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38th International Conference on Machine Learning, ICML 2021

作者： Kümmerle, Christian Verdun, Claudio Mayrink Department of Applied Mathematics & Statistics Johns Hopkins University Baltimore United States Department of Mathematics Department of Electrical and Computer Engineering Technical University of Munich Munich Germany

ISBN: (纸本)9781713845065

We propose an iterative algorithm for low-rank matrix completion that can be interpreted as an iteratively reweighted least squares (IRLS) algorithm, a saddle-escaping smoothing Newton method or a variable metric proximal gradient method applied to a non-convex rank surrogate. It combines the favorable data-efficiency of previous IRLS approaches with an improved scalability by several orders of magnitude. We establish the first local convergence guarantee from a minimal number of samples for that class of algorithms, showing that the method attains a local quadratic convergence rate. Furthermore, we show that the linear systems to be solved are well-conditioned even for very ill-conditioned ground truth matrices. We provide extensive experiments, indicating that unlike many state-of-the-art approaches, our method is able to complete very ill-conditioned matrices with a condition number of up to 1010 from few samples, while being competitive in its scalability. Copyright © 2021 by the author(s)

关键词： Scalability

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Differentially Private Synthetic Data with Private Density Estimation

arXiv

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arXiv 2024年

作者： Bojkovic, Nikolija Loh, Po-Ling School of Electrical Engineering University of Belgrade Belgrade Serbia Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom

The need to analyze sensitive data, such as medical records or financial data, has created a critical research challenge in recent years. In this paper, we adopt the framework of differential privacy, and explore mechanisms for generating an entire dataset which accurately captures characteristics of the original data. We build upon the work of Boedihardjo et al. [1], which laid the foundations for a new optimization-based algorithm for generating private synthetic data. Importantly, we adapt their algorithm by replacing a uniform sampling step with a private distribution estimator;this allows us to obtain better computational guarantees for discrete distributions, and develop a novel algorithm suitable for continuous distributions. We also explore applications of our work to several statistical *** Codes 62G07 © 2024, CC BY.

关键词： Sensitive data

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Comparative study of AI algorithms in boundary layer flow: Evaluating performance of levenberg-marquardt, bayesian, and scaled conjugate methods

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Thermal Science and engineering Progress 2025年 63卷

作者： Taha, Talal Abdal, Sohaib Ali, Liaqat Zulqarnain, Rana Muhammad Yook, Se-Jin Department of Mathematics and Statistics Faculty of Sciences International Islamic University Islamabad04436 Pakistan School of Mechanical Engineering Hanyang University 222 Wangsimni-ro Seongdong-gu Seoul04763 Korea Republic of School of Sciences Xi'an Technological University Xi'an710021 China Department of Mathematics Saveetha School of Engineering SlMATS Chennai India

The current research article presents an investigation into steady two-dimensional incompressible Sisko fluid flow, which is produced by magnetohydrodynamics (MHD), thermal radiation, and bioconvection over an exponential sheet. The obtained partial differential equations are reduced to coupled ordinary differential equations using appropriate similarity variables. The numerical results are first achieved through the BVP4C technique. After that, an artificial neural network will be performed to test the ability and accuracy of the model using the dataset in training, validation, and testing sets. The study mainly focuses on testing artificial neural networking (ANN) algorithms such as Levenberg-Marquardt, Bayesian regularization, and scaled conjugate gradient. After concluding the previous steps, it is found that the most suitable algorithm for this project is the Levenberg-Marquardt algorithm as it is efficient. Table 4 show the statistical difference between these three AI techniques. The research proves that an increase in fluid parameters improves the velocity profile, whereas greater thermal radiation strengthens the energy profile. This indicates the thermal and flow characteristics of Sisko fluid, where momentum and heat transfer are improved with these parameters. Both the Bayesian and Scaled Conjugate algorithms exhibit low performance;Scaled Conjugate displays fluctuating fitness, while Bayesian lacks validation data. Levenberg-Marquardt ensures accurate modeling by achieving high regression (∼1) and ideal MSE (≤10−8). Sisko fluid on a sheet containing gyrotactic bacteria improves bioconvective heat transmission and has applications in microfluidics and biomedicine. © 2025 Elsevier Ltd

