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Communications in Computer and Information science 2016年 613卷 610-620页

作者： Ahn, Hoo Young Lee, Yoon Joon Lee, Kyong-Ha School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Scientific Data Research Center KISTI Daejeon Korea Republic of

ISBN: (纸本)9783319340982

RAID is a popular storage architecture devised to improve both I/O performance and reliability of disk storages with disk arrays. With the declining price of NAND flash-based solid state drives and their performance gains, they have been widely deployed in systems ranging from portables to Internet-scale enterprise systems. By virtue of the benefits, studies on developing RAID gears with solid state drives have been performed in recent years. However, there are still some research issues on realizing a reliable RAID system with arrays of solid state drives due to the different characteristics of NAND flash memory. Moreover, the internal S/W architecture of the current commercial SSDs are not opened to the public so that it is hard to test SSD-based RAID systems with their devised algorithms. The fundamental algorithms in DBMS have been optimized for the use of hard disk drives, which prefer sequential data accesses. Recently, DBMS internals have been modified to make the best use of solid state drives rather than simple hardware replacement. To help the studies, we propose an open-source SSD-based RAID simulator named SRsim. SRSim helps researchers explore and experiment their ideas on an array os solid state drives more easily and accurately. © Springer International Publishing Switzerland 2016.

关键词： NAND circuits

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Correction to: The NYCKidSeq project: study protocol for a randomized controlled trial incorporating genomics into the clinical care of diverse New York City children

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Trials 2021年第1期22卷 146页

作者： Jacqueline A Odgis Katie M Gallagher Sabrina A Suckiel Katherine E Donohue Michelle A Ramos Nicole R Kelly Gabrielle Bertier Christina Blackburn Kaitlyn Brown Lena Fielding Jessenia Lopez Karla Lopez Aguiniga Estefany Maria Jessica E Rodriguez Monisha Sebastin Nehama Teitelman Dana Watnick Nicole M Yelton Avinash Abhyankar Noura S Abul-Husn Aaron Baum Laurie J Bauman Jules C Beal Toby Bloom Charlotte Cunningham-Rundles George A Diaz Siobhan Dolan Bart S Ferket Vaidehi Jobanputra Patricia Kovatch Thomas V McDonald Patricia E McGoldrick Rosamond Rhodes Michael L Rinke Mimsie Robinson Arye Rubinstein Lisa H Shulman Christian Stolte Steven M Wolf Elissa Yozawitz Randi E Zinberg John M Greally Bruce D Gelb Carol R Horowitz Melissa P Wasserstein Eimear E Kenny The Institute for Genomic Health Icahn School of Medicine at Mount Sinai New York NY USA. Department of Pediatrics Division of Pediatric Genetic Medicine Children's Hospital at Montefiore/Montefiore Medical Center/Albert Einstein College of Medicine Bronx NY USA. Department of Population Health Science and Policy Icahn School of Medicine at Mount Sinai New York NY USA. Institute for Health Equity Research Icahn School of Medicine at Mount Sinai New York NY USA. Molecular Diagnostics New York Genome Center New York NY USA. Department of Pediatrics Division of Pediatric Academic Medicine Children's Hospital at Montefiore/Montefiore Medical Center/Albert Einstein College of Medicine Bronx NY USA. Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY USA. Department of Medicine Icahn School of Medicine at Mount Sinai New York NY USA. Department of Health System Design and Global Health Icahn School of Medicine at Mount Sinai New York NY USA. Department of Pediatrics Division of Child Neurology Weill Cornell Medical New York NY USA. Department of Pediatrics Icahn School of Medicine at Mount Sinai New York NY USA. Department of Obstetrics and Gynecology and Women's Health (Reproductive and Medical Genetics) Albert Einstein College of Medicine Bronx NY USA. Department of Pathology and Cell Biology Columbia University Medical Center New York NY USA. Scientific Computing and Data Science Icahn School of Medicine at Mount Sinai New York NY USA. Department of Medicine (Cardiology) Montefiore/Montefiore Medical Center/Albert Einstein College of Medicine Bronx NY USA. Department of Pediatrics Division of Child Neurology New York Medical College Valhalla NY USA. Pediatric Neurology|Boston Children's Health Physicians/Maria Fareri Children's Hospital Hawthorne NY USA. Department of Medical Education Icahn School of Medicine at Mount Sinai New York NY USA. Bethel Gospel Assembly New York NY USA. Departm

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A Cloud Authentication Protocol using One-Time Pad

A Cloud Authentication Protocol using One-Time Pad

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IEEE Region 10 Conference

作者： Lexus Jun Hong Sim Shu Qin Ren Sye Loong Keoh Khin Mi Mi Aung School of Computing Science University of Glasgow G12 8QQ United Kingdom Data Center Technologies Division Data Storage Institute A*STAR Singapore 138932

