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作者： Xing, Chungui Qi, Feng Liu, Zhaoyang Wang, Yelong Guo, Shuxu State Key Laboratory on Integrated Optoelectronics College of Electronic Science and Engineering Jilin University Changchun China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang China Key Lab of Image Understanding and Computer Vision Shenyang China

Compressive sensing (CS) is a novel sampling modality, which indicates the signals can be sampled at a rate much below the Nyquist sampling rate. CS has increasing interest recently due to high demand of rapid, efficient, and in-expensive signal processing applications in the μmWave and mmWave frequencies, such as communication and imaging. There have been a lot of theoretical studies on this topic, but there is a lack of systematic experimental analysis of the implementation method itself. In this paper, we have investigated the influencing factors of terahertz compressive sensing based on experimental results, including illumination and the size of the pixel. Besides, to differentiate from current approaches, which generally make full use of the data, we propose to sort the data first and select a part of them based on amplitude, which might deliver a better image by prompting the mathematical calculations compulsively. We believe that such considerations given above would help to make a better system design and improve the performance of compressive imaging, and these results will also be helpful in the application of terahertz communication. © 2021 John Wiley & Sons, Ltd

关键词： Compressed sensing

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Evaluation Method of Node Importance in Temporal Satellite Networks Based on Time Slot Correlation

Research Square

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Research Square 2021年

作者： Xu, Rui Di, Xiaoqiang He, Xiongwen Qi, Hui School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China Jilin Province Key Laboratory of Network and Information Security Changchun University of Science and Technology Changchun130022 China Information Center Changchun University of Science and Technology Changchun130022 China Beijing Institute of Spacecraft System Engineering Beijing100094 China

Temporal satellite networks can accurately describe the dynamic process of satellite networks by considering the interaction relationship and interaction sequence between satellite nodes. In addition, the measurement of node importance in satellite networks plays a crucial role in understanding the structure and function of the network. The classical supra-adjacency matrix (SAM) temporal model identifies the key nodes in the temporal network to some extent, which ignores the differences of inter-layer connectivity relationships leading to the inability to reflect the dynamic variations of satellite nodes. Therefore, the evaluation method based on time slot correlation is proposed to measure the importance of satellite nodes in this paper. Firstly, the correlation coefficient of time slot nodes is defined to measure the coupling relationship of adjacent time slots. Secondly, the dynamic supra-adjacency matrix (DSAM) temporal network model is proposed considering the correlation between adjacent time slots and the characteristics of link time. Finally, the node importance ranking results in each time slot and a global perspective are obtained by utilizing the eigenvector centrality. Through experimental simulations of the Iridium and Orbcomm constellations, it is demonstrated that the DSAM method has a more accurate recognition rate and is more stable than the SAM method. © 2021, CC BY.

关键词： Satellites

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Advances in nuclear detection and readout techniques

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Nuclear science and Techniques 2023年第12期34卷 281-358页

作者： Rui He Xiao‑Yang Niu Yi Wang Hong‑Wei Liang Hong‑Bang Liu Ye Tian Hong‑Lin Zhang Chao‑Jie Zou Zhi‑Yi Liu Yun‑Long Zhang Hai‑Bo Yang Ju Huang Hong‑Kai Wang Wei‑Jia Han Bei Cao Gang Chen Cong Dai Li‑Min Duan Rui‑Rui Fan Fang‑Fa Fu Jian‑Hua Guo Dong Han Wei Jiang Xian‑Qin Li Xin Li Zhuo‑Dai Li Yu‑Tie Liang Shun Liao De‑Xu Lin Cheng‑Ming Liu Guo‑Rui Liu Jun‑Tao Liu Ze Long Meng‑Chen Niu Hao Qiu Hu Ran Xiang‑Ming Sun Bo‑Tan Wang Jia Wang Jin‑Xiang Wang Qi‑Lin Wang Yong‑Sheng Wang Xiao‑Chuan Xia Hao‑Qing Xie He‑Run Yang Hong Yin Hong Yuan Chun‑Hui Zhang Rui‑Guang Zhao Ran Zheng Cheng‑Xin Zhao Institute of Modern Physics Chinese Academy of SciencesLanzhou 730000China School of Nuclear Science and Technology University of Chinese Academy of SciencesBeijing 100049China Advanced Energy Science and Technology Guangdong Laboratory Huizhou 516000China Key Laboratory of Particle and Radiation Imaging Department of Engineering PhysicsTsinghua UniversityBeijing 100084China School of Integrated Circuits Dalian University of TechnologyDalian 116020China Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning 530004China School of Nuclear Science and Technology Lanzhou UniversityLanzhou 730000China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China Department of Radiation Oncology National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100021China Electronic Engineering School Heilongjiang UniversityHarbin 150080China Spallation Neutron Source Science Center Dongguan 523803China Micro‑electronic Department Harbin Institute of TechnologyHarbin 150001China Purple Mountain Observatory Chinese Academy of SciencesNanjing 210034China Liaoning Academy of Materials Shenyang 110167China Istituto Nazionale di Fisica Nucleare Sezione di PerugiaPerugiaItaly School of Mathematics and Physics Chengdu University of TechnologyChengdu 620035China PLAC Key Laboratory of Quark and Lepton Physics(MOE)Central China Normal UniversityWuhan 430079China School of Electronics and Information Northwestern Polytechnical UniversityXi’an 710129China State Key Lab of Superhard Materials College of PhysicsJilin UniversityChangchun 130012China School of Computer Science Northwestern Polytechnical UniversityXi’an 710129China

