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Utilizing probabilistic entanglement between sensors in quantum networks

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arXiv 2024年

作者： Van Milligen, Emily A. Gagatsos, Christos N. Kaur, Eneet Towsley, Don Guha, Saikat Department of Physics The University of Arizona 1630 East University Boulevard TucsonAZ85721 United States Department of Electrical and Computer Engineering The University of Arizona 1630 East University Boulevard TucsonAZ85721 United States Wyant College of Optical Sciences The University of Arizona 1630 East University Boulevard TucsonAZ85721 United States Program in Applied Mathematics The University of Arizona TucsonAZ85721 United States Cisco Quantum Lab Los Angeles United States College of Computer Science University of Massachusetts AmherstMA United States

One of the most promising applications of quantum networks is entanglement assisted sensing. The field of quantum metrology exploits quantum correlations to improve the precision bound for applications such as precision timekeeping, field sensing, and biological imaging. When measuring multiple spatially distributed parameters, current literature focuses on quantum entanglement in the discrete variable case—and quantum squeezing in the continuous variable case distributed amongst all of the sensors in a given network. However, it can be difficult to ensure all sensors pre-share entanglement of sufficiently high fidelity. This work probes the space between fully entangled and fully classical sensing networks by modeling a star network with probabilistic entanglement generation that is attempting to estimate the average of local parameters. The quantum Fisher information is used to determine which protocols best utilize entanglement as a resource for different network conditions. It is shown that without entanglement distillation there is a threshold fidelity below which classical sensing is preferable. For a network with a given number of sensors and links characterized by a certain initial fidelity and probability of success, this work outlines when and how to use entanglement, when to store it, and when it needs to be distilled. © 2024, CC BY.

关键词： Quantum entanglement

Multi-core synthesis and maximum satisfiability applied to optimal sizing of solar photovoltaic systems

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TechRxiv

TechRxiv 2021年

作者： Trindade, Alessandro Galvão, Edilson Cordeiro, Lucas The Post-graduate Program in Electrical Engineering Manaus Brazil Department of Electricity Federal University of Amazonas Manaus Brazil School of Computer Science The University of Manchester United Kingdom

Annual global energy consumption growth is around 1.3% with forecasts until 2040. Photovoltaic systems became a suitable alternative to nuclear and fossil energy generation. In order to support this technology’s dissemination, we develop and evaluate an automated formal synthesis approach that assists in decision-making for off-grid systems. Our proposed approach, called PVz, is based on a variant of the counterexample-guided inductive synthesis;it has a multi-core feature, which can obtain the optimal sizing of photovoltaic systems focusing on Life Cycle Cost analysis. Given the electrical needs of a home, we seek a set of electrical equipment with the best possible combination of devices that meet the specified requirements. We calculate all costs related to maintenance over 20 years. The results presented are based on seven case studies;some of them are real ones from the Amazon region in Brazil. The same case studies were solved by a commercial optimization tool. Our technique and the commercial tool results were validated with popular simulation software to perform a fair comparison. Furthermore, we analyze some topics such as run-time, optimal solution, and configuration of the resulting systems. We claim that our technique is advantageous compared to the existing approaches in the literature. © 2021, CC BY-NC-SA.

关键词： Decision making

Energy-efficient knowledge distillation for spiking Neural Networks

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arXiv 2021年

作者： Lee, Dongjin Park, Seongsik Kim, Jongwan Doh, Wuhyeong Yoon, Sungroh Department of Electrical and Computer Engineering Institute of New Media and Communications Interdisciplinary Program in Artificial Intelligence Automation and Systems Research Institute Seoul National University Korea Republic of

