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Hierarchical Regional Disparities and Potential Sector Identification Using Modified Agglomerative Clustering

IOP Conference Series: Materials Science and Engineering 2017年第1期180卷

作者： T A Munandar Azhari A Mushdholifah L Arsyad Doctoral Student of Computer Sc. and Electronic Dept. Math. And Natural Science Faculty Universitas Gadjah Mada Yogyakarta INDONESIA Informatics Eng. Dept. Information Technology Faculty Universitas Serang Raya Computer Sc. and Electronic Dept. Math. And Natural Science Faculty Universitas Gadjah Mada Yogyakarta INDONESIA Economics Department Faculty of Economics and Business Universitas Gadjah Mada Yogyakarta INDONESIA

Disparities in regional development methods are commonly identified using the Klassen Typology and Location Quotient. Both methods typically use the data on the gross regional domestic product (GRDP) sectors of a particular region. The Klassen approach can identify regional disparities by classifying the GRDP sector data into four classes, namely Quadrants I, II, III, and IV. Each quadrant indicates a certain level of regional disparities based on the GRDP sector value of the said region. Meanwhile, the Location Quotient (LQ) is usually used to identify potential sectors in a particular region so as to determine which sectors are potential and which ones are not potential. LQ classifies each sector into three classes namely, the basic sector, the non-basic sector with a competitive advantage, and the non-basic sector which can only meet its own necessities. Both Klassen Typology and LQ are unable to visualize the relationship of achievements in the development clearly of each region and sector. This research aimed to develop a new approach to the identification of disparities in regional development in the form of hierarchical clustering. The method of Hierarchical Agglomerative Clustering (HAC) was employed as the basis of the hierarchical clustering model for identifying disparities in regional development. Modifications were made to HAC using the Klassen Typology and LQ. Then, HAC which had been modified using the Klassen Typology was called MHACK while HAC which had been modified using LQ was called MACLoQ. Both algorithms can be used to identify regional disparities (MHACK) and potential sectors (MACLoQ), respectively, in the form of hierarchical clusters. Based on the MHACK in 31 regencies in Central Java Province, it is identified that 3 regencies (Demak, Jepara, and Magelang City) fall into the category of developed and rapidly-growing regions, while the other 28 regencies fall into the category of developed but depressed regions. Results of the MACLoQ imple

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Multimodal processes prototyping subject to fuzzy operation time constraints

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IFAC-PapersOnLine 2015年第3期28卷 2103-2108页

作者： Bocewicz, G. Banaszak, Z. Nielsen, I. Dept. of Computer Science and Management Koszalin University of Technology Sniadeckich 2 Koszalin75-453 Poland Dept. of Business Informatics Warsaw University of Technology Narbutta 85 Warsaw02-524 Poland Department of Mechanical and Manufacturing Engineering Aalborg University Aalborg East Denmark

The paper describes a Multimodal Transportation Network (MTN) in which several unimodal networks (Automated Guided Vehicles (AGVs), hoists, lifts, etc.) interact with each other via common shared workstations to provide a variety of demand-responsive material handling operations. Different material handling transport modes provide movement of work pieces between workstations along their manufacturing routes in the MTN. In this context the multimodal processes standing behind multiproduct production flow while executed in MTN can be seen as processes realized with synergic utilization of various local periodically acting processes. Such processes play a determining role in the evaluation of functioning efficiency inter alia in public transport systems, goods transport, energy and data transmission etc. The solution to a constraint satisfaction problem aimed at AGVs fleet match-up scheduling subject to fuzzy operation times constraints is our main contribution. In the considered case both production rate (production takt) and operations execution time are described by imprecise (fuzzy) data. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Constraint satisfaction problems Automatic guided vehicles Manufacture Mass transportation Materials handling Multimodal transportation Scheduling Transportation routes Automated guided vehicles Declarative models Fuzzy constraint Multi modal Multimodal transportation networks Multiproduct productions Public transport systems Responsive materials

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Nonlinear soft tissue mechanics based on polytopic Tensor Product modeling 14

Nonlinear soft tissue mechanics based on polytopic Tensor Pr...

