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Automated verification of nested DFS 20

20th International Workshop on Formal methods for Industrial Critical Systems, FMICS 2015

作者： van de Pol, Jaco C. Formal Methods and Tools Department of Computer Science CTIT University of Twente Enschede Netherlands

ISBN: (纸本)9783319194578

In this paper we demonstrate the automated verification of the Nested Depth-First Search (NDFS) algorithm for detecting accepting cycles. The starting point is a recursive formulation of the NDFS algorithm. We use Dafny to annotate the algorithm with invariants and a global specification. The global specification requires that NDFS indeed solves the accepting cycle problem. The invariants are proved automatically by the SMT solver Z3 underlying Dafny. The global specifications, however, need some inductive reasoning on paths in a graph. To prove these properties, some auxiliary lemmas had to be provided. The full specification is contained in this paper. It fits on 4 pages, is verified by Dafny in about 2 minutes, and was developed in a couple of weeks. © Springer International Publishing Switzerland 2015.

关键词： Specifications

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Numerical simulation of the energy deposition in the HIPIB irradiating process of Ti target

Numerical simulation of the energy deposition in the HIPIB i...

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International Conference on Applied Engineering, Materials and Mechanics (ICAEMM 2016)

作者： Ming GAO Rui HOU Yong YOU Meng ru LV College of Physical Science and Technology Shenyang Normal University Physics Group Xinmin Senior High School Department of Computer and Mathematics Basis Teaching Shenyang Normal University

ISBN: (纸本)9789813146570

In this study, the numerical method was used to investigate the interactions between the target of Ti and the High-intensity pulsed ion beam（HIPIB） of H and C. Calculations were made for the energy distribution and the range distributions of H and C with the initial energy of 100 ke V, 300 ke V and 450 *** result indicates that there were different characteristics of H and C. The H energy loss, which was the result of nuclear stopping, was significantly less than that of the C. Therefore, the H energy loss is negligible in the HIPIB of C and H. The range of H is greater than that of C. It can be concluded that the C affects the region of the shallow surface layer and the H affects the region of the deeper surface layer. The H has energy loss peak within Ti target. The C does not have energy loss peak, but it does deposit much more energy at the surface layer.

关键词： Numerical simulation of the energy deposition in the HIPIB irradiating process of Ti target simulation HIPIB

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Detection of Abnormalities in Driving Style Based on Moving Object Trajectories without Labels

Detection of Abnormalities in Driving Style Based on Moving ...

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IIAI International Congress on Advanced Applied Informatics

作者： Tian-Hsiang Huang Vladimir Nikulin Liang-Bi Chen Cloud Computing Research Center National Sun Yat-sen University Department of Mathematical Methods in Economy Vyatka State University Department of Computer Science and Engineering National Sun Yat-sen University

ISBN: (纸本)9781467389860

Being able automatically recognize the driving style is a very important and challenging task with many practical applications, including insurance. Note that hand labelling of the driving trajectories represents a very time-consuming task. To overcome this problem, we can reduce thousands of variable-length trajectories to the traditional rectangular format, using quantile-based technique. After that we can apply standard classifier to compare trajectories, corresponding to the particular driver, with trajectories of a few randomly selected drivers. We are assuming here that the most of the drivers have safe driving style with similar statistical characteristics. By comparing the current driver with a few randomly selected "good" drivers, we estimate the probability that the current driver is bad (or has significant deviations from usual statistical characteristics). Note as a distinguished particular feature of the presented method: it does not require availability of the training labels. The database includes 2736 drivers with 200 variable length driving trajectories each. We tested our model (with competitive results) online during Kaggle-based AXA Drivers Telematics Challenge in 2015.

关键词： Motion mining Unsupervised learning Big data

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Improving the fault tolerance of multi-robot networks through a combined control law strategy

Improving the fault tolerance of multi-robot networks throug...

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International Workshop on Reliable Networks Design and Modeling (RNDM)

作者： Cinara Ghedini Carlos H. C. Ribeiro Lorenzo Sabattini Computer Science Division Technological Institute of Aeronautics São José dos Campos – SP Brazil Department of Sciences and Methods for Engineering (DISMI) University of Modena and Reggio Emilia – Italy

Applications based on groups of self-organized mobile robots and - more generically - agents are becoming pervasive in communication, monitoring, traffic and transportation systems. Their advantage is the possibility of providing services without the existence of a previously defined infrastructure and with a high degree of autonomy. On the other hand, physical agents, in general, are prone to failures, adding uncertainty and unpredictability in the environments in which they operate. Therefore, a robust topology regarding failures is an imperative requirement. In this paper, we show that mechanisms based solely on connectivity maintenance are not enough to obtain a sufficiently resilient network, and a robustness-oriented approach is necessary. Thus, we propose a local combined control law that aims at maintaining the overall network connectivity while improving the network robustness via actions that reduce vulnerability to failures that might lead to network disconnection. The combined control law performance was validated from two perspectives: as a reactive and as a proactive mechanism. As a reactive mechanism, it was able to accommodate ongoing failures and postpone or avoid network fragmentation. As a proactive mechanism, the network topology was able to evolve from a potentially vulnerable topology w.r.t. failures to a more robust one.

