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Artificial Intelligence and Machine Learning for scientific Applications (AI4S), IEEE/ACM International Workshop on

作者： Nadav Schneider Matan Rusanovsky Raz Gvishi Gal Oren Technology Division Soreq Nuclear Research Center Yavne Israel Israel Atomic Energy Commission Scientific Computing Center Nuclear Research Center Negev Israel Department of Physics Nuclear Research Center Negev Israel Applied Physics Division Soreq Nuclear Research Center ─ Yavne Israel Department of Computer Science Technion Israel Institute of Technology Israel

ISBN: (纸本)9781665462082

High energy density physics (HEDP) experiments commonly involve a dynamic wave-front propagating inside a lowdensity foam. This effect affects its density and hence, its transparency. A common problem in foam production is the creation of defective foams. Accurate information on their dimension and homogeneity is required to classify the foams' quality. Therefore, those parameters are being characterized using a 3D-measuring laser confocal microscope. For each foam, five images are taken: two 2D images representing the top and bottom surface foam planes and three images of side cross-sections from 3D scannings. An expert has to do the complicated, harsh, and exhausting work of manually classifying the foam's quality through the image set and only then determine whether the foam can be used in experiments or not. Currently, quality has two binary levels of normal vs. defective. At the same time, experts are commonly required to classify a sub-class of normal-defective, i.e., defective foams but might be sufficient for the needed experiment. This sub-class is problematic due to inconclusive judgment that is primarily intuitive. In this work, we present a novel state─of─the─art multi-view deep learning classification model that mimics the physicist's perspective by automatically determining the foams' quality classification and thus aids the expert. Our model achieved 86% accuracy on upper and lower surface foam planes and 82% on the entire set, suggesting interesting heuristics to the problem. A significant added value in this work is the ability to regress the foam quality instead of binary deduction and even explain the decision visually. The source code used in this work, as well as other relevant sources, are available at: https://***/***.

关键词： Deep learning Three-dimensional displays Source coding Microscopy Conferences MIMICs Lasers

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Universal algebraic controllers and system identification

arXiv

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arXiv 2020年

作者： Vides, Fredy Scientific Computing Innovation Center School of Mathematics and Computer Science Universidad Nacional Autónoma de Honduras Tegucigalpa Honduras

In this document, some structured operator approximation theoretical methods for system identification of nearly eventually periodic systems, are presented. Let Cn×m denote the algebra of n × m complex matrices. Given Ε > 0, an arbitrary discrete-time dynamical system (Σ, T ) with state-space Σ contained in the finite dimensional Hilbert space Cn, whose state-transition map T : Σ × ([0, ∞) ∩ Z) → Σ is unknown or partially known, and needs to be determined based on some sampled data in a finite set Σ = {xt}1≤t≤m ⊂ Σ according to the rule T (xt, 1) = xt+1 for each 1 ≤ t ≤ m − 1, and given x ∈ Σ. We study the solvability of the existence problems for two triples (p, A, ) and (p, Aη, Φ) determined by a polynomial p ∈ C[z] with deg(p) ≤ m, a matrix root A ∈ Cm×m and an approximate matrix root Aη ∈ Cr×r of p(z) = 0 with r ≤ m, two completely positive linear multiplicative maps : Cm×m → Cn×n and Φ: Cr×r → Cn×n, such that kT (x, t) − (At)xk ≤ Ε and kΦ(Atη)x − (At)xk ≤ Ε, for each integer t ≥ 1 such that kT (x, t) − yk ≤ Ε for some y ∈ Σ. Some numerical implementations of these techniques for the reduced-order predictive simulation of dynamical systems in continuum and quantum mechanics, are *** Codes 93B28, 47N70 (primary) and 93C57, 93B40 (secondary) Copyright © 2020, The Authors. All rights reserved.

