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17th International Conference scientific and Statistical Database Management, SSDBM 2005

作者： De Brito, Halisson Matos Strauch, Julia De Souza, Jano Moreira Osthoff, Carla COPPE/UFRJ Systems Engineering and Computer Science Program Federal University of Rio de Janeiro PO Box 68511 ZIP Code: 21945-970 Rio de Janeiro RJ Brazil ENCE /IBGE National School of Statistical Sciences 106 S. 401 ZIP Code: 20231-050 R. André Cavalcanti Rio de Janeiro RJ Brazil LNCC National Laboratory for Scientific Computing Av. Getulio 333 Quitandinha Vargas Petrópolis RJ Brazil IM/UFRJ Institute of Mathematics Federal University of Rio de Janeiro PO Box 68511 ZIP Code: 21945-970 Rio de Janeiro RJ Brazil

This paper presents MODENA, an architecture for scientific models management using computational Grid platform. This architecture is comprised of two systems: ModManager and ModRunner. ModManager deals with knowledge management about scientific models, acting as a scientific models library allowing for cataloguing, searching, reutilization and generation of new models. To achieve this, a metamodel is proposed to classify models, in order to support the organization, searching and retrieving of models. ModRunner manages the execution of models in a Grid environment allowing for model composition to generate a scientific Grid Workflow to be executed by distributed services offered by Grid Services. An initial prototype of ModManager is presented.

关键词： Knowledge management

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25th Annual computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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The white rat of numerical reproducibility

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AIP Conference Proceedings 2021年第1期2365卷

作者： Michael Mascagni Departments of Computer Science Mathematics and Scientific Computing Florida State University Tallahassee FL 32306-4530 USA Applied and Computational Mathematics Division National Institute of Standards and Technology (NIST) Gaithersburg MD 20899-8910 USA

We explore an application from the author’s work in neuroscience. A code used to investigate neural development modeled 100 neurons with all-to-all excitatory connectivity. We used a simple ordinary differential equation system to model each neuron, and this 100-neuron model was used to produce a paper published in the Journal of Neurophysiology. Later a colleague used our code to continue this work, and found he could not reproduce our results. This lead us to thoroughly investigate this code and we discovered that it offered many different ways to thwart reproducibility that could be explained by round-off error arising from floating-point *** reproducibility is considered a task that directly follows from the determinism in computations. However, reproducibility has become an intense concern and and issue for research. In fact, the author developed an series of international workshops on numerical reproducibility and computational reproducibility that is now a regular offering at the annual international supercomputing conferences. We will show how this particular code provides a lack of reproducibility from the following three mechanisms: (i) the introduction of floating-point errors in an inner product; (ii) introduction of floating-point errors at each an increasing number of time steps during temporal refinement (ii); and (iii) differences in the output of library mathematical functions at the level of round-off error. This code’s sensitivity makes it a very powerful tool to explore many different manifestations of numerical reproducibility. However, this code is by no means exceptional, as in neuroscience these types of models are used extensively to gain insights on the functioning of the nervous system. In addition, these types of models are widely used in many other fields of study as they just nonlinear evolution equations.

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Rubric-based holistic review represents a change from traditional graduate admissions approaches in physics

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Physical Review Physics Education Research 2023年第1期19卷 010134-010134页

作者： Nicholas T. Young N. Verboncoeur Dao Chi Lam Marcos D. Caballero Department of Physics and Astronomy Michigan State University East Lansing Michigan 48824 USA Department of Computational Mathematics Science and Engineering Michigan State University East Lansing Michigan 48824 USA Department of Statistics Michigan State University East Lansing Michigan 48824 USA Center for Computing in Science Education and Department of Physics University of Oslo N-0316 Oslo Norway CREATE for STEM Institute Michigan State University East Lansing Michigan 48824 USA

Rubric-based admissions are claimed to help make the graduate admissions process more equitable, possibly helping to address the historical and ongoing inequities in the U.S. physics graduate school admissions process that have often excluded applicants from minoritized races, ethnicities, genders, and backgrounds. Yet, no studies have examined whether rubric-based admissions methods represent a fundamental change in the admissions process or simply represent a new tool that achieves the same outcome. To address that, we developed supervised machine learning models of graduate admissions data collected from our department over a seven-year period. During the first four years, our department used a traditional admission process and switched to a rubric-based process for the following three years, allowing us to compare which parts of the applications were driving admissions decisions. We find that faculty focused on applicants’ physics GRE scores and grade-point averages when making admissions decisions before the implementation of the rubric. While we were able to develop a sufficiently good model whose results we could trust for the data before the implementation of the rubric, we were unable to do so for the data collected after the implementation of the rubric, despite multiple modifications to the algorithms and data such as implementing Tomek Links. Our inability to model the second dataset despite being able to model the first combined with model comparison analyses suggests that rubric-based admission does change the underlying process. These results suggest that rubric-based holistic review is a method that could make the graduate admission process in physics more equitable.

