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arXiv 2024年

作者： Feriani, Sophia Julia Cosnefroy, Matthias Engsig-Karup, Allan Peter Warburton, Tim Pind, Finnur Jeong, Cheol-Ho Acoustic Technology Department of Electrical and Photonics Engineering Technical University of Denmark Ørsteds Plads Bygning 352 Kongens Lyngby2800 Denmark Treble Technologies Hafnartorg Kalkofnsvegur 2 Reykjavík101 Iceland Scientific Computing Department of Applied Mathematics and Computer Science Technical University of Denmark Richard Petersens Plads Building 324 Kongens Lyngby2800 Denmark Data & Decision Sciences Virginia Tech 727 Prices Fork Road BlacksburgVA24060 United States

In outdoor acoustics, the calculations of sound propagating in air can be computationally heavy if the domain is chosen large enough to fulfil the Sommerfeld radiation condition. By strategically truncating the computational domain with a efficient boundary treatment, the computational cost is lowered. One commonly used boundary treatment is the perfectly matched layer (PML) that dampens outgoing waves without polluting the computed solution in the inner domain. The purpose of this study is to propose and assess a new perfectly matched layer formulation for the 3D acoustic wave equation, using the nodal discontinuous Galerkin finite element method. The formulation is based on an efficient PML formulation that can be decoupled to further increase the computational efficiency and guarantee stability without sacrificing accuracy. This decoupled PML formulation is demonstrated to be long-time stable and an optimization procedure of the damping functions is proposed to enhance the performance of the formulation. Copyright © 2024, The Authors. All rights reserved.

关键词： computational efficiency

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Glaucoma is associated with poorer self-rated vision, vision concerns and health: Results from 59,168 questionnaire responses

AJO International

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AJO International 2024年第2期1卷 100035-100035页

作者： Rovelt, Jens Freiberg, Josefine Reimann, Marcel Benn, Marianne Torp-Pedersen, Christian Kolko, Miriam Department of Drug Design and Pharmacology University of Copenhagen Copenhagen 2100 Denmark Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs Lyngby 2800 Denmark Department of Clinical Biochemistry University of Copenhagen Rigshospitalet Copenhagen 2100 Denmark Department of Cardiology Nordsjællands Hospital Hillerød 3400 Denmark Department of Public Health University of Copenhagen Copenhagen 1353 Denmark Department of Ophthalmology Copenhagen University Hospital Rigshospitalet Glostrup 2600 Denmark

Purpose: This study aimed to examine self-rated vision, vision concerns, and general health in individuals with and without glaucoma. Such assessments offer a broad view of a person's overall well-being and potential health issues and provide valuable insights into the characteristics of glaucoma patients. Design: Cross-sectional. Methods: We used questionnaire responses from Project FOREVER (Finding Ophthalmic Risk and Evaluating the Value of Eye exams and their predictive Reliability) to evaluate self-rated vision and general health on a scale ranging from 1 (excellent), 2 (good), 3 (reasonable), 4 (poor) to 5 (very bad). Additionally, the vision concerns were rated on a scale from 1 (never), 2 (rarely), 3 (once in a while), 4 (often) to 5 (always). A glaucoma diagnosis was self-reported and obtained from the questionnaire. To investigate the strength and direction of associations, we used binomial logistic regression models including self-assessment, age, and sex as confounding variables and glaucoma status as the dependent variable. Results: This study analyzed questionnaire data from 59,168 participants, with an average age of 55.2 years (SD 15.0), and 62.0 % being women. Of these, 900 individuals (1.5 %) reported having a glaucoma diagnosis. Individuals having glaucoma reported poorer self-ratings in vision, vision concerns and general health. Specifically, the odds ratios for the poorest ratings were 2.46 for self-rated vision (95 % CI 1.70–3.55, p < 0.001), 8.04 for self-rated vision concerns (95 % CI 5.44–11.90, p < 0.001) and 1.86 for self-rated health (95 % CI 1.16–2.98, p = 0.010). Conclusion: Glaucoma status is associated with poorer self-assessments of vision, vision concerns, and health. This suggests that individuals with glaucoma experience more vision and health challenges than people without the disease and the three assessments can be used as tools to characterize glaucoma patients in risk profiling efforts. © 2024 The Author(s)

关键词： Glaucoma Self-rated health Self-rated vision Self-rated vision concerns

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Comparative analyses of multi-criteria methods in supplier selection problem

