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arXiv 2022年

作者： Kronfeld, Andreas S. Bhattacharya, Tanmoy Blum, Thomas Christ, Norman H. DeTar, Carleton Detmold, William Edwards, Robert Hasenfratz, Anna Lin, Huey-Wen Mukherjee, Swagato Orginos, Konstantinos Brower, Richard Cirigliano, Vincenzo Davoudi, Zohreh Jóo, Bálint Jung, Chulwoo Lehner, Christoph Meinel, Stefan Neil, Ethan T. Petreczky, Peter Richards, David G. Bazavov, Alexei Catterall, Simon Dudek, Jozef J. El-Khadra, Aida X. Engelhardt, Michael Fleming, George T. Giedt, Joel Gupta, Rajan Hansen, Maxwell T. Izubuchi, Taku Karsch, Frithjof Laiho, Jack Liu, Keh-Fei Meyer, Aaron S. Rinaldi, Enrico Savage, Martin Schaich, David Shanahan, Phiala E. Sharpe, Stephen R. Sufian, Raza Syritsyn, Sergey Van de Water, Ruth S. Wagman, Michael L. Weinberg, Evan Witzel, Oliver Aubin, Christopher Boyle, Peter Chandrasekharan, Shailesh Cloët, Ian C. Constantinou, Martha Cushman, Kimmy DeGrand, Thomas Fodor, Zoltan Foreman, Sam Gottlieb, Steven Hoying, Daniel Jang, Yong-Chull Jay, William I. Jin, Xiao-Yong Kelly, Christopher Kuti, Julius Lamm, Henry Lin, Meifeng Lin, Yin Lytle, Andrew T. Mackenzie, Paul Mandula, Jeffrey Meurice, Yannick Monahan, Christopher Morningstar, Colin Osborn, James C. Park, Sungwoo Simone, James N. Strelchenko, Alexei Tomii, Masaaki Vaquero, Alejandro Vranas, Pavlos Wang, Bigeng Wilcox, Walter Yoon, Boram Zhao, Yong Theory Division Fermi National Accelerator Laboratory BataviaIL60510 United States Group T-2 Los Alamos National Laboratory Los AlamosNM87545 United States Department of Physics University of Connecticut StorrsCT06269 United States RIKEN BNL Research Center Brookhaven National Laboratory UptonNY11973 United States Department of Physics Columbia University New YorkNY10027 United States Department of Physics and Astronomy University of Utah Salt Lake CityUT84112 United States Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA02139 United States The NSF Institute for Artificial Intelligence and Fundamental Interactions Theory Center Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States Department of Physics University of Colorado BoulderCO80309 United States Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Physics Department Brookhaven National Laboratory UptonNY11973 United States Department of Physics College of William & Mary WilliamsburgVA23187 United States Department of Physics Center for Computational Science Boston University BostonMA02215 United States Institute of Nuclear Theory University of Washington SeattleWA98195 United States Department of Physics Maryland Center for Fundamental Physics University of Maryland College ParkMD20742 United States Oak Ridge Leadership Computing Facility Oak Ridge National Laboratory Oak RidgeTN37831 United States Fakultät für Physik Universität Regensburg RegensburgD-93040 Germany Department of Physics University of Arizona TucsonAZ85721 United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Department of Physics Syracuse University SyracuseNY13244 United States Department of Physics University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Physics New Mexic

Lattice field theory provides a mathematically rigorous definition of quantum field theory, including gauge theories. The rigor provides a platform for computation of strongly-coupled gauge theories, not only quantum chromodynamics (QCD) at long-distances but also strongly-coupled sectors that might lie beyond the Standard Model (BSM) of particle physics. Indeed, the interpretation of many experiments in particle physics, nuclear physics, and astrophysics relies, often crucially, on results from lattice QCD or BSM. In quark-flavor physics, lattice QCD is essential for grounding theoretical predictions. Together with experimental measurements, lattice QCD is used to determine many of the fundamental parameters of the Standard Model's flavor sector: five out of six quark masses and the three mixing angles and CP-violating phase of the Cabibbo-Kobayashi-Maskawa (CKM) matrix. Further Standard-Model predictions requiring lattice QCD are for processes that could reveal signatures of new physics. This area of lattice QCD has become a precision science, as has the determination of the strong coupling, αs, which is key in many areas, for example for understanding Higgs-boson decay to gluons. Lattice-QCD calculations of the hadronic contributions to the anomalous magnetic moment of the muon are another area for which precision is both mandatory and achievable. While these contributions can be estimated via a combination of certain measurements and general theoretical concepts, a direct ab initio calculation from QCD is desirable to confirm robustly the tantalizingly large discrepancy between experiment and the Standard Model. Other indirect searches for new physics are complementary to quark-flavor physics and the muon anomalous magnetic moment. Lattice QCD is an essential tool for understanding newly discovered hadrons with quark content different from the usual baryons and mesons. Lattice-QCD calculations are needed to interpret bounds on electric dipole moments, proton dec

关键词： Calculations

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Original approach for reduction of high dimensionality in unsupervised learning

Original approach for reduction of high dimensionality in un...

