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arXiv 2023年

作者： Pedersen, Christian Tesileanu, Tiberiu Wu, Tinghui Golkar, Siavash Cranmer, Miles Zhang, Zijun Ho, Shirley Courant Institute of Mathematical Sciences New York University New York United States Center for Data Science New York University New York United States Center for Computational Astrophysics Flatiron Institute New York United States Center for Computational Neuroscience Flatiron Institute New York United States Division of Artificial Intelligence in Medicine Department of Medicine Cedars-Sinai Medical Center Los Angeles United States Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Institute of Astronomy University of Cambridge Cambridge United Kingdom Department of Astrophysical Sciences Princeton University Princeton United States Center for Computational Biology Flatiron Institute New York United States

In Elmarakeby et al., "Biologically informed deep neural network for prostatecancer discovery", a feedforward neural network with biologically informed,sparse connections (P-NET) was presented to model the state of prostate *** verified the reproducibility of the study conducted by Elmarakeby et al.,using both their original codebase, and our own re-implementation using moreup-to-date libraries. We quantified the contribution of network sparsificationby Reactome biological pathways, and confirmed its importance to P-NET\'ssuperior performance. Furthermore, we explored alternative neural architecturesand approaches to incorporating biological information into the networks. Weexperimented with three types of graph neural networks on the same trainingdata, and investigated the clinical prediction agreement between differentmodels. Our analyses demonstrated that deep neural networks with distinctarchitectures make incorrect predictions for individual patient that arepersistent across different initializations of a specific neural *** suggests that different neural architectures are sensitive to differentaspects of the data, an important yet under-explored challenge for clinicalprediction tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Diseases

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The Use of Different Image Recognition Techniques in Food Safety: A Study

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Journal of Food Quality 2021年第1期2021卷

作者： Khan, Rijwan Kumar, Santosh Dhingra, Niharika Bhati, Neha Department of Computer Science and Engineering Abes Institute of Technology Affiliated to Aktu Lucknow U.P Ghaziabad20109 India Department of Mathematics College of Natural and Applied Sciences University of Dar Es Salaam Dar Es Salaam Tanzania United Republic of

Food safety refers to preparing, transporting, and storing food to avoid foodborne sickness and harm. From farm to factory and factory to fork, food items may meet various health dangers. Therefore, food safety is crucial both monetarily and morally. The implications of failing to comply with food safety requirements are varied. The requirement for accurate, quick, and nonpartisan quality assessments of these features in food products continues to rise with increased demands for dietary materials and high-quality requirements. computer vision provides an automatic, nondestructive, and economic approach to achieving these aims. A substantial research has demonstrated its effectiveness for fruit and vegetable assessment and classification. It stresses the critical components of image processing technology and a survey of the most current advances across the food sector. This article outlines the essential parts of a computer vision system. In order to avoid foodborne disease and ensure food security, fast and effective detection of pathogenic microorganisms is crucial for public safety biomonitoring. Over the years, microorganism detection techniques have evolved. © 2021 Rijwan Khan et al.

关键词： Food safety

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Advancing quantum imaging through learning theory

arXiv

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arXiv 2025年

作者： Wang, Yunkai Oh, Changhun Liu, Junyu Jiang, Liang Zhou, Sisi Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada Department of Applied Mathematics University of Waterloo ONN2L 3G1 Canada Institute for Quantum Computing University of Waterloo ONN2L 3G1 Canada Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Computer Science The University of Pittsburgh PittsburghPA15260 United States Pritzker School of Molecular Engineering The University of Chicago ChicagoIL60637 United States Department of Physics and Astronomy University of Waterloo ONN2L 3G1 Canada

