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Spectral and semidefinite relaxations of the CLUHSIC algorithm

Spectral and semidefinite relaxations of the CLUHSIC algorit...

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10th SIAM International Conference on Data Mining, SDM 2010

作者： Yang, Wen-Yun Kwok, James T. Lu, Bao-Liang University of California Los Angeles CA United States Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University Shanghai China

CLUHSIC is a recent clustering framework that unifies the geometric, spectral and statistical views of clustering. In this paper, we show that the recently proposed discriminative view of clustering, which includes the DIFFRAC and DisKmeans algorithms, can also be unified under the CLUH-SIC framework. Moreover, CLUHSIC involves integer programming and one has to resort to heuristics such as iterative local optimization. In this paper, we propose two relaxations that are much more disciplined. The first one uses spectral techniques while the second one is based on semidefinite programming (SDP). Experimental results on a number of structured clustering tasks show that the proposed method significantly outperforms existing optimization methods for CLUHSIC. Moreover, it can also be used in semi-supervised classification. Experiments on real-world protein subcellu-lar localization data sets clearly demonstrate the ability of CLUHSIC in incorporating structural and evolutionary information. Copyright © by SIAM.

关键词： Integer programming

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Efficient online trace building using code replication

Efficient online trace building using code replication

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International Conference on Grid and Cloud computing

作者： Deng, Haipeng Chen, Kai Liu, Bo He, Yunchao Zhou, Kan Yang, Yindong Liang, Alei Guan, Haibing School of Software Shanghai Jiao Tong University China Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Information Security Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Scalable Computing and Systems China

ISBN: (纸本)9780769543130

Hot trace building plays an important role in enhancing the performance of dynamic binary translators, since in most cases 10% of code takes 90% of execution time of the whole program. Hot traces can promote the code position to make better the locality of the code;therefore programs can achieve a better performance. Thus how to build qualified and long hot traces is the key point. Considering the situation when one single basic block is needed by more than two hot traces, code replication can help build hot traces with cost of expansion of code size. In this paper, a code replication method to help with building hot traces as well as code positioning is presented, and experiments have been done to evaluate the details of changes that code replication brings to the system, like trace fragment length, memory size expansion, etc. With this method, a performance promotion by 0.5%-3.5% against the original trace building method has been achieved. © 2010 IEEE.

关键词： Virtual machine

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Studying the impact of synchronization frequency on scheduling tasks with dependencies in heterogeneous systems

Studying the impact of synchronization frequency on scheduli...

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作者： Andronikos, Theodore Ciorba, Florina M. Riakiotakis, Ioannis Papakonstantinou, George Chronopoulos, Anthony T. Department of Informatics Ionian University Corfu Greece Computing Systems Laboratory Department of Electrical and Computer Engineering National Technical University of Athens Greece Center for Advanced Vehicular Systems Mississippi State University MS United States Department of Computer Science University of Texas at San Antonio 6900 N. Loop 1604 West San Antonio TX 78249 United States

In this work, we develop and evaluate a theoretical model, which we then use to study the impact of the synchronization frequency on the performance of dynamic self-scheduling algorithms. These algorithms are used to parallelize loops with data dependencies on heterogeneous systems. The proposed model uses a formula to estimate the parallel time as a function of the synchronization frequency. Inter-node communication has been proven to be the dominant factor for the performance degradation of applications containing loops with data dependencies. The synchronization mechanism therefore requires careful fine-tuning in order to give the best possible performance. The proposed model determines the optimal synchronization frequency that results in the minimum parallel time. We use this model to study the impact of the synchronization frequency on the parallel execution of a computational kernel from image processing. For this kernel, the synchronization frequency giving the minimum parallel time predicted by our theoretical model was very close to the synchronization frequency giving the least parallel time in practice. We validate our model by extensive comparisons of the theoretically predicted parallel time and synchronization frequency against those obtained from practical experiments. The comparisons show that the proposed model is highly accurate, its predictions for the optimal synchronization frequency being within 0.0250% of the experimentally optimal synchronization frequency in the best case, and within 0.1750% of the experimentally optimal synchronization frequency in the worst case. Finally, the comparisons show that the proposed model improves on a previously existing model in heterogeneous systems, whereas it gives similar results in homogeneous systems. © 2010 Elsevier B.V. All rights reserved.

