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20th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2019

作者： Liao, Hao Liu, Qixin Vidmer, Alexandre Zhou, Mingyang Mao, Rui National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126166

In the past two decades, analyzing the information network has been intensively studied from various disciplines. Small world property and scale-free property prevail in network science research. The comparison and classification of different kinds of graphs are extremely important. However, how to design a robust and accurate classification with deep learning techniques for network property still lack enough attention, which is a vital task in various application scenarios. In this paper, we proposed the reliable network property classifier based on graph embedding(RNC) to classify the network property (scale free or small world property). In order to process non-euclidean data, we embedded each network into an image and use dimensional reduction, rasterization, and convolutional neural networks to complete the classification problem. The method can effectively accomplish classification tasks in not only artificial networks but also real networks. Besides, RNC wins greatly in terms of robustness on real networks, showing the robustness of RNC against the incomplete structure of the network. © 2019 IEEE.

关键词： Deep learning

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Wireless Large AI Model: Shaping the AI-Native Future of 6G and Beyond

arXiv

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arXiv 2025年

作者： Zhu, Fenghao Wang, Xinquan Li, Xinyi Zhang, Maojun Chen, Yixuan Huang, Chongwen Yang, Zhaohui Chen, Xiaoming Zhang, Zhaoyang Jin, Richeng Huang, Yongming Feng, Wei Yang, Tingting Bai, Baoming Gao, Feifei Yang, Kun Liu, Yuanwen Muhaidat, Sami Yuen, Chau Huang, Kaibin Wong, Kai-Kit Niyato, Dusit Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Automation Southeast University Nanjing210096 China Tsinghua University Beijing100084 China Pengcheng Laboratory Shenzhen518066 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China Suzhou215163 China The University of Hong Kong Hong Kong KU 6G Research Center Computer and Communication Engineering Khalifa University Abu Dhabi127788 United Arab Emirates School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is the wireless large AI model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, elucidating its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges hindering the practical implementation of WLAM and discuss pivotal future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Sorting

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IndexIt: Enhancing data locating services for parallel file systems 21

IndexIt: Enhancing data locating services for parallel file ...

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21st IEEE International Conference on high performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data science and Systems, HPCC/SmartCity/DSS 2019

作者： Cheng, Peng Wang, Yong Lu, Yutong Du, Yunfei Chen, Zhiguang College of Computer National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing Changsha China National Supercomputer Center in Guangzhou School of Data and Computer Science Sun Yat-Sen University Guangzhou China

ISBN: (纸本)9781728120584

While the ability to access a small fraction of data records from a large volume of scientific datasets is vital to accelerate scientific discovery, existing parallel file systems face serious challenges in managing scientific big data since data services have traditionally been decoupled from file systems. In this paper, we present IndexIt, an in-situ index and query middleware that aims to enhance record locating services for parallel file systems. When applications are writing data to parallel file systems, IndexIt allows users to index data while they are still in memory and bypass the performance bottleneck between memory and disks. By applying lightweight Bitmap-Range index and organizing the index as key-value pairs, IndexIt accelerates index building efficiently. Moreover, we propose a two-level query processing framework to process query requests. We build a prototype and evaluate its performance with real scientific datasets. Compared with existing data management tools, the index building time can be reduced dramatically. The proposed two-level query processing achieves up to 3 orders of magnitude speed-up than scanning the entire dataset and shows up to 1.35x speed-up than an existing tool. © 2019 IEEE.

关键词： Information management

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Susceptible-infected-susceptible model on networks with eigenvector localization

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Physical Review E 2020年第4期101卷 042310-042310页

作者： Zong-Wen Wei Bing-Hong Wang Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China Department of Modern Physics University of Science and Technology of China Hefei 230027 China

It is a longstanding debate on the absence of threshold for susceptible-infected-susceptible (SIS) model on networks with finite second order moment of degree distribution. The eigenvector localization of the adjacency matrix for a network gives rise to the inactive Griffiths phase featuring slow decay of the activity localized around highly connected nodes due to the dynamical fluctuation. We show how it dramatically changes our understanding of the SIS model, opening up new possibilities for the debate. We derive the critical condition for Griffiths to active phase transition: on average, an infected node can further infect another one in the characteristic lifespan of the star subgraph composed of the node and its nearest neighbors. The system approaches the critical point of avoiding the irreversible dynamical fluctuation and the trap of absorbing state. As a signature of the phase transition, the infection density of a node is not only proportional to its degree, but also proportional to the exponentially growing lifespan of the star. And the divergence of the average lifespan of the stars is responsible for the vanishing threshold in the thermodynamic limit. The eigenvector localization exponentially reinforces the infection of highly connected nodes, while it inversely suppresses the infection of small-degree nodes.

