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Adaptive Handover Decision Inspired By Biological Mechanism in Vehicle Ad-hoc Networks

Computers, Materials & Continua 2019年第9期61卷 1117-1128页

作者： Xuting Duan Jingyi Wei Daxin Tian Jianshan Zhou Haiying Xia Xin Li Kunxian Zheng Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems&SafetyControl Beijing100191China School of Transportation Science and Engineering Beihang UniversityBeijing100191China Department of Engineering and Design University of SussexBrightonBN19RHUK Research Institute of Highway Ministry Transport Beijing100088China Department of Computer Science and Technology Tsinghua UniversityHaidian DistrictBeijing100088China

In vehicle ad-hoc networks(VANETs),the proliferation of wireless communication will give rise to the heterogeneous access environment where network selection becomes *** by the self-adaptive paradigm of cellular attractors,this paper regards an individual communication as a cell,so that we can apply the revised attractor selection model to induce each connected *** at improving the Quality of Service(QoS),we presented the bio-inspired handover decision-making *** addition,we employ the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)for any vehicle to choose an access *** paper proposes a novel framework where the bio-inspired mechanism is combined with *** a dynamic and random mobility environment,our method achieves the coordination of performance of heterogeneous networks by guaranteeing the efficient utilization and fair distribution of network resources in a global *** experimental results confirm that the proposed method performs better when compared with conventional schemes.

关键词： Revised attraction selection model TOPSIS VANETs QoS

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Syntax-enhanced Pre-trained model

arXiv

引用

arXiv 2020年

作者： Xu, Zenan Guo, Daya Tang, Duyu Su, Qinliang Shou, Linjun Gong, Ming Zhong, Wanjun Quan, Xiaojun Jiang, Daxin Duan, Nan School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Microsoft Research Asia Beijing China Microsoft Search Technology Center Asia Beijing China Guangdong Key Laboratory Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. Such a problem would lead to the necessity of having human-annotated syntactic information, which limits the application of existing methods to broader scenarios. To address this, we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages. Our model is based on Transformer with a syntax-aware attention layer that considers the dependency tree of the text. We further introduce a new pre-training task of predicting the syntactic distance among tokens in the dependency tree. We evaluate the model on three downstream tasks, including relation classification, entity typing, and question answering. Results show that our model achieves state-of-the-art performance on six public benchmark datasets. We have two major findings. First, we demonstrate that infusing automatically produced syntax of text improves pre-trained models. Second, global syntactic distances among tokens bring larger performance gains compared to local head relations between contiguous tokens. Copyright © 2020, The Authors. All rights reserved.

关键词： Syntactics

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An IGS algorithm applied to DNA sequence design 17

An IGS algorithm applied to DNA sequence design

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17th IEEE International Conference on Dependable, Autonomic and Secure computing, IEEE 17th International Conference on Pervasive Intelligence and computing, IEEE 5th International Conference on Cloud and big data computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019

作者： Liu, Kaiqiang Wang, Bin Liu, Yuan Wu, Ranfeng Zhang, Xun Liu, Xin Zhang, Qiang Yin, Zhixiang Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China School of Mathematics and Big Data Anhui University of Science and Technology Dalian China

ISBN: (纸本)9781728130255;9781728130248

American scientist Adelman first used DNA molecules to solve the NP-complete problem, thus creating a new research field. As an advanced interdisciplinary subject, it has attracted many scholars because of its high parallelism, great storage density and low energy consumption. DNA sequence design is critical in the field of DNA computing. The success of DNA computing depends on the quality of DNA sequence. In this paper, we proposed an improved gravitational search algorithm for DNA sequence design. We improved the gravitational coefficient and introduced chaotic system and Gaussian mutation to the position update of the particles. We compared the result from our improved algorithm with the result obtained by other algorithms. The result shows that our proposed algorithm can generate high quality DNA sequence for DNA computing. © 2019 IEEE.

关键词： DNA computing DNA sequence design Gravitational search algorithm NP-complete problem

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Rely-Guarantee Reasoning about Messaging System for Autonomous Vehicles

Rely-Guarantee Reasoning about Messaging System for Autonomo...

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Theoretical Aspects of Software Engineering (TASE)

作者： Wenjing Xu Yongwang Zhao Dianfu Ma YuXin Zhang Qian Xiao State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang Univerisity Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China State Key Laboratory of Automotive Simulation and Control College of Automotive Engineering Jilin University Jilin China School of Computer Science and Technology Guizhou University Guiyang China

Messaging system as a communicating infrastructure is a safety-critical component of autonomous vehicles. For the purpose of safety certification of Level 4 autonomous driving systems, its necessary to provide a formally verified specification of messaging systems. This paper presents a realistic case study using the PiCore rely-guarantee framework to formally verify the DGPS (Differential Global Positioning System) of UISEE autonomous driving systems. We first create an axiom model of messaging systems by extending PiCore, which is reusable for concrete applications. The model supports dynamic configuration of message buffers as well as the automatic generation of the rely and guarantee conditions for compositional reasoning. It is instantiated when developing the formal specification of DGPS. Then, we use the rely-guarantee proof system of PiCore to verify the functional correctness and invariant of DGPS. The specification and its proof provide a strong evidence for the ongoing safety certification of DGPS.

