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arXiv 2024年

作者： Abrahao, Raphael A. Morin, Henri P.N. Pagé, Jordan T.R. Safari, Akbar Boyd, Robert W. Lundeen, Jeff S. Department of Physics University of Ottawa 25 Templeton Street OttawaONK1N 6N5 Canada Brookhaven National Laboratory UptonNY11973 United States Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada Department of Physics University of Wisconsin-Madison MadisonWI53706 United States Institute of Optics University of Rochester RochesterNY14627 United States Joint Centre for Extreme Photonics University of Ottawa National Research Council of Canada 100 Sussex Drive OttawaONK1A 0R6 Canada

Light, being massless, casts no shadow;under ordinary circumstances, photons pass right through each other unimpeded. Here, we demonstrate a laser beam acting like an object – the beam casts a shadow upon a surface when the beam is illuminated by another light source. We observe a regular shadow in the sense it can be seen by the naked eye, it follows the contours of the surface it falls on, and it follows the position and shape of the object (the laser beam). Specifically, we use a nonlinear optical process involving four atomic levels of ruby. We are able to control the intensity of a transmitted laser beam by applying another perpendicular laser beam. We experimentally measure the dependence of the contrast of the shadow on the power of the laser beam, finding a maximum of approximately of approximately 22%, similar to that of a shadow of a tree on a sunny day. We provide a theoretical model that predicts the contrast of the shadow. This work opens new possibilities for fabrication, imaging, and illumination. Copyright © 2024, The Authors. All rights reserved.

关键词： Laser beams

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Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation

arXiv

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arXiv 2021年

作者： Chen, Chen Qin, Chen Ouyang, Cheng Li, Zeju Wang, Shuo Qiu, Huaqi Chen, Liang Tarroni, Giacomo Bai, Wenjia Rueckert, Daniel Department of Computing Imperial College London United Kingdom Institute for Digital Communications School of Engineering University of Edinburgh United Kingdom Digital Medicine Research Centre School of Basic Medical Sciences Fudan University China Shanghai Key Laboratory of MICCAI Shanghai China CitAI Research Centre Department of Computer Science City University of London United Kingdom Department of Brain Sciences Imperial College London United Kingdom Data Science Institute Imperial College London United Kingdom Klinikum Rechts der Isar Technical University of Munich Germany

The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training. However, acquiring and manually labeling a large medical image set is resource-intensive, expensive, and sometimes impractical due to data sharing and privacy issues. To address this challenge, we propose AdvChain, a generic adversarial data augmentation framework, aiming at improving both the diversity and effectiveness of training data for medical image segmentation tasks. AdvChain augments data with dynamic data augmentation, generating randomly chained photo-metric and geometric transformations to resemble realistic yet challenging imaging variations to expand training data. By jointly optimizing the data augmentation model and a segmentation network during training, challenging examples are generated to enhance network generalizability for the downstream task. The proposed adversarial data augmentation does not rely on generative networks and can be used as a plug-in module in general segmentation networks. It is computationally efficient and applicable for both low-shot supervised and semi-supervised learning. We analyze and evaluate the method on two MR image segmentation tasks: cardiac segmentation and prostate segmentation with limited labeled data. Results show that the proposed approach can alleviate the need for labeled data while improving model generalization ability, indicating its practical value in medical imaging applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Image segmentation

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Blockchain enabled smart contract based applications: Deficiencies with the software development life cycle models

arXiv

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arXiv 2020年

作者： Miraz, Mahdi H. Ali, Maaruf Centre for Financial Regulation and Economic Development Chinese University of Hong Kong Sha Tin Hong Kong School of Applied Science Computing and Engineering Wrexham Glyndŵr University Wrexham United Kingdom Department of Computer Engineering Epoka University Tirana Albania

With the recent popularity of Blockchain and other Distributed Ledger Technologies (DLT), blockchain enabled smart contract applications has attracted increased research focus. However, the immutability of the blocks, where the smart contracts are stored, causes conflicts with the traditional Software Development Life Cycle (SDLC) models usually followed by software engineers. This clearly shows the unsuitability of the application of SDLC in designing blockchain enabled smart contract based applications. This research article addresses this current problem by first exploring the six traditional SDLC models, clearly identifying the conflicts in a table with the application of smart contracts and advocates that there is an urgent need to develop new standard model(s) to address the arising issues. The concept of both block immutability and contract is introduced. This is further set in a historical context from legacy smart contracts and blockchain enabled smart contracts extending to the difference between "shallow smart contracts" and "deep smart contracts". To conclude, the traditional SDLC models are unsuitable for blockchain enabled smart contract-based applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Smart contract

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An algorithm for heterogeneous wireless network connections for user preferences and services

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Scientific Reports 2025年第1期15卷 1-15页