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IEEE 14th International Symposium on Applied Machine Intelligence and informatics, SAMI 2016

作者： Takács, Árpád Haidegger, Tamás Galambos, Péter Kuti, József Rudas, Imre J. Antal Bejczy Center for Intelligent Robotics Óbuda University Bécsi út 96/b Budapest1034 Hungary Dept. of Telecommunication and Media Informatics Budapest University of Technology and Economics Magyar Tudósok Krt. 2. Budapest1117 Hungary Institute for Computer Science and Control Hungarian Academy of Sciences Kende u. 13-17 Budapest1111 Hungary Austrian Center for Medical Innovation and Technology Viktor-Kaplan-Strasse 2 Wiener Neustadt2700 Austria

ISBN: (纸本)9781467387408

Achieving reliable force control is one of the main design goals of robotic teleoperation. It is essential to grant safe and stable performance of these systems, regarding HMI control, even under major disturbing conditions such as time delay or model parameter uncertainties. This paper discusses the systematic derivation of polytopic qLPV model from the nonlinear dynamics of typical soft tissues of the human body based on recent experimental results. The derivation is based on the Tensor Product (TP) Model Transformation. The presented method is a crucial step in laying the foundations of adequate force control in telesurgery. The proposed approach could form the basis of LMI-based controller design. © 2016 IEEE.

关键词： Force control

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Measurement of the tt¯ cross section and its ratio to the Z production cross section using pp collisions at s=13.6 TeV with the ATLAS detector

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Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics 2024年 848卷

作者： Aad, G. Abbott, B. Abbott, D.C. Abdinov, O. Abud, A. Abed Abeling, K. Abhayasinghe, D.K. Abidi, S.H. AbouZeid, O.S. Abraham, N.L. Abramowicz, H. Abreu, H. Abulaiti, Y. Acharya, B.S. Achkar, B. Adachi, S. Adam, L. Bourdarios, C. Adam Adamczyk, L. Adamek, L. Adelman, J. Adersberger, M. Adiguzel, A. Adorni, S. Adye, T. Affolder, A.A. Afik, Y. Agapopoulou, C. Agaras, M.N. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J.A. Ahmadov, F. Ahmed, W.S. Ai, X. Aielli, G. Akatsuka, S. Åkesson, T.P.A. Akilli, E. Akimov, A.V. Al Khoury, K. Alberghi, G.L. Albert, J. Verzini, M. J. Alconada Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexandre, D. Alexopoulos, T. Alfonsi, A. Alhroob, M. Ali, B. Alimonti, G. Alison, J. Alkire, S.P. Allaire, C. Allbrooke, B.M.M. Allen, B.W. Allport, P.P. Aloisio, A. Alonso, A. Alonso, F. Alpigiani, C. Alshehri, A.A. Estevez, M. Alvarez Piqueras, D. Álvarez Alviggi, M.G. Coutinho, Y. Amaral Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Dos Santos, S. P. Amor Amoroso, S. Amrouche, C.S. An, F. Anastopoulos, C. Andari, N. Andeen, T. Anders, C.F. Anders, J.K. Andreazza, A. Andrei, V. Anelli, C.R. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Anthony, M.T. Antonelli, M. Antrim, D.J.A. Anulli, F. Aoki, M. Pozo, J. A. Aparisi Bella, L. Aperio Arabidze, G. Araque, J.P. Ferraz, V. Araujo Pereira, R. Araujo Arcangeletti, C. Arce, A.T.H. Arduh, F.A. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A.J. Armitage, L.J. Armstrong, A. Arnaez, O. Arnold, H. Artamonov, A. Artoni, G. Artz, S. Asai, S. Asbah, N. Asimakopoulou, E.M. Asquith, L. Assamagan, K. Astalos, R. Atkin, R.J. Atkinson, M. Atlay, N.B. Atmani, H. Augsten, K. Avolio, G. Avramidou, R. Ayoub, M.K. Azoulay, A.M. Azuelos, G. Baca, M.J. Bachacou, H. Bachas, K. Backes, M. Backman, F. Bagnaia, P. Bahmani, M. Bahrasemani, H. Bailey, A.J. Bailey, V.R. Baines, J.T. Bajic, M. Bakalis, C. Baker, O.K. Bakker, P.J. Gupta, D. Bakshi Balaji, S. Baldin, E.M. Balek, P. Balli, F. Balunas, W.K. Balz, J. Banas, E. Bandyopadhyay CPPM Aix-Marseille Université CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman OK United States Department of Physics University of Massachusetts Amherst MA United States Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan INFN Sezione di Pavia Italy Dipartimento di Fisica Università di Pavia Pavia Italy II. Physikalisches Institut Georg-August-Universität Göttingen Göttingen Germany Department of Physics Royal Holloway University of London Egham United Kingdom Department of Physics University of Toronto Toronto ON Canada Niels Bohr Institute University of Copenhagen Copenhagen Denmark Department of Physics and Astronomy University of Sussex Brighton United Kingdom Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion Israel Institute of Technology Haifa Israel High Energy Physics Division Argonne National Laboratory Argonne IL United States CERN Geneva Switzerland INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King's College London London United Kingdom International Center for Elementary Particle Physics Department of Physics University of Tokyo Tokyo Japan Institut für Physik Universität Mainz Mainz Germany IJCLab Université Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Department of Physics Northern Illinois University DeKalb IL United States Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Department of Physics Bogazici University Istanbul Turkey Istanbul University Dept. of Physics Istanbul Turkey Bruno Kessler Foundation Trento Italy Département de Physique Nucléaire et Corpusculaire Université de Genève Genève Switzerland Particle Physics Department Rutherford Appleton Laborato

