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23rd International Congress on Acoustics: Integrating 4th EAA Euroregio, ICA 2019

作者： Pind, Finnur Jeong, Cheol-Ho Hesthaven, Jan S. Engsig-Karup, Allan P. Strømann-Andersen, Jakob Henning Larsen Denmark Acoustic Technology Dept. of Electrical Engineering Technical University of Denmark Denmark Chair of Computational Mathematics and Simulation Science École polytechnique fédérale de Lausanne Switzerland Scientific Computing Dept. of Applied Mathematics and Computer Science Technical University of Denmark Denmark

ISBN: (纸本)9783939296157

Accurate modeling of boundary conditions is an important aspect in room acoustic simulations. It has been shown that the acoustics of rooms is not only dependent on the frequency characteristic of the complex boundary impedance, but also on the angle dependent properties of the impedance ("extended reaction"). This paper presents a computationally efficient method for modeling local-reaction (LR) and extended-reaction (ER) boundary conditions in high-order, nodal, time-domain finite element methods, such as the spectral element method (SEM) or the discontinuous Galerkin finite element method (DGFEM). The frequency and angle dependent boundary impedance is mapped to a multipole model and formulated in differential form. The solution of the boundary differential equations comes with minimal computational cost. In the ER model, wave splitting is applied at the boundary to separate the incident and reflected parts of the sound field. The directional properties of the incident sound field are determined from the incident particle velocity and the boundary conditions are adjusted continuously according to the wave angle of incidence. The accuracy of the boundary condition model is assessed by comparing simulations against measurements, where a significantly improved match between simulations and measurements is found when the ER model is used. © 2019 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： *fpin@ Extended-reaction High-order numerical methods Sound absorption Wave-based room acoustic simulations

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Investigation on agricultural land selection using hybrid fuzzy logic system ∗

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Scalable computing 2020年第4期21卷 569-582页

作者： Sengan, Sudhakar Vijayakumar, V. Krishnamoorthy, Sujatha Gunasekaran, S. Kumar, C. Sathiya Palani, Saravanan Subramaniyaswamy, V. Department of CSE Sree Sakthi Engineering College Coimbatore641 104 India School of Computer Science and Engineering University of New South Wales Sydney Australia Department of Computer Science Wenzhou Kean University Wenzhou Zhejing China Dept. of Computer Application King College of Arts & Science Tiruchengode637 215 India Department of Computational Intelligence Vellore Institute of Technology Vellore India School of Computing SASTRA Deemed University Thanjavur India

For maintaining the horticultural generation, Land Selection Investigation (LSI) is essential. Though incorporates estimation of the criteria assortment from the soil, territory to financial, market, and foundation, and these components are considerably enigmatically characterized and described by their inherent ambiguity. Multi-criteria basic leadership systems like positioning, rating, and so on are utilized for reasonableness examination. Master learning and judgment by leaders at different levels is integrated into this process. In the field of farming sciences, the Fuzzy Logic (FL) strategy has been effectively used to take care of numerous issues. Fuzzy with AHP is a Hybrid Fuzzy Logic (HFL) methodology. The policies Analytic Hierarchy Process (AHP), Fuzzy Numbers, Fuzzy Degree Investigation, Alpha Cut, and Lambda capacity are associated with it. As expressed, the procedure of necessary leadership includes a scope of criteria and a considerable measure of master learning and decisions. The components result from impacts extraordinarily. The capacity of three methods to demonstrate the affectability of the necessary leadership procedure is researched. Alpha cut and lambda esteem give and encourage considerable affectability investigation. All techniques are actualized to examine the reasonableness of the crop in the Indian nation. Test results when performed on Various Datasets, demonstrate that the proposed procedure removes more highlights just as gives more exactness when contrasted with existing techniques. © 2020 SCPE.