ISBN: (纸本)9781509025985

There is a significant increase in the amount of data breaches in corporate servers in the cloud environments. This includes username and password compromise in the cloud and account hijacking, thus leading to severe vulnerabilities of the cloud service provisioning. Traditional authentication schemes rely on the users to use their credentials to gain access to cloud service. However once the credential is compromised, the attacker will gain access to the cloud service easily. This paper proposes a novel scheme that does not require the user to present his credentials, and yet is able to prove ownership of access to the cloud service using a variant of zero-knowledge proof. A challenge-response protocol is devised to authenticate the user, requiring the user to compute a one-time pad (OTP) to authenticate himself to the server without revealing password to the server. A prototype has been implemented to facilitate the authentication of the user when accessing Dropbox, and the experiment results showed that the overhead incurred is insignificant.

关键词： amount data breaches

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Product-oriented review summarization and scoring

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Frontiers of Computer science 2015年第2期9卷 210-223页

作者： Rong ZHANG Wenzhe YU Chaofeng SHA Xiaofeng HE Aoying ZHOU Institute of Data Science and Engineering Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai 200062 China School of Computer Science Fudan University Shanghai 200433 China

Currently, mere are many onune review weo sites where consumers can freely write comments about different kinds of products and services. These comments are quite useful for other potential consumers. However, the number of online comments is often large and the number continues to grow as more and more consumers contribute. In addition, one comment may mention more than one product and con- tain opinions about different products, mentioning something good and something bad. However, they share only a single overall score, Therefore, it is not easy to know the quality of an individual product from these comments. This paper presents a novel approach to generate review summaries including scores and description snippets with re- spect to each individual product. From the large number of comments, we first extract the context （snippet） that includes a description of the products and choose those snippets that express consumer opinions on them. We then propose several methods to predict the rating （from 1 to 5 stars） of the snip- pets. Finally, we derive a generic framework for generating summaries from the snippets. We design a new snippet selec- tion algorithm to ensure that the returned results preserve the opinion-aspect statistical properties and attribute-aspect cov- erage based on a standard seat allocation algorithm. Through experiments we demonstrate empirically that our methods are effective. We also quantitatively evaluate each step of our ap- proach.

关键词： online transaction diversification review sum-marization review scoring

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Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network

arXiv

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arXiv 2017年

作者： Cao, Xiangyong Zhou, Feng Xu, Lin Meng, Deyu Xu, Zongben Paisley, John IEEE IEEE School of Mathematics and Statistics Xi’an Jiaotong University Xi’an710049 China National Laboratory of Radar Signal Processing Xidian University Xi’an China NYU Multimedia and Visual Computing Lab New York University Abu Dhabi United Arab Emirates Department of Electrical Engineering & Data Science Institute Columbia University New YorkNY United States Columbia University China Scholarship Council

This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent (SGD) and update the class labels of all pixel vectors using α-expansion min-cut-based algorithm. Compared with other state-of-the-art methods, the proposed classification method achieves better performance on one synthetic dataset and two benchmark HSI datasets in a number of experimental settings. Copyright © 2017, The Authors. All rights reserved.

关键词： Spectroscopy

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Maximum principle based algorithms for deep learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2017年第1期18卷

作者： Qianxiao Li Long Chen Cheng Tai E. Weinan Institute of High Performance Computing Agency for Science Technology and Research Singapore Peking University Beijing China Beijing Institute of Big Data Research and Peking University Beijing China Princeton University Princeton NJ and Beijing Institute of Big Data Research and Peking University Beijing China

The continuous dynamical system approach to deep learning is explored in order to devise alternative frameworks for training algorithms. Training is recast as a control problem and this allows us to formulate necessary optimality conditions in continuous time using the Pontryagin's maximum principle (PMP). A modification of the method of successive approximations is then used to solve the PMP, giving rise to an alternative training algorithm for deep learning. This approach has the advantage that rigorous error estimates and convergence results can be established. We also show that it may avoid some pitfalls of gradient-based methods, such as slow convergence on at landscapes near saddle points. Furthermore, we demonstrate that it obtains favorable initial convergence rate periteration, provided Hamiltonian maximization can be efficiently carried out - a step which is still in need of improvement. Overall, the approach opens up new avenues to attack problems associated with deep learning, such as trapping in slow manifolds and inapplicability of gradient-based methods for discrete trainable variables.

关键词： deep learning method of successive approximations optimal control pontryagin's maximum principle

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XPath translation to enhance the semantic matching of fine-grained Web service security policies 12

XPath translation to enhance the semantic matching of fine-g...

