检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)

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Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs

arXiv

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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Verification benchmarks for single-phase flow in three-dimensional fractured porous media

arXiv

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arXiv 2020年

作者： Berre, Inga Boon, Wietse M. Flemisch, Bernd Fumagalli, Alessio Gläser, Dennis Keilegavlen, Eirik Scotti, Anna Stefansson, Ivar Tatomir, Alexandru Brenner, Konstantin Burbulla, Samuel Devloo, Philippe Duran, Omar Favino, Marco Hennicker, Julian Lee, I-Hsien Lipnikov, Konstantin Masson, Roland Mosthaf, Klaus Chiara Nestola, Maria Giuseppina Ni, Chuen-Fa Nikitin, Kirill Schädle, Philipp Svyatskiy, Daniil Yanbarisov, Ruslan Zulianp, Patrick Department of Mathematics University of Bergen Allégaten 41 Bergen5007 Norway Department of Mathematics KTH Royal Institute of Technology Lindstedtsvägen 25 Stockholm11428 Sweden Department of Hydromechanics and Modelling of Hydrosystems University of Stuttgart Pfaffenwaldring 61 Stuttgart70569 Germany Laboratory for Modeling and Scientific Computing MOX Politecnico di Milano p.za Leonardo da Vinci 32 Milano20133 Italy Department of Applied Geology Geosciences Center University of Göttingen Goldschmidtstrasse 3 Göttingen37077 Germany Department of Earth Sciences Uppsala University Villavägen 16 UppsalaS-75236 Sweden University of Côte d’Azur CNRS INRIA LJAD Nice France Institute of Applied Analysis and Numerical Simulation University of Stuttgart Pfaffenwaldring 57 Stuttgart70569 Germany FEC Universidade Estadual de Campinas Cidade Universitária R. Josiah Willard Gibbs 85 Campinas-SPCEP 13083-839 Brazil Institute of Earth Sciences University of Lausanne Building Geopolis UNIL-Mouline Lausanne1015 Switzerland Section de Mathématiques Université de Genève 2-4 rue du Lièvre CP 64 Genève1211 Switzerland Graduate Institute of Applied Geology National Central University Taiwan Center for Environmental Studies National Central University Taiwan Los Alamos National Laboratory New Mexico United States Department of Environmental Engineering Technical University of Denmark Bygningstorvet Building 115 Kgs. Lyngby2800 Denmark Numerical Simulation in Science Medicine and Engineering Group Institute of Computational Science Università della Svizzera italiana. Via G. Buffi 13 Lugano Ticino6900 Switzerland Marchuk Institute of Numerical Mathematics of Russian Academy of Sciences Moscow Russia ETH Zürich Geothermal Energy and Geofluids Group Institute of Geophysics Zürich8092 Switzerland

Flow in fractured porous media occurs in the earth’s subsurface, in biological tissues, and in man-made materials. Fractures have a dominating influence on flow processes, and the last decade has seen an extensive development of models and numerical methods that explicitly account for their presence. To support these developments, we present a portfolio of four benchmark cases for single-phase flow in three-dimensional fractured porous media. The cases are specifically designed to test the methods’ capabilities in handling various complexities common to the geometrical structures of fracture networks. Based on an open call for participation, results obtained with 17 numerical methods were collected. This paper presents the underlying mathematical model, an overview of the features of the participating numerical methods, and their performance in solving the benchmark cases. Copyright © 2020, The Authors. All rights reserved.

关键词： Numerical methods

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Population ageing and deaths attributable to ambient PM_2·5 pollution: a global analysis of economic cost

