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arXiv 2023年

作者： Wu, Qiong Li, Jiahan Dai, Pingyang Ye, Qixiang Cao, Liujuan Wu, Yongjian Ji, Rongrong Institute of Artificial Intelligence The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221000 China The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China The Peng Cheng Laboratory Shenzhen518066 China The School of Electronics Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China The Media Analytics and Computing Lab Department of Computer Science School of Informatics Xiamen University Xiamen361005 China The Youtu Laboratory Tencent Shanghai200233 China The Fujian Engineering Research Center of Trusted Artificial Intelligence Analysis and Application Institute of Artificial Intelligence Xiamen University Xiamen361005 China

Unsupervised domain adaptation person re-identification (Re-ID) aims to identify pedestrian images within an unlabeled target domain with an auxiliary labeled source-domain dataset. Many existing works attempt to recover reliable identity information by considering multiple homogeneous networks. And take these generated labels to train the model in the target domain. However, these homogeneous networks identify people in approximate subspaces and equally exchange their knowledge with others or their mean net to improve their ability, inevitably limiting the scope of available knowledge and putting them into the same mistake. This paper proposes a Dual-level Asymmetric Mutual Learning method (DAML) to learn discriminative representations from a broader knowledge scope with diverse embedding spaces. Specifically, two heterogeneous networks mutually learn knowledge from asymmetric subspaces through the pseudo label generation in a hard distillation manner. The knowledge transfer between two networks is based on an asymmetric mutual learning manner. The teacher network learns to identify both the target and source domain while adapting to the target domain distribution based on the knowledge of the student. Meanwhile, the student network is trained on the target dataset and employs the ground-truth label through the knowledge of the teacher. Extensive experiments in Market-1501, CUHK-SYSU, and MSMT17 public datasets verified the superiority of DAML over state-of-the-arts. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Matching evaluation based on image content discriminative features for different image types

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The Journal of Photographic science 2024年第1期72卷

作者： Eman S. Sabry Salah Elagooz Fathi E. Abd El-Samie Nirmeen A. El-Bahnasawy Ghada M. El-Banby Rabie A. Ramadan a Department of Communications and Computers Engineering Higher Institute of Engineering El-Shorouk Academy El-Shorouk City Egypt b Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf Egyptc Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia d Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt e Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt f Computer Engineering Department College of Engineering Cairo University Giza Egypt

ABSTRACTSearch by image has taken the place of the prior wordy approach, considering spatial image comparison in the feature space. Several query image forms are available. Sketch-to-real image matching is very challenging problem, which is commonly based on edge feature detectors to extract features from sketches. However, they are inconvenient, and it is crucial to select different methods. Thus, methods assessment is conducted for similarity matching between sketched and real images for different drawing contents and clarity levels. The assessment approach includes comparing feature keypoints gathered using the Scale-Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) methods. In addition, the comparison includes matching of objects inside High-Deﬁnition (HD) images and 3D images based on their generated features. The textual-Hcontent image search is another search type that is frequently used in brand logo cases. Hence, it is presented for text-logo image search based on visual features. Besides, the motives and downsides of the considered methods are declared, including the space and time complexities within test cases.

关键词： SIFT ORB sketch-real image retrieval objects matching spatial distance 3D image matching

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Unraveling Ultrafast Photoionization in Hexagonal Boron Nitride

arXiv

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arXiv 2021年

作者： Xue, Lianjie Liu, Song Hang, Yang Summers, Adam M. Wilson, Derrek J. Wang, Xinya Chen, Pingping Folland, Thomas G. Hachtel, Jordan A. Shi, Hongyu Hosseini-Zavareh, Sajed Das, Suprem R. Lei, Shuting Zhang, Zhuhua Sorensen, Christopher M. Guo, Wanlin Caldwell, Joshua D. Edgar, James H. Blaga, Cosmin I. Trallero-Herrero, Carlos A. Department of Physics Kansas State University ManhattanKS66506 United States Tim Taylor Department of Chemical Engineering Kansas State University ManhattanKS66506 United States J. R. Macdonald Laboratory Department of Physics Kansas State University ManhattanKS66506 United States State Key Laboratory of Mechanics and Control of Mechanical Structures Institute of Nano Science Nanjing University of Aeronautics and Astronautics Nanjing210016 China Advanced Laser Light Source and few-cycle Inc. Institut Nationale de la Recherche Scientifique 1650 Boul. Lionel-Boulet VarennesQCJ3X 1P7 Canada Department of Industrial and Manufacturing Systems Engineering Kansas State University ManhattanKS66506 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Center for Nanophase Materials Science Oak Ridge National Laboratory Oak Ridge37830 United States Department of Electrical and Computer Engineering Kansas State University ManhattanKS66506 United States Department of Physics University of Connecticut StorrsCT06269 United States

