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Information Theory and Applications Workshop, ITA

作者： Hong Ju Park Boyu Li Ender Ayanoglu Center of Pervasive Communications and Computing Department of Electrical Engineering and Computer Science University of California Irvine USA

Multi-Input Multi-Output (MIMO) wireless communication systems commonly employ beamforming techniques with Singular Value Decomposition (SVD). In such systems, if no channel encoding is employed, the full diversity order provided by the channel is achieved when a single symbol is transmitted over multiple channels; however, this property is lost whenever multiple symbols are simultaneously transmitted. The full diversity order can be restored when channel coding is added to such a system. For example, when Bit-Interleaved Coded Modulation (BICM) is combined with this technique, the full diversity order of NM in an M × N MIMO channel, transmitting S parallel streams is possible; provided SR c ¿ 1 where R C is the BICM convolutional code rate. In this paper, we present multiple beamforming with constellation precoding which can achieve the full diversity order with both uncoded and BICM-coded SVD systems. An analytical proof of this property is provided. In addition, to reduce the computational complexity of Maximum Likelihood (ML) decoding, we introduce a Sphere Decoding (SD) technique. This technique achieves several orders of magnitude reduction in computational complexity not only with respect to conventional ML decoding, but also, with respect to conventional SD.

关键词： Array signal processing Maximum likelihood decoding MIMO Computational complexity Wireless communication Singular value decomposition Channel coding Interleaved codes Modulation coding Strontium

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Modelling data-driven CO2 sequestration using distributed HPC cyberinfrastructure 10

Modelling data-driven CO2 sequestration using distributed HP...

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2010 TeraGrid Conference, TG '10

作者： El-Khamra, Yaakoub Jha, Shantenu White, Christopher D. Craft and Hawkins Dept. of Petroleum Engineering Louisiana State University Texas Advanced Computing Center United States Department of Computer Science E-Science Institute Louisiana State University Edinburgh United States Craft and Hawkins Dept. of Petroleum Engineering Center for Computation and Technology Louisiana State University United States

ISBN: (纸本)9781605588186

In this paper we lay out the computational challenges involved in effectively simulating complex phenomena such as sequestering CO2 in oil and gas reservoirs. The challenges arise at multiple levels: (i) the computational complexity of simulating the fundamental processes;(ii) the resource requirements of the computationally demanding simulations;(iii) the need for integrating real-time data (intensive) and computationally intensive simulations;(iv) and the need to implement all of these in a robust, scalable and extensible approach. We will outline the architecture and implementation of the solution we develop in response to these requirements, and discuss results to validate claims that our solution scales to effectively solve desired problem sizes and thus provides the capability to generate novel scientific insight. Copyright 2010 ACM.

关键词： Carbon dioxide

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2011年 6844 LNCS卷

作者： Dehne, Frank Iacono, John Sack, Jörg-Rüdiger Carleton University School of Computer Science Parallel Computing and Bioinformatics Laboratory VISM Building 1125 Colonel By Drive OttawaONK1S 5B6 Canada Polytechnic Institute of NewYork University Department of Computer Science and Engineering 5 MetroTech Center NewYorkNY11201 United States Carleton University School of Computer Science Herzberg Laboratories Herzberg Building 1125 Colonel By Drive OttawaONK1S 5B6 Canada

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A comparative study on two large-scale hierarchical text classification tasks' solutions

A comparative study on two large-scale hierarchical text cla...

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International Conference on Machine Learning and Cybernetics

作者： Zhang, Jian Zhao, Hai Lu, Bao-Liang Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai 200240 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai 200240 China

ISBN: (纸本)9781424465262

Patent classification is a large scale hierarchical text classification (LSHTC) task. Though comprehensive comparisons, either learning algorithms or feature selection strategies, have been fully made in the text categorization field, few work was done for a LSHTC task due to high computational cost and complicated structural label characteristics. For the first time, this paper compares two popular learning frameworks, namely hierarchical support vector machine (SVM) and k nearest neighbor (k-NN) that are applied to a LSHTC task. Experiment results show that the latter outperforms the former in this LSHTC task, which is quite different from the usual results for normal text categorization tasks. Then this paper does a comparative study on different similarity measures and ranking approaches in k-NN framework for LSHTC task. Conclusions can be drawn that k-NN is more appropriate for the LSHTC task than hierarchical SVM and for a specific LSHTC task. BM25 outperforms other similarity measures and List Weak gains a better performance than other ranking approaches. We also find an interesting phenomenon that using all the labels of the retrieved neighbors can remarkably improve classification performance over only using the first label of the retrieved neighbors. © 2010 IEEE.