关键词： Fuzzy logic

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On complexity and convergence of high-order coordinate descent algorithms for smooth nonconvex box-constrained minimization

arXiv

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arXiv 2020年

作者： Amaral, V.S. Andreani, R. Birgin, Ernesto G. Marcondes, D.S. Martínez, J.M. Dept. of Applied Mathematics Institute of Mathematics Statistics and Scientific Computing University of Campinas SP Campinas13083-859 Brazil Dept. of Computer Science Institute of Mathematics and Statistics University of São Paulo Rua do Matão 1010 Cidade Universitária SP São Paulo05508-090 Brazil Dept. of Applied Mathematics Institute of Mathematics and Statistics University of São Paulo Rua do Matão 1010 Cidade Universitária SP São Paulo05508-090 Brazil

Coordinate descent methods have considerable impact in global optimization because global (or, at least, almost global) minimization is affordable for low-dimensional problems. Coordinate descent methods with high-order regularized models for smooth nonconvex box-constrained minimization are introduced in this work. High-order stationarity asymptotic convergence and first-order stationarity worst-case evaluation complexity bounds are established. The computer work that is necessary for obtaining first-order Ε-stationarity with respect to the variables of each coordinate-descent block is O(Ε−(p+1)/p) whereas the computer work for getting first-order Ε-stationarity with respect to all the variables simultaneously is O(Ε−(p+1)). Numerical examples involving multidimensional scaling problems are presented. The numerical performance of the methods is enhanced by means of coordinate-descent strategies for choosing initial *** Codes 90C30, 65K05, 49M37, 90C60, 68Q25 Copyright © 2020, The Authors. All rights reserved.

关键词： Constrained optimization

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Complexity and performance of an Augmented Lagrangian algorithm

arXiv

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arXiv 2019年

作者： Birgin, E.G. Martínez, J.M. Dept. of Computer Science Institute of Mathematics and Statistics University of São Paulo Rua do Matão 1010 Cidade Universitária São Paulo SP05508-090 Brazil Dept. of Applied Mathematics Institute of Mathematics Statistics and Scientific Computing State University of Campinas Campinas SP13083-859 Brazil

Algencan is a well established safeguarded Augmented Lagrangian algorithm introduced in [R. Andreani, E. G. Birgin, J. M. Martínez and M. L. Schuverdt, On Augmented Lagrangian methods with general lower-level constraints, SIAM Journal on Optimization 18, pp. 1286-1309, 2008]. Complexity results that report its worst-case behavior in terms of iterations and evaluations of functions and derivatives that are necessary to obtain suitable stopping criteria are presented in this work. In addition, the computational performance of a new version of the method is presented, which shows that the updated software is a useful tool for solving large-scale constrained optimization problems. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear programming

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Flow-based local graph clustering with better seed set inclusion

arXiv

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arXiv 2018年

作者： Veldt, Nate Klymko, Christine Gleich, David F. Mathematics Department Purdue University Center for Applied Scientific Computing Lawrence Livermore National Laboratory Dept. of Computer Science Purdue University

Flow-based methods for local graph clustering have received significant recent attention for their theoretical cut improvement and runtime guarantees. In this work we present two improvements for using flow-based methods in real-world semi-supervised clustering problems. Our first contribution is a generalized objective function that allows practitioners to place strict and soft penalties on excluding specific seed nodes from the output set. This feature allows us to avoid the tendency, often exhibited by previous flow-based methods, to contract a large seed set into a small set of nodes that does not contain all or even most of the seed nodes. Our second contribution is a fast algorithm for minimizing our generalized objective function, based on a variant of the push-relabel algorithm for computing preflows. We make our approach very fast in practice by implementing a global relabeling heuristic and employing a warm-start procedure to quickly solve related cut problems. In practice our algorithm is faster than previous related flow-based methods, and is also more robust in detecting ground truth target regions in a graph, thanks to its ability to better incorporate semi-supervised information about target clusters. Copyright © 2018, The Authors. All rights reserved.

关键词： Clustering algorithms

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EEG based epilepsy detection using approximation entropy and different classification strategies 8

EEG based epilepsy detection using approximation entropy and...

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8th International Conference on Intelligent computing and Information Systems, ICICIS 2017

作者： Naser, Aya Tantawi, Manal Shedeed, Howida A. Tolba, Mohamed F. Scientific Computing Dept. Faculty of Computer and Information Science Ain Shames University Cairo Egypt

ISBN: (纸本)9772371723

Epilepsy is the 4th prevalent neurological disorder, which distinguishes itself by frequent seizures. These seizures are unprovoked and can be detected by investigating the electroencephalogram (EEG) of the subject. In this paper, an automatic method is proposed to discriminate between normal and epileptic (interictal& ictal) classes. Approximation entropy (APEN) has been utilized as a single informative feature. Multiple classifiers such as: support vector machine (SVM), Probabilistic Neural Network (PNN) and Soft-max regression along with different strategies for classification have been examined. Experiments were carried out by the most popular dataset used in this domain. 96.875%, 53.75% and 65% are the best results achieved by soft-max regression classifier using multi-class strategy for normal, interictal and ictal classes respectively. © 2017 IEEE.

