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Darlington pair of quantum thermal transistors

Physical Review B 2021年第4期104卷 045405-045405页

作者： Ravi T. Wijesekara Sarath D. Gunapala Malin Premaratne Advanced Computing and Simulation Laboratory(AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton Victoria 3800 Australia Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 USA

Recent progress in manipulating individual quantum systems has led to the development of quantum thermal transistors, which control the thermal conductivity between two of its terminals according to an input signal on its third terminal. With several models for individual thermal transistors already developed, the next natural step is to investigate how multiple thermal transistors can interconnect to build useful composite devices. In electronics literature, the Darlington pair is a two-transistor configuration commonly used to construct electronic amplifiers with higher gain than is possible with a single transistor. We create the electronic Darlington pair's thermal equivalent using two individual thermal transistors in a similar configuration. Unlike previously studied models, multitransistor configurations like this contain internal transistor interconnections whose temperatures cannot be biased externally but are determined by the individual transistors' internal dynamics. We refine previous models to incorporate these transistor-transistor interactions and introduce an intermediate thermal bath to facilitate the thermal energy exchange between the Darlington pair transistors. We investigate temperature-based and optical field-based control strategies of the Darlington pair in terms of both steady-state and transient thermal flow characteristics through numerical simulations. Under both control strategies, the thermal Darlington pair's steady-state performance exhibits superior thermal amplification, sensitivity, and thermodynamic efficiency than an equivalent single thermal transistor. Our results closely mirror those expected from the corresponding electronic Darlington pair. Hence, we envision that we may readily adapt the intermediate bath formalism we developed in this work to translate a wide variety of useful electronic multitransistor configurations to their thermal equivalents.

关键词： Thermal properties Nanostructures Transistors

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Coherent Acoustic Phonons in Bi2Se3 Ultrathin Films Probed by Femtosecond, Optical Pump-Probe Spectroscopy

Coherent Acoustic Phonons in Bi2Se3 Ultrathin Films Probed b...

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Cheng, Jing Niherysh, Kiryl A. Francis, Peter Chakraborty, Debamitra Lombardi, Floriana Erts, Donats Prischepa, Serghej L. Laszcz, Adam Komissarov, Ivan V. Sobolewski, Roman Materials Science Graduate Program University of Rochester RochesterNY14627 United States Laboratory for Laser Energetics University of Rochester RochesterNY14623-1299 United States Department of Microtechnology and Nanoscience Chalmers University of Technology Gothenburg Sweden Institute of Chemical Physics University of Latvia Riga Latvia Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States Dipartimento di Fisica "E.R. Caianiello" Università degli Studi di Salerno Sa FiscianoI-84084 Italy Lukasiewicz Research Network Institute of Microelectronics and Photonics WarszawaPL-02668 Poland

ISBN: (纸本)9781957171395

The dynamics of coherent acoustic phonons in the Bi2Se3 layered crystal system are investigated. The findings reveal that the frequency evolution of breathing modes with the number of the material quintuple layers and the dispersion relation both can be explained by a modified chain model with a nonuniform coupling force. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Ultrathin films

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Recent advances in optical dynamic metaholography

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Opto-Electronic Advances 2021年第11期4卷 27-45页

作者： Hui Gao Xuhao Fan Wei Xiong Minghui Hong Wuhan National Laboratory for Optoelectronics School of Optical and Electronic InformationHuazhong University of Science and TechnologyWuhan 430074China Department of Electrical and Computer Engineering National University of SingaporeEngineering Drive 3Singapore 117576Singapore

Holography,with the capability of recording and reconstructing wavefronts of light,has emerged as an ideal approach for future deep-immersive naked-eye ***,the shortcomings(e.g.,small field of view,twin imaging,multiple or-ders of diffraction)of traditional dynamic holographic devices bring many challenges to their practical ***,planar artificial materials composed of subwavelength unit cells,have shown great potential in light field manipulation,which is useful for overcoming these ***,we review recent progress in the field of dynamic metasurface holography,from realization methods to design strategies,mainly including typical research works on dy-namic meta-holography based on tunable metasurfaces and multiplexed *** applications of dynam-ic meta-holography have been found in 3D display,optical storage,optical encryption,and optical information pro-cessing,which may accelerate the development of light field manipulation and micro/nanofabrication with higher dimen-sions.A number of potential applications and possible development paths are also discussed at the end.

