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Science China(Physics,Mechanics & Astronomy) 2019年第2期62卷 28-44页

作者： Anna L.Watts WenFei Yu Juri Poutanen Shu Zhang Sudip Bhattacharyya Slavko Bogdanov Long Ji Alessandro Patruno Thomas E.Riley Pavel Bakala Altan Baykal Federico Bernardini Ignazio Bombaci Edward Brown Yuri Cavecchi Deepto Chakrabarty Jér?me Chenevez Nathalie Degenaar Melania Del Santo Tiziana Di Salvo Victor Doroshenko Maurizio Falanga Robert D.Ferdman Marco Feroci Angelo F.Gambino MingYu Ge Svenja K.Greif Sebastien Guillot Can Gungor Dieter H.Hartmann Kai Hebeler Alexander Heger Jeroen Homan Rosario Iaria Jean in 't Zand Oleg Kargaltsev Aleksi KurkelaTheoretical Physics department CERN XiaoYu Lai Ang Li XiangDong Li ZhaoSheng Li Manuel Linares FangJun Lu Simin Mahmoodifar Mariano Méndez M.Coleman Miller Sharon Morsink Joonas N?ttil? Andrea Possenti Chanda Prescod-Weinstein JinLu Qu Alessandro Riggio Tuomo Salmi Andrea Sanna Andrea Santangelo Hendrik Schatz Achim Schwenk LiMing Song Eva?rámková Benjamin Stappers Holger Stiele Tod Strohmayer Ingo Tews Laura Tolos Gabriel T?r?k David Tsang Martin Urbanec Andrea Vacchi RenXin Xu YuPeng Xu Silvia Zane GuoBao Zhang ShuangNan Zhang WenDa Zhang ShiJie Zheng Xia Zhou Anton Pannekoek Institute for Astronomy University of Amsterdam Shanghai Astronomical Observatory Tuorla Observatory Department of Physics and Astronomy University of Turku Nordita KTH Royal Institute of Technology and Stockholm University Institute of High Energy Physics Chinese Academy Sciences Tata Institute of Fundamental Research Columbia Astrophysics Laboratory Columbia University Institut für Astronomie und Astrophysik Tübingen Universit?t Tübingen Leiden Observatory Leiden University Research Center for Computational Physics and Data Processing Silesian University in Opava Physics Department Middle East Technical University INAF Osservatorio Astronomico di Roma New York University Abu Dhabi Dipartimento di Fisica Enrico Fermi University of Pisa INFN Italian National Institute for Nuclear Physics Department of Physics and Astronomy Michigan State University Department of Astrophysical Sciences Princeton University Mathematical Sciences and STAG Research Centre University of Southampton MIT Kavli Institute for Astrophysics and Space Research DTU Space Technical University of Denmark INAF/IASF Palermo via Ugo La Malfa 153 Universita degli Studi di Palermo Dipartimento di Fisica e Chimica International Space Science Institute (ISSI) Faculty of Science University of East Anglia INAF Istituto di Astrofisica e Planetologie Spaziali Institut für Kernphysik Technische Universit?t Darmstadt ExtreMe Matter Institute EMMI GSI Helmholtzzentrum für Schwerionenforschung GmbH Instituto de Astrof'?sica Pontificia Universidad Católica de Chile Department of Physics & Astronomy Kinard Lab of Physics Clemson University School of Physics and Astronomy Monash University SRON Netherlands Institute for Space Research Department of Physics The George Washington University Faculty of Science and Technology University of Stavanger School of Physics and Mechanical & Electrical Engineering Hubei University of Education Department of Astronomy Xiamen University (Haiyun Campus) School of Astronomy and Space

In this White Paper we present the potential of the Enhanced X-ray Timing and Polarimetry(eXTP) mission for determining the nature of dense matter; neutron star cores host an extreme density regime which cannot be replicated in a terrestrial laboratory. The tightest statistical constraints on the dense matter equation of state will come from pulse profile modelling of accretion-powered pulsars, burst oscillation sources, and rotation-powered pulsars. Additional constraints will derive from spin measurements, burst spectra, and properties of the accretion flows in the vicinity of the neutron star. Under development by an international Consortium led by the institute of High Energy Physics of the Chinese Academy of Sciences, the eXTP mission is expected to be launched in the mid 2020 s.