关键词： Entropy

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Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper

arXiv

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arXiv 2022年

作者： Dorigo, Tommaso Giammanco, Andrea Vischia, Pietro Aehle, Max Bawaj, Mateusz Boldyrev, Alexey de Castro Manzano, Pablo Derkach, Denis Donini, Julien Edelen, Auralee Fanzago, Federica Gauger, Nicolas R. Glaser, Christian Baydin, Atılım G. Heinrich, Lukas Keidel, Ralf Kieseler, Jan Krause, Claudius Lagrange, Maxime Lamparth, Max Layer, Lukas Maier, Gernot Nardi, Federico Pettersen, Helge E.S. Ramos, Alberto Ratnikov, Fedor Röhrich, Dieter de Austri, Roberto Ruiz del Árbol, Pablo Martínez Ruiz Savchenko, Oleg Simpson, Nathan Strong, Giles C. Taliercio, Angela Tosi, Mia Ustyuzhanin, Andrey Zaraket, Haitham Istituto Nazionale di Fisica Nucleare Sezione di Padova Italy Université Catholique de Louvain Belgium Scientific Computing Technische Universität Kaiserslautern Germany Università di Perugia INFN Sezione di Perugia Italy HSE University Russia Université Clermont Auvergne Laboratoire de Physique de Clermont CNRS IN2P3 France SLAC National Accelerator Laboratory United States Department of Physics and Astronomy Uppsala University Sweden Department of Computer Science University of Oxford United Kingdom Physik-Department Technische Universität München Germany Center for Technology and Transfer University of Applied Sciences Worms Germany CERN Switzerland NHETC Dept. of Physics and Astronomy Rutgers University United States Università di Napoli "Federico II" Italy Germany Università degli Studi di Padova Italy Department of Oncology and Medical Physics Haukeland University Hospital Norway Instituto de Física Corpuscular UV-CSIC Spain Department of Physics and Technology University of Bergen Norway Instituto de Física de Cantabria UC-CSIC Spain Lund University Sweden Multi-Disciplinary Physics Laboratory Optics and Fiber Optics Group Faculty of Sciences Lebanese University Lebanon Lebanon

The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a superhuman task, given the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and information-extraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, "experience-driven" layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized by means of a systematic search of the configuration space. The stochastic nature of the involved quantum processes make the modeling of these systems an intractable problem from a classical statistics point of view, yet the construction of a fully differentiable pipeline and the use of deep learning techniques may allow the simultaneous optimization of all design parameters. In this document we lay down our plans for the design of a modular and versatile modeling tool for the end-to-end optimization of complex instruments for particle physics experiments as well as industrial and medical applications that share the detection of radiation as their basic ingredient. We consider a selected set of use cases to highlight the specific needs of different applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Medical applications

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EEG based Epilepsy Detection using Approximation Entropy and Different Classification Strategies

EEG based Epilepsy Detection using Approximation Entropy and...

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IEEE International Conference on Intelligent computing and Information Systems

作者： Aya Naser Manal Tantawi Howida A. Shedeed Mohamed F. Tolba Scientific Computing Dept. Ain Shames University Cairo Egypt Scientific Computing Dept. Faculty of Computer and Information Science Ain Shames University Cairo Egypt

Epilepsy is the 4th prevalent neurological disorder, which distinguishes itself by frequent seizures. These seizures are unprovoked and can be detected by investigating the electroencephalogram (EEG) of the subject. In this paper, an automatic method is proposed to discriminate between normal and epileptic (interictal & ictal) classes. Approximation entropy (APEN) has been utilized as a single informative feature. Multiple classifiers such as: support vector machine (SVM), Probabilistic Neural Network (PNN) and Soft-max regression along with different strategies for classification have been examined. Experiments were carried out by the most popular dataset used in this domain. 96.875%, 53.75% and 65% are the best results achieved by soft-max regression classifier using multi-class strategy for normal, interictal and ictal classes respectively.

