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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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Learning Cellular Objectives from Fluxes by Inverse Optimization

Learning Cellular Objectives from Fluxes by Inverse Optimiza...

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IEEE Annual Conference on Decision and control

作者： Qi Zhao Arion Stettner Ed Reznik Daniel Segre Ioannis Ch. Paschalidis Dept. of Electrical and Computer Eng. and Division of Systems Engineering Boston University Department of Biomedical Engineering Boston University Bioinformatics Program Department of Biology and Department of Biomedical Engineering Boston University

ISBN: (纸本)9781479978878

Flux Balance Analysis (FBA) is a widely used approach for studying biochemical networks, and in particular the genome-scale metabolic network reconstructions. It formulates the problem of predicting a cell's chemical reaction fluxes as the linear optimization problem of maximizing a cellular objective (e.g., growth) subject to constraints capturing stoichiometry mass balances of the metabolic network and bounds that reflect the composition of the growth medium. In practice, however, reaction fluxes of the cells under specific growth conditions are available to be measured, but the primal FBA objective function is not necessarily known. Understanding its structure can elucidate the cellular metabolic control mechanisms and infer important information regarding an organism's evolution. To that end, we have developed an Inverse Flux Balance Analysis (InvFBA) method which is a novel inverse optimization-based framework for inferring metabolic objective functions. Within this framework, we present three different forms of objective functions: linear, quadratic, and non-parametric. We show that in all cases, the inverse problem is tractable and can be solved efficiently. We provide several numerical examples to show that the inference of the objective function is consistent with simulated flux data and actual measurements.

关键词： Linear programming Kernel Optimization Biochemistry Biomass Biomedical measurement

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Abstracts from the 1st International Symposium on Decision Support in Anaesthesia and Intensive Care

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International journal of clinical monitoring and computing 2016年第1期14卷 49-68页

作者： D. Streifert N. Lutter E. Van der Vorst J. Mulier B. Schwilk U. Bothner R. Muche W. Friesdorf K. J. Ruskin A. F. de Geus G. E. Wiersma R. Huet H. Neuffer F. Fischer U. J. Christensen P. F. Jensen J. Jacobsen H. Ørding A. M. Brambrink V. Goel D. F. Hanley K. Becker H. D. Shaffner L. J. Martin N. V. Thakor R. C. Koehler R. J. Traystman M. Quintel M. Apin J. Martin M. Messelken R. Dieterle-Paterakis J. Hiller P. Milewski H. Gross U. Foehring N. Weiler B. Eberle W. Heinrichs W. Höltermann M. van Wickern D. M. Linton J. J. Ross D. G. Mason M. D. Pullman N. D. Edwards M. Doi R. J. Gajraj H. Mantzardis G. N. C. Kenny R. Markgraf G. Deutschinoff L. Pientka T. Scholten J. Maljers St. Walther A. Santevecci R. Ranieri Department of Anaesthesiology University of Erlangen-Nuernberg Erlangen Germany University Hospital Gasthuisberg B Leuven Belgium Department of Anaesthesiology University of Ulm Ulm Germany Yale University School of Medicine New Haven USA Department of Anesthesiology University Hospital Groningen RB The Netherlands Dept. Computer-Aided Circuit Design University of Erlangen-Nuernberg Erlangen Germany Dept. of Anaesthesiology Herlev Hospital University of Copenhagen Herlev Denmark Department of Anaesthesiology Johannes Gutenberg-University Medical School Mainz Germany The Johns Hopkins Medical Institution Baltimore USA Institut für Anästhesiolgie und operative Intensivmedizin Klinikum Mannheim Fakultät für klinische Medizin Universität Heidelberg Heidelberg Germany Department for Anaesthesiology and Intensive Care Medicine Hospital “Am Eichert” Goeppingen Germany Physikalisch-Technische Bundesanstalt Berlin Germany Klinik für Anaesthesiologie & Operative Intensivmedizin Klinikum Benjamin Franklin Freie Universitaet Berlin Berlin Germany Department of Anaesthesia and Intensive Care Medicine University Hospital Marburg Germany Medical Intensive Care Department of Medicine Hadassah Medical Organisation Jerusalem Israel Department of Anaesthetics University of Sheffield Northern General Hospital Sheffield UK Department of Automatic Control & Systems Engineering University of Sheffield Sheffield UK University Department of Anaesthesia HCI International Medical Centre Glasgow UK Department of Internal Medicine Allgemeines Krankenhaus für die Stadt Hagen University of Witten/Herdecke Witten Germany Plexus Medical Group Amsterdam Amsterdam The Netherlands Network Division USA Dept. of Anesthesiology and Intensive Care Università Cattolica del Sacro Cuore - Roma (Italy) Italy