关键词： neutron X-rays dense matter equation of state

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MODELING THERMAL SEQUENCE SIGNAL DECREASING FOR DUAL MODAL PASSWORD BREAKING

MODELING THERMAL SEQUENCE SIGNAL DECREASING FOR DUAL MODAL P...

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IEEE International Conference on image processing

作者： Duo Li Xiao-Ping Zhang Guangtao Zhai Xiaokang Yang Wenhan Zhu Xiao Gu Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering Ryerson University Toronto Canada

The thermal camera records trace of users' touch a while after they type in the password. People's password may be stolen through a thermal camera due to this phenomenon. In this paper, we model the procedure of the thermal sequence from the view point of physical process. Based on the Newton's Law of Cooling, we set up a physical model to describe the process of the keys' temperature decreasing. Then the model is used to estimate keystroke time instants by maximizing likelihood function method. We estimate the password after getting keystroke time instants of each key. The possibility that cheaters have to steal people's password is also explored in the experiment. Based on our findings in the experiment, we give several pieces of practical advice for people to protect their password.

关键词： Password Cameras Mathematical model Estimation Temperature measurement Videos Heat transfer

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Protecting privacy of users in brain-computer interface applications

arXiv

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arXiv 2019年

作者： Agarwal, Anisha Dowsley, Rafael McKinney, Nicholas D. Wu, Dongrui Lin, Chin-Teng De Cock, Martine Nascimento, Anderson C.A. School of Engineering and Technology University of Washington TacomaWA98402 United States Department of Computer Science Bar-Ilan University Israel School of Engineering and Technology University of Washington TacomaWA98402 United States Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan China School of Software University of Technology Sydney Australia Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on Secure Multiparty Computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving (PP) fashion, i.e. such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing based SMC in general, namely with 15 players involved in all the computations. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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OCTAVA: An open-source toolbox for quantitative analysis of optical coherence tomography angiography images

arXiv

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arXiv 2021年

作者： Untracht, Gavrielle R. Matos, Rolando Dikaios, Nikolaos Bapir, Mariam Durrani, Abdullah K. Butsabong, Teemapron Campagnolo, Paola Sampson, David D. Heiss, Christian Sampson, Danuta M. Optical+Biomedical Engineering Laboratory School of Electrical Electronic and Computer Engineering The University of Western Australia 35 Stirling Highway PerthWA6009 Australia Surrey Biophotonics Advanced Technology Institute School of Physics School of Biosciences and Medicine University of Surrey Surrey GuildfordGU2 7XH United Kingdom Department of Biochemical Sciences Department of Clinical and Experimental Medicine School of Biosciences and Medicine University of Surrey Surrey GuildfordGU2 7XH United Kingdom Mathematics Research Center Academy of Athens Athens106 79 Greece Surrey and Sussex Healthcare NHS Trust East Surrey Hospital Surrey RedhillRH1 5RH United Kingdom Surrey Biophotonics Centre for Vision Speech and Signal Processing School of Biosciences and Medicine The University of Surrey GuildfordGU2 7XH United Kingdom

Optical coherence tomography angiography (OCTA) performs non-invasive visualization and characterization of microvasculature in research and clinical applications mainly in ophthalmology and dermatology. A wide variety of instruments, imaging protocols, processing methods and metrics have been used to describe the microvasculature, such that comparing different study outcomes is currently not feasible. With the goal of contributing to standardization of OCTA data analysis, we report a user-friendly, open-source toolbox, OCTAVA (OCTA Vascular Analyzer), to automate the pre-processing, segmentation, and quantitative analysis of en face OCTA maximum intensity projection images in a standardized workflow. We present each analysis step, including optimization of filtering and choice of segmentation algorithm, and definition of metrics. We perform quantitative analysis of OCTA images from different commercial and non-commercial instruments and samples and show OCTAVA can accurately and reproducibly determine metrics for characterization of microvasculature. Wide adoption could enable studies and aggregation of data on a scale sufficient to develop reliable microvascular biomarkers for early detection, and to guide treatment, of microvascular disease. © 2021, CC BY-NC-SA.

