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Your search keyword '"*SCIENTIFIC computing"' showing total 7 results
7 results on '"*SCIENTIFIC computing"'

1. Advances on intelligent algorithms for scientific computing: an overview.

Author

Cheng Hua, Xinwei Cao, Bolin Liao, and Shuai Li

Subjects
ARTIFICIAL intelligence, OPTIMIZATION algorithms, SCIENTIFIC computing, ALGORITHMS, COMPUTER science
Abstract

The field of computer science has undergone rapid expansion due to the increasing interest in improving system performance. This has resulted in the emergence of advanced techniques, such as neural networks, intelligent systems, optimization algorithms, and optimization strategies. These innovations have created novel opportunities and challenges in various domains. This paper presents a thorough examination of three intelligent methods: neural networks, intelligent systems, and optimization algorithms and strategies. It discusses the fundamental principles and techniques employed in these fields, as well as the recent advancements and future prospects. Additionally, this paper analyzes the advantages and limitations of these intelligent approaches. Ultimately, it serves as a comprehensive summary and overview of these critical and rapidly evolving fields, offering an informative guide for novices and researchers interested in these areas. [ABSTRACT FROM AUTHOR]

Published
2023
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2. Mixed-Cell-Height Legalization Considering Technology and Region Constraints.

Author

Zhu, Ziran, Chen, Jianli, Zhu, Wenxing, and Chang, Yao-Wen

Subjects
*LEGALIZATION, *CELL motility, *ALGORITHMS, *TECHNOLOGY, *SCIENTIFIC computing
Abstract

Mixed-cell-height circuits have become popular in advanced technologies for better power, area, routability, and performance tradeoffs. With technology and region constraints imposed by modern circuit designs, the mixed-cell-height legalization problem has become even more challenging. Additionally, an ideal legalization method should minimize both the average and maximum cell movements to preserve the quality of a given placement as much as possible. In this article, we present an effective and efficient mixed-cell-height legalization algorithm to consider technology and region constraints while minimizing the average and maximum cell movements. We first present a fence region handling technique to unify the fence regions and the default region. To obtain a desired cell assignment, we then propose a movement-aware cell reassignment method by iteratively reassigning cells in locally dense areas to their desired rows. After cell reassignment, a technology-aware legalization is presented to remove cell overlaps while satisfying the technology constraints. Finally, we propose a technology-aware refinement to further reduce the average and maximum cell movements without increasing the technology constraints violations. Compared with the champion of the 2017 CAD Contest at ICCAD and the state-of-the-art work, experimental results based on the 2017 CAD Contest at ICCAD benchmarks show that our algorithm achieves the best average and maximum cell movements and significantly fewer technology constraint violations, in a comparable runtime. The experimental results based on the modified 2015 ISPD Contest benchmarks also demonstrate the effectiveness of our algorithm in minimizing the average and maximum cell movements, compared with state-of-the-art mixed-cell-height legalizers. [ABSTRACT FROM AUTHOR]

Published
2020
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3. Ghost Imputation: Accurately Reconstructing Missing Data of the Off Period.

Author

Rawassizadeh, Reza, Keshavarz, Hamidreza, and Pazzani, Michael

Subjects
*MISSING data (Statistics), *ACQUISITION of data, *MULTIPLE imputation (Statistics), *COMPUTATIONAL complexity, *ALGORITHMS, *SCIENTIFIC computing, *DATA
Abstract

Noise and missing data are intrinsic characteristics of real-world data, leading to uncertainty that negatively affects the quality of knowledge extracted from the data. The burden imposed by missing data is often severe in sensors that collect data from the physical world, where large gaps of missing data may occur when the system is temporarily off or disconnected. How can we reconstruct missing data for these periods? We introduce an accurate and efficient algorithm for missing data reconstruction (imputation), that is specifically designed to recover off-period segments of missing data. This algorithm, Ghost, searches the sequential dataset to find data segments that have a prior and posterior segment that matches those of the missing data. If there is a similar segment that also satisfies the constraint – such as location or time of day – then it is substituted for the missing data. A baseline approach results in quadratic computational complexity, therefore we introduce a caching approach that reduces the search space and improves the computational complexity to linear in the common case. Experimental evaluations on five real-world datasets show that our algorithm significantly outperforms four state-of-the-art algorithms with an average of 18 percent higher F-score. [ABSTRACT FROM AUTHOR]

Published
2020
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4. Sublinear-Time Algorithms for Compressive Phase Retrieval.