引用

The Lancet Planetary Health 2021年第6期5卷 e356-e367页

作者： Yin, Hao Brauer, Michael Zhang, Junfeng Cai, Wenjia Navrud, Ståle Burnett, Richard Howard, Courtney Deng, Zhu Kammen, Daniel M Schellnhuber, Hans Joachim Chen, Kai Kan, Haidong Chen, Zhan-Ming Chen, Bin Zhang, Ning Mi, Zhifu Coffman, D'Maris Cohen, Aaron J Guan, Dabo Zhang, Qiang Gong, Peng Liu, Zhu Ministry of Education Key Laboratory for Earth System modeling Department of Earth System Science Tsinghua University Beijing China School of Population and Public Health The University of British Columbia Vancouver BC Canada Energy and Resources Group University of California Berkeley CA United States Goldman School of Public Policy University of California Berkeley CA United States Renewable and Appropriate Energy Laboratory University of California Berkeley CA United States Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Nicholas School of the Environment and Duke Global Health Institute Duke University Durham NC United States Duke Kunshan University Kunshan Jiangsu China School of Economics and Business Norwegian University of Life Sciences Ås Norway Population Studies Division Health Canada Ottawa ON Canada Cumming School of Medicine University of Calgary Calgary AB Canada Planetary Health Dahdaleh Institute for Global Health Research York University Toronto ON Canada Potsdam Institute for Climate Impact Research Potsdam Germany Department of Environmental Health Sciences Yale School of Public Health New Haven CT United States School of Public Health Fudan University Shanghai China Department of Energy Economics School of Economics Renmin University of China Beijing China State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Beijing Normal University Beijing China Institute of Blue and Green Development Shandong University Weihai China The Bartlett School of Sustainable Construction University College London London United Kingdom Health Effects Institute Boston MA United States

Background: The health impacts of ambient air pollution impose large costs on society. Although all people are exposed to air pollution, the older population (ie, those aged ≥60 years) tends to be disproportionally affected. As a result, there is growing concern about the health impacts of air pollution as many countries undergo rapid population ageing. We investigated the spatial and temporal variation in the economic cost of deaths attributable to ambient air pollution and its interaction with population ageing from 2000 to 2016 at global and regional levels. Methods: In this global analysis, we developed an age-adjusted measure of the value of a statistical life-year (VSLY) to estimate the economic cost of deaths attributable to ambient PM_2·5 pollution using Global Burden of Diseases, Injuries, and Risk Factors Study 2017 data and country-level socioeconomic information. First, we estimated the global age-specific and cause-specific mortality and years of life lost (YLLs) attributable to PM_2·5 pollution using the global exposure mortality model and global estimates of exposure at 0·1° × 0·1° (about 11 km × 11 km at the equator) resolution. Second, for each year between 2000 and 2016, we translated the YLLs within each age group into a health-related cost using a country-specific, age-adjusted measure of VSLY. Third, we decomposed the major driving factors that contributed to the temporal change in health costs related to PM_2·5. Finally, we did a sensitivity test to analyse the variability of the estimated health costs to four alternative valuation measures. We identified the uncertainty intervals (UIs) from 1000 draws of the parameters and concentration–response functions by age, cause, country, and year. All economic values are reported in 2011 purchasing power parity-adjusted US dollars. All simulations were done with R, version 3.6.0. Findings: Globally, in 2016, PM_2·5 was estimated to have caused 8·42 million (95% U

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Supernova model discrimination with Hyper-Kamiokande

