检索结果-内蒙古大学图书馆

Faster GröBner Bases Via domain-Specific Ordering in Parameter Identifiability of Ode Models

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： Bessonov, Mariya Ilmer, Ilia Konstantinova, Tatiana Ovchinnikov, Alexey Pogudin, Gleb Soto, Pedro Brookhaven National Laboratory United States Department of Mathematics CUNY NYC College of Technology New YorkNY United States Ph.D. Program in Computer Science CUNY Graduate Center New YorkNY United States Department of Mathematics CUNY Queens College New YorkNY United States LIX CNRS École Polytechnique Institute Polytechnique de Paris Paris France Mathematical Institute University of Oxford Oxford United Kingdom

We consider a specific class of polynomial systems that arise in parameter identifiability problems of models of ordinary differential equations (OdE) and discover a method for speeding up the Gr¨obner basis computation by using specific variable ordering and weights coming from the structure of the OdE model. We provide empirical results that show improvement across different symbolic computing frameworks. © 2023, The Authors. All rights reserved.

关键词： Ordinary differential equations

Faster Gröbner bases for Lie derivatives of OdE systems via monomial orderings

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Bessonov, Mariya Ilmer, Ilia Konstantinova, Tatiana Ovchinnikov, Alexey Pogudin, Gleb Soto, Pedro Department of Mathematics CUNY NYC College of Technology New YorkNY United States Ph.D. Program in Computer Science CUNY Graduate Center New YorkNY United States Department of Mathematics CUNY Queens College New YorkNY United States LIX CNRS École Polytechnique Institute Polytechnique de Paris Paris France

Symbolic computation for systems of differential equations is often computationally expensive. Many practical differential models have a form of polynomial or rational OdE system with specified outputs. A basic symbolic approach to analyze these models is to compute and then symbolically process the polynomial system obtained by sufficiently many Lie derivatives of the output functions with respect to the vector field given by the OdE system. In this paper, we present a method for speeding up Gröbner basis computation for such a class of polynomial systems by using specific monomial ordering, including weights for the variables, coming from the structure of the OdE model. We provide empirical results that show improvement across different symbolic computing frameworks and apply the method to speed up structural identifiability analysis of OdE models. © 2022, CC BY-NC-SA.

关键词： Polynomials

ALGEBRAIC IdENTIFIABILITY OF PARTIAL dIFFERENTIAL EQUATION MOdELS

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Byrne, Helen M. Harrington, Heather A. Ovchinnikov, Alexey Pogudin, Gleb Rahkooy, Hamid Soto, Pedro Mathematical Institute Radcliffe Observatory Quarter Woodstock Road OxfordOX2 6GG United Kingdom Ludwig Institute for Cancer Research Oxford Nuffield Department of Clinical Medicine Old Road Campus Research Building off Roosevelt Drive Headington OxfordOX3 7DQ United Kingdom Faculty of Mathematics Technische Universitat Dresden Dresden01062 Germany Pfotenhauerstrasse 108 Dresden01307 Germany Pfotenhauerstrasse 108 Dresden01307 Germany Department of Mathematics CUNY Queens College and Ph.D. Programs in Mathematics and Computer Science CUNY Graduate Center New York United States LIX CNRS École Polytechnique Institute Polytechnique de Paris 1 rue Honoré d’Estienne d’Orves Palaiseau91120 France Wellcome Centre for Human Genetics Roosevelt Drive OxfordOX3 7BN United Kingdom

differential equation models are crucial to scientific processes. The values of model parameters are important for analyzing the behaviour of solutions. A parameter is called globally identifiable if its value can be uniquely determined from the input and output functions. To determine if a parameter estimation problem is well-posed for a given model, one must check if the model parameters are globally identifiable. This problem has been intensively studied for ordinary differential equation models, with theory and several efficient algorithms and software packages developed. A comprehensive theory of algebraic identifiability for PdEs has hitherto not been developed due to the complexity of initial and boundary conditions. Here, we provide theory and algorithms, based on differential algebra, for testing identifiability of polynomial PdE models. We showcase this approach on PdE models arising in the *** Codes 92B05, 12H05, 35R30, 93C20, 93B25, 93B30 © 2024, CC BY.