The inclusive top-quark-pair production cross section σtt¯ and its ratio to the Z-boson production cross section have been measured in proton–proton collisions at s=13.6 TeV, using 29 fb−1 of data collected in ...

The inclusive top-quark-pair production cross section σ_tt¯ and its ratio to the Z-boson production cross section have been measured in proton–proton collisions at s=13.6 TeV, using 29 fb⁻¹ of data collected in 2022 with the ATLAS experiment at the Large Hadron Collider. Using events with an opposite-charge electron-muon pair and b-tagged jets, and assuming Standard Model decays, the top-quark-pair production cross section is measured to be σ_tt¯=850±3(stat.)±18(syst.)±20(lumi.) pb. The ratio of the tt¯ and the Z-boson production cross sections is also measured, where the Z-boson contribution is determined for inclusive e⁺e⁻ and μ⁺μ⁻ events in a fiducial phase space. The relative uncertainty on the ratio is reduced compared to the tt¯ cross section, thanks to the cancellation of several systematic uncertainties. The result for the ratio, R_tt¯/Z=1.145±0.003(stat.)±0.021(syst.)±0.002(lumi.) is consistent with the Standard Model prediction using the PDF4LHC21 PDF set. © 2023 The Author(s)

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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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Using personalized finger gestures for navigating virtual characters 12

Using personalized finger gestures for navigating virtual ch...

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12th Workshop on Virtual Reality Interactions and Physical Simulations, VRIPHYS 2015

作者： Ouzounis, Christos Mousas, Christos Anagnostopoulos, Christos-Nikolaos Newbury, Paul Dept. of Media Production Ostwestfalen-Lippe University of Applied Sciences Germany Dept. of Computer Science Dartmouth College United States Dept. of Cultural Technology and Communication University of the Aegean Greece Dept. of Informatics University of Sussex United Kingdom

ISBN: (纸本)9783905674989

In this paper, we present and evaluate a method to navigate a character into a virtual environment based on personalized finger gestures. The methodology that has been developed allows a user to generate his/her own finger gestures that are associated with the actions of a character. Specifically, in a pre-processing stage, the user wishes to perform specific gestures for specific actions of a character creating a dataset of gestures. During the runtime of the application, Dynamic Time Warping (DTW) and template matching methods were used to compute the similarity of the input and examples of gestures. The system recognizes the input gesture of a user and generates the motion required to navigate a character into the virtual environment. To demonstrate the efficiency and possible use of such a character navigation method, a number of users participated in an evaluation process. The results of the evaluation process indicate the possible use of personalized finger gestures for navigating a character into a virtual environment. © The Eurographics Association 2015.