关键词： metasurface dynamic meta-holography tunable meta-holography multiplexed meta-holography

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Multimodal Self-Supervised Learning for Lesion Localization

Multimodal Self-Supervised Learning for Lesion Localization

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IEEE International Symposium on Biomedical Imaging

作者： Hao Yang Hong-Yu Zhou Cheng Li Weijian Huang Jiarun Liu Yong Liang Guangming Shi Hairong Zheng Qiegen Liu Shanshan Wang Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Peng Cheng Laboratory Shenzhen China University of Chinese Academy of Sciences Beijing China Department of Computer Science The University of Hong Kong Pokfulam China Pazhou Laboratory (Huangpu) Guangzhou China Department of Electronic Information Engineering Nanchang University China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Multimodal deep learning utilizing imaging and diagnostic reports has made impressive progress in the field of medical imaging diagnostics, demonstrating a particularly strong capability for auxiliary diagnosis in cases where sufficient annotation information is lacking. Nonetheless, localizing diseases accurately without detailed positional annotations remains a challenge. Although existing methods have attempted to utilize local information to achieve fine-grained semantic alignment, their capability in extracting the fine-grained semantics of the comprehensive context within reports is limited. To address this problem, a new method is introduced that takes full sentences from textual reports as the basic units for local semantic alignment. This approach combines chest X-ray images with their corresponding textual reports, performing contrastive learning at both global and local levels. The leading results obtained by this method on multiple datasets confirm its efficacy in the task of lesion localization.

关键词： Location awareness Deep learning Annotations Semantics Robustness Lesions Task analysis

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Secure state estimation over Markov wireless communication channels

Secure state estimation over Markov wireless communication c...

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IEEE Conference on Decision and Control

作者： Anastasia Impicciatore Anastasios Tsiamis Yuriy Zacchia Lun Alessandro D’Innocenzo George J. Pappas Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy Automatic Control Laboratory ETH Zürich Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA United States

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This note studies state estimation in wireless networked control systems with secrecy against eavesdropping. Specifically, a sensor transmits a system state information to the estimator over a legitimate user link, and an eavesdropper overhears these data over its link independent of the user link. Each connection may be affected by packet losses and is modeled by a finite-state Markov channel (FSMC), an abstraction widely used to design wireless communication systems. This paper presents a novel concept of optimal mean square expected secrecy over FSMCs and delineates the design of a secrecy parameter requiring the user mean square estimation error (MSE) to be bounded and eavesdropper MSE unbounded. We illustrate the developed results on an example of an inverted pendulum on a cart whose parameters are estimated remotely over a wireless link exposed to an eavesdropper.

关键词： Wireless communication Wireless sensor networks Estimation error Networked control systems Channel estimation Packet loss Markov processes

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Parallel chaotic sources based on microcombs

Parallel chaotic sources based on microcombs

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Conference on Lasers and Electro-Optics (CLEO)

作者： Bitao Shen Haowen Shu Weiqiang Xie Ruixuan Chen Zhi Liu Xuguang Zhang Yimeng Wang Yunhao Zhang Buwen Cheng Shaohua Yu John E. Bowers Lin Chang Xingjun Wang State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara CA USA State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Beijing China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China Frontiers Science Center for Nano-Optoelectronics Peking University Beijing China

Massively parallel optical chaotic sources with large bandwidth are demonstrated based on chaotic microcombs in the AlGaAsOI platform. 30-channel parallel random bit generation with 30 Gbit/s per channel is realized b... 详细信息

ISBN: (纸本)9781957171258

关键词： Diode lasers Integrated photonics Kerr effect Nonlinear effects Optical microscopy Random number generation

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Local-to-Global Cross-Modal Attention-Aware Fusion for HSI-X Semantic Segmentation

arXiv

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arXiv 2024年

作者： Zhang, Xuming Yokoya, Naoto Gu, Xingfa Tian, Qingjiu Bruzzone, Lorenzo The International Institute for Earth System Science Nanjing University Nanjing210023 China The Department of Complexity Science and Engineering Graduate School of Frontier Sciences The University of Tokyo Chiba277-8561 Japan The RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan The Key Laboratory of Computational Optical Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The Department of Information Engineering and Computer Science University of Trento Trento38050 Italy

Hyperspectral image (HSI) classification has recently reached its performance bottleneck. Multimodal data fusion is emerging as a promising approach to overcome this bottleneck by providing rich complementary information from the supplementary modality (X-modality). However, achieving comprehensive cross-modal interaction and fusion that can be generalized across different sensing modalities is challenging due to the disparity in imaging sensors, resolution, and content of different modalities. In this study, we propose a Local-to-Global Cross-modal Attention-aware Fusion (LoGoCAF) framework for HSI-X classification that jointly considers efficiency, accuracy, and generalizability. LoGoCAF adopts a pixel-to-pixel two-branch semantic segmentation architecture to learn information from HSI and X modalities. The pipeline of LoGoCAF consists of a local-to-global encoder and a lightweight multilayer perceptron (MLP) decoder. In the encoder, convolutions are used to encode local and high-resolution fine details in shallow layers, while transformers are used to integrate global and low-resolution coarse features in deeper layers. The MLP decoder aggregates information from the encoder for feature fusion and prediction. In particular, two cross-modality modules, the feature enhancement module (FEM) and the feature interaction and fusion module (FIFM), are introduced in each encoder stage. The FEM is used to enhance complementary information by combining the feature from the other modality across direction-aware, position-sensitive, and channel-wise dimensions. With the enhanced features, the FIFM is designed to promote cross-modality information interaction and fusion for the final semantic prediction. Extensive experiments demonstrate that our LoGoCAF achieves superior performance and generalizes well on various multimodal fusion and classification tasks. The code will be made publicly available. Copyright © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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Large-area SNSPDs for up to 7.4 μm wavelengths

