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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8881卷 750-763页

作者： Hay, David Shirazipourazad, Shahrzad Sen, Arunabha School of Computer Science and Engineering Hebrew University Jerusalem Israel School of Computing Informatics and Decision System Engineering Arizona State University TempeAZ85281 United States

Target tracking problems have been studied fairly extensively by researchers in the last few years. However, the problem of continuous tracking of all mobile targets using the fewest number of mobile trackers, even when the trajectories of all the targets are known in advance, has received very little attention. In this paper we study this problem, where the goal is to find the fewest number of trackers needed to track all the targets for the entire period of observation. Specifically, given a set of n targets moving in n different (known) trajectories in a two (or three) dimensional space, our objective is to find the fewest number of velocity-bounded UAVs (mobile sensors, trackers) and their trajectories, so that all the targets are tracked during the entire period of observation. We also study two other versions of the problem where not only the number of trackers but also the time during which the trackers are active is also taken into account. We formulate these problems as network flow problems and propose algorithms for their solution. We evaluate the performance of our algorithms through simulation and study the impact of parameters such as the speed and sensing range of the trackers. © Springer International Publishing Switzerland 2014.

关键词： Trajectories

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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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Cell analytics in compound hit selection of bacterial inhibitors

Cell analytics in compound hit selection of bacterial inhibi...

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IEEE International Conference on Big Data

作者： Robert P. Trevino Steve A. Kawamoto Thomas J. Lamkin Huan Liu Arizona State University Tempe AZ US UES Inc. Dayton Ohio USA Air Force Research Laboratory Dayton OH USA School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe Arizona USA

Identifying novel drugs that inhibit bacterial infection has gained a considerable amount of attention in recent years. This is in part due to the increased number of highly resistant bacteria and the serious health threat it poses around the world. In order to combat this threat, a significant hurdle to overcome is the relatively low success rate of identifying novel chemical compounds that are effective at inhibiting bacterial infection. Despite increasing the vast amount of data that is currently generated during drug discovery endeavors, traditional analysis methods have not increased the overall success rate. In this paper, we investigate whether multivariate Image-based high content screening (HCS) platforms can identify chemical compounds using significantly reduced data while retaining its competitiveness. Image-based HCS is still predominantly used in biological compound activity assessments (bioassays) with univariate methods, not utilizing the data to its full potential. We propose a novel method that uses a small number of cells in high dimensional space to analyze interactions between cells, bacteria, and chemical compounds. Our results further indicate that our method can identify compounds that inhibit bacterial infection with a fraction of the control data generated.

关键词： Compounds Microorganisms Chemical compounds Drugs Bacterial infections Aerospace electronics

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Prototype Development of a Responsive Emotive Sensing System (DRESS) to aid older persons with dementia to dress independently

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Gerontechnology : international journal on the fundamental aspects of technology to serve the ageing society 2015年第3期13卷 345-358页

作者： Diane Feeney Mahoney Winslow Burleson Cecil Lozano Vijay Ravishankar Edward Leo Mahoney MGH Institute of Health Professions School of Nursing Boston MA USA. New York University College of Nursing New York NY USA Motivational Environment Research Group School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe Az USA. EDEE Gerontechnology Consulting Charlestown MA 02129 USA.

BACKGROUND:Prior research has critiqued the lack of attention to the stressors associated with dementia related dressing issues, stigmatizing patient clothing, and wearable technology challenges. This paper describes the conceptual development and feasibility testing of an innovative 'smart dresser' context aware affective system (DRESS) to enable dressing by people with moderate memory loss through individualized audio and visual task prompting in real time. METHODS:Mixed method feasibility study involving qualitative focus groups with 25 Alzheimer's family caregivers experiencing dressing difficulties to iteratively inform system design and a quantitative usability trial with 10 healthy subjects in a controlled laboratory setting to assess validity of technical operations. RESULTS:Caregivers voiced the need for tangible dressing assistance to reduce their frustration from time spent in repetitive cueing and power struggles over dressing. They contributed 6 changes that influenced the prototype development, most notably adding a dresser top iPad to mimic a familiar 'TV screen' for the audio and visual cueing. DRESS demonstrated promising overall functionality, however the validity of identification of dressing status ranged from 0% for the correct pants dressing to 100% for all shirts dressing scenarios. Adjustments were made to the detection components of the system raising the accuracy of detection of all acted dressing scenarios for pants from 50% to 82%. CONCLUSIONS:Findings demonstrate family caregiver acceptability of the proposed system, the successful interoperability of the built system's components, and the system's ability to interpret correct and incorrect dressing actions in controlled laboratory simulations. Future research will advance the system to the alpha stage and subsequent testing with end users in real world settings.

