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arXiv 2025年

作者： Cheng, Sibo Bocquet, Marc Ding, Weiping Finn, Tobias Sebastian Fu, Rui Fu, Jinlong Guo, Yike Johnson, Eleda Li, Siyi Liu, Che Moro, Eric Newton Pan, Jie Piggott, Matthew Quilodran, Cesar Sharma, Prakhar Wang, Kun Xiao, Dunhui Xue, Xiao Zeng, Yong Zhang, Mingrui Zhou, Hao Zhu, Kewei Arcucci, Rossella CEREA ENPC EDF R&D Institut Polytechnique de Paris Île-de-France France School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong226019 China School of Mathematical Sciences Key Laboratory of Intelligent Computing and Applications Tongji University Shanghai200092 China School of Engineering and Materials Science Faculty of Science and Engineering Queen Mary University of London LondonE1 4NS United Kingdom Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Computer Science and Engineering Hong Kong university of science and technology Hong Kong Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom Tianjin Key Laboratory of Imaging and Sensing Microelectronics Technology School of Microelectronics Tianjin University Tianjin300072 China Centre for Health Informatics Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Department of Community Health Sciences Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Undaunted Grantham Institute for Climate Change and the Environment Imperial College London LondonSW7 2AZ United Kingdom Culham Campus AbingdonOX14 3DB United Kingdom Centre for Computational Science Department of Chemistry University College London LondonWC1E 6BT United Kingdom Concordia Institute for Information Systems Engineering Concordia University MontrealQCH3G 1M8 Canada School of Mechanical Medical and Process Engineering Faculty of Engineering Queensland University of Technology BrisbaneQLD Australia Department of Chemical Engineering University College London LondonWC1E 6BT United Kingdom

Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field. © 2025, CC BY-NC-SA.

关键词： Mesh generation

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Evolutionary federated learning on EEG-data 19

Evolutionary federated learning on EEG-data

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19th Conference Information Technologies - Applications and Theory, ITAT 2019

作者： Szegedi, Gábor Kiss, Péter Horváth, Tomáš Department of Data Science and Engineering ELTE – Eötvös Loránd University Faculty of Informatics Budapest Pázmány Péter sétány 1/C. BudapestH-1117 Hungary

With the spread of digitalization across every aspects of society and economy, the amount of data generated keeps increasing. In some domains, this generation happens in such a massively distributed fashion that poses challenges for even collecting the data to build machine learning (ML) models on it, not to mention the processing power necessary for training. An important aspect of processing information that has been generated at users is privacy concerns, that is, users might be unwilling to expose anything that would enable one to draw any conclusion regarding to confidential information they possess. In this work, we present a experiment on a genetic algorithm based federated learning (FL) algorithm, that reduces the data transfer from individual users to the learner to a single fitness value. Copyright ©2019 for this paper by its authors.

关键词： data transfer

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Reducing EHR navigation time with an advanced hyperlipidemia management Tool: An evaluation of efficiency

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International Journal of Medical informatics 2025年

作者： Chin-Chen Chen Yu-Chen Liu Guan-Ling Lin Annisa Ristya Rahmant Der-Ming Liou Hsuan-Chia Yang Information Technology Department Cardinal Tien Hospital New Taipei City Taiwan Information Technology Department Yonghe Cardinal Tien Hospital New Taipei City Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan School of Nursing College of Medicine National Taiwan University Taipei Taiwan Department Health Policy and Management Faculty of Medicine Public Health and Nursing Universitas Gadjah Mada Yogyakarta Indonesia Department of Computer Science Faculty of Science and Technology Middlesex University London UK Master Program in Medical Informatics National Tsing Hua University Hsinchu City Taiwan International Center for Health Information Technology (ICHIT) Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Research Center of Big Data and Meta-analysis Wanfang Hospital Taipei Medical University Taipei Taiwan

Background Effective management of hyperlipidemia is crucial for preventing cardiovascular diseases. However, traditional monitoring systems often lack efficiency, particularly in navigating electronic health records (EHRs) to identify and track at-risk patients. Objective This study aims to evaluate the impact of a Hyperlipidemia Medication Reminder System (HLD-MRS) implemented in a district hospital to streamline EHR navigation and enhance patient management. Methods A pre-post prospective study was conducted at a district hospital. This study designed the HLD-MRS to integrate patient data, guideline recommendations, and risk factor assessments into a single interface, simplifying EHR navigation. This study compared EHR navigation times and patient tracking rates before and after HLD-MRS implementation across the Cardiology, Endocrinology, and Family Medicine departments. Results Among 750 patients with follow-up appointments, 334 were identified as having HLD or being at high-risk status. The HLD-MRS significantly reduced mean EHR navigation time from 83.9 s (SD = 8.6) to 53.8 s (SD = 6.6) across all departments (p < 0.001). The proportion of actively monitored HLD patients increased from 42.5 % to 56.6 %, while cases with unknown or withheld assessment status decreased from 57.5 % to 28.4 %. Conclusion The HLD-MRS improved efficiency in EHR navigation and increased the frequency of active patient monitoring. This guideline-driven decision support system shows promise for enhancing hyperlipidemia care in high-volume outpatient settings.

