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arXiv 2021年

作者： Weissmann, Simon Chada, Neil K. Schillings, Claudia Tong, Xin T. Interdisciplinary Center for Scientific Computing University of Heidelberg Heidelberg69120 Germany Applied Mathematics and Computational Science Program King Abdullah University of Science and Technology Thuwal KSA 23955 Saudi Arabia Mannheim School of Computer Science and Mathematics University of Mannheim Mannheim68131 Germany Department of Mathematics National University of Singapore Singapore119077 Singapore

Ensemble Kalman inversion (EKI) is a derivative-free optimizer aimed at solving inverse problems, taking motivation from the celebrated ensemble Kalman filter. The purpose of this article is to consider the introduction of adaptive Tikhonov strategies for EKI. This work builds upon Tikhonov EKI (TEKI) which was proposed for a fixed regularization constant. By adaptively learning the regularization parameter, this procedure is known to improve the recovery of the underlying unknown. For the analysis, we consider a continuous-time setting where we extend known results such as well-posdeness and convergence of various loss functions, but with the addition of noisy observations. Furthermore, we allow a time-varying noise and regularization covariance in our presented convergence result which mimic adaptive regularization schemes. In turn we present three adaptive regularization schemes, which are highlighted from both the deterministic and Bayesian approaches for inverse problems, which include bilevel optimization, the MAP formulation and covariance learning. We numerically test these schemes and the theory on linear and nonlinear partial differential equations, where they outperform the non-adaptive TEKI and *** Codes 65M32, 60G35, 65C35, 70F17 Copyright © 2021, The Authors. All rights reserved.

关键词： Continuous time systems

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SHELLEY: Exploring Learning-Based Network Alignment on Biological Data

SHELLEY: Exploring Learning-Based Network Alignment on Biolo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Riccardo De Luca Manuela Petti Pietro H. Guzzi Paolo Tieri MSc Program in Data Science Sapienza University of Rome Rome Italy DIAG Dept. Comp. Contr. Manag. Eng. Sapienza University of Rome Rome Italy Dept. UMG Magna Graecia University Catanzaro Italy IAC Institute for Applied Computing CNR National Research Council Rome Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Global network alignment is the computational problem of determining the similarity between nodes of different networks to establish a one-to-one correspondence between them. It has important applications in the biological field, particularly for discovering similar roles between the elements of different systems or for transferring knowledge from a well-studied system to another. In this paper, we present SHELLEY, a tool that facilitates the development, testing, and combination of learning-based network alignment algorithms by providing a set of modules that allow for the recreation and combination of both representation learning methods (RLMs) and deep matching methods (DMMs). We then present a case study in which we apply this tool to a protein-protein interaction network (PPI), demonstrating how the representation phase of RLMs is crucial for model robustness against *** code of SHELLEY is available at: https://***/rickydeluca/shelley

关键词： Representation learning Proteins Protein engineering Codes Biological system modeling Noise Robustness Data models Biological information theory Testing

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Bullen’s parameter as a seismic observable for spin crossovers in the lower mantle

arXiv

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arXiv 2017年

作者： Valencia-Cardona, Juan J. Williams, Quentin Shukla, Gaurav Wentzcovitch, Renata M. Scientific Computing Program University of Minnesota MinneapolisMN United States Department of Earth and Planetary Sciences University of California Santa Cruz Santa CruzCA United States Department of Earth Ocean and Atmospheric Science Florida State University TallahasseeFL United States Department of Applied Physics and Applied Mathematics Columbia University New York CityNY United States Lamont-Doherty Earth Observatory Columbia University PalisadesNY United States