The non-linear response of dielectrics to intense, ultrashort electric fields has been a sustained topic of interest for decades with one of its most important applications being femtosecond laser micro/nano-machining. More recently, renewed interests in strong field physics of solids were raised with the advent of mid-infrared femtosecond laser pulses, such as high-order harmonic generation, optical-field-induced currents, etc. All these processes are underpinned by photoionization (PI), namely the electron transfer from the valence to the conduction bands, on a time scale too short for phononic motion to be of relevance. Here, in hexagonal boron nitride, we reveal that the bandgap can be finely manipulated by femtosecond laser pulses as a function of field polarization direction with respect to the lattice, in addition to the field’s intensity. It is the modification of bandgap that enables the ultrafast PI processes to take place in dielectrics. We further demonstrate the validity of the Keldysh theory in describing PI in dielectrics in the few TW/cm2 regime. © 2021, CC BY.

关键词： Photoionization

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Structured light by discrete-phase orbital angular momentum holograms

arXiv

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arXiv 2018年

作者： Vijayakumar, A. Rosales-Guzman, Carmelo Rai, Mani Ratnam Rosen, Joseph Minin, Oleg V. Minin, Igor V. Forbes, Andrew Department of Electrical and Computer Engineering Ben-Gurion University of the Negev P.O. Box 653 Beer-Sheva8410501 Israel School of Physics University of the Witwatersrand Private Bag X5 Johannesburg2050 South Africa Wang Da-Heng Collaborative Innovation Center for Quantum manipulation & Control Harbin University of Science and Technology Harbin150080 China National Research Tomsk State University Lenin Avenue 36 Tomsk634050 Russia National Research Tomsk Polytechnic University Lenin Avenue 30 Tomsk634050 Russia

Structured light has been created by a myriad of near- and far-field techniques and has found both classical and quantum applications. In the case of orbital angular momentum (OAM), continuous spiral phase patterns in dynamic or geometric phase are often employed with the phase patterns existing across the entire transverse plane. Here we exploit the uncertainty relation between OAM and angle to create structured OAM fields using multilevel OAM holograms. We show theoretically and experimentally that only a multilevel angular phase contour in the near-field is needed to create structured OAM light in the far-field, exploiting the reciprocal nature of angular momentum and angle. We use this approach to demonstrate exotic 3D structured light control to show the evolution of the Poynting vector in such fields and to highlight the physics underlying this phenomenon. Copyright © 2018, The Authors. All rights reserved.

关键词： Angular momentum

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Face Mask Integrated with Flexible and Wearable Manganite Oxide Respiration Sensor

SSRN

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SSRN 2023年

作者： Ye, Lianxu Wu, Fan Xu, Ruixing Zhang, Di Lu, Juanjuan Wang, Chuanlong Dong, Anjiang Xu, Sichen Xue, Lejun Fan, Zixin Xu, Longjie Li, Kaifeng Li, Dong Kursumovic, Ahmed Zhao, Run Tang, Rujun Qiu, Lei Wang, Haiyan MacManus-Driscoll, Judith L. Jing, Qingshen Li, Weiwei Yang, Hao College of Physics MIIT Key Laboratory of Aerospace Information Materials and Physics Nanjing University of Aeronautics and Astronautics Nanjing211106 China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China School of Microelectronics & Data Science Anhui University of Technology Maanshan243002 China School of Materials Engineering School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Jiangsu Key Laboratory of Thin Films School of Physical Science and Technology Soochow University Suzhou215006 China Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China Department of Materials Science & Metallurgy University of Cambridge 27 Charles Babbage Road CambridgeCB3 0FS United Kingdom James Watt School of Engineering University of Glasgow GlasgowG12 8LT United Kingdom State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Nanjing211106 China