关键词： Learning algorithms

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An Empirical Study on Development Set Selection Strategy for Machine Translation Learning∗ 5

An Empirical Study on Development Set Selection Strategy for...

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Joint 5th Workshop on Statistical Machine Translation and MetricsMATR, WMT 2010 at the 48th Conference of the Associationfor Computational Linguistics, ACL 2010

作者： Hui, Cong Zhao, Hai Song, Yan Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dong Chuan Rd. Shanghai 200240 China Department of Chinese Translation and Linguistics City University of Hong Kong Hong Kong

ISBN: (纸本)9781932432718

This paper describes a statistical machine translation system for our participation for the WMT10 shared task. Based on MOSES, our system is capable of translating German, French and Spanish into English. Our main contribution in this work is about effective parameter tuning. We discover that there is a significant performance gap as different development sets are adopted. Finally, ten groups of development sets are used to optimize the model weights, and this does help us obtain a stable evaluation result. © 2010 Association for Computational Linguistics

关键词： Machine translation

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Design of a tree-structured data generator

Design of a tree-structured data generator

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IIE Annual Conference and Expo 2010

作者： Chowdhary, Abhinav Romanowski, Carol J. Department of Computer Science Rochester Institute of Technology Golisano College of Computing and Information Sciences Rochester NY 14623 United States Center for Multidisciplinary Studies Rochester Institute of Technology College of Applied Sciences and Technology Rochester NY 14623 United States

Engineering data, especially bills of material, can be represented as unordered trees. Since BOMs tend to be proprietary, it is very difficult to obtain BOM datasets to use in testing data mining algorithms such as clustering and classification. Clustering in particular is a useful method for determining groups of similar BOMs in cases where intelligent product numbering or product families do not exist. This research develops a program to generate suitable datasets of unordered trees subject to a user's specification of tree depth and number of leaf nodes.

关键词： Data mining

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Sequential prefetch cache sizing for maximal hit rate

Sequential prefetch cache sizing for maximal hit rate

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International Symposium on Modeling, Analysis and Simulation of computer and Telecommunication Systems

作者： Bhatia, Swapnil Varki, Elizabeth Merchant, Arif Computing Science Laboratory Palo Alto Research Center Palo Alto CA 94304 United States Department of Computer Science University of New Hampshire Durham NH 03824 United States Storage Platforms Laboratory Hewlett-Packard Laboratories Palo Alto CA 94304 United States

ISBN: (纸本)9780769541976

We propose a prefetch cache sizing module for use with any sequential prefetching scheme and evaluate its impact on the hit rate. Disk array caches perform sequential prefetching by loading data contiguous to I/O request data into the array cache. If the I/O workload has sequential locality, then data prefetched in response to sequential accesses in the workload will receive hits. Different schemes prefetch different data, so the prefetch cache size requirement varies. Moreover, the proportion of sequential and random requests in the workload and their interleaving pattern affects the size requirement. If the cache is too small, then prefetched data would get evicted from the cache before a request for the data arrives, thus lowering the hit rate. If the cache is too large, then valuable cache space is wasted. We present a simple sizing module that can be added to any prefetching scheme to ensure that the prefetch cache size is adequately matched to the requirement of the prefetching scheme on a dynamic workload comprising multiple streams. We analytically compute the maximal hit rate achievable by popular prefetching schemes and through simulations, show that our sizing module maintains the prefetch cache at a size that nearly achieves this maximal hit rate. © 2010 IEEE.

关键词： I/O request data I/O workload cache cache storage disc storage disk array cache dynamic hit rate interleaved storage interleaving pattern maximal hit rate prefetching sequential prefetch cache sizing size storage

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Quantifying performance benefits of overlap using MPI-2 in a seismic modeling application 10

Quantifying performance benefits of overlap using MPI-2 in a...

