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Post-2000 Nonlinear Optical Materials and Measurements: Data Tables and Best Practices

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arXiv 2023年

作者： Vermeulen, Nathalie Espinosa, Daniel Ball, Adam Ballato, John Boucaud, Philippe Boudebs, Georges Campos, Cecília L.A.V. Dragic, Peter Gomes, Anderson S.L. Huttunen, Mikko J. Kinsey, Nathaniel Mildren, Rich Neshev, Dragomir Padilha, Lázaro A. Pu, Minhao Secondo, Ray Tokunaga, Eiji Turchinovich, Dmitry Yan, Jingshi Yvind, Kresten Dolgaleva, Ksenia Van Stryland, Eric W. Brussels Photonics B-PHOT Department of Applied Physics and Photonics IR-TONA Vrije Universiteit Brussel Pleinlaan 2 Brussel1050 Belgium School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada Dept. Of Electrical & Computer Engineering Virginia Commonwealth University RichmondVA23284 United States Dept of Materials Science and Engineering Clemson University ClemsonSC29634 United States Université Côte d’Azur CNRS CRHEA Rue Bernard Grégory Sophia-Antipolis06905 France Univ Angers LPHIA SFR MATRIX AngersF-49000 France Departamento de Física Universidade Federal de Pernambuco PE Recife50670-901 Brazil Dept. Of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Tampere University Photonics Laboratory Physics Unit TampereFI-33014 Finland MQ Photonics Research Centre Macquarie University NSW2109 Australia ARC Centre of Excellence for Transformative Meta-Optical Systems TMOS Department of Electronic Materials Engineering Research School of Physics Australian National University CanberraACT2601 Australia Instituto de Fisica "Gleb Wataghin" Universidade Estadual de Campinas Campinas Sao Paulo13083-970 Brazil Department of Electrical and Photonics engineering Technical University of Denmark Kgs. LyngbyDK-2800 Denmark Department of Physics Faculty of Science Tokyo University of Science 1-3 Kagurazaka Shinjuku-ku Tokyo162-8601 Japan Fakultät für Physik Universität Bielefeld Universitätsstr. 25 Bielefeld33615 Germany CREOL The College of Optics and Photonics University of Central Florida OrlandoFL United States

In its 60 years of existence, the field of nonlinear optics has gained momentum especially over the past two decades thanks to major breakthroughs in material science and technology. In this article, we present a new set of data tables listing nonlinear-optical properties for different material categories as reported in the literature since 2000. The papers included in the data tables are representative experimental works on bulk materials, solvents, 0D-1D-2D materials, metamaterials, fiber waveguiding materials, on-chip waveguiding materials, hybrid waveguiding systems, and materials suitable for nonlinear optics at THz frequencies. In addition to the data tables, we also provide best practices for performing and reporting nonlinear-optical experiments. These best practices underpin the selection process that was used for including papers in the tables. While the tables indeed show strong advancements in the field over the past two decades, we encourage the nonlinear-optics community to implement the identified best practices in future works. This will allow a more adequate comparison, interpretation and use of the published parameters, and as such further stimulate the overall progress in nonlinear-optical science and applications. © 2023, CC BY.

关键词： Nonlinear optics

A Perspective on Quantum Sensors from Basic Research to Commercial Applications

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arXiv 2024年

作者： Oh, Eun Gregoire, Maxwell D. Black, Adam T. Hughes, K. Jeramy Kunz, Paul D. Larsen, Michael Lautier-Gaud, Jean Lee, Jongmin Schwindt, Peter D.D. Mouradian, Sara L. Narducci, Frank A. Sackett, Charles A. Laboratory for Physical Sciences College ParkMD20740 United States Air Force Research Laboratory AlbuquerqueNM87117 United States Naval Research Laboratory WashingtonDC20375 United States Fieldline Inc. BoulderCO80301 United States Army Research Laboratory AdelphiMD20783 United States Northrup Grumman Woodland HillsCA91367 United States Rue François Mitterrand Talence33400 France Sandia National Laboratories AlbuquerqueNM87123 United States AFRL/RVBYT Space Vehicles Directorate Section Head Quantum Optics Optical Sciences Division Fieldline Industries Inc. Quantum Science & Technology Branch Chief DEVCOM Army Research Laboratory Department of Physics The University of Texas at Austin AustinTX78712 United States Quantum Science Architect Mission Systems Sector Emerging Capabilities Development iXblue Quantum Sensors The Technical Staff Atom-Optic Sensing Department The Technical Staff Atom-Optic Sensing Department Electrical and Computer Engineering Department Department of Physics Physics Department University of Washington SeattleWA98105 United States Naval Postgraduate School MontereyCA93943 United States University of Virginia CharlottesvilleVA22904 United States

