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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Jie Huang Xuan Zhang Guangjie Han Gangyong Jia Huizi Liyou Jian Wan Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou China Department of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Department of Information & Communication Systems Hohai University Chanqzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou

ISBN: (纸本)9781509056972

A new generation memory, Non-Volatile Memory (NVM), such as Phase-Change Memory (PCM), has been adopted together with DRAM in the main memory to form the hybrid main memory for low energy consumption and high capacity. The biggest challenge of hybrid memory is how to decrease the average memory access cost for the higher cost of NVM's read/write operation. Currently, most researches are based on migration. However, the page migration itself is a high cost operation. And the migration based policy produces many migration operations, which induces high cost in memory access. Therefore, in order to decrease the cost, we present a virtual page behavior based page management policy (VBPM) in this paper. According to the virtual pages' behavior, we allocate virtual pages into DRAM or PCM physical pages correspondingly. The whole process is migration independent. The experimental results show our VBPM decreases the average memory access time by 24%, moreover, VBPM improves real-time performance in critical path.

关键词： Phase change materials Random access memory Memory management Algorithm design and analysis Resource management Nonvolatile memory Cloud computing

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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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Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

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Scientific data 2021年第1期8卷 72页

作者： Gilberto Pastorello Carlo Trotta Eleonora Canfora Housen Chu Danielle Christianson You-Wei Cheah Cristina Poindexter Jiquan Chen Abdelrahman Elbashandy Marty Humphrey Peter Isaac Diego Polidori Markus Reichstein Alessio Ribeca Catharine van Ingen Nicolas Vuichard Leiming Zhang Brian Amiro Christof Ammann M Altaf Arain Jonas Ardö Timothy Arkebauer Stefan K Arndt Nicola Arriga Marc Aubinet Mika Aurela Dennis Baldocchi Alan Barr Eric Beamesderfer Luca Belelli Marchesini Onil Bergeron Jason Beringer Christian Bernhofer Daniel Berveiller Dave Billesbach Thomas Andrew Black Peter D Blanken Gil Bohrer Julia Boike Paul V Bolstad Damien Bonal Jean-Marc Bonnefond David R Bowling Rosvel Bracho Jason Brodeur Christian Brümmer Nina Buchmann Benoit Burban Sean P Burns Pauline Buysse Peter Cale Mauro Cavagna Pierre Cellier Shiping Chen Isaac Chini Torben R Christensen James Cleverly Alessio Collalti Claudia Consalvo Bruce D Cook David Cook Carole Coursolle Edoardo Cremonese Peter S Curtis Ettore D'Andrea Humberto da Rocha Xiaoqin Dai Kenneth J Davis Bruno De Cinti Agnes de Grandcourt Anne De Ligne Raimundo C De Oliveira Nicolas Delpierre Ankur R Desai Carlos Marcelo Di Bella Paul di Tommasi Han Dolman Francisco Domingo Gang Dong Sabina Dore Pierpaolo Duce Eric Dufrêne Allison Dunn Jiří Dušek Derek Eamus Uwe Eichelmann Hatim Abdalla M ElKhidir Werner Eugster Cacilia M Ewenz Brent Ewers Daniela Famulari Silvano Fares Iris Feigenwinter Andrew Feitz Rasmus Fensholt Gianluca Filippa Marc Fischer John Frank Marta Galvagno Mana Gharun Damiano Gianelle Bert Gielen Beniamino Gioli Anatoly Gitelson Ignacio Goded Mathias Goeckede Allen H Goldstein Christopher M Gough Michael L Goulden Alexander Graf Anne Griebel Carsten Gruening Thomas Grünwald Albin Hammerle Shijie Han Xingguo Han Birger Ulf Hansen Chad Hanson Juha Hatakka Yongtao He Markus Hehn Bernard Heinesch Nina Hinko-Najera Lukas Hörtnagl Lindsay Hutley Andreas Ibrom Hiroki Ikawa Marcin Jackowicz-Korczynski Dalibor Janouš Wilma Jans Rachhpal Jassal Shicheng Jiang Tomomichi Kato Myr Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. gzpastorello@lbl.gov. DIBAF University of Tuscia Viterbo 01100 Italy. Euro-Mediterranean Centre on Climate Change Foundation (CMCC) Lecce 73100 Italy. Climate & Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Department of Civil Engineering California State University Sacramento CA 95819 USA. Department of Geography Environment and Spatial Sciences Michigan State University East Lansing MI 48823 USA. Department of Computer Science University of Virginia Charlottesville VA 22904 USA. TERN Ecosystem Processes Menzies Creek VIC3159 Australia. Max Planck Institute for Biogeochemistry Jena 07701 Germany. Laboratoire des Sciences du Climat et de l'Environnement (LSCE) CEA CNRS UVSQ UPSACLAY Gif sur Yvette 91190 France. Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China. Department of Soil Science University of Manitoba Winnipeg MB R3T2N2 Canada. Department of Agroecology and Environment Agroscope Research Institute Zürich 8046 Switzerland. School of Geography and Earth Sciences McMaster University L8S4K1 Hamilton ON Canada. Department of Physical Geography and Ecosystem Science Lund University Lund 22362 Sweden. Department of Agronomy and Horticulture University of Nebraska-Lincoln Lincoln NE 68583 USA. School of Ecosystem and Forest Sciences The University of Melbourne Richmond VIC3121 Australia. Department of Biology Research Group PLECO University of Antwerp Antwerp 2610 Belgium. Joint Research Centre European Commission Ispra 21027 Italy. TERRA Teaching and Research Center University of Liege Gembloux B-5030 Belgium. Finnish Meteorological Institute Helsinki 00560 Finland. ES

