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Author Correction: Sub-femtonewton force sensing in solution by super-resolved photonic force microscopy

Nature Photonics 2024年 998页

作者： Xuchen Shan Dajing Wang Jinlong Shi Hongyan Zhu Xiaolan Zhong Baolei Liu Weichang Hao Fan Wang Lei Ding Qian Peter Su Shihui Wen Dayong Jin Lei Jiang Chaohao Chen Yang Wang Zhaocun Huang Lingqian Chang Shen S. J. Wang Peter John Reece Wei Ren Xunyu Lu Jie Lu Lingdong Sun School of Physics Beihang University Beijing People’s Republic of China Science and Technology Center for Quantum Biology National Institute of Extremely-Weak Magnetic Field Infrastructure Hangzhou People’s Republic of China School of Biomedical Engineering Faculty of Engineering and Information Technology University of Technology Sydney Sydney New South Wales Australia Centre for Atomaterials and Nanomanufacturing School of Science RMIT University Melbourne Victoria Australia Institute for Biomedical Materials and Devices (IBMD) School of Mathematical and Physical Sciences Faculty of Science University of Technology Sydney Sydney New South Wales Australia Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo People’s Republic of China Key Laboratory of Bio-Inspired Materials and Interfaces Sciences Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing People’s Republic of China Australian Research Council Centre of Excellence for Transformative Meta-Optical Systems Department of Electronic Materials Engineering Research School of Physics The Australian National University Canberra Australian Capital Territory Australia Key Laboratory of Biomechanics and Mechanobiology (Ministry of Education) Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing People’s Republic of China Computing and Information Systems Department Melbourne School of Engineering University of Melbourne Melbourne Victoria Australia School of Physics The University of New South Wales Sydney New South Wales Australia School of Chemistry & Chemical Engineering Shaanxi Normal University Xi’an People’s Republic of China Particles and Catalysis Research Laboratory School of Chemical Engineering The University of New South Wales Sydney New South Wales Australia Australian Artificial Intelligence Institute Faculty of Engineering and IT University of Technology

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Correction to: Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation

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Archives of Computational Methods in Engineering 2021年第7期28卷 5039-5039页

作者： Ismail, Leila Materwala, Huned Tayefi, Maryam Ngo, Phuong Karduck, Achim P. Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Norwegian Centre for E-Health Research Tromsø Norway Faculty of Informatics Furtwangen University Furtwangen Germany

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HealthFog: An Ensemble Deep Learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in Integrated IoT and Fog computing Environments

arXiv

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arXiv 2019年

作者： Tuli, Shreshth Basumatary, Nipam Gill, Sukhpal Singh Kahani, Mohsen Arya, Rajesh Chand Wander, Gurpreet Singh Buyya, Rajkumar Laboratory School of Computing and Information Systems University of Melbourne Australia Delhi India Madras India Queen Mary University of London United Kingdom Web Technologies Laboratory Ferdowsi University Of Mashhad Iran Department of Cardiology Hero Heart Institute Dayanand Medical College and Hospital LudhianaPunjab India

cloud computing provides resources over the Internet and allows a plethora of applications to be deployed to provide services for different industries. The major bottleneck being faced currently in these cloud frameworks is their limited scalability and hence inability to cater to the requirements of centralized Internet of Things (IoT) based compute environments. The main reason for this is that latency-sensitive applications like health monitoring and surveillance systems now require computation over large amounts of data (Big Data) transferred to centralized database and from database to cloud data centers which leads to drop in performance of such systems. The new paradigms of fog and edge computing provide innovative solutions by bringing resources closer to the user and provide low latency and energy efficient solutions for data processing compared to cloud domains. Still, the current fog models have many limitations and focus from a limited perspective on either accuracy of results or reduced response time but not both. We proposed a novel framework called HealthFog for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis. HealthFog delivers healthcare as a fog service using IoT devices and efficiently manages the data of heart patients, which comes as user requests. Fog-enabled cloud framework, FogBus is used to deploy and test the performance of the proposed model in terms of power consumption, network bandwidth, latency, jitter, accuracy and execution time. HealthFog is configurable to various operation modes which provide the best Quality of Service or prediction accuracy, as required, in diverse fog computation scenarios and for different userrequirements. Copyright © 2019, The Authors. All rights reserved.