作者： Krishnan, S. Dinesh Daniel, A. Ayyasamy, S. Balusamy, Balamurugan Selvarajan, Shitharth Al-Shehari, Taher Alsadhan, Nasser A. B V Raju Institute of Technology Telangana Narsapur India Amity University Madhya Pradesh Gwalior India Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore India Associate Dean-Student Engagement Shiv Nadar University Gautam Buddha Nagar India Department of Computer Science Kebri Dehar University Kebri Dehar 250 Ethiopia Computer Skills Department of Self-Development Skill Common First Year Deanship King Saud University Riyadh 11362 Saudi Arabia Computer Science Department College of Computer and Information Sciences King Saud University Riyadh 12372 Saudi Arabia School of Built Environment Engineering and Computing Leeds Beckett University Leeds United Kingdom Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India

Heterogeneous wireless networks (HWNs) present a challenge in selecting the optimal network for user devices due to the overlapping availability of multiple networks. In order to help users choose the best HWN connection, this research is trying to build a decision-making framework that takes user preferences and network performance characteristics into account. Using a multi-attribute decision-making (MADM) method that incorporates fuzzy logic and the Fuzzy Analytic Hierarchy Process (FAHP), our goal is to improve the decision-making process for network selection. The suggested system takes into account a number of network metrics, including latency, jitter, bandwidth, and cost, and uses user preferences to determine the relative importance of each to guarantee a tailored and adaptable recommendation. Our results demonstrate that the algorithm greatly enhances the efficiency of network selection and the level of user happiness, with UMTS being the best option for conversational services, WiMAX being the best for streaming, and LTE being the best for interactive services. Through the incorporation of user-centric decision-making into the network selection process, this research enhances adaptive wireless communication systems, leading to better user experience and network efficiency. © The Author(s) 2025.

关键词： Fuzzy analysis hierarchy process Fuzzy logic Heterogeneous wireless networks Machine learning Multi-attribute decision-making approach

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The medical algorithmic audit (vol 4, pg e384, 2022)

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LANCET DIGITAL HEALTH 2022年第6期4卷 E405-E405页

作者： Liu, X. Glocker, B. McCradden, M. M. Ghassemi, M. Denniston, A. K. Oakden-Rayner, L. Academic Unit of Ophthalmology Institute of Inflammation and Ageing College of Medical and Dental Sciences University of Birmingham UK Department of Ophthalmology University Hospitals Birmingham NHS Foundation Trust Birmingham UK Moorfields Eye Hospital NHS Foundation Trust London UK Health Data Research UK London UK Birmingham Health Partners Centre for Regulatory Science and Innovation University of Birmingham Birmingham UK Biomedical Image Analysis Group Department of Computing Imperial College London London UK The Hospital for Sick Children Toronto ON Canada Dalla Lana School of Public Health Toronto ON Canada Institute for Medical Engineering and Science and Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA National Institute of Health Research Biomedical Research Centre for Ophthalmology Moorfields Hospital London NHS Foundation Trust London UK University College London Institute of Ophthalmology London UK Australian Institute for Machine Learning University of Adelaide Adelaide SA Australia. lauren.oakden-rayner@adelaide.edu.au

Artificial intelligence systems for health care, like any other medical device, have the potential to fail. However, specific qualities of artificial intelligence systems, such as the tendency to learn spurious correlates in training data, poor generalisability to new deployment settings, and a paucity of reliable explainability mechanisms, mean they can yield unpredictable errors that might be entirely missed without proactive investigation. We propose a medical algorithmic audit framework that guides the auditor through a process of considering potential algorithmic errors in the context of a clinical task, mapping the components that might contribute to the occurrence of errors, and anticipating their potential consequences. We suggest several approaches for testing algorithmic errors, including exploratory error analysis, subgroup testing, and adversarial testing, and provide examples from our own work and previous studies. The medical algorithmic audit is a tool that can be used to better understand the weaknesses of an artificial intelligence system and put in place mechanisms to mitigate their impact. We propose that safety monitoring and medical algorithmic auditing should be a joint responsibility between users and developers, and encourage the use of feedback mechanisms between these groups to promote learning and maintain safe deployment of artificial intelligence *** © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Iterative pseudo-labeling based adaptive copy-paste supervision for semi-supervised tumor segmentation

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Knowledge-Based Systems 2025年 324卷

作者： Qiangguo Jin Hui Cui Junbo Wang Changming Sun Yimiao He Ping Xuan Linlin Wang Cong Cong Leyi Wei Ran Su School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China Department of Computer Science and Information Technology La Trobe University Melbourne Australia CSIRO Data61 Sydney Australia Department of Computer Science School of Engineering Shantou University Guangdong China Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences Shandong China Australian Institute of Health Innovation (AIHI) Macquarie University Australia School of Software Shandong University Shandong China Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science Macao Polytechnic University Macao Special Administrative Region of China School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China