关键词： Diversity & inclusion Educational policy Graduate students

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Classification of open-ended responses to a research-based assessment using natural language processing

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Physical Review Physics Education Research 2022年第1期18卷 010141-010141页

作者： Joseph Wilson Benjamin Pollard John M. Aiken Marcos D. Caballero H. J. Lewandowski Department of Physics University of Colorado Boulder Boulder Colorado 80309 USA JILA National Institute of Standards and Technology and the University of Colorado Boulder Colorado 80309 USA Department of Physics Worcester Polytechnic Institute Worcester Massachusetts 01609 USA The Njord Centre Departments of Geosciences and Physics University of Oslo 0316 Oslo Norway Center for Computing in Science Education and Department of Physics University of Oslo 0316 Oslo Norway Department of Physics and Astronomy Department of Computational Mathematics Sciences and Engineering and CREATE for STEM Institute Michigan State University East Lansing Michigan 48824 USA

Surveys have long been used in physics education research to understand student reasoning and inform course improvements. However, to make analysis of large sets of responses practical, most surveys use a closed-response format with a small set of potential responses. Open-ended formats, such as written free response, can provide deeper insights into student thinking, but take much longer to analyze, especially with a large number of responses. Here, we explore natural language processing as a computational solution to this problem. We create a machine learning model that can take student responses from the Physics Measurement Questionnaire as input, and output a categorization of student reasoning based on different reasoning paradigms. Our model yields classifications with the same level of agreement as that between two humans categorizing the data, but can be done by a computer, and thus can be scaled for large datasets. In this work, we describe the algorithms and methodologies used to create, train, and test our natural language processing system. We also present the results of the analysis and discuss the utility of these approaches for analyzing open-response data in education research.

关键词： Assessment Research methodology

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Residual-based a posteriori error control for Space-time finite element approximations of parabolic optimal control problems

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PAMM 2014年第1期14卷

作者： Wei Gong Michael Hinze Zhaojie Zhou Institute of Computational Mathematics The State Key Laboratory of Scientific and Engineering Computing (LSEC) Academy of Mathematics and Systems Science Chinese Academy of Sciences. University of Hamburg Department of Mathematics Bundesstraße 55 20146 Hamburg. School of Mathematics Science Shendong Normal University 250014 Ji'nan China.

In this paper we investigate a space-time finite element approximation of parabolic optimal control problems. The first order optimality conditions are transformed into an elliptic equation of fourth order in space and second order in time involving only the state or the adjoint state in the space-time domain. We derive a priori and a posteriori error estimates for the time discretization of the state and the adjoint state. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)

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Detection of features from the internet of things customer attitudes in the hotel industry using a deep neural network model

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Measurement: Sensors 2022年 22卷

作者： Rajesh, Sudha Abd Algani, Yousef Methkal Al Ansari, Mohammed Saleh Balachander, Bhuvaneswari Raj, Roop Muda, Iskandar Kiran Bala, B. Balaji, S. Dept. of Computational Intelligence College of Engineering and Technology School of Computing SRMIST Kattankulathur Chennai India Department of Mathematics Sakhnin College Israel College of Engineering Department of Chemical Engineering University of Bahrain Bahrain Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Lecturer in Economics Education Department Government of Haryana Haryana Panchkula India Department of Doctoral Program Faculty Economic and Business Universitas Sumatera Utara 20222 Jl. Prof TM Hanafiah 12 USU Campus Padang bulan Medan20155 Indonesia Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Tamil Nadu Trichy India Department of CSE Panimalar Engineering College Chennai India Department of Mathematics The Arab Academic College for Education in Israel-Haifa Israel

Tourism and the hotel business have benefited greatly from the use of digital social networking. Using social big data research, the application of deep learning seems to have been beneficial in a marketing strategies and customer preference estimate. Recognizing human psychology, which is critical to industrial success, has benefited greatly from digital technology and social media. The Internet of Things (IoT) provides a chance for a hotel sector to improve the customer experience although lowering operational expenses. The ratings are determined by the following factors: Value, Apartment, Location, Hygiene, Front Office, Facilities, Professional Service, Internet, and Parking. Traditional techniques which anticipate hotel evaluations through minimal precision add difficulty to the rating assessment. As a result, efficient deep learning algorithms are employed to evaluate reviews designed to help consumers in selecting better hotels. To predict qualities, multiple classification techniques, including convolutional neural network-based deep learning (CNN-DL) and support vector machine (SVM) network-based deep learning, were used in this research. The system examines system efficiency by using the TripAdvisor website, this is a well American database. The research results reveal that the CNN-DL method outperforms another method in terms of classification efficiency and failure *** graphical results could also be utilized to enhance the effectiveness of the suggested model and offer insights into response tactics, demonstrating the study's academic and conceptual achievements. Although it is feasible to conclude from such research that the possibility of IoT within the hotel industry has not yet been fully investigated, as researchers commonly speculate on using IoT for implementations that might quickly be of involvement to a hotel sector, but refuse to recognize that possibility as a massive market. © 2022 The Author(s)

