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Principal Component Pursuit with reduced linear measurements

Principal Component Pursuit with reduced linear measurements

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IEEE International Symposium on Information Theory

作者： Arvind Ganesh Kerui Min John Wright Yi Ma Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign USA Department of Electrical Engineering Columbia University USA Visual Computing Group Microsoft Research Asia China

ISBN: (纸本)9781467325790

In this paper, we study the problem of decomposing a superposition of a low-rank matrix and a sparse matrix when a relatively few linear measurements are available. This problem arises in many data processing tasks such as aligning multiple images or rectifying regular texture, where the goal is to recover a low-rank matrix with a large fraction of corrupted entries in the presence of nonlinear domain transformation. We consider a natural convex heuristic to this problem which is a variant to the recently proposed Principal Component Pursuit. We prove that under suitable conditions, this convex program guarantees to recover the correct low-rank and sparse components despite reduced measurements. Our analysis covers both random and deterministic measurement models.

关键词： Sparse matrices Manganese Matrix decomposition Information theory Linear matrix inequalities Numerical models Robustness

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Compressive principal component pursuit

Compressive principal component pursuit

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IEEE International Symposium on Information Theory

作者： John Wright Arvind Ganesh Kerui Min Yi Ma Department of Electrical Engineering Columbia University USA Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign USA Visual Computing Group Microsoft Research Asia China

ISBN: (纸本)9781467325790

We consider the problem of recovering a target matrix that is a superposition of low-rank and sparse components, from a small set of linear measurements. This problem arises in compressed sensing of structured high-dimensional signals such as videos and hyperspectral images, as well as in the analysis of transformation invariant low-rank recovery. We analyze the performance of the natural convex heuristic for solving this problem, under the assumption that measurements are chosen uniformly at random. We prove that this heuristic exactly recovers low-rank and sparse terms, provided the number of observations exceeds the number of intrinsic degrees of freedom of the component signals by a polylogarithmic factor. Our analysis introduces several ideas that may be of independent interest for the more general problem of compressive sensing of superpositions of structured signals.

关键词： Sparse matrices Matrix decomposition Manganese Robustness Q measurement Compressed sensing Image coding

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A GPU-based accelerator for chinese word segmentation

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14th Asia Pacific Web Technology Conference, APWeb 2012

作者： Gu, Xiwu Li, Ruixuan Wen, Kunmei Peng, Bei Xiao, Weijun Intelligent and Distributed Computing Lab College of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China Department of Electrical and Computer Engineering University of Minnesota twin cities 200 Union Street SE Minneapolis MN 55455 United States

ISBN: (纸本)9783642292521

The task of Chinese word segmentation is to split sequence of Chinese characters into tokens so that the Chinese information can be more easily retrieved by web search engine. Due to the dramatic increase in the amount of Chinese literature in recent years, it becomes a big challenge for web search engines to analyze massive Chinese information in time. In this paper, we investigate a new approach to high-performance Chinese information processing. We propose a CPU-GPU collaboration model for Chinese word segmentation. In our novel model, a dictionary-based word segmentation approach is proposed to fit GPU architecture. Three basic word segmentation algorithms are applied to evaluate the performance of this model. In addition, we present several optimization strategies to fully exploit the potential computing power of GPU. Our experimental results show that our model can achieve significant performance speedups up to 3-fold compared with the implementations on CPU. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Graphics processing unit

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Unifying software and product configuration: A research roadmap

Unifying software and product configuration: A research road...

引用

Workshop on Configuration at ECAI, ConfWS 2012

作者： Hubaux, Arnaud Jannach, Dietmar Drescher, Conrad Murta, Leonardo Männistö, Tomi Czarnecki, Krzysztof Heymans, Patrick Nguyen, Tien Zanker, Markus PReCISE Research Center University of Namur Belgium Department of Computer Science TU Dortmund Germany Computing Laboratory University of Oxford United Kingdom Fluminense Federal University Niterói Brazil Aalto University School of Science Finland Generative Software Development Lab University of Waterloo Canada Electrical and Computer Engineering Department Iowa State University United States Alpen-Adria-Universität Klagenfurt Austria

For more than 30 years, knowledge-based product configuration systems have been successfully applied in many industrial domains. Correspondingly, a large number of advanced techniques and algorithms have been developed in academia and industry to support different aspects of configuration reasoning. While traditional research in the field focused on the configuration of physical artefacts, recognition of the business value of customizable software products led to the emergence of software product line engineering. Despite the significant overlap in research interests, the two fields mainly evolved in isolation. Only limited attempts were made at combining the approaches developed in the different fields. In this paper, we first aim to give an overview of commonalities and differences between software product line engineering and product configuration. We then identify opportunities for cross-fertilization between these fields and finally develop a research agenda to combine their respective techniques. Ultimately, this should lead to a unified configuration approach.

关键词： Knowledge based systems

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Facial Action Detection Using Block-Based Pyramid Appearance Descriptors

Facial Action Detection Using Block-Based Pyramid Appearance...

