检索结果-内蒙古大学图书馆

5th Pacific Rim Conference on Multimedia, PCM 2004

作者： Lee, Liang-Teh Li, Kuan-Ching Yang, Chao-Tung Tseng, Chia-Ying Liu, Kang-Yuan Hung, Chih-Hung Dept. of Computer Science and Engineering Tatung University Taipei104 Taiwan Parallel and Distributed Processing Center Dept. of Computer Science and Information Management Providence University Taichung433 Taiwan High Performance Computing Laboratory Dept. of Computer Science and Information Engineering Tunghai University Taichung407 Taiwan Networks and Multimedia Institute Institute for Information Industry Taipei106 Taiwan

ISBN: (纸本)9783540239857

In order to obtain a low-cost, low-power consumption and small sized digital home processing center, especially for multimedia applications, we propose a scalable wallet-size cluster computing system in this paper. Such system uses low-cost, low-power consumption and small sized microprocessors as computing nodes to form a cluster system. The main reason why we are interested to investigate and build such system is to increase the computing power and to accelerate the H.264 video processing. We analyzed the H.264 decoder scheme and distributed the most heavy-load computations in the decoder scheme to each computing node (or microprocessor) of the cluster system for the processing of signals, in parallel. To run multimedia applications in this cluster system, an efficient real-time video processing is implemented. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Computing power

来源：评论

学校读者我要写书评

暂无评论

Attention-based fault-tolerant approach for multi-agent reinforcement learning systems

arXiv

引用

arXiv 2019年

作者： Geng, Mingyang Xu, Kele Li, Yiying Liu, Shuqi Ding, Bo Wang, Huaimin National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha410073 China National Key Laboratory of Big Data Management and Analysis Northeastern University Shenyang110000 China

The aim of multi-agent reinforcement learning systems is to provide interacting agents with the ability to collaboratively learn and adapt to the behavior of other agents. In many real-world applications, the agents can only acquire a partial view of the world. However, in realistic settings, one or more agents that show arbitrarily faulty or malicious behavior may suffice to let the current coordination mechanisms fail. In this paper, we study a practical scenario considering the security issues in the presence of agents with arbitrarily faulty or malicious behavior. Under these circumstances, learning an optimal policy becomes particularly challenging, even in the unrealistic case that an agent's policy can be made conditional upon all other agents' observations. To overcome these difficulties, we present an Attention-based Fault-Tolerant (FT-Attn) algorithm which selects correct and relevant information for each agent at every time-step. The multi-head attention mechanism enables the agents to learn effective communication policies through experience concurrently to the action policies. Empirical results have shown that FT-Attn beats previous state-of-the-art methods in some complex environments and can adapt to various kinds of noisy environments without tuning the complexity of the algorithm. Furthermore, FT-Attn can effectively deal with the complex situation where an agent needs to reach multiple agents' correct observation at the same time. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

An Efficient Label Routing on High-Radix Interconnection Networks

An Efficient Label Routing on High-Radix Interconnection Net...

引用

International Conference on parallel and distributed Systems (ICPADS)

作者： Fei Lei Dezun Dong Xiangke Liao College of Computer National University of Defense Technology China National Laboratory for Parallel and Distributed Processing National University of Defense Technology China Collaborative Innovation Center of High Performance Computing National University of Defense Technology China

ISBN: (纸本)9781538632086

Cost-effective adaptive routing has a significant impact on overall performance for high-radix hierarchical topologies, such as Dragonfly, which achieve a lower network diameter than traditional topologies, Torus and Fat tree, but exhibit a lower degree of adaptiveness for shortest-path rout- ing. Existing adaptive routing methods for those hierarchical topologies improve the adaptiveness by increasing path length, i.e. local or global adaptive routing, and thus suffer from complex and costly deadlock avoidance. This work aims to maximize the routing adaptiveness at the minimum cost of deadlock avoidance. We propose a label routing method for high-radix hierarchical networks. This label routing utilizes a co-design methodology and coordinates the two pipelines, input queue and routing computation, in the router microarchitec- ture. Packets in the input buffer are labeled by our routing algorithm depending on network states. We reorganize the input buffer and develop a label routing algorithm, named Green-Red Routing, GRR. GRR relaxes the requirement of using virtual channels to eliminate routing deadlock, and mitigates buffer resources dedicated to deadlock avoidance. GRR manages the buffer resources and balance its utilization elaborately, and achieve fully adaptive routing efficiently. We conduct extensive experiments to evaluate the performance of GRR on Dragonfly and compare it with state-of-the-art works. The results show that GRR achieves 10%-35% higher performance than existing routing algorithms under most traffic patterns.

关键词： Routing System recovery Network topology Topology Resource management Adaptive systems Organizations

来源：评论

学校读者我要写书评

暂无评论

Enabling Tissue-Scale Cardiac Simulations Using Heterogeneous Computing on Tianhe-2

Enabling Tissue-Scale Cardiac Simulations Using Heterogeneou...