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Erratum to: The whole genome sequence of the Mediterranean fruit fly, Ceratitis capitata (Wiedemann), reveals insights into the biology and adaptive evolution of a highly invasive pest species

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Genome biology 2017年第1期18卷 11页

作者： Alexie Papanicolaou Marc F Schetelig Peter Arensburger Peter W Atkinson Joshua B Benoit Kostas Bourtzis Pedro Castañera John P Cavanaugh Hsu Chao Christopher Childers Ingrid Curril Huyen Dinh HarshaVardhan Doddapaneni Amanda Dolan Shannon Dugan Markus Friedrich Giuliano Gasperi Scott Geib Georgios Georgakilas Richard A Gibbs Sarah D Giers Ludvik M Gomulski Miguel González-Guzmán Ana Guillem-Amat Yi Han Artemis G Hatzigeorgiou Pedro Hernández-Crespo Daniel S T Hughes Jeffery W Jones Dimitra Karagkouni Panagiota Koskinioti Sandra L Lee Anna R Malacrida Mosè Manni Kostas Mathiopoulos Angela Meccariello Monica Munoz-Torres Shwetha C Murali Terence D Murphy Donna M Muzny Georg Oberhofer Félix Ortego Maria D Paraskevopoulou Monica Poelchau Jiaxin Qu Martin Reczko Hugh M Robertson Andrew J Rosendale Andrew E Rosselot Giuseppe Saccone Marco Salvemini Grazia Savini Patrick Schreiner Francesca Scolari Paolo Siciliano Sheina B Sim George Tsiamis Enric Ureña Ioannis S Vlachos John H Werren Ernst A Wimmer Kim C Worley Antigone Zacharopoulou Stephen Richards Alfred M Handler Hawkesbury Institute for the Environment Western Sydney University Sydney Australia. Justus-Liebig-University Giessen Institute for Insect Biotechnology 35394 Giessen Germany. Department of Biological Sciences Cal Poly Pomona Pomona CA 91768 USA. Department of Entomology and Center for Disease Vector Research University of California Riverside Riverside CA 92521 USA. Interdepartmental Graduate Program in Genetics Genomics & Bioinformatics University of California Riverside Riverside CA 92521 USA. Department of Biological Sciences University of Cincinnati Cincinnati OH 45221 USA. Insect Pest Control Laboratory Joint FAO/IAEA Programme of Nuclear Techniques in Food and Agriculture Seibersdorf Vienna Austria. Department of Environmental and Natural Resources Management University of Patras Agrinio Greece. Department of Environmental Biology Centro de Investigaciones Biológicas CSIC 28040 Madrid Spain. Human Genome Sequencing Center Department of Human and Molecular Genetics Baylor College of Medicine 77030 Houston TX USA. National Agricultural Library USDA 20705 Beltsville MD USA. Georg-August-Universität Göttingen Johann-Friedrich-Blumenbach-Institut für Zoologie und Anthropologie 37077 Göttingen Germany. Department of Biology University of Rochester 14627 Rochester NY USA. Department of Biological Sciences Wayne State University 48202 Detroit MI USA. Department of Biology and Biotechnology University of Pavia 27100 Pavia Italy. USDA-ARS Pacific Basin Agricultural Research Center 96720 Hilo HI USA. DIANA-Lab Department of Electrical & Computer Engineering University of Thessaly 382 21 Volos Greece and Hellenic Pasteur Institute 11521 Athens Greece. Department of Entomology University of Illinois at Urbana-Champaign 61801 Urbana IL USA. Department of Biological Sciences Oakland University 48309 Rochester MI USA. Department of Biochemistry and Biotechnology University of Thessaly Larissa Greece. Department of Biology Univer

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Botnet detection using social graph analysis 52

Botnet detection using social graph analysis

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2014 52nd Annual Allerton Conference on Communication, control, and Computing, Allerton 2014

