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Erratum: An intrinsic mechanism controls reactivation of neural stem cells by spindle matrix proteins

Nature communications 2017年第1期8卷 1298页

作者： Song Li Chwee Tat Koe Su Ting Tay Angie Lay Keng Tan Shenli Zhang Yingjie Zhang Patrick Tan Wing-Kin Sung Hongyan Wang Neuroscience and Behavioural Disorders Program Duke-NUS Medical School 8 College Road Singapore 169857 Singapore. Cancer and Stem Cell Biology Program Duke-NUS Medical School 8 College Road Singapore 169857 Singapore. NUS Graduate School for Integrative Sciences and Engineering National University of Singapore 28 Medical Drive Singapore 117456 Singapore. Cellular and Molecular Research National Cancer Centre Singapore 169610 Singapore. Cancer Science Institute of Singapore National University of Singapore Singapore 119074 Singapore. Genome Institute of Singapore 60 Biopolis Street Genome 02-01 Singapore 138672 Singapore. Department of Computer Science National University of Singapore Singapore 117417 Singapore. Neuroscience and Behavioural Disorders Program Duke-NUS Medical School 8 College Road Singapore 169857 Singapore. hongyan.wang@duke-nus.edu.sg. NUS Graduate School for Integrative Sciences and Engineering National University of Singapore 28 Medical Drive Singapore 117456 Singapore. hongyan.wang@duke-nus.edu.sg. Department of Physiology Yong Loo Lin School of Medicine National University of Singapore Singapore 117597 Singapore. hongyan.wang@duke-nus.edu.sg.

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Multi-class classification of objects in images using principal component analysis and genetic programming

Multi-class classification of objects in images using princi...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Manassés Ribeiro Heitor Silvério Lopes Federal Catarinense Institute of Education Science and Technology (IFC) Videira Santa Catarina Brazil Graduate Program in Electrical and Computer Engineering (CPGEI) Federal University of Technology of Paraná (UTFPR) Curitiba Paraná Brazil

This work presents a methodology for using Principal Component Analysis (PCA) and Genetic programming (GP) for the classification of multi-class objects found in digital images. The image classification process is performed by using features extracted from images, through feature extraction algorithms, reduced by PCA and labeled by similarity comparing with other previously classified objects. GP uses two sets of elements: terminals, composed by the features extracted by PCA; and non-terminals, composed by algebraic operations. The fitness function was defined by the product of sensibility and specificity, two performance measures. A penalty term is also used to decrease the number of nodes of the tree, while minimally affecting the quality of solutions. The proposed approach was applied to set of 2739 digital images divided into objects representing airplanes, motorbikes, background from google, faces and watch classes, provided by the Caltech101 image database. The proposed approach was compared with SVM, Naïve Bayes and C4.5. Results suggest that the approach PCA+GP is able to evolve solutions for the problem as a simple classification rule with true positive rate above 70%. Additionally, we observe that PCA+PG obtained results slightly better than SVM and C4.5, besides these methods give a result that is not comprehensible by humans.

关键词： Principal component analysis Image color analysis Feature extraction Histograms Shape Genetic programming Support vector machines

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2D and 3D photogrammetric models for respiratory analysis in adolescents

2D and 3D photogrammetric models for respiratory analysis in...

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13th Mediterranean Conference on Medical and Biological engineering and Computing 2013, MEDICON 2013

作者： Ripka, W.L. Ulbricht, Leandra Neves, E.B. Gewehr, P.M. Federal University of Technology-Paraná Graduate Program in Electrical Engineering and Computer Science Curitiba Brazil Federal University of Technology-Paraná Graduate Program in Biomedical Engineering Curitiba Brazil