Quantum sensors represent a new generation of sensors with improved precision, accuracy, stability, and robustness to environmental effects compared to their classical predecessors. After decades of laboratory development, several types of quantum sensors are now commercially available or are part-way through the commercialization process. This article provides a brief description of the operation of a selection of quantum sensors that employ the principles of atom-light interactions and discusses progress toward packaging those sensors into products. This article covers quantum inertial and gravitational sensors, including gyroscopes, accelerometers, gravimeters, and gravity gradiometers that employ atom interferometry, nuclear magnetic resonance gyroscopes, atomic and spin-defect magnetometers, and Rydberg electric field sensors. © 2024, CC BY-NC-ND.

关键词： Gyroscopes

Higher-order topological in-bulk corner state in pure diffusion systems

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arXiv 2022年

作者： Liu, Zhoufei Cao, Pei-Chao Xu, Liujun Xu, Guoqiang Li, Ying Huang, Jiping Department of Physics State Key Laboratory of Surface Physics Key Laboratory of Micro and Nano Photonic Structures MOE Fudan University Shanghai200438 China State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy Zhejiang University Haining314400 China Shaoxing Institute Zhejiang University Shaoxing312000 China Graduate School China Academy of Engineering Physics Beijing100193 China Department of Electrical and Computer Engineering National University of Singapore Kent Ridge 117583 Singapore

Compared with conventional topological insulator that carries topological state at its boundaries, the higher-order topological insulator exhibits lower-dimensional gapless boundary states at its corners and hinges. Leveraging the form similarity between Schrödinger equation and diffusion equation, researches on higher-order topological insulators have been extended from condensed matter physics to thermal diffusion. Unfortunately, all the corner states of thermal higher-order topological insulator reside within the band gap. Another kind of corner state, which is embedded in the bulk states, has not been realized in pure diffusion systems so far. Here, we construct higher-dimensional Su-Schrieffer-Heeger models based on sphere-rod structure to elucidate these corner states, which we term "in-bulk corner states". Due to the antiHermitian properties of diffusive Hamiltonian, we investigate the thermal behaviour of these corner states through theoretical calculation, simulation, and experiment. Furthermore, we study the different thermal behaviours of in-bulk corner state and in-gap corner state. Our results would open a different gate for diffusive topological states and provide a distinct application for efficient heat dissipation. © 2022, CC BY.

关键词： Topology

Smart grid security through fusion-enhanced federated learning against adversarial attacks

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engineering Applications of Artificial Intelligence 2025年 157卷

作者： Shabbir, Attia Manzoor, Habib Ullah Zoha, Ahmed Halim, Zahid Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi Pakistan James Watt School of Engineering University Of Glasgow GlasgowG12 8QQ United Kingdom Department of Electrical Engineering University of Engineering and Technology Lahore Pakistan Machine Intelligence and Affective Systems Laboratory National Yunlin University of Science and Technology 123 University Road Section 3 Douliu Yunlin64002 Taiwan

The proliferation of smart grids introduces significant challenges for energy networks in managing and securing the vast data they generate. Federated Learning (FL) provides a cost-effective and privacy-preserving framework for distributed model training, addressing critical concerns around customer data privacy and security. However, FL remains vulnerable to adversarial threats, particularly data poisoning attacks, which can significantly impair model performance. This study presents a novel data poisoning attack and proposes a mitigation framework tailored for resource-constrained smart grids. The Centroid-Based Anomaly Aware Federated Averaging (CBAA-FedAvg) framework is introduced, achieving a Mean Absolute Percentage Error (MAPE) of 2.7 percent, closely aligning with baseline performance while maintaining robustness. CBAA-FedAvg integrates advanced fusion techniques, including parameter quantization (from 32-bit floating point to 8-bit fixed point) and dynamic clustering, to minimize computational complexity and optimize data processing. Furthermore, an automatic stopping criterion optimizes convergence, reducing energy consumption and computation time. Experimental results demonstrate that CBAA-FedAvg exhibits remarkable resilience to both data and model poisoning attacks, leveraging fusion strategies to enhance security and efficiency. This framework provides a scalable and effective solution for improving the fusion, security, and operational efficiency of smart grids. © 2025

