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检索条件"任意字段=16th Euromicro International Conference on Parallel, Distributed and Network-Based Processing"

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distributed Deep Convolutional network: A Generalized SVD Perspective 16

Distributed Deep Convolutional Network: A Generalized SVD Pe...

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16th international conference on Wireless Communications and Signal processing, WCSP 2024

作者： Ding, Yi Shi, Junpeng Shi, Shuyun Qian, Jialong Chen, Qinxian College of Electronic Engineering National University of Defense Technology Hefei China

ISBN: (纸本)9798350390643

With the rapid advancement of distributed systems technology, deep learning-based methods have become a common scheme to implement multiple data processing. this paper presents a novel multi-channel encoder-decoder architecture (called as D2CN), which is explained by integrating generalized singular value decomposition (GSVD) with the principles of Hankel convolution framelet. Specifically, we employ the feature extraction capability of GSVD to perform data interactions by forward/backward propagation, where numerous inputs are designed using the common bases and the reliable performance is achieved by training shared right bases. the network intuitively shows the interactions of multiple data in propagation, which is suitable for a wide range of inverse problems. Finally, we demonstrate the superiority of D2CN over other fundamental networks through numerical experiments conducted on inverse problem tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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A parallel software pipeline to select relevant genes for pathway enrichment 30

A parallel software pipeline to select relevant genes for pa...

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30th euromicro international conference on parallel, distributed and network-based processing (PDP)

作者： Agapito, Giuseppe Cannataro, Mario Magna Graecia Univ Catanzaro Dept Law Econ & Social Sci Catanzaro Italy Magna Graecia Univ Catanzaro Data Analyt Res Ctr Catanzaro Italy Magna Graecia Univ Catanzaro Dept Surg & Med Sci Catanzaro Italy

ISBN: (纸本)9781665469586

the continuous technological development of experimental omics technologies such as microarrays, allows to perform large scale genomics studies. After the initial enthusiasm, it became pretty clear that even the results provided by microarrays in form of lists of differential expressed genes (DEGs), were mainly as enigmatic as the first sequence of the genome, because these lists of DEGs are detached from the influenced biological mechanisms. Pathway enrichment analysis (PEA) supports researchers to provide the clues necessary to link DEGs to the influenced biological pathways and consequently to the underlying biological mechanisms and processes. Putting DEGs data sets in a suitable format for the PEA can be a tedious error-prone and laborious process even for bioinformaticians, who needs to perform it manually before to be ready for the PEA. To fill this lack, we present a parallel software pipeline which uploads a list of DEGs and automatically provides as results the enriched pathways. the parallel software pipeline is implemented in Python and provides the following automated actions: i) parallel splitting of DEGs in groups;ii) parallel building of the similarity matrices related to the DEGs groups;iii) parallel mapping of similarity matrices in networks;iv) parallel pathway enrichment analysis for each group of identified DEGs. Preliminary results shown that the pipeline can help to analyze DEGs and easily generate in a few minutes a list of pathway enrichment results that otherwise would require numerous hours of manual work and several different scripts. the parallel software pipeline provides a two-fold benefits: first, it contributes to speed up the computation of pathway enrichment, automating several steps currently performed manually. Second, it provides a more peculiar list of DEGs to calculate pathway enrichment, contributing to improve the relevance and significance of the enriched pathways.

关键词： Pathway Enrichment Analysis parallel Computing Omics Sciences Statistical Analysis network Analysis

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Deep Unrolling-based Joint Active User Detection and Channel Estimation in distributed Antenna Systems 16

Deep Unrolling-Based Joint Active User Detection and Channel...

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16th international conference on Wireless Communications and Signal processing, WCSP 2024

作者： Liu, Junhui Xu, Qirui Zhu, Shihao Zhao, Ming Zhou, Wuyang University of Science and Technology of China Cas Key Laboratory of Wireless-Optical Communications Anhui Hefei China

ISBN: (纸本)9798350390643

this paper addresses the challenge of joint active user detection (AUD) and channel estimation (CE) for grant-free random access within massive machine-type communications (mMTC) enabled distributed antenna systems (DAS). Sparse Bayesian learning (SBL) integrated with variational Bayesian inference (VBI) methods exploit channel sparsity effectively and are commonly employed for AUD and CE. Nevertheless, these techniques generally require matrix inversion operations and numerous iterations, leading to high computational complexity. To mitigate these issues, we propose a deep unrolling neural network based on a fast inversion algorithm, introducing learnable parameters to accelerate convergence. Additionally, we consider the pilot collision in grant-free random access, which is often overlooked in most literature, and presents a pilot collision handling method that leverages the spatial sparsity of distributed antennas. Simulation results validate the reliability of the proposed network and the method for pilot collision processing. © 2024 IEEE.