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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RETRACTED: Graphic and haptic simulation for transvaginal cholecystectomy training in NOTES

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Journal of Biomedical Informatics 2016年 60卷 410-421页

作者： Jun J. Pan Woojin Ahn Saurabh Dargar Tansel Halic Bai C. Li Ganesh Sankaranarayanan Kurt Roberts Steven Schwaitzberg Suvranu De State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Center for Modeling Simulation and Imaging in Medicine Rensselaer Polytechnic Institute Troy USA Computer Science Department University of Central Arkansas Conway USA Gastrointestinal Surgery Yale School of Medicine Yale University New Heaven USA Department of Surgery Cambridge Health Alliance Harvard Medical School Boston USA

This article has been retracted: please see Elsevier Policy on Article Withdrawal ( http://***/locate/withdrawalpolicy ). This article has been retracted at the request of the Editor-in-Chief. The Publisher and Editor have been informed that the article was submitted to the journal without the consent of all the coauthors. The coauthor Suvranu De expressed concerns regarding the validity of some of the techniques and data reported in the paper, particularly in Figure 18 and Table 3. In addition, we identified in the article a significant portion of copied text that came from the following publications: Metaballs-based physical modeling and deformation of organs for virtual surgery Junjun Pan, Chengkai Zhao, Xin Zhao, Aimin Hao, Hong Qin The Visual computer , June 2015, Volume 31, Issue 6, pp 947–957 DOI: 10.1007/s00371-015-1106-y Virtual reality training and assessment in laparoscopic rectum surgery Jun J. Pan, Jian Chang, Xiaosong Yang, Hui Liang, Jian J. Zhang, Tahseen Qureshi, Robert Howell and Tamas Hickish The International Journal of Medical Robotics and computer Assisted Surgery , Volume 11, Issue 2, pages 194–209 DOI: 10.1002/rcs.1582 As such, this article represents an abuse of the scientific publishing system, which we believe warrants the retraction of the article. The scientific community takes a very strong view on this matter and apologies are offered to the readers of the journal that our processes, which have since been updated, did not adequately verify the assent of the coauthors and their approval of the paper’s content.

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Thompson Sampling for multi-objective multi-armed bandits problem 23

Thompson Sampling for multi-objective multi-armed bandits pr...

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23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015

作者： Yahyaa, Saba Manderick, Bernard Computational Modeling Group Artificial Intelligence Lab Computer Science Department Vrije Universiteit Brussel Pleinlaan 2 Brussels1050 Belgium

ISBN: (纸本)9782875870148

The multi-objective multi-armed bandit (MOMAB) problem is a sequential decision process with stochastic rewards. Each arm generates a vector of rewards instead of a single scalar reward. Moreover, these multiple rewards might be conflicting. The MOMAB-problem has a set of Pareto optimal arms and an agent's goal is not only to find that set but also to play evenly or fairly the arms in that set. To find the Pareto optimal arms, linear scalarized function or Pareto dominance relations can be used. The linear scalarized function converts the multiobjective optimization problem into a single objective one and is a very popular approach because of its simplicity. The Pareto dominance relations optimizes directly the multi-objective problem. In this paper, we extend the Thompson Sampling policy to be used in the MOMAB problem. We propose Pareto Thompson Sampling and linear scalarized Thompson Sampling approaches. We compare empirically between Pareto Thompson Sampling and linear scalarized Thompson Sampling on a test suite of MOMAB problems with Bernoulli distributions. Pareto Thompson Sampling is the approach with the best empirical performance.