“A Craftsman Must Sharpen His Tools to Do His Job,”said *** detection and readout techniques are the foundation of particle physics,nuclear physics,and particle astrophysics to reveal the nature of the ***,they are being increasingly used in other disciplines like nuclear power generation,life sciences,environmental sciences,medical sciences,*** article reviews the short history,recent development,and trend of nuclear detection and readout techniques,covering Semiconductor Detector,Gaseous Detector,Scintillation Detector,Cherenkov Detector,Transition Radiation Detector,and Readout *** explaining the principle and using examples,we hope to help the interested reader underst and this research field and bring exciting information to the community.

关键词： Silicon detector Wide bandgap detector Time projection chamber(TPC) Multigap resistive plate chamber detector(MRPC) Scintillation detector Calorimeters Cherenkov detector Transition radiation detector Artificial intelligence Trigger-less readout

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Electromagnetic scattering by arbitrary-shaped magnetic particles and multipole decomposition: Analytical and numerical approaches

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Physical Review B 2023年第15期107卷 155425-155425页

作者： Andrey B. Evlyukhin Vladimir R. Tuz Institute of Quantum Optics Leibniz University Hannover Welfengarten 1 30167 Hannover Germany Cluster of Excellence PhoenixD Leibniz University Hannover Welfengarten 1A 30167 Hannover Germany State Key Laboratory on Integrated Optoelectronics College of Electronic Science and Engineering International Center of Future Science Jilin University 2699 Qianjin Street Changchun 130012 China School of Radiophysics Biomedical Electronics and Computer Systems V. N. Karazin Kharkiv National University 4 Svobody Square Kharkiv 61022 Ukraine

We develop a theoretical approach that makes it possible to analyze the role of multipole contributions in the scattering spectra of arbitrarily shaped magnetic particles with the relative permittivity and permeability given in a general tensor-valued form. The method for calculating the exact multipole moments with the inclusion of induced electric polarization and magnetization is suggested and explicitly applied for dipole and quadrupole terms. The final expressions for the dipole and quadrupole moments are obtained in such a form that they can be easily implemented in numerical solvers. Successful verification of the analytical expressions for multipole moments and the multipole decomposition of the scattered field and scattering cross section is provided by comparing the results obtained for differently shaped magnetic particles using the analytical Mie theory, numerical discrete dipole approximation, and comsol multiphysics software. As a particular example, the manifestation of the Faraday effect for fields scattered by a ferrite sphere is discussed in the framework of the multipole decomposition method with the derived magnetization terms. Accounting for the magnetic properties of particles in the multipole analysis of the electromagnetic scattering significantly expands the method's capabilities for studying and modeling metamaterial properties in the spectral ranges where natural materials can have a relative permeability different from unity.