关键词： Optical tomography

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Quality Inspection of Phalaenopsis Hybrids Using Hyperspectral Band Selection Techniques

Quality Inspection of Phalaenopsis Hybrids Using Hyperspectr...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yen-Chieh Ouyang Bo-Han Chen Meng-Chueh Lee Tsang-Sen Liu Mang Ou-Yang Hsian-Min Chen Chao-Cheng Wu Chia-Hsien Wen Min-Shao Shih Chein-I Chang Yung-Jhe Yan Department of Electrical Engineering National Chung Hsing University Taiwan Institute of communication Engineering National Chung Hsing University Taiwan Taiwan Agriculture Research Institute Nantou Taiwan Department of Electrical and Computer Engineering National Chiao-Tung University Hsinchu City Taiwan Center for Quantitative Imaging in Medicine Taichung Veterans General Hospital Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Management Providence University Taiwan Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore MD USA

Fusarium wilt on Phalaenopsis is a disease that makes farmers suffer seriously. Although Phalaenopsis does not die immediately with Fusarium wilt, it seriously decreases the quality that buyers cannot accept. In this paper, we introduce an emerging method to detect Fusarium wilt at the base of Phalaenopsis stems. The detection model divides Phalaenopsis samples into two categories, healthy and infection. The band selection (BS) processing technique based on band prioritization (BP) is applied to extract significant bands and eliminate redundant bands. Subsequently, some algorithms which are constrained energy minimization (CEM), spectral information divergence(SID) and SeQuential N-FINDER to detect the Fusarium wilt, and we hope the research would help farmers decrease their losses.

关键词： Hyperspectral imaging Support vector machines Diseases Signal to noise ratio Hybrid fiber coaxial cables Correlation

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ACOUSLIC-AI challenge report: Fetal abdominal circumference measurement on blind-sweep ultrasound data from low-income countries

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Medical image Analysis 2025年 105卷

作者： M. Sofia Sappia Chris L. de Korte Bram van Ginneken Dean Ninalga Satoshi Kondo Satoshi Kasai Kousuke Hirasawa Tanya Akumu Carlos Martín-Isla Karim Lekadir Victor M. Campello Jorge Fabila Anette Beverdam Jeroen van Dillen Chase Neff Keelin Murphy Diagnostic Image Analysis Group Department of Medical Imaging Radboud University Medical Center Geert Grooteplein Zuid 10 Nijmegen 6525 GA Gelderland The Netherlands Medical Ultrasound Imaging Center Department of Medical Imaging Radboud University Medical Center Geert Grooteplein Zuid 10 Nijmegen 6525 GA Gelderland The Netherlands Independent researcher Toronto Canada Muroran Institute of Technology 27-1 Mizumoto-cho Muroran 050-8585 Hokkaido Japan Niigata University of Health and Welfare 1398 Shimami-cho Kita-ku Niigata 950-3198 Niigata Japan Konica Minolta Inc. 1-2 Sakura-machi Takatsuki 569-8503 Osaka Japan Department of Mathematics and Computer Science Universitat de Barcelona Gran Via de les Corts Catalanes 585 Barcelona 08007 L’Eixample Spain Institució Catalana de Recerca i Estudis Avançats Passeig Lluís Companys 23 Barcelona 08010 L’Eixample Spain Department of Obstetrics and Gynaecology Radboud University Medical Center Geert Grooteplein Zuid 10 Nijmegen 6525 GA Gelderland The Netherlands