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International Conference on Logistics and Operations Management (GOL)

作者： Harchli Fidae Es-safi Abdelatif Ettaouil Mohamed Scientific Computing and Computer Sciences Engineering Sciences Modeling and Scientific Computing Laboratory Fez Morocco

ISBN: (纸本)9781467385725

With the appearance of web 2.0, the huge amount of web services and the increasing number of information, articles, and products placed on line, the quantity of data is exploding throughout the world. In addition, this huge amount of data is qualified as high-dimensional data. Analyzing large datasets is an urgent problem of great practical importance. Precisely, the major concerns are directed to the reduction of high dimensionality of the feature space owing to computational complexity and accuracy consideration. Consequently, variations of methods have been originally introduced in the literature to select an optimal number of features. In this paper, we propose an original method based on a new version of k-means algorithm to reduce the dimension of large data sets thanks to a new proposing process of features' clustering.

关键词： Clustering algorithms Feature extraction Algorithm design and analysis Classification algorithms scientific computing Clustering methods Data models

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Network slicing for service-oriented networks under resource constraints

arXiv

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arXiv 2017年

作者： Zhang, Nan Liu, Ya-Feng Farmanbar, Hamid Chang, Tsung-Hui Hong, Mingyi Luo, Zhi-Quan School of Mathematical Sciences Peking University State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Huawei Canada Research Center Ottawa Canada Department of Electrical and Computer Engineering University of Minnesota United States Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China

To support multiple on-demand services over fixed communication networks, network operators must allow flexible customization and fast provision of their network resources. One effective approach to this end is network virtualization, whereby each service is mapped to a virtual subnetwork providing dedicated on-demand support to network users. In practice, each service consists of a prespecified sequence of functions, called a service function chain (SFC), while each service function in a SFC can only be provided by some given network nodes. Thus, to support a given service, we must select network function nodes according to the SFC and determine the routing strategy through the function nodes in a specified order. A crucial network slicing problem that needs to be addressed is how to optimally localize the service functions in a physical network as specified by the SFCs, subject to link and node capacity constraints. In this paper, we formulate the network slicing problem as a mixed binary linear program and establish its strong NP-hardness. Furthermore, we propose efficient penalty successive upper bound minimization (PSUM) and PSUM-R(ounding) algorithms, and two heuristic algorithms to solve the problem. Simulation results are shown to demonstrate the effectiveness of the proposed algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Network function virtualization

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Flexible multiple base station association and activation for downlink heterogeneous networks

arXiv

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arXiv 2017年

作者： Shen, Kaiming Liu, Ya-Feng Ding, David Yiwei Yu, Wei Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

This letter shows that the flexible association of possibly multiple base stations (BSs) to each user over multiple frequency bands, along with the joint optimization of BS transmit power that encourages the turning-off of the BSs at off-peak time, can significantly improve the performance of a downlink heterogeneous wireless cellular network. We propose a gradient projection algorithm for optimizing BS association and an iteratively reweighting scheme together with a novel proximal gradient method for optimizing power in order to find the optimal tradeoff between network utility and power consumption. Simulation results reveal significant performance improvement as compared to the conventional single-BS association. Copyright © 2017, The Authors. All rights reserved.

关键词： Chemical activation

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An efficient geosciences workflow on multi-core processors and GPUs:a case study for aerosol optical depth retrieval from MODIS satellite data

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International Journal of Digital Earth 2016年第8期9卷 748-765页

作者： Jia Liu Dustin Feld Yong Xue Jochen Garcke Thomas Soddemann Peiyuan Pan Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina Faculty of Life Sciences and Computing London Metropolitan UniversityLondonUK Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt AugustinGermany Institute for Numerical Simulation University of BonnBonnGermany University of Chinese Academy of Sciences BeijingChina Department of Computer Science University of CologneCologneGermany

