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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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A Comparative Study of Two Complex Ontologies in Air Traffic Management

A Comparative Study of Two Complex Ontologies in Air Traffic...

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Conference on Digital Avionics Systems (DASC)

作者： Eduard Gringinger Richard M. Keller Audun Vennesland Christoph G. Schuetz Bernd Neumayr Corporate Research Frequentis AG Vienna Austria Intelligent Systems Division NASA Ames Research Center Moffet Field CA USA Software Engineering Safety and Security SINTEF Trondheim Norway Institute of Business Informatics - Data & Knowledge Engineering Johannes Kepler University Linz Linz Austria

ISBN: (数字)9781728106496

ISBN: (纸本)9781728114972

Over the past 25 years, multiple different data models have been introduced to standardize information management and facilitate data exchange and integration in the aviation domain. As a next step in the evolution of aviation data management, ontologies capturing the semantics (concepts, properties, and relationships) have been produced based on those models. In this paper, we describe a study comparing two recently released and independently-developed complex ontologies focused on Air Traffic Management (ATM) - the NASA ATM Ontology and an ontology derived from the ATM Information Reference Model. We develop a methodology for manually comparing two ontologies and identifying what we describe as exact, light, and mismatches between concepts in the two ontologies. We also describe a classification scheme that characterizes mismatches in terms of the general reason for the mismatch. This approach can be applied to improve existing ATM ontologies and foster interoperability, which will benefit aviation stakeholders.

关键词： Information Exchange Models ATM Information Reference Model NASA ATM Ontology Ontology Matching Ontology Air traffic management Automatic Teller Machines classification scheme MISMATCH aviation data exchange data models Information management

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Improving data latency in ETL with filtering algorithms for stream processing an experimental setup

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AIP Conference Proceedings 2024年第1期2991卷

作者： Adilah Sabtu Mahadi Bahari Nurulhuda Firdaus Mohd Azmi Nor Azizah Ali Zuraidah Sulaiman Nur Aqidah Mohd Fauzi Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia 54100 Kuala Lumpur Malaysia Azman Hashim International Business School Universiti Teknologi Malaysia 81310 Johor Bharu Johor Malaysia UTM Big Data Centre Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia 81310 Johor Bharu Johor Malaysia School of Computing Faculty of Engineering Universiti Teknologi Malaysia 81310 Johor Bharu Johor Malaysia School of Professional and Continuing Education Universiti Teknologi Malaysia 81310 Johor Bharu Johor Malaysia

The Extract, Transform and Load (ETL) system is a standard approach of managing and sustaining movement and transaction of data assets. The exponential growth of data necessitates the development of better approaches and ways of surpassing the traditional ETL limits and effectively manage these big data in near real-time, in terms of availability, speed of delivery or latency and scalability in processing, thus enhance the functionality and increase data value. The purpose of this research is to identify approaches and series of combination of implementing ETL for streaming and near real-time data with special focus on data latency. It is important to note that while the approaches might improve latency, it is also possible that other aspects such as availability and scalability could be compromised, as well as other limitations. The research then presents an experimental setup to simulate a near real-time scenario for landslide mapping using Lidar data, introduces 3 sets of algorithms combo: (1) data model transformation, (2) alert threshold (3) query filter techniques in stream processing with the aim of achieving low latency. Furthermore, the research expects to contribute some modified filtering techniques for accelerating queries for prioritized datasets and reduce overall data filtering time. The experiment will be performed on a Hadoop platform.

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Network constraints on the mixing patterns of binary node metadata

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Physical Review E 2020年第6期102卷 062310-062310页

作者： Matteo Cinelli Leto Peel Antonio Iovanella Jean-Charles Delvenne Ca' Foscari University of Venice Department of Environmental Sciences Informatics and Statistics 30172 Mestre (VE) Italy Applico Lab CNR-ISC 00185 Rome Italy Institute of Data Science Faculty of Science and Engineering Maastricht University Maastricht The Netherlands Department of Data Analytics and Digitalisation School of Business and Economics Maastricht University Maastricht The Netherlands University of Rome “Tor Vergata” Via del Politecnico 1 Rome Italy ICTEAM Université catholique de Louvain Louvain-la-Neuve Belgium CORE Université catholique de Louvain Louvain-la-Neuve Belgium

