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IEEE International Workshop on Hardware-Oriented Security and Trust (HOST)

作者： Owen Millwood Fei Hongming Prosanta Gope Oğuz Narlı Meltem Kurt Pehlivanoğlu Elif Bilge Kavun Biplab Sikdar Department of Computer Science The University of Sheffield Sheffield UK Department of Electrical and Computer Engineering The National University of Singapore Singapore Department of Computer Engineering Kocaeli University Kocaeli Turkey Secure Intelligent Systems Research Group University of Passau Passau Germany

With the ubiquitous and distributed nature of the Internet-of-Things (IoT), various qualities of traditional communication methods for end devices and their verifiers prove insufficient in solving the challenges this new paradigm faces. Many new hardware and software technologies are proposed in an attempt to provide IoT systems with desired security properties while meeting performance requirements. Physically Unclonable Functions (PUFs) are one such technology receiving particular interest from the wider research community by promising to provide low-cost and highly secure key data to enable lightweight authentication protocols for devices operating over publicly accessible networks. PUFs have been the target of Machine Learning Modelling Attacks (ML-MA), which aim to clone the intrinsic behaviour of the PUF to undermine their security. While many PUF-based schemes have been proposed to defend against adversaries who are guaranteed to be dishonest, an area which has not seen significant consideration is one where a normal communication participant cannot always be assumed to act honestly. To the best of our knowledge, this work is the first to consider the concept of ‘semi-honest verifier’ for PUFbased authentication, taking initial steps to shed light on this prominent issue in IoT by proposing a privacy-preserving mutual authentication protocol which considers security against MLMA in the presence of such verifiers. Furthermore, this work describes hardware-level considerations for PUF obfuscation by utilising a combination of strong PUF, configurable One-Way Function (OWF) and secure DRAM-PUF and is, therefore, one of the first to integrate PUF obfuscation comprehensively at the protocol level.

关键词： Performance evaluation Protocols Authentication Cloning Machine learning Physical unclonable function Hardware

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Advancing athermal silica fiber lasers to watt-level power and beyond 7

Advancing athermal silica fiber lasers to watt-level power a...

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Photonic Heat Engines: Science and Applications VII 2025

作者： Chen, Chun-Wei Balliu, Enkeleda Meehan, Bailey Hawkins, Thomas W. Ballato, John Dragic, Peter D. Boilard, Tommy Bernier, Martin Digonnet, Michel J.F. Edward L. Ginzton Laboratory Stanford University StanfordCA94305 United States Cobolt AB HÜBNER Photonics Solna171 54 Sweden Department of Materials Science and Engineering Clemson University SC United States Department of Electrical Computer Engineering University of Illinois at Urbana-Champaign IL United States Université Laval Québec Canada

ISBN: (数字)9781510685079

ISBN: (纸本)9781510685062

Waste heat in fiber lasers poses major challenges in frequency, power, and pointing stabilities, as well as beam quality and power scaling. Using anti-Stokes-fluorescence cooling, we developed a radiation-balanced Yb-doped silica fiber amplifier with a novel aluminophosphosilicate core-glass composition that delivers 575 mW with no net internal heat generation by amplifying a 0.9-mW seed at 1064 nm (28.0 dB of gain). With a lower seed power (0.4 mW), a radiation-balanced gain of 31.5 dB was achieved. We discuss the strategies and recent progress in silicate-glass engineering that enabled this breakthrough performance in a small-core fiber at atmospheric pressure, as well as the potential for power-scaling radiation-balanced fiber lasers to the 100-watt level through advanced fiber designs. Our findings point to a promising path for stable high-power operation free from thermal limitations. © 2025 SPIE.

关键词： Laser cooling

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Understanding Uncertainty-based Active Learning Under Model Mismatch

arXiv

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arXiv 2024年

作者： Rahmati, Amir Hossein Fan, Mingzhou Zhou, Ruida Urban, Nathan M. Yoon, Byung-Jun Qian, Xiaoning The Department of Electrical and Computer Engineering Texas A&M University College StationTX77843 United States The Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States Brookhaven National Laboratory UptonNY11973 United States

Instead of randomly acquiring training data points, Uncertainty-based Active Learning (UAL) operates by querying the label(s) of pivotal samples from an unlabeled pool selected based on the prediction uncertainty, thereby aiming at minimizing the labeling cost for model training. The efficacy of UAL critically depends on the model capacity as well as the adopted uncertainty-based acquisition function. Within the context of this study, our analytical focus is directed toward comprehending how the capacity of the machine learning model may affect UAL efficacy. Through theoretical analysis, comprehensive simulations, and empirical studies, we conclusively demonstrate that UAL can lead to worse performance in comparison with random sampling when the machine learning model class has low capacity and is unable to cover the underlying ground truth. In such situations, adopting acquisition functions that directly target estimating the prediction performance may be beneficial for improving the performance of UAL. © 2024, CC BY-NC-SA.