Quantitative remote sensing retrieval algorithms help understanding the dynamic aspects of Digital ***,the Big Data and complex models in Digital Earth pose grand challenges for computation *** this article,taking the aerosol optical depth(AOD)retrieval as a study case,we exploit parallel computing methods for high efficient geophysical parameter *** present an efficient geocomputation workflow for the AOD calculation from the Moderate Resolution Imaging Spectroradiometer(MODIS)satellite *** to their individual potential for parallelization,several procedures were adapted and implemented for a successful parallel execution on multicore processors and Graphics Processing Units(GPUs).The benchmarks in this paper validate the high parallel performance of the retrieval workflow with speedups of up to 5.x on a multi-core processor with 8 threads and 43.x on a *** specifically address the time-consuming model retrieval part,hybrid parallel patterns which combine the multicore processor’s and the GPU’s compute power were implemented with static and dynamic workload distributions and evaluated on two systems with different CPU–GPU *** is shown that only the dynamic hybrid implementation leads to a greatly enhanced overall exploitation of the heterogeneous hardware environment in varying circumstances.

关键词： Digital earth highperformance computing GPU multi-core hybrid parallel pattern aerosol optical depth retrieval workflow

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Comparing recent PTA results on the nanohertz stochastic gravitational wave background

arXiv

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arXiv 2023年

作者： Agazie, G. Antoniadis, J. Anumarlapudi, A. Archibald, A.M. Arumugam, P. Arumugam, S. Arzoumanian, Z. Askew, J. Babak, S. Bagchi, M. Bailes, M. Bak Nielsen, A.-S. Baker, P.T. Bassa, C.G. Bathula, A. Bécsy, B. Berthereau, A. Bhat, N.D.R. Blecha, L. Bonetti, M. Bortolas, E. Brazier, A. Brook, P.R. Burgay, M. Burke-Spolaor, S. Burnette, R. Caballero, R.N. Cameron, A. Case, R. Chalumeau, A. Champion, D.J. Chanlaridis, S. Charisi, M. Chatterjee, S. Chatziioannou, K. Cheeseboro, B.D. Chen, S. Chen, Z.-C. Cognard, I. Cohen, T. Coles, W.A. Cordes, J.M. Cornish, N.J. Crawford, F. Cromartie, H.T. Crowter, K. Curylo, M. Cutler, C.J. Dai, S. Dandapat, S. Deb, D. DeCesar, M.E. DeGan, D. Demorest, P.B. Deng, H. Desai, S. Desvignes, G. Dey, L. Dhanda-Batra, N. Di Marco, V. Dolch, T. Drachler, B. Dwivedi, C. Ellis, J.A. Falxa, M. Feng, Y. Ferdman, R.D. Ferrara, E.C. Fiore, W. Fonseca, E. Franchini, A. Freedman, G.E. Gair, J.R. Garver-Daniels, N. Gentile, P.A. Gersbach, K.A. Glaser, J. Good, D.C. Goncharov, B. Gopakumar, A. Graikou, E. Grießmeier, J.-M. Guillemot, L. Gültekin, K. Guo, Y.J. Gupta, Y. Grunthal, K. Hazboun, J.S. Hisano, S. Hobbs, G.B. Hourihane, S. Hu, H. Iraci, F. Islo, K. Izquierdo-Villalba, D. Jang, J. Jawor, J. Janssen, G.H. Jennings, R.J. Jessner, A. Johnson, A.D. Jones, M.L. Joshi, B.C. Kaiser, A.R. Kaplan, D.L. Kapur, A. Kareem, F. Karuppusamy, R. Keane, E.F. Keith, M.J. Kelley, L.Z. Kerr, M. Key, J.S. Kharbanda, D. Kikunaga, T. Klein, T.C. Kolhe, N. Kramer, M. Krishnakumar, M.A. Kulkarni, A. Laal, N. Lackeos, K. Lam, M.T. Lamb, W.G. Larsen, B.B. Lazio, T.J.W. Lee, K.J. Levin, Y. Lewandowska, N. Littenberg, T.B. Liu, K. Liu, T. Liu, Y. Lommen, A. Lorimer, D.R. Lower, M.E. Luo, J. Luo, R. Lynch, R.S. Lyne, A.G. Ma, C.-P. Maan, Y. Madison, D.R. Main, R.A. Manchester, R.N. Mandow, R. Mattson, M.A. McEwen, A. McKee, J.W. McLaughlin, M.A. McMann, N. Meyers, B.W. Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 MilwaukeeWI53201 United States Institute of Astrophysics FORTH N. Plastira 100 Heraklion70013 Greece Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 Bonn53121 Germany Newcastle University NE1 7RU United Kingdom Department of Physics Indian Institute of Technology Roorkee Roorkee247667 India Department of Electrical Engineering IIT Hyderabad Telangana Kandi502284 India X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 GreenbeltMD20771 United States Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia Australia Université Paris Cité CNRS Astroparticule et Cosmologie Paris75013 France The Institute of Mathematical Sciences C.I.T. Campus Taramani Chennai600113 India Homi Bhabha National Institute Training School Complex Anushakti Nagar Mumbai400094 India Fakultät für Physik Universität Bielefeld Postfach 100131 Bielefeld33501 Germany Department of Physics and Astronomy Widener University One University Place ChesterPA19013 United States ASTRON Netherlands Institute for Radio Astronomy Oude Hoogeveensedijk 4 Dwingeloo7991 PD Netherlands Department of Physical Sciences Indian Institute of Science Education and Research Punjab Mohali140306 India Department of Physics Oregon State University CorvallisOR97331 United States Laboratoire de Physique et Chimie de l'Environnement et de l'Espace Université d'Orléans CNRS Orléans45071 Cedex 02 France Observatoire Radioastronomique de Nançay Observatoire de Paris Université PSL Université d'Orléans CNRS Nançay18330 France International Centre for Radio Astronomy Research Curtin University BentleyWA6102 Australia Physics Department University of Florida GainesvilleFL32611 United States Dipartimento di Fisica "G. Occhialini" Universitá degli Studi di Milano-Bicocca

