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arXiv 2022年

作者： Xu, Fei Xu, Jianian Chen, Jiabin Chen, Li Shang, Ruitao Zhou, Zhi Liu, Fangming The Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University 3663 N. Zhongshan Road Shanghai200062 China The School of Computing and Informatics University of Louisiana at Lafayette 301 East Lewis Street LafayetteLA70504 United States The Guangdong Key Laboratory of Big Data Analysis and Processing School of Computer Science and Engineering Sun Yat-sen University 132 E. Waihuan Road Guangzhou510006 China The National Engineering Research Center for Big Data Technology and System The Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology 1037 Luoyu Road Wuhan430074 China

GPUs are essential to accelerating the latency-sensitive deep neural network (DNN) inference workloads in cloud datacenters. To fully utilize GPU resources, spatial sharing of GPUs among co-located DNN inference workloads becomes increasingly compelling. However, GPU sharing inevitably brings severe performance interference among co-located inference workloads, as motivated by an empirical measurement study of DNN inference on EC2 GPU instances. While existing works on guaranteeing inference performance service level objectives (SLOs) focus on either temporal sharing of GPUs or reactive GPU resource scaling and inference migration techniques, how to proactively mitigate such severe performance interference has received comparatively little attention. In this paper, we propose iGniter, an interference-aware GPU resource provisioning framework for cost-efficiently achieving predictable DNN inference in the cloud. iGniter is comprised of two key components: (1) a lightweight DNN inference performance model, which leverages the system and workload metrics that are practically accessible to capture the performance interference;(2) A cost-efficient GPU resource provisioning strategy that jointly optimizes the GPU resource allocation and adaptive batching based on our inference performance model, with the aim of achieving predictable performance of DNN inference workloads. We implement a prototype of iGniter based on the NVIDIA Triton inference server hosted on EC2 GPU instances. Extensive prototype experiments on four representative DNN models and datasets demonstrate that iGniter can guarantee the performance SLOs of DNN inference workloads with practically acceptable runtime overhead, while saving the monetary cost by up to 25% in comparison to the state-of-the-art GPU resource provisioning strategies. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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A Lagrangian traffic flow model considering lane changing behavior: Formulation and numerical implementation

A Lagrangian traffic flow model considering lane changing be...

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2020 Forum on Integrated and Sustainable Transportation Systems, FISTS 2020

作者： Lu, Liang Zheng, Fangfang Liu, Xiaobo Yuan, Yufei Southwest Jiaotong University Western Hi-tech Zone Chengdu School of Transportation and Logistics Sichuan611756 China Southwest Jiaotong University School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology 611756 China Delft University of Technology Department of Transport and Planning Delft Netherlands

ISBN: (数字)9781728195032

ISBN: (纸本)9781728195032

This paper proposes a multilane traffic flow model based on the notions of conservation laws in Lagrangian coordinates. Both the mathematical formulation and the graphical representation are provided. A logit choice model is applied to describe drivers' lane choice probability. The lane changing rate is estimated by employing the Incremental Transfer (IT) principle and the lane choice probability model. The numerical implementation of the model in the case of two lanes is discussed. The simulation results reveal that the proposed Lagrangian model is able to describe lane changing dynamics of vehicle platoons;while the lane changing equilibrium curve at the macroscopic level is consistent with that from the multilane Eulerian model as well as the observed data on the highway. © 2020 IEEE.

关键词： Lagrange multipliers

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Cooperative Sensing and Heterogeneous Information Fusion in VCPS: A Multi-agent Deep Reinforcement Learning Approach

arXiv

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arXiv 2022年

作者： Xu, Xincao Liu, Kai Dai, Penglin Xie, Ruitao Cao, Jingjing Luo, Jiangtao The College of Computer Science Chongqing University Chongqing400040 China The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China The National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The School of Transportation and Logistics Engineering Wuhan University of Technology Hubei430063 China The Electronic Information and Networking Research Institute Chongqing University of Posts and Telecommunications Chongqing400065 China

Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing a new metric called Age of View (AoV). Specifically, we first present the system architecture where heterogeneous information can be cooperatively sensed and uploaded via vehicle-to-infrastructure (V2I) communications in vehicular edge computing (VEC). Logical views are constructed by fusing the heterogeneous information at edge nodes. Further, we formulate the problem by deriving a cooperative sensing model based on the multi-class M/G/1 priority queue, and defining the AoV by modeling the timeliness, completeness and consistency of the logical views. On this basis, a multi-agent deep reinforcement learning solution is proposed. In particular, the system state includes vehicle sensed information, edge cached information and view requirements. The vehicle action space consists of the sensing frequencies and uploading priorities of information. A difference-reward-based credit assignment is designed to divide the system reward, which is defined as the VCPS quality, into the difference reward for vehicles. Edge node allocates V2I bandwidth to vehicles based on predicted vehicle trajectories and view requirements. Finally, we build the simulation model and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solution. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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$$\alpha $$ -UNet++: A data-Driven Neural Network Architecture for Medical Image Segmentation 2nd

$$\alpha $$ -UNet++: A Data-Driven Neural Network Architectu...