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Procedia computer science 2022年 207卷 4593-4602页

作者： Bartosz Paradowski Zdzisław Szyjewski Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Żołnierska 49 71-210 Szczecin Poland Institute of Management University of Szczecin ul. Cukrowa 8 71-004 Szczecin Poland

The ever-growing interest in multi-criteria decision-making methods requires research that would compare them with each other as the decision-makers would need help choosing the appropriate method. It is important to highlight the differences between them, because using different methods may result in discrepancies in the obtained rankings. Additionally, it is worthwhile for the research to take into account the problems whose solution is extremely valuable. To this end, we present a comparison of five multi-criteria decision-making methods: TOPSIS, VIKOR, COMET, SPOTIS, and MARCOS in the supplier selection problem. The conducted research showed significant differences in the evaluation of the preferences of the alternatives and some discrepancies in the obtained rankings.

关键词： MCDM MCDA supplier selection comparison rank similarity

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Super-Resolution without High-Resolution Labels for Black Hole Simulations

arXiv

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arXiv 2024年

作者： Helfer, Thomas Edwards, Thomas D.P. Dafflon, Jessica Wong, Kaze W.K. Olson, Matthew Lyle Institute for Advanced Computational Science Stony Brook University Stony BrookNY11794 United States William H. Miller III Department of Physics and Astronomy Johns Hopkins University BaltimoreMD21218 United States Valence Labs 6666 Rue Saint-Urbain MontrealQCH2S 3H1 Canada Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD21218 United States Data Science and AI Institute Johns Hopkins University BaltimoreMD21218 United States Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States Intel Labs Santa ClaraCA United States

Generating high-resolution simulations is key for advancing our understanding of one of the universe's most violent events: Black Hole mergers. However, generating Black Hole simulations is limited by prohibitive computational costs and scalability issues, reducing the simulation's fidelity and resolution achievable within reasonable time frames and resources. In this work, we introduce a novel method that circumvents these limitations by applying a super-resolution technique without directly needing high-resolution labels, leveraging the Hamiltonian and momentum constraints-fundamental equations in general relativity that govern the dynamics of spacetime. We demonstrate that our method achieves a reduction in constraint violation by one to two orders of magnitude and generalizes effectively to out-of-distribution simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Black holes

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Predicting Solar Proton Events of Solar Cycles 22 - 24 using GOES Proton & soft X-ray flux features

arXiv

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arXiv 2023年

作者： Ali, Aatiya Sadykov, Viacheslav Kosovichev, Alexander Kitiashvili, Irina N. Oria, Vincent Nita, Gelu M. Illarionov, Egor O'Keefe, Patrick M. Francis, Fraila Chong, Chun-Jie Kosovich, Paul Marroquin, Russell D. Physics & Astronomy Department Georgia State University AtlantaGA30303 United States Physics Department New Jersey Institute of Technology NewarkNJ07102 United States NASA Ames Research Center Moffett FieldCA94035 United States Computer Science Department New Jersey Institute of Technology NewarkNJ07102 United States Department of Mechanics and Mathematics Moscow State University Moscow119991 Russia Moscow Center of Fundamental and Applied Mathematics Moscow119234 Russia Department of Physics University of California San Diego La Jolla CA92093 United States

Solar Energetic Particle (SEP) events and their major subclass, Solar Proton Events (SPEs), can have unfavorable consequences on numerous aspects of life and technology, making them one of the most harmful effects of solar activity. Garnering knowledge preceding such events by studying operational data flows is essential for their forecasting. Considering only Solar Cycle (SC) 24 in our previous study (Sadykov et al. 2021), we found that it may be sufficient to utilize only proton and soft X-ray (SXR) parameters for SPE forecasts. Here, we report a catalog recording ≥10 MeV ≥10 particle flux unit SPEs with their properties, spanning SCs 22-24, using NOAA's Geostationary Operational Environmental Satellite flux data. We report an additional catalog of daily proton and SXR flux statistics for this period, employing it to test the application of machine learning (ML) on the prediction of SPEs using a Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost). We explore the effects of training models with data from one and two SCs, evaluating how transferable a model can be across different time periods. XGBoost proved to be more accurate than SVMs for almost every test considered, while outperforming operational SWPC NOAA predictions and a persistence forecast. Interestingly, training done with SC 24 produces weaker TSS and HSS2, even when paired with SC 22 or SC 23, indicating transferability issues. This work contributes towards validating forecasts using long-spanning data- an understudied area in SEP research that should be considered to verify the cross-cycle robustness of ML-driven forecasts. © 2023, CC BY.