The Australian, Chinese, European, Indian, and North American pulsar timing array (PTA) collaborations recently reported, at varying levels, evidence for the presence of a nanohertz gravitational wave background (GWB). Given that each PTA made different choices in modeling their data, we perform a comparison of the GWB and individual pulsar noise parameters across the results reported from the PTAs that constitute the International Pulsar Timing Array (IPTA). We show that despite making different modeling choices, there is no significant difference in the GWB parameters that are measured by the different PTAs, agreeing within 1σ. The pulsar noise parameters are also consistent between different PTAs for the majority of the pulsars included in these analyses. We bridge the differences in modeling choices by adopting a standardized noise model for all pulsars and PTAs, finding that under this model there is a reduction in the tension in the pulsar noise parameters. As part of this reanalysis, we"extended" each PTA's data set by adding extra pulsars that were not timed by that PTA. Under these extensions, we find better constraints on the GWB amplitude and a higher signal-to-noise ratio for the Hellings and Downs correlations. These extensions serve as a prelude to the benefits offered by a full combination of data across all pulsars in the IPTA, i.e., the IPTA's Data Release 3, which will involve not just adding in additional pulsars, but also including data from all three PTAs where any given pulsar is timed by more than as single PTA. Copyright © 2023, The Authors. All rights reserved.

关键词： Pulsars

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A method for contactless palm ROI extraction 11

A method for contactless palm ROI extraction

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11th International Conference on computer Engineering and Systems, ICCES 2016

作者： ElSayed, Ahmed S. Ebeid, Hala M. Roushdy, Mohamed Fayed, Zaki T. Computer Science Dept. Faculty of Computers and Information Sciences Ain Shams University Cairo Egypt Scientific Computing Department Faculty of Computers and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9781509032679

Palmprint can be extracted from a hand using a low-cost webcam in a contactless manner. Using a webcam makes the enrollment process fast and convenient for users. Being contactless solve the hygiene issue and avoid copying the latent prints from sensor's surface. However, a number of challenges arise in such environment;geometric transformations, the existence of finger rings, hand accessories, and other false objects. This paper proposes a palm ROI extraction method that is robust to these challenges. The method is based on blob analysis, morphological and geometrical operations without a need to pre-Train or parameter adjustment. It's tested on three available hand databases that cover these challenges;namely, Sfax, IITD and PolyU 3D/2D. The palm ROI is considered to be wrongly extracted if it contains part of the background. The method achieves an extraction error of 0%, 0.27% and 0.26% for the three DBs, respectively. Applying a massive rotation and scaling tests leads to a minor increase in the extraction errors by 0.24%, 0.35% and 0.84% for the three DBs, respectively. © 2016 IEEE.