Fetal growth restriction, affecting up to 10% of pregnancies, is a critical factor contributing to perinatal mortality and morbidity. Ultrasound measurements of the fetal abdominal circumference (AC) are a key aspect of monitoring fetal growth. However, the routine practice of biometric obstetric ultrasounds is limited in low-resource settings due to the high cost of sonography equipment and the scarcity of trained sonographers. To address this issue, we organized the ACOUSLIC-AI (Abdominal Circumference Operator-agnostic UltraSound measurement in Low-Income Countries) challenge to investigate the feasibility of automatically estimating fetal AC from blind-sweep ultrasound scans acquired by novice operators using low-cost devices. Training data, collected from three Public Health Units (PHUs) in Sierra Leone are made publicly available. Private validation and test sets, containing data from two PHUs in Tanzania and a European hospital, are provided through the Grand-Challenge platform. All sets were annotated by experienced readers. Sixteen international teams participated in this challenge, with six teams submitting to the Final Test Phase. In this article, we present the results of the three top-performing AI models from the ACOUSLIC-AI challenge, which are publicly accessible. We evaluate their performance in fetal abdomen frame selection, segmentation, abdominal circumference measurement, and compare their performance against clinical standards for fetal AC measurement. Clinical comparisons demonstrated that the limits of agreement (LoA) for A2 in fetal AC measurements are comparable to the interobserver LoA reported in the literature. The algorithms developed as part of the ACOUSLIC-AI challenge provide a benchmark for future algorithms on the selection and segmentation of fetal abdomen frames to further minimize fetal abdominal circumference measurement variability.

关键词： Prenatal ultrasound Low-income countries Fetal abdominal circumference Fetal biometry Free-hand sweeps Point-of-care ultrasound Deep learning

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NCEM: Network structural similarity metric-based clustering for noisy cryo-EM single particle images

NCEM: Network structural similarity metric-based clustering ...

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Chinese Automation Congress

作者： Yin Shuo Biao Zhang Hong-Bin Shen Yang Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Computer Science Shanghai Jiao Tong University Shanghai China

Cryo-EM single particle image reconstruction is currently a powerful technique for revealing the structure of biomacromolecules. Compared to traditional structural biology techniques like X-Ray, it requires fewer restrictions on specimens and is highly efficient with image processing tools. In this single particle reconstruction protocol, the ultimate goal is to identify different particle projection orientations. Since the picked single particle images are highly noisy, clustering is an important step to refrain noise by dividing images with similar projection angles into groups and averaging these images. The goal of clustering analysis is to assign similar particles into same class, so similarity measurement between particles is an important part in all clustering algorithms. Directly measuring the similarity of two particle images will be unreliable due to their low SNR. In this study, we propose a novel network structural similarity metric-based clustering algorithm NCEM for clustering the single particle images. We first construct a complex network for all particle images, where each node represents a particle. Then calculating the similarity between two nodes using structural similarity. This new network-based single particle image similarity metric has advantages over direct measurement for its noise resistance by using the structural information of the network. Our experiments on both artificial and experimental datasets demonstrate its effectiveness.

关键词： Cryo-EM Single particle image clustering Complex network Structural similarity

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Corrigendum to “Fuzzy implication functions based on powers of continuous t-norms” [Int. J. Approx. Reason. 83 (2017) 265–279]

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International Journal of Approximate Reasoning 2019年 104卷 144-147页

作者： Sebastia Massanet Jordi Recasens Joan Torrens Soft Computing Image Processing and Aggregation (SCOPIA) research group Dept. of Mathematics and Computer Science University of the Balearic Islands Ctra. de Valldemossa Km.7.5 07122 Palma de Mallorca Spain Balearic Islands Health Research Institute (IdISBa) 07010 Palma Spain Secció Matemàtiques i Informàtica ETS Arquitectura del Vallès Universitat Politècnica de Catalunya 08190 Sant Cugat del Vallès Spain