关键词： Federated learning

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge

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arXiv 2023年

作者： Holste, Gregory Zhou, Yiliang Wang, Song Jaiswal, Ajay Lin, Mingquan Zhuge, Sherry Yang, Yuzhe Kim, Dongkyun Nguyen-Mau, Trong-Hieu Tran, Minh-Triet Jeong, Jaehyup Park, Wongi Ryu, Jongbin Hong, Feng Verma, Arsh Yamagishi, Yosuke Kim, Changhyun Seo, Hyeryeong Kang, Myungjoo Celi, Leo Anthony Lu, Zhiyong Summers, Ronald M. Shih, George Wang, Zhangyang Peng, Yifan Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States Department of Population Health Sciences Weill Cornell Medicine New YorkNY10065 United States School of Information Systems Carnegie Mellon University PittsburghPA15213 United States Department of Electrical Engineering and Computer Science Massachussetts Institute of Technology CambridgeMA02139 United States School of Computer Science Carnegie Mellon University PittsburghPA15213 United States University of Science VNU-HCM Ho Chi Minh City70000 Viet Nam KT Research & Development Center KT Corporation Seoul06763 Korea Republic of Department of Software and Computer Engineering Ajou University Suwon16499 Korea Republic of Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China Wadhwani Institute for Artificial Intelligence Mumbai400079 India Division of Radiology and Biomedical Engineering Graduate School of Medicine The University of Tokyo Tokyo113-0033 Japan BioMedical AI Team AIX Future R&D Center SK Telecom Seoul04539 Korea Republic of Seoul National University Seoul02504 Korea Republic of Department of Mathematical Sciences Seoul National University Seoul02504 Korea Republic of Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA02139 United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA02215 United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA02115 United States National Center for Biotechnology Information National Library of Medicine BethesdaMD20894 United States Clinical Center National Institutes of Health BethesdaMD20892 United States Department of Radiology Weill Cornell Medicine New YorkNY10065 United States

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" – there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification. © 2023, CC BY-NC-SA.

关键词： computer aided diagnosis

EXPPO: EXecution Performance Profiling and Optimization for CPS Co-simulation-as-a-Service

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EXPPO: EXecution Performance Profiling and Optimization for ...

International Symposium on Object-Oriented Real-Time Distributed Computing

作者： Yogesh D. Barve Himanshu Neema Zhuangwei Kang Hongyang Sun Aniruddha Gokhale Thomas Roth Department of Electrical Engineering and Computer Science Vanderbilt University Nashville TN USA Smart Grid and Cyber-Physical Systems Program Office Engineering Laboratory National Institute of Standards and Technology Gaithersburg MD USA

ISBN: (数字)9781728169583

ISBN: (纸本)9781728169590

A co-simulation may comprise several heterogeneous federates with diverse spatial and temporal execution characteristics. In an iterative time-stepped simulation, a federation exhibits the Bulk Synchronous parallel (BSP) computation paradigm in which all federates perform local operations and synchronize with their peers before proceeding to the next round of computation. In this context, the lowest performing (i.e., slowest) federate dictates the progression of the federation logical time. One challenge in co-simulation is performance profiling for individual federates and entire federations. The computational resource assignment to the federates can have a large impact on federation performance. Furthermore, a federation may comprise federates located on different physical machines as is the case for cloud and edge computing environments. As such, distributed profiling and resource assignment to the federation is a major challenge for operationalizing the co-simulation execution at scale. This paper presents the Execution Performance Profiling and Optimization (EXPPO) methodology, which addresses these challenges by using execution performance profiling at each simulation execution step and for every federate in a federation. EXPPO uses profiling to learn performance models for each federate, and uses these models in its federation resource recommendation tool to solve an optimization problem that improves the execution performance of the co-simulation. Using an experimental testbed, the efficacy of EXPPO is validated to show the benefits of performance profiling and resource assignment in improving the execution runtimes of co-simulations while also minimizing the execution cost.