Author

Li, Yi and Nakos, Vasileios

Subjects
*SCIENTIFIC computing, *ALGORITHMS, *IMAGE reconstruction algorithms, *APPROXIMATION algorithms
Abstract

In the problem of compressed phase retrieval, the goal is to reconstruct a sparse or approximately $k$ -sparse vector $x \in \mathbb {C} ^{n}$ given access to $y= |\Phi x|$ , where $|v|$ denotes the vector obtained from taking the absolute value of $v\in \mathbb {C} ^{n}$ coordinate-wise. In this paper we present sublinear-time algorithms for a few for-each variants of the compressive phase retrieval problem which are akin to the variants considered for the classical compressive sensing problem in theoretical computer science. Our algorithms use pure combinatorial techniques and near-optimal number of measurements. [ABSTRACT FROM AUTHOR]

Published
2020
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5. ALGORITHMICITY OF EVOLUTIONARY ALGORITHMS.

Author

Leciejewski, Sławomir and Szynkiewicz, Mariusz

Subjects
*SCIENTIFIC computing, *COLLOQUIAL language, *ALGORITHMS, *SCIENTIFIC language, *EVOLUTIONARY algorithms, *EVOLUTIONARY computation, *COMPUTER science
Abstract

In the first part of our article we will refer the penetration of scientific terms into colloquial language, focusing on the sense in which the concept of an algorithm currently functions outside its original scope. The given examples will refer mostly to disciplines not directly related to computer science and to the colloquial language. In the next part we will also discuss the modifications made to the meaning of the term algorithm and how this concept is now understood in computer science. Finally, we will discuss the problem of algorithmicity of evolutionary algorithms, i.e. we will try to answer the question whether - and to what extent - they are still algorithms in the classical sense. [ABSTRACT FROM AUTHOR]

Published
2020
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6. SciPy 1.0: fundamental algorithms for scientific computing in Python: 24 February 2020 : An amendment to this paper has been published and can be accessed via a link at the top of the paper