关键词： Internet of things

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Design and Implementation of RFID based Smart Shopping Booth

Design and Implementation of RFID based Smart Shopping Booth

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International Conference on information Science and Control Engineering (ICISCE)

作者： Ehtisham Lodhi Fenghua Zhu Zeeshan Lodhi Qasim Saleem Gang Xiong FeiYue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering COMSATS University Islamabad Pakistan School of Automation Science and Electrical Engineering Beihang University Beijing China The Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728157122

ISBN: (纸本)9781728157139

In this modern era, technology has advanced to such an extent to make our daily life more proficient and convenient. This paper presents the design and implementation of vending machine (VM) for students, which operates on radio frequency identification technology (RFID). Hence, it turns the VM into smart shopping booth (SSB). The SSB gives the access only through RFID card and vend different products according to the desire of student. The SSB is equipped with RFID reader and student identity card that holds the RFID tag. When the student identity card is scanned for verification and then after selecting the required products, will be dispensed off if a student has available balance in his/her identity card and SMS will be sent to students about remaining balance in their cards by GSM technology. The programming for controller is done by using proton IDE language. The SSB is the customized idea of vending machine and it attempts to resolve various issues that appeared in coin/note based VM. The student identity card RFID based technology is a breakthrough towards a paperless and cashless environment. Moreover, it is very beneficial in saving the precious time of students due to fast paced life and also user friendly. RFID based SSB also improved the communication and sensing capabilities of VM. Hence, our suggested approach outperforms in all aspects.

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An Advanced Quantum-Resistant Signature Scheme for cloud Based on Eisenstein Ring

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Computers, Materials & Continua 2018年第7期56卷 19-34页

作者： Faguo Wu Xiao Zhang Wang Yao Zhiming Zheng Lipeng Xiang Wanpeng Li Key Laboratory of Mathematics Informatics and Behavioral SemanticsMinistry of EducationSchool of Mathematics and Systems ScienceBeihang UniversityBeijing100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing100191China The Fifth Electronics Research Institute of Ministry of Industry and Information Technology Guangzhou510610China Department of Electrical and Electronic Engineering City University of LondonUK

Signature,widely used in cloud environment,describes the work as readily identifying its *** existing signature schemes in the literature mostly rely on the Hardness assumption which can be easily solved by quantum *** this paper,we proposed an advanced quantum-resistant signature scheme for cloud based on Eisenstein Ring(ETRUS)which ensures our signature scheme proceed in a lattice with higher *** proved that ETRUS highly improve the performance of traditional lattice signature ***,the Norm of polynomials decreases significantly in ETRUS which can effectively reduce the amount of polynomials convolution ***,storage complexity of ETRUS is smaller than classical ***,according to all convolution of ETRUS enjoy lower degree polynomials,our scheme appropriately accelerate 56.37%speed without reducing its security level.

关键词： Signature quantum-resistant Eisenstein Ring ETRUS

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One pathogen does not an epidemic make: A review of interacting contagions, diseases, beliefs, and stories