作者： Wang, Jing Paschalidis, Ioannis Ch. Division of Systems Engineering Boston University 8 St. Mary's St. BostonMA02215 United States Dept. of Electrical and Computer Eng. Boston University 8 Mary's St. BostonMA02215 United States

ISBN: (纸本)9781479980093

Signature-based botnet detection methods identify botnets by recognizing Command and control (C&C) traffic and can be ineffective for botnets that use new and sophisticate mechanisms for such communications. To address these limitations, we propose a novel botnet detection method that analyzes the social relationships among nodes. The method consists of two stages: (i) anomaly detection in an 'interaction' graph among nodes using large deviations results on the degree distribution, and (ii) community detection in a social 'correlation' graph whose edges connect nodes with highly correlated communications. The latter stage uses a refined modularity measure and formulates the problem as a non-convex optimization problem for which appropriate relaxation strategies are developed. We apply our method to real-world botnet traffic and compare its performance with other community detection methods. The results show that our approach works effectively and the refined modularity measure improves the detection accuracy. © 2014 IEEE.

关键词： Convex optimization

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Botnet detection using social graph analysis

Botnet detection using social graph analysis

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Annual Allerton Conference on Communication, control, and Computing

作者： Jing Wang Ioannis Ch. Paschalidis Division of Systems Engineering Boston University Boston MA Dept. of Electrical & Computer Eng. Boston University Boston MA

ISBN: (纸本)9781479980109

Signature-based botnet detection methods identify botnets by recognizing Command and control (C&C) traffic and can be ineffective for botnets that use new and sophisticate mechanisms for such communications. To address these limitations, we propose a novel botnet detection method that analyzes the social relationships among nodes. The method consists of two stages: (i) anomaly detection in an "interaction" graph among nodes using large deviations results on the degree distribution, and (ii) community detection in a social "correlation" graph whose edges connect nodes with highly correlated communications. The latter stage uses a refined modularity measure and formulates the problem as a non-convex optimization problem for which appropriate relaxation strategies are developed. We apply our method to real-world botnet traffic and compare its performance with other community detection methods. The results show that our approach works effectively and the refined modularity measure improves the detection accuracy.

关键词： Communities Correlation computer crime Monitoring Erbium Image edge detection Vectors

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Fair resource allocation in multiuser OFDMA downlink with passive eavesdropping

Fair resource allocation in multiuser OFDMA downlink with pa...

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IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Sotiris Karachontzitis Stelios Timotheou Ioannis Krikidis Kostas Berberidis Computer Engineering and Informatics Dept. & RACTI/RU-8 University of Patras Greece KIOS Research Center for Intelligent Systems and Networks University of Cyprus Cyprus Department of Electrical and Computer Engineering University of Cyprus Cyprus

In this paper, we study the problem of resource allocation for a multiuser orthogonal frequency-division multiple access (OFDMA) downlink with eavesdropping. The considered setup consists of a base station, several users and a single eavesdropper that indents to wiretap the transmitted message within each OFDMA subchannel. By taking into consideration the existence of the eavesdropper, the base station aims to assign subchannels and allocate the available power in order to optimize the max-min fairness criterion over the users' secrecy rate. The investigated problem is hard to be solved because of its combinatorial and nonlinear nature. Thus, optimality is discussed for two particular cases where the available power tends to infinity and zero, respectively. The optimal solution is obtained by formulating a mixed integer linear problem in the first case and a series of linear sum assignment problems in the second. In addition, two low-complexity solutions are presented which are based on decoupling the subchannel and the power allocation subproblems. Numerical results are provided to illustrate the performance of the presented solutions.

关键词： Resource management Wireless communication Security Physical layer Conferences Mobile computing Downlink

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A Predictive Model for the Anticoagulant Bivalirudin Administered to Cardiac Surgical Patients

A Predictive Model for the Anticoagulant Bivalirudin Adminis...

