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Metalenses phase characterization by multidistance phase retrieval

Light(Science & Applications) 2024年第9期13卷 1898-1907页

作者： Bowen Liu Jialuo Cheng Maoxiong Zhao Jin Yao Xiaoyuan Liu Shaohu Chen Lei Shi Din Ping Tsai Zihan Geng Mu Ku Chen State Key Laboratory of Surface Physics Key Laboratory of Micro-and Nano-Photonic Structures(Ministry of Education)and Department of PhysicsFudan University200433 ShanghaiChina Department of Electrical Engineering City University of Hong KongKowloonHong Kong SARChina State Key Laboratory of Terahertz and Millimeter Waves City University of Hong KongKowloonHong Kong SARChina Institute for Nanoelectronic Devices and Quantum Computing Fudan University200438 ShanghaiChina Collaborative Innovation Center of Advanced Microstructures Nanjing University210093 NanjingJiangsuChina Shanghai Research Center for Quantum Sciences 201315 ShanghaiChina Centre for Biosystems Neuroscienceand NanotechnologyCity University of Hong KongKowloonHong Kong SARChina Institute of Data and Information Tsinghua Shenzhen International Graduate SchoolTsinghua University518071 ShenzhenGuangdongChina

Metalens,characterized by their unique functions and distinctive physical properties,have gained significant attention for their potential *** further optimize the performance of metalens,it is necessary to characterize the phase modulation of the *** this study,we present a multi-distance phase retrieval system based on optical field scanning and discuss its convergence and *** findings indicate that the system is capable of retrieving the phase distribution of the metalens as long as the measurement noise is low and the total length of the scanned light field is sufficiently *** system enables the analysis of focal length and aberration by utilizing the computed phase *** extend our investigation to measure the phase distribution of the metalens operating in the near-infrared(NIR)spectrum and identify the impact of defects in the sample on the ***,we conduct a comparative analysis of the phase distribution of the metalens in air and ethanol and observe the variations in the phase modulation of the metalens in different working *** system provides a straightforward method for the phase characterization of metalens,aiding in optimizing the metalens design and functionality.

关键词： phase system distribution

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Online Test Paper Composition Based on Genetic Algorithm

Online Test Paper Composition Based on Genetic Algorithm

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2018 3rd International Conference on Modelling, Simulation and Applied Mathematics(MSAM 2018)

作者： Wanli Song School of Information Engineering Nanjing Xiaozhuang University Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing XiaoZhuang University

The application of online examination is used more and more widely. Test paper composition is the core function in this application. Online examination system requires that the test paper should be quick and flexible, and the composition of questions should be reasonable and random. Test paper should not only meet the specific needs of users, ensure fairness, impartiality, but also be accurate and efficient. The intelligent test paper composition algorithm based on genetic algorithm is studied in this paper. Then this method is used to organize the structure of the test paper automatically, composites the examination content. Proposals are put forward to examines the degree of students' mastery of knowledge based on this method. This algorithm can automatically composite test papers according to the conditions of difficulty degree, knowledge coverage, and the proportion of questions. It can automatically composite test papers for online assessment. The practice of online testing system based on this algorithm shows that the algorithm is scientific and effective. On the premise of guaranteeing the quality of the test paper, it greatly improves the efficiency and pertinence of online testing. It is beneficial to improve students' learning efficiency and intellectualization of education.

关键词： test paper composition education intellectualization genetic algorithm E-learning

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Deep latent low-rank fusion network for progressive subspace discovery 29

Deep latent low-rank fusion network for progressive subspace...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Zhang, Zhao Ren, Jiahuan Zhang, Zheng Liu, Guangcan Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China School of Computer Science and Technology Soochow University China Bio-Computing Research Center Harbin Institute of Technology Shenzhen China School of Information and Control Nanjing University of Information Science and Technology China