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Medical Image Segmentation Review: The Success of U-Net

arXiv

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arXiv 2022年

作者： Azad, Reza Aghdam, Ehsan Khodapanah Rauland, Amelie Jia, Yiwei Avval, Atlas Haddadi Bozorgpour, Afshin Karimijafarbigloo, Sanaz Cohen, Joseph Paul Adeli, Ehsan Merhof, Dorit The Institute of Imaging and Computer Vision RWTH Aachen University Aachen52074 Germany The Department of Electrical Engineering Shahid Beheshti University Tehran*** Iran The School of Medicine Mashhad University of Medical Sciences Mashhad*** Iran The Department of Computer Engineering Sharif University of Technology Tehran*** Iran The Center for Artificial Intelligence in Medicine & Imaging Stanford University Palo AltoCA94304 United States Stanford University StanfordCA94305 United States The Faculty of Informatics and Data Science University of Regensburg Regensburg93053 Germany The Fraunhofer Institute for Digital Medicine MEVIS Bremen28359 Germany

Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most widespread image segmentation architecture due to its flexibility, optimized modular design, and success in all medical image modalities. Over the years, the U-Net model achieved tremendous attention from academic and industrial researchers. Several extensions of this network have been proposed to address the scale and complexity created by medical tasks. Addressing the deficiency of the naive U-Net model is the foremost step for vendors to utilize the proper U-Net variant model for their business. Having a compendium of different variants in one place makes it easier for builders to identify the relevant research. Also, for ML researchers it will help them understand the challenges of the biological tasks that challenge the model. To address this, we discuss the practical aspects of the U-Net model and suggest a taxonomy to categorize each network variant. Moreover, to measure the performance of these strategies in a clinical application, we propose fair evaluations of some unique and famous designs on well-known datasets. We provide a comprehensive implementation library with trained models for future research. In addition, for ease of future studies, we created an online list of U-Net papers with their possible official implementation. All information is gathered in https://***/NITR098/Awesome-U-Net repository. © 2022, CC BY.

关键词： Computer aided diagnosis

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Optimization in federated learning 19

Optimization in federated learning

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19th Conference Information Technologies - Applications and Theory, ITAT 2019

作者： Felbab, Vukasin Kiss, Péter Horváth, Tomáš Department of Data Science and Engineering ELTE Eötvös Loránd University Faculty of Informatics Budapest Pázmány Péter sétány 1/C. BudapestH-1117 Hungary

Federated learning (FL) is an emerging branch of machine learning (ML) research, that is examining the methods for scenarios, where individual nodes possess parts of the data, and the task is to form a single common model that fits to the whole distribution. In FL, we generally use mini batch gradient descent for optimizing weights of the models that appears to work very well for federated scenarios. For traditional machine learning setups, a number of modifications has been proposed to accelerate the learning process and to help to get over challenges posed by the high dimensionality and non-convexity of search spaces of the parameters. In this paper we present our experiments on applying different popular optimization methods for training neural networks in a federated manner. Copyright ©2019 for this paper by its authors.

关键词： Machine learning

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SNPViz v2.0: A web-based tool for enhanced haplotype analysis using large scale resequencing datasets and discovery of phenotypes causative gene using allelic variations

SNPViz v2.0: A web-based tool for enhanced haplotype analysi...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuai Zeng Mária Škrabišová Zhen Lyu Yen On Chan Kristin Bilyeu Trupti Joshi University of Missouri-Columbia Columbia USA Faculty of Science Palacký University in Olomouc Olomouc Czech Republic MU Institute for Data Science and Informatics University of Missouri-Columbia Columbia USA USDA/ARS Plant Genetics Research Unit University of Missouri-Columbia Columbia USA