关键词： Housing and daily activities gerontechnology smart dresser

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Conditional expectation algorithms for covering arrays

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Journal of Combinatorial Mathematics and Combinatorial computing 2014年 90卷 97-115页

作者： Colbourn, Charles J. School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85287-8809 United States State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China

An efficient conditional expectation algorithm for gencrating covering arrays has established a number of the best known upper bounds on covering array numbers. Despite its theoretical efficiency, the method requires a large amount of storage and time. In order to extend the range of its application, we generalize the method to find covering arrays that are invariant under the action of a group, reducing the search to consider only orbit representatives of interactions to be covered. at the same time, we extend the method to construct a generalization of covering arrays called quilting arrays. The extended conditional expectation algorithm, as expected, provides a technique for generating covering and quilting arrays that reduces the time and storage required. Remarkably, it also improves on the best known bounds on covering array numbers in a variety of parameter situations.

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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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Author Correction: The challenge of mapping the human connectome based on diffusion tractography

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Nature communications 2019年第1期10卷 5059页

作者： Klaus H Maier-Hein Peter F Neher Jean-Christophe Houde Marc-Alexandre Côté Eleftherios Garyfallidis Jidan Zhong Maxime Chamberland Fang-Cheng Yeh Ying-Chia Lin Qing Ji Wilburn E Reddick John O Glass David Qixiang Chen Yuanjing Feng Chengfeng Gao Ye Wu Jieyan Ma Renjie He Qiang Li Carl-Fredrik Westin Samuel Deslauriers-Gauthier J Omar Ocegueda González Michael Paquette Samuel St-Jean Gabriel Girard François Rheault Jasmeen Sidhu Chantal M W Tax Fenghua Guo Hamed Y Mesri Szabolcs Dávid Martijn Froeling Anneriet M Heemskerk Alexander Leemans Arnaud Boré Basile Pinsard Christophe Bedetti Matthieu Desrosiers Simona Brambati Julien Doyon Alessia Sarica Roberta Vasta Antonio Cerasa Aldo Quattrone Jason Yeatman Ali R Khan Wes Hodges Simon Alexander David Romascano Muhamed Barakovic Anna Auría Oscar Esteban Alia Lemkaddem Jean-Philippe Thiran H Ertan Cetingul Benjamin L Odry Boris Mailhe Mariappan S Nadar Fabrizio Pizzagalli Gautam Prasad Julio E Villalon-Reina Justin Galvis Paul M Thompson Francisco De Santiago Requejo Pedro Luque Laguna Luis Miguel Lacerda Rachel Barrett Flavio Dell'Acqua Marco Catani Laurent Petit Emmanuel Caruyer Alessandro Daducci Tim B Dyrby Tim Holland-Letz Claus C Hilgetag Bram Stieltjes Maxime Descoteaux Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. k.maier-hein@dkfz.de. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. Sherbrooke Connectivity Imaging Lab (SCIL) Université de Sherbrooke Sherbrooke QC J1K 0A5 QC Canada. Department of Intelligent Systems Engineering School of Informatics and Computing Indiana University Bloomington IN 47408 USA. Krembil Research Institute University Health Network Toronto Canada M5G 2C4. Department of Neurological Surgery University of Pittsburgh School of Medicine Pittsburgh PA 15213 USA. IMT-Institute for Advanced Studies Lucca 55100 Italy. Department of Diagnostic Imaging St. Jude Children's Research Hospital Memphis TN 38105 USA. University of Toronto Institute of Medical Science Toronto Canada M5S 1A8. Institute of Information Processing and Automation Zhejiang University of Technology Hangzhou 310023 Zhejiang China. United Imaging Healthcare Co Shanghai 201807 China. Shanghai Advanced Research Institute Shanghai 201210 China. Laboratory of Mathematics in Imaging Harvard Medical School Boston MA 02215 USA. Center for Research in Mathematics Guanajuato 36023 Mexico. PROVIDI Lab Image Sciences Institute University Medical Center Utrecht Utrecht 3508 The Netherlands. Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University Maindy Road Cardiff CF24 4HQ UK. Department of Radiology University Medical Center Utrecht Utrecht 3508 The Netherlands. Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM) Université de Montréal Montreal QC Canada H3W 1W5. Sorbonne Universités UPMC Univ Paris 06 CNRS INSERM Laboratoire d'Imagerie Biomédicale (LIB) 75013 Paris France. Center for Advanced Research in Sleep Medicine Hôpital du Sacré-Coeur de Montréal Montreal Canada H4J 1C5. Neuroimaging Unit Institute of Bioimaging and Molecular Physiology (IBFM) Nat

