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Structuring Nonlinear Wavefront Emitted from Monolayer Transition-Metal Dichalcogenides

Research 2020年第1期2020卷 1721-1730页

作者： Xuanmiao Hong Guangwei Hu Wenchao Zhao Kai Wang Shang Sun Rui Zhu Jing Wu Weiwei Liu Kian Ping Loh Andrew Thye Shen Wee Bing Wang Andrea Alù Cheng-Wei Qiu Peixiang Lu Wuhan National Laboratory for Optoelectronics and School of Physics Huazhong University of Science and TechnologyWuhan 430074China Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583 Advanced Science Research Center City University of New YorkNew York 10031USA Department of Physics National University of Singapore2 Science Drive 3Singapore 117542 Institute of Materials Research and Engineering Agency for ScienceTechnology and Research2 Fusionopolis WayInnovis#08-03Singapore 138634 Department of Chemistry National University of Singapore3 Science Drive 3Singapore 17543 Centre for Advanced 2D Materials National University of SingaporeBlock S146 Science Drive 2Singapore 117546 Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of TechnologyWuhan 430205China

The growing demand for tailored nonlinearity calls for a structure with unusual phase discontinuity that allows the realization of nonlinear optical chirality,holographic imaging,and nonlinear wavefront ***-metal dichalcogenide(TMDC)monolayers offer giant optical nonlinearity within a few-angstrom thickness,but limitations in optical absorption and domain size impose restriction on wavefront control of nonlinear emissions using classical light *** contrast,noble metal-based plasmonic nanosieves support giant field enhancements and precise nonlinear phase control,with hundred-nanometer pixellevel resolution;however,they suffer from intrinsically weak nonlinear ***,we report a multifunctional nonlinear interface by integrating TMDC monolayers with plasmonic nanosieves,yielding drastically different nonlinear functionalities that cannot be accessed by either *** a hybrid nonlinear interface allows second-harmonic(SH)orbital angular momentum(OAM)generation,beam steering,versatile polarization control,and holograms,with an effective SH nonlinearityχ^((2))of~25 nm/*** designer platform synergizes the TMDC monolayer and plasmonic nanosieves to empower tunable geometric phases and large field enhancement,paving the way toward multifunctional and ultracompact nonlinear optical devices.

关键词： nonlinearity Transition yielding

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Dense-View GEIs Set: View space covering for gait recognition based on Dense-View GAN

arXiv

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arXiv 2020年

作者： Liao, Rijun An, Weizhi Yu, Shiqi Li, Zhu Huang, Yongzhen Department of Computer Science and Electrical Engineering University of Missouri Kansas City United States Department of Computer Science and Engineering University of Texas Arlington United States Department of Computer Science and Engineering Southern University of Science and Technology China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China Watrix technology limited co. ltd China

Gait recognition has proven to be effective for long-distance human recognition. But view variance of gait features would change human appearance greatly and reduce its performance. Most existing gait datasets usually collect data with a dozen different angles, or even more few. Limited view angles would prevent learning better view invariant feature. It can further improve robustness of gait recognition if we collect data with various angles at 1◦ interval. But it is time consuming and labor consuming to collect this kind of dataset. In this paper, we, therefore, introduce a Dense-View GEIs Set (DV-GEIs) to deal with the challenge of limited view angles. This set can cover the whole view space, view angle from 0◦ to 180◦ with 1◦ interval. In addition, Dense-View GAN (DV-GAN) is proposed to synthesize this dense view set. DV-GAN consists of Generator, Discriminator and Monitor, where Monitor is designed to preserve human identification and view information. The proposed method is evaluated on the CASIA-B and OU-ISIR dataset. The experimental results show that DV-GEIs synthesized by DV-GAN is an effective way to learn better view invariant feature. We believe the idea of dense view generated samples will further improve the development of gait recognition. Copyright © 2020, The Authors. All rights reserved.

关键词： Gait analysis

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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Visualizing Functional Network Connectivity Difference between Healthy Control and Major Depressive Disorder Using an Explainable Machine-learning Method

Visualizing Functional Network Connectivity Difference betwe...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ji Ye Chun Mohammad S. E. Sendi Jing Sui Dongmei Zhi Vince D. Calhoun Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences and University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Major depressive disorder (MDD) is a complex mental disorder characterized by a persistent sad feeling and depressed mood. Recent studies reported differences between healthy control (HC) and MDD by looking to brain networks including default mode and cognitive control networks. More recently there has been interest in studying the brain using advanced machine learning-based classification approaches. However, interpreting the model used in the classification between MDD and HC has not been explored yet. In the current study, we classified MDD from HC by estimating whole-brain connectivity using several classification methods including support vector machine, random forest, XGBoost, and convolutional neural network. In addition, we leveraged the SHapley Additive exPlanations (SHAP) approach as a feature learning method to model the difference between these two groups. We found a consistent result among all classification method in regard of the classification accuracy and feature learning. Also, we highlighted the role of other brain networks particularly visual and sensory motor network in the classification between MDD and HC subjects.

