检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Banad, Yaser M. Hasan, Syed Mohammad Abid Sharif, Sarah S. Veronis, Georgios Gartia, Manas Ranjan The School of Electrical and Computer Engineering University of Oklahoma NormanOK73019 United States The Department of Mechanical and Industrial Engineering Louisiana State University Baton RougeLA70803 United States The Center for Quantum Research and Technology University of Oklahoma NormanOK73019 United States The School of Electrical Engineering and Computer Science Louisiana State University Baton RougeLA70803 United States Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States

Whispering gallery mode (WGM) resonators are garnering significant attention due to their unique characteristics and remarkable properties. When integrated with optical sensing and processing technology, WGM resonators offer numerous advantages, including compact size, high sensitivity, rapid response, and tunability. This paper comprehensively investigates the optical properties and behavior of WGMs in complex microsphere resonator configurations. The findings underscore the potential of WGMs in sensing applications and their role in advancing future optical information processing. The study explores the impact of configuration, size, excitation, polarization, and coupling effects on the WGMs’ properties. The paper provides crucial insights and valuable guidance for designing and optimizing microsphere resonator systems, enabling their realization for practical applications. © 2023, CC BY.

关键词： Optical data processing

Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control Under Switching Topologies

学校读者我要写书评

暂无评论

Cooperative Learning with Gaussian Processes for Euler-Lagra...

American Control Conference (ACC)

作者： Zewen Yang Songbo Dong Armin Lederer Xiaobing Dai Siyu Chen Stefan Sosnowski Georges Hattab Sandra Hirche Center for Artificial Intelligence in Public Health Research (ZKI - PH) Robert Koch Institute Berlin Germany Chair of Information-oriented Control (ITR) TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Computer Science ETH Zurich Zurich Switzerland Walker Department of Mechanical Engineering University of Texas at Austin Austin TX USA Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This work presents an innovative learning-based approach to tackle the tracking control problem of Euler-Lagrange multi-agent systems with partially unknown dynamics operating under switching communication topologies. The approach leverages a correlation-aware cooperative al-gorithm framework built upon Gaussian process regression, which adeptly captures inter-agent correlations for uncertainty predictions. A standout feature is its exceptional efficiency in deriving the aggregation weights achieved by circumventing the computationally intensive posterior variance calculations. Through Lyapunov stability analysis, the distributed control law ensures bounded tracking errors with high probability. Simulation experiments validate the protocol's efficacy in effectively managing complex scenarios, establishing it as a promising solution for robust tracking control in multi-agent systems characterized by uncertain dynamics and dynamic communication structures.

关键词： Uncertainty Protocols Correlation Decentralized control Switches Gaussian processes Topology

Quasi-Phase-Matching Enabled by van der Waals Stacking

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Tang, Yilin Sripathy, Kabilan Qin, Hao Lu, Zhuoyuan Guccione, Giovanni Janousek, Jiri Zhu, Yi Hasan, Md Mehedi Iwasa, Yoshihiro Lam, Ping Koy Lu, Yuerui School of Engineering College of Engineering Computing and Cybernetics The Australian National University CanberraACT2601 Australia ARC Centre for Quantum Computation and Communication Technology The Australian National University CanberraACT2601 Australia Research School of Physics College of Science The Australian National University CanberraACT2601 Australia Department of Computer Engineering TUM School of Computation Information and Technology Technical University of Munich Munich80333 Germany Clarendon Laboratory University of Oxford OxfordOX1 3PU United Kingdom RIKEN Center for Emergent Matter Science Hirosawa 2-1 Wako Saitama351-0198 Japan 2 Fusionopolis Way Innovis #08-03 Singapore138634 Singapore