arXiv

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arXiv 2021年

作者： Abe, K. Adrich, P. Aihara, H. Akutsu, R. Alekseev, I. Ali, A. Ameli, F. Anghel, I. Anthony, L.H.V. Antonova, M. Araya, A. Asaoka, Y. Ashida, Y. Aushev, V. Ballester, F. Bandac, I. Barbi, M. Barker, G.J. Barr, G. Batkiewicz-Kwasniak, M. Bellato, M. Berardi, V. Bergevin, M. Bernard, L. Bernardini, E. Berns, L. Bhadra, S. Bian, J. Blanchet, A. Blaszczyk, F.D.M. Blondel, A. Boiano, A. Bolognesi, S. Bonavera, L. Booth, N. Borjabad, S. Boschi, T. Bose, D. Boyd, S.B. Bozza, C. Bravar, A. Bravo-Berguño, D. Bronner, C. Brown, L. Bubak, A. Buchowicz, A. Avanzini, M. Buizza Cafagna, F.S. Calabria, N.F. Calvo-Mozota, J.M. Cao, S. Cartwright, S.L. Carroll, A. Catanesi, M.G. Cebriàn, S. Chabera, M. Chakraborty, S. Checchia, C. Choi, J.H. Choubey, S. Cicerchia, M. Coleman, J. Collazuol, G. Cook, L. Cowan, G. Cuen-Rochin, S. Danilov, M. Lopez, G. Daz de la Fuente, E. de Perio, P. de Rosa, G. Dealtry, T. Densham, C.J. Dergacheva, A. Deshmukh, N. Devi, M.M. Di Lodovico, F. Di Meo, P. Di Palma, I. Doyle, T.A. Drakopoulou, E. Drapier, O. Dumarchez, J. Dunne, P. Dziewiecki, M. Eklund, L. El Hedri, S. Ellis, J. Emery, S. Esmaili, A. Esteve, R. Evangelisti, A. Feely, M. Fedotov, S. Feng, J. Fernandez, P. Fernández-Martinez, E. Ferrario, P. Ferrazzi, B. Feusels, T. Finch, A. Finley, C. Fiorentini, A. Fiorillo, G. Fitton, M. Frankiewicz, K. Friend, M. Fujii, Y. Fukuda, Y. Galinski, G. Gao, J. Garde, C. Garfagnini, A. Garode, S. Gialanella, L. Giganti, C. Gomez-Cadenas, J.J. Gonin, M. González-Nuevo, J. Gorin, A. Gornea, R. Gousy-Leblanc, V. Gramegna, F. Grassi, M. Grella, G. Guigue, M. Gumplinger, P. Hadley, D.R. Harada, M. Hartfiel, B. Hartz, M. Hassani, S. Hastings, N.C. Hayato, Y. Hernando-Morata, J.A. Herrero, V. Hill, J. Hiraide, K. Hirota, S. Holin, A. Horiuchi, S. Hoshina, K. Hultqvist, K. Iacob, F. Ichikawa, A.K. Ibnsalih, W. Idrissi Iijima, T. Ikeda, M. Inomoto, M. Inoue, K. Insler, J. Ioannisian, A. Ishida, T. Ishidoshiro, K. AGH University of Science and Technology Faculty of Computer Science Electronics and Telecommunications Krakow Poland Boston University Department of Physics BostonMA United States University of British Columbia Department of Physics and Astronomy VancouverBC Canada British Columbia Institute of Technology Physics Department BurnabyBC Canada Università della Campania L. Vanvitelli INFN Sezione di Napoli Napoli Italy Carleton University Department of Physics OttawaON Canada University of California Davis Department of Physics DavisCA United States University of California Irvine Department of Physics and Astronomy IrvineCA United States California State University Department of Physics CarsonCA United States Charles University IPNP FMF Prague Czech Republic Chonnam National University Department of Physics Gwangju Korea Republic of Dongshin University Laboratory for High Energy Physics Naju Korea Republic of Donostia International Physics Center Ikerbasque Foundation Basque Country Spain Ecole Polytechnique IN2P3-CNRS Laboratoire Leprince-Ringuet Palaiseau France University of Edinburgh School of Physics and Astronomy Edinburgh United Kingdom ETH Zurich Institute for Particle and Astroparticle Physics Zurich Switzerland University of Geneva Section de Physique DPNC Geneva Switzerland GIST College Gwangju Institute of Science and Technology Gwangju Korea Republic of University of Glasgow School of Physics and Astronomy Glasgow United Kingdom Universidad de Guadalajara CUCEI Departamento de Fisica Jal Guadalajara Mexico Universidad de Guadalajara CUCEA IT.Ph.D. program Jal Guadalajara Mexico H. Niewodniczá nski Institute of Nuclear Physics PAN Cracow Poland University of Hawaii Department of Physics and Astronomy HonoluluHI United States Imperial College London Department of Physics London United Kingdom Indian Institute of Technology Guwahati Guwahati India Indian Institute of Technology Jodhpur Department of Physics Raj

