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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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Detecting anomalous citation groups in journal networks

arXiv

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arXiv 2020年

作者： Kojaku, Sadamori Livan, Giacomo Masuda, Naoki Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Computer Science University College London LondonWC1E 6EA United Kingdom Systemic Risk Centre London School of Economics and Political Science LondonWC2A 2AE United Kingdom Department of Mathematics University at Buffalo State University of New York BuffaloNY United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York BuffaloNY14260-2900 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

The ever-increasing competitiveness in the academic publishing market incentivizes journal editors to pursue higher impact factors. This translates into journals becoming more selective, and, ultimately, into higher publication standards. However, the fixation on higher impact factors leads some journals to artificially boost impact factors through the coordinated effort of a "citation cartel" of journals. "Citation cartel" behavior has become increasingly common in recent years, with several instances being reported. Here, we propose an algorithm—-named CIDRE—to detect anomalous groups of journals that exchange citations at excessively high rates when compared against a null model that accounts for scientific communities and journal size. CIDRE detects more than half of the journals suspended from Journal Citation Reports due to anomalous citation behavior in the year of suspension or in advance. Furthermore, CIDRE detects many new anomalous groups, where the impact factors of the member journals are lifted substantially higher by the citations from other member journals. We describe a number of such examples in detail and discuss the implications of our findings with regard to the current academic climate. Copyright © 2020, The Authors. All rights reserved.

关键词： Publishing

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Derivative Free Levenberg-Marquardt Method for Solving Fuzzy Nonlinear Equation

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IOP Conference Series: Materials Science and engineering 2021年第1期1115卷

作者： A U Omesa I M Sulaiman M Mamat M Y Waziri A Shadi M A Zaini Kalfin I Sumiati Department of Mathematics College of Agriculture Zuru Kebbi Nigeria Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Kuala Nerus 22200 Terengganu Malaysia Department of Mathematics Bayero University Kano Nigeria Institute of Engineering Mathematics Universiti Malaysia Perlis Kangar Malaysia Doctor Program of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Indonesia Master Program in Mathematics Faculty of Mathematics and Natural Science Universitas Padjadjaran Indonesia

In this paper, we present a derivative-free computational method for solving fuzzy nonlinear equation. Derivative-free technique avoids computing the derivative by generating an estimate to the derivative. This is made possible by inserting the estimate of in Levenberg-Marquardt's method. Numerical experiments are carried out which shows that, the method is efficient.

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Valley notch filter in a graphene strain superlattice: Green's function and machine learning approach

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Physical Review B 2019年第20期100卷 205411-205411页

作者： V. Torres P. Silva E. A. T. de Souza L. A. Silva D. A. Bahamon Instituto de Física Universidade Federal Fluminense Niterói Av. Litorânea sn 24210-340 RJ-Brazil MackGraphe – Graphene and Nano-Materials Research Center Mackenzie Presbyterian University Rua da Consolação 896 01302-907 São Paulo SP Brazil School of Computing and Informatics & Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University Rua da Consolação 896 01302-907 São Paulo SP Brazil

The valley transport properties of a superlattice of out-of-plane Gaussian deformations are calculated using a Green's function and a machine learning approach. Our results show that periodicity significantly improves the valley filter capabilities of a single Gaussian deformation; these manifest themselves in the conductance as a sequence by valley filter plateaus. We establish that the physical effect behind the observed valley notch filter is the coupling between counterpropagating transverse modes; the complex relationship between the design parameters of the superlattice and the valley filter effect make it difficult to estimate in advance the valley filter potentialities of a given superlattice. With this in mind, we show that a deep neural network can be trained to predict valley polarization with a precision similar to the Green's function but with much less computational effort.

关键词： Ballistic transport Quantum transport Strain Graphene Landauer formula Machine learning Tight-binding model

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Lessons learned from the 2017 Antarctic deployment of the Icefin unmanned underwater vehicle

Lessons learned from the 2017 Antarctic deployment of the Ic...

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AUVSI XPONENTIAL 2018

作者： Spears, Anthony Meister, Matthew Dichek, Daniel Hurwitz, Benjamin Lutz, Joshua Lawrence, J. Phileo, Kit Schmidt, Britney E. School of Earth and Atmospheric Sciences Georgia Institute of Technology 30332 United States College of Computing Georgia Institute of Technology 30332 United States Ocean Science and Engineering Program Georgia Institute of Technology 30332 United States

