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Information system for diagnosis in pregnant chronic energy deficiency using a rule based system algorithm 10

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Information system for diagnosis in pregnant chronic energy ...

10th International Seminar on New Paradigm and Innovation of Natural Sciences and itsApplication, ISNPINSA 2020

作者： Sustamy, R.P. Widyawati, M.N. Suryono, S. Master of Applied Science in Midwifery Program Poltekkes Kemenkes Semarang Indonesia Departement of Physics Faculty of Science and Mathematics University Diponegoro Semarang Indonesia

The condition of Chronic Energy Deficiency (CED) has become one of the causes of high maternal mortality. Improper handling of CED will have an impact on the health problems of both mother and baby. This is because when the diagnosis is still using conventional methods, so it takes a long time or is not time efficient, an alternative is needed to improve the quality of information system-based services in diagnosing mothers with CED. In this research, an information system design was carried out using the SDLC (System Development Life Cylce) method with the waterfall model which includes: needs analysis, design, implementation, and offer. Analysis of speed data using respondents 15 chronic energy deficiency pregnant and static test using Mann Whitney obtained p-value 0.000 p © Published under licence by IOP Publishing Ltd.

关键词： Information systems

CONTINUOUS-TIME ANALYSIS OF ADAPTIVE OPTIMIZATION AND NORMALIZATION

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arXiv 2024年

作者： Gould, Rhys Tanaka, Hidenori Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom CBS-NTT Physics of Intelligence Program Harvard University Physics & Informatics Laboratories NTT Research Inc United States

Adaptive optimization algorithms, particularly Adam and its variant AdamW, are fundamental components of modern deep learning. However, their training dynamics lack comprehensive theoretical understanding, with limited insight into why common practices—such as specific hyperparameter choices and normalization layers—contribute to successful generalization. This work presents a continuous-time formulation of Adam and AdamW, facilitating a tractable analysis of training dynamics that can shed light on such practical questions. We theoretically derive a stable region for Adam’s hyperparameters (β, γ) that ensures bounded updates, empirically verifying these predictions by observing unstable exponential parameter growth outside of this stable region. Furthermore, we theoretically justify the success of normalization layers by uncovering an implicit meta-adaptive effect of scale-invariant architectural components. This insight leads to an explicit optimizer, 2-Adam, which we generalize to k-Adam—an optimizer that applies an adaptive normalization procedure k times, encompassing Adam (corresponding to k = 1) and Adam with a normalization layer (corresponding to k = 2). Overall, our continuous-time formulation of Adam facilitates a principled analysis, offering deeper understanding of optimal hyperparameter choices and architectural decisions in modern deep learning. © 2024, CC BY.

关键词： Adaptive algorithms

Unbiased Parameter Estimation for Bayesian Inverse Problems

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arXiv 2025年

作者： Chada, Neil K. Jasra, Ajay Maama, Mohamed Tempone, Raul Department of Mathematics City University of Hong Kong China School of Data Science The Chinese University of Hong Kong Shenzhen CN Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we consider the estimation of unknown parameters in Bayesian inverse problems. In most cases of practical interest, there are several barriers to performing such estimation, This includes a numerical approximation of a solution of a differential equation and, even if exact solutions are available, an analytical intractability of the marginal likelihood and its associated gradient, which is used for parameter estimation. The focus of this article is to deliver unbiased estimates of the unknown parameters, that is, stochastic estimators that, in expectation, are equal to the maximizer of the marginal likelihood, and possess no numerical approximation error. Based upon the ideas of [4] we develop a new approach for unbiased parameter estimation for Bayesian inverse problems. We prove unbiasedness and establish numerically that the associated estimation procedure is faster than the current state-of-the-art methodology for this problem. We demonstrate the performance of our methodology on a range of problems which include a PDE and ODE. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