关键词： Channel estimation

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Applications of Large-Scale Quantum Neural networks based on distributed Quantum Computing 16

Applications of Large-Scale Quantum Neural Networks Based on...

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16th international conference on Wireless Communications and Signal processing, WCSP 2024

作者： Kan, Yin He, Junyuan Xue, Cheng Anhui University AHU-IAI Ai Joint Laboratory Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Hefei China Institute of Artificial Intelligence Comprehensive National Science Center Hefei China

ISBN: (纸本)9798350390643

this research investigates the limitations of current quantum hardware in fulfilling the computational demands of quantum neural networks. It introduces an optimized circuit computed method leveraging bit splitting within the framework of distributed quantum computing. the research exemplifies this approach by decomposing a 25-qubit variational quantum circuit into several smaller 5-qubit circuits, thereby enabling distributed quantum computations. A hybrid quantum-classical neural network architecture employing this methodology was implemented and evaluated on selected computer vision datasets. the results were promising, affirming the viability of the approach. the successful implementation of this architecture suggests that future distributed quantum computing systems will likely consist of scalable, heterogeneous quantum systems interconnected through an extensive quantum network. this configuration is anticipated to significantly enhance computational power and versatility, expanding the scope of quantum computing applications in real-world scenarios. this advancement not only validates the feasibility of distributed quantum architectures but also underscores their potential to revolutionize various technological domains through enhanced computational capabilities. © 2024 IEEE.

关键词： Quantum electronics

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Proceedings - 2023 IEEE international conference on parallel and distributed processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and networking, ISPA/BDCloud/SocialCom/SustainCom 2023

Proceedings - 2023 IEEE International Conference on Parallel...

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21st IEEE international Symposium on parallel and distributed processing with Applications, 13th IEEE international conference on Big Data and Cloud Computing, 16th IEEE international conference on Social Computing and networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

ISBN: (纸本)9798350329223

the proceedings contain 153 papers. the topics discussed include: transaction data management optimization based on multi-partitioning in blockchain systems;semi-asynchronous federated learning optimized for NON-IID data communication based on tensor decomposition;HKTGNN: hierarchical knowledge transferable graph neural network-based supply chain risk assessment;DQR-TTS: semi-supervised text-to-speech synthesis with dynamic quantized representation;deep reinforcement learning-based network moving target defense in DPDK;iNUMAlloc: towards intelligent memory allocation for AI accelerators with NUMA;and predictive queue-based low latency congestion detection in data center networks.

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Proceedings - 2017 25th euromicro international conference on parallel, distributed and network-based processing, PDP 2017

Proceedings - 2017 25th Euromicro International Conference o...

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25th euromicro international conference on parallel, distributed and network-based processing, PDP 2017

ISBN: (纸本)9781509060580

the proceedings contain 90 papers. the topics discussed include: cloud storage cost modeling for cryptographic file systems;high performance I/O for seismic wave propagation simulations;modeling low power compute clusters for cloud simulation;MERCURY: a transparent guided I/O framework for high performance I/O stacks;parallel satisfiability solver based on hybrid partitioning method;elastic scaling for distributed latency-sensitive data stream operators;parallelization of machine learning applied to call graphs of binaries for malware detection;fault-tolerant parallel execution of workflows with deadlines;and a rapid data communication exploration tool for Hybrid CPU-FPGA architectures.

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parallelizing Multipacting Simulation for the Design of Particle Accelerator Components 31

Parallelizing Multipacting Simulation for the Design of Part...

