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arXiv 2023年

作者： Jasra, Ajay Maama, Mohamed Ombao, Hernando Applied Mathematics and Computational Science Program United States Statistics 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 filtering of partially observed multi-dimensional diffusion processes that are observed regularly at discrete times. This is a challenging problem which requires the use of advanced numerical schemes based upon time-discretization of the diffusion process and then the application of particle filters. Perhaps the state-of-the-art method for moderate dimensional problems is the multilevel particle filter of [12]. This is a method that combines multilevel Monte Carlo and particle filters. The approach in that article is based intrinsically upon an Euler discretization method. We develop a new particle filter based upon the antithetic truncated Milstein scheme of [10]. We show that for a class of diffusion problems, for ϵ > 0 given, that the cost to produce a mean square error (MSE) in estimation of the filter, of O(ϵ2) is O(ϵ−2 log(ϵ)2). In the case of multidimensional diffusions with non-constant diffusion coefficient, the method of [12] has a cost of O(ϵ−2.5) to achieve the same MSE. We support our theory with numerical results in several examples. Copyright © 2023, The Authors. All rights reserved.

关键词： Mean square error

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Direct Machine Translation Indonesian-Batak Toba

Direct Machine Translation Indonesian-Batak Toba

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Conference on New Media Studies (CoNMedia)

作者： Zaenal Abidin Akmal Junaidi Wamiliana Frengky Marulitua Togatorop Imam Ahmad Ajeng Savitri Puspaningrum Doctoral Program of Mathematics and Natural Sciences Universitas Lampung Bandarlampung Indonesia Department of Computer Science Universitas Lampung Bandarlampung Indonesia Department of Mathematics Universitas Lampung Bandarlampung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia

The translation application was developed using Python and Visual Studio Code as the primary programming environment. Technological progress has led to the younger generation forsaking the Batak language in their daily interactions, perceiving it as inconsequential to their social circle and contributing to the decline of regional languages. There is concern that regional languages, namely the Batak language, may lose significance or face extinction if this trend persists. This translation program is being implemented to aid in preserving regional languages, specifically the Batak language, enabling it to conform to contemporary circumstances. The development of this translation engine utilized the Batak language dictionary. The process of designing, coding, and testing is responsible for creating this direct machine translation. The BLEU score is employed to assess the caliber of machine translation. The study's findings indicate that the % BLEU value for ten sentences was 32.3%. This score falls within the category of sentences whose meaning can be understood, but the sentence structure still needs development. The direct machine translation developed in this Web platform is highly proficient and practical.

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

arXiv

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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

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Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

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computers, Materials & Continua 2022年第11期73卷 2423-2439页

作者： Sara A.Althubiti JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Romany F.Mansour Fayadh Alenezi Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Research Center-CIENS Escuela Naval de Suboficiales A.R.C.“Barranquilla”BarranquillaColombia Electronics and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga 72511Egypt Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and ***,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several *** Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality *** OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate *** this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart *** primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and ***,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the *** addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature ***,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification *** design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s *** experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

关键词： Ovarian cancer decision support system smart healthcare IoMT deep learning feature selection

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Domain Adaptation in Color-Shifted Datasets Through Adversarial Learning Balancing

Domain Adaptation in Color-Shifted Datasets Through Adversar...

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Hidayaturrahman Agung Trisetyarso Iman Herwidiana Kartowisastro Widodo Budiharto Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusatara University Jakarta Indonesia Mathematics Department School of Computer Science Bina Nusatara University Jakarta Indonesia Computer Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Due to the disparity in the levels of difficulty presented by the several tasks, doing domain adaptation in an adversarial way may result in an imbalanced learning process. In the MNIST dataset, this phenomenon also manifests itself in the form of domain adaptation for color-shifted distribution. In this particular situation, the domain classifier has a higher tendency to fit more quickly, but the category classifier fits quite poorly in the learning process. In order to address this problem, a new hyper-parameter has been added to the loss function in order to strike a compromise between the learning speed of the domain and the categorical classifier. By using this technique, the categorical classifier may better match the data while still maintaining the same level of performance as the domain classifier. In order to determine whether or not making use of this hyper-parameter is useful, the phenomena in question is examined using three distinct color-shifted settings. Following the evaluations, it was discovered that the newly introduced hyper-parameter is capable of coping with imbalanced learning while simultaneously engaging in domain adaptation.