We consider the network constraints on the bounds of the assortativity coefficient, which aims to quantify the tendency of nodes with the same attribute values to be connected. The assortativity coefficient can be considered as the Pearson's correlation coefficient of node metadata values across network edges and lies in the interval [−1,1]. However, properties of the network, such as degree distribution and the distribution of node metadata values, place constraints upon the attainable values of the assortativity coefficient. This is important as a particular value of assortativity may say as much about the network topology as about how the metadata are distributed over the network—a fact often overlooked in literature where the interpretation tends to focus simply on the propensity of similar nodes to link to each other, without any regard on the constraints posed by the topology. In this paper we quantify the effect that the topology has on the assortativity coefficient in the case of binary node metadata. Specifically, we look at the effect that the degree distribution, or the full topology, and the proportion of each metadata value has on the extremal values of the assortativity coefficient. We provide the means for obtaining bounds on the extremal values of assortativity for different settings and demonstrate that under certain conditions the maximum and minimum values of assortativity are severely limited, which may present issues in interpretation when these bounds are not considered.

关键词： Clustering Network structure Topological tools

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Culture versus Policy: More Global Collaboration to Effectively Combat COVID-19

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The Innovation 2020年第2期1卷 15-16页

作者： Jianping Li Kun Guo Enrique Herrera Viedma Heesoek Lee Jiming Liu Ning Zhong Luiz Flavio Autran Monteiro Gomes Florin Gheorghe Filip Shu-Cherng Fang Mujgan SagirÖzdemir Xiaohui Liu Guoqing Lu Yong Shi School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China Department of Computer Science and Artificial Intelligence E.T.S.de Ingenieria Informatica y de TelecomunicacionesUniversity of Granada18071 GranadaSpain Department of Information Management Korea Advanced Institute of Science and TechnologySeoul 207-43 Korea Department of Computer Science and HKBU-CSD&NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control Hong Kong Baptist UniversityHong KongChina Department of Life Science and Informatics Maebashi Institute of TechnologyMaebashi 371-0816Japan Ibmec University Center Av.Presidente Wilson118Office#111020030-020 Rio de JaneiroBrazil The Romanian Academy Bucharest010071Romania Industrial and Systems Engineering Department North Carolina State UniversityRaleighNC 27695USA Department of Industrial Engineering Eskisehir Osmangazi University26480 EskisehirTurkey Department of Computer Science Brunel University LondonLondonUB83PHUK Department of Biology and School of Interdisciplinary Informatics University of Nebraska at OmahaOmahaNE 68182USA College of Information Science and Technology University of Nebraska at OmahaOmahaNE 68182USA

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each *** countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

关键词： seeking overcome policy

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Innovative informatics methods for process mining in health care

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Journal of Biomedical informatics 2020年 109卷 103551-103551页

作者： Munoz-Gama, Jorge Martin, Niels Fernandez-Llatas, Carlos Johnson, Owen Sepúlveda, Marcos Department of Computer Science School of Engineering Pontificia Universidad Católica de Chile Santiago Chile Research Group Business Informatics Hasselt University Hasselt Belgium Data Analytics Laboratory Vrije Universiteit Brussel Brussels Belgium ITACA Institute – Process Mining 4 Health Lab Universitat Politècnica de Valencia Valencia Spain Karolinska Institutet Stockholm Sweden School of Computing Leeds University Leeds United Kingdom

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An Integrated AI-Enabled System Using One Class Twin Cross Learning (OCT-X) for Early Gastric Cancer Detection

arXiv

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arXiv 2025年

作者： Liu, Xian-Xian Wei, Yuanyuan Xu, Mingkun Guo, Yongze Zhang, Hongwei Dong, Huicong Song, Qun Zhao, Qi Luo, Wei Tien, Feng Gao, Juntao Fong, Simon Department of Computer and Information Science University of Macau 999078 China Department of Biomedical Engineering The Chinese University of Hong Kong Shatin999077 Hong Kong Department of Neurology David Geffen School of Medicine University of California Los AngelesCA90095 United States Guangdong Institute of Intelligence Science and Technology Hengqin Zhuhai519031 China Department of Precision Instrument Tsinghua University Beijing100084 China Department of Gastroenterology Affiliated Hospital of Hebei University of Engineering Handan China Institute of Artificial Intelligence Chongqing Technology and Business University CO Chongqing400067 China Cancer Centre Institute of Translational Medicine Faculty of Health Sciences University of Macau Taipa519000 China MoE Frontiers Science Center for Precision Oncology University of Macau Taipa519000 China Institute of Clinical Medicine The First People’s Hospital of Foshan Guangzhou1528000 China Hebei Key Laboratory of Medical Data Science Institute of Biomedical Informatics School of Medicine Hebei University of Engineering Hebei Province Handan056038 China Tsinghua University Beijing China