Elastic anomalies produced by the spin crossover in ferropericlase have been documented by both first principles calculations and high pressure-temperature experiments. The predicted signature of this spin crossover in the lower mantle is, however, subtle and difficult to geophysically observe within the mantle. Indeed, global seismic anomalies associated with spin transitions have not yet been recognized in seismologic studies of the deep mantle. A sensitive seismic parameter is needed to determine the presence and amplitude of such a spin crossover signature. The effects of spin crossovers on Bullen’s parameter, η, are assessed here for a range of compositions, thermal profiles, and lateral variations in temperature within the lower mantle. Velocity anomalies associated with the spin crossover in ferropericlase span a depth range near 1,000 km for typical mantle temperatures. Positive excursions of Bullen’s parameter with a maximum amplitude of ∼ 0.03 are calculated to be present over a broad depth range within the mid-to-deep lower mantle: these are largest for peridotitic and harzburgitic compositions. These excursions are highest in amplitude for model lower mantles with large lateral thermal variations, and with cold downwellings having longer lateral length-scales relative to hot upwellings. We conclude that predicted deviations in Bullen’s parameter due to the spin crossover in ferropericlase for geophysically relevant compositions may be sufficiently large to resolve in accurate seismic inversions of this parameter, and could shed light on both the lateral variations in temperature at depth within the lower mantle, and the amount of ferropericlase at depth. Copyright © 2017, The Authors. All rights reserved.

关键词： Seismology

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High-order Discretization of a Gyrokinetic Vlasov Model in Edge Plasma Geometry

arXiv

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arXiv 2017年

作者： Dorr, Milo R. Colella, Phillip Dorf, Mikhail A. Ghosh, Debojyoti Hittinger, Jeffrey A.F. Schwartz, Peter O. Center for Applied Scientific Computing Lawrence Livermore National Laboratory 7000 East Avenue L-561 LivermoreCA94550 United States Fusion Energy Program Lawrence Livermore National Laboratory 7000 East Avenue L-630 LivermoreCA94550 United States Applied Numerical Algorithms Group Lawrence Berkeley National Laboratory One Cyclotron Road Mail Stop 50A-1148 BerkeleyCA94720 United States

We present a high-order spatial discretization of a continuum gyrokinetic Vlasov model in axisymmetric tokamak edge plasma geometries. Such models describe the phase space advection of plasma species distribution functions in the absence of collisions. The gyrokinetic model is posed in a four-dimensional phase space, upon which a grid is imposed when discretized. To mitigate the computational cost associated with high-dimensional grids, we employ a high-order discretization to reduce the grid size needed to achieve a given level of accuracy relative to lower-order methods. Strong anisotropy induced by the magnetic field motivates the use of mapped coordinate grids aligned with magnetic flux surfaces. The natural partitioning of the edge geometry by the separatrix between the closed and open field line regions leads to the consideration of multiple mapped blocks, in what is known as a mapped multiblock (MMB) approach. We describe the specialization of a more general formalism that we have developed for the construction of high-order, finite-volume discretizations on MMB grids, yielding the accurate evaluation of the gyrokinetic Vlasov operator, the metric factors resulting from the MMB coordinate mappings, and the interaction of blocks at adjacent boundaries. Our conservative formulation of the gyrokinetic Vlasov model incorporates the fact that the phase space velocity has zero divergence, which must be preserved discretely to avoid truncation error accumulation. We describe an approach for the discrete evaluation of the gyrokinetic phase space velocity that preserves the divergence-free property to machine precision. A distinguishing feature of an edge geometry is the X point, where the poloidal field component vanishes. The inability to construct fully flux-surface aligned MMB coordinate systems that are smooth up to and through the X point requires the relaxation of alignment in the vicinity of this point. We therefore describe an approach for the generation of s

关键词： Finite volume method

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Transverse magnetic defect modes in two-dimensional triangular-lattice photonic crystals

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Physical Review E 2001年第5期64卷 056614-056614页

作者： N. Stojić J. Glimm Y. Deng J. W. Haus Department of Physics and Astronomy State University of New York at Stony Brook Stony Brook New York 11794-3381 Center for Data Intensive Computing Brookhaven National Laboratory Building 463B Upton New York 11973-5751 Department of Applied Mathematics and Statistics State University of New York at Stony Brook Stony Brook New York 11794-3600 Electro-Optics Program The University of Dayton Dayton Ohio 45469-0254

We present a numerical study of the localized transverse magnetic (TM) defect modes in a two-dimensional, triangular-lattice photonic crystal. The sample consists of an array of circular, air cylinders in a dielectric medium (GaAs). The defect modes were calculated by using a parallel version of the finite-difference time-domain method on the Yee mesh. To validate our computations the results for the transverse electric case were checked against experimental results and the numerical results using a different method. We study the spatial symmetry for TM modes, obtained by changing the dipole excitation frequency. Also, we vary the defect-cylinder radius to tune the resonant frequency across the band gap. The TM mode is found to be highly localized at the defect in the photonic lattice.

