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Chemical Electron Tomography at Lower Dose and Higher Resolution

Microscopy and Microanalysis 2024年第SUPPLEMENT_1期30卷

作者： Hovden, Robert Schwartz, Jonathan Sung, Suk Hyun Di, Zichao Wendy Jiang, Yi Manassa, Jason Pietryga, Jacob Qian, Yiwen Cho, Min Gee Rowell, Jonathan L Zheng, Huihuo Robinson, Richard D Gu, Junsi Kirilin, Alexey Rozeveld, Steve Ercius, Peter Scott, Mary Department of Materials Science and Engineering University of Michigan Ann Arbor MI Applied Physics Program University of Michigan Ann Arbor MI Chan Zuckerberg Initiative Redwood City CA Rowland Institute at Harvard Cambridge MA. Mathematics and Computer Science Division Argonne National Laboratory Lemont IL Advanced Photon Source Facility Argonne National Laboratory Lemont IL Department of Materials Science and Engineering U.C. Berkeley Berkeley CA National Center for Electron Microscopy Molecular Foundry Berkeley CA Department of Chemistry and Chemical Biology Cornell University Ithaca NY Argonne Leadership Computing Facility Argonne National Laboratory Lemont IL Dow Chemical Co. Terneuzen Netherlands Dow Chemical Co. Midland MI

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Interpretable Independent Recurrent Networks for Forecasting Stroke in Atrial Fibrillation

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JACC. Asia 2025年

作者： Jung-Chi Hsu Yi-Hsien Hsieh Yen-Yun Yang Shu-Lin Chuang Che Lin Lian-Yu Lin Department of Internal Medicine National Taiwan University Hospital Jinshan Branch New Taipei City Taiwan Division of Cardiology Department of Internal Medicine National Taiwan University Hospital and College of Medicine Taipei Taiwan. Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan. Department of Medical Research National Taiwan University Hospital Taipei Taiwan. Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine National Taiwan University Taipei Taiwan Smart Medicine and Health Informatics Program National Taiwan University Taipei Taiwan. Electronic address: chelin@ntu.edu.tw. Division of Cardiology Department of Internal Medicine National Taiwan University Hospital and College of Medicine Taipei Taiwan Smart Medicine and Health Informatics Program National Taiwan University Taipei Taiwan Cardiovascular Center National Taiwan University Hospital Taipei Taiwan Department of Internal Medicine National Taiwan University Hospital Yunlin Branch Yunlin Taiwan. Electronic address: hspenos@***.

BACKGROUND:Atrial fibrillation (AF) is a major risk factor for transient ischemic attack (TIA)/ischemic stroke (IS). OBJECTIVES:Given the dynamic nature of IS risk, this study aimed to predict IS risk in AF patients using a high-dimensional time-series model. METHODS:We conducted a cohort study at the National Taiwan University Hospital from 2014 to 2019, including 7,710 AF patients, with external validation in 6,822 patients from the National Taiwan University Hospital Yunlin Branch. The Forecasting Strokes via Interpretable Independent Networks (ForeSIIN) model, based on gated recurrent units, was proposed. Kaplan-Meier analysis with log-rank test evaluated risk group differences. RESULTS:The annual TIA/IS incidence rate ranged from 181.96 (95% CI: 164.42-200.93) to 15.81 (95% CI: 12.38-20.18) per 1,000 person-years, with an overall incidence of 42.40 (95% CI: 39.60-45.39). The ForeSIIN model achieved the best prediction with an area under the receiver-operating characteristics curve of 0.764 (95% CI: 0.722-0.810), compared with the CHADS-VASc score (AUC: 0.650; 95% CI: 0.596-0.699) and other nonsequential models: extreme gradient boosting AUC: 0.722 (95% CI: 0.676-0.769), support vector machine AUC 0.691 (95% CI: 0.637-0.741), random forest AUC: 0.689 (95% CI: 0.637-0.742). External validation showed area under the receiver-operating characteristics curve of 0.646 (95% CI: 0.618-0.671) and area under the precision-recall curve of 0.222 (95% CI: 0.184-0.259). Feature impact analysis identified the top 5 factors: history of TIA/IS, estimated glomerular filtration rate, C-reactive protein, hematocrit, and plasma fasting glucose. Kaplan-Meier analysis showed significant risk differences between ForeSIIN groups (log-rank P < 0.001). CONCLUSIONS:The innovative ForeSIIN model demonstrated accurate stroke prediction in AF patients and enhanced the interpretation of dynamic risk factors over time.