关键词： Robustness Robots Network topology Maintenance engineering Topology Laplace equations Electronic mail

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Analysis of the causes and consequences of falls from scaffolding using the Polish construction industry as an example

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IOP Conference Series: Materials science and Engineering 2017年第1期251卷

作者： A Hoła B Hoła M Szóstak Arch. PhD Eng.Wroclaw University of Science and Technology Faculty of Civil Engineering Department of Building Physics and Computer Aided Design Poland Prof. WUST PhD Eng. Wroclaw University of Science and Technology Faculty of Civil Engineering Department of Construction Methods and Managements Poland MSc Eng. Wroclaw University of Science and Technology Faculty of Civil Engineering Department of Construction Methods and Managements Poland

The results of research published in many publications indicate that the scale of the problem of accidents at work in the construction industry is significant. Fatal and serious accidents, which in the construction industry are often a result of a fall from a height and associated with working on scaffolding, have a particularly strong impact on society. Systematic controls carried out on Polish construction sites indicate irregularities in the area of ensuring safety on scaffolding. As a result of conducted own studies, dozens of accidents caused by falls from scaffolding were selected from the total number of accidents at work in the construction industry and then analysed. The aim of the research was to identify the causes of these accidents and their consequences. The results of studies conducted in this area will be the content of the article.

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A Social Network Analysis of the Emergency Medical Command During a Live CBRNE Exercise

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Prehospital and Disaster Medicine 2017年第S1期32卷 S223-S224页

作者： Carl-Oscar Jonson Erik Prytz Ove Jansson Centre For Teaching And Research In Disaster Medicine And Traumatology And Department Of Clinical And Experimental Medicine Linköping University Linköping/Sweden Department Of Computer And Information Science Linköping University Linköping/Sweden

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Introducing high performance computing to undergraduate students

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computers in Education Journal 2016年第4期16卷 104-112页

作者： Cui, Suxia Wang, Yonghui Li, Lin Peng, Xiaobo Yalvac, Bugrahan Engineering Department Prairie View A and M University United States Computer Science Department Prairie View A and M University United States Dept. of Teaching Learning and Culture Texas A and M University College Station United States

Recently, President Obama issued an Executive Order to ensure the United States' leadership in computing. Necessary hardware and software design skills should be introduced into university curricula. Computing has been advanced to High Performance Computing (HPC) throughout the past decades. However, undergraduate students are still lacking of experience in how HPC functions especially in minority-serving institutions, because our current computing curricula do not adequately cover HPC contents. To address this problem, a team of faculty members have obtained external funding supports to improve undergraduate computing education through enhanced courses and research opportunities. The goal is to incorporate HPC concepts and training across the computing curricula in multiple disciplines in order to motivate students' interests in computing and improve their problem-solving skills. This three-year project has already finished the second year of implementation. During the first year, a diverse teaching environment was established, including a HPC cluster and embedded HPC platforms. Both platforms supported students' learning and research in parallel programming, embedded systems design, and data cloud. In the second project year, several courses were revised or developed across three departments: Electrical and computer Engineering, computer science, and Engineering Technology. New course materials integrating the parallel and distributed computing concepts were developed and offered to undergraduate students. Project-based learning was introduced into classroom. More advanced concepts, such as computer vision and machine learning were explored by undergraduate students. At the same time, the research results were disseminated in junior and senior level courses. Faculty members applied effective pedagogy to teach new generation computing. For all the classes involved in this project, student surveys were collected to guide future project implementation. This article s

关键词： Parallel programming

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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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Hierarchical Task Analysis as a Method to Support Emergency Response Planning

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Prehospital and Disaster Medicine 2017年第S1期32卷 S19-S20页

作者： Carl-Oscar Jonson Simon Rosenqvist Rebecca Forsberg Jonas Aléx Erik Prytz Centre For Teaching And Research In Disaster Medicine And Traumatology And Department Of Clinical And Experimental Medicine Linköping University Linköping/Sweden Department Of Computer And Information Science Linköping University Linköping/Sweden Research And Development Center For Disaster Medicine Unit Of Surgery Department Of Surgical And Perioperative Science Umeå University Umeå/Sweden

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