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12th IEEE/ACS International Conference of Computer Systems and Applications, AICCSA 2015

作者： Brahim, Monia Ben Chaari, Tarak Jmaiel, Mohamed ReDCAD Laboratory National School of Engineers of Sfax BP 1173 Sfax3038 Tunisia Research Center for Computer Science Multimedia and Digital Data Processing of Sfax B.P. 275 Sakiet Ezzit Sfax3021 Tunisia Higher Institute of Computing Sciences and Multimedia of Gabes BP 122 Gabes6033 Tunisia

ISBN: (纸本)9781509004782

Fine-grained Web service security policies contain protection assertions that include XPath expressions pointing to the message data to be secured. However, existing security policy (SP) matching approaches don't take into account the structural heterogeneities that may exist between the requestor and provider data. In this paper, we show how this can lead to wrong SP matching results and propose an XPath translation approach that makes the XPath expressions in a requestor and provider SPs refer to the same XML schema so that they may be correctly compared. We also implement our XPath translator, integrate it into our previous SP matcher, and validate the correctness of matching fine-grained protection assertions through several tests and a real-world use case. © 2015 IEEE.

关键词： Semantics

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PennyLane: Automatic differentiation of hybrid quantum-classical computations

arXiv

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arXiv 2018年

作者： Bergholm, Ville Izaac, Josh Schuld, Maria Gogolin, Christian Ahmed, Shahnawaz Ajith, Vishnu Alam, M. Sohaib Alonso-Linaje, Guillermo AkashNarayanan, B. Asadi, Ali Arrazola, Juan Miguel Azad, Utkarsh Banning, Sam Blank, Carsten Bromley, Thomas R. Cordier, Benjamin A. Ceroni, Jack Delgado, Alain Matteo, Olivia Di Dusko, Amintor Garg, Tanya Guala, Diego Hayes, Anthony Hill, Ryan Ijaz, Aroosa Isacsson, Theodor Ittah, David Jahangiri, Soran Jain, Prateek Jiang, Edward Khandelwal, Ankit Kottmann, Korbinian Lang, Robert A. Lee, Christina Loke, Thomas Lowe, Angus McKiernan, Keri Meyer, Johannes Jakob Montañez-Barrera, J.A. Moyard, Romain Niu, Zeyue O’Riordan, Lee James Oud, Steven Panigrahi, Ashish Park, Chae-Yeun Polatajko, Daniel Quesada, Nicolás Roberts, Chase Sá, Nahum Schoch, Isidor Shi, Borun Shu, Shuli Sim, Sukin Singh, Arshpreet Strandberg, Ingrid Soni, Jay Száva, Antal Thabet, Slimane Vargas-Hernández, Rodrigo A. Vincent, Trevor Vitucci, Nicola Weber, Maurice Wierichs, David Wiersema, Roeland Willmann, Moritz Wong, Vincent Zhang, Shaoming Killoran, Nathan Xanadu 777 Bay Street Toronto Canada Wallenberg Centre for Quantum Technology Department of Microtechnology and Nanoscience Chalmers University of Technology Gothenburg412 96 Sweden Indian Institute of Information Technology Kottayam India NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States data cybernetics Martin-Kolmsperger-Str 26 Landsberg86899 Germany Department of Medical Informatics and Clinical Epidemiology Oregon Health and Science University PortlandOR97202 United States Dept. of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Department of Physics Indian Institute of Technology Roorkee Uttarakhand Roorkee India qBraid 5235 South Harper Court ChicagoIL60615 United States Factal Analytics Level 2 Chimes Building Plot 61 Sector - 44 Haryana Gurgaon122003 India Centre for High Energy Physics Indian Institute of Science Karnataka Bengaluru560012 India ICFO - Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Av. Carl Friedrich Gauss 3 Castelldefels Barcelona08860 Spain Chemical Physics Theory Group Department of Chemistry University of Toronto Canada DUG Technology 76 Kings Park Rd West PerthWA6005 Australia Rigetti Computing 2919 Seventh Street BerkeleyCA94710 United States Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute for Advanced Simulation Jülich Supercomputing Centre Forschungszentrum Jülich Jülich52425 Germany University of Amsterdam Netherlands School of Physical Sciences National Institute of Science Education and Research HBNI Odisha Jatni752 050 India Cervest United Kingdom Centro Brasileiro de Pesquisas Físicas Rua Dr. Xavier Sigaud 150 Rio de Janeiro22290-180 Brazil Zurich8092 Switzerland Neo4j UK Ltd United Kingdom Zapata Computing Inc. United States ITC Infotech Bangalore India Department of Microtechnology and Nanoscienc

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane’s core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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Erratum: First Fermi-LAT Solar Flare Catalog (2021, ApJS, 252, 13)

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The Astrophysical Journal Supplement Series 2021年第1期256卷