关键词： atrial fibrillation explainable artificial intelligence gated recurrent unit recurrent neural network (RNN) stroke

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Exploring the effect of image enhancement techniques on COVID‐19 detection using chest X‐rays images

arXiv

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arXiv 2020年

作者： Rahman, Tawsifur Khandakar, Amith Qiblawey, Yazan Tahir, Anas Kiranyaz, Serkan Kashem, Saad Bin Abul Islam, Mohammad Tariqul Maadeed, Somaya Al Zughaier, Susu M. Khan, Muhammad Salman Chowdhury, Muhammad E.H. Department of Biomedical Physics & Technology University of Dhaka Dhaka 1000 Bangladesh Department of Electrical Engineering Qatar University Doha2713 Qatar Faculty of Robotics and Advanced Computing Qatar Armed Forces‐Academic Bridge Program Qatar Foundation Doha24404 Qatar Dept. of Electrical Electronics and Systems Engineering Universiti Kebangsaan Malaysia BangiSelangor43600 Malaysia Department of Computer Science and Engineering Qatar University Doha2713 Qatar Department of Basic Medical Sciences College of Medicine Biomedical and Pharmaceutical Research Unit QU Health Qatar University Doha2713 Qatar University of Engineering and Technology Peshawar Pakistan

The use of computer-aided diagnosis in the reliable and fast detection of corona virus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the medical infrastructure. Chest X-ray (CXR) imaging has several advantages over other imaging techniques as it is cheap, easily accessible, fast and portable. CXR images are sometimes of poor quality and so image enhancement techniques can help the machine learning models to extract valuable discriminating features from the image. Numerous works have been reported on COVID-19 detection from smaller set of original X-ray images. However, the effect of image enhancement in COVID-19 detection was not reported in the literature. This paper explores the effect of various popular image enhancement techniques and states the © 2020, CC BY.

关键词： Image enhancement

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Prototype Selection Using Self-Organizing-Maps and Entropy for Overlapped Classes and Imbalanced Data

Prototype Selection Using Self-Organizing-Maps and Entropy f...

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International Joint Conference on Neural Networks

作者： Marcio Rubbo Leandro A. Silva Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University Sao Paulo SP Brazil Computing and Informatics Faculty & Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University Sao Paulo SP Brazil

The k nearest neighbor (kNN) is a traditional supervised classifier used in data mining tasks. However, when used in real applications, mainly in a dataset with class imbalance or class overlap, kNN suffers with problems in accuracy performance. In this paper, we propose three prototype selection methods using self-organizing maps (SOM) and information entropy to increase the effectiveness of the kNN classifier in datasets with these conditions. The methods, named SOMEntropyKnn, were able to increase the effectiveness of the kNN classifier in all the 14 datasets used in the experiment, increasing the accuracy performance from datasets with imbalance or overlap problems.

关键词： Prototypes Entropy Self-organizing feature maps Neurons Training Information entropy Electrical engineering

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Approximate Sum of Absolute Transformed Differences Hardware Accelerator

Approximate Sum of Absolute Transformed Differences Hardware...

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IEEE International Conference on Electronics, Circuits and Systems

作者： Matheus F. Stigger Victor H. S. Lima Leonardo B. Soares Claudio M. Diniz Sergio Bampi Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil Federal Institute of Rio Grande do Sul (IFRS) Rio Grande Brazil Graduate Program in Microelectronics (PGMicro) Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

Motion Estimation and mode decision are the most compute-intensive tasks in recent video encoders. Sum of Absolute Transformed Differences (SATD) is a distortion metric used inside ME and mode decision to decide the best block to be used as prediction for each block to be encoded. To reduce the complexity of SATD calculation, this work proposes an approximate SATD hardware accelerator based on 1D 4 × 4 Hadamard Transform, in contrast with the precise SATD hardware accelerator that contains a 2D 4 × 4 Hadamard Transform. The proposed approximate SATD accelerator reduces power dissipation in 66.86% compared to the precise SATD accelerator while increasing BD-BR only by less than 0.748%, which is a better result than SAD accelerator that increases BD-BR in up to 0.830% compared to precise SATD.