The Icefin vehicle represents a custom robotic platform design specifically targeted to the unique challenges of sub-ice deployments in Antarctica. A previous iteration of the vehicle was deployed to McMurdo, Antarctica in 2014 and provided valuable data and lessons learned. A refinement of the vehicle design has followed, yielding a more robust and capable system for a second Antarctic deployment over the 2017 field season (October 2017 - January 2018). The mechanical design of this system is largely limited by the small diameter deployment holes drilled in the thick ice shelves. This, in turn, drives optimization of the design to permit a large scientific sensor suite and computational capabilities of the system. Presented here is an overview of the lessons learned during the vehicle redesign process, including mechanical, electrical, software, and autonomy considerations. Also presented are early results and lessons learned from the 2017 deployment of the vehicle to McMurdo, Antarctica. We hope that by presenting our path and lessons learned through robotic system development and field deployment, we can inspire other research teams and lower the barrier to entry for science-enabling platforms. Field robotics represents a challenging technological area and by presenting our development and deployment experience we can enable more rapid advancement in the state of the possible. In particular, we highlight how simultaneous system & science design increases the utility of such vehicles and offers insight into solving new challenges. © 2018 IQPC. All rights reserved.

关键词： Digital storage

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Modeling subgrid-scale force and divergence of heat flux of compressible isotropic turbulence by artificial neural network

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Physical Review Fluids 2019年第10期4卷 104605-104605页

作者： Chenyue Xie Ke Li Chao Ma Jianchun Wang Shenzhen Key Laboratory of Complex Aerospace Flows Center for Complex Flows and Soft Matter Research Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen 518055 People's Republic of China Institute of Computational Mathematics and Scientific Engineering Computing Chinese Academy of Sciences Beijing 100190 People's Republic of China The Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

In this paper, the subgrid-scale (SGS) force and the divergence of SGS heat flux of compressible isotropic turbulence are modeled directly by an artificial neural network (ANN), which serves as a data-driven SGS modeling tool for large-eddy simulations (LESs). The unclosed SGS force and divergence of SGS heat flux are modeled based on the local stencil geometry with Galilean invariance. The input features include the first-order and second-order derivatives of filtered velocity and temperature, filtered density, and its first-order derivative. It is shown that the proposed ANN-F7 model shows an advantage over the gradient model in the a priori test. Specifically, the ANN-F7 model gives larger correlation coefficients and smaller relative errors than the gradient model. In an a posteriori analysis, the ANN-F7 model performs better than the dynamic Smagorinsky model (DSM) and dynamic mixed model (DMM) in the prediction of the statistical properties of flow fields at the Taylor microscale Reynolds number Reλ ranging from 180 to 250. The DSM and DMM models lead to the typical tilted spectral distribution of velocity, where low wave numbers are too energy rich, while those near the cutoff are damped too strongly. In contrast, it is shown that the velocity spectrum predicted by the ANN-F7 model almost overlaps with the filtered direct numerical simulation data. Besides, the ANN-F7 model reconstructs the probability density functions of SGS force and divergence of SGS heat flux much better than the DSM and DMM models. An artificial neural network with reasonable physical input features can deepen our understanding of turbulence modeling.

关键词： Compressible flows Turbulence Artificial neural networks

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Conventional and adaptive strategies applied to DC motor control using microcontroller PIC18F4520 7

Conventional and adaptive strategies applied to DC motor con...

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7th Brazilian Electrical Systems Symposium, SBSE 2018

作者： Taveiros, Filipe Emanuel Vieira Barros, Luciano Sales Barros, Camila Mara Vital Electrical Engineering and Computing Graduate Program of Federal University of Rio Grande Do Norte Natal Brazil Electrical Engineering Department of Federal University of Rio Grande Do Norte Natal Brazil Computer Systems Department of Federal University of Paraíba João Pessoa Brazil

ISBN: (纸本)9781538633632

In this paper strategies to control the speed of a generic DC motor system are discussed. They range from the conventional, like PID and state-feedback, to reference model adaptive algorithms such as MRAC, VS-MRAC and DMARC. The goal of this study is to determine the best control strategy for high-performance applications regarding to stability, robustness and optimal controller effort. In theory and sometimes in practice, all these control strategies proof themselves reliable, however, in heavy-duty set-point references the application requires high performance of the controller under disturbances, unmodeled dynamics and variable speed reference conditions. The use of appropriate control strategies can highly contribute to this purpose. To support the conclusions, the control strategies have been embedded in a microcontroller PIC18F4520 and applied to a test motor in order to obtain experimental results. © 2018 IEEE.