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31st euromicro international conference on parallel, distributed and network-based processing, PDP 2023

作者： Galarza, J. Navaridas, J. Pascual, J.A. Romero, T. Munoz, J.L. Bustinduy, I. University of the Basque Country Donostia Spain Donostia International Physics Center Donostia Spain Ess Bilbao Bilbao Spain

ISBN: (纸本)9798350337631

Particle trajectory and collision simulation is a critical step of the design and construction of novel particle accelerator components. However it requires a huge computational effort which can slow down the design process. We started from a sequential simulation program which is used to study an event called 'Multipacting'. Our work explains the physical problem that is simulated and the implications it can have on the behavior of the components. then we analyze the original program's operation to find the best options for parallelization. We first developed a parallel version of the Multipacting simulation and were able to accelerate the execution up to 35× with 48 or 56 cores. In the best cases, parallelization efficiency was maintained up to 16 cores ( 95%) and the speed-up plateaus at around 40 to 48 cores. When this first parallelization effort was tried for multi-power simulations, we found that parallelism was severely limited with a maximum of 20× speed-up. For this reason, we introduced a new method to improve the parallelization efficiency for this second use case. this method uses a shared processor pool for all simulations of electrons (OnePool). OnePool improved scalability by pushing the speed-up to over 32×. © 2023 IEEE.

关键词： parallel programming

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Towards parallel Data Stream processing on System-on-Chip CPU+GPU Devices 30

Towards Parallel Data Stream Processing on System-on-Chip CP...

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30th euromicro international conference on parallel, distributed and network-based processing, PDP 2022

作者： Mencagli, Gabriele Griebler, Dalvan Danelutto, Marco University of Pisa Department of Computer Science Italy Do Rio Grande Do Sul Pontificia Universidade Catolica Porto Alegre Brazil

ISBN: (纸本)9781665469586

Data Stream processing is a pervasive computing paradigm with a wide spectrum of applications. Traditional streaming systems exploit the processing capabilities provided by homogeneous Clusters and Clouds. Due to the transition to streaming systems suitable for IoT/Edge environments, there has been the urgent need of new streaming frameworks and tools tailored for embedded platforms, often available as System-onChips composed of a small multicore CPU and an integrated onchip GPU. Exploiting this hybrid hardware requires special care in the runtime system design. In this paper, we discuss the support provided by the WindFlow library, showing its design principles and its effectiveness on the NVIDIA Jetson Nano board. © 2022 IEEE.

关键词： System-on-chip

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MLN-Subdue: Substructure Discovery In Homogeneous Multilayer networks 16

MLN-Subdue: Substructure Discovery In Homogeneous Multilayer...

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16th international conference on Knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th international Joint conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024

作者： Rai, Anish Roy, Anamitra Santra, Abhishek Chakravarthy, Sharma The University of Texas at Arlington ArlingtonTX76019 United States

ISBN: (纸本)9789897587160

Substructure discovery is a well-researched problem for graphs (both simple and attributed) for knowledge discovery. Recently, multilayer networks (or MLNs) have been shown to be better suited for modeling complex datasets that have multiple entity and relationship types. However, the MLN representation brings new challenges in finding substructures due to the presence of layers, and substructure discovery methods for MLNs are currently not available. this paper proposes a substructure discovery algorithm for homogeneous MLNs using the decoupling approach. In HoMLNs, each layer has same or a common subset of nodes but different intralayer connectivity. this algorithm has been implemented using the Map/Reduce framework to handle arbitrarily large layers and to improve the response time through distributed and parallel processing. In the decoupled approach, each layer is processed independently (without using any information from other layers) and in parallel and the substructures generated from each layer are combined after each iteration to generate substructures that span layers. the focus is on the correctness of the algorithm and resource utilization based on the number of layers. the proposed algorithm is validated through extensive experimental analysis on large real-world and synthetic graphs with diverse graph characteristics. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： network architecture

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A Multi-attention based CNN-BiLSTM Intrusion Detection Model for In-vehicle networks 21

A Multi-attention Based CNN-BiLSTM Intrusion Detection Model...

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作者： Gao, Kai Huang, Hao Liu, Linhong Du, Ronghua Zhang, Jinlai Changsha University of Science & Technology College of Automotive and Mechanical Engineering Changsha410114 China

ISBN: (纸本)9798350329223

the development of vehicular networking technology continuously enhances the internet connectivity of modern vehicles. However, for in-vehicle networks, constant communication with the outside world dramatically increases vehicle security risks, posing serious security threats to automotive systems and occupants. this paper proposes a CNN-BiLSTM vehicular network intrusion detection model based on multi-head attention by analyzing the information security vulnerabilities in the Controller Area network (CAN) bus. By extracting only the communication features of CAN messages, including CAN ID and Data field, this paper conducted a multiclass classification study on the real-time traffic dataset CarHacking. the model achieved a detection accuracy of 99.992%. the reduction of features effectively simplifies the model and improves its computation efficiency. © 2023 IEEE.

关键词： Anomaly detection

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