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IEEE Annual Conference on Decision and control

作者： Qi Zhao Thomas Edrich Ioannis Ch. Paschalidis Division of Systems Engineering Boston University Boston MA 02215 Department of Anesthesia Perioperative and Pain medicine Brigham and Women's Hospital Boston MA 02215 Dept. of Electrical & Computer Eng. Division of Systems Eng. and Center for Information & Systems Eng. Boston University 8 St. Mary's St. Boston MA 02215

ISBN: (纸本)9781467357159

Bivalirudin is used in patients with heparin-induced thrombocytopenia and is a direct thrombin inhibitor. Since it is a rarely used drug, clinical experience with its dosing is sparse. We develop a model that predicts the effect of bivalirudin, measured by the Partial Thromboplastin Time (PTT), based on its past fusion rates. We learn population-wide model parameters by solving a nonlinear optimization problem that uses a training set of patient data. More interestingly, we devise an adaptive algorithm based on the extended Kalman filter that can adapt model parameters to individual patients. The latter adaptive model emerges as the most promising as it reduces both the mean error and, drastically, the per-patient error variance. The model accuracy we demonstrate on actual patient measurements is sufficient to be useful in guiding optimal therapy.

关键词： Adaptation models Predictive models Mathematical model Coagulation Prediction algorithms Time measurement

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Generation expansion planning under wide-scale RES energy penetration

Generation expansion planning under wide-scale RES energy pe...

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2012 International Conference on Renewable Energies for Developing Countries, REDEC 2012

作者： Tigas, K. Mantzaris, J. Giannakidis, G. Nakos, C. Sakellaridis, N. Pyrgioti, E. Alexandridis, A.T. Energy Systems Analysis Laboratory Energy Policy Division Centre for Renewable Energy Sources and Savings Pikermi Attiki Greece Electric Power Systems Section Dept. of Electrical and Computer Engineering University of Patras Patra Greece

ISBN: (纸本)9781467324403

Renewable energy penetration in electricity is expected to have a spectacular growth in the forthcoming years. Power systems and the codes of their operation will be modified to take into account the specific characteristics of variable renewable energy operation (wind energy, photovoltaics). A number of issues arise in the context of developing new generation expansion planning methodologies, under a large scale penetration of renewable energy. A probabilistic approach is necessary due to the strong stochastic nature of variable (non dispatchable) renewables incorporating the statistics of the customer load and the non dispatchable RES generation. The level of penetration of variable renewable energy has to be calculated according to restrictions implied by the energy curtailment which can occur when the customer load is low and RES generation is high. Hydro pumped storage plants decrease this curtailment and consequently increase the level of RES penetration. In addition, fast reserve capacity is required to deal with big variations of variable RES energy generation. In summary the necessary storage and reserve capacity has to be calculated and incorporated in the costs of different scenarios related to expansion planning or operation simulation. In addition, grid costs mainly related to the Transmission System Expansion have to be calculated since they are related to the penetration of areas with high RES potential. In the present paper an attempt is made to incorporate such costs in the TIMES model in order to have a more accurate approach of generation expansion scenarios, incorporating storage, reserve plants and Transmission System expansion in the cost of RES technologies. Using the results as an input, a more detailed probabilistic approach is used to calculate the relevant production costs under a wide-scale penetration of RES. Methodologies presented here are based on the so called residual load duration curves approach. © 2012 IEEE.

关键词： Costs

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Adaptive contingency control: Wind turbine operation integrated with blade condition monitoring

Adaptive contingency control: Wind turbine operation integra...

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50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

作者： Frost, Susan A. Balas, Mark J. Goebel, Kai Wright, Alan D. NASA Ames Research Center Moffett Field CA 94035 United States University of Wyoming Laramie WY 82070 United States National Renewable Energy Laboratory Golden CO 80401 United States Intelligent Systems Division M/S 269-3 POB 1 United States Electrical and Computer Engineering Dept. United States National Wind Technology Center M/S 3811 1617 Cole Blvd. United States

Increasing turbine up-time and reducing maintenance costs are key technology drivers for wind turbine operators. Components within wind turbines are subject to considerable stresses due to unpredictable environmental conditions resulting from rapidly changing local dynamics. systems health management has the aim to assess the state-of-health of components within a wind turbine, to estimate remaining life, and to aid in autonomous decision-making to minimize damage. Advanced adaptive contingency control can provide the mechanism to enable safe and efficient turbine operation and provide enabling technology for systems Health Management goals. The work reported herein explores the integration of condition monitoring of wind turbine blades with adaptive controls capable of responding to potentially damaging environmental conditions. Results are demonstrated using a high fidelity simulator of a utility-scale wind turbine.

关键词： Condition monitoring

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