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24th ACM International Conference on Supercomputing, ICS'10

作者： Potluri, Sreeram Lai, Ping Tomko, Karen Sur, Sayantan Cui, Yifeng Tatineni, Mahidhar Schulz, Karl W. Barth, William L. Majumdar, Amitava Panda, Dhabhaleswar K. Department of Computer Science and Engineering Ohio State University Columbus OH 43210 United States Ohio Supercomputer Center Columbus OH 43212 United States San Diego Supercomputer Center San Diego CA 92037 United States Texas Advanced Computing Center Austin TX 78758-4497 United States

ISBN: (纸本)9781450300186

AWM-Olsen is a widely used ground motion simulation code based on a parallel finite difference solution of the 3-D velocity-stress wave equation. This application runs on tens of thousands of cores and consumes several million CPU hours on the TeraGrid Clusters every year. A significant portion of its run-time (37% in a 4,096 process run), is spent in MPI communication routines. Hence, it demands an optimized communication design coupled with a low-latency, high-bandwidth network and an efficient communication subsystem for good performance. In this paper, we analyze the performance bottlenecks of the application with regard to the time spent in MPI communication calls. We find that much of this time can be overlapped with computation using MPI non-blocking calls. We use both two-sided and MPI-2 one-sided communication semantics to re-design the communication in AWM-Olsen. We find that with our new design, using MPI-2 one-sided communication semantics, the entire application can be sped up by 12% at 4K processes and by 10% at 8K processes on a state-of-the-art InfiniBand cluster, Ranger at the Texas Advanced computing center (TACC). © 2010 ACM.

关键词： Semantics

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Combining Color Quantization with Curvelet Transform for Image Retrieval

Combining Color Quantization with Curvelet Transform for Ima...

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International Conference on Artificial Intelligence and Computational Intelligence (AICI)

作者： Yungang Zhang Lijin Gao Wei Gao Jun Liu Department of Computer Science Yunnan Normal University Kunming China Department of Computer Science University of Liverpool Liverpool United Kingdom Computing Center Kunming Metallurgy College Kunming China

Color and shape descriptions of an image are the most widely used visual features in content-based image retrieval systems. Feature vectors for shape and color can be combined to improve the performance of the content-based image retrieval systems. In this paper, a novel image retrieval method integrating HSV color quantization and curve let transform is proposed. By analyzing properties of HSV(Hue, Saturation, Value) color space, a new dividing method to quantize the HSV color space into 24 non-uniform bins based on HSV soft decision is introduced and used for color histogram generation. Digital curve let transform is employed for extracting shape features in images, as it has been proved that the curve let transform is an almost optimal sparse representation of objects with edges. The generated HSV color histogram and the curve let feature are then combined and weighted for image retrieval, using Manhattan distance metric as the similiarity measure. Experiments on an image database of 565 images show that the combined feature performs well in precision and adaptability.

关键词： Image color analysis Transforms Image retrieval Feature extraction Histograms Pixel

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A Carbon 2.0 Framework Based on Cloud computing

A Carbon 2.0 Framework Based on Cloud Computing

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International Conference on computer and Information science (ACIS)

作者： Jiandun Li Wu Zhang Junjie Peng Zhou Lei Yu Lei School of Computer Engineering & Science High Performance Computing Center Shanghai University Shanghai China Department of Computer Science & Engineering University of Texas Arlington Arlington USA

The Copenhagen climate conference 2009 has concluded that we have to change the way how we live. Currently, several carbon management systems are being developed to meet enterprises' requirement. In addition, a number of carbon footprint calculation tools have been developed to fulfill carbon calculation needs from industries, households and individuals. However, under the current circumstance, it is still a challenging issue on how to take advantage of the latest IT technologies to improve carbon calculations with comprehensive functionality. In this paper, we extend carbon 2.0, the concept proposed by previous studies, and introduce a new carbon framework based on cloud computing. This new framework is massively scalable and can configure and deliver carbon-related services dynamically and on demand. Moreover, it is a real low-carbon framework itself!

关键词： Carbon Carbon dioxide Cloud computing Companies Decision making computer architecture

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