ISBN: (纸本)9783319008455

The aim of this study was to test the correlation between a two dimensional (2D) and three dimensional (3D) biomechanical photogrammetric model, with inspiratory and expiratory lung capacities in adolescents. This was a cross sectional study, in which 40 adolescents of both genders were assessed, aged between 14 and 17 years old. Anthropometric data were collected (body mass, height and thorax length). Then, three tests were performed for forced vital capacity (FVC) in the supine position, using a spirometer. For FVC proof assessments, the volunteers were subjected to a mapping of body structures with adhesive markers and, after the test, one was selected with better respiratory performance for computational analysis of photogrammetric thoracoabdominal mobility. The results showed average values of 15.40, 61.29, 1.66 and 25.54, for age (years), body mass (kg), height (m) and thorax length (cm), respectively. The analysis found respiratory values of: 3:22 l for forced expiratory volume in one second, 3.87 l for forced vital capacity, 6.50 l/s for peak expiratory flow and 3.59 l for deep inspiration. The correlation showed better values for 3D photogrammetry as compared to 2D. The inclusion of the third dimension has been applied in order to adequate photogrammetric methodology to existing kinematics analysis tools as videogrammetry computed and plethysmography. © Springer International Publishing Switzerland 2014.

关键词： Photogrammetry

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Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges

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Scientific data 2017年第1期4卷 170077-170077页

作者： Hesham Elhalawani Abdallah S.R. Mohamed Aubrey L. White James Zafereo Andrew J. Wong Joel E. Berends Shady AboHashem Bowman Williams Jeremy M. Aymard Aasheesh Kanwar Subha Perni Crosby D. Rock Luke Cooksey Shauna Campbell Yao Ding Clifton D. Fuller Stephen Y. Lai Elisabeta G. Marai David Vock Guadalupe M. Canahuate John Freymann Keyvan Farahani Jayashree Kalpathy-Cramer Department of Radiation Oncology University of Texas MD Anderson Cancer Center Houston 77030 Texas USA Department of Clinical Oncology University of Alexandria Alexandria 21527 Egypt McGovern Medical School at University of Texas Health Science Center at Houston (UTHealth) Houston 77030 Texas USA University of Texas Health Science Center San Antonio 78229 Texas USA Department of Cardiology Harvard Medical School and Massachusetts General Hospital Boston 02115 Massachusetts USA Furman University Greenville 29613 South Carolina USA Abilene Christian University Abilene 79601 Texas USA Texas Tech University Health Sciences Center School of Medicine Lubbock 79905 Texas USA Columbia College of Physicians and Surgeons New York 10032 Massachusetts USA Texas Tech Health Sciences Center El Paso Paul L. Foster School of Medicine 79905 Texas USA Department of Radiation Oncology Cleveland Clinic Foundation Cleveland 44124 Ohio USA Department of Imaging Physic University of Texas MD Anderson Cancer Center Houston 77030 Texas USA Medical Physics Program University of Texas Graduate School of Biomedical Sciences Houston 77030 Texas USA Department of Head and Neck Surgery University of Texas MD Anderson Cancer Center Houston 77030 Texas USA Department of Computer Science University of Illinois at Chicago Chicago 60607 Illinois USA Department of Biostatistics University of Minnesota of Public Health Minneapolis 55455 Minnesota USA Department of Electrical & Computer Engineering University of Iowa Iowa City 52242 Iowa USA Leidos Biomedical Research Inc Frederick National Laboratory for Cancer Research Frederick 21701 Maryland USA The Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins Medical Institutions Baltimore 21287 Maryland USA National Cancer Institute National Institutes of Health Bethesda 20892 Maryland USA Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging Massachusetts Ge

Cancers arising from the oropharynx have become increasingly more studied in the past few years, as they are now epidemic domestically. These tumors are treated with definitive (chemo)radiotherapy, and have local recurrence as a primary mode of clinical failure. Recent data suggest that 'radiomics', or extraction of image texture analysis to generate mineable quantitative data from medical images, can reflect phenotypes for various cancers. Several groups have shown that developed radiomic signatures, in head and neck cancers, can be correlated with survival outcomes. This data descriptor defines a repository for head and neck radiomic challenges, executed via a Kaggle in Class platform, in partnership with the MICCAI society 2016 annual *** public challenges were designed to leverage radiomics and/or machine learning workflows to discriminate HPV phenotype in one challenge (HPV status challenge) and to identify patients who will develop a local recurrence in the primary tumor volume in the second one (Local recurrence prediction challenge) in a segmented, clinically curated anonymized oropharyngeal cancer (OPC) data set.

