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arXiv 2020年

作者： Kim, Jungeun Lee, Kookjin Lee, Dongeun Jin, Sheo Yon Park, Noseong Department of AI Yonsei University Korea Republic of Extreme Scale Data Science & Analytics Department Sandia National Laboratory United States Department of Computer Science and Information Systems Texas A&M University at Commerce United States IT Engineering Department Sookmyung Women’s University Korea Republic of Department of AI & CS Yonsei University Korea Republic of

We present a method for learning dynamics of complex physical processes described by time-dependent nonlinear partial differential equations (PDEs). Our particular interest lies in extrapolating solutions in time beyond the range of temporal domain used in training. Our choice for a baseline method is physics-informed neural network (PINN) [Raissi et al., J. Comput. Phys., 378:686–707, 2019] because the method parameterizes not only the solutions, but also the equations that describe the dynamics of physical processes. We demonstrate that PINN performs poorly on extrapolation tasks in many benchmark problems. To address this, we propose a novel method for better training PINN and demonstrate that our newly enhanced PINNs can accurately extrapolate solutions in time. Our method shows up to 72% smaller errors than existing methods in terms of the standard L2-norm metric. Copyright © 2020, The Authors. All rights reserved.

关键词： Extrapolation

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A clarification of misconceptions, myths and desired status of artificial intelligence

arXiv

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arXiv 2020年

作者： Emmert-Streib, Frank Yli-Harja, Olli Dehmer, Matthias Predictive Society and Data Analytics Lab Faculty of Information Technology and Communication Sciences Tampere University Tampere Finland Institute of Biosciences and Medical Technology Tampere University of Technology Tampere Finland Department of Mechatronics and Biomedical Computer Science UMIT Hall in Tyrol Austria

The field artificial intelligence (AI) has been founded over 65 years ago. Starting with great hopes and ambitious goals the field progressed though various stages of popularity and received recently a revival in the form of deep neural networks. Some problems of AI are that so far neither’intelligence’ nor the goals of AI are formally defined causing confusion when comparing AI to other fields. In this paper, we present a perspective on the desired and current status of AI in relation to machine learning and statistics and clarify common misconceptions and myths. Our discussion is intended to uncurtain the veil of vagueness surrounding AI to see its true countenance. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Correction: Content and Dynamics of Websites Shared Over Vaccine-Related Tweets in COVID-19 Conversations: Computational Analysis

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Journal of medical Internet research 2023年 25卷 e43279页

作者： Iain Cruickshank Tamar Ginossar Jason Sulskis Elena Zheleva Tanya Berger-Wolf Center for Computational Analysis of Social and Organizational Systems Carnegie Mellon University Pittsburgh PA United States. Department of Communication and Journalism University of New Mexico Albuquerque NM United States. Department of Computer Science The University of Illinois at Chicago Chicago IL United States. Translational Data Analytics Institute The Ohio State University Colombus OH United States.

[This corrects the article DOI: 10.2196/29127.].

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Cosmological constraints with deep learning from KiDS-450 weak lensing maps

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Physical Review D 2019年第6期100卷 063514-063514页

作者： Janis Fluri Tomasz Kacprzak Aurelien Lucchi Alexandre Refregier Adam Amara Thomas Hofmann Aurel Schneider Institute of Particle Physics and Astrophysics Department of Physics 8093 ETH Zurich Switzerland Data Analytics Lab Department of Computer Science 8006 ETH Zurich Switzerland