arXiv

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arXiv 2025年

作者： Hébert-Dufresne, Laurent Ahn, Yong-Yeol Allard, Antoine Crothers, Jessica W. Dodds, Peter Sheridan Galesic, Mirta Ghanbarnejad, Fakhteh Gravel, Dominique Hammond, Ross A. Lerman, Kristina Lovato, Juniper Openshaw, John J. Redner, S. Scarpino, Samuel V. St-Onge, Guillaume Tangherlini, Timothy R. Young, Jean-Gabriel Vermont Complex Systems Institute University of Vermont BurlingtonVT United States Translational Global Infectious Disease Research Center University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Département de physique de génie physique et d’optique Université Laval QuébecQC Canada Department of Pathology and Laboratory Medicine Robert Larner M.D. College of Medicine University of Vermont BurlingtonVT United States Santa Fe Institute Santa FeNM United States Complexity Science Hub Vienna Austria School of Technology and Architecture SRH University of Applied Sciences Heidelberg Leipzig Germany Département de biologie Université de Sherbrooke SherbrookeQC Canada Brown School Washington University in St. Louis St. LouisMO United States The Brookings Institution WashingtonDC United States Information Sciences Institute University of Southern California United States Division of Infectious Diseases and Geographic Medicine Stanford University StanfordCA United States Institute for Experiential AI Northeastern University BostonMA United States Laboratory for the Modeling of Biological and Socio-technical Systems Northeastern University BostonMA United States The Roux Institute Northeastern University PortlandME United States Department of Scandinavian School of Information University of California Berkeley BerkeleyCA United States Department of Mathematics & Statistics University of Vermont BurlingtonVT United States

From pathogens and computer viruses to genes and memes, contagion models have found widespread utility across the natural and social sciences. Despite their success and breadth of adoption, the approach and structure of these models remain surprisingly siloed by field. Given the siloed nature of their development and widespread use, one persistent assumption is that a given contagion can be studied in isolation, independently from what else might be spreading in the population. In reality, countless contagions of biological and social nature interact within hosts (interacting with existing beliefs, or the immune system) and across hosts (interacting in the environment, or affecting transmission mechanisms). Additionally, from a modeling perspective, we know that relaxing these assumptions has profound effects on the physics and translational implications of the models. Here, we review mechanisms for interactions in social and biological contagions, as well as the models and frameworks developed to include these interactions in the study of the contagions. We highlight existing problems related to the inference of interactions and to the scalability of mathematical models and identify promising avenues of future inquiries. In doing so, we highlight the need for interdisciplinary efforts under a unified science of contagions and for removing a common dichotomy between social and biological contagions. Copyright © 2025, The Authors. All rights reserved.

关键词： Histology

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Global and local epidemiology of group a Streptococcus indicates that naturally-acquired immunity is enduring and strain-specific

arXiv

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arXiv 2021年

作者： Chisholm, Rebecca H. Lacey, Jake A. Kokko, Jan Campbell, Patricia T. McDonald, Malcolm I. Corander, Jukka Davies, Mark R. Tong, Steven Y.C. McVernon, Jodie Geard, Nicholas School of Engineering and Mathematical Sciences La Trobe University BundooraVIC Australia Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health The University of Melbourne MelbourneVIC Australia Doherty Department University of Melbourne at the Peter Doherty Institute for Infection and Immunity MelbourneVIC Australia Department of Mathematics and Statistics University of Helsinki Victorian Infectious Diseases Reference Laboratory Epidemiology University of Melbourne at the Peter Doherty Institute for Infection and Immunity MelbourneVIC Australia Centre for Chronic Disease Prevention Cairns Campus JamesCook University CairnsQLD Australia Helsinki Institute for Information Technology Department of Mathematics and Statistics University of Helsinki Helsinki Finland Department of Biostatistics University of Oslo Oslo Norway Parasites and Microbes Wellcome Sanger Institute Wellcome Genome Campus Cambridge Hinxton United Kingdom Department of Microbiology and Immunology University of Melbourne at the Peter Doherty Institute for Infection and Immunity MelbourneVIC Australia Victorian Infectious Diseases Service The Royal Melbourne Hospital Global and Tropical Health Division Menzies School of Health Research Charles Darwin University Darwin NT Australia School of Computing and Information Systems Melbourne School of Engineering The University of Melbourne MelbourneVIC Australia

The bacterium Group A Streptococcus (Streptococcus pyogenes, GAS) is a human-specific pathogen and a major cause of global morbidity and mortality. Despite decades of research our knowledge of GAS infection and immunity is incomplete, hampering vaccine design and other efforts to reduce disease prevalence. Epidemiological studies indicate positive associations between the prevalence of GAS-related disease, the diversity of circulating strains and the degree of poverty in host populations. However, the infection and immune mechanisms underlying these associations are not clear. In this work, we use an agent-based model to demonstrate that observed diversity and prevalence are best accounted for by the hypothesis that GAS infection confers enduring strain-specific immunity, with reduced or absent cross-protection against infection by other strains. Our results suggest that the success of GAS vaccines will depend on their ability to elicit long-lasting cross-protective immunity over multiple strain types. © 2021, CC BY-NC-ND.