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Conference on Advances in Signal Processing (CASP)

作者： Shri Promoad Gorhe Pradeep K. Sinha Madhuri A. Joshi M. B. Khambete Prachi Mukherji James L Garrison Pramod Kale Hemant A. Patil Aditya Abhyankar Arnab Das Maharshi Karve Stree Shikshan Samstha New Raipur University FIET (U.K.) FIE (I) FIETE (I) IET Pune Local Network College of Engineering Pune CCEW General Chair CASP-2016 (E&TC) CCEW Co Chair CASP-2016 Aeronautics and Astronautics Electrical and Computer Engineering (by courtesy) Ecological Sciences & Engineering Interdisciplinary Graduate Program Purdue University USA ICIT Pvt. Ltd. Pune Former Director ISRO DA-IICT Gandhinagar Gujarat State Faculty of Technology University of Pune Department of Technology University of Pune Acoustic Research Lab of the Tropical Marine Science Institute at the National University of Singapore Retd. Electrical Officer Indian Navy (1994 - 2015) Adjunct Faculty at IIT Delhi and DIAT Pune

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

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Abstracts and Workshops 7th National Spinal Cord Injury Conference November 9-11, 2017 Fallsview Casino Resort Niagara Falls, Ontario, Canada Abstracts

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JOURNAL OF SPINAL CORD MEDICINE 2017年第6期40卷 813-869页

作者： [Anonymous] a NET Team Toronto Rehabilitation Institute - University Health Network Toronto ON Canada. fo Neural Engineering and Therapeutic Team Lyndhurst Centre Toronto Rehabilitation Institute - University Health Network Toronto ON Canada. c Rick Hansen Institute Vancouver BC Canada. ev Rick Hansen Institute Vancouver BC Canada. fc Stan Cassidy Hospital University of New Brunswick Fredericton NB Canada. fd Dalhousie University Halifax NS Canada. e University of Saskatchewan Saskatoon SK Canada. aa University of Calgary Calgary AB Canada. y Dalhousie University Halifax NS Canada. fa Health Sciences Centre Winnipeg MB Canada. fb University of Manitoba Winnipeg MB Canada. h University of Alberta Edmonton AB Canada. i Universite Laval Quebe c City QC Canada. bd Rick Hansen Institute Vancouver BC Canada. fz Clinician Investigator Lyndhurst Centre Toronto Rehabilitation Institute University Health Network and Assistant Professor Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto. v University of Toront o Toronto ON Canada. n Institute of Biomaterials and Biomedical Engineering University of Toronto Toronto ON Canada. o Toronto Rehabilitation Institute - University Health Network Toronto ON Canada. ga Clinical Scientist Lyndhurst Centre Toronto Rehabilitation Institute University Health Network and Assistant Professor Department of Physical Therapy University of Toronto. dx Department of Electrical and Computer Engineering University of Toronto Toronto ON Canada. fm Lawson Health Research Institute London ON Canada. r Parkwood Institute London ON Canada. bi Parkwood Institute London ON Canada. s University of British Columbia Vancouver BC Canada. t Simon Fraser University Burnaby BC Canada. u DeltaQuest Foundation Concord MA USA. z Institut national d'excellence en santé et en services sociaux (INESSS) Quebec City QC Ca

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Performance Evaluation of Hypervisors for HPC Applications

Performance Evaluation of Hypervisors for HPC Applications

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IEEE International Conference on Systems, Man and Cybernetics

作者： David Beserra Felipe Oliveira Jean Araujo Felipe Fernandes Alberto Araújo Patrícia Endo Paulo Maciel Edward David Moreno Graduate Program in Computer Science University of Sergipe Aracaju Brazil Academic Unit of Garanhuns Federal Rural University of Pernambuco Garanhuns Brazil Informatics Center Federal University of Pernambuco Recife Brazil Graduate in Systems and Computing Military Institute of Engineering Rio de Janeiro Brazil University of Pernambuco Caruaru Brazil

ISBN: (纸本)9781479986989

High Performance Computing (HPC) aggregates computing power in order to solve large and complex problems in different knowledge areas. Nowadays, HPC users can utilize virtualized infrastructures as a low-cost alternative to deploy their applications. However, virtualization brings some challenges for HPC, specially in regard to overhead caused by hyper visors. In this work, our main goal is to analyze the performance of two hyper visors (KVM and Virtual Box) under HPC activities, considering full virtualization, and Para virtualization approaches. We used the HPC Challenge Benchmark (HPCC) to evaluate processor, RAM, inter-process communication and network communication performance. Our results show KVM in Para virtualization mode has a similar performance of a native cluster.