关键词： Natural convection

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Spatial Reconstruction of Precipitation Fields Using the Effective Distribution Methodology with Compound Poisson Gamma Marginals

SSRN

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SSRN 2024年

作者： Baxevani, Anastassia Andreou, Christos Hristopulos, Dionissios T. Department of Mathematics and Statistics University of Cyprus Cyprus School of Electrical and Computer Engineering Technical University of Crete Crete Chania73100 Greece

Modeling daily precipitation remains a challenge due to the intermittent, non-Gaussian, and spatially dependent structure of the *** propose a novel stochastic spatial precipitation generator which uses the compound Poisson gamma distribution (CPG) to handle simultaneously the intermittency and non-Gaussianity of daily precipitation combined with the effective distribution method (EDM) to model spatial dependence. We present an estimation approach for the model parameters from daily precipitation time series at a number of sampling locations. In addition, we employ EDM with computationally efficient simulation algorithms that account for intermittency and correlations at neighboring sites, in order to generate realistic spatial precipitation *** performance of the proposed CPG-EDM method is assessed by fitting the model to reanalysis datasets and calculating univariate and joint-dependence cross-validation measures. © 2024, The Authors. All rights reserved.

关键词： Stochastic models

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Discrete Morse Theory on Join of Digraphs

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Wuhan University Journal of Natural Sciences 2022年第4期27卷 303-312页

作者： WANG Chong ZHAO Suqian CUI Shuwen Department of Mathematics and Statistics Cangzhou Normal UniversityCangzhou 061000HebeiChina School of Sciences Hebei University of Science and TechnologyShijiazhuang 050018HebeiChina Department of Physics and Information Engineering Cangzhou Normal UniversityCangzhou 061000HebeiChina

For given two digraphs,we can construct a larger digraph through *** two digraphs that make up the join are called the factors of the *** this paper,we give a necessary and sufficient condition that the function on the join determined by the discrete Morse functions on factors is a discrete Morse ***,we further prove the discrete Morse theory on join when the factors satisfy certain conditions.

关键词： path homology transitive closure digraph join

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Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing

Communication-Efficient Decentralized Federated Learning via...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Shenglong Zhou Kaidi Xu Geoffrey Ye Li The School of Mathematics and Statistics Beijing Jiaotong University Beijing China Department of Electrical and Electronic Engineering Imperial College London London UK

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

Decentralized federated learning (DFL) has gained popularity due to its practicality across various applications. Compared to the centralized version, training a shared model among a large number of nodes in DFL is more challenging, as there is no central server to coordinate the training process. Especially when distributed nodes suffer from limitations in communication or computational resources, DFL will experience extremely inefficient and unstable training. Motivated by these challenges, in this paper, we develop a novel algorithm based on the framework of the inexact alternating direction method (iADM). On one hand, our goal is to train a shared model with a sparsity constraint. This constraint enables us to leverage one-bit compressive sensing (1BCS), allowing transmission of one-bit information among neighbour nodes. On the other hand, communication between neighbour nodes occurs only at certain steps, reducing the number of communication rounds. Therefore, the algorithm exhibits notable communication efficiency. Additionally, as each node selects only a subset of neighbours to participate in the training, the algorithm is robust against stragglers. Additionally, complex items are computed only once for several consecutive steps and subproblems are solved inexactly using closed-form solutions, resulting in high computational efficiency. Finally, numerical experiments showcase the algorithm's effectiveness in both communication and computation.