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Single nucleotide polymorphisms (SNPs) and insertions/deletions (Indels) are widely spread across all chromosomes of the genome and act as biological markers, which aid in identification of genes associated with traits or phenotypes. With the advances in next-generation sequencing (NGS) technology, large amounts of SNPs and Indels data have become available, making it difficult to effectively perform analysis across multiple samples and intuitively integrate, compare and/or visualize them simultaneously. Genome-wide association studies (GWAS) is a widely used method to find genetic variations associated with a trait, but it lacks an efficient way to investigate genomic variant functions. To tackle these issues, we have developed SNPViz v2.0, a web-based tool to visualize large-scale haplotype blocks with detailed SNPs and Indels grouped by their chromosomal coordinates, along with their overlapping gene models, phenotype to genotype accuracies, Gene Ontology (GO) annotations, protein families (Pfam) annotations, genomic variant annotations, and their functional effects. Moreover, SNPViz v2.0 integrates several large scale soybean SNPs and Indels datasets including G. Soja, GWAS, NAM41, USB-15x, USB-40x, MSMC and Zhou302, available from multiple studies. SNPViz v2.0 is deployed for all organisms and available in both SoyKB and KBCommons frameworks. For soybean data only, the SNPViz 2.0 is publicly available at http://***/SNPViz2/. For other organisms such as Arabidopsis thaliana, Mus musculus and Zea mays, SNPViz 2.0 is publicly available in their respective knowledge bases at https://***.

关键词： Tools data visualization Bioinformatics Genomics databases Annotations Organisms

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COVID-19 vaccination coverage for half a million non-EU migrants and refugees in England( vol8, pg288, 2024)

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NATURE HUMAN BEHAVIOUR 2024年第2期8卷 399-399页

作者： Burns, Rachel Wyke, Sacha Eyre, Max T. Boukari, Yamina Sorensen, Tina B. Tsang, Camille Campbell, Colin N. J. Beale, Sarah Zenner, Dominik Hargreaves, Sally Campos-Matos, Ines Harron, Katie Aldridge, Robert W. Centre for Public Health Data Science Institute of Health Informatics University College London London UK Institute of Epidemiology and Health Care University College London London UK Department of Health and Social Care Office for Health Improvement and Disparities London UK UK Health Security Agency London UK Department of Disease Control Faculty of Infectious and Tropical Diseases London School of Hygiene and Tropical Medicine London UK Immunisation and Vaccine Preventable Diseases UK Health Security Agency London UK Global Public Health Unit Wolfson Institute of Population Health Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK Infection and Population Health Department Institute of Global Health University College London London UK Institute for Infection and Immunity St George’s University of London Cranmer Terrace London London UK UCL Great Ormond Street Institute of Child Health University College London London UK

Despite evidence suggesting that some migrants are at risk of under-immunization and have experienced severe health inequities during the pandemic, data are limited on migrants’ COVID-19 vaccine coverage globally. Here we linked data from non-European Union migrants and resettled refugees to the national COVID-19 vaccination dataset in England. We estimated patterns in second and third dose delays and overdue doses between 12 December 2020 and 20 April 2022 by age, visa type and ethnicity. Of the 465,470 linked records, 91.8% (427,073/465,470) of migrants received a second dose and 51.3% (238,721/465,470) received a third. Refugees had the highest risk of delayed second (adjusted odds ratio 1.66; 95% confidence interval 1.55–1.79) and third dose (1.55; 1.43–1.69). Black migrants were twice as likely to have a second dose delayed (2.37; 2.23–2.54) than white migrants, but this trend reversed for the third dose. Older migrants (>65 years) were four times less likely to have received their second or third dose compared with the general population in England aged >65 or older. Policymakers, researchers and practitioners should work to understand and address personal and structural barriers to vaccination for diverse migrant populations.

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Clinician data Scientists—Preparing for the Future of Medicine in the Digital World

健康数据科学（英文）

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健康数据科学（英文） 2022年第4期 1-3页

作者： Fulin Wang Lin Ma Georgina Moulton Mai Wang Luxia Zhang National Institute of Health Data Science at Peking University BeijingChina Institute of Medical Technology Peking University Health Science CenterBeijingChina Peking University Health Science Center BeijingChina Division of Informatics Imaging and Data SciencesFaculty of BiologyMedicine and HealthThe University of ManchesterManchesterUK National Institute of Health Data Science at Peking University BeijingChina Advanced Institute of Information Technology Peking UniversityChina National Institute of Health Data Science at Peking University BeijingChina Advanced Institute of Information Technology Peking UniversityChina Institute of Nephrology Peking University First HospitalChina

*** Clinician-scientists have a unique strength in translational research and medical advances that improve the quality of care and patient *** big data analytics and advanced tech-nologies such as artificial intelligence are being continuously applied in the healthcare scenario,it not only transforms patient care but also creates tremendous opportunities for data-driven *** a digital health era,clinician-scientists proficient in data science knowledge——that is clinician data scientists——are central to harnessing the power of big data analytics and advanced technologies in medicine.