We quantify performance of quantum imaging by modeling it as a learning task and calculating the Resolvable Expressive Capacity (REC). Compared to the traditionally applied Fisher information matrix approach, REC provides a single-parameter interpretation of overall imaging quality for specific measurements that applies in the regime of finite samples. We first examine imaging performance for two-point sources and generally distributed sources, referred to as compact sources, both of which have intensity distributions confined within the Rayleigh limit of the imaging system. Our findings indicate that REC increases stepwise as the sample number reaches certain thresholds, which are dependent on the source’s size. Notably, these thresholds differ between direct imaging and superresolution measurements (e.g., spatial-mode demultiplexing (SPADE) measurement in the case of Gaussian point spread functions (PSF)). REC also enables the extension of our analysis to more general scenarios involving multiple compact sources, beyond the previously studied scenarios. For closely spaced compact sources with Gaussian PSFs, our newly introduced orthogonalized SPADE method outperforms the naively separate SPADE method, as quantified by REC. Copyright © 2025, The Authors. All rights reserved.

关键词： Fisher information matrix

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Prediction and Prevention of Pandemics via Graphical Model Inference and Convex Programming

arXiv

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arXiv 2021年

作者： Krechetov, Mikhail Sikaroudi, Amir Mohammad Esmaieeli Efrat, Alon Polishchuk, Valentin Chertkov, Michael Skolkovo Institute of Science and Technology Moscow121205 Russia University of Arizona Department of Computer Science TucsonAZ85721 United States University of Arizona Program in Applied Mathematics TucsonAZ85721 United States Linköping Univeristy Communications and Transport Systems Norrkoping60174 Sweden University of Arizona Department of Mathematics TucsonAZ85721 United States

Hard-to-predict bursts of COVID-19 pandemic revealed significance of statistical modeling which would resolve spatio-temporal correlations over geographical areas, for example spread of the infection over a city with census tract granularity. In this manuscript, we provide algorithmic answers to the following two inter-related public health challenges of immense social impact which have not been adequately addressed (1) Inference Challenge: assuming that there are N census blocks (nodes) in the city, and given an initial infection at any set of nodes, e.g. any N of possible single node infections, any N(N-1)=2 of possible two node infections, etc, what is the probability for a subset of census blocks to become infected by the time the spread of the infection burst is stabilized? (2) Prevention Challenge: What is the minimal control action one can take to minimize the infected part of the stabilized state footprint? To answer the challenges, we build a Graphical Model of pandemic of the attractive Ising (pair-wise, binary) type, where each node represents a census tract and each edge factor represents the strength of the pairwise interaction between a pair of nodes, e.g. representing the inter-node travel, road closure and related, and each local bias/field represents the community level of immunization, acceptance of the social distance and mask wearing practice, etc. Resolving the Inference Challenge requires finding the Maximum-A-Posteriory (MAP), i.e. most probable, state of the Ising Model constrained to the set of initially infected nodes. (An infected node is in the +1 state and a node which remained safe is in the-1 state.) We show that almost all attractive Ising Models on dense graphs result in either of the two possibilities (modes) for the MAP state: either all nodes which were not infected initially became infected, or all the initially uninfected nodes remain uninfected (susceptible). This bi-modal solution of the Inference Challenge allows us to re-sta

关键词： Ising model

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Gender-age effects in assessing attitudes toward statistics among undergraduate students: structural equation approach

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Research in Statistics 2024年第1期2卷

作者： Isaac Ofori Asare Dorothy Anima Frimpong Reuben Dzotefe Alice Constance Mensah Samuel Baffoe a Accra Technical University Accra Ghana b Computer Science Department Accra Technical University Accra Ghana c Mamprobi Polyclinic Accra Ghana d Mathematics and Statistics Department Accra Technical University Accra Ghana e Applied Mathematics Department Koforidua Technical University Koforidua Ghana

Students’ ability to be abreast with the basic concepts in statistics and its applications would help to stay relevant in the modern world due to the dynamic ways of doing things currently. The aims of the study were to assess the psychometric properties of the SATS in the Ghanaian context, to explore attitudes influencing factors and the relationship between psychometric properties of SATS and to examine gender-age effects in assessing statistical literacy among students. In this research the study was conducted to acquire a valid instrument to measure attitudes toward statistics in the Ghanaian context. The study surveyed 1000 undergraduate students from 10 universities. In this study, structural equation modeling through confirmatory factor analysis was used to validate the six-factor structure of the SATS scale and resulted in good model fit indices. A multi-group analysis was performed, which suggests gender and age have significant influence on some of the psychometric properties of the SATS components.