Quasi-phase matching (QPM) is a technique extensively utilized in nonlinear optics for enhancing the efficiency and stability of frequency conversion processes. However, the conventional QPM relies on periodically poled ferroelectric crystals, which are limited in availability. The 3R phase of molybdenum disulfide (3R-MoS2), a transition metal dichalcogenide (TMDc) with the broken inversion symmetry, stands out as a promising candidate for QPM, enabling efficient nonlinear process. Here, we experimentally demonstrate the QPM at nanoscale, utilizing van der Waals stacking of 3R-MoS2 layers with specific orientation to realize second harmonic generation (SHG) enhancement beyond the non QPM limit. We have also demonstrated enhanced spontaneous parametric down-conversion (SPDC) via QPM of 3R-MoS2 homo-structure, enabling more efficient generation of entangled photon pairs. The tunable capacity of 3R-MoS2 van der Waals stacking provides a platform for tuning phase-matching condition. This technique opens interesting possibilities for potential applications in nonlinear process and quantum technology. Copyright © 2024, The Authors. All rights reserved.

关键词： Van der Waals forces

Touchstone benchmark: are we on the right way for evaluating AI algorithms for medical segmentation? 24

学校读者我要写书评

暂无评论

Touchstone benchmark: are we on the right way for evaluating...

Proceedings of the 38th International Conference on neural Information Processing Systems

作者： Pedro R. A. S. Bassi Wenxuan Li Yucheng Tang Fabian Isensee Zifu Wang Jieneng Chen Yu-Cheng Chou Saikat Roy Yannick Kirchhoff Maximilian Rokuss Ziyan Huang Jin Ye Junjun He Tassilo Wald Constantin Ulrich Michael Baumgartner Klaus H. Maier-Hein Paul Jaeger Yiwen Ye Yutong Xie Jianpeng Zhang Ziyang Chen Yong Xia Zhaohu Xing Lei Zhu Yousef Sadegheih Afshin Bozorgpour Pratibha Kumari Reza Azad Dorit Merhof Pengcheng Shi Ting Ma Yuxin Du Fan Bai Tiejun Huang Bo Zhao Haonan Wang Xiaomeng Li Hanxue Gu Haoyu Dong Jichen Yang Maciej A. Mazurowski Saumya Gupta Linshan Wu Jiaxin Zhuang Hao Chen Holger Roth Daguang Xu Matthew B. Blaschko Sergio Decherchi Andrea Cavalli Alan L. Yuille Zongwei Zhou Department of Computer Science Johns Hopkins University and Department of Pharmacy and Biotechnology University of Bologna and Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Department of Computer Science Johns Hopkins University NVIDIA Division of Medical Image Computing German Cancer Research Center (DKFZ) and Helmholtz Imaging German Cancer Research Center (DKFZ) ESAT-PSI KU Leuven Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University and HIDSS4Health - Helmholtz Information and Data Science School for Health Shanghai Jiao Tong University Shanghai Artificial Intelligence Laboratory Division of Medical Image Computing German Cancer Research Center (DKFZ) Division of Medical Image Computing German Cancer Research Center (DKFZ) and Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Helmholtz Imaging German Cancer Research Center (DKFZ) and Interactive Machine Learning Group (IML) DKFZ School of Computer Science and Engineering Northwestern Polytechnical University Australian Institute for Machine Learning The University of Adelaide College of Computer Science and Technology Zhejiang University Hong Kong University of Science and Technology (Guangzhou) Hong Kong University of Science and Technology (Guangzhou) and Hong Kong University of Science and Technology Faculty of Informatics and Data Science University of Regensburg Faculty of Electrical Engineering and Information Technology RWTH Aachen University Faculty of Informatics and Data Science University of Regensburg and Fraunhofer Institute for Digital Medicine MEVIS Electronic & Information Engineering School Harbin Institute of Technology (Shenzhen) Shanghai Jiao Tong University and Beijing Academy of Artificial Intelligence (BAAI) S

ISBN: (纸本)9798331314385

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.