关键词： Three term control systems

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A smartphone application to detection and classification of coffee leaf miner and coffee leaf rust

arXiv

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arXiv 2019年

作者： Manso, Giuliano L. Knidel, Helder Krohling, Renato A. Ventura, José A. Labcin-Laboratory of Computing and Engineering Inspired by Nature UFES-Federal University of Espirito Santo Vitória Brazil PPGI-Graduate Program in Computer Science UFES Vitória Brazil Production Engineering Department UFES Vitória Brazil Incaper Rua Afonso Sarlo Vitória ES29052-010 Brazil

Generally, the identification and classification of plant diseases and/or pests are performed by an expert . One of the problems facing coffee farmers in Brazil is crop infestation, particularly by leaf rust Hemileia vastatrix and leaf miner Leucoptera coffeella. The progression of the diseases and or pests occurs spatially and temporarily. So, it is very important to automatically identify the degree of severity. The main goal of this article consists on the development of a method and its i implementation as an App that allow the detection of the foliar damages from images of coffee leaf that are captured using a smartphone, and identify whether it is rust or leaf miner, and in turn the calculation of its severity degree. The method consists of identifying a leaf from the image and separates it from the background with the use of a segmentation algorithm. In the segmentation process, various types of backgrounds for the image using the HSV and YCbCr color spaces are tested. In the segmentation of foliar damages, the Otsu algorithm and the iterative threshold algorithm, in the YCgCr color space, have been used and compared to k-means. Next, features of the segmented foliar damages are calculated. For the classification, artificial neural network trained with extreme learning machine have been used. The results obtained shows the feasibility and effectiveness of the approach to identify and classify foliar damages, and the automatic calculation of the severity. The results obtained are very promising according to experts. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural networks

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Density of states method for symplectic gauge theories at finite temperature

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Physical Review D 2025年第11期111卷 114511-114511页

作者： Ed Bennett Biagio Lucini David Mason Maurizio Piai Enrico Rinaldi Davide Vadacchino Swansea Academy of Advanced Computing Swansea University Fabian Way SA1 8EN Swansea Wales United Kingdom Department of Mathematics Faculty of Science and Engineering Swansea University Fabian Way SA1 8EN Swansea Wales United Kingdom School of Mathematical Sciences Queen Mary University of London Mile End Road London E1 4NS United Kingdom Department of Physics Faculty of Science and Engineering Swansea University Singleton Park SA2 8PP Swansea Wales United Kingdom Jülich Supercomputing Centre Forschungszentrum Jülich D-52425 Jülich Germany Interdisciplinary Theoretical and Mathematical Science Program RIKEN (iTHEMS) 2-1 Hirosawa Wako Saitama 351-0198 Japan Centre for Mathematical Sciences University of Plymouth Plymouth PL4 8AA United Kingdom

We study the finite-temperature behavior of the Sp(4) Yang-Mills lattice theory in four dimensions, by applying the logarithmic linear relaxation algorithm. We demonstrate the presence of coexisting (metastable) phases, when the system is in the proximity of the transition. We measure observables such as the free energy, the expectation value of the plaquette operator and of the Polyakov loop, as well as the specific heat, and the Binder cumulant. We use these results to obtain a high-precision measurement of the critical coupling at the confinement-deconfinement transition, and assess its systematic uncertainty, for one value of the lattice extent in the time direction. Furthermore, we perform an extensive study of the finite-volume behavior of the lattice system, by repeating the measurements for fixed lattice time extent, while increasing the spatial size of the lattice. We hence characterize the first-order transition on the lattice and present the first results in the literature on this theory for the infinite volume extrapolation of lattice quantities related to latent heat and interface tension. Gauge theories with Sp(4) group have been proposed as new dark sectors to provide a fundamental origin for the current phenomenological evidence of dark matter. A phase transition at high temperature, in such a new dark sector, occurring in the early Universe, might have left a relic stochastic background of gravitational waves. Our results represent a milestone toward establishing whether such a new physics signal is detectable in future experiments, as they enter the calculation of the parameters, α and β, controlling the power spectrum of gravitational waves. We also outline the process needed in the continuum extrapolation of our measurements and test its feasibility on one additional choice of temporal extent of the lattice.

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Regularization of Extreme Learning Machines with information of spatial relations of the projected data

Regularization of Extreme Learning Machines with information...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Lourenço R. G. Araújo Luiz C. B. Torres Leonardo J. Silvestre Carla Takahashi Antônio P. Braga Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Department of Computing and Systems Federal University of Ouro Preto Jo??o Monlevade Brazil Department of Computing and Electronics Federal University of Esp??rito Santo S??o Mateus ES Brazil Loggi Tecnologia Alameda Santos S??o Paulo SP Brazil Department of Electronics Engineering Federal University of Minas Gerais Belo Horizonte MG Brazil

The following work presents a new approach to automatic selection of Tikhonov's regularization parameter, responsible for controlling the weight value of an ELM neural network. A strategy based on measurements obtained from data projection (Fisher-Score) is introduced. Seven datasets are tested and results are compared to those obtained when the regularization parameter is selected through cross-validation. The strategy shows satisfactory classification performance (in terms of p-value), while presenting significant training time reduction.

关键词： Neurons Training Cybernetics Minimization Testing Biological neural networks Aerospace electronics

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