关键词： Student Sstatistics literacy reliability confirmatory factor analysis attitude

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Sparse spectral-galerkin method on an arbitrary tetrahedron using generalized koornwinder polynomials

arXiv

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arXiv 2021年

作者： Jia, Lueling Li, Huiyuan Zhang, Zhimin Beijing Computational Science Research Center Beijing100193 China State Key Laboratory of Computer Science Laboratory of Parallel Computing Institute of Software Chinese Academy of Sciences Beijing100190 China Department of Mathematics Wayne State University DetroitMI48202 United States

In this paper, we propose a sparse spectral-Galerkin approximation scheme for solving the second-order partial differential equations on an arbitrary tetrahedron. Generalized Koornwinder polynomials are introduced on the reference tetrahedron as basis functions with their various recurrence relations and differentiation properties being explored. The method leads to well-conditioned and sparse linear systems whose entries can either be calculated directly by the orthogonality of the generalized Koornwinder polynomials for differential equations with constant coefficients or be evaluated efficiently via our recurrence algorithm for problems with variable coefficients. Clenshaw algorithms for the evaluation of any polynomial in an expansion of the generalized Koornwinder basis are also designed to boost the efficiency of the method. Finally, numerical experiments are carried out to illustrate the effectiveness of the proposed Koornwinder spectral *** Codes 65N25, 65N35 © 2021, CC0.

关键词： Polynomials

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Determination of the main parameters of the photovoltaic solar module 3

Determination of the main parameters of the photovoltaic sol...

引用

3rd International Conference on Renewable Energy and Environment Engineering, REEE 2020

作者： Urmashev, Baydaulet A. Kunelbayev, Murat Temirbekov, Almas N. Kassenov, Syrym Zhaksylykova, Zhadra Amenova, Farida Department of Computer Science Kazakh National University Named after Al-Farabi Almaty Kazakhstan Institute Information and Computational Technologies Cs Mes Rk Almaty Kazakhstan Faculty of Mechanical Mathematics Al-Farabi Kazakh National University Almaty Kazakhstan Abay Kazakh National Pedagogical University Almaty Kazakhstan S. Amanzholov East Kazakhstan State University Department of Mathematics Faculty of Science and Technology Ust-Kamenogorsk Kazakhstan

This article deals with the determination of the main operating parameters of a photovoltaic solar module. In laboratory tests, the study of the dependence of current, voltage and power on time and density of solar radiation, as well as monitoring of environmental parameters: temperature and humidity of the outside air. Analysis of the test results shows that a photoelectric module with an installed capacity of 800 W and a total battery capacity of 800 Ah provides the electric power industry with a daily consumption of 2.0.. 2.2 kWh. The discharge time of the battery varies from 11.7 to 3.5 hours when the average electric load of the consumer changes from 300 to 1000 watts. © 2020 The Authors, published by EDP sciences.

关键词： Electric industry

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Non-rotational measures in the Bargmann space, and entanglement of bipartite squeezed coherent states

arXiv

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arXiv 2024年

作者： Górska, K. Horzela, A. Kolaczek, D. Spisak, B.J. Szafraniec, F.H. H. Niewodniczański Institute of Nuclear Physics Polish Academy of Sciences ul.Eljasza-Radzikowskiego 152 KrakówPL 31342 Poland Department of Applied Mathematics University of Agriculture in Kraków ul. Balicka 253c Kraków30–198 Poland AGH University of Krakow Faculty of Physics and Applied Computer Science al. A. Mickiewicza 30 Krakow30-059 Poland Instytut Matematyki Uniwersytet Jagielloński ul. Lojasiewicza 6 Kraków30 348 Poland