关键词： Cognition Safety Formal specifications Vehicle dynamics Autonomous vehicles Certification Global Positioning System

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Adversarial directed graph embedding

arXiv

引用

arXiv 2020年

作者： Zhu, Shijie Li, Jianxin Peng, Hao Wang, Senzhang He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Computer Science and Engineering Lehigh University BethlehemPA United States

Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector. However, these methods learn the source and target vectors separately. For the node with very low indegree or outdegree, the corresponding target vector or source vector cannot be effectively learned. In this paper, we propose a novel Directed Graph embedding framework based on Generative Adversarial Network, called DGGAN. The main idea is to use adversarial mechanisms to deploy a discriminator and two generators that jointly learn each node's source and target vectors. For a given node, the two generators are trained to generate its fake target and source neighbor nodes from the same underlying distribution, and the discriminator aims to distinguish whether a neighbor node is real or fake. The two generators are formulated into a unified framework and could mutually reinforce each other to learn more robust source and target vectors. Extensive experiments show that DGGAN consistently and significantly outperforms existing state-of-the-art methods across multiple graph mining tasks on directed graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Directed graphs

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An auto code generator for stencil on SW26010 21

An auto code generator for stencil on SW26010

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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： Zhu, Xiaomin Zeng, Yunhui Wei, Yanjie Feng, Shengzhong Liu, Weiguo Balaji, Pavan Joint Engineering Research Center Health Big Data Intelligent Analysis Technology Center for High Performance Computing Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shandong Computer Science Center National SuperComputer Center in Jinan Jinan China Sclool of Software Shandong University Jinan China Argonne National Laboratory United States

ISBN: (纸本)9781728120584

Stencil is a basic building block widely used in many HPC areas and applications. It generally dominates the time cost and is critical to the overall performance. Given that heterogeneous many-core is frequently adopted to build super computers, porting and optimizing stencil codes on modern accelerator-based architectures is important. Porting is non-trivial, and optimization is more difficult as it requires better understanding of the underlying architecture. As a result, auto tuning targeting on accelerators such as GPU becomes a hot research topic. In this paper, we focus on tuning stencil automatically on SW26010, which is a heterogeneous many-core CPU equipped in Sunway TaihuLight SuperComputer, and mainly work on 2D stencil variants code in ROMS (Regional Ocean Modeling System). The code generator can generate codes running on slave cores for 50+ different FORTRAN stencil-alike loops in ROMS in seconds, and optimization methods are integrated into the generator as well. The performance is better than the directive based method (OpenACC) and the hand-drafted code by a junior programmer, and it is comparable compared with the code written by a senior programmer with several years of programming experience. © 2019 IEEE.

关键词： Supercomputers

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Unveiling dark matter free streaming at the smallest scales with the high redshift Lyman-alpha forest

引用

Physical Review D 2024年第4期109卷 043511-043511页

作者： Vid Iršič Matteo Viel Martin G. Haehnelt James S. Bolton Margherita Molaro Ewald Puchwein Elisa Boera George D. Becker Prakash Gaikwad Laura C. Keating Girish Kulkarni Kavli Institute for Cosmology University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Cavendish Laboratory University of Cambridge 19 J. J. Thomson Avenue Cambridge CB3 0HE United Kingdom SISSA—International School for Advanced Studies Via Bonomea 265 34136 Trieste Italy INFN—National Institute for Nuclear Physics Via Valerio 2 I-34127 Trieste Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 34151 Trieste Italy INAF Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 I-34131 Trieste Italy ICSC—Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy Institute of Astronomy University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom School of Physics and Astronomy University of Nottingham University Park Nottingham NG7 2RD United Kingdom Leibniz-Institut für Astrophysik Potsdam An der Sternwarte 16 14482 Potsdam Germany Department of Physics and Astronomy University of California Riverside California 92521 USA Max-Planck-Institut für Astronomie Königstuhl 17 D-69117 Heidelberg Germany Institute for Astronomy University of Edinburgh Blackford Hill Edinburgh EH9 3HJ United Kingdom Tata Institute of Fundamental Research Homi Bhabha Road Mumbai 400005 India

This study introduces novel constraints on the free streaming of thermal relic warm dark matter (WDM) from Lyman-α forest flux power spectra. Our analysis utilizes a high resolution, high redshift sample of quasar spectra observed using the HIRES and UVES spectrographs (z=4.2–5.0). We employ a Bayesian inference framework and a simulation-based likelihood that encompasses various parameters including the free streaming of dark matter, cosmological parameters, the thermal history of the intergalactic medium, and inhomogeneous reionization to establish lower limits on the mass of a thermal relic WDM particle of 5.7 keV (at 95% CL). This result surpasses previous limits from the Lyman-α forest through reduction of the measured uncertainties due to a larger statistical sample and by measuring clustering to smaller scales (kmax=0.2 km−1 s). The approximately two-fold improvement due to the expanded statistical sample suggests that the effectiveness of Lyman-α forest constraints on WDM models at high redshifts are limited by the availability of high quality quasar spectra. Restricting the analysis to comparable scales and thermal history priors as in prior studies (kmax<0.1 km−1 s) lowers the bound on the WDM mass to 4.1 keV. As the precision of the measurements increases, it becomes crucial to examine the instrumental and modeling systematics. On the modeling front, we argue that the impact of the thermal history uncertainty on the WDM particle mass constraint has diminished due to improved independent observations. At the smallest scales, the primary source of modeling systematic arises from the structure in the peculiar velocity of the intergalactic medium and inhomogeneous reionization.