ISBN: (纸本)9780999241165

Low-rank representation is powerful for recovering and clustering the subspace structures, but it cannot obtain deep hierarchical information due to the single-layer mode. In this paper, we present a new and effective strategy to extend the single-layer latent low-rank models into multiple-layers, and propose a new and progressive Deep Latent Low-Rank Fusion Network (DLRF-Net) to uncover deep features and structures embedded in input data. The basic idea of DLRF-Net is to refine features progressively from the previous layers by fusing the subspaces in each layer, which can potentially obtain accurate features and subspaces for representation. To learn deep information, DLRF-Net inputs shallow features of the last layers into subsequent layers. Then, it recovers the deeper features and hierarchical information by congregating the projective subspaces and clustering subspaces respectively in each layer. Thus, one can learn hierarchical subspaces, remove noise and discover the underlying clean subspaces. Note that most existing latent low-rank coding models can be extended to multilayers using DLRF-Net. Extensive results show that our network can deliver enhanced performance over other related frameworks. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

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Network Security Situation Assessment Method Based on Absorbing Markov Chain

Network Security Situation Assessment Method Based on Absorb...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Gao, Hongbin Wang, Shangxing Zhang, Hongbin Liu, Bin Zhao, Dongmei Liu, Zhen Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China Hebei University of Science and Technology School of Information Science and Engineering Hebei Key Laboratory of Network and Information Security Shijiazhuang China Hebei University of Science and Technology School of Economics and Management Research Center of Big Data and Social Computing Shijiazhuang China Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

This paper has a new network security evaluation method as an absorbing Markov chain-based assessment method. This method is different from other network security situation assessment methods based on graph theory. It effectively refinement issues such as poor objectivity of other methods, incomplete consideration of evaluation factors, and mismatching of evaluation results with the actual situation of the network. Firstly, this method collects the security elements in the network. Then, using graph theory combined with absorbing Markov chain, the threat values of vulnerable nodes are calculated and sorted. Finally, the maximum possible attack path is obtained by blending network asset information to determine the current network security status. The experimental results prove that the method fully considers the vulnerability and threat node ranking and the specific case of system network assets, which makes the evaluation result close to the actual network situation. © 2022 IEEE.

关键词： Network security

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Clustering by orthogonal NMF model and non-convex penalty optimization

Clustering by orthogonal NMF model and non-convex penalty op...

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作者： Wang, Shuai Chang, Tsung-Hui Cui, Ying Pang, Jong-Shi Shenzhen Research Institute of Big Data and School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China Guangdong Provincial Key Laboratory of Big Data Computing and School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Industrial and Systems Engineering University of Southern California Los AngelesCA90089 United States

The non-negative matrix factorization (NMF) model with an additional orthogonality constraint on one of the factor matrices, called the orthogonal NMF (ONMF), has been found a promising clustering model and can outperform the classical K-means. However, solving the ONMF model is a challenging optimization problem because the coupling of the orthogonality and non-negativity constraints introduces a mixed combinatorial aspect into the problem due to the determination of the correct status of the variables (positive or zero). Most of the existing methods directly deal with the orthogonality constraint in its original form via various optimization techniques, but are not scalable for large-scale problems. In this paper, we propose a new ONMF based clustering formulation that equivalently transforms the orthogonality constraint into a set of norm-based non-convex equality constraints. We then apply a non-convex penalty (NCP) approach to add them to the objective as penalty terms, leading to a problem that is efficiently solvable. One smooth penalty formulation and one non-smooth penalty formulation are respectively studied. We build theoretical conditions for the penalized problems to provide feasible stationary solutions to the ONMF based clustering problem, as well as proposing efficient algorithms for solving the penalized problems of the two NCP methods. Experimental results based on both synthetic and real datasets are presented to show that the proposed NCP methods are computationally time efficient, and either match or outperform the existing K-means and ONMF based methods in terms of the clustering performance. © 1991-2012 IEEE.

关键词： Non-negative matrix factorization

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Non-Gaussian Lagrangian Stochastic Model for Wind Field Simulation in the Surface Layer

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Advances in Atmospheric Sciences 2020年第1期37卷 90-104页

作者： Chao LIU Li FU Dan YANG David R.MILLER Junming WANG Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing UniversityChongqing 401331China School of Big Data and Software Engineering Chongqing UniversityChongqing 401331China Department of Natural Resources and Environment University of ConnecticutStorrsCT 06268USA Climate and Atmospheric Science Section Illinois State Water SurveyPrairie Research InstituteUniversity of Illinois at Urbana-ChampaignChampaignIL 61820USA

