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Quantum Complementarity Approach to Device-Independent Security

Physical Review Letters 2023年第14期131卷 140801-140801页

作者： Xingjian Zhang Pei Zeng Tian Ye Hoi-Kwong Lo Xiongfeng Ma Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing 100084 China Centre for Quantum Information and Quantum Control (CQIQC) Department of Electrical & Computer Engineering and Department of Physics University of Toronto Toronto Ontario M5S 3G4 Canada Quantum Bridge Technologies Inc. 100 College Street Toronto Ontario M5G 1L5 Canada

Complementarity is an essential feature of quantum mechanics. The preparation of an eigenstate of one observable implies complete randomness in its complementary observable. In quantum cryptography, complementarity allows us to formulate security analyses in terms of phase-error correction. However, the concept becomes much subtler in the device-independent regime that offers security without device characterization. Security proofs of device-independent quantum cryptography tasks are often complex and quite different from those of their more standard device-dependent cousins. The existing proofs pose huge challenges to experiments, among which large data-size requirement is a crux. Here, we show the complementarity security origin of the device-independent tasks. By linking complementarity with quantum nonlocality, we recast the device-independent scheme into a quantum error correction protocol. Going beyond the identical-and-independent-distribution case, we consider the most general attack. We generalize the sample entropy in classical Shannon theory for the finite-size analysis. Our method exhibits good finite-size performance and brings the device-independent scheme to a more practical regime. Applying it to the data in a recent ion-trap-based device-independent quantum key distribution experiment, one could reduce the requirement on data size to less than a third. Furthermore, the operational meaning of complementarity naturally extends device-independent scenarios to advantage key distillation, easing experiments by tolerating higher loss and lower transmittance.

关键词： Nonlocality Quantum cryptography Quantum entanglement

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Soliton Vortex Comb Generation in an AlGaAs Microresonator for Self-Torque Pulse Synthesis

Soliton Vortex Comb Generation in an AlGaAs Microresonator f...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Y. Liu B. Chen Y. Zhou C. Ye Q. Cao P. Huang C. Kim Y. Zheng L. K. Oxenløwe K. Yvind J. Li Y. Zhang C. Dong S. Fu Q. Zhan X. Wang J. Liu M. Pu Department of Electrical and Photonics Engineering DTU Electro Technical University of Denmark Denmark State Key Laboratory of Optoelectronic Materials and Technologies School of Physics School of Electronics and Information Technology Sun Yat-sen University Guangzhou China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China Zhangjiang Laboratory Shanghai China Institute of Advanced Photonics Technology School of Information Engineering Guangdong University of Technology Guangzhou China Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen China

We demonstrate bright soliton generation at room temperature within a grating-dressed AlGaAs-on-insulator microresonator and the simultaneous emission of phase-locked vortices, enabling the synthesis of self-torque pu... 详细信息

ISBN: (纸本)9798350369311

关键词： Solitons Lasers and electrooptics Microcavities Gratings Electro-optical waveguides

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Deep Learning-Based Object Pose Estimation: A Comprehensive Survey

arXiv

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arXiv 2024年

作者： Liu, Jian Sun, Wei Yang, Hui Zeng, Zhiwen Liu, Chongpei Zheng, Jin Liu, Xingyu Rahmani, Hossein Sebe, Nicu Mian, Ajmal The National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China The School of Architecture and Art Central South University Changsha410082 China The Department of Automation Tsinghua University Beijing100084 China The School of Computing and Communications Lancaster University LA1 4YW United Kingdom The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy The Department of Computer Science The University of Western Australia WA6009 Australia

Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, i.e., instance-level, category-level, and unseen object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We also keep tracing the latest works at Awesome-Object-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Black Phosphorus Ink Formulation for Aerosol Jet Printing of Planar and Conformal Optoelectronics

Black Phosphorus Ink Formulation for Aerosol Jet Printing of...