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Nearest Level Control Based Modular Multi-level Converters For Power Electronics Building Blocks Concept In Electric Ship System

Nearest Level Control Based Modular Multi-level Converters F...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Ali Moghassemi Laxman Timilsina S M Imrat Rahman Ali Arsalan Phani Kumar Chamarthi Gokhan Ozkan Behnaz Papari Christopher S. Edrington Zheyu Zhang Holcombe Department of Electrical and Computer Engineering Clemson University Clemson SC USA Department of Automotive Engineering Clemson University Greenville SC USA Dominion Energy Innovation Center Clemson University North Charleston SC USA Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

Power electronics building blocks (PEBBs) involve the integration of fundamental components into blocks with defined functionality, stacked in series and parallel, to extend converter power ratings to meet naval ship systems’ various power conversion needs. The PEBBs concept in literature is based on modular multilevel converters (MMCs). MMCs are promising candidates for medium and high-power system applications owing to their unique features, such as modularity/scalability/simplicity in structure, low switching losses, low quantization on voltage/current, high reliability, and high efficiency. However, a promising switching control method is required in MMCs to balance the capacitor voltage and suppress the circulating current. This paper presents the nearest level control (NLC) method for the PEBBs concept in modern electric ship systems to simultaneously improve capacitor voltage balancing, circulating current, and power quality. The simulation is conducted in MATLAB/Simulink software. A three-phase five-level MMC converter is considered for the simulation to analyze and compare the converter’s performance based on the proposed NLC and traditional sinusoidal pulse-width modulation (SPWM) switching methods.

关键词： Multilevel converters Capacitors Power quality Transportation Switching loss Switches Power electronics

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Mid-infrared Photons Released by NAD^(+)Reduction in the Tricarboxylic Acid Cycle of Myelinated Neuron

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Neuroscience Bulletin 2023年第7期39卷 1146-1150页

作者： Daoling Peng Wenpeng Qi Feng Zhang Bo Song Key Laboratory of Theoretical Chemistry of Environment Ministry of EducationSchool of EnvironmentSouth China Normal UniversityGuangzhou 510006China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China Key Laboratory of Optical Technology and Instruments for Medicine Ministry of EducationShanghai Key Lab of Modern Optical SystemsSchool of Optical-Electrical Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Dear Editor,Life can utilize energy with high efficiency,especially with the help of aerobic respiration,where the tricarboxylic acid(TCA)cycle acts as an important metabolic engine for mitochondria to produce the cell energy currency of adenosine triphosphate(ATP),and nicotinamide adenine dinucleotide(NAD)plays a particular role of energy management molecule in the TCA cycle[1].

关键词： carboxylic utilize cycle

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AUGFL: Augmenting Federated Learning with Pretrained Models

arXiv

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arXiv 2025年

作者： Yue, Sheng Qin, Zerui Deng, Yongheng Ren, Ju Zhang, Yaoxue Zhang, Junshan School of Cyber Science and Technology Sun Yatsen University China Department of Computer Science and Technology Tsinghua University China Zhong-guancun Laboratory China Department of Electrical and Computer Engineering University of California Davis United States

Federated Learning (FL) has garnered widespread interest in recent years. However, owing to strict privacy policies or limited storage capacities of training participants such as IoT devices, its effective deployment is often impeded by the scarcity of training data in practical decentralized learning environments. In this paper, we study enhancing FL with the aid of (large) pretrained models (PMs), that encapsulate wealthy general/domain-agnostic knowledge, to alleviate the data requirement in conducting FL from scratch. Specifically, we consider a networked FL system formed by a central server and distributed clients. First, we formulate the PM-aided personalized FL as a regularization-based federated meta-learning problem, where clients join forces to learn a meta-model with knowledge transferred from a private PM stored at the server. Then, we develop an inexact-ADMM-based algorithm, AUGFL, to optimize the problem with no need to expose the PM or incur additional computational costs to local clients. Further, we establish theoretical guarantees for AUGFL in terms of communication complexity, adaptation performance, and the benefit of knowledge transfer in general non-convex cases. Extensive experiments corroborate the efficacy and superiority of AUGFL over existing baselines. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Low-Cost Dielectrophoresis-Based Microfluidic Chip for Label-Free Particle Separation with 3D Electrodes

Low-Cost Dielectrophoresis-Based Microfluidic Chip for Label...