关键词： Ordinary differential equations

The Smart Analysis of Performing Scalable Inference for Big data Analytics

学校读者我要写书评

暂无评论

The Smart Analysis of Performing Scalable Inference for Big ...

International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Vishakha d Bhandarkar Aruna Sri Rongali Mohamed dawood Shamout Prof. Sachin Pund Gigih Forda Nama Shrishailappa Patil Applied Mathematics and Humanities Yeshwantrao Chavan College of Engineering India Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US Department of Management College of Business Administration University of Sharjah Sharjah United Arab Emirates Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Computer engineering Vishwakarma Institute of Technology Pune

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the “performing Scalable Inference” technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way..

关键词：

Enhancing healthcare AI stability with edge computing and machine learning for extubation prediction

学校读者我要写书评

暂无评论

Scientific Reports 2025年第1期15卷 1-11页

作者： Huang, Kuo-Yang Hsu, Ying-Lin Chung, Che-Liang Chen, Huang-Chi Horng, Ming-Hwarng Lin, Ching-Hsiung Liu, Ching-Sen Xu, Jia-Lang Division of Chest Medicine Department of Internal Medicine Changhua Christian Hospital Changhua Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan Ph.D. Program in Medical Biotechnology National Chung Hsing University Taichung Taiwan Institute of Statistics National Chung Hsing University Taichung Taiwan Division of Chest Medicine Department of Internal Medicine Yuanlin Christian Hospital Changhua Taiwan Department of Applied Mathematics National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung Taiwan

The advancement of the Internet of Medical Things (IoMT) has revolutionized data acquisition and processing in critical care settings. Given the pivotal role of ventilators, accurately predicting extubation outcomes is essential to optimize patient care. This study presents an edge computing-based framework that incorporates machine learning algorithms to predict ventilator extubation success using real-time data collected directly from ventilators. The system was deployed on edge devices to enable on-site inference with minimal latency. Among the evaluated models, Random Forest and XGBoost, the latter demonstrated superior predictive performance under both holdout and tenfold cross-validation schemes. Notably, the edge-based architecture reduced server data transmissions by 83.33%, while improving system stability, resilience, and sustainability. This paper details the model evaluation and demonstrates the feasibility and efficiency of edge intelligence in ventilator weaning decision support. © The Author(s) 2025.

关键词： Clinical decision support system Edge-computing Extubation Internet of medical things Machine learning

Parameter Identifiability and Input-Output Equations

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Ovchinnikov, Alexey Pogudin, Gleb Thompson, Peter Department of Mathematics CUNY Queens College and Ph.D. Programs in Mathematics and Computer Science CUNY Graduate Center NY United States LIX CNRS École Polytechnique Institute Polytechnique de Paris France Department of Computer Science National Research University Higher School of Economics Moscow Russia Courant Institute of Mathematical Sciences New York University New York United States Ph.D. Program in Mathematics CUNY Graduate Center New York United States

Structural parameter identifiability is a property of a differential model with parameters that allows for the parameters to be determined from the model equations in the absence of noise. One of the standard approaches to assessing this problem is via input-output equations and, in particular, characteristic sets of differential ideals. The precise relation between identifiability and input-output identifiability is subtle. The goal of this note is to clarify this relation. The main results are: • identifiability implies input-output identifiability;• these notions coincide if the model does not have rational first integrals;• the field of input-output identifiable functions is generated by the coefficients of a "minimal" characteristic set of the corresponding differential ideal. We expect that some of these facts may be known to the experts in the area, but we are not aware of any articles in which these facts are stated precisely and rigorously proved. Copyright © 2020, The Authors. All rights reserved.