Face masks are key personal protective equipment for reducing exposure to viruses and other environmental hazards such as air pollution. Integrating flexible and wearable sensors into face masks can provide valuable insights into personal and public health. The advantages that a breath-monitoring face mask requires, including multi-functional sensing ability and continuous, long-term dynamic breathing process monitoring, have been underdeveloped to date. Here, we design an effective human breath monitoring face mask based on a flexible La0.7Sr0.3MnO3 (LSMO)/Mica respiration sensor. The sensor’s capabilities and systematic measurements are investigated under two application scenes, namely clinical monitoring mode and daily monitoring mode, to monitor, recognise, and analyse different human breath status, i.e., cough, normal breath, and deep breath. This sensing system exhibits super-stability and multi-modal capabilities in continuous and long-time monitoring of the human breath. We determine that during monitoring human breath, thermal diffusion in LSMO is responsible for the change of resistance in flexible LSMO/Mica sensor. Both simulated and experimental results demonstrate good discernibility of the flexible LSMO/Mica sensor operating at different breath status. Our work opens a route for the design of novel flexible and wearable electronic devices. © 2023, The Authors. All rights reserved.

关键词： Mica

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Enhancing quantum control by bootstrapping a quantum processor of 12 qubits

arXiv

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arXiv 2017年

作者： Lu, Dawei Li, Keren Li, Jun Katiyar, Hemant Park, Annie Jihyun Feng, Guanru Xin, Tao Li, Hang Long, Guilu Brodutch, Aharon Baugh, Jonathan Zeng, Bei Laflamme, Raymond Department of Physics Southern University of Science and Technology Shenzhen518055 China Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Beijing Computational Science Research Center Beijing100193 China Max-Planck-Institutfür Quantenoptik GarchingD-85748 Germany Center for Quantum Information and Quantum Control Department of Physics Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3H6 Canada Department of Mathematics and Statistics University of Guelph GuelphONN1G 2W1 Canada Perimeter Institute for Theoretical Physics Waterloo ONN2L 2Y5 Canada

Accurate and efficient control of quantum systems is one of the central challenges for quantum information processing. Current state-of-the-art experiments rarely go beyond 10 qubits and in most cases demonstrate only limited control. Here we demonstrate control of a 12-qubit system, and show that the system can be employed as a quantum processor to optimize its own control sequence by using measurement-based feedback control (MQFC). The final product is a control sequence for a complex 12-qubit task: preparation of a 12-coherent state. The control sequence is about 10% more accurate than the one generated by the standard (classical) technique, showing that MQFC can correct for unknown imperfections. Apart from demonstrating a high level of control over a relatively large system, our results show that even at the 12-qubit level, a quantum processor can be a useful lab instrument. As an extension of our work, we propose a method for combining the MQFC technique with a twirling protocol, to optimize the control sequence that produces a desired Clifford gate. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum optics

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Automated osteoporosis classification and T-score prediction using hip radiographs via deep learning algorithm

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Therapeutic Advances in Musculoskeletal Disease 2024年 16卷

作者： Chen, Yu-Pin Chan, Wing P. Zhang, Han-Wei Tsai, Zhi-Ren Peng, Hsiao-Ching Huang, Shu-Wei Jang, Yeu-Chai Kuo, Yi-Jie Department of Orthopedics Wan Fang Hospital Taipei Medical University Taipei City Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan Department of Radiology Wan Fang Hospital Taipei Medical University Taipei City Taiwan Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan Biomedica Corporation New Taipei City Taiwan Program for Aging China Medical University Taichung City Taiwan Institute of Population Health Sciences National Health Research Institutes Miaoli County Taiwan Department of Electrical and Computer Engineering Institute of Electrical Control Engineering National Yang Ming Chiao Tung University Hsinchu County Hsinchu City Taiwan Department of Computer Science and Information Engineering Asia University Taichung City Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung City Taiwan Center for Precision Medicine Research Asia University Taichung City Taiwan Department of Applied Science National Taitung University Taitung County Taitung City Taiwan Department of Orthopedics Wan Fang Hospital Taipei Medical University No. 111 Sec. 3 Xinglong Road Wenshan Taipei 11696 Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