关键词： Virtualization Virtual machine monitors Bandwidth Benchmark testing Random access memory Performance evaluation Kernel

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Time Series Prediction Using Restricted Boltzmann Machines and Backpropagation

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Procedia computer science 2015年 55卷 990-999页

作者： Rafael Hrasko André G.C. Pacheco Renato A. Krohling Graduate Program in Computer Science PPGI UFES - Universidade Federal do Espirito Santo Av. Fernando Ferrari 514 Vitória 29075-910 Brazil Production Engineering Department UFES - Universidade Federal do Espirito Santo Av. Fernando Ferrari 514 Vitória 29075-910 Brazil

Time series prediction appear in many real-world problems, e.g., financial market, signal processing, weather forecasting among others. The underlying models and time series data of those problems are generally complex in a way that reasonable accurate estimation cannot be easily achieved, thus requiring more advanced techniques. Statistical models are the classical approaches for tackling this problem. Many works extended different architectures of Artificial neural networks to work with time series prediction, such as Feedforward, Boltzmann Machines and Deep Belief Network. A Deep Belief Network based on hybridization between Gaussian-Bernoulli Restricted Boltzmann Machine and the Backpropagation algorithm is presented. The hybrid algorithm is tested on three time series databases showing promising results.

关键词： Time Series Prediction Gaussian-Bernoulli Restrict Boltzmann Machine Deep Belief Network

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A-TOPSIS – An Approach Based on TOPSIS for Ranking Evolutionary Algorithms

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Procedia computer science 2015年 55卷 308-317页

作者： Renato A. Krohling André G.C. Pacheco Production Engineering Department UFES - Federal University of Espirito Santo Av. Fernando Ferrari 514 Vitória CEP: 29060-270 Brazil Graduate Program in Computer Science PPGI UFES - Federal University of Espirito Santo Av. Fernando Ferrari 514 Vitória CEP: 29060-270 Brazil

In this paper, we propose an alternative novel method based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve the problem of ranking and comparing algorithms. In evolutionary computation, algorithms are executed several times and then a statistic in terms of mean values and standard deviations are calculated. In order to compare algorithms performance it is very common to handle such issue by means of statistical tests. Ranking algorithms, e.g., by means of Friedman test may also present limitations since they consider only the mean value and not the standard deviation of the results. Since the TOPSIS is not able to handle directly this kind of data, we develop an approach based on TOPSIS for algorithm ranking named as A-TOPSIS. In this case, the alternatives consist of the algorithms and the criteria are the benchmarks. The rating of the alternatives with respect to the criteria are expressed by means of a decision matrix in terms of mean values and standard deviations. A case study is used to illustrate the method for evolutionary algorithms. The simulation results show the feasibility of the A-TOPSIS to find out the ranking of algorithms under evaluation.

关键词： Algorithms comparison ranking TOPSIS evolutionary algorithms

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Distributed keyword vector representation for document categorization

Distributed keyword vector representation for document categ...

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International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Yu-Lun Hsieh Shih-Hung Liu Yung-Chun Chang Wen-Lian Hsu Department of Computer Science National Chengchi University Taiwan Social Networks and Human-Centered Computing Program Academia Sinica Taiwan Institute of Information Science Academia Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Departmem of Information Management National Taiwan University Taiwan

ISBN: (纸本)9781467396073

In the age of information explosion, efficiently categorizing the topic of a document can assist our organization and comprehension of the vast amount of text. In this paper, we propose a novel approach, named DKV, for document categorization using distributed real-valued vector representation of keywords learned from neural networks. Such a representation can project rich context information (or embedding) into the vector space, and subsequently be used to infer similarity measures among words, sentences, and even documents. Using a Chinese news corpus containing over 100,000 articles and five topics, we provide a comprehensive performance evaluation to demonstrate that by exploiting the keyword embeddings, DKV paired with support vector machines can effectively categorize a document into the predefined topics. Results demonstrate that our method can achieve the best performances compared to several other approaches.

关键词： Computational modeling Manuals

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