Wind field simulation in the surface layer is often used to manage natural resources in terms of air quality,gene flow(through pollen drift),and plant disease transmission(spore dispersion).Although Lagrangian stochastic(LS)models describe stochastic wind behaviors,such models assume that wind velocities follow Gaussian ***,measured surface-layer wind velocities show a strong skewness and *** paper presents an improved model,a non-Gaussian LS model,which incorporates controllable non-Gaussian random variables to simulate the targeted non-Gaussian velocity distribution with more accurate skewness and *** velocity statistics generated by the non-Gaussian model are evaluated by using the field data from the Cooperative Atmospheric Surface Exchange Study,October 1999 experimental dataset and comparing the data with statistics from the original Gaussian *** show that the non-Gaussian model improves the wind trajectory simulation by stably producing precise skewness and kurtosis in simulated wind velocities without sacrificing other features of the traditional Gaussian LS model,such as the accuracy in the mean and variance of simulated *** improvement also leads to better accuracy in friction velocity(i.e.,a coupling of three-dimensional velocities).The model can also accommodate various non-Gaussian wind fields and a wide range of skewness–kurtosis ***,improved skewness and kurtosis in the simulated velocity will result in a significantly different dispersion for wind/particle ***,the non-Gaussian model is worth applying to wind field simulation in the surface layer.

关键词： Lagrangian stochastic model wind field simulation non-Gaussian wind velocity surface layer

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Multi-modal Fine-grained Retrieval with Local and Global Cross-Attention

Multi-modal Fine-grained Retrieval with Local and Global Cro...

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International Conference on Ubiquitous and Future Networks, ICUFN

作者： Qiaosong Chen Ye Zhang Junzhuo Liu Zhixiang Wang Xin Deng Jin Wang Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications Chongqing P.R.China Department of Radiation Oncology (Maastro) GROW-School Maastricht University Medical Centre+ Maastricht The Netherlands

The goal of cross-modal retrieval is that the user gives any sample as a query sample, and the system retrieves and feeds back various modal samples related to the query sample. At present, the cross-modal retrieval method mainly focuses on coarse-grained, which is far from being satisfied in practical application. However, there are many difficulties in fine-grained retrieval, such as the heterogeneous gap and semantic gap between multi-modal data, the difficulty of similarity measurement, and the small difference in fine-grained sample features. To overcome these limitations, we propose a novel multi-modal fine-grained retrieval method with the LAGC-Attention module, which can fully extract and fuse feature information from different modalities and represent them in a common space. Specifically, we use local and global cross self-attention to extract the neighboring and global context information for each single modal data, which greatly enhances the feature representation capability of each modality (image, text, audio, video), and especially reduce the gap between different feature distributions. Finally, Extensive experiments and ablation studies demonstrate that our method achieves state-of-the-art on the public dataset PKU FG-XMedia.

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture

A Graph Sequence Generator and Multi-head Self-attention Mec...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yuejia Wu Jian-Tao Zhou College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models.

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Using Rough Sets to Improve the High-dimensional data Anomaly Detection Method Based on Extended Isolation Forest

Using Rough Sets to Improve the High-dimensional Data Anomal...

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International Conference on Computer Supported Cooperative Work in Design

作者： Hanlin Liu Jiantao Zhou Hua Li College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods.

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EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced Graph Sample and Aggregate Inductive Learning Algorithm

EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced ...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuejia Wu Jian-Tao Zhou Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph is an important research field that involves the storage and management of knowledge, but the incompleteness and sparsity of Knowledge Graphs hinder their application in many fields. Knowledge Graph Reasoning aims to alleviate this problem by completing missing paths or identifying wrong paths between entities. Graph Convolution Network (GCN) based methods are one of the state-of-the-art approaches to this work. However, it is difficult to directly generalize to unknown nodes and utilizes valid information from the local neighborhood which results in poor flexibility and extensibility and will loss of important information. This paper presents EG-KGR, a plug-and-play knowledge reasoning model based on enhanced graph sampling and aggregate inductive learning algorithm to relieve the above problems and enhance existing GCN-based methods. Specifically, EG-KGR supports incremental characteristics, uses inductive learning to replace transductive learning, and designs random sampling and local information sampling optimization methods to improve the model's generalization ability, prediction accuracy, and running speed. Extensive experimental results show that our EG-KGR can achieve optimal results.

关键词： Training Knowledge engineering Convolution Aggregates Optimization methods Knowledge graphs Learning (artificial intelligence)

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