关键词： Cosmology Dark matter Large scale structure of the Universe Particle dark matter Astrophysical & cosmological simulations

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A threshold effect of coupling delays on intra-layer synchronization in duplex networks

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Science China(Technological Sciences) 2018年第12期61卷 1907-1914页

作者： TANG LongKun LU JunAn Lü JinHu Fujian Province University Key Laboratory of Computation Science School of Mathematical Sciences Huaqiao University Department of Mathematics & Statistics Georgia State University School of Mathematics and Statistics Wuhan University School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates the impact of inter-layer coupling functions and intra-layer coupling delays on intra-layer synchronization regions and sychronizability. It is found that the inter-layer coupling functions have great influence on intra-layer synchronization regions, as well as on the intra-layer synchronizability. In particular, there exists an inter-layer coupling function such that the inter-layer coupling strength neither improves nor weakens the intra-layer synchronizability. Furthermore, no matter which one of three inter-layer coupling functions is chosen, a small intra-layer delay always keeps the intra-layer synchronized regions almost unchanged, implying that the small delay neither enhances nor suppresses the intra-layer synchronizability. At the same time the delay greatly frustrates the synchronizability in each layer when it is greater than some threshold. Our results may have potential applications for interconnected technological networks where communication delays are inevitably present.

关键词： multiplex network master stability function intra-layer synchronization synchronized region time delay

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Intelligent identification of two-dimensional nanostructures by machine-learning optical microscopy

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Nano Research 2018年第12期11卷 6316-6324页

作者： Xiaoyang Lin Zhizhong Si Wenzhi Fu Jianlei Yang Side Guo Yuan Cao Jin Zhang Xinhe Wang Peng Liu Kaili Jiang Weisheng Zhao Fert Beijing Research Institute School ofMicroelectronics & Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC)Beihang UniversityBeijing 100191China Beihang-GoertekJoint Microelectronics Institute Qingdao Research InstituteBeihang UniversityQingdao 266000China Fert Beijing Research Institute School of Computer Science and Engineering & Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC)Beihang UniversityBeijing 100191China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics & Tsinghua-Foxconn Nanotechnology Research CenterCollaborative Innovation Center of Quantum MatterTsinghua UniversityBeijing 100084China

Two-dimensional (2D) materials and their heterostructures, with wafer-scale synthesis methods and fascinating properties, have attracted significant interest and triggered revolutions in corresponding device applications. However, facile methods to realize accurate, intelligent, and large-area characterizations of these 2D nanostructures are still highly desired. Herein, we report the successful application of machine-learning strategy in the optical identification of 2D nanostructures. The machine-learning optical identification (MOI) method endows optical microscopy with intelligent insight into the characteristic color information of 2D nanostructures in the optical photograph. The experimental results indicate that the MOI method enables accurate, intelligent, and large-area characterizations of graphene, molybdenum disulfide, and their heterostructures, including identifications of the thickness, existence of impurities, and even stacking order. With the convergence of artificial intelligence and nanoscience, this intelligent identification method can certainly promote fundamental research and wafer-scale device applications of 2D nanostructures.

关键词： machine-learning optical identification two-dimensional (2D) material heterostructure artificial intelligence

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Efficient Block Pruning Based on Kernel and Feature Stablization

Efficient Block Pruning Based on Kernel and Feature Stabliza...

引用

Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Sheng Xu Hanlin Chen Kexin Liu Jinhu Lii Baochang Zhang School of Automatic Science and Electrical Engineering Beihang University China State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

With the development of computer vision research, the architecture of convolutional neural network becomes more and more complex to reach the state-of-the-art performance. Is the complexity of the model necessarily proportional to its accuracy? To answer this, the compression of the network has attracted much attention in the academy and industry. Existing network pruning methods mostly rely on the scoring mechanism of complexity or diversity of kernels to compress the network, and then build the network model after removing the kernels by tuning or training on the input data. These methods are cumbersome and depend on a well-trained pre-trained model. In this paper, we propose an end-to-end block pruning method based on kernel and feature stability by pruning blocks efficiently. To accomplish this, we firstly introduce a mask to scale the output of the blocks, and the L1 regularization term to monitor the mask update. Second, we introduce the Center Loss to guarantee that the feature does not deviate greatly during learning. To converge fast, we introduce fast iterative shrinkage-thresholding algorithm (FISTA) to optimize the mask, by which a more fast and reliable pruning process is achieved. We implement experiments on different datasets, including CIFAR-10 and ImageNet ILSVRC2012. All the experiments have achieved the state-of-the-art accuracy.

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