关键词： Electromagnetic wave propagation

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An Improved Approach for Semantic Segmentation of Fundus Lesions using R2U-Net

An Improved Approach for Semantic Segmentation of Fundus Les...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Alejandro Pereira Carlos Santos Marilton Aguiar Daniel Welfer Marcelo Dias Rafaela de Menezes Paulo Roberto Ferreira Fábio Rossi Marcos d’Ornellas Carlos Jesus Haygert Juliano Kazienko Fábio Comim Aurélio Hoppe Juliano Weber Leandro Tavares Leonardo Guths Rodrigo Guerra Postgraduate Program in Computing Federal University of Pelotas - UFPel Pelotas Brazil Federal Institute of Education Science and Technology Farroupilha - IFFar Alegrete Brazil Department of Applied Computing Federal University of Santa Maria - UFSM Santa Maria Brazil Department of Clinical Medicine Federal University of Santa Maria - UFSM Santa Maria Brazil Industrial Technical College Federal University of Santa Maria - UFSM Santa Maria Brazil Department of Clinical Medicine Federal University of Minas Gerais - UFMG Belo Horizonte Brazil Department of Information Systems and Computing Regional University of Blumenau - FURB Blumenau Brazil Federal Institute of Education Science and Technology Farroupilha - IFFar Santo Ângelo Brazil Postgraduate Program in Computing Federal University of Rio Grande do Sul - UFRGS Porto Alegre Brazil Center of Computational Sciences Federal University of Rio Grande - FURG Rio Grande Brazil

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Diabetic Retinopathy (DR) is a microvascular complication related to diabetes that affects approximately 33% of individuals with this condition and, if not detected and treated early, can lead to irreversible vision loss. Fundus lesions such as Hard and Soft Exudates, Hemorrhages, and Microaneurysms typically identify DR. The development of computational methods to segment these lesions plays a fundamental role in the early diagnosis of the disease. This paper proposes a new approach that uses an R2U-Net combined with data augmentation techniques for segmenting fundus lesions. We trained, adjusted, and evaluated the proposed work in the DDR dataset, achieving an accuracy of 99.87% and a mean Intersection over Union (mIoU) equal 59.69%. Furthermore, we assessed it in the IDRiD dataset, achieving an mIoU of 49.92%. The results obtained in the experiments highlight the potential contribution of the model in the lesion annotations for creating new DR datasets, which is essential given the scarcity of annotations in publicly available datasets.

关键词： Measurement Diabetic retinopathy Accuracy Annotations Semantic segmentation Feature extraction Data augmentation Data models Lesions Hemorrhaging

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Optimizing Hammerstein-Wiener Model for Forecasting Confirmed Cases of Covid-19

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IAENG International Journal of applied Mathematics 2022年第1期52卷 1页

作者： Abdullahi, Sunusi Bala Ibrahim, Abdulkarim Hassan Abubakar, Auwal Bala Kambheera, Abhiwat General Investigation Zonal Criminal Investigation Section Force Criminal Investigation and Intelligence Department Nigeria Police Abuja900211 Nigeria Department of Applied Mathematics King Mongkut’s University of Technology Thonburi Bangkok Thailand Department of Mathematical Sciences Faculty of Physical Sciences Bayero University Kano700241 Nigeria Mathematics and Computing Science Program Faculty of Science and Technology Phetchabun Rajabhat University Phetchabun 67000 Thailand

Noise poses challenge to nonlinear Hammerstein-Wiener (HW) subsystem model application, because HW subsystem need large number of parameter interactions. However, flexibility, soft computing, and automatic adjustment to dynamic observation for best model fitting make it potential for forecasting nonlinear data. In this article, we adopted improved HW inference from Levenberg-Marquardt optimization algorithm to optimize HW subsystem and to select best model parameters. Therefore, the adopted model is tested on COVID-19 confirmed reported cases, to estimate transmission rate of COVID-19 virus for period from 15th March 2020 to 29th April 2020. Model validation is carried out on small dataset, which outperforms some existing models. The adopted model is further evaluated using statistical metrics and reported best accuracy of 0.127 and 0.998 for Mean Absolute percentage error (MAPE) and coefficient of determination (R2) respectively, with best model complexity of 1.86. The obtained results are promising enough in predicting spread of COVID-19 virus and may inspire as guidance to relax lockdown restriction policies. © 2022, IAENG International Journal of applied Mathematics. All Rights Reserved.