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2nd MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2020, and the 1st MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2020

作者： Chen, Yaxin Ma, Benteng Xia, Yong Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Institute of Medical Research Northwestern Polytechnical University Xi’an710072 China

ISBN: (纸本)9783030605476

UNet++, an encoder-decoder architecture constructed based on the famous UNet, has achieved state-of-the-art results on many medical image segmentation tasks. Despite improved performance, UNet++ introduces densely connected decoding blocks, some of which, however, are redundant for a specific task. In this paper, we propose-UNet++ that allows us to automatically identify and discard redundant decoding blocks without the loss of precision. To this end, we design an auxiliary indicator function layer to compress the network architecture via removing a decoding block, in which all individual responses are less than a given threshold. We evaluated the segmentation architecture obtained respectively for liver segmentation and nuclei segmentation, denoted by UNet++, against UNet and UNet++. Comparing to UNet++, our UNet++ reduces the parameters by 18.89% in liver segmentation and 34.17% in nuclei segmentation, yielding an average improvement of IoU by 0.27% and 0.11% on two tasks. Our results suggest that the UNet++ produced by the proposed-UNet++ not only improves the segmentation accuracy slightly but also reduces the model complexity considerably. © 2020, Springer Nature Switzerland AG.

关键词： Decoding

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Hardware architecture design of HEVC entropy decoding

Hardware architecture design of HEVC entropy decoding

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Shiyu Wang Shengbing Zhang Jihe Wang Libo Chang Liangyou Feng Xiaoya Fan Engineering Research Center of Embedded System Integration Ministry of Education School of Computer Science Northwestern Polytechnical University Xian China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xian China

As the latest generation of digital video coding standard, HEVC has technically optimized multiple modules such as related frame prediction, block processing and entropy coding in the frequency coding and decoding framework. However, flexible and efficient coding algorithms make the amount of calculation in the video decoding and reconstruction process increase dramatically. The energy efficiency of traditional processors is limited, and the decoding calculation process is difficult to meet the current needs of ultra-high-definition video playback. For the most important and time-consuming bitstream analysis and entropy decoding part of the HEVC decoding process, a new hardware architecture strategy is provided, which can effectively improve the HEVC decoding performance. In this paper, the designed data access module, bitstream analysis and entropy decoding unit are simulated and verified. Build an FPGA verification platform, and use the main tier standard test sequence to simulate and verify the designed hardware acceleration architecture. The experimental results show that the hardware architecture of bitstream analysis and entropy decoding designed in this paper can reach the functions and performance indicators specified by the HEVC standard Level-4 main tier, and the bitstream analysis acceleration effect is good. This thesis aims at the current HEVC decoding calculation characteristics, and through the design of hardware architecture verification, it provides new solutions and hardware architecture ideas for subsequent HEVC encoding and decoding performance optimization.

关键词： Analytical models Program processors Video sequences Bit rate Syntactics Prediction algorithms Entropy

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Hub location of air cargo company in air alliance

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Journal of Southeast University(English Edition) 2020年第3期36卷 341-348页

作者： Zhang Jin Yan Yan Tang Qiuyu School of Transportation and Logistics Southwest Jiaotong UniversityChengdu 611756China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong UniversityChengdu 611756China National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong UniversityChengdu 611756China

Based on the characteristics of the air alliance environment saving transport mileage,the hub location problem of the air cargo network was ***,the air alliance selection probability model was introduced to determine the alliance self-operation or outsourcing probability in different ***,according to the location center rule,with the goal of minimizing the total cost,the hub location model was *** improved immune chaos genetic algorithm was used to solve this *** results show that the improved algorithm has stronger convergence and better effect than the immune genetic *** the number of hubs increases,the fixed cost increases,but the transportation cost *** greater the discount factor,the fixed cost,and the self operating cost sharing coefficient,the higher the total network *** airline which joins the air alliance can greatly reduce the operating cost of ***,airlines should consider joining the alliance.

关键词： air alliance air alliance selection probability hub location improved algorithm

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Sensitive Labels Matching Privacy Protection in Multi-Social Networks

Sensitive Labels Matching Privacy Protection in Multi-Social...