关键词： Complex systems Epidemic Network phase transitions Nonequilibrium statistical mechanics Spreading

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An Improved spectral graph partition intelligent clustering algorithm for low-power wireless networks

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Journal of Ambient Intelligence and Humanized computing 2019年 1-9页

作者： Chen, Ling Chen, Yan Wang, Yu Zhengzhou Science and Technology Institute Zhengzhou450002 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China College of Computer Science Henan University of Engineering Zhengzhou451191 China

Aiming at the problem of energy imbalance and low-energy efficiency in wireless sensor network, a novel energy-balanced clustering routing algorithm based on spectral graph partition is proposed in this paper, which firstly calculates the optimum single-hop distance, balances the energy consumption of cluster heads, and avoids that the number of cluster heads is not fixed and cluster heads are clustered by using the traditional probability function. In addition, the transmission cost function is designed to construct the optimum transmission path to reduce energy consumption. The experimental results show that the network performance of our proposed algorithm is improved, which achieves on low-energy network for loss reduction and load balancing. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cost functions

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Multiplex Symbolic Execution: Exploring Multiple Paths by Solving Once

Multiplex Symbolic Execution: Exploring Multiple Paths by So...

引用

IEEE International Conference on Automated Software Engineering (ASE)

作者： Yufeng Zhang Zhenbang Chen Ziqi Shuai Tianqi Zhang Kenli Li Ji Wang College of Computer Science and Electronic Engineering Hunan University Changsha China College of Computer National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering National Supercomputing Center in Changsha Hunan University Changsha China College of Computer State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

ISBN: (数字)9781450367684

ISBN: (纸本)9781728172811

Path explosion and constraint solving are two challenges to symbolic execution's scalability. Symbolic execution explores the program's path space with a searching strategy and invokes the underlying constraint solver in a black-box manner to check the feasibility of a path. Inside the constraint solver, another searching procedure is employed to prove or disprove the feasibility. Hence, there exists the problem of double searchings in symbolic execution. In this paper, we propose to unify the double searching procedures to improve the scalability of symbolic execution. We propose Multiplex Symbolic Execution (MuSE) that utilizes the intermediate assignments during the constraint solving procedure to generate new program inputs. MuSE maps the constraint solving procedure to the path exploration in symbolic execution and explores multiple paths in one time of solving. We have implemented MuSE on two symbolic execution tools (based on KLEE and JPF) and three commonly used constraint solving algorithms. The results of the extensive experiments on real-world benchmarks indicate that MuSE has orders of magnitude speedup to achieve the same coverage.

关键词： Multiplexing Scalability Tools Search problems Explosions Space exploration Software engineering

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Parallel computing for large-scale author name disambiguation in medline 21

Parallel computing for large-scale author name disambiguatio...

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作者： Tang, Anyao Wu, Chengkun Liu, Jie Wang, Wei Yang, Xi Xing, Yuting Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha410073 China State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China College of Computer National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9781728120584

Author name disambiguation (AND) is an important task in the field of scientific data mining. It has become a great challenge with the rapid growth of academic digital libraries. The task of AND for a large number of authors is computationally intensive. In particular, an author's name in MEDLINE is represented by full last name and initials, like 'Zhang S', which leads to a lot of identical strings that actually represent different names. In this paper, we proposed an efficient algorithm for parallel AND computation. The proposed algorithm mainly addresses the load balancing issue across many computing nodes. It involves the following strategies:(1) Author-based load balancing, which splits the computation load for each core by author name labels. (2) Matrix-based strategy, which calculates the pairwise similarity between publications and saves them in a matrix globally shared by all processes. Then group them by width-first search. We combine the above two strategies, the second of which is used to calculate authors with a large number of documents, and the other authors apply the first. We constructed a publications database written by Chinese authors from MEDLINE, the biggest public database for biomedical literature (abstracts). For benchmark testing, we experimented our algorithm with a dataset of 1 million publications on the Tianhe-2A supercomputer. Firstly, we trained an AND classifier that can achieve 98.1% of F1. The serial computation time is estimated to be approximately 246 hours, while the parallel execution time is approximately 66 hours in the case of four cores on a single node (with a speedup of 3.7x). Finally, we reduced the total parallel computing time of 1 million documents to about 2 hours and achieved 65.8% of parallelism efficiency using 200 cores on 90 nodes. © 2019 IEEE.