关键词： COVID-19

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Employing texture features of chest x-ray images and machine learning in covid-19 detection and classification

Mendel

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Mendel 2021年第1期27卷 9-17页

作者： Alquran, Hiam Alsleti, Mohammad Alsharif, Roaa Qasmieh, Isam Abu Alqudah, Ali Mohammad Harun, Nor Hazlyna Binti Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid21163 Jordan The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman11855 Jordan College of applied medical Sciences Radiological Science Program King Saud University Jeddah21435 Saudi Arabia Data Science Research Lab School of Computing Universiti Utara Malaysia Sintok Kedah06010 Malaysia

The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient;as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival. © 2021, Brno University of Technology. All rights reserved.

关键词： COVID-19

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Agent-Based Behavioral Model and Simulation of Fire Egress

Agent-Based Behavioral Model and Simulation of Fire Egress

引用

International Conference and Workshop on computing and Communication (IEMCON)

作者： Andrew J. Park Gisung Han Valerie Spicer Ryan Ficocelli Herbert H. Tsang Eunju Hwang Department of Computing Science Thompson Rivers University Kamloops BC Canada Program in Urban Regeneration Korea University Seoul South Korea Institute for Canadian Urban Research Studies Simon Fraser University Burnaby BC Canada Applied Research Lab Trinity Western University BC Canada Dept of Apparel Housing and Resource Management Virginia Tech VA USA

ISBN: (纸本)9781728125312

Disastrous fires can cause death, injuries, and property damage. In certain cases, mass evacuations of buildings are also required to ensure safety. Building fires are particularly dangerous because of added factors such as confinement and height of buildings. Fire egress is essential to fire safety in buildings. Evaluating such safety is not easy since simulating fire egress with real subjects (and possibly with a real fire or smoke) poses ethical issues. There have been computer simulations of fire egress. However, current evacuee models are too simple and do not reflect the behavior of actual evacuees. This paper presents a comprehensive behavioral model and simulation of evacuees in a building fire. The fire egress simulation system is based on the behavioral model that is verified using simulation experiments. This simulation system can be used to evaluate the fire egress in actual buildings and can assist in determining fire safety standards. Simulation results are presented and future research is discussed.

关键词： Sensors Computational modeling Windows Safety Computer simulation Rivers

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Fault Tolerant Quantum Error Mitigation

arXiv

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arXiv 2023年

作者： Gonzales, Alvin Babu, Anjala M. Liu, Ji Saleem, Zain Byrd, Mark Intelligence Community Postdoctoral Research Fellowship Program Argonne National Laboratory LemontIL United States School of Physics and Applied Physics Southern Illinois University CarbondaleIL United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States School of Computing Southern Illinois University CarbondaleIL United States

Typically, fault-tolerant operations and code concatenation are reserved for quantum error correction due to their resource overhead. Here, we show that fault tolerant operations have a large impact on the performance of symmetry based error mitigation techniques. We also demonstrate that similar to results in fault tolerant quantum computing, code concatenation in fault-tolerant quantum error mitigation (FTQEM) can exponentially suppress the errors to arbitrary levels. For a family of circuits, we provide analytical error thresholds for FTQEM with the repetition code. These circuits include a set of quantum circuits that can generate all of reversible classical computing. The post-selection rate in FTQEM can also be increased by correcting some of the outcomes. Our threshold results can also be viewed from the perspective of quantifying the number of gate operations we can delay checking the stabilizers in a concatenated code before errors overwhelm the encoding. The benefits of FTQEM are demonstrated with numerical simulations and hardware demonstrations. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum computers

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