Convolutional neural networks (CNNs) have recently been demonstrated on synthetic data to improve upon the precision of cosmological inference. In particular, they have the potential to yield more precise cosmological constraints from weak lensing mass maps than the two-point functions. We present the cosmological results with a CNN from the KiDS-450 tomographic weak lensing dataset, constraining the total matter density Ωm, the fluctuation amplitude σ8, and the intrinsic alignment amplitude AIA. We use a grid of N-body simulations to generate a training set of tomographic weak lensing maps. We test the robustness of the expected constraints to various effects, such as baryonic feedback, simulation accuracy, a different value of H0, or the light cone projection technique. We train a set of ResNet-based CNNs with varying depths to analyze sets of tomographic KiDS mass maps divided into 20 flat regions, with applied Gaussian smoothing of σ=2.34 arc min. The uncertainties on shear calibration and n(z) error are marginalized in the likelihood pipeline. Following a blinding scheme, we derive constraints on S8=σ8(Ωm/0.3)0.5=0.777−0.036+0.038 with our CNN analysis, with AIA=1.398−0.724+0.779. We compare this result to the power spectrum analysis on the same maps and likelihood pipeline and find an improvement of about 30% for the CNN. We discuss how our results offer excellent prospects for the use of deep learning in future cosmological data analysis.

关键词： Cosmological parameters Large scale structure of the Universe Astrophysical & cosmological simulations

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M2: Mixed models with preferences, popularities and transitions for next-basket recommendation

arXiv

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arXiv 2020年

作者： Peng, Bo Ren, Zhiyun Parthasarathy, Srinivasan Ning, Xia Department of Computer Science and Engineering The Ohio State University ColumbusOH43210 United States Department of Biomedical Informatics Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States Department of Biomedical Informatics The Ohio State University ColumbusOH43210 United States

Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed model with preferences, popularities and transitions (M2) for the next-basket recommendation. This method models three important factors in next-basket generation process: 1) users' general preferences, 2) items' global popularities and 3) transition patterns among items. Unlike existing recurrent neural network-based approaches, M2does not use the complicated networks to model the transitions among items, or generate embeddings for users. Instead, it has a simple encoder-decoder based approach (ed-Trans) to better model the transition patterns among items. We compared M2with different combinations of the factors with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets in recommending the first, second and third next basket. Our experimental results demonstrate that M2significantly outperforms the state-of-the-art methods on all the datasets in all the tasks, with an improvement of up to 22.1%. In addition, our ablation study demonstrates that the ed-Trans is more effective than recurrent neural networks in terms of the recommendation performance. We also have a thorough discussion on various experimental protocols and evaluation metrics for the evaluation of next-basket recommendation methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

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The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

arXiv

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arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

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Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring data

Short Term Blood Glucose Prediction based on Continuous Gluc...

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

作者： Ali Mohebbi Alexander R. Johansen Nicklas Hansen Peter E. Christensen Jens M. Tarp Morten L. Jensen Henrik Bengtsson Morten Mørup Department of Applied Mathematics and Computer Science Cognitive Systems Technical University of Denmark (DTU) Denmark Novo Nordisk A/S Data Analytics Søborg Denmark Novo Nordisk A/S Medical & Science Søborg Denmark Novo Nordisk A/S Device R&D Hillerød Denmark

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Continuous Glucose Monitoring (CGM) has enabled important opportunities for diabetes management. This study explores the use of CGM data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction and compare the RNNs to conventional time-series forecasting using Autoregressive Integrated Moving Average (ARIMA). A prediction horizon up to 90 min into the future is considered. In this context, we evaluate both population-based and patient-specific RNNs and contrast them to patient-specific ARIMA models and a simple baseline predicting future observations as the last observed. We find that the population-based RNN model is the best performing model across the considered prediction horizons without the need of patient-specific data. This demonstrates the potential of RNNs for STBG prediction in diabetes patients towards detecting/mitigating severe events in the STBG, in particular hypoglycemic events. However, further studies are needed in regards to the robustness and practical use of the investigated STBG prediction models.