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False Noise Attack Detection for differentially-private distributed control of microgrids

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Automatica 2025年 179卷

作者： Ye, Feng Cao, Xianghui Cai, Lin Chow, Mo-Yuen School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Department of Electrical and Computer Engineering University of Victoria Victoria BCV8W 3P6 Canada UM-SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Privacy preserving in distributed control is getting more attention, and differential privacy (DP) is the common tool to protect data privacy, in which additive noise is applied in the algorithm function. However, DP can be leveraged by false noise (FN) attacks because attack vectors can be disguised as artificial noise in DP. FN attacks are a concern as the stealth attacks are hard to detect. Moreover, DP in distributed control makes FN attack detection more difficult. Hence, detecting FN attacks in privacy-preserving distributed control is critical and challenging. In this paper, taking distributed energy management systems as the control object, we propose a novel peer-to-peer attack detection approach, named False Noise Attack Detection (FNAD). In FNAD, each device observes the power decisions of its neighbors based on the data from its two-hop neighbors, estimates the power decisions of its neighbors by a Kalman filter, and updates the detection index of each neighbor according to the residues of the Kalman filter at each iteration. The detection index is developed based on information entropy, without any prior knowledge of the FN attacks. If a device's detection index is out of well-defined thresholds, its neighbors can perform a majority vote to decide whether it is malicious. We theoretically prove the detection effect of FNAD against three representative attacks in the literature and analyze the advantages of FNAD compared with the traditional methods. The effectiveness of FNAD is demonstrated by extensive simulations. © 2025 Elsevier Ltd

关键词： State estimation

A Reinforcement Learning Approach for Fast Frequency Control in Low-Inertia Power systems

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arXiv 2020年

作者： Stanojev, Ognjen Kundacina, Ognjen Markovic, Uros Vrettos, Evangelos Aristidou, Petros Hug, Gabriela EEH - Power Systems Laboratory ETH Zurich Switzerland Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Cyprus Swissgrid AG Laufenburg Switzerland Department of Power Electronic and Communications Engineering University of Novi Sad Serbia

The electric grid is undergoing a major transition from fossil fuel-based power generation to renewable energy sources, typically interfaced to the grid via power electronics. The future power systems are thus expected to face increased control complexity and challenges pertaining to frequency stability due to lower levels of inertia and damping. As a result, the frequency control and development of novel ancillary services is becoming imperative. This paper proposes a data-driven control scheme, based on Reinforcement Learning (RL), for grid-forming Voltage Source Converters (VSCs), with the goal of exploiting their fast response capabilities to provide fast frequency control to the system. A centralized RL-based controller collects generator frequencies and adjusts the VSC power output, in response to a disturbance, to prevent frequency threshold violations. The proposed control scheme is analyzed and its performance evaluated through detailed time-domain simulations of the IEEE 14-bus test system. Copyright © 2020, The Authors. All rights reserved.

关键词： Time domain analysis

Multi-agent maintenance scheduling based on the coordination between central operator and decentralized producers in an electricity market

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arXiv 2020年

作者： Rokhforoz, Pegah Gjorgiev, Blazhe Sansavini, Giovanni Fink, Olga Intelligent Maintenance Systems ETH Zurich Switzerland School of Electrical and Computer Engineering University of Tehran Tehran Iran Reliability and Risk Engineering Laboratory Institute of Energy Technology Department of Mechanical and Process Engineering ETH Zurich Switzerland

Condition-based and predictive maintenance enable early detection of critical system conditions and thereby enable decision makers to forestall faults and mitigate them. However, decision makers also need to take the operational and production needs into consideration for optimal decision-making when scheduling maintenance activities. Particularly in network systems, such as power grids, decisions on the maintenance of single assets can affect the entire network and are, therefore, more complex. This paper proposes a two-level multi-agent decision support systems for the generation maintenance decision (GMS) of power grids in an electricity markets. The GMS plays an important role in increasing the reliability at the network level. The aim of the GMS is to minimize the generation cost while maximizing the system reliability. The proposed framework integrates a central coordination system, i.e. the transmission system operator (TSO), and distributed agents representing power generation units that act to maximize their profit and decide about the optimal maintenance time slots while ensuring the fulfilment of the energy demand. The objective function of agents (power generation companies) is based on the reward and the penalty that they obtain from the interplay between power production and loss of production due to failure, respectively. The optimal strategy of agents is then derived using a distributed algorithm, where agents choose their optimal maintenance decision and send their decisions to the central coordinating system. The TSO decides whether to accept the agents’ decisions by considering the market reliability aspects and power supply constraints. To solve this coordination problem, we propose a negotiation algorithm using an incentive signal to coordinate the agents’ and central system’s decisions such that all the agents’ decisions can be accepted by the central system. We demonstrate the efficiency of our proposed algorithm using a IEEE 39 bus system. Co

关键词： Decision making