关键词： Virtual reality

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Interim design report

arXiv

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arXiv 2020年

作者： Abramowicz, H. Jovin, T. Agatonovic Alonso, O. Amjad, M.S. An, F. Andricek, L. Anduze, M. Anguiano, J. Antonov, E. Aoki, Y. Arteche, F. Attié, D. Büscher, V. Bach, O. Balagura, V. Baudot, J. Begunov, V. Behera, S. Behnke, T. Bellerive, A. Belver, D. Benhammou, Y. Berggren, M. Berriaud, C. Bertolone, G. Besançon, M. Besson, A. Beyer, J. Bezshyyko, O. Bhattacharya, D.S. Bhattacharya, P. Blazey, G. Bocharnikov, V. Boronat, M. Borysov, O. Bosley, R. Boudry, V. Boumediene, D. Bourgeois, C. Jelisavcic, I. Bozovic Breton, D. Brianne, E. Brient, J.-C. Briggl, K. Buesser, K. Callier, S. Alamillo, E. Calvo Carrillo, C. Catalán, A. Chadeeva, M. Chau, P. Chera, M. Chetverushkin, B. Chrzaszcz, M. Claus, G. Colas, P. Colledani, C. Combaret, C. Cornat, R. Corriveau, F. Danilov, M. de la Taille, C. Deguchi, Y. Desch, K. Dieguez, A. Diener, R. Dixit, M. Dong, M. Dorokhov, A. Dozière, G. Drutskoy, A. Dulucq, F. Dyshkant, A. Edy, E. Eigen, G. Einhaus, U. El Bitar, Z. Elkhalii, A. Emberger, L. Esperante, D. Eté, R. Fang, Y. Fedorchuk, O. Firlej, M. Fiutowski, T. Fleck, I. Fourches, N. Fouz, M.C. Francis, K. Fujii, K. Fujii, K. Fullana, E. Fusayasu, T. Fuster, J. Göttlicher, P. Gadow, K. Gaede, F. Gallas, A. Ganjour, S. García, I. Cabrera, H. García Garillot, G. Garutti, E. Gastaldi, F. Ghislain, P. Goffe, M. Gomis, P. Gong, W. Gonnin, A. Goswami, D. Goto, K. Graf, C. Gregor, I.-M. Grenier, G. Guillaumat, R. Habermehl, M. Hagge, L. Hartbrich, O. Hartjes, F. Henschel, H. Heuchel, D. Hidalgo, S. Himmi, A. Hu, T. Hu-Guo, C. Ianigro, J.-C.T. Idzik, M. Irles, A. Ishihara, H. Ishikawa, A. Jönsson, L. Jaaskelainen, K. Jeans, D. Jeglot, J. Kacarevic, G. Kachel, M. Kajiwara, S. Kalinowski, J. Kaminski, J. Kamiya, Y. Karl, R. Kato, Y. Kato, Y. Kawada, S.-I. Kawagoe, K. Khan, S.A. Kleinwort, C. Kluit, P. Kobayashi, M. Koffmane, C. Komamiya, S. Korpachev, S. Kotera, K. Krämer, U. Krüger, K. Kunath, J. Kurata, M. Kurca, T. AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland University of Bergen Institute for Physics and Technology Bergen Norway University of Birmingham School of Physics and Astronomy Edgbaston United Kingdom Budker Institute of Nuclear Physics Siberian Branch Russian Academy of Sciences Novosibirsk State University Novosibirsk Russia University of Bonn Physikalisches Institut Bonn Germany Bergische Universität Wuppertal Fakultät 4/ Physik Wuppertal Germany Carleton University Ottawa Canada Irfu CEA Université Paris Saclay Gif sur Yvette France CIEMAT - Centro de Investigaciones Energéticas Medioambientales y Tecnológicas Madrid Spain Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France CSIC University of Cantabria Instituto de Física de Cantabria Santander Spain Deutsches Elektronen-Synchrotron Hamburg Germany Dhofar University Salalah Oman University of Hamburg Faculty of Mathematics Informatics and Natural Sciences Hamburg Germany Institute of High Energy Physics Austrian Academy of Science Wien Austria Halbeiterlabor der Max-Planck-Gesellschaft München Germany Keldysh Institute of Applied Mathematics Russian Academy of Sciences Moscow Russia University of Tokyo Tokyo Japan CSIC-University of Valencia Instituto de Física Corpuscular Paterna Spain Kraków Poland Institute of High Energy Physics Chinese Academy of Science Beijing China Indian Institute of Technology GuwahatiAssam India Barcelona Spain Institute of Physics of the Czech Academy of Sciences Prague 8 Czech Republic Institut Pluridisciplinaire Hubert Curien Strasbourg France Institut de Physique des deux infinis de Lyon Villeurbanne France Instituto Tecnológico de Aragòn Zaragoza Spain Iwate University Morioka Japan University of Kansas Department of Physics and Astronomy LawrenceKS United States High Energy Accelerator Research Organisation KEK TsukubaIbaraki Japan Kindai University Department of Physics Higashi Os