关键词： Radio frequency Support vector machines Visualization Functional magnetic resonance imaging Machine learning Hospitals Brain modeling

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Spectrum Manipulation for Sound with Effective Gauge Fields in Cascading Temporally Modulated Waveguides

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Physical Review Applied 2019年第6期11卷 064012-064012页

作者： Chengzhi Qin Yugui Peng Ying Li Xuefeng Zhu Bing Wang Cheng-Wei Qiu Peixiang Lu School of Physics and Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan 430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore 119620 Singapore Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan 430205 China

Spectrum is a basic dimension of waves ranging from electromagnetic to acoustic waves where information can be encoded and multiplexed. The manipulation of the sound spectrum is desirable in applications of acoustic communication and voice encryption, which, however, is challenging to realize. Here, based on temporally modulated waveguides, we create effective gauge fields to generate frequency domain Bloch oscillations (FBOs) to control the spectrum of sound. The modulation can induce mode transitions in the waveguide band and form a discrete frequency lattice where the wave vector mismatch during transitions acts as an effective electric field that drives FBOs. Furthermore, we find the modulation phase accompanying transitions serves as an effective gauge potential that can control the initial oscillation phase. We report that multiple FBOs with judiciously designed oscillation phases can be further cascaded to realize acoustic spectrum reconstruction, unidirectional transduction, and bandwidth engineering. This study reveals the significance of gauge fields in FBOs and functionalizes its cascaded configurations for advanced control of the sound spectrum. This paradigm may find versatile applications in acoustic secure communication, information encryption, and processing.

关键词： Acoustic signal processing & noise Acoustic wave phenomena Elastic modulus Geometric & topological phases Synthetic gauge fields Waveguides Bloch wave theory

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AIM 2020 Challenge on Image Extreme Inpainting 16th

AIM 2020 Challenge on Image Extreme Inpainting

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Workshops held at the 16th European Conference on computer Vision, ECCV 2020

作者： Ntavelis, Evangelos Romero, Andrés Bigdeli, Siavash Timofte, Radu Hui, Zheng Wang, Xiumei Gao, Xinbo Shin, Chajin Kim, Taeoh Son, Hanbin Lee, Sangyoun Li, Chao Li, Fu He, Dongliang Wen, Shilei Ding, Errui Bai, Mengmeng Li, Shuchen Zeng, Yu Lin, Zhe Yang, Jimei Zhang, Jianming Shechtman, Eli Lu, Huchuan Zeng, Weijian Ni, Haopeng Cai, Yiyang Li, Chenghua Xu, Dejia Wu, Haoning Han, Yu Nadim, Uddin S. M. Jang, Hae Woong Ahmed, Soikat Hasan Yoon, Jungmin Jung, Yong Ju Li, Chu-Tak Liu, Zhi-Song Wang, Li-Wen Siu, Wan-Chi Lun, Daniel P. K. Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Narang, Pratik Mandal, Murari Chauhan, Pranjal Singh Computer Vision Lab ETH Zürich Zürich Switzerland CSEM Neuchâtel Switzerland School of Electronic Engineering Xidian University Xi’an China Image and Video Pattern Recognition Laboratory School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Baidu Inc. Beijing China Beijing China Dalian University of Technology Dalian China Adobe San Jose United States Rensselaer Polytechnic Institute Troy United States Peking University Beijing China Lab Gachon University Seongnam Korea Republic of Centre for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong China Indian Institute of Technology Madras Chennai India BITS Pilani Pilani India MNIT Jaipur Jaipur India

ISBN: (纸本)9783030670696

This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Efficient Mode Transfer on a Compact Silicon Chip by Encircling Moving Exceptional Points

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Physical Review Letters 2020年第15期124卷 153903-153903页

作者： Qingjie Liu Shuyi Li Bing Wang Shaolin Ke Chengzhi Qin Kai Wang Weiwei Liu Dingshan Gao Pierre Berini Peixiang Lu School of Physics and Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan 430074 China Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan 430205 China School of Electrical Engineering and Computer Science University of Ottawa Ottawa Ontario K1N 6N5 Canada Department of Physics and Center for Research in Photonics University of Ottawa Ottawa Ontario K1N 6N5 Canada CAS Center for Excellence in Ultra-Intense Laser Science Shanghai 201800 China