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IEEE International Conference on Communications (ICC)

作者： Wei Wang Qilin Mu Yanhong Pu Dapeng Man Wu Yang Xiaojiang Du Harbin Engineering University Improving the Governance Capability of Big Data Application Counting National Engineering Laboratory CETC Big Data Research Institute Co. Ltd Harbin China Big Data Application on Improving Government Governance Capabilities National Engineering Laboratory Guiyang College of Computer Science and Technology Harbin Engineering University Harbin China Department of Computer and Information Sciences Temple University Philadelphia USA

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

In social networks, some private information, such as the personal name, age gender, the number of friends, can be obtained by others. This paper defines a combination degree-neighborhood label matching attack model based on group maps obtained from multi-social networks. Based on the heuristic combination degree attack model, the target combination degree and neighborhood labels are used as the background knowledge of the attacker to obtain the candidate vertices set. The singularity of the sensitive label matching results will expose the sensitive information of the vertex being attacked. In order to solve this privacy attack, this paper proposes a group graph sensitive label generalization L diversity algorithm. This algorithm reduces the probability of sensitive labels being identified by designing a group map sensitive label generalization tree. According to the background knowledge, the number of sensitive labels in the candidate set and the number of sensitive labels obtained by matching are not less than L, so as to protect the sensitive information of the attacked target. The algorithm was evaluated by using three sets of data with different ratios. The experiment results show that the privacy protection algorithm effectively prevents sensitive label privacy attacks consisting of combination degree-domain label matching and better maintains the availability of graph data.

关键词： Social network services data privacy Privacy big data applications Sensitivity Heuristic algorithms

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Remote sensing monitoring of areca yellow leaf disease based on UAV multi-spectral images

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Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural engineering 2020年第8期36卷 54-61页

作者： Zhao, Jinling Jin, Yu Ye, Huichun Huang, Wenjiang Dong, Yingying Fan, Lingling Ma, Huiqin Jiang, Jing National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei230601 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Hainan Key Laboratory of Earth Observation Sanya572029 China

Yellow leaf disease is a serious disease that endangers the growth of areca, it is urgent to monitor the infection severity and spatial distribution in time and accurately. However, the traditional monitoring methods are mainly depend on visual inspection and manual investigation, which affects the efficiency and spatial scope of monitoring. Low altitude UAV remote sensing technology can effectively solve the problems of insufficient optical satellite images acquisition caused by cloudy and rainy weather in areca planting area, and improve the real-time monitoring of areca yellow leaf disease. In this paper, in order to identify the severities and spatial distribution of areca yellow leaf disease, five band(including blue, green, red, near-infrared and red-edge wavebands) multispectral images were obtained by using the MicaSense RedEdge-M multispectral camera mounted on the DJI Phantom 4 Pro V2.0. Based on the Minimum Redundancy Maximum Relevance (mRMR), three sensitive features were selected from 15 potential vegetation indexes. Using Back Propagation Neural Network(BPNN), Random Forest(RF) and Support Vector Machine(SVM) classification algorithms respectively, a monitoring model of areca yellow leaf disease severity was constructed. A total sixty in-situ sampling points were selected and the disease index (DI) were obtained, according to the characteristics of the disease and the separability of remote sensing images, the severities of the disease were divided into three grades: health (DI © 2020, Editorial Department of the Transactions of the Chinese Society of Agricultural engineering. All right reserved.

关键词： Neural network models

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Real-time Illumination Estimation for Mixed Reality on Mobile Devices

Real-time Illumination Estimation for Mixed Reality on Mobil...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Di Xu Zhen Li Yanning Zhang National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University China

ISBN: (数字)9781728165325

ISBN: (纸本)9781728165332

We present a lightweight lighting estimation method for the purpose of realistic mixed reality (MR) on mobile devices. Given a single RGB image, our method estimates the environment lighting and renders the virtual object in real-time. While previous works tackled this problem by reconstructing the complicated high dynamic range (HDR) environment maps, our deep neural network directly infers the corresponding spherical harmonics in extensive environments. Compared to previous approaches, our method is more robust and efficient as it works for both indoor and outdoor scenes in real-time. Experiments show that our approach achieves realistic rendering in various MR scenarios.

关键词： Lighting Virtual reality Real-time systems Mobile handsets Estimation Robustness Image reconstruction

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Image super-resolution with an enhanced group convolutional neural network

arXiv

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arXiv 2022年

作者： Tian, Chunwei Yuan, Yixuan Zhang, Shichao Lin, Chia-Wen Zuo, Wangmeng Zhang, David School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China Department of Electrical Engineering City University of Hong Kong Hong Kong School of Computer Science and Engineering Central South University Hunan Changsha410083 China Department of Electrical Engineering the Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is useful to inherit more long-distance contextual information for resolving long-term dependency. An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. Extensive experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR. Code is found at https://***/hellloxiaotian/ESRGCNN. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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