Author

Virtanen, P., Gommers, R., Oliphant, T.E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., Walt, S.J., Brett, M., Wilson, J., Millman, K.J., Mayorov, N., Nelson, A.R.J., Jones, E., Kern, R., Larson, E., Carey, C.J., Polat, I., Feng, Y., Moore, E.W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E.A., Harris, C.R., Archibald, A.M., Ribeiro, A.H., Pedregosa, F., Mulbregt, P., Vijaykumar, A., Bardelli, A.P., Rothberg, A., Hilboll, A., Kloeckner, A., Scopatz, A., Lee, A., Rokem, A., Woods, C.N., Fulton, C., Masson, C., Häggström, C., Fitzgerald, C., Nicholson, D.A., Hagen, D.R., Pasechnik, D.V., Olivetti, E., Martin, E., Wieser, E., Lenders, F., Silva, Fabrice, Wilhelm, F., Young, G., Price, G.A., Ingold, G.-L., Allen, G.E., Lee, G.R., Audren, H., Probst, Irvin, Dietrich, J.P., Silterra, J., Webber, J.T., Slavič, J., Nothman, J., Buchner, J., Kulick, J., Schönberger, J.L., Miranda Cardoso, J.V., Reimer, J., Harrington, J., Rodríguez, J.L.C., Nunez-Iglesias, J., Kuczynski, J., Tritz, K., Thoma, M., Newville, M., Kümmerer, M., Bolingbroke, M., Tartre, M., Pak, M., Smith, N.J., Nowaczyk, N., Shebanov, N., Pavlyk, O., Brodtkorb, P.A., Lee, P., McGibbon, R.T., Feldbauer, R., Lewis, S., Tygier, S., Sievert, S., Vigna, S., Peterson, S., More, S., Pudlik, T., Oshima, T., Pingel, T.J., Robitaille, T.P., Spura, T., Jones, T.R., Cera, T., Leslie, T., Zito, T., Krauss, T., Upadhyay, U., Halchenko, Y.O., Vázquez-Baeza, Y., SciPy 1.0, Contributors, Low Temperature Laboratory, TKK Helsinki University of Technology (TKK), Centre Interdisciplinaire de Nanoscience de Marseille (CINaM), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Tribologie et Dynamique des Systèmes (LTDS), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-École Nationale des Travaux Publics de l'État (ENTPE)-Ecole Nationale d'Ingénieurs de Saint Etienne-Centre National de la Recherche Scientifique (CNRS), SNECMA Villaroche [Moissy-Cramayel], Safran Group, New Technologies Research Centre [Plzeň] (NTC), University of West Bohemia [Plzeň ], Institute of Environmental Physics [Bremen] (IUP), University of Bremen, Istituto Nazionale di Ricerca Metrologica (INRiM), Laboratoire Chrono-environnement - CNRS - UBFC (UMR 6249) (LCE), Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Laboratoire de Mécanique et d'Acoustique [Marseille] (LMA ), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Sons, Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), European Synchrotron Radiation Facility (ESRF), Lab-STICC_ENSTAB_CID_PRASYS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Max Planck Institute for Solar System Research (MPS), Max-Planck-Gesellschaft, University of Chicago, Consortium of Advanced Radiation Sciences, Advanced Photon Source, GSECARS, University of Chicago, Laboratoire de physique des gaz et des plasmas (LPGP), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), The Open University [Milton Keynes] (OU), Nobeyama Radio Observatory, Gommers, Ralf [0000-0002-0300-3333], Haberland, Matt [0000-0003-4806-3601], Reddy, Tyler [0000-0003-2364-6157], van der Walt, Stéfan J. [0000-0001-9276-1891], Millman, K. Jarrod [0000-0002-5263-5070], Nelson, Andrew R. J. [0000-0002-4548-3558], Laxalde, Denis [0000-0002-5540-4825], Ribeiro, Antônio H. [0000-0003-3632-8529], van Mulbregt, Paul [0000-0002-2382-8308], Apollo - University of Cambridge Repository, van der Walt, Stéfan J [0000-0001-9276-1891], Millman, K Jarrod [0000-0002-5263-5070], Nelson, Andrew RJ [0000-0002-4548-3558], Ribeiro, Antônio H [0000-0003-3632-8529], Quansight LLC, California Polytechnic State University [San Luis Obispo] (CAL POLY), Los Alamos National Laboratory (LANL), AUTRES, National Research University Higher School of Economics [St. Petersburg], Tallinn Technical University, University of California [Berkeley] (UC Berkeley), University of California (UC), University of Birmingham [Birmingham], Skolkovo Innovation Center, Australian Nuclear Science and Technology Organisation [Australie] (ANSTO), Enthought Inc, University of Washington [Seattle], University of Massachusetts [Amherst] (UMass Amherst), University of Massachusetts System (UMASS), Bruker BioSpin Corporation, Université de Lyon-Université de Lyon-École Nationale des Travaux Publics de l'État (ENTPE)-Ecole Nationale d'Ingénieurs de Saint Etienne (ENISE)-Centre National de la Recherche Scientifique (CNRS), University of Texas at Austin [Austin], Astronomical Institute Anton Pannekoek (AI PANNEKOEK), University of Amsterdam [Amsterdam] (UvA), Universidade Federal de Minas Gerais [Belo Horizonte] (UFMG), Google LLC, Laboratoire Chrono-environnement (UMR 6249) (LCE), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), and Max-Planck-Institut für Sonnensystemforschung = Max Planck Institute for Solar System Research (MPS)

Subjects
FOS: Computer and information sciences, Computer science, 01 natural sciences, Biochemistry, Computer Science - Software Engineering, 631/45/56, Data Structures and Algorithms (cs.DS), 010303 astronomy & astrophysics, computer.programming_language, 0303 health sciences, Signal processing, Signal Processing, Computer-Assisted, Computational Physics (physics.comp-ph), ddc, Linear algebra, Perspective, Minification, Physics - Computational Physics, Algorithm, Algorithms, Biotechnology, De facto standard, FOS: Physical sciences, Image processing, History, 21st Century, Models, Biological, 706/703/559, Python (Computer program language), Computational science, 03 medical and health sciences, Computer Science - Data Structures and Algorithms, 0103 physical sciences, Computer Simulation, [INFO]Computer Science [cs], ddc:530, Cluster analysis, Molecular Biology, Scientific computing, 030304 developmental biology, Sparse matrix, software, Computational Biology, Cell Biology, Python (programming language), History, 20th Century, [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA], Software Engineering (cs.SE), Nonlinear Dynamics, Linear Models, Computer Science - Mathematical Software, Programming Languages, 631/114, computer, Mathematical Software (cs.MS), Python
Abstract

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments., Comment: Article source data is available here: https://github.com/scipy/scipy-articles

Published
2020

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