Exceptional points (EPs) are branch point singularities of self-intersecting Riemann sheets, and they can be observed in a non-Hermitian system with complex eigenvalues. It has been revealed recently that dynamically encircling EPs by adiabatically changing the parameters of a system composed of lossy optical waveguides could lead to asymmetric (input-output) mode transfer. However, the length of the waveguides had to be considerable to ensure adiabatic evolution. Here we demonstrate that the parameters can change adiabatically along a smaller encircling loop by utilizing moving EPs, leading to significant shortening of the structures compared to fixed EPs. Meanwhile, the mode transmittance is remarkably improved and the transfer efficiency persists at ∼90%. Moving EPs are very promising for applications such as highly integrated broadband optical switches and convertors operating at telecommunication wavelengths.

关键词： Metamaterials Optical materials & elements Photonics Waveguides

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Frangi-net

Frangi-net

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Workshop on Bildverarbeitung fur die Medizin, 2018

作者： Fu, Weilin Breininger, Katharina Schaffert, Roman Ravikumar, Nishant Würfl, Tobias Fujimoto, Jim Moult, Eric Maier, Andreas Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-University Erlangen-Nuremberg Erlangen Germany Erlangen Germany Department Electrical Engineering and Computer Science Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge United States

ISBN: (纸本)9783662580950

In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network ("Frangi-Net"), and illustrate that the Frangi-Net is equivalent to the original Frangi filter. Furthermore, we show that, as a neural network, Frangi-Net is trainable. We evaluate the proposed method on a set of 45 high resolution fundus images. After fine-tuning, we observe both qualitative and quantitative improvements in the segmentation quality compared to the original Frangi measure, with an increase up to 17% in F1 score. © Springer-Verlag GmbH Deutschland 2018.

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Overview of the recent experimental research on the J-TEXT tokamak

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Nuclear Fusion 2024年第11期64卷 112005-112005页

作者： Ding, Yonghua Wang, Nengchao Chen, Zhongyong Xia, Donghui Yang, Zhoujun Chen, Zhipeng Zheng, Wei Yan, Wei Li, Da Zhou, Song Xu, Xin Zhang, Xiaoyi Mao, Feiyue Fang, Jiangang Ren, Zhengkang Chen, Xixuan Zhang, Junli Zhang, Xiaobo He, Ying Zhang, Qi Li, You Bai, Wei Mou, Lingke Li, Feng Long, Ting Ke, Rui Gao, Li Shi, Peng Shen, Chengshuo Liu, Jiaxing Guo, Weixin Wang, Lu Li, Hanhui Jiang, Zhonghe Zhang, Xiaoqing Li, Jianchao Rao, Bo Cheng, Zhifeng Zhu, Ping Liu, Minghai Xu, Tao Ma, Shaoxiang Yang, Yong Li, Chuan Wang, Zhijiang Zhang, Ming Yu, Kexun Hu, Xiwei Liang, Y. Yu, Q. Gentle, K.W. Pan, Yuan Gentle, Kenneth William Yu, Qingquan Liang, Yunfeng Jiang, Wei Yi, Lin Zhang, Ya Hu, Qiming Li, Jianchao Huang, Mingxiang Li, Haolong Huang, Zhuo Huang, Jie Wang, Weijun He, Mei Zhao, Qing Xu, Jiayu Zhou, Gangyi Ba, Weigang Yang, Cheng Yao, Junjie Wang, Chuliang He, Bo Hu, Yongzhen Deng, Mingyang Fu, Jingwei Liang, Meiling Guan, Yehong Wu, Hongyan Zhuang, Ge Ding, Weixing Lan, Tao Zeng, Shiyong Hussain, Malik Sadam Lin, Zhifang Liu, Hai Sun, Youwen Zeng, Long Liu, Shaocheng Liao, Liang Zhong, Wulyu Shi, Zhongbing Jiang, Min Dong, Yunbo Xu, Min Wang, Aike Zhang, Xiaolong Liu, Linzi Tong, Ruihai Cheng, Jun Nie, Lin Li, Hui Xu, Jianqiang Yang, Zengchen Wen, Jie Liang, Anshu Zhang, Yipo Wu, Yifan Chen, Chengyuan Zhao, Kaijun Shi, Yuejiang Huang, He Austin, Max Lee, Sanggon Luhmann, Neville C. Domier, Calvin W. Zhu, Yilun Brower, David L. Chen, Jie Ida, K. Takemura, Y. Suzuki, Y. Nagasaki, K. Drews, P. Knieps, A. Han, Xiang Krämer-Flecken, A. Henkel, M. He, Jiyang Zhou, Yinan Jia, Ruo Peng, Yue Wang, Pengyu Li, Jiawei Yang, Jie Lu, Conghui Lu, Yiwei Wang, Rumeng Zhang, Hongqi Zeng, Yingqin Zhang, Guodong Ma, Fangyuan Bala, Abba Alhaji Li, Dingchen Hua, Jiankun Ai, Xinkun Tao, Qinlin Zhong, Yu Li, Yangbo He, Xiangrui Wan, Sui Huang, Shanni Ma, Yiming Qu, Zitong Yu, Shimin Huang, Zhangsheng Lei, Chi School of Electric and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China Southwestern Institute of Physics Chengdu China Institute of Plasma Physics Chinese Academy of Sciences Hefei China Guizhou University of Commerce Guiyang China Wuhan Institute of Technology Wuhan China ITER Organization Route de Vinon-sur-Verdon CS 90 046 Saint-Paul-lez-DuranceCedex 13067 France Forschungszentrum Jülich GmbH IEK-Plasmaphysik Jülich Germany Max-Planck-Institut für Plasmaphysik Garching Germany Institute of Fusion Studies University of Texas at Austin AustinTX United States University of Texas at Austin AustinTX78712 United States School of Physics Huazhong University of Science and Technology Wuhan430074 China Department of Physics Wuhan University of Technology Wuhan430070 China Princeton Plasma Physics Laboratory PO Box 451 PrincetonNJ08543 United States Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan430205 China College of Computer Science South-Central University for Nationalities Wuhan430074 China Shenzhen University Shenzhen518060 China University of Science and Technology of China Hefei230026 China School of Electrical Engineering & Automation Jiangsu Normal University Xuzhou221116 China Southwest Jiaotong University Chengdu China College of Nuclear Science and Engineer East China University of Technology PO Box Nanchang330013 China ENN Science and Technology Development Co. Ltd Hebei Key Laboratory of Compact Fusion Langfang065001 China National Fusion Research Institute Daejeon305-333 Korea Republic of University of California DavisCA95616 United States University of California Los Angeles Los AngelesCA90095 United States National Institute for Fusion Science Toki509-5292 Japan Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8527 Japan Institute of Advanced Energy Kyoto University Gokasho Ky