关键词： Hotels

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How AD can help solve differential-algebraic equations

arXiv

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arXiv 2017年

作者： Pryce, John D. Nedialkov, Nedialko S. Tan, Guangning Li, Xiao Cardiff University School of Mathematics Senghennydd Rd CardiffCF24 4AG United Kingdom McMaster University Department of Computing & Software HamiltonL8S 4K1 Canada Massachusetts Institute of Technology Process Systems Engineering Laboratory Boston United States McMaster University School of Computational Science and Engineering HamiltonL8S 4K1 Canada

A characteristic feature of differential-algebraic equations is that one needs to find derivatives of some of their equations with respect to time, as part of so called index reduction or regularisation, to prepare them for numerical solution. This is often done with the help of a computer algebra system. We show in two significant cases that it can be done efficiently by pure algorithmic differentiation. The first is the Dummy Derivatives method;here we give a mainly theoretical description, with tutorial examples. The second is the solution of a mechanical system directly from its Lagrangian formulation. Here we outline the theory and show several non-trivial examples of using the "Lagrangian facility" of the Nedialkov-Pryce initial-value solver DAETS, namely: a spring-mass-multipendulum system;a prescribed-trajectory control problem;and long-time integration of a model of the outer planets of the solar system, taken from the DETEST testing package for ODE solvers. Copyright © 2017, The Authors. All rights reserved.

关键词： Differential equations

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GW190521: Tracing imprints of spin-precession on the most massive black hole binary

arXiv

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arXiv 2023年

作者： Miller, Simona J. Isi, Maximiliano Chatziioannou, Katerina Varma, Vijay Mandel, Ilya Department of Physics California Institute of Technology PasadenaCA91125 United States LIGO Laboratory California Institute of Technology PasadenaCA91125 United States Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States Department of Mathematics Center for Scientific Computing and Data Science Research University of Massachusetts DartmouthMA02747 United States Max Planck Institute for Gravitational Physics Albert Einstein Institute PotsdamD-14476 Germany School of Physics Astronomy Monash University ClaytonVIC3800 Australia OzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery ClaytonVIC3800 Australia

GW190521 is a remarkable gravitational-wave signal on multiple fronts: its source is the most massive black hole binary identified to date and could have spins misaligned with its orbit, leading to spin-induced precession;an astrophysically consequential property linked to the binary's origin. However, due to its large mass, GW190521 was only observed during its final 3-4 cycles, making precession constraints puzzling and giving rise to alternative interpretations, such as eccentricity. Motivated by these complications, we trace the observational imprints of precession on GW190521 by dissecting the data with a novel time domain technique, allowing us to explore the morphology and interplay of the few observed cycles. We find that precession inference hinges on a quiet portion of the pre-merger data that is suppressed relative to the merger-ringdown. Neither pre-merger nor post-merger data alone are the sole driver of inference, but rather their combination;in the quasi-circular scenario, precession emerges as a mechanism to accommodate the lack of a stronger pre-merger signal in light of the observed post-merger. In terms of source dynamics, the pre-merger suppression arises from a tilting of the binary with respect to the observer. Establishing such a consistent picture between the source dynamics and the observed data is crucial for characterizing the growing number of massive binary observations and bolstering the robustness of ensuing astrophysical claims. © 2023, CC BY.

关键词： Stars

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The physics GRE does not help applicants "stand out"

arXiv

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arXiv 2020年

作者： Young, Nicholas T. Caballero, Marcos D. Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Center for Computing in Science Education Department of Physics University of Oslo OsloN-0316 Norway CREATE for STEM Institute Michigan State University East LansingMI48824 United States

One argument for keeping the physics GRE is that it can help applicants who might otherwise be missed in the admissions process stand out. In this work, we evaluate whether this claim is supported by physics graduate school admissions decisions. We used admissions data from five PhD-granting physics departments over a 2-year period (N=2537) to see how the fraction of applicants admitted varied based on their physics GRE scores. We compared applicants with low GPAs to applicants with higher GPAs, applicants from large undergraduate universities to applicants from smaller undergraduate universities, and applicants from selective undergraduate institutions to applicants from less selective undergraduate institutions. We also performed a mediation and moderation analysis to provide statistical rigor and to better understand the previous relationships. We find that for applicants who might otherwise have been missed (e.g. have a low GPA or attended a small or less selective school) having a high physics GRE score did not seem to increase the applicant’s chances of being admitted to the schools. However, having a low physics GRE score seemed to penalize otherwise competitive applicants. Thus, our work suggests that the physics GRE does not, in fact, help applicants who might otherwise be missed stand out. Copyright © 2020, The Authors. All rights reserved.

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