关键词： Covariance matrix

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Erratum to: Higher order numerical methods for fractional delay differential equations

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Indian Journal of Pure and Applied Mathematics 2024年 1-1页

作者： Kumar, Manoj Jhinga, Aman Daftardar-Gejji, Varsha Department of Mathematics National Defence Academy Pune India Department of Mathematics and Computer Science West University of Timişoara Timişoara Romania Department of Scientific Computing Modeling and Simulation Savitribai Phule Pune University Pune India

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GPU-Accelerated DNS of Compressible Turbulent Flows

arXiv

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arXiv 2022年

作者： Kim, Youngdae Ghosh, Debojyoti Constantinescu, Emil M. Balakrishnan, Ramesh Computational Science Division Argonne National Laboratory 9700 S. Cass Ave. LemontIL60439 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory 7000 East Ave. LivermoreCA94550 United States Mathematics and Computer Science Division Argonne National Laboratory 9700 S. Cass Ave. LemontIL60439 United States

This paper explores strategies to transform an existing CPU-based high-performance computational fluid dynamics solver, HyPar, for compressible flow simulations on emerging exascale heterogeneous (CPU+GPU) computing platforms. The scientific motivation for developing a GPU-enhanced version of HyPar is to simulate canonical turbulent flows at the highest res-MSC Codes 76F65, 65Y05, 76F05, 35Q30, 65M06 Copyright © 2022, The Authors. All rights reserved.

关键词： Turbulent flow

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A novel transfer learning-based model for diagnosing malaria from parasitized and uninfected red blood cell images

引用

Decision Analytics Journal 2023年 9卷

作者： Qadri, Azam Mehmood Raza, Ali Eid, Fatma Abualigah, Laith Institute of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan 64200 Pakistan Technology Management Stony Brook University 11794 NY United States Department of Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Seoul 03063 South Korea Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied Science Research Center Applied science private university Amman 11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia

Malaria represents a potentially fatal communicable illness triggered by the Plasmodium parasite. This disease is transmitted to humans through the bites of Anopheles mosquitoes that carry the infection. This disease has significant and devastating consequences on the health systems of fragile countries, particularly in sub-Saharan Africa. Malaria affects red blood cells by invading and replicating within them, destroying them, and releasing toxic byproducts into the bloodstream. The parasite's ability to stick and modify the surface of red blood cells can cause them to become sticky, obstructing blood flow in vital organs such as the brain and spleen. Therefore, efficient approaches for the early detection of malaria are critical to saving patients’ lives. The main aim of this study is to develop an efficient model for early malaria diagnosis. We used malaria images based on parasitized and uninfected red blood cells for the study experiments. We applied neural network-based approaches such as Neural Search Architecture Network (NASNet) and compared its performance with machine learning techniques. Moreover, we proposed a novel NNR (NASNet Random forest) method for feature engineering. The proposed NNR approach first extracts spatial features from input malaria images, then class prediction probability features are extracted from these spatial features. The feature set obtained from the data extraction trains machine learning models. Our comprehensive experiments show that the support vector machine outperformed state-of-the-art models, achieving a high-performance score of 99% and having an inference time near 0.025 s. We validated the performance using k-fold cross-validation and optimized the hyperparameters through tuning. Our proposed research has improved the early diagnosis of malaria and can assist medical specialists in reducing the mortality rate. © 2023 The Author(s)

关键词： Deep learning Machine learning Malaria diagnosis Neural search architecture network Red blood cells Transfer learning

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Characterization of n-Dimensional Toric and Burst-Error-Correcting Quantum Codes from Lattice Codes

arXiv

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arXiv 2024年

作者： Trinca, Cibele Cristina Palazzo, Reginaldo Interlando, J. Carmelo Watanabe, Ricardo Augusto de Albuquerque, Clarice Dias de Carvalho, Edson Donizete de Andrade, Antonio Aparecido The Department of Biotechnology and Bioprocess Engineering Federal University of Tocantins TO Gurupi Brazil The School of Electrical and Computer Engineering State University of Campinas Brazil The Department of Mathematics and Statistics San Diego State University San DiegoCA United States The School of Mathematics Statistics and Scientific Computing State University of Campinas Brazil Brazil The Science and Technology Center Federal University of Cariri Juazeiro do Norte Brazil The Department of Mathematics São Paulo State University SP Ilha Solteira Brazil The Department of Mathematics São Paulo State University Brazil