In Massanet et al. (2017) [1] a new property of fuzzy implication functions, called the invariance property with respect to powers of a continuous t-norm, was introduced and its application in approximate reasoning was studied. In the same paper, the novel family of power based implications was presented as a family of fuzzy implication functions that satisfy such invariance property. Unfortunately, this fact is not entirely true since as we will prove in this paper, there exist some power based implications generated from specific ordinal sum t-norms which do not fulfil the invariance property. Thus, a characterization of which continuous t-norms can be used to generate power based implications satisfying the power based invariance property is presented. Additionally, as an alternative solution, we introduce in this paper a slight modification of this property in such a way that all power based implications satisfy it.

关键词： Fuzzy implication function Power based implications Invariance with respect to powers of a continuous t-norm

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Towards real-time eyeblink detection in the wild: Dataset, theory and practices

arXiv

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arXiv 2019年

作者： Hu, Guilei Xiao, Yang Cao, Zhiguo Meng, Lubin Fang, Zhiwen Zhou, Joey Tianyi Yuan, Junsong National Key Laboratory of Science and Technology on Multi-Spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical Engineering Southern Medical University China School of Energy and Mechanicalelectronic Engineering Hunan University of Humanities Science and Technology China Institute of High Performance Computing A∗STAR Singapore Computer Science and Engineering Department of University at Buffalo State University of New York United States

Effective and real-time eyeblink detection is of widerange applications, such as deception detection, drive fatigue detection, face anti-spoofing. Despite previous efforts, most of existing focus on addressing the eyeblink detection problem under constrained indoor conditions with relative consistent subject and environment setup. Nevertheless, towards practical applications, eyeblink detection in the wild is highly preferred, and of greater challenges. In this paper, we shed the light to this research topic. A labelled eyeblink in the wild dataset (i.e., HUST-LEBW) of 673 eyeblink video samples (i.e., 381 positives, and 292 negatives) is first established. These samples are captured from the unconstrained movies, with the dramatic variation on face attribute, head pose, illumination condition, imaging configuration, etc. Then, we formulate eyeblink detection task as a binary spatial-temporal pattern recognition problem. After locating and tracking human eyes using SeetaFace engine and KCF (Kernelized Correlation Filters) tracker respectively, a modified LSTM model able to capture the multi-scale temporal information is proposed to verify eyeblink. A feature extraction approach that reveals the appearance and motion characteristics simultaneously is also proposed. The experiments on HUSTLEBW reveal the superiority and efficiency of our approach. The comparisons with the existing state-of-the-art methods validate the advantages of our manner for eyeblink detection in the wild. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

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Opinion target extraction for the Chinese formal text based on dependency relations 1st

Opinion target extraction for the Chinese formal text based ...

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1st International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2017

作者： Yang, Xiao-Yan Xu, Ge Zhang, Fu-Quan Liao, Xiang-Wen Xu, Lin Department of Computer Science Minjiang University FuzhouFujian350121 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control FuzhouFujian350121 China School of Software Beijing Institute of Technology Beijing10081 China College of Mathematics and Computer Science Fuzhou University FuzhouFujian350108 China Innovative Information Industry Research Institute Fujian Normal University FuzhouFujian350300 China

ISBN: (纸本)9783319707297

Due to the increasing amount of opinion data on the internet, opinion mining has become a hot topic, in which extracting opinion targets is a key step. The state-of-the-art approaches only use direct dependency relation patterns to extract opinion targets and the indirect dependency relation patterns have not been used. In this paper, the dependency relations between opinion target and opinion word are defined, and direct and indirect dependency relation patterns are designed. Then, a bootstrapping approach is used to extract and evaluate both candidate patterns and opinion targets. The experimental results show that in formal text, the approach improves the performance compared with the state-of-the-art approaches for opinion target extraction. © Springer International Publishing AG 2018.

关键词： Sentiment analysis

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