关键词： Matrix algebra

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Reconfigurable Intelligent Surface Assisted high-Speed Train Communications: Coverage performance Analysis and Placement Optimization

arXiv

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arXiv 2023年

作者： Liu, Changzhu He, Ruisi Niu, Yong Han, Zhu Ai, Bo Gao, Meilin Ma, Zhangfeng Wang, Gongpu Zhong, Zhangdui The State Key Laboratory of Advanced Rail Autonomous Operation The School of Electronics and Information Engineering The Frontiers Science Center for Smart High-speed Railway System The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The Tsinghua Space Center the Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100091 China The College of Information Engineering Shaoyang University Shaoyang422000 China The Beijing Key Lab of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

Reconfigurable intelligent surface (RIS) emerges as maximization problem is formulated to facilitate RIS discrete an efficient and promising technology for the next wireless phase design and RIS placement optimization, which is subject generation networks and has attracted a lot of attention owing to coverage probability constraint. Simulation results validate to the capability of extending wireless coverage by reflecting that better coverage performance and higher travel distance signals toward targeted receivers. In this paper, we consider can be achieved with deployment of RIS. The impacts of some a RIS-assisted high-speed train (HST) communication system key system parameters including transmission power, signal-to-to enhance wireless coverage and improve coverage probability. noise ratio threshold, number of RIS elements, number of RIS First, coverage performance of the downlink single-input-single- quantization bits, horizontal distance between base station and output system is investigated, and the closed-form expression RIS, and speed of HST on system performance are investigated. of coverage probability is derived. Moreover, travel distance In addition, it is found that RIS can well improve coverage probability with limited power consumption for HST communications. Copyright © 2023, The Authors. All rights reserved.

关键词： Railroads

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Fast Linear Prediction Hyperspectral Image Visualization Algorithm

引用

IOP Conference Series: Earth and Environmental science 2021年第1期693卷

作者： Yunpeng Xu Yuanxi Peng Jian Long State Key Laboratory of High-Performance Computing College of Computer National University of Defense Technology Changsha 410073 China

How to generate a color image with as much information as possible from hyperspectral images on a trichromatic display is a subject of great concern. In a sense, effective bands containing important information can be extracted from hyperspectral data for image visualization through the method of band selection. In this paper, two improved methods are proposed for the linear prediction (LP) band selection algorithm. According to the information entropy, the band with the greatest difference in information domain is selected as initial ***, the max pooling method is used to select the pixel which can better reflect the image features. Finally, the band similarity is used to select the most distinctive band for hyperspectral image visualization. The experimental results are measured by the metrics such as feature separability, standard deviation, operation efficiency and image displaying *** final experimental results show that fast linear prediction (LP) algorithm is an effective method for hyperspectral image visualization.

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Many-body localization in a non-Hermitian quasiperiodic system

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Physical Review B 2020年第6期102卷 064206-064206页

作者： Liang-Jun Zhai Shuai Yin Guang-Yao Huang The School of Mathematics and Physics Jiangsu University of Technology Changzhou 213001 China School of Physics Sun Yat-Sen University Guangzhou 510275 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 China

In the present study, the interplay among interaction, topology, quasiperiodicity, and non-Hermiticity is studied. The hard-core bosons model on a one-dimensional lattice with asymmetry hoppings and quasiperiodic onsite potentials is selected. This model, which preserves time-reversal symmetry (TRS), will exhibit three types of phase transition: Real-complex transition of eigenenergies, topological phase transition, and many-body localization (MBL) phase transition. For the real-complex transition, it is found that the imaginary parts of the eigenenergies are always suppressed by the MBL. Moreover, by calculating the winding number, a topological phase transition can be revealed with the increase of potential amplitude, and we find that the behavior is quite different from the single-particle systems. Based on our numerical results, we conjecture that these three types of phase transition occur at the same point in the thermodynamic limit, and the MBL transition of quasiperiodic system and disordered system should belong to different universality classes. Finally, we demonstrate that these phase transitions can profoundly affect the dynamics of the non-Hermitian many-body system.

关键词： Phase transitions Quantum phase transitions Topological phase transition Topological phases of matter

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