Quantum error correction is essential for the development of any scalable quantum computer. In this work we introduce a generalization of a quantum interleaving method for combating clusters of errors in toric quantum error-correcting codes. We present new n-dimensional toric quantum codes, where n ≥ 5, which are featured by lattice codes and apply the proposed quantum interleaving method to such new n-dimensional toric quantum codes. Through the application of this method to these novel n-dimensional toric quantum codes we derive new n-dimensional quantum burst-error-correcting codes. Consequently, n-dimensional toric quantum codes and burst-error-correcting quantum codes are provided offering both a good code rate and a significant coding gain when it comes to toric quantum codes. Another important consequence from the presented n-dimensional toric quantum codes is that if the Golomb and Welch conjecture in [1] regarding the Lee sphere in n dimensions for the respective close packings holds true, then it follows that these n-dimensional toric quantum codes are the only possible ones to be obtained from lattice codes. Moreover, such a methodology can be applied for burst error correction in cases involving localized errors, quantum data storage and quantum channels with memory. © 2024, CC BY.

关键词： Quantum channel

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Random sections of p-ellipsoids, optimal recovery and Gelfand numbers of diagonal operators

arXiv

引用

arXiv 2021年

作者： Hinrichs, Aicke Prochno, Joscha Sonnleitner, Mathias Institute of Analysis University of Linz Altenbergerstrasse 69 Linz4040 Austria Faculty of Computer Science and Mathematics University of Passau Innstrasse 33 Passau94032 Germany Department of Mathematics and Scientific Computing University of Graz Heinrichstrasse 36 Graz8010 Austria

We study the circumradius of a random section of an p-ellipsoid, 0 p-ellipsoid for which the radius of optimal information is given by the Gelfand numbers of a diagonal operator. In the case where the semiaxes decay polynomially and 1 ≤ p ≤ ∞, we conjecture that, as the amount of information increases, the radius of random information either decays like the radius of optimal information or is bounded from below by a constant, depending on whether the exponent of decay is larger than the critical value 1 - 1/p or not. If 1 ≤ p ≤ 2, we prove this conjecture by providing a matching lower bound. This extends the recent work of Hinrichs et al. [Random sections of ellipsoids and the power of random information, Trans. Amer. Math. Soc., 2021+] for the case p = *** Codes Primary 52A23, 65Y20, Secondary 60G15 © 2021, CC BY.

关键词： Recovery

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FEniCS-preCICE: Coupling FEniCS to other simulation software

arXiv

引用

arXiv 2021年

作者： Rodenberg, Benjamin Desai, Ishaan Hertrich, Richard Jaust, Alexander Uekermann, Benjamin Scientific Computing in Computer Science Department of Informatics Technical University of Munich Germany Usability and Sustainability of Simulation Software Institute for Parallel and Distributed Systems University of Stuttgart Germany Simulation of Large Systems Institute for Parallel and Distributed Systems University of Stuttgart Germany

The new software FEniCS-preCICE is a middle software layer, sitting in between the existing finite-element library FEniCS and the coupling library preCICE. The middle layer simplifies coupling (existing) FEniCS application codes to other simulation software via preCICE. To this end, FEniCS-preCICE converts between FEniCS and preCICE mesh and data structures, provides easy-to-use coupling conditions, and manages data checkpointing for implicit coupling. The new software is a library itself and follows a FEniCS-native style. Only a few lines of additional code are necessary to prepare a FEniCS application code for coupling. We illustrate the functionality of FEniCS-preCICE by two examples: a FEniCS heat conduction code coupled to OpenFOAM and a FEniCS linear elasticity code coupled to SU2. The results of both scenarios are compared with other simulation software showing good agreement. © 2021, CC BY-NC-ND.

