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Automating the Practice of Science – Opportunities, Challenges, and Implications

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arXiv 2024年

作者： Musslick, Sebastian Bartlett, Laura K. Chandramouli, Suyog H. Dubova, Marina Gobet, Fernand Griffiths, Thomas L. Hullman, Jessica King, Ross D. Nathan Kutz, J. Lucas, Christopher G. Mahesh, Suhas Pestilli, Franco Sloman, Sabina J. Holmes, William R. Institute of Cognitive Science Osnabrück University Osnabrück49090 Germany Department of Cognitive Linguistic & Psychological Sciences Brown University ProvidenceRI02912 United States Centre for Philosophy of Natural and Social Science London School of Economics Lakatos Building Houghton Street LondonWC2A 2AE United Kingdom AaltoFI-00076 Finland Department of Computing Science University of Alberta 8900 114 St NW EdmontonABT6G 2S4 Canada Cognitive Science Program Indiana University 1101 E 10th St BloomingtonIN47405 United States School of Psychology University of Roehampton LondonSW15 4JD United Kingdom Departments of Psychology and Computer Science Princeton University PrincetonNJ United States Department of Computer Science Northwestern University IL United States Department of Chemical Engineering and Biotechnology University of Cambridge CambridgeCB3 0AS United Kingdom Department of Computer Science and Engineering Chalmers University of Technology Gothenburg412 96 Sweden Department of Applied Mathematics and Electrical and Computer Engineering University of Washington Seattle98195 United States School of Informatics University of Edinburgh 10 Crichton St. EH8 9AB United Kingdom Department of Materials Science and Engineering University of Toronto Canada Department of Psychology Department of Neuroscience The University of Texas AustinTX United States Department of Computer Science University of Manchester M13 9PL United Kingdom

Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: Where do the greatest opportunities lie for automation in scientific practice?;What are the current bottlenecks of automating scientific practice?;and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice. © 2024, CC BY.

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

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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

Learning to Match 2D Images and 3D LiDAR Point Clouds for Outdoor Augmented Reality

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Learning to Match 2D Images and 3D LiDAR Point Clouds for Ou...

Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Weiquan Liu Baiqi Lai Cheng Wang Xuesheng Bian Wentao Yang Yan Xia Xiuhong Lin Shang-Hong Lai Dongdong Weng Jonathan Li Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China Photogrammetry and Remote Sensing Technical University of Munich Munich Germany Department of Computer Science National Tsing Hua University Hsinchu Taiwan Beijing Engineering Research Center of Mixed Reality and Advanced Display School of Optics and Photonics Beijing Institute of Technology Beijing China Department of Geography and Environmental Management University of Waterloo Waterloo Canada

ISBN: (数字)9781728165325

ISBN: (纸本)9781728165332

Large-scale Light Detection and Ranging (LiDAR) point clouds provide basic 3D information support for Augmented Reality (AR) in outdoor environments. Especially, matching 2D images across to 3D LiDAR point clouds can establish the spatial relationship of 2D and 3D space, which is a solution for the virtual-real registration of AR. This paper first provides a precise 2D-3D patch-volume dataset, which contains paired matching 2D image patches and 3D LiDAR point cloud volumes, by using the Mobile Laser Scanning (MLS) data from the urban scene. Second, we propose an end-to-end network, Siam2D3D-Net, to jointly learn local feature representations for 2D image patches and 3D LiDAR point cloud volumes. Experimental results indicate the proposed Siam2D3D-Net can match and establish 2D-3D correspondences from the query 2D image to the 3D LiDAR point cloud reference map. Finally, an application is used to evaluate the possibility of the proposed virtual-real registration of AR in outdoor environments.

关键词： Three-dimensional displays Two dimensional displays Laser radar Cameras Feature extraction Augmented reality Remote sensing

Model architecture-oriented combat system effectiveness simulation based on MDE

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Journal of Systems Engineering and Electronics 2017年第5期28卷 900-922页

作者： Yonglin Lei Ning Zhu Jian Yao Hessam Sarjoughian Weiping Wang School of Information Systems and Management National University of Defense Technology School of Computing Informatics and Decision Systems Engineering Arizona State University