Entanglement of bipartite squeezed states generated by holomorphic Hermite functions of two complex variables is investigated using phase-space approach based on the Wigner distribution function. Orthogonality of the holomorphic Hermite functions implies the relationship between certain real parameter associated with the non-rotational measure in the Bargmann space and the squeezing parameter. The mutual relation between squeezing and entanglement is elucidated with the help of Peres-Horodecki positive partial transpose criterion formulated in the phase-space version for continuous-variable systems. The quantitative characteristics of the entanglement is determined using the log-negativity criterion. The oscillator-like model of a two-particle quantum-mechanical system is developed to illustrate the presented findings. © 2024, CC BY.

关键词： Phase space methods

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Employing feature engineering strategies to improve the performance of machine learning algorithms on echocardiogram dataset

引用

Digital Health 2023年 9卷

作者： Huang, Huang-Nan Chen, Hong-Ming Lin, Wei-Wen Huang, Chau-Jian Chen, Yung-Cheng Wang, Yu-Huei Yang, Chao-Tung Department of Applied Mathematics Tunghai University Taichung City Taiwan Cardiovascular Center Taichung Veterans General Hospital Taichung City Taiwan Department of PostBaccalaureate Medicine National Chung Hsing University Taichung Taiwan Department of Life Science Tunghai University Taichung City Taiwan Department of Information Management ShuZen junior College of Medicine and Management Kaohsiung City Taiwan Department of Computer Science Tunghai University Taichung City Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung City Taiwan

Objectives: This study mainly uses machine learning (ML) to make predictions by inputting features during training and inference. The method of feature selection is an important factor affecting the accuracy of ML models, and the process includes data extraction, which is the collection of all data required for ML. It also needs to import the concept of feature engineering, namely, this study needs to label the raw data of the cardiac ultrasound dataset with one or more meaningful and informative labels so that the ML model can learn from it and predict more accurate target values. Therefore, this study will enhance the strategies of feature selection methods from the raw dataset, as well as the issue of data scrubbing. Methods: In this study, the ultrasound dataset was cleaned and critical features were selected through data standardization, normalization, and missing features imputation in the field of feature engineering. The aim of data scrubbing was to retain and select critical features of the echocardiogram dataset while making the prediction of the ML algorithm more accurate. Results: This paper mainly utilizes commonly used methods in feature engineering and finally selects four important feature values. With the ML algorithms available on the Azure platform, namely, Random Forest and CatBoost, a Voting Ensemble method is used as the training algorithm, and this study also uses visual tools to gain a clearer understanding of the raw data and to improve the accuracy of the predictive model. Conclusion: This paper emphasizes feature engineering, specifically on the cleaning and analysis of missing values in the raw dataset of echocardiography and the identification of important critical features in the raw dataset. The Azure platform is used to predict patients with a history of heart disease (individuals who have been under surveillance in the past three years and those who haven’t). Through data scrubbing and preprocessing methods in feature engineering, th

关键词： correlation matrix data scrubbing feature selection machine learning Precision medicine

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Probing reaction channels via reinforcement learning

arXiv

引用

arXiv 2023年

作者： Liang, Senwei Singh, Aditya N. Zhu, Yuanran Limmer, David T. Yang, Chao Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Chemistry University of California Berkeley BerkeleyCA94720 United States Chemical Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Materials Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Kavli Energy Nanoscience Institute at Berkeley BerkeleyCA94720 United States

We propose a reinforcement learning based method to identify important configurations that connect reactant and product states along chemical reaction paths. By shooting multiple trajectories from these configurations, we can generate an ensemble of configurations that concentrate on the transition path ensemble. This configuration ensemble can be effectively employed in a neural network-based partial differential equation solver to obtain an approximation solution of a restricted Backward Kolmogorov equation, even when the dimension of the problem is very high. The resulting solution, known as the committor function, encodes mechanistic information for the reaction and can in turn be used to evaluate reaction rates. © 2023, CC BY.

关键词： Reinforcement learning

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