关键词： Finite element method

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Mapping 3d Grain and Precipitate Structure During in Situ Mechanical Testing of Open-Cell Metal Foam Using Micro-Computed Tomography and High-Energy X-Ray Diffraction Microscopy

SSRN

引用

SSRN 2022年

作者： Johnson, Quinton C. Kenesei, Peter Petruzza, Steve Plumb, Jayden Sharma, Hemant Park, Jun-Sang Marsden, Elliott Matheson, Kristoffer Czabaj, Michael W. Spear, Ashley D. Department of Mechanical Engineering University of Utah Salt Lake CityUT84112 United States Scientific Computing and Imaging Institute University of Utah Salt Lake CityUT84112 United States Department of Computer Science Utah State University LoganUT84322 United States Advanced Photon Source Argonne National Laboratory LemontIL60439 United States

Open-cell metal foams are ultra-low-density cellular metals with complex hierarchical structures that span bulk, cell, ligament, and sub-ligament scales and give rise to desirable properties such as high strength-to-weight ratio and excellent energy absorption. Although literature suggests that intrinsic material structures at sub-ligament length scales (e.g., grains and precipitates) play an important role in mechanical behavior of open-cell metal foams, there are very few experimental measurements of such structures in three dimensions and for meaningful volumes of foam. This study seeks to map and track the three-dimensional (3D) grain and precipitate structures of an intact volume of open-cell aluminum foam by advancing microstructural characterization techniques that leverage X-ray micro-computed tomography ( µ CT) and far-field high-energy X-ray diffraction microscopy (FF-HEDM). A 6%-relative-density aluminum foam sample was mechanically tested in compression while µ CT and FF-HEDM measurements were collected at interrupted loading states at beamline 1-ID of the Advanced Photon Source. A new scanning strategy and reconstruction algorithm were established to enable characterization of a foam volume with diameter approximately four times wider than the nominal width of the X-ray beam. The result is a set of maps that detail both the 3D grain and precipitate structures throughout the foam volume at successive strain steps. A novel grain tracking procedure was developed to track individual grains within the foam volume by accounting for the large rigid-body motions that individual ligaments can undergo during mechanical loading. The ability to track grains and precipitate structures in three dimensions throughout large bulk deformation of ulta-low-density polycrystalline materials enables new possibilities for validating numerical models and investigating local failure mechanisms. Furthermore, the methods and procedures developed in this study could be applied to

关键词： Synchrotron radiation

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Visualizing Air Voids and Synthetic Fibers from X-Ray Computed Tomographic Images of Concrete 1

Visualizing Air Voids and Synthetic Fibers from X-Ray Comput...

引用

1st Intermountain Engineering, Technology and computing, IETC 2020

作者： Bordelon, Amanda C. Hong, Sungmin Bearzi, Yohann Vachet, Clement Gerig, Guido Utah Valley University Dept of Engineering Orem United States New York University Dept of Computer Science and Engineering Brooklyn United States Research Development Kitware Clifton Park United States Scientific Computing Institute University of Utah Salt Lake City United States

ISBN: (纸本)9781728142913

A challenge in quality control for synthetic fiber-reinforced concrete is determining the actual spatial distribution of fibers. This paper presents the first computer algorithm to identify synthetic macrofibers within hardened concrete that has been scanned in an industrial X-ray computed tomographic scanner. The algorithm can also be used to obtain the spatial distribution of other inclusions such as air voids or steel fibers as well. Visualization of synthetic fibers was the primary focus of this work. The heterogeneous nature of concrete results in a noisy image which makes identifying contrast edge segmentation difficult for to the image processing. In order to identify only fibers, the air voids touching the fibers must be identified separately because they are similar in grayscale as the synthetic fibers. These air voids are assumed to be spherical in shape, and once identified can be extracted from the remaining fiber-aggregate-cement system. In this study, it was determined that the algorithm works best for straight macrosynthetic fibers where the pixel resolution is similar or smaller than the diameter of the fibers and if the fibers remain straight lines in the 3D matrix. © 2020 IEEE.

关键词： Visualization

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