关键词： Strain

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent systems and computing 2021年 1262卷 xv-xvi页

作者： Giri, Debasis Buyya, Rajkumar Ponnusamy, S. De, Debashis Adamatzky, Andrew Hussien, Jemal Department of Information Technology Maulana Abul Kalam Azad University of Technology HaringhataWest Bengal India School of Computing and Information Systems University of Melbourne MelbourneVIC Australia Department of Mathematics Indian Institute of Technology Madras Chennai India Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology HaringhataWest Bengal India Unconventional Computing Laboratory Department of Computer Science and Creative Technologies University of the West of England Bristol United Kingdom Faculty of Science Engineering and Built Environment Deakin University Geelong GeelongVIC Australia

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Robust Camera Model Identification Based on Richer Convolutional Feature Network

Robust Camera Model Identification Based on Richer Convoluti...

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Asia-Pacific Signal and information Processing Association Annual Summit and Conference (APSIPA)

作者： Ze-Yu Zou Yun-Xia Liu Wen-Na Zhang Yue-Hui Chen Yun-Li Zang Yang Yang Bonnie Ngai-Fong Law Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China Integrated Electronic Systems Lab Co. Ltd Jinan China School of Information Science and Engineering Shandong University Qingdao China The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9781728132488

ISBN: (纸本)9781728132495

Based on convolutional neural network (CNN), the problem of robust patch level camera model identification is studied in this paper. Firstly, an effective feature representation is proposed by concatenating a multiscale residual prediction module as well as the original RGB images. Motivated by exploration of multi-scale characteristic, the multiscale residual prediction module automatically learn the residual images to avoid the subsequent CNN being affected by the scene content. Color channel information is integrated for enhanced diversity of CNN inputs. Secondly, a modified richer convolutional feature network is presented for robust camera model identification by fully exploiting the learnt features. Finally, the effectiveness of the proposed method is verified by abundant experimental results at the patch level, which is more difficult than image level experiments.

关键词： Cameras Kernel Convolution Image color analysis Feature extraction Robustness Training

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PointNLM: Point N onlocal M eans for vegetation segmentation based on middle echo point clouds

arXiv

引用

arXiv 2019年

作者： Li, Jonathan Wu, Rongren Chen, Yiping Zhu, Qing Luo, Zhipeng Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University School of Information Science and Engineering Xiamen Fujian361005 China Departments of Geography Environmental Management and Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Middle echo which cover s one or a few corresponding points is a specific type of 3D point cloud acquired by a multi echo laser scanner. In this paper, we propose a novel approach for automatic segmentation of trees that leverages middle echo information from LiDAR point clouds . First, using a convolution classification method, the proposed type of point clouds reflected by the middle echoes are identified from all point clouds T he middle echo point clouds are distinguished from the first and last echoes. Hence , the crown positions of the tree s are quickly detect ed from the huge number of point clouds. Second, to accurately extract trees from all point clouds, we propose a 3D deep learning network , PointNLM to semantically segment tree crowns. PointNLM captures the long range relationship betw een the point clouds via a non local branch and extracts high level features via max pooling applied to unordered points. T he whole framework is evaluated using the Semantic 3D reduced test set . T he IoU of tree point cloud segmentation reached 0.864. In a ddition, the semantic segmentation network was tested using the Paris Lille 3D dataset T he average IoU outperformed several other popular methods . The experiment al results indicate that the proposed algorithm provide s an excellen t solution for vegetation segmentation from LiDAR point clouds. Copyright © 2019, The Authors. All rights reserved.

关键词： Vegetation

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