Journal of Computational Mathematics and Data Science最新文献_第8页

Contagion-induced risk: An application to the global export network 传染风险:在全球出口网络中的应用

Journal of Computational Mathematics and Data Science Pub Date : 2021-10-01 DOI: 10.1016/j.jcmds.2021.100010

E. Vicente, A. Mateos, E. Mateos

引用次数: 0

Testing pairs of continuous random variables for independence: A simple heuristic 检验连续随机变量对的独立性:一个简单的启发式方法

Journal of Computational Mathematics and Data Science Pub Date : 2021-10-01 DOI: 10.1016/j.jcmds.2021.100012

M. Khatun, S. Siddiqui

引用次数: 0

The Yun transform in probabilistic and statistical contexts: Weibull baseline case and its applications in reliability theory 概率和统计背景下的Yun变换:威布尔基线情况及其在可靠性理论中的应用

Journal of Computational Mathematics and Data Science Pub Date : 2021-09-01 DOI: 10.1016/j.jcmds.2021.100002

Christophe Chesneau , M. Girish Babu , Hassan S. Bakouch

{"title":"The Yun transform in probabilistic and statistical contexts: Weibull baseline case and its applications in reliability theory","authors":"Christophe Chesneau , M. Girish Babu , Hassan S. Bakouch","doi":"10.1016/j.jcmds.2021.100002","DOIUrl":"https://doi.org/10.1016/j.jcmds.2021.100002","url":null,"abstract":"<div><p>In this paper, we present a new family of distributions based on a particular case of a transform introduced by Yun (2014). Among others, this transform demonstrates great flexibility and nice mathematical properties which can be useful in a statistical context (continuous derivatives of all order, simplicity of the inverse transform, etc.). We propose a new three-parameter distribution from this family, namely the Yun–Weibull (YW) distribution. Some statistical properties of this distribution are studied, involving flexible hazard rate shapes. Subsequently, the statistical inference of the YW distribution is investigated. The parameters are estimated by employing the maximum likelihood estimation method. We establish the existence and uniqueness of the obtained estimators. The YW distribution is applied to fit two practical data sets. As a main result of our analysis, the new distribution is found to be more appropriate to these data sets than other competitive distributions. Moreover, the uniqueness of the parameter estimates of the YW distribution is studied using the profile log-likelihood function visually under the two practical data sets.</p></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"1 ","pages":"Article 100002"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.jcmds.2021.100002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91678001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning 2D Gabor filters by infinite kernel learning regression 利用无限核学习回归学习二维Gabor滤波器

Journal of Computational Mathematics and Data Science Pub Date : 2021-09-01 DOI: 10.1016/j.jcmds.2021.100016

Kamaledin Ghiasi-Shirazi

{"title":"Learning 2D Gabor filters by infinite kernel learning regression","authors":"Kamaledin Ghiasi-Shirazi","doi":"10.1016/j.jcmds.2021.100016","DOIUrl":"https://doi.org/10.1016/j.jcmds.2021.100016","url":null,"abstract":"<div><p>Gabor functions have wide-spread applications both in analyzing the visual cortex of mammalians and in designing machine vision algorithms. It is known that the receptive field of neurons of V1 layer in the visual cortex can be accurately modeled by Gabor functions. In addition, Gabor functions are extensively used for feature extraction in machine vision tasks. In this paper, we prove that Gabor functions are translation-invariant positive-definite kernels and show that the problem of image representation with Gabor functions can be formulated as infinite kernel learning regression. Specifically, we use the stabilized infinite kernel learning regression algorithm that has already been introduced for learning translation-invariant positive-definite kernels and has enough flexibility and generality to embrace the class of Gabor kernels. The algorithm yields a representation of the image as a support vector expansion with a compound kernel that is a finite mixture of Gabor functions. The problem with this representation is that all Gabor functions are present at all support vector pixels. Using LASSO, we propose a method for sparse representation of an image with Gabor functions in which each Gabor function is positioned at a very sparse set of pixels. As a practical application, we introduce a novel method for learning a dataset-specific set of Gabor filters that can be used subsequently for feature extraction. Our experiments on CMU-PIE and Extended Yale B datasets show that use of the learned Gabor filters significantly improves the recognition accuracy of a recently introduced face recognition algorithm.</p></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"1 ","pages":"Article 100016"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772415821000080/pdfft?md5=af240b6063e9a7317487e5c2e4f5c43f&pid=1-s2.0-S2772415821000080-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91678000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Neural networks as smooth priors for inverse problems for PDEs 神经网络作为PDEs逆问题的平滑先验

Journal of Computational Mathematics and Data Science Pub Date : 2021-09-01 DOI: 10.1016/j.jcmds.2021.100008