Background: Despite being the gold standard for diagnosing osteoporosis, dual-energy X-ray absorptiometry (DXA) is an underutilized screening tool for osteoporosis. Objectives: This study proposed and validated a controllable feature layer of a convolutional neural network (CNN) model with a preprocessing image algorithm to classify osteoporosis and predict T-score on the proximal hip region via simple hip radiographs. Design: This was a single-center, retrospective study. Methods: An image dataset of 3460 unilateral hip images from 1730 patients (age ⩾50 years) was retrospectively collected with matched DXA assessment for T-score for the targeted proximal hip regions to train (2473 unilateral hip images from 1430 patients) and test (497 unilateral hip images from 300 patients) the proposed CNN model. All images were processed with a fully automated CNN model, X1AI-Osteo. Results: The proposed screening tool illustrated a better performance (sensitivity: 97.2%;specificity: 95.6%;positive predictive value: 95.7%;negative predictive value: 97.1%;area under the curve: 0.96) than the open-sourced CNN models in predicting osteoporosis. Moreover, when combining variables, including age, body mass index, and sex as features in the training metric, there was high consistency in the T-score on the targeted hip regions between the proposed CNN model and the DXA (r = 0.996, p < 0.001). Conclusion: The proposed CNN model may identify osteoporosis and predict T-scores on the targeted hip regions from simple hip radiographs with high accuracy, highlighting the future application for population-based opportunistic osteoporosis screening with low cost and high adaptability for a broader population at risk. Trial registration: TMU-JIRB N201909036. © The Author(s), 2024.

关键词： deep learning neural network osteoporosis radiographs T-score

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An optimized energy management strategy for fuel cell hybrid vehicles

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IOP Conference Series: Materials science and engineering 2019年第4期612卷

作者： Wenxin Xiao Lei Wang Dongyin Liu Wenwen Zhang Chinese-German College Tongji University Shanghai China Department of Electrical and Computer Engineering Technische Universität München Munich Germany Research Center of Control Science and Intelligent System University of Electronic Science and Technology of China Chengdu China College of Electronics and Information Engineering Tongji University Shanghai China

Fuel cell hybrid vehicles are environmentally friendly and have good development prospects. Their energy management is very important, but there are currently few studies on this topic. In this paper, an optimized energy management strategy specifically applied to fuel cell hybrid vehicles is proposed. Fuel cell hybrid vehicles have some different characteristics from ordinary hybrid vehicles, which need to be considered in energy management. For the fuel cell to remain activated during driving, its output power must remain above a minimum power. In addition, it takes some time for the fuel cell to start up. These characteristics are considered in the vehicle model. The energy management strategy proposed in this paper maximizes energy use efficiency by controlling the SOC value of the battery. The SOC value of the battery is in the proper range during driving. At high speeds, fuel is consumed as a source of energy and the battery is charged to keep its SOC at a high level. When approaching the destination or when the vehicle is traveling at a low speed, the battery is discharged to reduce fuel consumption. Applying this strategy to the ECE-15 and EUDCL environments, the simulation results show that this optimized energy management strategy improves the energy efficiency of the fuel cell and enables the battery to operate at the appropriate power.

关键词：

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2024年 1076 LNNS卷 v-vii页

作者： Biele, Cezary Kacprzyk, Janusz Kopeć, Wieslaw Możaryn, Jakub Owsiński, Jan W. Romanowski, Andrzej Sikorski, Marcin National Information Processing Institute Warszaw Poland Polish Academy of Sciences Systems Research Institute Warsaw Poland Polish-Japanese Academy of Information Technology Warsaw Poland Institute of Automatic Control and Robotics Faculty of Mechatronics Warsaw University of Technology Warsaw Poland Institute of Applied Computer Science Faculty of Electrical Electronic Computer and Control Engineering Lódż University of Technology LODZ Poland Department of Applied Informatics in Management Faculty of Management and Economics Gdańsk University of Technology Gdańsk Poland

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