Combat system effectiveness simulation (CSES) is a special type of complex system simulation. Three non-functional requirements (NFRs), i.e. model composability, domain specific modeling, and model evolvability, are gaining higher priority from CSES users when evaluating different modeling methodologies for CSES. Traditional CSES modeling methodologies are either domain-neutral (lack of domain characteristics consideration and limited support for model composability) or domain-oriented (lack of openness and evolvability) and fall short of the three NFRs. Inspired by the concept of architecture in systems engineering and software engineering fields, we extend it into a concept of model architecture for complex simulation systems, and propose a model architecture-oriented modeling methodology in which the model architecture plays a central role in achieving the three NFRs. Various model-driven engineering (MDE) approaches and technologies, including simulation modeling platform (SMP), unified modeling language (UML), domain specific modeling (DSM), eclipse modeling framework (EMF), graphical modeling framework (GMF), and so forth, are applied where possible in representing the CSES model architecture and its components' behaviors from physical and cognitive domain aspects. A prototype CSES system, called weapon effectiveness simulation system (WESS), and a non-trivial air-combat simulation example are presented to demonstrate the methodology.

关键词： combat system effectiveness simulation (CSES) model architecture model-driven engineering (MDE) simulation modeling platform (SMP) domain specific modeling (DSM) weapon effectiveness simulation system (WESS)

Scientific elite revisited: Patterns of productivity, collaboration, authorship and impact

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arXiv 2020年

作者： Li, Jichao Yin, Yian Fortunato, Santo Wang, Dashun College of Systems Engineering National University of Defense Technology Changsha China Center for Science of Science and Innovation Northwestern University EvanstonIL United States Northwestern Institute on Complex Systems Northwestern University EvanstonIL United States Kellogg School of Management Northwestern University EvanstonIL United States McCormick School of Engineering Northwestern University EvanstonIL United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Indiana University BloomingtonIN United States

Throughout history, a relatively small number of individuals have made a profound and lasting impact on science and society. Despite long-standing, multi-disciplinary interests in understanding careers of elite scientists, there have been limited attempts for a quantitative, career-level analysis. Here, we leverage a comprehensive dataset we assembled, allowing us to trace the entire career histories of nearly all Nobel laureates in physics, chemistry, and physiology or medicine over the past century. We find that, although Nobel laureates were energetic producers from the outset, producing works that garner unusually high impact, their careers before winning the prize follow relatively similar patterns as ordinary scientists, being characterized by hot streaks and increasing reliance on collaborations. We also uncovered notable variations along their careers, often associated with the Nobel prize, including shifting coauthorship structure in the prize-winning work, and a significant but temporary dip in the impact of work they produce after winning the Nobel. Together, these results document quantitative patterns governing the careers of scientific elites, offering an empirical basis for a deeper understanding of the hallmarks of exceptional careers in science. Copyright © 2020, The Authors. All rights reserved.

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A Performance Investigation of SAC-OCDMA System Based on a Spectral Efficient 2D Cyclic Shift Code for Next Generation Passive Optical Network

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Research Square

Research Square 2021年

作者： Cherifi, Abdelhamid Meftah, Khaled Dahani, Ameur Alayedi, Mohanad Mrabet, Hichem Electronics Department Dr. Moulay Tahar University of Saida Saida Algeria Computational Physics of Materiasl Laboratory Djillali Liabès University of Sidi Bel-Abbès Sidi Bel Abbès22000 Algeria Department of Electronics Faculty of Technology Dr. Tahar Moulay University of Saïda Saïda 20000 Algeria Electronics Department Ferhat Abbas University of Setif 1 Setif Algeria Laboratory of SER’COM Tunisia Polytechnic School Carthage University Tunis 1054 Tunisia Department of IT College of Informatics and Computing Saudi Electronic University Medina Saudi Arabia

A two dimensional spectral/spatial cyclic shift (2D-CS) optical code division multiple access (OCDMA) systems is proposed for a potentially next generation passive optical network (NG-PON) implementation called (2D-CS NG-OCDMA-PON) system. The2D-CS proposed code is characterized by a high capacity and a zero cross correlation property leads to completely eliminating the multiple access interference (MAI) effect that is considered as the main OCDMA system drawback. Firstly, the 2D-CScode construction is investigated from 1D-CS code. Secondly, a system description is provided by exhibiting the transmitter and receiver architecture in the PON context. Analytical analysis reveals that our proposed 2D-CScode outperforms similar codes such as perfect difference (2D-PD) and dynamic cyclic shift (2D-DCS) codes in terms of spectral efficiency, simultaneous network subscribers and data bit rate. In addition, based on numerical analysis 2D-CS NG-OCDMA-PON system shows a good system performance by means of avery low BER and a high Q-factor values equal to10−26 and 10.41 dB, respectively. Likewise, for four users and free-amplification the achievable reach ability distance of the NG-OCDMA-PON system is 63.21 km, 43.57 km and 33.2 km while Q-factor is equal to 6 dB at a bit rate of 622Mb/s, 1Gb/s and 1.5 Gb/s, respectively. On the other side, according to the system setup the number of single mode fiber (SMF) is reduced to the half compared to other 2D-OCDMA-PON systems based on enhanced multi diagonal (EMD) and single weight ZCC (SWZCC) codes. © 2021, CC BY.