Spiking neural networks (SNNs) have been gaining interest as energy-efficient alternatives of conventional artificial neural networks (ANNs) due to their event-driven computation. Considering the future deployment of SNN models to constrained neuromorphic devices, many studies have applied techniques originally used for ANN model compression, such as network quantization, pruning, and knowledge distillation, to SNNs. Among them, existing works on knowledge distillation reported accuracy improvements of student SNN model. However, analysis on energy efficiency, which is also an important feature of SNN, was absent. In this paper, we thoroughly analyze the performance of the distilled SNN model in terms of accuracy and energy efficiency. In the process, we observe a substantial increase in the number of spikes, leading to energy inefficiency, when using the conventional knowledge distillation methods. Based on this analysis, to achieve energy efficiency, we propose a novel knowledge distillation method with heterogeneous temperature parameters. We evaluate our method on two different datasets and show that the resulting SNN student satisfies both accuracy improvement and reduction of the number of spikes. On MNIST dataset, our proposed student SNN achieves up to 0.09% higher accuracy and produces 65% less spikes compared to the student SNN trained with conventional knowledge distillation method. We also compare the results with other SNN compression techniques and training methods. © 2021, CC BY.

关键词： Energy efficiency

Shaping Integrity: Why Generative Artificial Intelligence Does Not Have to Undermine Education

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arXiv 2024年

作者： Tan, Myles Joshua Toledo Maravilla, Nicholle Mae Amor Tan Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL32611 United States Department of Epidemiology College of Public Health & Health Professions College of Medicine University of Florida GainesvilleFL32610 United States Biology Program College of Arts and Sciences University of St. La Salle Negros Occidental Bacolod City6100 Philippines Department of Natural Sciences College of Arts and Sciences University of St. La Salle Negros Occidental Bacolod City6100 Philippines Department of Chemical Engineering College of Engineering and Technology University of St. La Salle Negros Occidental Bacolod City6100 Philippines Department of Electronics Engineering College of Engineering and Technology University of St. La Salle Negros Occidental Bacolod City6100 Philippines Yo-Vivo Corporation Negros Occidental Bacolod City6100 Philippines

This paper examines the role of generative artificial intelligence (GAI) inpromoting academic integrity within educational settings. It explores how AIcan be ethically integrated into classrooms to enhance learning experiences,foster intrinsic motivation, and support voluntary behavior change amongstudents. By analyzing established ethical frameworks and educational theoriessuch as deontological ethics, consequentialism, constructivist learning, andSelf-Determination Theory (SDT), the paper argues that GAI, when usedresponsibly, can enhance digital literacy, encourage genuine knowledgeconstruction, and uphold ethical standards in education. This researchhighlights the potential of GAI to create enriching, personalized learningenvironments that prepare students to navigate the complexities of the modernworld ethically and effectively. © 2024, CC BY-NC-SA.

关键词： Contrastive Learning

FaSTrack: A modular framework for real-time motion planning and guaranteed safe tracking

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arXiv 2021年

作者： Chen, Mo Herbert, Sylvia L. Hu, Haimin Pu, Ye Fisac, Jaime F. Bansal, Somil Han, SooJean Tomlin, Claire J. School of Computing Science Simon Fraser University Canada Department of Mechanical and Aerospace Engineering UC San Diego United States Department of Electrical Engineering Princeton University United States Department of Electrical and Electronic Engineering University of Melbourne Australia Department of Electrical Engineering and Computer Sciences UC Berkeley United States Control and Dynamical Systems Program California Institute of Technology United States

Real-time, guaranteed safe trajectory planning is vital for navigation in unknown environments. However, real-time navigation algorithms typically sacrifice robustness for computation speed. Alternatively, provably safe trajectory planning tends to be too computationally intensive for real-time replanning. We propose FaSTrack, Fast and Safe Tracking, a framework that achieves both real-time replanning and guaranteed safety. In this framework, real-time computation is achieved by allowing any trajectory planner to use a simplified planning model of the system. The plan is tracked by the system, represented by a more realistic, higher-dimensional tracking model. We precompute the tracking error bound (TEB) due to mismatch between the two models and due to external disturbances. We also obtain the corresponding tracking controller used to stay within the TEB. The precomputation does not require prior knowledge of the environment. We demonstrate FaSTrack using Hamilton-Jacobi reachability for precomputation and three different real-time trajectory planners with three different tracking-planning model pairs. Copyright © 2021, The Authors. All rights reserved.