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International Workshop on Thin Films for Electronics, Electro-Optics, Energy and Sensors 2022

作者： Muramutsa, Florent Pedersen, Samuel V. Logan, Jonathan Eppel, Michael Busuladzic-Begic, Mia Eixenberger, Joshua Wood, Joshua D. Ouchen, Fahima Subbaraman, Harish Husko, Chad Jaques, Brian J. Heckman, Emily M. Estrada, David Micron School of Materials Science and Engineering Boise State University BoiseID83725 United States Iris Light Technologies Inc. ChicagoIL60622 United States Department of Physics Boise State University BoiseID83725 United States Center for Advanced Energy Studies Idaho FallsID83401 United States Promethean Consulting LLC Oak ParkIL60302 United States Air Force Research Laboratory Sensors Directorate Wright-Patterson AFBOH45433 United States Department of Electrical and Computer Engineering Boise State University BoiseID83725 United States Idaho National Laboratory Idaho FallsID83402 United States

ISBN: (纸本)9781510660618

The ink formulation process of stable black phosphorus (BP) inks for aerosol jet printing (AJP) is presented. Formulating BP stable photonic inks involves tailoring the flake size and ink solvents to AJP. Single crystal BP inks were used to print both backgated FET devices with a resistivity as low as 105Ω-cm and the resulting thin films showed photoluminescence emission in the NIR regime paving the way to printed optoelectronic devices. © 2023 SPIE. All rights reserved.

关键词： Optoelectronic devices

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CTtrack: A CNN+Transformer-based framework for fiber orientation estimation & tractography

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Neuroscience Informatics 2022年第4期2卷 100099-100099页

作者： Hosseini, S.M.H. Hassanpour, M. Masoudnia, S. Iraji, S. Raminfard, S. Nazem-Zadeh, M. Department of Electrical and Computer Engineering University of Tehran Tehran Iran Research Center for Molecular and Cellular Imaging Advanced Medical Technologies and Equipment Institute (AMTEI) Iran Medical Physics and Biomedical Engineering Department Tehran University of Medical Sciences Tehran Iran

In recent years, multiple data-driven fiber orientation distribution function (fODF) estimation algorithms and automatic tractography pipelines have been proposed to address the limitations of traditional methods. However, these approaches lack precision and generalizability. To tackle these shortcomings, we introduce CTtrack, a CNN+Transformer-based pipeline to estimate fODFs and perform tractography. In this approach, a convolutional neural network (CNN) module is employed to project the resampled diffusion-weighted magnetic resonance imaging (DW-MRI) data to a lower dimension. Then, a transformer model estimates the fiber orientation distribution functions using the projected data within a local block around each voxel. The proposed model represents the extracted fODFs by spherical harmonics coefficients. The predicted fiber ODFs can be used for both deterministic and probabilistic tractography. Our pipeline was tested in terms of the precision and robustness in estimating fODFs and performing tractography using both simulated and real diffusion data. The Tractometer tool was employed to compare our method with the classical and data-driven tractography approaches. The qualitative and quantitative assessments illustrate the competitive performance of our framework compared to other available algorithms. © 2022 The Authors

关键词： Diffusion-weighted MRI Fiber tractography fODF estimation Transformer

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Localization of Chiral Edge States by the Non-Hermitian Skin Effect

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Physical Review Letters 2024年第11期132卷 113802-113802页

作者： Gui-Geng Liu (刘癸庚) Subhaskar Mandal Peiheng Zhou Xiang Xi Rimi Banerjee Yuan-Hang Hu Minggui Wei Maoren Wang Qiang Wang Zhen Gao Hongsheng Chen Yihao Yang Yidong Chong Baile Zhang Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link Singapore 637371 Singapore National Engineering Research Center of Electromagnetic Radiation Control Materials State Key Laboratory of Electronic Thin Film and Integrated Devices University of Electronic Science and Technology of China Chengdu 610054 China School of Electrical Engineering and Intelligentization Dongguan University of Technology Dongguan 523808 China School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing Jiangsu 210093 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen 518055 China Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Science and Technology Innovation Center College of Information Science and Electronic Engineering Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang ZJU-UIUC Institute Zhejiang University Hangzhou 310027 China Centre for Disruptive Photonic Technologies The Photonics Institute Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore

Quantum Hall systems host chiral edge states extending along the one-dimensional boundary of any two-dimensional sample. In solid state materials, the edge states serve as perfectly robust transport channels that produce a quantized Hall conductance; due to their chirality, and the topological protection by the Chern number of the bulk band structure, they cannot be spatially localized by defects or disorder. Here, we show experimentally that the chiral edge states of a lossy quantum Hall system can be localized. In a gyromagnetic photonic crystal exhibiting the quantum Hall topological phase, an appropriately structured loss configuration imparts the edge states’ complex energy spectrum with a feature known as point-gap winding. This intrinsically non-Hermitian topological invariant is distinct from the Chern number invariant of the bulk (which remains intact) and induces mode localization via the “non-Hermitian skin effect.” The interplay of the two topological phenomena—the Chern number and point-gap winding—gives rise to a non-Hermitian generalization of the paradigmatic Chern-type bulk-boundary correspondence principle. Compared to previous realizations of the non-Hermitian skin effect, the skin modes in this system have superior robustness against local defects and disorders.