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Transfer learning with deep Convolutional Neural Network (CNN) for pneumonia detection using chest X-ray

arXiv

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arXiv 2020年

作者： Rahman, Tawsifur Chowdhury, Muhammad E.H. Khandakar, Amith Islam, Khandaker R. Islam, Khandaker F. Mahbub, Zaid B. Kadir, Muhammad A. Kashem, Saad Department of Biomedical Physics & Technology University of Dhaka Dhaka1000 Bangladesh Department of Electrical Engineering Qatar University Doha2713 Qatar Department of Orthodentics Bangabandhu Sheikh Mujib Medical University Dhaka1000 Bangladesh Department of Mathematics and Physics North South University Dhaka1229 Bangladesh Faculty of Robotics and Advanced Computing Qatar Armed Forces-Academic Bridge Program Qatar Foundation Doha24404 Qatar

Pneumonia is a life threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life endangering if not acted upon in the right time and thus early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using the digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN): AlexNet, ResNet18, DenseNet201 and SqueezeNet were used for transfer learning. 5247 chest x-ray images consisting of bacterial, viral and normal chest x-rays were used and through preprocessing techniques, the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95% and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster diagnosing pneumonia by the radiologist and can help in the fast airports screening of the pneumonia patients. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Dynamic compressed sensing for real-Time tomographic reconstruction

arXiv

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arXiv 2020年

作者： Schwartz, Jonathan Zheng, Huihuo Hanwell, Marcus Jiang, Yi Hovden, Robert Department of Material Science and Engineering Ann ArborUniversity of Michigan MichiganMI United States Argonne Leadership Computing Facility Argonne National Laboratory LemontIL United States Kitware Inc Clifton ParkNY United States Advanced Photon Source Facility Argonne National Laboratory LemontIL United States Applied Physics Program University of Michigan Ann ArborMI United States

Electron tomography has achieved higher resolution and quality at reduced doses with recent advances in compressed sensing. Compressed sensing (CS) theory exploits the inherent sparse signal structure to effciently reconstruct three-dimensional (3D) volumes at the nanoscale from un-dersampled measurements. However, the process bottlenecks 3D reconstruction with computation times that run from hours to days. Here we demonstrate a framework for dynamic compressed sens-ing that produces a 3D specimen structure that updates in real-Time as new specimen projections are collected. Researchers can begin interpreting 3D specimens as data is collected to facilitate high-throughput and interactive analysis. Using scanning transmission electron microscopy (STEM), we show that dynamic compressed sensing accelerates the convergence speed by 3-fold while also re-ducing its error by 27% for a Au/SrTiO3 nanoparticle specimen. Before a tomography experiment is completed, the 3D tomogram has interpretable structure within 33% of completion and fine details are visible as early as 66%. Upon completion of an experiment, a high-fidelity 3D visualization is produced without further delay. Additionally, reconstruction parameters that tune data delity can be manipulated throughout the computation without rerunning the entire process. Copyright © 2020, The Authors. All rights reserved.

关键词： Compressed sensing

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Estimation of Grounding Resistance of Transmission Line Towers based on Artificial Neural Networks: A Practical Analysis based on a developed Android App

Estimation of Grounding Resistance of Transmission Line Towe...

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International Symposium on Lightning Protection (XII SIPDA)

作者： Raphael Batista João R. Souza Graduate Program in Automation and Control Engineering IFMG R. Itaguaçu 595 Betim MG Brazil Dept. of Electroeletronics and Computing CEFET-MG Alameda das Perdizes 61 Contagem MG Brazil

This work presents a practical evaluation of an Android app developed for grounding resistance estimation of typical transmission line (TL) towers arrangement. Such arrangements, composed by counterpoise cables, are evaluated for a two-layered soil model by a trained artificial neural network (ANN), which derives in a significative low computational cost for the solution problem. The developed algorithm is evaluated with measurements made in TL towers and compared to the solution provided by a rigorous numerical routine. The results indicate a good accuracy, with median and standard deviation lower than 7%, of the developed app for conditions where the input parameters have values within the range used to training ANN. Values outside the specified range can reach significative errors up to 5 and 17 times, respectively, for median and standard deviation.

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Position: Bayesian deep learning is needed in the age of large-scale AI 24

Position: Bayesian deep learning is needed in the age of lar...

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Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.

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

arXiv

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arXiv 2019年

作者： Torres, V. Silva, P. De, E.A.T. Souza Silva, L.A. Bahamon, D.A. Instituto de Física Universidade Federal Fluminense Niterói Av. Litorânea sn RJ24210-340 Brazil MackGraphe Mackenzie Presbyterian University Rua da Consolação 930 SP01302-907 Brazil School of Computing and Informatics Graduate Program in Electrical Engineering and Computing

The valley transport properties of a superlattice of out-of-plane Gaussians 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 counter-propagating transverse modes;the complex relationship between the design parameters of the superlattice and the valley filter effect make 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 transmission with a precision similar to the Green’s function but with much less computational effort. Copyright © 2019, The Authors. All rights reserved.

关键词： Landforms

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