关键词： Multiobjective optimization

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An efficient storage technology for object-based distributed storage system

International Journal of Control and Automation

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International Journal of Control and Automation 2015年第11期8卷 337-348页

作者： Zhang, Jilin Su, Yucheng Wan, Jian Zhou, Li Wang, Jue Zhang, Lei College of Electrical Engineering Zhejiang University Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education China Supercomputing Center of Computer Network Information Center Chinese Academy of Sciences Beijing China Computer Science Department Beijing University of Civil Engineering and Architecture Beijing China

There have two effective ways to improve the overall efficiency of the object-based distributed storage system, one is network caching technology and the other is metadata server cluster load balancing strategy. Under the heterogeneous storage environments, object-based distributed file system face the key challenge of lacking caching mechanism for the high-frequency access files, metadata server load imbalance also reduce the overall efficiency. To address these challenges, we discuss the design and implementation of an object-based distributed file storage caching system, and propose a weight-based metadata server cluster load balancing strategy. It effectively improve the metadata processing capacity. Based on the results, it demonstrated the feasibility and the efficiency of our methods. © 2015 SERSC.

关键词： Metadata

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Tracking and Counting Motion for Monitoring Food Intake Based-On Depth Sensor and UDOO Board: A Comprehensive Review

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IOP Conference Series: Materials science and Engineering 2017年第1期226卷

作者： Muhammad Fuad bin Kassim Mohd Norzali Haji Mohd Embedded Computing Systems (EmbCos) Department of Computer Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia 86400 Parit Raja Batu Pahat Johor Malaysia Modeling and Simulation (BIOMEMS) Research Group Department of Electronic Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia 86400 Parit Raja Batu Pahat Johor Malaysia

Technology is all about helping people, which created a new opportunity to take serious action in managing their health care. Moreover, Obesity continues to be a serious public health concern in the Malaysia and continuing to rise. Obesity has been a serious health concern among people. Nearly half of Malaysian people overweight. Most of dietary approach is not tracking and detecting the right calorie intake for weight loss, but currently used tools such as food diaries require users to manually record and track the food calories, making them difficult for daily use. We will be developing a new tool that counts the food intake bite by monitoring hand gesture and face jaw motion movement of caloric intake. The Bite count method showed a good significant that can lead to a successful weight loss by simply monitoring the bite taken during eating. The device used was Kinect Xbox One which used a depth camera to detect the motion on person hand and face during food intake. Previous studies showed that most of the method used to count bite device is worn type. The recent trend is now going towards non-wearable devices due to the difficulty when wearing devices and it has high false alarm ratio. The proposed system gets data from the Kinect that will be monitoring the hand and face gesture of the user while eating. Then, the gesture of hand and face data is sent to the microcontroller board to recognize and start counting bite taken by the user. The system recognizes the patterns of bite taken from user by following the algorithm of basic eating type either using hand or chopstick. This system can help people who are trying to follow a proper way to reduce overweight or eating disorders by monitoring their meal intake and controlling eating rate.

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Research and implementation of CDP disaster recovery system based on oracle

International Journal of Control and Automation

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International Journal of Control and Automation 2015年第11期8卷 325-336页

作者： Ren, Yongjian Ye, Jiaolong Zhang, Jilin Zhou, Li Wang, Jue Zhang, Lei School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education China College of Electrical Engineering Zhejiang University Hangzhou China Supercomputing Center of Computer Network Information Center Chinese Academy of Sciences Beijing China Computer Science Department Beijing University of Civil Engineering and Architecture Beijing China

The OraCDP disaster recovery system uses CDP disaster recovery technology based on the combination of block level, combining with the communication coupling between the underlying I/O and Oracle database. The experimental results show that the OraCDP system can not only maintain the normal operation of the original system without any negative influence, but also guarantee the recovery of data and service at specific time quickly, fully and accurately. © 2015 SERSC.

关键词： Recovery

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Rule-based modelling and simulation of drug-administration policies 2

Rule-based modelling and simulation of drug-administration p...

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2nd modeling and simulation in Medicine Symposium, MSM 2015, Part of the 2015 Spring simulation Multi-Conference, SpringSim 2015

作者： Bortolussi, Luca Krüger, Thilo Lehr, Thorsten Wolf, Verena Modelling and Simulation Group Department of Computer Science Saarland University Germany Clinical Pharmacy Department of Pharmacy Saarland University Germany DMG University of Trieste Italy

ISBN: (纸本)9781510810600

We consider rule-based models extended with time-dependent reaction rates, a suitable formalism to describe the effect of drug administration on biochemical systems. In the paper, we provide a novel and efficient rejection-based simulation algorithm that samples exactly the trajectory space of such models. Furthermore, we investigate a model of drug administration in the context of a plaque-formation process of Alzheimer's disease, a polymerization process best described by a rule-based model to counteract the intrinsic combinatorial explosion of the underlying reaction network. Further-more, time-dependent rates are needed to model the effect of drugs. We apply the simulation algorithm to study the efficacy of different drug administration policies. © 2015 Society for modeling & simulation International (SCS).

关键词： Reaction rates

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