关键词： Synchronization

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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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Vigilance detection based on sparse representation of EEG

Vigilance detection based on sparse representation of EEG

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Hongbin Yu Hongtao Lu Tian Ouyang Hongjun Liu Bao-Liang Lu Department of Computer Science and Engineering Shanghai Jiaotong University China MOE-Microsoft Laboratory of Intelligent Computing and Intelligent Systems and the Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China

Electroencephalogram (EEG) based vigilance detection of those people who engage in long time attention demanding tasks such as monotonous monitoring or driving is a key field in the research of brain-computer interface (BCI). However, robust detection of human vigilance from EEG is very difficult due to the low SNR nature of EEG signals. Recently, compressive sensing and sparse representation become successful tools in the fields of signal reconstruction and machine learning. In this paper, we propose to use the sparse representation of EEG to the vigilance detection problem. We first use continuous wavelet transform to extract the rhythm features of EEG data, and then employ the sparse representation method to the wavelet transform coefficients. We collect five subjects' EEG recordings in a simulation driving environment and apply the proposed method to detect the vigilance of the subjects. The experimental results show that the algorithm framework proposed in this paper can successfully estimate driver's vigilance with the average accuracy about 94.22 %. We also compare our algorithm framework with other vigilance estimation methods using different feature extraction and classifier selection approaches, the result shows that the proposed method has obvious advantages in the classification accuracy.

关键词： Electroencephalography Feature extraction Accuracy Training Continuous wavelet transforms Sleep

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A Taxonomy of Autonomic Application Management in Grids

A Taxonomy of Autonomic Application Management in Grids

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Mustafizur Rahman Rajiv Ranjan Rajkumar Buyya Cloud Computing and Distributed Systems Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia Service Oriented Computing Research Group School of Computer Science and Engineering University of New South Wales Australia

In this paper, we propose a taxonomy that characterizes and classifies different components of autonomic application management in Grids. We also survey several representative Grid systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the similarities and differences of state-of-the-art technologies utilized in autonomic application management from the perspective of Grid computing, but also identifies the areas that require further research initiatives.

关键词： Monitoring Resource management Taxonomy Processor scheduling Decision making Dynamic scheduling

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Estimate vigilance level in driving simulation based on sparse representation

Estimate vigilance level in driving simulation based on spar...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hong-Jun Liu Hong-Bin Yu Qing-Sheng Ren Hong-Tao Lu MOE-Microsoft Laboratory of Intelligent Computing and Intelligent Systems Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China

Avoiding fatal accidents caused by low vigilance level in driving is very important in our daily lives. Electroencephalography(EEG) has been proved very effective for measuring the level of vigilance. In this paper, we distinguish vigilance level into three classes which are 'alert', 'fatigue' and 'sleeping' by using sparse representation classification(SRC). Six features from each frequency band are got from samples of EEG data. Random feature is used to reduce the dimension of features. Actually there is almost no training process before the classification. The accuracy in classification of three classes reaches about 90% on average.

关键词： Electroencephalography Variable speed drives Support vector machines Surgery

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Performance tuning towards a KVM-based low latency virtualization system

Performance tuning towards a KVM-based low latency virtualiz...

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2nd International Conference on Information engineering and computer science, ICIECS 2010

作者： Zuo, Baojing Chen, Kai Liang, Alei Guan, Haibing Zhang, Jun Ma, Ruhui Yang, Hongbo School of Software Shanghai Jiao Tong University Shanghai 200240 China School of Information Security Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781424479412

Utilizing virtualization technology to combine real-time operating system (RTOS) and off-the-shelf time-sharing general purpose operating system (GPOS) is attracting much more interest recently. Such combination has the potential to provide a large application base, and to guarantee timely deterministic response to real-time applications, yet there is no convincible experimental result about its real-time property. In this paper, we analyze the interrupt latency of RTOS running on Linux KVM based on some preliminary tunings, and find out System Management Interrupt (SMI) is the main factor which makes the maximum latency unideal, so we propose a method to limit the worst-case interrupt latency in an acceptable interval. Furthermore, we also find out that boosting priority may result in wastes of CPU resources when RTOS is not executing real-time tasks, so we design a co-scheduling mechanism to improve the CPU throughput of the GPOS system. ©2010 IEEE.

关键词： Virtualization

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Vigilance Analysis Based on Continuous Wavelet Transform of EEG Signals

Vigilance Analysis Based on Continuous Wavelet Transform of ...

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International Conference on Biomedical engineering and computer science

作者： Tian Ouyang Hong-Tao Lu MOE-MS Key Laboratory for Intelligent Computing and Intelligent Systems Department of Computer Science and Engineering Shanghai Jiao Tong University

ISBN: (纸本)9781424453153

Electroencephalography (EEG) is considered a reliable indicator of a person's vigilance level. In this paper, we use EEG recordings to discriminate three vigilance states of a person, namely alert, drowsy, and sleep, while driving a car in a simulation environment. The proposed framework explores the use of continuous wavelet transform in EEG signal processing. A large set of features is extracted from the wavelet coefficients, which are computed from EEG signals with multiple wavelet functions. We use random forest to rank the plenty of features and select the most important ones for later classification. Samples of EEG data are then trained and classified by SVM (Support Vector Machine). On datasets acquired from 5 subjects, our method reveals high classification accuracy (over 96%).

关键词： Electroencephalography (EEG) Vigilance Alert Drowsy Sleep Continuous Wavelet Transform Random Forest SVM

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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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