Early detection of gastric cancer, a leading cause of cancer-related mortality worldwide, remains hampered by the limitations of current diagnostic technologies, leading to high rates of misdiagnosis and missed diagnoses. To address these challenges, we propose an integrated system that synergizes advanced hardware and software technologies to balance speed-accuracy. Our study introduces the One Class Twin Cross Learning (OCT-X) algorithm. Leveraging a novel fast double-threshold grid search strategy (FDT-GS) and a patch-based deep fully convolutional network, OCT-X maximizes diagnostic accuracy through real-time data processing and seamless lesion surveillance. The hardware component includes an all-in-one point-of-care testing (POCT) device with high-resolution imaging sensors, real-time data processing, and wireless connectivity, facilitated by the NI CompactDAQ and LabVIEW software. Our integrated system achieved an unprecedented diagnostic accuracy of 99.70%, significantly outperforming existing models by up to 4.47%, and demonstrated a 10% improvement in multirate adaptability. These findings underscore the potential of OCT-X as well as the integrated system in clinical diagnostics, offering a path toward more accurate, efficient, and less invasive early gastric cancer detection. Future research will explore broader applications, further advancing oncological diagnostics. Code is available at https://***/ liu37972/*** © 2025, CC BY-NC-ND.

关键词： Computer hardware description languages

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PRE-ACT: Prediction of Radiotherapy side Effects using explainable AI for patient Communication and Treatment modification

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Journal of Cancer Policy 2025年 43卷

作者： Yuqin Liang Paolo Bajardi Guido Bologna Francesco Bonchi Gabriella Cortellessa Andre Dekker Francesca Fracasso Manuela Joore Nikos Paragios Tim Rattay Sofia Rivera Cheryl Roumen Johan van Soest Alberto Traverso Karolien Verhoeven Iordanis Koutsopoulos Christopher J. Talbot Department of Radiation Oncology (Maastro) GROW School for Oncology and Reproduction Maastricht University Medical Centre Maastricht Netherlands CENTAI Institute Torino Italy University of Applied Sciences and Arts of Western Switzerland HES-SO Geneva Switzerland Consiglio Nazionale delle Ricerche (CNR) ISTC Roma Italy Maastricht University Maastricht Netherlands Therapanacea Paris France Leicester Cancer Research Centre University of Leicester Leicester United Kingdom UNICANCER Paris France Institut Gustave Roussy Paris France Medical Data Works Maastricht Netherlands Brightlands Institute for Smart Society (BISS) Faculty of Science and Engineering Maastricht University Heerlen the Netherlands Athens University of Economics and Business Department of Informatics Athens Greece Department of Genetics Genomics & Cancer Sciences University of Leicester Leicester United Kingdom

PRE-ACT leverages Artificial Intelligence (AI) to predict radiotherapy side effects in breast cancer patients, integrating complex imaging and genomics data. This Horizon Europe project aims to enhance shared decision-making by providing explainable predictions and tailored interventions, advancing personalised radiotherapy and improving patient outcomes.

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Partitioning vectors into quadruples: Worst-case analysis of a matching-based algorithm 29

Partitioning vectors into quadruples: Worst-case analysis of...

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29th International Symposium on Algorithms and Computation, ISAAC 2018

作者： Ficker, Annette M.C. Erlebach, Thomas Mihalák, Matúš Spieksma, Frits C.R. Faculty of Economics and Business KU Leuven Leuven Belgium Department of Informatics University of Leicester Leicester United Kingdom Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9783959770941

Consider a problem where 4k given vectors need to be partitioned into k clusters of four vectors each. A cluster of four vectors is called a quad, and the cost of a quad is the sum of the component-wise maxima of the four vectors in the quad. The problem is to partition the given 4k vectors into k quads with minimum total cost. We analyze a straightforward matching-based algorithm and prove that this algorithm is a23 -approximation algorithm for this problem. We further analyze the performance of this algorithm on a hierarchy of special cases of the problem and prove that, in one particular case, the algorithm is a54 -approximation algorithm. Our analysis is tight in all cases except one. © Annette M. C. Ficker, Thomas Erlebach, Matúš Mihalák, and Frits C. R. Spieksma;licensed under Creative Commons License CC-BY

关键词： Approximation algorithms

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