关键词： Edge states Photonic crystals Quantum Hall effect Chern insulators Non-Hermitian systems

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Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding

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IFAC-PapersOnLine 2023年第2期56卷 7071-7076页

作者： Min Li Luefeng Chen Min Wu Min Ding Kaoru Hirota Witold Pedrycz School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China Tokyo Institute of Technology Yokohama 226-8502 Japan Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada

A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students’ intentions in the university teaching scene. Specifically, feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotional behavior recognition, which reduce the influence of the complex structure and slow network updates in deep learning. Multivariate analysis of variance is used to select the key personal information affecting the intention and obtain the coefficient of influence degree. Finally, fuzzy inference is used to understand the intention. According to the recognition results, the accuracy on FABO database of our proposal is 1.89%, 12.21% and 0.78% higher than those of the Residual Network combined with geodesic flow kernel (ResNet-101+GFK), a fuzzy deep neural network with sparse autoencoder (FDNNSA), and an affect recognition on a video-skeleton of bimodal information with a hierarchical classification fusion strategy (HCFS), respectively, indicating that our proposal can effectively capture the emotional intention of students in the teaching scene.

关键词： Convolution Neural Networks Broad Learning Emotional Intention Human-Robot Interaction

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Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene

Partially Occluded Face Expression Recognition with CBAM-Bas...

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Chinese Automation Congress (CAC)

作者： Yanxing Bai Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration School of Automation Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan

In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene.

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Output consensus control for linear port-Hamiltonian systems

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IFAC-PapersOnLine 2022年第30期55卷 230-235页

作者： Shuai Feng Yu Kawano Michele Cucuzzella Jacquelien M.A. Scherpen School of Automation Nanjing University of Science and Technology Nanjing 210094 China Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima 739-8527 Japan Department of Electrical Computer and Biomedical Engineering University of Pavia 27100 Pavia PV Italy Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen 9747 AG Groningen the Netherlands

In this paper, we study output consensus of coupled linear port-Hamiltonian systems on graphs in the presence of constant disturbances, where couplings are allowed to be both static and dynamic. Utilizing port-Hamiltonian structures, we present dynamic controllers achieving output consensus where the consensus values are determined by the disturbances. Finally, the utility of the proposed controller is illustrated by applying it to current sharing of DC microgrids.

关键词： port-Hamiltonian systems networked systems output consensus

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Mapping Phonon Polaritons with Visible Light

arXiv

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arXiv 2024年

作者： Arledge, Kiernan E. Ellis, Chase T. Sarabi, Nazli Rasouli Whiteside, Vincent R. Kim, Chul Soo Kim, Mijin Ratchford, Daniel C. Meeker, Michael A. Weng, Binbin Tischler, Joseph G. Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman73019 United States Center for Quantum Research and Technologies University of Oklahoma Norman73019 United States U.S. Naval Research Laboratory WashingtonDC20375 United States School of Electrical and Computer Engineering University of Oklahoma Norman73019 United States Advanced Research Center City University of New York New York10017 United States

Phonon polaritons (PhPs) are hybrid photon-phonon waves which enable strong light-matter interactions and subdiffractional confinement, potentially empowering applications in sensing, nonlinear optics and nanoscale energy manipulation. In this work, we use confocal Raman microscopy to investigate the coupling between bulk phonon modes and localized surface phonon polariton (SPhP) modes in indium phosphide (InP) nanopillars and 4H-silicon carbide (4H-SiC) gratings. The Raman intensity within the nanostructures is described in terms of the SPhP eigenmodes and used to reconstruct the field intensity, providing a method to map SPhP eigenmodes using visible and near-IR light. Our results indicate that, contrary to expectation, all Raman-active bulk phonon modes of InP and 4H-SiC couple to the localized SPhP modes. Further, we confirm that polarizability selection rules form the predominant coupling mechanism between phonons and SPhP modes, with electron-phonon coupling playing a role for certain phonon modes (A1(LO) and E1(TO) in 4H-SiC). These observations provide a method for extending Raman studies of PhP modes to achieve full 3D reconstruction of the PhP eigenmodes and visualize light-matter interactions within nanostructures, thus advancing Raman scattering as a technique for understanding PhP modes. Copyright © 2024, The Authors. All rights reserved.

关键词： Indium phosphide

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