关键词： Field emission Light-induced magnetic effects Magneto-optics

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Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms

arXiv

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arXiv 2022年

作者： Kou, Lei Li, Yang Zhang, Fangfang Gong, Xiaodong Hu, Yinghong Yuan, Quande Ke, Wende Institute of Oceanographic Instrumentation Qilu University of Technology Shandong Academy of Sciences Qingdao266075 China School of Electrical Engineering Northeast Electric Power University Jilin City132012 China School of Electrical Engineering and Automation Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Electric Power Research Institute State Grid Jibei Electric Power Company Limited Beijing100054 China School of Computer Technology and Engineering Changchun Institute of Technology Changchun130012 China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China

In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind powe has been developing in the direction of digitization and intelligence. It is of great significance to carry ou research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit fo the reduction of the operation and maintenance costs, the improvement of the power generation efficiency improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of "offshore wind power engineering and biological and environment", the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of powe equipment, and digital *** Codes 90B25 Copyright © 2022, The Authors. All rights reserved.

关键词： Offshore wind farms

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Modern deep learning in bioinformatics

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Journal of Molecular Cell Biology 2020年第11期12卷 823-827页

作者： Haoyang Li Shuye Tian Yu Li Qiming Fang Renbo Tan Yijie Pan Chao Huang Ying Xu Xin Gao Cancer Systems Biology Center The China-Japan Union HospitalJilin UniversityChangchun 130033China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Biology Southern University of Science and TechnologyShenzhen 518055China Computational Bioscience Research Center(CBRC) Computer Electrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Ningbo Institute of Information Technology Application Chinese Academy of SciencesNingbo 315040China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensGA 30602USA

Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A number of comprehensive reviews have been published on such applications,ranging from high-level reviews with future perspectives to those mainly serving as tutorials.

关键词： has such she

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A Survey of Learning on Small Data: Generalization, Optimization, and Challenge

arXiv

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arXiv 2022年

作者： Cao, Xiaofeng Bu, Weixin Huang, Shengjun Zhang, Minling Tsang, Ivor W. Ong, Yew Soon Kwok, James T. The School of Artificial Intelligence Jilin University Changchun130012 China The MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China The School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China The Australian Artificial Intelligence Institute University of Technology SydneyNSW2008 Australia Singapore The School of Computer Science and Engineering Nanyang Technological University Nanyang Avenue Singapore639798 Singapore Artificial Intelligence Scientist of A∗STAR Singapore The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Learning on big data brings success for artificial intelligence (AI), but the annotation and training costs are expensive. In future, learning on small data that approximates the generalization ability of big data is one of the ultimate purposes of AI, which requires machines to recognize objectives and scenarios relying on small data as humans. A series of learning topics is going on this way such as active learning and few-shot learning. However, there are few theoretical guarantees for their generalization performance. Moreover, most of their settings are passive, that is, the label distribution is explicitly controlled by finite training resources from known distributions. This survey follows the agnostic active sampling theory under a PAC (Probably Approximately Correct) framework to analyze the generalization error and label complexity of learning on small data in model-agnostic supervised and unsupervised fashion. Considering multiple learning communities could produce small data representation and related topics have been well surveyed, we thus subjoin novel geometric representation perspectives for small data: the Euclidean and non-Euclidean (hyperbolic) mean, where the optimization solutions including the Euclidean gradients, non-Euclidean gradients, and Stein gradient are presented and discussed. Later, multiple learning communities that may be improved by learning on small data are summarized, which yield data-efficient representations, such as transfer learning, contrastive learning, graph representation learning. Meanwhile, we find that the meta-learning may provide effective parameter update policies for learning on small data. Then, we explore multiple challenging scenarios for small data, such as the weak supervision and multi-label. Finally, multiple data applications that may benefit from efficient small data representation are surveyed. Copyright © 2022, The Authors. All rights reserved.

关键词： Big data

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Trapped-mode excitation in all-dielectric metamaterials with loss and gain

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Physical Review B 2022年第15期106卷 155429-155429页

作者： Anton V. Hlushchenko Denis V. Novitsky Vladimir R. Tuz State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering International Center of Future Science Jilin University 2699 Qianjin Street Changchun 130012 China National Science Center “Kharkiv Institute of Physics and Technology ” National Academy of Sciences of Ukraine 1 Akademicheskaya Street Kharkiv 61108 Ukraine School of Radiophysics Biomedical Electronics and Computer Systems V. N. Karazin Kharkiv National University 4 Svobody Square Kharkiv 61022 Ukraine

Non-Hermitian photonics based on combining loss and gain media within a single optical system provides a number of approaches to control and generate the flow of light. In this paper, we show that by introducing non-Hermitian perturbation into the system with loss and gain constituents, the high-quality resonances known as trapped modes can be excited without the need to change the symmetry of the unit cell geometry. To demonstrate this idea, we consider a widely used all-dielectric planar metamaterial whose unit cell consists of a pair of rectangular nanoantennae made of ordinal (with loss) and doped (with gain) silicon. Since the quality factor of the trapped-mode resonance can be controlled by changing both spatial symmetry and non-Hermiticity, varying loss and gain allows us to compensate for the influence of asymmetry and restore the quality factor of the localized mode. The results obtained suggest new ways to achieve high-quality resonances in non-Hermitian metamaterials that are promising for many practical applications in nanophotonics.