Core-collapse supernovae are among the most magnificent events in the observable universe. They produce many of the chemical elements necessary for life to exist and their remnants-neutron stars and black holes-are interesting astrophysical objects in their own right. However, despite millennia of observations and almost a century of astrophysical study, the explosion mechanism of core-collapse supernovae is not yet well understood. Hyper-Kamiokande is a next-generation neutrino detector that will be able to observe the neutrino flux from the next galactic core-collapse supernova in unprecedented detail. We focus on the first 500 ms of the neutrino burst, corresponding to the accretion phase, and use a newly-developed, high-precision supernova event generator to simulate Hyper-Kamiokande's response to five different supernova models. We show that Hyper-Kamiokande will be able to distinguish between these models with high accuracy for a supernova at a distance of up to 100 kpc. Once the next galactic supernova happens, this ability will be a powerful tool for guiding simulations towards a precise reproduction of the explosion mechanism observed in nature. Copyright © 2021, The Authors. All rights reserved.

关键词： Supernovae

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Experiments in Routing Vehicles for Municipal Services

Experiments in Routing Vehicles for Municipal Services

引用

International Conference on High Performance Computing and simulation

作者： Imran Mahmood Sahar Idwan Junaid Ahmed Zubairi Izzeddin Matar Center for Research in Modeling & Simulation School of Electrical Engineering and Computer Science National University of Sciences and Technology CS and Engineering Department School of Engineering American U of Ras Al Khaimah Ras al Khaimah UAE and Comp Sci & Apps Dept Hashemite University Computer and Information Sciences Department College of Liberal Arts and Sciences State University of New York at Fredonia Faculty of Information Technology Software Engineering Department University of Petra

ISBN: (纸本)9781538678800

In this paper, route planning of waste collection trucks using R&R (Ruin and Recreate) approach is explored. We assume the trucks are guided by the central depot in selecting the optimal route for waste collection. Heuristic algorithms are simulated to find the optimal routes for the waste management fleet. Through the use of smart dumpsters that can communicate the current level of waste using sensors and communication modules, we aim to reduce the number of trucks used, the total time taken and total distance traveled by the fleet in a day. Our work is based on variations of CVRPTW (Capacitated Vehicle Routing Problem with Time Window Constraint). The central management system selects the dumpsters, based on their waste levels, in descending order, and dispatches appropriate number of trucks, with path assignments using Ruin and Recreate (R&R) approach of the VRPTW strategy. The municipal authority saves transit time, fuel cost and service time by using our approach, through a simulation of smart waste collection.

关键词： Smart Dumpster Capacitated Vehicle Routing Problem with Time Window Constraint Ruin & Recreate Search heuristic Waste management simulation

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Fine-grained image recognition via weakly supervised click data guided bilinear CNN model

Fine-grained image recognition via weakly supervised click d...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Guangjian Zheng Min Tan Jun Yu Qing Wu Jianping Fan Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Department of Computer Science University of North Carolina at Charlotte

ISBN: (纸本)9781509060689

Bilinear convolutional neural networks (BCNN) model, the state-of-the-art in fine-grained image recognition, fails in distinguishing the categories with subtle visual differences. We design a novel BCNN model guided by user click data (C-BCNN) to improve the performance via capturing both the visual and semantical content in images. Specially, to deal with the heavy noise in large-scale click data, we propose a weakly supervised learning approach to learn the C-BCNN, namely W-C-BCNN. It can automatically weight the training images based on their reliability. Extensive experiments are conducted on the public Clickture-Dog dataset. It shows that: (1) integrating CNN with click feature largely improves the performance; (2) both the click data and visual consistency can help to model image reliability. Moreover, the method can be easily customized to medical image recognition. Our model performs much better than conventional BCNN models on both the Clickture-Dog and medical image dataset.

关键词： Reliability Visualization Image recognition Data models Training Feature extraction Computational modeling

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Efficient NAS Benchmark Kernels with C++ Parallel Programming

Efficient NAS Benchmark Kernels with C++ Parallel Programmin...