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IEEE International Conference on and IEEE International Confernece on Social computing (SocialCom) Privacy, Security, Risk and Trust (PASSAT)

作者： Bihan Jiang Michel F. Valstar Maja Pantic Department of Computing Imperial College London UK Mixed Reality Lab School of Computer Science University of Nottingham UK Faculty of Electrical Engineering University of Twente Netherlands

Facial expression is one of the most important non-verbal behavioural cues in social signals. Constructing an effective face representation from images is an essential step for successful facial behaviour analysis. Most existing face descriptors operate on the same scale, and do not leverage coarse v.s. fine methods such as image pyramids. In this work, we propose the sparse appearance descriptors Block-based Pyramid Local Binary Pattern (B-PLBP) and Block-based Pyramid Local Phase Quantisation (B-PLPQ). The effectiveness of our proposed descriptors is evaluated by a real-time facial action recognition system. The performance of B-PLBP and B-PLPQ is also compared with Block-based Local Binary Patterns (B-LBP) and Block-based Local Phase Quantisation (B-LPQ). The system proposed here enables detection a much larger range of facial behaviour by detecting 22 facial muscle actions (Action Units, AUs), which can be practically applied for social behaviour analysis and synthesis. Results show that our proposed descriptor B-PLPQ outperforms all other tested methods for the problem of FACS Action Unit analysis and that systems which utilise a pyramid representation outperform those that use basic appearance descriptors.

关键词： Face Histograms Feature extraction Kernel Gold Support vector machines Face recognition

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Survivable Virtual Infrastructure Mapping in Virtualized Data Centers

Survivable Virtual Infrastructure Mapping in Virtualized Dat...

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IEEE International Conference on Cloud computing, CLOUD

作者： Jielong Xu Jian Tang Kevin Kwiat Weiyi Zhang Guoliang Xue Department of Electrical Engineering and Computer Science at Syracuse University Air Force Research Lab (AFRL) AT&T Labs Research School of Computing Informatics and Decision Systems Engineering at Arizona State University

In a virtualized data center, survivability can be enhanced by creating redundant Virtual Machines (VMs) as backup for VMs such that after VM or server failures, affected services can be quickly switched over to backup VMs. To enable flexible and efficient resource management, we propose to use a service-aware approach in which multiple correlated VMs and their backups are grouped together to form a Survivable Virtual Infrastructure (SVI) for a service or a tenant. A fundamental problem in such a system is to determine how to map each SVI to a physical data center network such that operational costs are minimized subject to the constraints that each VM's resource requirements are met and bandwidth demands between VMs can be guaranteed before and after failures. This problem can be naturally divided into two sub-problems: VM Placement(VMP) and Virtual Link Mapping (VLM). We present a general optimization framework for this mapping problem. Then we present an efficient algorithm for the VMP sub problem as well as a polynomial-time algorithm that optimally solves the VLM sub problem, which can be used as subroutines in the framework. We also present an effective heuristic algorithm that jointly solves the two sub problems. It has been shown by extensive simulation results based on the real VM data traces collected from the green data center at Syracuse University that compared with the First Fit Descending (FFD) and single shortest path based baseline algorithm, both our VMP+VLM algorithm and joint algorithm significantly reduce the reserved bandwidth, and yield comparable results in terms of the number of active servers.

关键词： Servers Bandwidth Heuristic algorithms Cloud computing Joining processes Virtual machining

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Utilizing RFID-WSNs for reducing the footprint of the Oil Sands industry

Utilizing RFID-WSNs for reducing the footprint of the Oil Sa...

引用

IEEE Globecom Workshops

作者： Ashraf Al-Fagih Ahmad El Kouche Sharief Oteafy Abdallah Alma'aitah Telecommunications Research Laboratory School of Computing Queens University Kingston ONT Canada Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Telecommunications Research Lab School of Computing Kingston Ontario Canada Department of Electrical and Computer Engineering Queen's University Kingston Ontario Canada

In harsh operational conditions, machines lose components on a frequent basis, causing a significant footprint to the surrounding environment. This affects operational up-time, safety measures and cost effectiveness. This paper proposes the use of an integrated RFID-WSN architecture to reduce the footprint of mining equipment used in the Canadian Oil Sands industry. Sensors and RFID tags provide both identification and positioning data for detecting broken parts from Ground Engaging Tools (GETs), such as huge shovel teeth. A delay-tolerant delivery approach utilizes mobile couriers, placed on loading trucks available in the vicinity, to store and carry data from readers and relays to remote base-stations. Delivering data in such harsh mining environments poses several challenges including power scarcity, vast transmission distances and the lack of an accessible communication infrastructure. We provide a use case to demonstrate how this architecture successfully overcomes these challenges within the harsh industrial environment of the Oil Sands.