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Time-Space Network Based Exact Models for Periodical Monitoring Routing Problem

Time-Space Network Based Exact Models for Periodical Monitor...

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American Control Conference

作者： Muhong Zhang Derek Kingston School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85287 USA Wright Patterson Air Force Base Vehicles Division 2210 8th St. B20146 R301 Wright Patterson OH 45433

ISBN: (纸本)9781479917730

Unmanned Air Vehicles (UAVs) have been used cooperatively with Unattended Ground Sensors (UGSs) to monitor and identify intruders on a road network system. The current practice includes four phases. In the patrol phase, UAVs visits UGSs periodically to detect the possible alarms. Once an alarm is detected, the second isolation phase is activated to determine the region of the intruders on the network. Delivery/classification and response are the two following phases, which need more human involvements. This work focuses on the first patrol phase to determine the optimal patrol route visiting the UGSs periodically with minimal weighted revisit time interval. Since the objective focuses on a single UGS with worst performance (longest revisit time) and there are large number of alternative optimal solutions, a secondary objective is considered to minimize the duration of the total reiterative tour. Two mixed integer programming models are considered on a time-space discretized network. Numerical experiments show the nontrivial solutions on a small network. The second model improves the computational performance from the first model. The quality of the solutions are evaluated by upper and lower bounds.

关键词： Routing Problem revisit time Unmanned Aerial Vehicle Alarm Phase Intruder computational performance Lower bound Unattended Ground Sensor Integer programming models

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Characterization and modeling of the conduction and switching mechanisms of HfO_x based RRAM

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Environment and Development Economics 2014年第1期1631卷 1-2页

作者： Yu, Shimeng Wong, H.-S. Philip School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe 85281 AZ United States Department of Electrical Engineering Stanford University Stanford 94305 CA United States

The conduction and switching mechanism of resistive random access memory (RRAM) is reviewed in this paper. The resistive switching in oxides is generally attributed to the conductive filament (made up of oxygen vacancies) formation and rupture in the oxide due to field assisted oxygen ion migration. As a model system for device physics study, HfOx based RRAM devices were fabricated and characterized. To identify the electron conduction mechanism, various electrical characterization techniques such as I-V measurements at various temperatures, low-frequency noise measurements, and AC conductance measurements were employed. It was suggested that the trap-assisted-tunneling is the dominant conduction mechanism. In order to explore the oxygen ion migration dynamics, pulse switching measurements were performed. An exponential voltage-time relationship was found between the switching time and the applied voltage. To obtain a first-order understanding of the variability of resistive switching, a Kinetic Monte Carlo (KMC) numerical simulator was developed. The generation/recombination/migration probabilities of oxygen vacancies and oxygen ions were calculated, and the conductive filament configuration was updated stochastically according to those probabilities. The KMC simulation can reproduce many experimental observations in the DC I-V sweep, pulse switching, endurance cycling, and retention baking, etc. The tail bits in the resistance distribution are attributed to the oxygen vacancy left over in the gap region due to a competition between the oxygen vacancy generation and recombination. To enable circuit and system development using RRAM, a compact device model was developed. The compact model, implemented in MATLAB, HSPICE, and Verilog-A, which can be employed in many commonly available circuit simulators using the SPICE engine. Copyright © Materials Research Society 2014.

关键词： dielectric properties electrical properties metal-insulator transition

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