Semi-supervised learning (SSL) has attracted considerable attention in medical image processing. The latest SSL methods use a combination of consistency regularization and pseudo-labeling to achieve remarkable success. However, most existing SSL studies focus on segmenting large organs, neglecting the challenging scenarios where there are numerous tumors or tumors of small volume. Furthermore, the extensive capabilities of data augmentation strategies, particularly in the context of both labeled and unlabeled data, have yet to be thoroughly investigated. To tackle these challenges, we introduce a straightforward yet effective approach, termed iterative pseudo-labeling based adaptive copy-paste supervision (IPA-CP), for tumor segmentation in CT scans. IPA-CP incorporates a two-way uncertainty based adaptive augmentation mechanism, aiming to inject tumor uncertainties present in the mean teacher architecture into adaptive augmentation. Additionally, IPA-CP employs an iterative pseudo-label transition strategy to generate more robust and informative pseudo labels for the unlabeled samples. Extensive experiments on both in-house and public datasets show that our framework outperforms state-of-the-art SSL methods in medical image segmentation. Ablation study results demonstrate the effectiveness of our technical contributions.

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Sparse code multiple access for 6G wireless communication networks: Recent advances and future directions

arXiv

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arXiv 2021年

作者： Yu, Lisu Liu, Zilong Wen, Miaowen Cai, Donghong Dang, Shuping Wang, Yuhao Xiao, Pei School of Information Engineering Nanchang University Nanchang330031 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Electronics Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China College of Cyber Security Jinan University Guangzhou510632 China Thuwal23955-6900 Saudi Arabia Institute for Communication Systems 5G Innovation Centre University of Surrey GuildfordGU2 7XH United Kingdom

As 5G networks rolling out in many different countries nowadays, the time has come to investigate how to upgrade and expand them towards 6G, where the latter is expected to realize the interconnection of everything as well as the development of a ubiquitous intelligent mobile world for intelligent life. To enable this epic leap in communications, this article provides an overview and outlook on the application of sparse code multiple access (SCMA) for 6G wireless communication systems, which is an emerging disruptive non-orthogonal multiple access (NOMA) scheme for the enabling of massive connectivity. We propose to apply SCMA to a massively distributed access system (MDAS), whose architecture is based on fiber-based visible light communication (FVLC), ultra-dense network (UDN), and NOMA. Under this framework, we consider the interactions between optical front-hauls and wireless access links. In order to stimulate more upcoming research in this area, we outline a number of promising directions associated with SCMA for faster, more reliable, and more efficient multiple access in future 6G communication networks. © 2021, CC BY-NC-ND.

关键词： Visible light communication

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The computer Vision-based Tolerancing Callout Detection Model

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Procedia CIRP 2020年 92卷 134-139页

作者： Xin Deng Tukun Li Yuanping Xu Yanlong Cao Chao Kong Errui Zhang School of Software Engineering Chengdu University of Information Technology Chengdu China EPSRC Future Advanced Metrology Hub Centre for Precision Technologies School of Computing and Engineering University of Huddersfield Huddersfield HD1 3DH UK Shandong Industrial Technology Research Institute of Zhejiang University Shandong 277500 China College of Engineering and Computer Science Australia National University Canberra ACT 2600 Australia

Tolerancing symbols play an important role in mechanical product drawings, and they directly determine the functions, mating properties, interchangeability and working life of geometrical products. A symbolic tolerancing callout containing a set of symbols represents a set of pre-ordered operations with attributes. It includes an amount of knowledge from the drawing and standard documents, which is often reconstructed manually by engineers. Thus, at the same time, a symbolic tolerancing callout makes it difficult for the end-user to understand and interpret these callouts manually. To this end, this study puts forward a tolerancing callout detection model via the use of off-the-shelf costumer-grade cameras on current mobile devices for extracting and recognizing tolerancing callout blocks and symbols in them intelligently. This model has four core components: image preprocessing, callout location and extraction, symbol and character segmentation, and deep learning-based symbol recognition. The image preprocessing component is developed to remove the interferences on the target technical drawings through the corresponding morphological methods. This study proposes a novel solution on callout block locations and extractions in callout intensive scenarios since the callout locations and extractions can directly affect the accuracy of symbol and character recognitions. Then, Huff Transform and improved projection methods have been devised to symbol and character segmentations. Finally, this study constructed a convolutional neural network (CNN) to train a symbol recognition model. The experimental results show that the proposed model gains applicability on intelligent callout extractions and the corresponding symbol recognitions.

关键词： Tolerancing symbols Callout location extraction Symbol character segmentation CNN

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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