关键词： Trajectories

Non-Hermitian metasurface with non-trivial topology

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Non-Hermitian metasurface with non-trivial topology

Conference on Lasers and Electro-Optics (CLEO)

作者： Ciril S. Prasad Frank Yang Weijian Li Rosemary Lach Henry O. Everitt Gururaj V. Naik Department of Electrical and Computer Engineering Rice University Houston TX USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA U.S. Army DEVCOM Army Research Laboratory-South Houston TX USA

The synergy between topology and non-Hermiticity in photonics holds potential for optical devices that are robust against defects. We demonstrate a non-Hermitian plasmonic-dielectric metasurface in the visible with no... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Analytical models Laser theory Optical switches Optical resonators Metasurfaces Laser modes Topology

Smartphone sensing of multiple heavy metal ion contaminants in drinking water

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Smartphone sensing of multiple heavy metal ion contaminants ...

Optics and Photonics for Sensing the Environment, ES 2020 - Part of Optical Sensors and Sensing Congress 2020

作者： Nguyen, Hoang Misbah, Ibrahim Shih, Wei-Chuan Department of Electrical and Computer Engineering University of Houston 4800 Calhoun Rd HoustonTX77204 United States Program of Materials Science and Engineering University of Houston 4800 Calhoun Rd HoustonTX77204 United States

We report an array-based smartphone sensor for multiplex detection and quantitation of lead and mercury ions in drinking water without the addition of any solution reagent. The detection limits for lead and mercury ar... 详细信息

ISBN: (纸本)9781557528209

关键词： Potable water

A comprehensive review of solid-state lithium batteries: Fast Charging characteristics and in-operando diagnostics

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Nano Energy 2025年 142卷

作者： Ruoxu Shang Talyah Nelson Thanh Vy Nguyen Charlotte Nelson Harsha Antony Brian Abaoag Mihrimah Ozkan Cengiz S. Ozkan Materials Science and Engineering Program University of California Riverside CA USA Department of Mechanical Engineering University of California Riverside CA USA Department of Bioengineering University of California Riverside CA USA Department of Electrical and Computer Engineering University of California Riverside CA USA

This comprehensive review article delves into the evolving landscape of solid-state batteries (SSBs), presenting a critical evaluation beyond the conventional lithium-ion technology. It meticulously explores the performance analysis, fast-charging capabilities, and in-operando characterization of SSBs. Amid growing demands for safer, higher energy density, and more sustainable energy storage solutions, SSBs emerge as a compelling alternative, promising to revolutionize applications from portable electronics to electric vehicles. This review begins with a detailed examination of the fundamental principles underpinning SSBs, including their unique architecture and the roles of solid electrolytes in enhancing safety and energy density. It then shifts focus to the pivotal aspect of fast charging – a critical parameter for consumer acceptance and market penetration of electric vehicles. The analysis extends to the intricacies of in-operando characterization techniques, which are essential for understanding the dynamic processes within SSBs during operation, thus offering insights into optimizing performance and longevity. By juxtaposing SSBs with conventional lithium-ion batteries, the review highlights the technological strides and hurdles ahead. Furthermore, it discusses the latest advancements in materials science, engineering strategies, and characterization methods that potentially address the current limitations of SSBs. Conclusively, this review not only underscores the significant progress in SSB technology but also outlines the roadmap and future directions for researchers and industry stakeholders aiming to harness the full potential of SSBs in next-generation energy storage systems.

关键词： Solid-state batteries (SSBs) Fast charging Operando Lithium-ion Batteries

Perspective—The Feasibility of Continuous Protein Monitoring in Interstitial Fluid

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ECS Sensors Plus 2023年第2期2卷 027001-027001页

作者： Young, Thomas Clark, Vincent Arroyo-Currás, Netzahualcóyotl Heikenfeld, Jason Department of Biomedical Engineering University of Cincinnati Cincinnati 45221 OH United States Chemistry-Biology Interface Program Zanvyl Krieger School of Arts & Sciences Johns Hopkins University Baltimore 21218 MD United States Department of Electrical & Computer Engineering University of Cincinnati Cincinnati 45221 OH United States Department of Pharmacology and Molecular Sciences Johns Hopkins University School of Medicine Baltimore 21205 MD United States

Highlights Continuous monitoring or sensing of proteins. Biological challenges to continuous sensors for proteins. Biosensor sensitivity, longevity, and antifouling are areas for future research. © 2023 The Autho...

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