关键词： Lasers Metamaterials Mie scattering

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Merging high localization and TE-TM polarization degeneracy of guided waves in dielectric metasurfaces

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Physical Review Applied 2025年第1期23卷 014084-014084页

作者： Rui Li Sergey Polevoy Vladimir R. Tuz Oleh Yermakov State Key Laboratory on Integrated Optoelectronics College of Electronic Science and Engineering International Center of Future Science Jilin University 2699 Qianjin Street Changchun 130012 China Department of Radiospectroscopy O. Ya. Usikov Institute for Radiophysics and Electronics of the NAS of Ukraine Kharkiv 61085 Ukraine School of Radiophysics Biomedical Electronics and Computer Systems V. N. Karazin Kharkiv National University 4 Svobody Square Kharkiv 61022 Ukraine Department of Computational Physics V. N. Karazin Kharkiv National University 4 Svobody Square Kharkiv 61022 Ukraine Department of Fiber Photonics Leibniz Institute of Photonic Technology 9 Albert-Einstein-Straße Jena 07745 Germany

The polarization degree of freedom is an inherent feature of plane waves propagating in an isotropic homogeneous medium. The miniaturization of optical systems leads to the high localization of electromagnetic waves, but also to the loss of polarization control, namely, breaking TE-TM polarization degeneracy. In this work, we discover the near-field polarization degree of freedom for highly localized guided waves propagating along a dielectric metasurface. We demonstrate the opportunity to create a metasurface with the degenerate TE-TM polarization spectrum for the required operating wavelength and different constitutive materials. In particular, we analyze several possible implementations including silicon nitride and ceramic metasurfaces consisting of disk-shaped resonators, and evaluate the impact of substrates. Finally, we experimentally implement one of the metasurface designs and verify its broadband degenerate TE-TM polarization spectrum. The obtained results form an alternative platform for the planar polarization devices utilizing the polarization degree of freedom of localized light.

关键词： Silicon nitride

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Sclera Segmentation and Joint Recognition Benchmarking Competition: SSRBC 2023

Sclera Segmentation and Joint Recognition Benchmarking Compe...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Abhijit Das Saurabh Atreya Aritra Mukherjee Matej Vitek Haiqing Li Caiyong Wang Guangzhe Zhao Fadi Boutros Patrick Siebke Jan Niklas Kolf Naser Damer Sun Ye Lu Hexin Fan Aobo You Sheng Sabari Nathan R. Suganya R. S. Rampriya Geetanjali Sharma P Priyanka Aditya Nigam Peter Peer Umapada Pal Vitomir Štruc Birla Institute of Technology and Science Pilani (BITS Pilani) India University of Ljubljana (UL Slovenia) Beijing University of Civil Engineering and Architecture (BUCEA) China Fraunhofer Institute for Computer Graphics Research IGD (IGD) Germany TU Darmstadt Germany Jilin University (JLU China) Cougar Inc (Japan) Vellore Institure of Technology Vellore (VIT India) Indian Institute of Technology Mandi (IIT-MD India) Indian Statistical Institute Kolkata (ISI India)

This paper presents the summary of the Sclera Segmentation and Joint Recognition Benchmarking Competition (SSRBC 2023) held in conjunction with IEEE International Joint Conference on Biometrics (IJCB 2023). Different from the previous editions of the competition, SSRBC 2023 not only explored the performance of the latest and most advanced sclera segmentation models, but also studied the impact of segmentation quality on recognition performance. Five groups took part in SSRBC 2023 and submitted a total of six segmentation models and one recognition technique for scoring. The submitted solutions included a wide variety of conceptually diverse deep-learning models and were rigorously tested on three publicly available datasets, i.e., MASD, SBVPI and MOBIUS. Most of the segmentation models achieved encouraging segmentation and recognition performance. Most importantly, we observed that better segmentation results always translate into better verification performance.

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