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Bayesian Parameter Inference for Partially Observed Stochastic Volterra Equations

arXiv

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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

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Exploring 3D Reconstruction with Drone Images: Advances and Challenges in Urban Environments 50

Exploring 3D Reconstruction with Drone Images: Advances and ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Miranda Baldivieso, Thiago João da Silva Batista, Taise Grazielle Chagas, Fabio Suim Rodrigues Velho, Luiz Carlos Pacheco Ferreira Rosa, Paulo Fernando Defense Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil State Institute of the Environment Inea RJ Rio de Janeiro Brazil Department of Science and Industry systems University of South-Eastern Norway Kongsberg Norway Department of Computer Graphics Institute for Pure and Applied Mathematics Rio de Janeiro Brazil Electrical Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil

ISBN: (纸本)9781665464543

This working-in-progress paper aims to present a three-dimensional reconstruction using aerial images in different environments. The experiments were conducted with aircraft in both external and internal settings, starting with image acquisition, followed by the application of specific photogrammetry software-both commercial and open-source-and concluding with a qualitative evaluation of the results. © 2024 IEEE.

关键词： Aerial photography

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Bayesian Spectral Graph Denoising with Smoothness Prior

Bayesian Spectral Graph Denoising with Smoothness Prior

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Annual Conference on Information sciences and Systems (CISS)

作者： Sam Leone Xingzhi Sun Michael Perlmutter Smita Krishnaswamy Program for Applied Mathematics Yale University Department of Computer Science Yale University Department of Mathematics Boise State University Department of Genetics Yale School of Medicine Wu Tsai Institute Yale University FAIR Meta AI Computational Biology and Bioinformatics Program Yale University

Here we consider the problem of denoising features associated to complex data, modeled as signals on a graph, via a smoothness prior. This is motivated in part by settings such as single-cell RNA where the data is very high-dimensional, but its structure can be captured via an affinity graph. This allows us to utilize ideas from graph signal processing. In particular, we present algorithms for the cases where the signal is perturbed by Gaussian noise, dropout, and uniformly distributed noise. The signals are assumed to follow a prior distribution defined in the frequency domain which favors signals which are smooth across the edges of the graph. By pairing this prior distribution with our three models of noise generation, we propose Maximum A Posteriori (M.A.P.) estimates of the true signal in the presence of noisy data and provide algorithms for computing the M.A.P. Finally, we demonstrate the algorithms’ ability to effectively restore signals from white noise on image data and from severe dropout in single-cell RNA sequence data.

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Revisiting Fuel Subsidies in Indonesia using K-Means, PAM, and CLARA

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2023年第3期50卷 858页

作者： Prasetyo, Fajar Agung Caraka, Rezzy Eko Kim, Yunho Goldameir, Noor Ell Sulistyowati Tyasti, Avia Enggar Gio, Prana Ugiana Anggoro, Faisal Ramadhani, Muthia Pardamean, Bens Faculty of Economics and Business Universitas Indonesia Indonesia Indonesia Department of Mathematics Ulsan National Institute of Science and Technology Korea Republic of Department of Mathematics Statistics Study Program Riau University Riau Pekanbaru Indonesia Institut Teknologi Adhi Tama Surabaya East Java Indonesia International Trade of ASEAN and RRT Region Polytechnic of APP DKI Jakarta Indonesia Department of Mathematics Universitas Sumatera Utara Medan20155 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

Indonesia is one of the countries in the world that still applies subsidies for fuel oil. By the law, the Indonesian government must ensure the supply and distribution of fuel for all Indonesian people. To implement this policy properly, understanding the pattern of fuel consumption is fundamental. In this study, clustering will be used to determine the categories of districts and cities based on subsidized fuel consumption patterns. Our research used several methods to compare which cluster method is the most optimal;the methods include k-means, Partitioning Around Medoids (PAM), and Clustering Large Applications (CLARA). The results show that k-means is the best clustering method with the highest Dunn index and Silhouette coefficient compared to others. The optimum cluster number we get is two and represents fuel consumption from several districts and cities. Some districts and cities had under-average consumption and needed to be monitored. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： K-means clustering

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UNRAVELING THE GEOGRAPHY OF INFECTION SPREAD: HARNESSING SUPER-AGENTS FOR PREDICTIVE MODELING

arXiv

引用

arXiv 2023年

作者： Sikaroudi, Amir Mohammad Esmaieeli Efrat, Alon Chertkov, Michael Computer Science Department University of Arizona TucsonAZ85721 United States Program in Applied Mathematics Department of Mathematics University of Arizona TucsonAZ85721 United States

Our study presents an intermediate-level modeling approach that bridges the gap between complex Agent-Based Models (ABMs) and traditional compartmental models for infectious diseases. We introduce "super-agents" to simulate infection spread in cities, reducing computational complexity while retaining individual-level interactions. This approach leverages real-world mobility data and strategic geospatial tessellations for efficiency. Voronoi Diagram tessellations, based on specific street network locations, outperform standard Census Block Group tessellations, and a hybrid approach balances accuracy and efficiency. Benchmarking against existing ABMs highlights key optimizations. This research improves disease modeling in urban areas, aiding public health strategies in scenarios requiring geographic specificity and high computational efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： Complex networks

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