FLUID-STRUCTURE INTERACTION WITH POROUS MEDIA: THE BEAVER-JOSEPH CONDITION IN THE STRONG SENSE

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arXiv 2025年

作者： Binz, Tim Hieber, Matthias Roy, Arnab Princeton University Program in Applied & Computational Mathematics Fine Hall Washington Road PrincetonNJ08544 United States Technische Universität Darmstadt Schloßgartenstraße 7 Darmstadt64289 Germany

This article considers fluid structure interaction describing the motion of a fluid contained in a porous medium. The fluid is modelled by Navier-Stokes equations and the coupling between fluid and the porous medium is described by the classical Beaver-Joseph or the Beaver-Joseph-Saffman interface condition. In contrast to previous work these conditions are investigated for the first time in the strong sense and it is shown that the coupled system admits a unique, global strong solution in critical spaces provided the data are small enough. Furthermore, a Serrin-type blow-up criterium is developed and higher regularity estimates at the interface are established, which say that the solution is even analytic provided the forces are so. Copyright © 2025, The Authors. All rights reserved.

关键词： Navier Stokes equations

BLIS-Net: Classifying and Analyzing Signals on Graphs

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arXiv 2023年

作者： Xu, Charles Goldman, Laney Guo, Valentina Hollander-Bodie, Benjamin Trank-Greene, Maedee Adelstein, Ian De Brouwer, Edward Ying, Rex Krishnaswamy, Smita Perlmutter, Michael Department of Mathematics Yale University United States Department of Computer Science Yale University United States Department of Mathematics Harvey Mudd College United States Department of Genetics Yale School of Medicine United States Department of Applied Mathematics University of Colorado Boulder United States Department of Mathematics Boise State University United States Program in Computing Boise State University United States

Graph neural networks (GNNs) have emerged as a powerful tool for tasks such as node classification and graph classification. However, much less work has been done on signal classification, where the data consists of many functions (referred to as signals) defined on the vertices of a single graph. These tasks require networks designed differently from those designed for traditional GNN tasks. Indeed, traditional GNNs rely on localized low-pass filters, and signals of interest may have intricate multi-frequency behavior and exhibit long range interactions. This motivates us to introduce the BLIS-Net (Bi-Lipschitz Scattering Net), a novel GNN that builds on the previously introduced geometric scattering transform. Our network is able to capture both local and global signal structure and is able to capture both low-frequency and high-frequency information. We make several crucial changes to the original geometric scattering architecture which we prove increase the ability of our network to capture information about the input signal and show that BLIS-Net achieves superior performance on both synthetic and real-world data sets based on traffic flow and fMRI data. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

Unbiased and Multilevel Methods for a Class of Diffusions Partially Observed via Marked Point Processes

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arXiv 2023年

作者： Alvarez, Miguel Jasra, Ajay Ruzayqat, Hamza Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

In this article we consider the filtering problem associated to partially observed diffusions, with observations following a marked point process. In the model, the data form a point process with observation times that have its intensity driven by a diffusion, with the associated marks also depending upon the diffusion process. We assume that one must resort to time-discretizing the diffusion process and develop particle and multilevel particle filters to recursively approximate the filter. In particular, we prove that our multilevel particle filter can achieve a mean square error (MSE) of O(∈2) (∈ > 0 and arbitrary) with a cost of O(∈-2.5) versus using a particle filter which has a cost of O(∈-3) to achieve the same MSE. We then show how this methodology can be extended to give unbiased (that is with no time-discretization error) estimators of the filter, which are proved to have finite variance and with high-probability have finite cost. Finally, we extend our methodology to the problem of online static-parameter estimation. Key words: Unbiased Methods, Multilevel Monte Carlo, Non-Linear Filtering, Point Processes, Parameter *** Codes 60G55, 60G35, 62M20, 62F30 © 2023, CC BY.