关键词： Training Vehicular and wireless technologies Technological innovation Federated learning Computational modeling Computational efficiency Servers

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Deep Learning for Lung Disease Classification Using Transfer Learning and a Customized CNN Architecture with Attention 2

Deep Learning for Lung Disease Classification Using Transfer...

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2nd IEEE International Conference on Sensors, Electronics and Computer engineering, ICSECE 2024

作者： Liu, Xiaoyi Yu, Zhou Tan, Lianghao Ira A. Fulton Schools of Engineering Arizona State University Tempe United States University of Illinois at Chicago Department of Mathematics Statistics and Computer Science Chicago United States W. P. Carey School of Business Arizona State University Tempe United States

ISBN: (纸本)9798350373646

Many people die from lung-related diseases every year. X-ray is an effective way to test if one is diagnosed with a lung-related disease or not. This study concentrates on categorizing three distinct types of lung X-rays: those depicting healthy lungs, those showing lung opacities, and those indicative of viral pneumonia. Accurately diagnosing the disease at an early phase is critical. In this paper, five different pre-trained models will be tested on the Lung X-ray Image Dataset. SqueezeNet, VGG11, ResNet18, DenseNet, and MobileNetV2 achieved accuracies of 0.64, 0.85, 0.87, 0.88, and 0.885, respectively. MobileNetV2, as the best-performing pre-trained model, will then be further analyzed as the base model. Eventually, our own model, MobileNet-Lung based on MobileNetV2, with fine-tuning and an additional layer of attention within feature layers, was invented to tackle the lung disease classification task and achieved an accuracy of 0.933. This result is significantly improved compared with all five pre-trained models. © 2024 IEEE.

关键词： Lung cancer

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Computable Bounds on the Solution to Poisson’s Equation for General Harris Chains

arXiv

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arXiv 2025年

作者： Glynn, Peter W. Lin, Na Liu, Yuanyuan Department of Management Science and Engineering Institute for Computational and Mathematical Engineering Stanford University United States School of Mathematics and Statistics HNP-LAMA Central South University China

Poisson’s equation is fundamental to the study of Markov chains, and arises in connection with martingale representations and central limit theorems for additive functionals, perturbation theory for stationary distributions, and average reward Markov decision process problems. In this paper, we develop a new probabilistic representation for the solution of Poisson’s equation, and use Lyapunov functions to bound this solution representation explicitly. In contrast to most prior work on this problem, our bounds are computable. Our contribution is closely connected to recent work of Hervé and Ledoux [2025], in which they focus their study on a special class of Harris chains satisfying a particular small set condition. However, our theory covers general Harris chains, and often provides a tighter bound. In addition to the new bound and representation, we also develop a computable uniform bound on marginal expectations for Harris chains, and a computable bound on the potential kernel representation of the solution to Poisson’s equation. Copyright © 2025, The Authors. All rights reserved.

关键词： Poisson equation

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Emergency Decision-Making Based on q-Rung Orthopair Fuzzy Rough Aggregation Information

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Computers, Materials & Continua 2021年第12期69卷 4077-4094页

作者： Ahmed B.Khoshaim Saleem Abdullah Shahzaib Ashraf Muhammad Naeem Department of Mechanical Engineering King Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Abdul Wali Khan University MardanMardan23200Pakistan Department of Mathematics and Statistics Bacha Khan UniversityCharsadda24420Pakistan Deanship of Combined First Year Umm Al-Qura UniversityMakkahSaudi Arabia

With the frequent occurrences of emergency events,emergency decision making(EDM)plays an increasingly significant role in coping with such situations and has become an important and challenging research area in recent *** is essential for decision makers to make reliable and reasonable emergency decisions within a short span of time,since inappropriate decisions may result in enormous economic losses and social *** handle emergency effectively and quickly,this paper proposes a new EDM method based on the novel concept of q-rung orthopair fuzzy rough(q-ROPR)set.A novel list of q-ROFR aggregation information,detailed description of the fundamental characteristics of the developed aggregation operators and the q-ROFR entropy measure that determine the unknown weight information of decision makers as well as the criteria weights are *** an algorithm is given to tackle the uncertain scenario in emergency to give reliable and reasonable emergency *** using proposed list of q-ROFR aggregation information all emergency alternatives are ranked to get the optimal *** this,the q-ROFR entropy measure method is used to determine criteria and experts’weights objectively in the EDM ***,through an illustrative example of COVID-19 analysis is compared with existing EDM *** results verify the effectiveness and practicability of the proposed methodology.