J. Berg, K. Nyström

引用次数: 9

Efficient adaptive exponential time integrators for nonlinear Schrödinger equations with nonlocal potential 具有非局部势的非线性Schrödinger方程的有效自适应指数时间积分器

Journal of Computational Mathematics and Data Science Pub Date : 2021-09-01 DOI: 10.1016/j.jcmds.2021.100014

Winfried Auzinger , Iva Březinová , Alexander Grosz , Harald Hofstätter , Othmar Koch , Takeshi Sato

{"title":"Efficient adaptive exponential time integrators for nonlinear Schrödinger equations with nonlocal potential","authors":"Winfried Auzinger , Iva Březinová , Alexander Grosz , Harald Hofstätter , Othmar Koch , Takeshi Sato","doi":"10.1016/j.jcmds.2021.100014","DOIUrl":"10.1016/j.jcmds.2021.100014","url":null,"abstract":"<div><p>The performance of exponential-based numerical integrators for the time propagation of the equations associated with the multiconfiguration time-dependent Hartree–Fock (MCTDHF) method for the approximation of the multi-particle Schrödinger equation in one space dimension is assessed. Among the most popular integrators such as Runge–Kutta methods, time-splitting, exponential integrators and Lawson methods, exponential Lawson multistep methods with one predictor–corrector step provide the best stability and accuracy at the least effort. This assessment is based on the observation that the evaluation of the nonlocal terms associated with the potential is the computationally most demanding part of such a calculation in our setting. In addition, the predictor step provides an estimator for the local time-stepping error, thus allowing for adaptive time-stepping which reflects the smoothness of the solution and enables to reliably control the accuracy of a computation in a robust way, without the need to guess an optimal stepsize a priori. One-dimensional model examples are studied to compare different time integrators and demonstrate the successful application of our adaptive methods.</p></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"1 ","pages":"Article 100014"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772415821000079/pdfft?md5=37b574c538b0935ceb3731af09bc19cf&pid=1-s2.0-S2772415821000079-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77624988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Neural networks as smooth priors for inverse problems for PDEs 神经网络作为PDEs逆问题的平滑先验

Journal of Computational Mathematics and Data Science Pub Date : 2021-09-01 DOI: 10.1016/j.jcmds.2021.100008

Jens Berg, Kaj Nyström

引用次数: 8

Jordan canonical forms for systems of elliptic equations 椭圆方程系统的约当规范形式

Journal of Computational Mathematics and Data Science Pub Date : 2021-09-01 DOI: 10.1016/j.jcmds.2021.100006

Mosito Lekhooana , Motlatsi Molati , Celestin Wafo Soh

引用次数: 1

Testing pairs of continuous random variables for independence: A simple heuristic 检验连续随机变量对的独立性:一个简单的启发式方法

Journal of Computational Mathematics and Data Science Pub Date : 2021-09-01 DOI: 10.1016/j.jcmds.2021.100012

Mahfuza Khatun , Sikandar Siddiqui

引用次数: 0

Contagion-induced risk: An application to the global export network 传染风险:在全球出口网络中的应用

Journal of Computational Mathematics and Data Science Pub Date : 2021-09-01 DOI: 10.1016/j.jcmds.2021.100010

E. Vicente, A. Mateos, E. Mateos

{"title":"Contagion-induced risk: An application to the global export network","authors":"E. Vicente, A. Mateos, E. Mateos","doi":"10.1016/j.jcmds.2021.100010","DOIUrl":"https://doi.org/10.1016/j.jcmds.2021.100010","url":null,"abstract":"<div><p>In many systems, the state of each of their components can itself be a source of risk affecting the other components, and it is not easy to aggregate these individual values together with the interconnecting structural elements of the network. There are simulation models in the literature that establish propagation curves for the population as a whole, especially in the epidemiological case, but these models do not provide a clear analytical expression of the risk borne by each of the network nodes. Moreover, classical models, such as the generalized cascade model, are not necessarily convergent. Neither can the individual values of each node be aggregated on the same scale as they were measured. This paper proposes a mathematical model that makes it possible to analyze the propagation of risk in the face of a given adverse event that may reach all the elements of a network and precisely calculate the risk borne by each node according to its own vulnerability and the relationships with the other nodes, which may be more or less vulnerable and constitute additional sources of risk. It is shown that the new model ensures convergence and that the aggregated results can be interpreted in terms of the risk measurement scale previously given for each node. In addition, the global import–export network is used to illustrate how political or economic instability in one state can generate crises in other states.</p></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"1 ","pages":"Article 100010"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772415821000055/pdfft?md5=82f3893819e0ad2c6abff625b636d520&pid=1-s2.0-S2772415821000055-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91677865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0