Large-area SNSPDs for up to 7.4 μm wavelengths

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CLEO: QELS_Fundamental science, QELS 2022

作者： Colangelo, Marco Walter, Alexander B. Korzh, Boris Schmidt, Ekkehart Bumble, Bruce Lita, Adriana E. Beyer, Andrew D. Allmaras, Jason P. Briggs, Ryan M. Kozorezov, Alexander Wollman, Emma E. Shaw, Matthew D. Berggren, Karl K. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Dr. PasadenaCA United States National Institute of Standards and Technology BoulderCO United States Department of Physics Lancaster University LancasterLA1 4YB United Kingdom

ISBN: (纸本)9781557528209

关键词： Particle beams

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Needles in Needle Stacks: Meaningful Clinical Information Buried in Noisy Waveform Data

arXiv

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arXiv 2024年

作者： Nagaraj, Sujay Goodwin, Andrew J. Lopushanskyy, Dmytro Eytan, Danny Greer, Robert W. Goodfellow, Sebastian D. Assadi, Azadeh Jayarajan, Anand Goldenberg, Anna Mazwi, Mjaye L. Department of Computer Science University of Toronto Toronto Canada Department of Genetics & Genome Biology SickKids Toronto Canada Vector Institute Toronto Canada Department of Critical Care Medicine SickKids Toronto Canada School of Biomedical Engineering University of Sydney Sydney Australia Department of Computer Science University of Oxford Oxford United Kingdom Technion - Israel Institute of Technology Haifa Israel Department of Civil & Mineral Engineering University of Toronto Toronto Canada Canadian Institute for Advanced Research Toronto Canada Department of Laboratory Medicine & Pathobiology University of Toronto Toronto Canada Division of Critical Care Medicine Seattle Children's Hospital Seattle United States

Central Venous Lines (C-Lines) and Arterial Lines (A-Lines) are routinely used in the Critical Care Unit (CCU) for blood sampling, medication administration, and high-frequency blood pressure measurement. Judiciously accessing these lines is important, as over-utilization is associated with significant in-hospital morbidity and mortality. Documenting the frequency of line-access is an important step in reducing these adverse outcomes. Unfortunately, the current gold-standard for documentation is manual and subject to error, omission, and bias. The high-frequency blood pressure waveform data from sensors in these lines are often noisy and full of artifacts. Standard approaches in signal processing remove noise artifacts before meaningful analysis. However, from bedside observations, we characterized a distinct artifact that occurs during each instance of C-Line or A-Line use. These artifacts are buried amongst physiological waveform and extraneous noise. We focus on Machine Learning (ML) models that can detect these artifacts from waveform data in real-time - finding needles in needle stacks, in order to automate the documentation of line-access. We built and evaluated ML classifiers running in real-time at a major children's hospital to achieve this goal. We demonstrate the utility of these tools for reducing documentation burden, increasing available information for bedside clinicians, and informing unit-level initiatives to improve patient safety. © 2024, CC BY.

关键词： Blood pressure

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Deficient Excitation in Parameter Learning

arXiv

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arXiv 2025年

作者： Cao, Ganghui Wang, Shimin Guay, Martin Wang, Jinzhi Duan, Zhisheng Polycarpou, Marios M. State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China Massachusetts Institute of Technology CambridgeMA02139 United States Queen’s University KingstonONK7L 3N6 Canada KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus

This paper investigates parameter learning problems under deficient excitation (DE). The DE condition is a rank-deficient, and therefore, a more general evolution of the well-known persistent excitation condition. Under the DE condition, a proposed online algorithm is able to calculate the identifiable and non-identifiable subspaces, and finally give an optimal parameter estimate in the sense of least squares. In particular, the learning error within the identifiable subspace exponentially converges to zero in the noise-free case, even without persistent excitation. The DE condition also provides a new perspective for solving distributed parameter learning problems, where the challenge is posed by local regressors that are often insufficiently excited. To improve knowledge of the unknown parameters, a cooperative learning protocol is proposed for a group of estimators that collect measured information under complementary DE conditions. This protocol allows each local estimator to operate locally in its identifiable subspace, and reach a consensus with neighbours in its non-identifiable subspace. As a result, the task of estimating unknown parameters can be achieved in a distributed way using cooperative local estimators. Application examples in system identification are given to demonstrate the effectiveness of the theoretical results developed in this paper. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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