关键词： Rational functions

EFFECTIVIZING LUSIN'S THEOREM

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Miller, Russell Dept. of Mathematics Queens College Ph.D. Programs in Mathematics & Computer Science Graduate Center City University of New York United States

Lusin's Theorem states that, for every Borel-measurable function f on and every Ε > 0, there exists a continuous function g on which is equal to f except on a set of measure in place of R. The distinct processes showing generalized lowness for generic sets and for a set of full measure are seen to explain the differences between versions of Lusin's *** Codes 03d80 (primary) 03d78, 03F60, 03E15, 26A21, 26A15 (secondary) Copyright © 2019, The Authors. All rights reserved.

关键词： Computability and decidability

Promoting Inclusion by Tracing Our Mathematical Roots

学校读者我要写书评

暂无评论

The National Teaching & Learning Forum 2019年第5期28卷 4-6页

作者： Linda McGuire ph.d. Professor Of Mathematics Department Of Mathematics & Computer Science Muhlenberg College Contact: Linda McGuire Ph.D. Professor of Mathematics Department of Mathematics & Computer Science Muhlenberg College Allentown PA 18104

学校读者我要写书评

暂无评论

AmberTools

Journal of Chemical Information and Modeling 2023年第20期63卷 6183-6191页

作者： Case, david A. Aktulga, Hasan Metin Belfon, Kellon Cerutti, david S. Cisneros, G. Andrés Cruzeiro, Vinícius Wilian d. Forouzesh, Negin Giese, Timothy J. Götz, Andreas W. Gohlke, Holger Izadi, Saeed Kasavajhala, Koushik Kaymak, Mehmet C. King, Edward Kurtzman, Tom Lee, Tai-Sung Li, Pengfei Liu, Jian Luchko, Tyler Luo, Ray Manathunga, Madushanka Machado, Matias R. Nguyen, Hai Minh O’Hearn, Kurt A. Onufriev, Alexey V. Pan, Feng Pantano, Sergio Qi, Ruxi Rahnamoun, Ali Risheh, Ali Schott-Verdugo, Stephan Shajan, Akhil Swails, Jason Wang, Junmei Wei, Haixin Wu, Xiongwu Wu, Yongxian Zhang, Shi Zhao, Shiji Zhu, Qiang Cheatham, Thomas E. Roe, daniel R. Roitberg, Adrian Simmerling, Carlos York, darrin M. Nagan, Maria C. Merz, Kenneth M. Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Computer Science and Engineering Michigan State University East LansingMI48824-1322 United States FOG Pharmaceuticals Inc. CambridgeMA02140 United States Psivant 451 D Street Suite 205 BostonMA02210 United States Department of Physics Department of Chemistry and Biochemistry University of Texas at Dallas RichardsonTX75801 United States Department of Chemistry and The PULSE Institute Stanford University StanfordCA94305 United States Department of Computer Science California State University Los AngelesCA90032 United States Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States San Diego Supercomputer Center University of California San Diego La Jolla CA92093-0505 United States Institute for Pharmaceutical and Medicinal Chemistry Heinrich Heine University Düsseldorf Düsseldorf40225 Germany Forschungszentrum Jülich GmbH Jülich52425 Germany Pharmaceutical Development Genentech Inc. South San FranciscoCA94080 United States Laufer Center for Physical and Quantitative Biology Department of Chemistry Stony Brook University Stony BrookNY11794 United States Departments of Molecular Biology and Biochemistry Chemical and Biomolecular Engineering Materials Science and Engineering and Biomedical Engineering Graduate Program in Chemical and Materials Physics University of California IrvineCA92697 United States Ph.D. Programs in Chemistry Biochemistry and Biology The Graduate Center of the City University of New York 365 Fifth Avenue New YorkNY10016 United States Department of Chemistry Lehman College 250 Bedford Park Blvd West BronxNY10468 United States Department of Chemistry and Biochemistry Loyola University Chicago ChicagoIL60660 United States Beijing National Laboratory for Molecular Science

AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note. ... 详细信息

关键词： Free energy Ligands Molecular mechanics Molecular modeling QM/MM