The J-TEXT capability is enhanced compared to two years ago with several upgrades of its diagnostics and the increase of electron cyclotron resonance heating (ECRH) power to 1 MW. With the application of electron cyclotron wave (ECW), the ECW assisted plasma startup is achieved;the tearing mode is suppressed;the toroidal injection of 300 kW ECW drives around 24 kA current;fast electrons are generated with toroidal injected ECW and the runaway current conversion efficiency increases with ECRH power. The mode coupling between 2/1 and 3/1 modes are extensively studied. The coupled 2/1 and 3/1 modes usually lead to major disruption. Their coupling can be either suppressed or avoided by external resonant magnetic perturbation fields and hence avoids the major disruption. It is also found that the 2/1 threshold of external field is significantly reduced by a pre-excited 3/1 mode, which can be either a locked island or an external kink mode. The disruption control is studied by developing prediction methods capable of cross tokamak application and by new mitigation methods, such as the biased electrode or electromagnetic pellet injector. The high-density operation and related disruptions are studied from various aspects. Approaching the density limit, the collapse of the edge shear layer is observed and such collapse can be prevented by applying edge biasing, leading to an increased density limit. The density limit is also observed to increase, if the plasma is operated in the poloidal divertor configuration or the plasma purity is increased by increasing the pre-filled gas pressure or ECRH power during the start-up phase. © 2024 The Author(s).

关键词： Tokamak devices

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QuaSI – Quantile sparse image: A prior for spatio-temporal denoising of retinal OCT data

QuaSI – Quantile sparse image: A prior for spatio-temporal ...

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Workshop on Bildverarbeitung fur die Medizin, 2018

作者： Schirrmacher, Franziska Köhler, Thomas Husvogt, Lennart Fujimoto, James G. Hornegger, Joachim Maier, Andreas K. Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Electrical Engineering & Computer Science Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge United States

Optical Coherence Tomography (OCT) is a standard non-invasive imaging modality widely used in opthalmology. Due to its high spatial resolution OCT has become a standard imaging technique. However, speckle noise caused... 详细信息

ISBN: (纸本)9783662580950

关键词： Optical tomography

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