关键词： Forecasting

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The Effectiveness of the Finite Differences Method on Physical and Medical Images Based on a Heat Diffusion Equation 2

The Effectiveness of the Finite Differences Method on Physic...

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2nd International Virtual Conference on Pure science, 2IVCPS 2021

作者： Rasheed, Mohammed Ali, Ali Hassan Alabdali, Osama Shihab, Suha Rashid, Ahmed Rashid, Taha Abed Hamad, Saad Hussein Applied Sciences Department University of Technology Baghdad Iraq Doctoral School of Mathematical and Computational Sciences University of Debrecen Pf. 400 DebrecenH-4002 Hungary Department of Mathematics College of Education for Pure Sciences University of Basrah Basrah Iraq College of Education for Pure Sciences University of Anbar Al-Anbar Iraq Al Iraqia University College of Arts Baghdad Iraq Skudai Johor Bahru81310 Malaysia College of Computer Science and Information Technology Al-Diwaniyah Iraq

In this paper, we study the influence of applying the well-known finite differences method on medical and physical images. These images will be used as coefficients in the steps of the solution after the images being imported and converted to arrays. The aim of the study is to show and analyze the changes that could happen to images for the sake of an enhancement. Experiments of one dimension and two dimensions will be illustrated by applying the explicit and the implicit methods using MATLAB to explain the way of how these methods affect images. Furthermore, we will study and measure the quality of the proposed images in each phase of the experiments using statistical metrics such as PSNR (Peak Signal to Noise Ratio), MSE (Mean Squared Error), MD (Maximum Difference) and some other statistical metrics. © Published under licence by IOP Publishing Ltd.

关键词： Finite difference method

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Error-bounded correction of noisy labels 37

Error-bounded correction of noisy labels

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37th International Conference on Machine Learning, ICML 2020

作者： Zheng, Songzhu Wu, Pengxiang Goswami, Aman Goswami, Mayank Metaxas, Dimitris Chen, Chao Department of Applied Mathematics and Statistics Stony Brook University NY United States Department of Computer Science Rutgers University NJ United States Bain and Company Bangalore India Department of Computer Science City University of New York NY United States Department of Biomedical Informatics Stony Brook University NY United States

ISBN: (纸本)9781713821120

To collect large scale annotated data, it is inevitable to introduce label noise, i.e., incorrect class labels. To be robust against label noise, many successful methods rely on the noisy classifiers (i.e., models trained on the noisy training data) to determine whether a label is trustworthy. However, it remains unknown why this heuristic works well in practice. In this paper, we provide the first theoretical explanation for these methods. We prove that the prediction of a noisy classifier can indeed be a good indicator of whether the label of a training data is clean. Based on the theoretical result, we propose a novel algorithm that corrects the labels based on the noisy classifier prediction. The corrected labels are consistent with the true Bayesian optimal classifier with high probability. We incorporate our label correction algorithm into the training of deep neural networks and train models that achieve superior testing performance on multiple public datasets. © 2020 by the Authors All rights reserved.

关键词： Deep neural networks

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Reconstructions of the fast-ion deuterium distribution in a tritium-rich plasma in the JET DTE2 campaign 50

Reconstructions of the fast-ion deuterium distribution in a ...

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50th EPS Conference on Plasma Physics, EPS 2024