作者： M. Ajello L. Baldini D. Bastieri R. Bellazzini A. Berretta E. Bissaldi R. D. Blandford R. Bonino P. Bruel S. Buson R. A. Cameron R. Caputo E. Cavazzuti C. C. Cheung G. Chiaro D. Costantin S. Cutini F. D'Ammando F. de Palma R. Desiante N. Di Lalla L. Di Venere F. Fana Dirirsa S. J. Fegan Y. Fukazawa S. Funk P. Fusco F. Gargano D. Gasparrini F. Giordano M. Giroletti D. Green S. Guiriec E. Hays J. W. Hewitt D. Horan G. Jóhannesson M. Kovac'evic' M. Kuss S. Larsson L. Latronico J. Li F. Longo M. N. Lovellette P. Lubrano S. Maldera A. Manfreda G. Martí-Devesa M. N. Mazziotta I. Mereu P. F. Michelson T. Mizuno M. E. Monzani A. Morselli I. V. Moskalenko M. Negro N. Omodei M. Orienti E. Orlando D. Paneque Z. Pei M. Persic M. Pesce-Rollins V. Petrosian F. Piron T. A. Porter G. Principe J. L. Racusin S. Rainò R. Rando B. Rani M. Razzano S. Razzaque A. Reimer O. Reimer D. Serini C. Sgrò E. J. Siskind G. Spandre P. Spinelli D. Tak E. Troja J. Valverde K. Wood G. Zaharijas Department of Physics and Astronomy Clemson University Kinard Lab of Physics Clemson SC 29634-0978 USA Università di Pisa and Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova I-35131 Padova Italy Dipartimento di Fisica e Astronomia 'G. Galilei ' Università di Padova I-35131 Padova Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy melissa.pesce.rollins@pi.infn.it Dipartimento di Fisica Università degli Studi di Perugia I-06123 Perugia Italy Dipartimento di Fisica 'M. Merlin' dell'Università e del Politecnico di Bari via Amendola 173 I-70126 Bari Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari I-70126 Bari Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics and SLAC National Accelerator Laboratory Stanford University Stanford CA 94305 USA nicola.omodei@stanford.edu vahep@stanford.edu Istituto Nazionale di Fisica Nucleare Sezione di Torino I-10125 Torino Italy Dipartimento di Fisica Università degli Studi di Torino I-10125 Torino Italy Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 F-91128 Palaiseau France Institut für Theoretische Physik und Astrophysik Universität Würzburg Campus Hubland Nord Emil-Fischer-Str. 31 D-97074 Würzburg Germany NASA Goddard Space Flight Center Greenbelt MD 20771 USA Italian Space Agency Via del Politecnico snc I-00133 Roma Italy Space Science Division Naval Research Laboratory Washington DC 20375-5352 USA INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica Milano via E. Bassini 15 I-20133 Milano Italy University of Padua Department of Statistical Science Via 8 Febbraio 2 35122 Padova Italy Istituto Nazionale di Fisica Nucleare Sezione di Perugia I-06123 Perugia Italy INAF Istituto di Radioastronomia I-40129 Bologna Italy Department of Physics University of Johannesburg P.O. Box 524 Auckland Park 2006 So

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A Prediction Modelling and Pattern Detection Approach for the First-Episode Psychosis Associated to Cannabis Use

A Prediction Modelling and Pattern Detection Approach for th...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Wajdi Alghamdi Daniel Stamate Katherine Vang Daniel Stahl Marco Colizzi Giada Tripoli Diego Quattrone Olesya Ajnakina Robin M. Murray Marta Di Forti Data Science & Soft Computing Lab. and Department of Computing Goldsmiths. University of London Department of Rinstatistics Institute of Psychiatry Psychology and Neuroscience London MRC Social Genetic and Developmental Psychiatry Centre Institute of Psychiatry Psychology and Neuroscience London Department of Psychosis Studies Institute of Psychology and Neuroscience London

ISBN: (纸本)9781509061686

Over the last two decades, a significant body of research has established a link between cannabis use and psychotic outcomes. In this study, we aim to propose a novel symbiotic machine learning and statistical approach to pattern detection and to developing predictive models for the onset of first-episode psychosis. The data used has been gathered from real cases in cooperation with a medical research institution, and comprises a wide set of variables including demographic, drug-related, as well as several variables specifically related to the cannabis use. Our approach is built upon several machine learning techniques whose predictive models have been optimised in a computationally intensive framework. The ability of these models to predict first-episode psychosis has been extensively tested through large scale Monte Carlo simulations. Our results show that Boosted Classification Trees outperform other models in this context, and have significant predictive ability despite a large number of missing values in the data. Furthermore, we extended our approach by further investigating how different patterns of cannabis use relate to new cases of psychosis, via association analysis and Bayesian techniques.

关键词： Predictive models Computational modeling data models Psychiatry Monte Carlo methods Bayes methods Machine learning algorithms

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