关键词： Passive optical networks

Metrics Reloaded: Recommendations for image analysis validation

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

An empirical study of handover control parameter on LTE handover algorithms: An evaluation on quality of service and quality of experience

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AIP Conference Proceedings 2022年第1期2617卷

作者： Noormadinah Allias Megat Norulazmi Megat Mohamed Noor Mohd Taha Ismail Mohd Nazri Ismail 1Universiti Kuala Lumpur-Malaysian Institute of Information Technology 1016 Jalan Sultan Ismail 50250 Kuala Lumpur Malaysia 2School of Computing and Informatics Albukhary International University Jalan Tun Abdul Razak 05200 Alor Setar Kedah Malaysia 3National Defense University of Malaysia Kem Perdana Sungai Besi 57000 Kuala Lumpur Malaysia

For the past few years, the Third Generation Partnership Project (3GPP) has introduced the Long-Term Evolution (LTE) as a critical answer to supporting the users' demand for a faster data connection. Nevertheless, another problem arises as the users move from one location to another; they will face a service interruption. As a result, switching from one base station to another base station is required. This process is known as handover. The LTE handover can be done using two algorithms: the A3RSRP and A2A4RSRQ. Both algorithms are associated with two Handover Control Parameters (HCPs). Earlier, many studies have been conducted to evaluate the handover algorithms' impact on the LTE network of Handover Control Parameters (HCPs). However, the studies solely focused on the Quality of Service (QoS), thus neglecting the Quality of Experience (QoE). Therefore, this paper presents an empirical analysis of the impact of the Handover Control Parameters based on user trajectories in terms of the QoS (Throughput and delay) and QoE (time to complete file download). The study found that the A2A4RSRQ algorithm performed better than the A3RSRP algorithm once the mobile user moved in a range of Angle-1 and Angle-2 directions. On the other side, different Handover Control Parameters should be assigned based on user trajectories to obtain good handover performances.

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Dispensed transformer network for unsupervised domain adaptation

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arXiv 2021年

作者： Li, Yunxiang Li, Jingxiong Dan, Ruilong Wang, Shuai Jin, Kai Zeng, Guodong Wang, Jun Pan, Xiangji Zhang, Qianni Zhou, Huiyu Jin, Qun Wang, Li Wang, Yaqi College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Artificial Intelligence and Biomedical Image Analysis Lab Westlake University Hangzhou China School of Mechanical Electrical and Information Engineering Shandong University Weihai China Department of Ophthalmology Hospital of Zhejiang University Hangzhou China sitem Center for Translational Medicine and Biomedical Entrepreneurship University of Bern Bern Switzerland School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Computing and Mathematical Sciences University of Leicester United Kingdom Department of Human Informatics and Cognitive Sciences Faculty of Human Sciences Waseda University Tokyo Japan Department of Radiology and Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill United States College of Media Engineering Communication University of Zhejiang Hangzhou China

Accurate segmentation is a crucial step in medical image analysis and applying supervised machine learning to segment the organs or lesions has been substantiated effective. However, it is costly to perform data annotation that provides ground truth labels for training the supervised algorithms, and the high variance of data that comes from different domains tends to severely degrade system performance over cross-site or cross-modality datasets. To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper. Our novel DTNet contains three modules. First, a dispensed residual transformer block is designed, which realizes global attention by dispensed interleaving operation and deals with the excessive computational cost and GPU memory usage of the Transformer. Second, a multi-scale consistency regularization is proposed to alleviate the loss of details in the low-resolution output for better feature alignment. Finally, a feature ranking discriminator is introduced to automatically assign different weights to domain-gap features to lessen the feature distribution distance, reducing the performance shift of two domains. The proposed method is evaluated on large fluorescein angiography (FA) retinal nonperfusion (RNP) cross-site dataset with 676 images and a wide used cross-modality dataset from the MM-WHS challenge. Extensive results demonstrate that our proposed network achieves the best performance in comparison with several state-of-the-art techniques. © 2021, CC BY.

关键词： Supervised learning