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Iranian Conference on electrical engineering (ICEE)

作者： Fatemeh Esmaeili Zeynab Alipour Mehdi Fardmanesh Electrical and Computer Engineering Department University of Toronto Toronto Canada Electrical Engineering Department Superconducting Electronics Research Laboratory Sharif University of Technology Tehran Iran

In this article, an easy and low-cost fabrication method for microfluidic chips with 3D electrodes is proposed. As an application of this method, the fabricated chip has been employed as a particle separator using the dielectrophoresis force. This new design does not require a sheath current, and the separation can be done only by using an injection pump, which reduces cost and complexity. The developed microfluidic channel and electrodes are engraved simultaneously using a laser, and therefore the depth of the channel and the electrodes are equal, which increases the efficiency of the chip. Then the electrodes' engraved regions were filled up by silver paste to the level of that of the microchannel. This structure is used to separate live and dead yeast cells using dielectrophoresis force, generated efficiently throughout the channel using 3D electrodes. Simulations and experimental results, both confirm the effective performance of the microfluidic chip in this design.

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Generalized Haldane map from the matrix product state path integral to the critical theory of the J1−J2 chain

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Physical Review Research 2025年第1期7卷 L012037-L012037页

作者： F. Azad Adam J. McRoberts Chris Hooley A. G. Green Electrical Engineering and Computer Science Department Technische Universität Berlin 10587 Berlin Germany London Centre for Nanotechnology University College London Gordon Street London WC1H 0AH United Kingdom International Centre for Theoretical Physics Strada Costiera 11 34151 Trieste Italy Max Planck Institute for the Physics of Complex Systems Nöthnitzer Straße 38 01187 Dresden Germany

We study the J1−J2 spin-1/2 chain using a path integral constructed over matrix product states (MPS). By virtue of its nontrivial entanglement structure, the MPS ansatz captures the key phases of the model even at a semiclassical, saddle-point level, and, as a variational state, is in good agreement with the field theory obtained by Abelian bosonization. Going beyond the semiclassical level, we show that the MPS ansatz facilitates a physically motivated derivation of the field theory of the critical phase: By carefully taking the continuum limit—a generalization of the Haldane map—we recover from the MPS path integral a field theory with the correct topological term and emergent SO(4) symmetry, constructively linking the microscopic states and topological field-theoretic structures. Moreover, the dimerization transition is particularly clear in the MPS formulation—an explicit dimerization potential becomes relevant, gapping out the magnetic fluctuations.

关键词： Topology

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Platform independent integration of telluride thin films for high speed on-chip photodetection

Platform independent integration of telluride thin films for...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Ahn, Geun Ho White, Alexander D. Kim, Hyungjin Higashitarumizu, Naoki Mayor, Felix M. Herrmann, Jason F. Safavi-Naeini, Amir H. Javey, Ali Vučković, Jelena E. L. Ginzton Laboratory Stanford University StanfordCA United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA United States

We demonstrate platform-independent and scalable integration of tellurium thin film with photonic structures in silicon, silicon nitride and lithium niobate for ultra-broadband photodetection with a bandwidth of more ... 详细信息

ISBN: (纸本)9781957171258

关键词： Thin films

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Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling

arXiv

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arXiv 2023年

作者： Fabbro, Nicolò Dal Mitra, Aritra Pappas, George J. The Department of Information Engineering University of Padova Italy The Electrical and Computer Engineering Department North Carolina State University United States The Electrical and Systems Engineering Department University of Pennsylvania United States

Federated learning (FL) has recently gained much attention due to its effectiveness in speeding up supervised learning tasks under communication and privacy constraints. However, whether similar speedups can be established for reinforcement learning remains much less understood theoretically. Towards this direction, we study a federated policy evaluation problem where agents communicate via a central aggregator to expedite the evaluation of a common policy. To capture typical communication constraints in FL, we consider finite capacity up-link channels that can drop packets based on a Bernoulli erasure model. Given this setting, we propose and analyze QFedTD - a quantized federated temporal difference learning algorithm with linear function approximation. Our main technical contribution is to provide a finite-sample analysis of QFedTD that (i) highlights the effect of quantization and erasures on the convergence rate;and (ii) establishes a linear speedup w.r.t. the number of agents under Markovian sampling. Notably, while different quantization mechanisms and packet drop models have been extensively studied in the federated learning, distributed optimization, and networked control systems literature, our work is the first to provide a non-asymptotic analysis of their effects in multi-agent and federated reinforcement learning. © 2023, CC BY.

关键词： Reinforcement learning

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