关键词： Temperature sensors Radiofrequency identification Temperature measurement Hydrocarbons Monitoring Vibrations

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Reports of the AAAI 2011 conference workshops

Reports of the AAAI 2011 conference workshops

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作者： Agmon, Noa Agrawal, Vikas Aha, David W. Aloimonos, Yiannis Buckley, Donagh Doshi, Prashant Geib, Christopher Grasso, Floriana Green, Nancy Johnston, Benjamin Kaliski, Burt Kiekintveld, Christopher Law, Edith Lieberman, Henry Mengshoel, Ole J. Metzler, Ted Modayil, Joseph Oard, Douglas W. Onder, Nilufer O'Sullivan, Barry Pastra, Katerina Precup, Doina Ramachandran, Sowmya Reed, Chris Sariel-Talay, Sanem Selker, Ted Shastri, Lokendra Singh, Satinder Smith, Stephen F. Srivastava, Siddharth Sukthankar, Gita Uthus, David C. Williams, Mary-Anne Department of Computer Science University of Texas at Austin United States Center for Knowledge Driven Information Systems Infosys Labs India Adaptive Systems Section Naval Research Laboratory Washington DC United States Department of Computer Science University of Maryland College Park United States EMC's Center of Excellence in Ireland Research Europe at EMC Ireland Department of Computer Science University of Georgia United States School of Informatics University of Edinburgh United Kingdom Department of Computer Science University of Liverpool United Kingdom Department of Computer Science University of North Carolina Greensboro United States Faculty of Engineering and Information Technology University of Technology Sydney Australia VeriSign Inc. United States Department of Computer Science University of Texas at El Paso United States Machine Learning Department Carnegie Mellon University United States Massachusetts Institute of Technology Media Laboratory United States Carnegie Mellon University's Silicon Valley Campus United States Darrell W. Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computing Science University of Alberta Canada College of Information Studies Institute for Advanced Computer Studies University of Maryland College Park United States Department of Computer Science Michigan Technological University United States Department of Computer Science University College Cork Ireland Cognitive Systems Research Insitute Athens Greece School of Computer Science McGill University Canada Stottler Henke Inc. United States School of Computing University of Dundee United Kingdom Department of Computer Engineering Istanbul Technical University Turkey Convergence Lab Infosys Technologies Ltd. United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Robotics Institute Carnegie Mellon University United States University of Wisconsin

The AAAI-11 workshop program was held Sunday and Monday, August 7-18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages;Analyzing Microtext;Applied Adversarial Reasoning and Risk Modeling;Artificial Intelligence and Smarter Living: The Conquest of Complexity;Artifiicial Intelligence for Data Center Management and Cloud computing;Automated Action Planning for Autonomous Mobile Robots;Computational Models of Natural Argument;Generalized Planning;Human Computation;Human-Robot Interaction in Elder Care;Interactive Decision Theory and Game Theory, 2010;Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action, and Language;Lifelong Learning from Sensorimotor Experience;Plan, Activity, and Intent Recognition;and Scalable Integration of Analytics and visualization. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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Trusted framework for health information exchange

Trusted framework for health information exchange

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2011 9th International Conference on Frontiers of Information Technology, FIT 2011

作者： Afzal, Muhammad Hussain, Maqbool Ahmad, Mahmood Anwar, Zahid Semantic Systems Research Lab. School of Electrical Engineering and Computer Science Pakistan Department of Computing School of Electrical Engineering and Computer Science Pakistan

ISBN: (纸本)9780769546254

Healthcare Interchange Exchange (HIE) is set of healthcare entities that link regions and nations around the world to share healthcare information about patients. HIE provides a unified interface to various stakeholders to access patient data. For example, individuals want to access personal data, practitioners want to import their patient data from Personal Health Records (PHR) and researchers want to access anonymized patient data for research purposes with ensured individual privacy. Keeping in view requirements of various stakeholders it is challenging to design an HIE system that ensures the security and privacy of patient information, such that the health records remain interoperable and scalable. This paper proposes, an HIE framework that will provide secure access to patient data and adhere to the interoperability rules. Implementation of l-diversity algorithm ensures individual privacy especially when the data is made available publicly for statistical analysis. © 2011 IEEE.

关键词： Interoperability

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Eyebrow shape-based features for biometric recognition and gender classification: A feasibility study

Eyebrow shape-based features for biometric recognition and g...

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2011 International Joint Conference on Biometrics, IJCB 2011

作者： Dong, Yujie Woodard, Damon L. Department of Electrical and Computer Engineering School of Computing Clemson University United States Biometrics and Pattern Recognition Lab School of Computing Clemson University United States

ISBN: (纸本)9781457713583

A wide variety of applications in forensic, government, and commercial fields require reliable personal identification. However, the recognition performance is severely affected when encountering non-ideal images caused by motion blur, poor contrast, various expressions, or illumination artifacts. In this paper, we investigated the use of shape-based eyebrow features under non-ideal imaging conditions for biometric recognition and gender classification. We extracted various shape-based features from the eyebrow images and compared three different classification methods: Minimum Distance Classifier (MD), Linear Discriminant Analysis Classifier (LDA) and Support Vector Machine Classifier (SVM). The methods were tested on images from two publicly available facial image databases: The Multiple Biometric Grand Challenge (MBGC) database and the Face Recognition Grand Challenge (FRGC) database. Obtained recognition rates of 90% using the MBGC database and 75% using the FRGC database as well as gender classification recognition rates of 96% and 97% for each database respectively, suggests the shape-based eyebrow features maybe be used for biometric recognition and soft biometric classification. © 2011 IEEE.

关键词： Database systems

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