关键词：

A Coarse-to-fine Cascaded Evidence-Distillation neural Network for Explainable Fake News Detection

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Yang, Zhiwei Ma, Jing Chen, Hechang Lin, Hongzhan Luo, Ziyang Chang, Yi College of Computer Science and Technology Jilin University Changchun China Department of Computer Science Hong Kong Baptist University Hong Kong School of Artificial Intelligence Jilin University China International Center of Future Science Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China

Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances. However, they often tailor automated solutions on manual fact-checked reports, suffering from limited news coverage and debunking delays. When a piece of news has not yet been fact-checked or debunked, certain amounts of relevant raw reports are usually disseminated on various media outlets, containing the wisdom of crowds to verify the news claim and explain its verdict. In this paper, we propose a novel Coarse-to-fine Cascaded Evidence-Distillation (CofCED) neural network for explainable fake news detection based on such raw reports, alleviating the dependency on fact-checked ones. Specifically, we first utilize a hierarchical encoder for web text representation, and then develop two cascaded selectors to select the most explainable sentences for verdicts on top of the selected top-K reports in a coarse-to-fine manner. Besides, we construct two explainable fake news datasets, which are publicly available. Experimental results demonstrate that our model significantly outperforms state-of-the-art baselines and generates high-quality explanations from diverse evaluation perspectives. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

Nanoplasmonic magneto-optical isolator [Invited]

学校读者我要写书评

暂无评论

Chinese Optics Letters 2021年第8期19卷 130-137页

作者： Vahid Foroughi Nezhad Chenglong You Georgios Veronis School of Electrical Engineering and Computer Science Louisiana State UniversityBaton RougeLouisiana 70803USA Center for Computation and Technology Louisiana State UniversityBaton RougeLouisiana 70803USA Department of Physics and Astronomy Louisiana State UniversityBaton RougeLouisiana 70803USA

We introduce a nanoplasmonic isolator that consists of a cylindrical resonator placed close to a metal-dielectric-metal(MDM) waveguide. The material filling the waveguide and resonator is a magneto-optical(MO) material, and the structure is under an externally applied static magnetic field. We theoretically investigate the properties of the structure and show that the cavity mode without MO activity splits into two modes when the MO activity is present. In addition, we find that the presence of the MDM waveguide leads to a second resonance due to the geometrical asymmetry caused by the existence of the waveguide. We also show that, when MO activity is present, the cavity becomes a traveling wave resonator. Thus, the transmission of the structure depends on the direction of the incident light, and the proposed structure operates as an optical isolator.

关键词： plasmonics metal optics magneto-optic systems

Identifying electronic transitions of defects in hexagonal boron nitride for quantum memories

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Cholsuk, Chanaprom Çakan, Aslı Suwanna, Sujin Vogl, Tobias Department of Computer Engineering School of Computation Information and Technology Technical University of Munich Munich80333 Germany Abbe Center of Photonics Institute of Applied Physics Friedrich Schiller University Jena Jena07745 Germany Optical and Quantum Physics Laboratory Department of Physics Faculty of Science Mahidol University Bangkok10400 Thailand

A quantum memory is a crucial keystone for enabling large-scale quantum networks. Applicable to the practical implementation, specific properties, i.e., long storage time, selective efficient coupling with other systems, and a high memory efficiency are desirable. Though many quantum memory systems are developed thus far, none of them can perfectly meet all requirements. This work herein proposes a quantum memory based on color centers in hexagonal boron nitride (hBN), where its performance is evaluated based on a simple theoretical model of suitable defects in a cavity. Employing density functional theory calculations, 257 triplet and 211 singlet spin electronic transitions are investigated. Among these defects, it is found that some defects inherit the Λ electronic structures desirable for a Raman-type quantum memory and optical transitions can couple with other quantum systems. Further, the required quality factor and bandwidth are examined for each defect to achieve a 95% writing efficiency. Both parameters are influenced by the radiative transition rate in the defect state. In addition, inheriting triplet-singlet spin multiplicity indicates the possibility of being a quantum sensing, in particular, optically detected magnetic resonance. This work therefore demonstrates the potential usage of hBN defects as a quantum memory in future quantum networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Boron nitride

Modeling the performance and bandwidth of single-atom adiabatic quantum memories

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Nateeboon, Takla Cholsuk, Chanaprom Vogl, Tobias Suwanna, Sujin Optical and Quantum Physics Laboratory Department of Physics Faculty of Science Mahidol University Bangkok10400 Thailand Department of Computer Engineering TUM School of Computation Information and Technology Technical University of Munich Munich80333 Germany Abbe Center of Photonics Institute of Applied Physics Friedrich Schiller University Jena07745 Germany