引用

International Conference on parallel and distributed Systems (ICPADS)

作者： Johannes Langguth Qiang Lan Namit Gaur Xing Cai Mei Wen Chun-Yuan Zhang Simula Research Laboratory Lysaker Norway College of Computer National University of Defense Technology Changsha China National Key Laboratory of Parallel and Distributed Processing Changsha China Department of Informatics University of Oslo Oslo Norway

ISBN: (纸本)9781509053827

We develop a simulator for 3D tissue of the human cardiac ventricle with a physiologically realistic cell model and deploy it on the supercomputer Tianhe-2. In order to attain the full performance of the heterogeneous CPU-Xeon Phi design, we use carefully optimized codes for both devices and combine them to obtain suitable load balancing. Using a large number of nodes, we are able to perform tissue-scale simulations of the electrical activity and calcium handling in millions of cells, at a level of detail that tracks the states of trillions of ryanodine receptors. We can thus simulate arrythmogenic spiral waves and other complex arrhythmogenic patterns which arise from calcium handling deficiencies in human cardiac ventricle tissue. Due to extensive code tuning and parallelization via OpenMP, MPI, and SCIF/COI, large scale simulations of 10 heartbeats can be performed in a matter of hours. Test results indicate excellent scalability, thus paving the way for detailed whole-heart simulations in future generations of leadership class supercomputers.

关键词： Calcium Computational modeling Mathematical model Performance evaluation Hardware Instruction sets Supercomputers

来源：评论

学校读者我要写书评

暂无评论

Learning generic diffusion processes for image restoration

arXiv

引用

arXiv 2018年

作者： Qiao, Peng Dou, Yong Chen, Yunjin Feng, Wensen Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China ULSee Inc. Hangzhou China College of Computer Science & Software Engineering Shenzhen University Shenzhen China

Image restoration problems are typical ill-posed problems where the regularization term plays an important role. The regularization term learned via generative approaches is easy to transfer to various image restoration, but offers inferior restoration quality compared with that learned via discriminative approaches. On the contrary, the regularization term learned via discriminative approaches are usually trained for a specific image restoration problem, and fail in the problem for which it is not trained. To address this issue, we propose a generic diffusion process (genericDP) to handle multiple Gaussian denoising problems based on the Trainable Non-linear Reaction Diffusion (TNRD) models. Instead of one model, which consists of a diffusion and a reaction term, for one Gaussian denoising problem in TNRD, we enforce multiple TNRD models to share one diffusion term. The trained genericDP model can provide both promising denoising performance and high training efficiency compared with the original TNRD models. We also transfer the trained diffusion term to non-blind deconvolution which is unseen in the training phase. Experiment results show that the trained diffusion term for multiple Gaussian denoising can be transferred to image non-blind deconvolution as an image prior and provide competitive performance. Copyright © 2018, The Authors. All rights reserved.

关键词： Diffusion

来源：评论

学校读者我要写书评

暂无评论

Research on the weighted reconstruction of metabolic networks

Research on the weighted reconstruction of metabolic network...

引用

第五届中国蛋白质组学大会暨首届粤港蛋白质组学学术交流会

作者： Zhou Tingting Wang Zhenghua Chan, K.C.C. Beijing Proteome Research Center BeijingP.R.China102206 National Laboratory for Parallel & Distributed ProcessingNational University of Deference and TechnologyChangshaP.R.China 410073 Department of computingthe HongKong Polytechnic UniversityHonghumHongkong Department of computing the HongKong Polytechnic UniversityHonghumHongkong National Laboratory for Parallel & Distributed Processing National University of Deference and TechnologyChangshaP.R.China 410073

来源：评论

学校读者我要写书评

暂无评论

SAWSDL-iMatcher: A Customizable and Effective Semantic Web Service Matchmaker

SSRN

引用

SSRN 2018年

作者： Wei, Dengping Wang, Ting Wang, Ji Bernstein, Abraham National University of Defense Technology Changsha410073 China University of Zürich Binzmühlestrasse 14 ZürichCH-8050 Switzerland National Laboratory for Parallel and Distributed Processing Changsha410073 China Dynamic and Distributed Information Systems Group

As the number of publicly available services grows, discovering proper services becomes an important issue and has attracted amount of attempts. This paper presents a new customizable and effective matchmaker, called SAWSDL-iMatcher. It supports a matchmaking mechanism, named iXQuery, which extends XQuery with various similarity joins for SAWSDL service discovery. Using SAWSDL-iMatcher, users can flexibly customize their preferred matching strategies according to different application requirements. SAWSDL-iMatcher currently supports several matching strategies, including syntactic and semantic matching strategies as well as several statistical-model-based matching strategies which can effectively aggregate similarity values from matching on various types of service description information such as service name, description text, and semantic annotation. Besides, we propose a semantic matching strategy to measure the similarity among SAWSDL semantic annotations. These matching strategies have been evaluated in SAWSDL-iMatcher on SAWSDL-TC2 and Jena Geography Dataset (JGD). The evaluation shows that different matching strategies are suitable for different tasks and contexts, which implies the necessity of a customizable matchmaker. In addition, it also provides evidence for the claim that the effectiveness of SAWSDL service matching can be significantly improved by statistical-model-based matching strategies. Our matchmaker is competitive with other matchmakers on benchmark tests at S3 contest 2009. © 2018, The Authors. All rights reserved.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Sample dropout for audio scene classification using multi-scale dense connected convolutional neural network

arXiv

引用

arXiv 2018年

作者： Feng, Dawei Xu, Kele Mi, Haibo Liao, Feifan Zhou, Yan Science and Technology on Parallel and Distributed Laboratory School of Computer National University of Defense Technology Changsha410073 China School of Information and Communication National University of Defense Technology Wuhan430010 China