关键词： q-rung orthopair fuzzy rough set q-ROFR entropy measure aggregation information emergency decision making

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Phytochemical Analysis, Computational Study, and in vitro Assay of Etlingera elatior Inflorescence Extract Towards Inducible Nitric Oxide Synthase

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Journal of Experimental Pharmacology 2025年 17卷 123-141页

作者： Prayoga, Deshanda Kurniawan Pitaloka, Dian Ayu Eka Aulifa, Diah Lia Budiman, Arif Levita, Jutti Jiranusornkul, Supat Nguyen, Binh Phu Program in Pharmacy Faculty of Pharmacy Universitas Padjadjaran Sumedang Indonesia Department of Pharmacology and Clinical Pharmacy Faculty of Pharmacy Universitas Padjadjaran Sumedang Indonesia Department of Pharmaceutical Analysis and Medicinal Chemistry Faculty of Pharmacy Universitas Padjadjaran Sumedang Indonesia Department of Pharmaceutics and Pharmaceutical Technology Faculty of Pharmacy Universitas Padjadjaran Sumedang Indonesia Department of Pharmaceutical Science Faculty of Pharmacy Chiang Mai University Chiang Mai Thailand School of Mathematics and Statistics Victoria University of Wellington Wellington New Zealand

Background: Overproduction of nitric oxide (NO), catalyzed by inducible nitric oxide synthase (iNOS), in the gastric mucosa, contributes to the inflammatory process caused by oxidative stress. Current medications for gastric ulcers, such as proton pump inhibitors and histamine-2 receptor antagonists, have been reported to generate adverse reactions. Purpose: To obtain the phytochemical profile of Etlingera elatior inflorescence extract, computational studies, and in vitro assay of the extract towards iNOS. Methods: Fresh E. elatior inflorescence petals collected from West Java, Indonesia, were extracted using ethanol, and their nutritional composition, anthocyanin content, and levels of vitamin C, C3G, and quercetin in the extract were determined. Drug-likeness and ADMET properties were predicted, and the binding affinity and stability of the phytoconstituents towards iNOS were studied using molecular docking and molecular dynamic simulation, and in vitro assay of the extract towards human iNOS. Results: The extract contains protein 21.81%, fat 0.99%, carbohydrate 38.27%, water 24.56%, and ash 14.37%. The total anthocyanin and vitamin C levels were 47.535 mg/100 g and 985.250 mg/100 g, respectively. The levels of C3G and quercetin were 0.0007% w/w, 0.004% w/w, and 0.0005% w/w, respectively. Drug-likeness and ADMET properties of the constituents showed that most followed Lipinski Rules of Five (Ro5), with few violations. All phytoconstituents occupied the catalytic site by binding to Glu377, and Trp372, similar to S-ethylisothiourea (SEITU) and quinazoline, the iNOS inhibitors. Among these, the flavylium cation of cyanidin, demethoxycurcumin, C3G, cyanidin, and quercetin showed the best binding affinities. Root mean square deviation (RMSD), root mean square fluctuation (RMSF), solvent-accessible surface area (SASA), and radius of gyration (Rg) graphs confirmed the stability of the complexes. E. elatior inflorescence extract inhibited human iNOS with an IC50 value of

关键词： anti-inflammation cyanidin-3-glucoside polyphenols proximate analysis quercetin

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