关键词： Extraction

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New modeling of multilayer perceptron architecture optimization with regularization: An application to pattern classification

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2017年第3期44卷 261-269页

作者： Ramchoun, H. Idrissi, M.A. Janati Ghanou, Y. Ettaouil, M. Laboratory of modeling and scientific computing Faculty of Sciences and Technology of Fez Morocco Department of Computer Engineering High School of Technology Moulay Ismaïl University B. P. 3103 Toulal Meknes50000 Morocco Faculty of Sciences and Technology Sidi Mohamed Ben Abdellah University B.P. 2202 Imouzzer route Fez Morocco

The Multilayer Perceptron (MLP) is the most useful artificial neural network to estimate the functional structure in the non-linear systems, but the determination of its architecture and weights is a fundamental problem due to their direct impact on the network convergence and performance. This paper presents an extension of our latest work where we have treated the problem of optimizing the number of connections weights and hidden layers in MLP. We introduce an objective function regularized by the sum of connections used by the network and weight decay, to solve the resulting model, we use a genetic algorithm and we train the network with back-propagation method. To test our methodology we use a data classification: wine, iris, seeds and medical data (Cancer, thyroid). We compare the obtained results with those of similar works existing in the literature. The numerical results illustrate the advantages of our approach as a new method of architecture optimization and training.

关键词： Genetic algorithms

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Ten simple rules for measuring the impact of workshops

arXiv

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arXiv 2018年

作者： Sufi, Shoaib Duckles, Beth Simera, Iveta Nurmikko-Fuller, Terhi Bellis, Louisa Miah, Wadud Wilde, Adriana Nenadic, Aleksandra Silva, Raniere de Beyer, Jennifer A. Struthers, Caroline Emsley, Iain Philippe, Olivier Balzano, Melissa Coelho, Sara Ford, Heather Jones, Catherine Higgins, Vanessa School of Computer Science University of Manchester Manchester United Kingdom Portland State University PortlandOR United States UK EQUATOR Centre Centre for Statistics in Medicine NDORMS University of Oxford Oxford United Kingdom College of Arts and Social Sciences Australian National University Canberra Australia Cancer Research UK Cambridge Centre University of Cambridge Cambridge United Kingdom Numerical Algorithms Group Oxford United Kingdom School of Computer Science University of St Andrews St Andrews United Kingdom Oxford e-Research Centre University of Oxford Oxford United Kingdom Electronics and Computer Science University of Southampton Southampton United Kingdom ELIXIR Hub Wellcome Genome Campus Cambridge United Kingdom EGI Foundation Amsterdam Netherlands School of Media and Communication University of Leeds Leeds United Kingdom Scientific Computing Department Science and Technology Facilities Council Rutherford Appleton Laboratory Didcot United Kingdom School of Social Sciences University of Manchester Manchester United Kingdom

Workshops are used to explore a specific topic, transfer knowledge, solve identified problems or create something new. In funded research projects and other research endeavours, workshops are the mechanism to gather the wider project, community or interested people together around a particular topic. However, natural questions arise: how do we measure the impact of these workshops? Do we know whether they are meeting the goals and objectives we set for them? What indicators should we use? In response to these questions, this paper will outline rules that will improve the measurement of the impact of workshops. Copyright © 2018, The Authors. All rights reserved.

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A real time dynamic model for optimizing hydrocarbons' production based on deep recurrent network 11

A real time dynamic model for optimizing hydrocarbons' produ...

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11th International Conference on computer Engineering and Systems, ICCES 2016

作者： Elons, A.S. Elgendy, M.Y. Magdy, Dalia Ahmed Scientific Computing Department Ain-Shams University Cairo Egypt Research and Development Department Apex Company Cairo Egypt Computer and Information System Department Sadat Academy for Management Sciences Cairo Egypt

ISBN: (纸本)9781509032679

Contrary to common sense that oil and gas products do exist on a constant vertical depth in a specific region, they exist on varying depths in the same region. This fact forces the drilling directions to change deviations to keep track in the production zone 'Pay-Zone'. Drilling a new borehole can be directed by the knowledge of the surrounding already drilled boreholes. Geo-steering process is a real-Time determining for drilling directions based on drilling sensors readings and surrounding boreholes data to maximize oil and gas production inside a new borehole. A previous work was done by us that proposes an initial pre-drilling plan based on sensors measurements of the drilled surrounding boreholes. This work extends it by adding a dynamic adaptation feature for this plan based on the current borehole sensors measurements. This adaptation considers the current borehole previous measurements to re-direct the drilling path to keep track the Pay-Zone. This paper presents Long Short-Term Memory (LSTM) Recurrent Neural Network for modeling the previous sensors measurements sequence to adapt the drilling directions. The dataset consists of 3 groups of boreholes in 3 different Areas, each group consists of 5 boreholes, 4 of them considered as surrounding boreholes for the 5th. Two groups is exploited as training set while the 3rd is used for testing issue. The results shows that LSTM Recurrent network outperforms both the conventional Recurrent Network and Deep Forward Network. The prediction results reached 98% accuracy for the testing data. © 2016 IEEE.

关键词： Long short-term memory

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