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Nonlinear soft tissue mechanics based on polytopic Tensor Product modeling

Nonlinear soft tissue mechanics based on polytopic Tensor Pr...

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International Symposium on Applied Machine Intelligence and informatics (SAMI)

作者： Árpád Takács Tamás Haidegger Péter Galambos József Kuti Imre J. Rudas Antal Bejczy Center for Intelligent Robotics Óbuda University Budapest Hungary Austrian Center for Medical Innovation and Technology Wiener Neustadt Austria Institute for Computer Science and Control Hungarian Academy of Sciences Budapest Hungary Dept. of Telecommunication & Media Informatics Budapest University of Technology and Economics Budapest Hungary

关键词： Optimized production technology Mathematical model Biological systems Biological system modeling

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Analysis and experimental evaluation of time-dependent distance oracles 17

Analysis and experimental evaluation of time-dependent dista...

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17th Workshop on Algorithm Engineering and Experiments, ALENEX 2015

作者： Kontogiannis, Spyros Michalopoulos, George Papastavrou, Georgia Paraskevopoulos, Andreas Wagner, Dorothea Zaroliagis, Christos Computer Science and Engineering Dept. University of Ioannina Ioannina45110 Greece Computer Engineering and Informatics Dept. University of Patras Rio26500 Greece Computer Technology Institute and Press Diophantus Rio26504 Greece Am Fasanengarten 5 Karlsruhe76131 Germany

ISBN: (纸本)9781611973754

Urban road networks are represented as directed graphs, accompanied by a metric which assigns cost functions (rather than scalars) to the arcs, e.g. representing time-dependent arc-traversal-times. In this work, we present oracles for providing time-dependent min-cost route plans, and conduct their experimental evaluation on a real-world data set (city of Berlin). Our oracles are based on precomputing all landmark-to-vertex shortest travel-time functions, for properly selected landmark sets. The core of this preprocessing phase is based on a novel, quite efficient and simple oneto-all approximation method for creating approximations of shortest travel-time functions. We then propose three query algorithms, including a PTAS, to efficiently provide mincost route plan responses to arbitrary queries. Apart from the purely algorithmic challenges, we deal also with several implementation details concerning the digestion of raw traffic data, and we provide heuristic improvements of both the preprocessing phase and the query algorithms. We conduct an extensive, comparative experimental study with all query algorithms and six landmark sets. Our results are quite encouraging, achieving remarkable speedups (at least by two orders of magnitude) and quite small approximation guarantees, over the time-dependent variant of Dijkstra's algorithm. Copyright © 2015. by the Society for Industrial and Applied Mathematics.

关键词： Directed graphs

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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