关键词： Diabetes Predictive models Training data models Sociology Statistics Insulin

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Harnessing artificial intelligence to fill global shortfalls in biodiversity knowledge

Nature Reviews Biodiversity

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Nature Reviews Biodiversity 2025年第3期1卷 166-182页

作者： Laura J. Pollock David Rolnick Justin Kitzes Sara Beery Kaitlyn M. Gaynor Marta A. Jarzyna Tanya Berger-Wolf Oisin Mac Aodha Bernd Meyer Graham W. Taylor Devis Tuia Department of Biology McGill University Montréal Quebec Canada Mila—Quebec AI Institute Montréal Quebec Canada School of Computer Science McGill University Montréal Quebec Canada Department of Biological Sciences University of Pittsburgh Pittsburgh PA USA Faculty of AI and Decision Making Massachusetts Institute of Technology Cambridge MA USA Department of Botany University of British Columbia Vancouver British Columbia Canada Department of Zoology University of British Columbia Vancouver British Columbia Canada Department of Evolution Ecology and Organismal Biology The Ohio State University Columbus OH USA Translational Data Analytics Institute The Ohio State University Columbus OH USA Department of Computer Science and Engineering The Ohio State University Columbus OH USA Department of Electrical & Computer Engineering The Ohio State University Columbus OH USA School of Informatics University of Edinburgh Edinburgh UK Department of Data Science and AI Monash University Clayton Victoria Australia School of Engineering University of Guelph Guelph Ontario Canada Vector Institute Toronto Ontario Canada School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland

Large, well described gaps exist in both what we know and what we need to know to address the biodiversity crisis. Artificial intelligence (AI) offers new potential for filling these knowledge gaps, but where the biggest and most influential gains could be made remains unclear. To date, biodiversity-related uses of AI have largely focused on tracking and monitoring of wildlife populations. Rapid progress is being made in the use of AI to build phylogenetic trees and species distribution models. However, AI also has considerable unrealized potential in the re-evaluation of important ecological questions, especially those that require the integration of disparate and inherently complex data types, such as images, video, text, audio and DNA. This Review describes the current and potential future use of AI to address seven clearly defined shortfalls in biodiversity knowledge. Recommended steps for AI-based improvements include the re-use of existing image data and the development of novel paradigms, including the collaborative generation of new testable hypotheses. The resulting expansion of biodiversity knowledge could lead to science spanning from genes to ecosystems — advances that might represent our best hope for meeting the rapidly approaching 2030 targets of the Global Biodiversity Framework.

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Maintaining Voting Integrity using Blockchain

Maintaining Voting Integrity using Blockchain

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International Conference on Electronics, computer and Computation (ICECCO)

作者： Steve A. Adeshina Adegboyega Ojo Department of Computer Science Nile University of Nigeria Insight Centre for Data Analytics National University of Ireland Galway Republic of Ireland

ISBN: (数字)9781728151601

ISBN: (纸本)9781728151618

The potentials of using blockchains and distributed ledgers to support voting processes have attracted significant attention in the electronic voting community. Most of these recent ideas are centered on blockchain-based e-voting protocols. Others focus on how blockchain can be exploited to simultaneously deliver auditability and anonymity of voters in the voting process. A common feature of these research efforts is the use of blockchain within e-voting contexts. We elaborate in this work the integrity requirements that must be supported by blockchain in online voting as well as offline voting prevalent in developing countries. The framework conditions for blockchain-based voting are also discussed.

关键词： cryptography data integrity data privacy developing countries distributed databases government data processing Government data processing distributed databases data privacy data Quality cryptography Vote Developing Countries Electronic voting Anonymity

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A Hybrid Approach to Dynamic Enterprise data Platform

A Hybrid Approach to Dynamic Enterprise Data Platform

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IEEE International Conference on Big data

作者： Mehmet Selman Sezgin Ahmet Tuğrul Bayrak Olcay Taner Ytldtz İstanbul Turkey Data Science and Analytics Etstur İstanbul Turkey Department of Computer Engineering Işɩk Univesity İstanbul Turkey

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Today, corporations aim to make maximum use of the data produced in business applications. One of the most important goals is to convert the data to the commercial benefit in the fastest way. For this purpose, it is critical to receive the data from source systems, process this data and use it as a support for business decisions. There are many approaches to the proceeding of acquiring, processing and making the data useful. In this study, we took advantage of most of the existing approaches and produced a hybrid solution. This solution can be integrated with new data sources very quickly and reduces the amount of time for data integration, preprocessing, deduplication and entity mapping by using open source software components.

关键词： Distributed databases data warehouses Big data Companies Google

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