关键词： Conferences Information systems

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Virtual machine provisioning, code management, and data movement design for the Fermilab HEPCloud Facility 22

Virtual machine provisioning, code management, and data move...

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22nd International Conference on computing in High Energy and Nuclear Physics, CHEP 2016

作者： Timm, S. Cooper, G. Fuess, S. Garzoglio, G. Holzman, B. Kennedy, R. Grassano, D. Tiradani, A. Krishnamurthy, R. Vinayagam, S. Raicu, I. Wu, H. Ren, S. Noh, S.-Y. Scientific Computing Division Fermilab BataviaIL60563 United States Computer Science Dept. Illinois Institute of Technology ChicagoIL60616 United States Korea Institute of Science and Technology Information Daejeon Korea Republic of

The Fermilab HEPCloud Facility Project has as its goal to extend the current Fermilab facility interface to provide transparent access to disparate resources including commercial and community clouds, grid federations, and HPC centers. This facility enables experiments to perform the full spectrum of computing tasks, including data-intensive simulation and reconstruction. We have evaluated the use of the commercial cloud to provide elasticity to respond to peaks of demand without overprovisioning local resources. Full scale data-intensive workflows have been successfully completed on Amazon Web Services for two High Energy Physics Experiments, CMS and NOνA, at the scale of 58000 simultaneous cores. This paper describes the significant improvements that were made to the virtual machine provisioning system, code caching system, and data movement system to accomplish this work. The virtual image provisioning and contextualization service was extended to multiple AWS regions, and to support experiment-specific data configurations. A prototype Decision Engine was written to determine the optimal availability zone and instance type to run on, minimizing cost and job interruptions. We have deployed a scalable on-demand caching service to deliver code and database information to jobs running on the commercial cloud. It uses the frontiersquid server and CERN VM File System (CVMFS) clients on EC2 instances and utilizes various services provided by AWS to build the infrastructure (stack). We discuss the architecture and load testing benchmarks on the squid servers. We also describe various approaches that were evaluated to transport experimental data to and from the cloud, and the optimal solutions that were used for the bulk of the data transport. Finally, we summarize lessons learned from this scale test, and our future plans to expand and improve the Fermilab HEP Cloud Facility. © Published under licence by IOP Publishing Ltd.

关键词： Virtual machine

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Layer-parallel training of deep residual neural networks

arXiv

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arXiv 2018年

作者： Günther, S. Ruthotto, L. Schroder, J.B. Cyr, E.C. Gauger, N.R. Scientific Computing Group TU Kaiserslautern Germany Department of Mathematics and Computer Science Emory University AtlantaGA United States Dept. of Mathematics and Statistics University of New Mexico AlbuquerqueNM United States Computational Mathematics Department Sandia National Laboratories AlbuquerqueNM United States

Residual neural networks (ResNets) are a promising class of deep neural networks that have shown excellent performance for a number of learning tasks, e.g., image classification and recognition. Mathematically, ResNet architectures can be interpreted as forward Euler discretizations of a nonlinear initial value problem whose time-dependent control variables represent the weights of the neural network. Hence, training a ResNet can be cast as an optimal control problem of the associated dynamical system. For similar time-dependent optimal control problems arising in engineering applications, parallel-in-time methods have shown notable improvements in scalability. This paper demonstrates the use of those techniques for efficient and effective training of ResNets. The proposed algorithms replace the classical (sequential) forward and backward propagation through the network layers by a parallel nonlinear multigrid iteration applied to the layer domain. This adds a new dimension of parallelism across layers that is attractive when training very deep networks. From this basic idea, we derive multiple layer-parallel methods. The most efficient version employs a simultaneous optimization approach where updates to the network parameters are based on inexact gradient information in order to speed up the training process. Using numerical examples from supervised classification, we demonstrate that the new approach achieves similar training performance to traditional methods, but enables layer-parallelism and thus provides speedup over layer-serial methods through greater concurrency. Copyright © 2018, The Authors. All rights reserved.

关键词： Optimal control systems

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