-and-forget model that enables execution of Python functions across any resource to which a user has access. However, while Globus Compute has relieved users from many of the challenges of remote computing, we have observed some inefficiencies that remain in terms of use. For example, many users wrap external applications, such as C/C++, Fortran, and even MPI applications, in Python functions and users must deploy many endpoints on a single computer to exploit different configurations. In this paper we describe enhancements to Globus Compute to address these barriers: an asynchronous, future-based executor interface for submitting and monitoring tasks, shell and MPI-based function types, and a multi-user endpoint that can be deployed by administrators and used by authorized users.

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Private data sharing between decentralized users through the privGAN architecture 24

Private data sharing between decentralized users through the...

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24th IEEE international Enterprise distributed Object computing conference (IEEE EDOC)

作者： Rajotte, Jean-Francois Ng, Raymond T. Univ British Columbia Data Sci Inst Vancouver BC Canada Univ British Columbia Dept Comp Sci Vancouver BC Canada

ISBN: (数字)9781728164717

ISBN: (纸本)9781728164717

More data is almost always beneficial for analysis and machine learning tasks. In many realistic situations however, an enterprise cannot share its data, either to keep a competitive advantage or to protect the privacy of the data sources, the enterprise's clients for example. We propose a method for data owners to share synthetic or fake versions of their data without sharing the actual data, nor the parameters of models that have direct access to the data. the method proposed is based on the privGAN architecture where local GANs are trained on their respective data subsets with an extra penalty from a central discriminator aiming to discriminate the origin of a given fake sample. We demonstrate that this approach, when applied to subsets of various sizes, leads to better utility for the owners than the utility from their real small datasets. the only shared pieces of information are the parameter updates of the central discriminator. the privacy is demonstrated with white-box attacks on the most vulnerable elments of the architecture and the results are close to random guessing. this method would apply naturally in a federated learning setting.

关键词： Synthetic data GAN Privacy distributed data Federated Learning

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Towards a Core Ontology of Economic Exchanges for Multilateral Accounting Information systems 24

Towards a Core Ontology of Economic Exchanges for Multilater...

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24th IEEE international Enterprise distributed Object computing conference (IEEE EDOC)

作者： Blums, Ivars Weigand, Hans SIA ODO Riga Latvia Univ Tilburg Tilburg Netherlands

ISBN: (纸本)9781728164731

Enterprises collaborate in government-regulated markets by developing and realizing exchange relationships. However, current Accounting Information systems (AIS) are organized in an enterprise-centric way. We introduce the Multilateral AIS (MAIS) perspective on top of the Bilateral (Shared Ledger) and Unilateral Enterprise-Specific perspectives for Corporate Reporting. We reconsider our previous conceptualization in the OntoUML language of the Economic Exchange lifecycle in Bilateral AIS from an Economic System and Market perspective. We identify requirements for existing accounting framework improvement in MAIS. An online platform case is provided as an illustration.

关键词： IFRS AIS UFO Economic Exchanges

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