Quantum memories are essential for quantum repeaters that will form the backbone of the future quantum internet. Such memory can capture a signal state for a controllable amount of time after which this state can be retrieved. In this work, we theoretically investigated how atomic material and engineering parameters affect the performance and bandwidth of a quantum memory. We have applied a theoretical model for quantum memory operation based on the Lindblad master equation and adiabatic quantum state manipulation. The materials’ properties and their uncertainty are evaluated to determine the performance of Raman-type quantum memories by showcasing two defects in two-dimensional hexagonal boron nitride (hBN). We have derived a scheme to calculate the signal bandwidth based on the material parameters as well as the maximum efficiency that can be realized. The bandwidth depends on four factors: the signal photon frequency, the dipole transition moments in the electronic structure, cavity volume, and the strength of the external control electric field. As our scheme is general and independent of materials, it can be applied to many other quantum materials with a suitable three-level structure. We therefore provided a promising route for designing and selecting materials for quantum memories. Our work is therefore an important step toward the realization of a large-scale quantum network. Copyright © 2023, The Authors. All rights reserved.

关键词： Bandwidth

Data-Efficient CLIP-Powered Dual-Branch Networks for Source-Free Unsupervised Domain Adaptation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Li, Yongguang Cao, Yueqi Li, Jindong Wang, Qi Wang, Shengsheng College of Software Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China School of Artificial Intelligence Jilin University Changchun130012 China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Ministry of Education Jilin University Changchun130012 China

Source-free Unsupervised Domain Adaptation (SF-UDA) aims to transfer a model’s performance from a labeled source domain to an unlabeled target domain without direct access to source samples, addressing critical data privacy concerns. However, most existing SF-UDA approaches assume the availability of abundant source domain samples, which is often impractical due to the high cost of data annotation. To address the dual challenges of limited source data and privacy concerns, we introduce a data-efficient, CLIP-powered dual-branch network (CDBN). This architecture consists of a cross-domain feature transfer branch and a target-specific feature learning branch, leveraging high-confidence target domain samples to transfer text features of source domain categories while learning target-specific soft prompts. By fusing the outputs of both branches, our approach not only effectively transfers source domain category semantic information to the target domain but also reduces the negative impacts of noise and domain gaps during target training. Furthermore, we propose an unsupervised optimization strategy driven by accurate classification and diversity, preserving the classification capability learned from the source domain while generating more confident and diverse predictions in the target domain. CDBN achieves near state-of-the-art performance with far fewer source domain samples than existing methods across 31 transfer tasks on seven datasets. © 2024, CC BY.

关键词： Adversarial machine learning

Switching between singular points and exceptional-point-enhanced sensing in non-Hermitian photonic structures 13

学校读者我要写书评

暂无评论

Switching between singular points and exceptional-point-enha...

Active Photonic Platforms XIII 2021

作者： Veronis, Georgios Huang, Yin Wang, Lanyan Shen, Yuecheng School of Electrical Engineering and Computer Science Louisiana State University Baton RougeLA70803 United States Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States Department of Optoelectrics Information Science and Engineering School of Physics and Electronics Central South University Hunan Changsha410012 China State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-Sen University Guangdong Guangzhou510275 China

ISBN: (纸本)9781510644304

We investigate the exceptional points in a two-layer cylindrical waveguide structure consisting of absorbing and nonabsorbing dielectrics. We show that by tuning the parameters of the structure the complex effective indices of two waveguide modes can coalesce so that an exceptional point is formed. We show that the sensitivity of the effective index of the waveguide mode is enhanced at the exceptional point. We also investigate using phase-change materials in multilayer structures to switch between singular points. We show that in multilayer structures consisting of phasechange, lossless dielectric, lossy, and gain materials, absorbing or spectral singularities can be switched to exceptional points, and self-dual spectral singularities can be switched to unidirectional spectral singularities by switching the phasechange material from its crystalline to its amorphous phase. Our results could be important for developing new compact reconfigurable singularity-enhanced optical devices. © 2021 SPIE.

关键词： Physical optics