引用

Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Dalvan Griebler Junior Loff Gabriele Mencagli Marco Danelutto Luiz Gustavo Fernandes Pontificia Universidade Catolica do Rio Grande do Sul Porto Alegre RS BR Parallel Application Modeling Group (GMAP) Pontifical Catholic University of Rio Grande do Sul (PUCRS) Porto Alegre Brazil Computer Science Department University of Pisa (UNIPI) Pisa Italy

Benchmarking is a way to study the performance of new architectures and parallel programming frameworks. Well-established benchmark suites such as the NAS Parallel Benchmarks (NPB) comprise legacy codes that still lack portability to C++ language. As a consequence, a set of high-level and easy-to-use C++ parallel programming frameworks cannot be tested in NPB. Our goal is to describe a C++ porting of the NPB kernels and to analyze the performance achieved by different parallel implementations written using the Intel TBB, OpenMP and FastFlow frameworks for Multi-Cores. The experiments show an efficient code porting from Fortran to C++ and an efficient parallelization on average.

关键词： C++ languages Kernel Parallel programming Benchmark testing Parallel processing Three-dimensional displays Libraries

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Vehicular ad hoc network (VANET): A survey, challenges, and applications

Advances in Intelligent Systems and Computing

引用

Advances in Intelligent Systems and Computing 2017年 548卷 39-51页

作者： Rasheed, Asim Gillani, Saira Ajmal, Sana Qayyum, Amir Capital University of Science & Technology Islamabad Pakistan Department of Computer Science COMSATS Institute of Information Technology Islamabad Pakistan Center of Modeling and Simulation National University of Sciences & Technology Islamabad Pakistan

An ad hoc network consisting of vehicles has emerged as an interesting but challenging domain where a lot of new application may find their place. Though research in this field is on since last two decades, large-scale practical implementation still require some time. In this paper, a survey of current challenges and potential applications, incorporating medium access control schemes, routing approaches, hardware and spectrum issues, and security and privacy issues for VANETs, is presented. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Surveys

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Three Layer Game Theoretic Decision Framework for Cyber-Investment and Cyber-Insurance 8th

Three Layer Game Theoretic Decision Framework for Cyber-Inve...

引用

8th International Conference on Decision and Game Theory for Security, GameSec 2017

作者： Tosh, Deepak K. Vakilinia, Iman Shetty, Sachin Sengupta, Shamik Kamhoua, Charles A. Njilla, Laurent Kwiat, Kevin Department of Computer Science Norfolk State University NorfolkVA United States Department of Computer Science and Engineering University of Nevada RenoNV United States Virginia Modeling Analysis and Simulation Center Old Dominion University NorfolkVA United States Network Security Branch Army Research Laboratory AdelphiMD United States Cyber Assurance Branch Air Force Research Laboratory RomeNY United States

ISBN: (纸本)9783319687100

Cyber-threat landscape has become highly complex, due to which isolated attempts to understand, detect, and resolve cybersecurity issues are not feasible in making a time constrained decisions. Introduction of cyber-threat information (CTI) sharing has potential to handle this issue to some extent, where knowledge about security incidents is gathered, exchanged across organizations for deriving useful information regarding the threat actors and vulnerabilities. Although, sharing security information could allow organizations to make informed decision, it may not completely eliminate the risks. Therefore, organizations are also inclined toward considering cyber-insurance for transferring risks to the insurers. Also, in networked environment, adversaries may exploit the information sharing to successfully breach the participating organizations. In this paper, we consider these players, i.e. organizations, adversary, and insure, to model a three layer game, where players play sequentially to find out their optimal strategies. Organizations determine their optimal self-defense investment to make while participating in CTI sharing and cyber-insurance. The adversary looks for an optimal attack rate while the insurer targets to maximize its profit by offering suitable coverage level to the organizations. Using backward induction approach, we conduct subgame perfect equilibrium analysis to find optimal strategies for the involved players. We observe that when cyber-insurance is not considered, attacker prefers to increase its rate of attack. This motivates the organizations to consider cyber-insurance option for transferring the risks on their critical assets. © 2017, Springer International Publishing AG.

关键词： Game theory

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