关键词： medicine future digital world data clinician preparing scientists

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Enabling machine learning algorithms for credit scoring - Explainable artificial intelligence (XAI) methods for clear understanding complex predictive models

arXiv

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arXiv 2021年

作者： Biecek, Przemyslaw Chlebus, Marcin Gajda, Janusz Gosiewska, Alicja Kozak, Anna Ogonowski, Dominik Sztachelski, Jakub Wojewnik, Piotr Faculty of Mathematics and Information Science Warsaw University of Technology ul. Koszykowa 7 Warsaw00-662 Poland Faculty of Economic Sciences University of Warsaw ul. Dluga 44/50 Warsaw00-241 Poland Faculty of Mathematics Informatics and Mechanics University of Warsaw ul. Banacha 2 Warsaw02-097 Poland Data Juice Lab sp. z o.o. ul. Platanowa 11 Warsaw03-054 Poland ul. Zygmunta Modzelewskiego 77a Warsaw02-679 Poland

Rapid development of advanced modelling techniques gives an opportunity to develop tools that are more and more accurate. However as usually, everything comes with a price and in this case, the price to pay is to loose interpretability of a model while gaining on its accuracy and precision. For managers to control and effectively manage credit risk and for regulators to be convinced with model quality the price to pay is too high. So, it prevents them from using advanced models due to the lack of their interpretability. In this paper, we show how to take credit scoring analytics in to the next level, namely we present comparison of various predictive models (logistic regression, logistic regression with weight of evidence transformations and modern artificial intelligence algorithms) and show that advanced tree based models give best results in prediction of client default. What is even more important and valuable we also show how to boost advanced models using techniques which allow to interpret them and made them more accessible for credit risk practitioners, resolving the crucial obstacle in widespread deployment of more complex, "black box" models like random forests, gradient boosted or extreme gradient boosted trees. All this will be shown on the large dataset obtained from the Polish Credit Bureau to which all the banks and most of the lending companies in the country do report the credit files. This huge extent of data ensures high quality of the model inputs and objectivity of the conclusions. In this paper the data from lending companies were used. The paper then compares state of the art best practices in credit risk modelling with new advanced modern statistical tools boosted by the latest developments in the field of interpretability and explainability of artificial intelligence algorithms. We believe that this is a valuable contribution when it comes to presentation of different modelling tools but what is even more important it is showing which method

关键词： Decision trees

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Counting stabilizer codes for arbitrary dimension

arXiv

引用

arXiv 2022年

作者： Singal, Tanmay Chiang, Che Hsu, Eugene Kim, Eunsang Goan, Hsi-Sheng Hsieh, Min-Hsiu Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University GrudziĄdzka 5/7 Toruń87-100 Poland Department of Physics Center for Theoretical Physics National Taiwan University Taipei10617 Taiwan Quantum information center Chung Yuan Christian University No. 200 Zhongbei Rd. Zhongli Dist. Taoyuan City320314 Taiwan Department of mathematical Data Science Hanyang University Gyeonggi-do Ansan15588 Korea Republic of Center for Quantum Science and Engineering National Taiwan University Taipei10617 Taiwan Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan Hon Hai Quantum Computing Research Center Taipei Taiwan

In this work, we compute the number of [[n, k]]d stabilizer codes made up of ddimensional qudits, for arbitrary positive integers d. In a seminal work by Gross (Ref. [23]) the number of [[n, k]]d stabilizer codes was computed for the case when d is a prime (or the power of a prime, i.e., d = pm, but when the qudits are Galois-qudits). The proof in Ref. [23] is inapplicable to the non-prime case. For our proof, we introduce a group structure to [[n, k]]d codes, and use this in conjunction with the Chinese remainder theorem to count the number of [[n, k]]d codes. Our work overlaps with Ref. [23] when d is a prime and in this case our results match exactly, but the results differ for the more generic case. Despite that, the overall order of magnitude of the number of stabilizer codes scales agnostic of whether the dimension is prime or non-prime. This is surprising since the method employed to count the number of stabilizer states (or more generally stabilizer codes) depends on whether d is prime or not. The cardinality of stabilizer states, which was so far known only for the prime-dimensional case (and © 2022, CC BY.

关键词： Codes (symbols)

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