作者： Järleblad, H. Reman, B.C.G. Dong, Y. Nocente, M. Eriksson, J. Valentini, A. Rud, M. Dal Molin, A. Garcia, J. Kazakov, Ye O. Keeling, D. King, D. Lerche, E.A. Lorenzini, R. Maggi, C. Maslov, M. Moseev, D. Rigatoni, D. Schmidt, B. Štancar, Ž. Tardocchi, M. Salewski, M. Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. LyngbyDK-2800 Denmark Department of Physics Technical University of Denmark Kgs. LyngbyDK-2800 Denmark Centre for Fusion Space and Astrophysics Department of Physics Warwick University CoventryCV4 7AL United Kingdom Laboratory for Plasma Physics LPP-ERM/KMS BrusselsB-1000 Belgium Department of Physics University of Milano-Bicocca Milano20126 Italy Department of Physics and Astronomy Uppsala University Uppsala751 20 Sweden Institute for Plasma Science and Technology National Research Council Milan20125 Italy CEA IRFM Saint-Paul-lez-DuranceF-13108 France CCFE Culham Science Centre Abingdon OxfordshireOX14 3DB United Kingdom Consorzio RFX CNR Research Area Corso Stati Uniti Padua4-35127 Italy UKAEA Culham Centre for Fusion Energy Abingdon OxfordshireOX14 3DB United Kingdom Max-Planck-Institut für Plasmaphysik Teilinstitut Greifswald Mecklenburg-Vorpommern Greifswald Germany University of California IrvineCA United States Jožef Stefan Institute Ljubljana1000 Slovenia

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Structural Properties of Hyperuniform Networks

arXiv

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arXiv 2024年

作者： Newby, Eli Shi, Wenlong Jiao, Yang Albert, Reka Torquato, Salvatore Department of Physics Pennsylvania State University University ParkPA16802 United States Materials Science and Engineering Arizona State University TempeAZ85287 United States Department of Physics Arizona State University TempeAZ85287 United States Department of Chemistry Princeton University PrincetonNJ08544 United States Department of Physics Princeton University PrincetonNJ08544 United States Princeton Institute of Materials Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Disordered hyperuniform many-particle systems are recently discovered exotic states of matter, characterized by a complete suppression of normalized infinite-wavelength density fluctuations, as in perfect crystals, and lack of conventional long-range order, as in liquids and glasses. In this work, we begin a program to quantify the structural properties of nonhyperuniform and hyperuniform networks. In particular, large two-dimensional (2D) Voronoi networks (graphs) containing approximately 10,000 nodes are created from a variety of different point configurations, including the antihyperuniform hyperplane intersection process (HIP), nonhyperuniform Poisson process, nonhyperuniform random sequential addition (RSA) saturated packing, and both non-stealthy and stealthy hyperuniform point processes. We carry out an extensive study of the Voronoi-cell area distribution of each of the networks through determining multiple metrics that characterize the distribution, including their higher-cumulants (i.e., skewness 1 and excess kurtosis 2). We show that the HIP distribution is far from Gaussian, as evidenced by a high skewness (γ1 = 3.16) and large positive excess kurtosis (γ2 = 16.2). The Poisson (with γ1 = 1.07 and γ2 = 1.79) and non-stealthy hyperuniform (with γ1 = 0.257 and γ2 = 0.0217) distributions are Gaussian-like distributions, since they exhibit a small but positive skewness and excess kurtosis. The RSA (with γ1 = 0.450 and γ2 = -0.0384) and the highest stealthy hyperuniform distributions (with γ1 = 0.0272 and γ2 = -0.0626) are also non-Gaussian because of their low skewness and negative excess kurtosis, which is diametrically opposite non-Gaussian behavior of the HIP. The fact that the cell-area distributions of large, finite-sized RSA and stealthy hyperuniform networks (e.g., with N ≈ 10, 000 nodes) are narrower, have larger peaks, and smaller tails than a Gaussian distribution implies that in the thermodynamic limit the distributions should exhibit compact suppo

关键词： Gaussian distribution

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IoT device profiling: From MUD files to S×C contracts

IoT device profiling: From MUD files to S×C contracts

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2020 Open Identity Summit

作者： Matthíasson, Guoni Giaretta, Alberto Dragoni, Nicola Department of Applied Mathematics and Computer Science Anker Engelunds Vej 1 Kgs. Lyngby2800 Denmark Örebro University AASS Research Centre Fakultetsgatan 1 Örebro702 81 Sweden

ISBN: (纸本)9783885796992

Security is a serious, and often neglected, issue in the Internet of Things (IoT). In order to improve IoT security, researchers proposed to use Security-by-Contract (S×C), a paradigm originally designed for mobile application platforms. However, S×C assumes that manufacturers equip their devices with security contracts, which makes hard to integrate legacy devices with S×C. In this paper, we explore a method to extract S×C contracts from legacy devices' Manufacturer Usage Descriptions (MUDs). We tested our solution on 28 different MUD files, and we show that it is possible to create basic S×C contracts, paving the way to complete extraction tools. © 2020 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Internet of things

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