Acoustic scene classification is an intricate problem for a machine. As an emerging field of research, deep Convolutional Neural Networks (CNN) achieve convincing results. In this paper, we explore the use of multi-scale Dense connected convolutional neural network (DenseNet) for the classification task, with the goal to improve the clas-sification performance as multi-scale features can be extracted from the time-frequency representation of the audio signal. On the other hand, most of previous CNN-based audio scene classification approaches aim to improve the classification accuracy, by employing different regularization techniques, such as the dropout of hidden units and data augmentation, to reduce overfitting. It is widely known that outliers in the training set have a high negative inuence on the trained model, and culling the outliers may improve the classification performance, while it is often under-explored in previous studies. In this paper, inspired by the silence removal in the speech signal processing, a novel sample dropout approach is proposed, which aims to remove outliers in the training dataset. Using the DCASE 2017 audio scene classification datasets, the experimental results demonstrates the proposed multi-scale DenseNet providing a su-perior performance than the traditional single-scale DenseNet, while the sample dropout method can further improve the classification robustness of multi-scale DenseNet. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistics

来源：评论

学校读者我要写书评

暂无评论

Correntropy induced metric based graph regularized non-negative matrix factorization

Correntropy induced metric based graph regularized non-negat...

引用

International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Bin Mao Naiyang Guan Dacheng Tao Xuhui Huang Zhigang Luo College of Science National University of Defense Technology Changsha China College of Computer National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory Changsha China Department of Computer Science and Technology College of Computer National University of Defense Technology Changsha China Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW Australia

Non-negative matrix factorization (NMF) is an efficient dimension reduction method and plays an important role in many pattern recognition and computer vision tasks. However, conventional NMF methods are not robust since the objective functions are sensitive to outliers and do not consider the geometric structure in datasets. In this paper, we proposed a correntropy graph regularized NMF (CGNMF) to overcome the aforementioned problems. CGNMF maximizes the correntropy between data matrix and its reconstruction to filter out the noises of large magnitudes, and expects the coefficients to preserve the intrinsic geometric structure of data. We also proposed a modified version of our CGNMF which construct the adjacent graph by using sparse representation to enhance its reliability. Experimental results on popular image datasets confirm the effectiveness of CGNMF.

关键词： Robustness Vectors Clustering algorithms Mutual information Measurement Computer integrated manufacturing

来源：评论

学校读者我要写书评

暂无评论

An Efficient Algorithm for Tensor Principal Component Analysis via Proximal Linearized Alternating Direction Method of Multipliers

An Efficient Algorithm for Tensor Principal Component Analys...

引用

International Conference on Advanced Cloud and Big Data (CBD)

作者： Linbo Qiao Bofeng Zhang Lei Zhuang Jinshu Su College of Computer National University of Defense Technology ChanzSha China National Laboratory for Parallel and Distributed Processing National University of Defense Technology ChangSha China Department of Statistics Shanghai University of Finance and Economics Shanghai China

ISBN: (纸本)9781509036783

In paper, we focus on the computation of the principal components for a general tensor, known as the tensor principal component analysis (PCA) problem. It has been proven that the general tensor PCA problem is reducible to its matricization form when the order is even. Usually it is considered as low-rank matrix completion problems theoretically. It is common to consider nuclear norm as a surrogate of the rank operator since it is the tightest convex lower bound of the rank operator under certain condition. However, most nuclear norm minimization based approaches involve numbers of singular value decomposition (SVD) operations. Given a matrix X ∈ R m × n , the time complexity of SVD operation is O(mn 2 ), which brings prohibitive computational burden to apply these methods in real applications. However, the problem is non-convex, and the proximal mapping associated with non-convex regularization is not easy to compute. It is always solved by the Linearized Alternating Direction Method of Multipliers (LADMM). Despite the success of LADMM in practice, it remains unknown if LADMM is convergent in solving such non-convex compositely regularized optimization. In this paper, we firstly present a detailed convergence analysis of the LADMM algorithm for solving non-convex compositely regularized optimization with a large class of non-convex penalties. Furthermore, we propose a new efficient and scalable algorithm for matrix principal component analysis called Proximal Linearized Alternating Direction Method of Multipliers for Principal Component Analysis(PLADMPCA). Different from traditional matrix factorization methods, PLADMPCA utilizes the linearization technique to formulate the matrix as an outer product of vectors, which greatly improves the computational efficacy compared to matrix factorization method. We empirically evaluate the proposed algorithm PLADMPCA on synthetic tensor data with different order. Results have shown that PLADMPCA have much better computational

关键词： Tensile stress Principal component analysis Algorithm design and analysis Convergence Matrix decomposition Optimization Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：