关键词： Diffusion

Bayesian Parameter Inference for Partially Observed Stochastic Volterra Equations

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arXiv 2023年

作者： Jasra, Ajay Ruzayqat, Hamza Wu, Amin Applied Mathematics & Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

In this article we consider Bayesian parameter inference for a type of partially observed stochastic Volterra equation (SVE). SVEs are found in many areas such as physics and mathematical finance. In the latter field they can be used to represent long memory in unobserved volatility processes. In many cases of practical interest, SVEs must be time-discretized and then parameter inference is based upon the posterior associated to this time-discretized process. Based upon recent studies on time-discretization of SVEs (e.g. [24]) we use Euler-Maruyama methods for the afore-mentioned discretization. We then show how multilevel Markov chain Monte Carlo (MCMC) methods [19] can be applied in this context. In the examples we study, we give a proof that shows that the cost to achieve a 4 mean square error (MSE) of O(ϵ2), ϵ > 0, is O(ϵ− 4/2H+1 ), where H is the Hurst parameter. If one uses a single level MCMC method then the cost is O(ϵ−2(2H+3)/ 2H+1 ) to achieve the same MSE. We illustrate these results in the context of state-space and stochastic volatility models, with the latter applied to real *** Codes 62F15, 62M20, 60J10, 60J22, 65C40 © 2023, CC BY.

关键词： Mean square error

Multilevel Monte Carlo Methods for the Grad-Shafranov Free Boundary Problem

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SSRN

SSRN 2023年

作者： Elman, Howard C. Liang, Jiaxing Sánchez-Vizuet, Tonatiuh Department of Computer Science Institute for Advanced Computer Studies University of Maryland College Park United States Applied Mathematics & Statistics and Scientific Computing Program University of Maryland College Park United States Department of Mathematics The University of Arizona United States

The equilibrium configuration of a plasma in an axially symmetric reactor is described mathematically by a free boundary problem associated with the celebrated Grad-Shafranov equation. The presence of uncertainty in the model parameters introduces the need to quantify the variability in the predictions. This is often done by computing a large number of model solutions on a computational grid for an ensemble of parameter values and then obtaining estimates for the statistical properties of solutions. In this study, we explore the savings that can be obtained using multilevel Monte Carlo methods, which reduce costsby performing the bulk of the computations on a sequence of spatial grids that are coarser than the one that would typically be used for a simple Monte Carlo simulation. We examine this approach using both a set of uniformly refined grids and a set of adaptively refined grids guided by a discrete error estimator. Numerical experiments show that multilevel methods dramatically reduce the cost of simulation, with cost reductionstypically on the order of 60 or more and possibly as large as 200. Adaptive gridding results in more accurate computation of geometric quantities such as x-points associated with the model. © 2023, The Authors. All rights reserved.

关键词： Monte Carlo methods

大气阻塞Charney-De Vore准地转方程的能量稳定性

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中国科学：数学 2022年第7期52卷 823-844页

作者：陈志敏熊向明深圳大学数学与统计学院深圳518060 The Program in Applied and Computational Mathematics Princeton UniversityrincetonJ08544USA

Charney和DeVore(1979)发现,由于地形的影响,β平面槽道中正压大气的准地转流动有多个平衡态.本文证明了这种流动的基本流在某个参数范围内(包括平坦地表的特殊情形)是渐近稳定的,因此排除了这一参数范围内存在多个平衡态的可能性.本文... 详细信息

Charney和DeVore(1979)发现,由于地形的影响,β平面槽道中正压大气的准地转流动有多个平衡态.本文证明了这种流动的基本流在某个参数范围内(包括平坦地表的特殊情形)是渐近稳定的,因此排除了这一参数范围内存在多个平衡态的可能性.本文作者还发现,为了使Charney-DeVore准地转方程适定,需要补充一个平均经向力或平均经向速度的条件.本文在不同的地表起伏幅度下,采用拟弧长延拓法证实至少存在3个平衡态;还通过高精度直接数值模拟研究了这些平衡态的稳定性.

关键词：准地转方程正压流动 β平面槽道模型地形效应非线性稳定性拟弧长延拓法