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IEEE International Conference on Robotics and Automation (ICRA)

作者： Navid Feizi Zahra Bahrami S. Farokh Atashzar Mehrdad R. Kermani Rajni V. Patel Canadian Surgical Technologies and Advanced Robotics (CSTAR) Lawson Health Research Institute London ON Canada School of Biomedical Engineering Western University London ON Canada Institute of Geography University of Erlangen-Nuremberg Erlangen Germany Department of Mechanical and Aerospace Engineering the Department of Electrical and Computer Engineering and the Department of Biomedical Engineering New York University (NYU) New York NY USA NYU WIRELESS and NYU CUSP Department of Electrical and Computer Engineering Western University London ON Canada Department of Electrical and Computer Engineering the Department of Surgery and the Department of Clinical Neurological Sciences School of Biomedical Engineering Western University London ON Canada

Electroadhesive clutches have attracted a great deal of interest in the last decade as semi-active actuators for human-robot interaction due to their lightweight, low power consumption, and tunable high-torque output capability. However, because of the complexity of their dynamics, in most cases, they are utilized in an ON/OFF -control strategy. In this regard, the non-autonomous (time-dependent) degradation of electroadhesive behavior is an inherent challenge that injects unpredictability and uncertainty into the behavior of this family of semi-active clutches. We propose a novel approach to preventing degradation of electroadhesion using a segmented electrode design that modulates the electrical field on the dielectric surface while using a direct current signal and securing low power consumption. This paper, for the first time, presents an optimization process based on a novel analytic model of the proposed actuator. It also develops a data-driven model augmentation using a hybrid shallow learning approach composed of a long short-term memory (LSTM) architecture which is combined with the analytical model. The performance of the proposed semi-active clutch and the data-driven hybrid model is experimentally validated in this paper.

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Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels

arXiv

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arXiv 2024年

作者： Yan, Jianhao Yan, Pingchuan Chen, Yulong Li, Jing Zhu, Xianchao Zhang, Yue Zhejiang University Zhejiang China School of Engineering Westlake University Zhejiang China University College London London United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom LanBridge Group Sichuan China Institute of Advanced Technology Westlake Institute for Advanced Study China

This study presents a comprehensive evaluation of GPT-4’s translation capabilities compared to human translators of varying expertise levels. Through systematic human evaluation using the MQM schema, we assess translations across three language pairs (Chinese←→English, Russian←→English, and Chinese←→Hindi) and three domains (News, Technology, and Biomedical). Our findings reveal that GPT-4 achieves performance comparable to junior-level translators in terms of total errors, while still lagging behind senior translators. Unlike traditional Neural Machine Translation systems, which show significant performance degradation in resource-poor language directions, GPT-4 maintains consistent translation quality across all evaluated language pairs. Through qualitative analysis, we identify distinctive patterns in translation approaches: GPT-4 tends toward overly literal translations and exhibits lexical inconsistency, while human translators sometimes over-interpret context and introduce hallucinations. This study represents the first systematic comparison between LLM and human translators across different proficiency levels, providing valuable insights into the current capabilities and limitations of LLM-based translation systems. © 2024, CC BY.

关键词： Neural machine translation

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Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging

arXiv

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arXiv 2022年

作者： Angelopoulos, Anastasios N. Kohli, Amit P. Bates, Stephen Jordan, Michael I. Malik, Jitendra Alshaabi, Thayer Upadhyayula, Srigokul Romano, Yaniv Department of Electrical Engineering and Computer Science University of California Berkeley United States Advanced Bioimaging Center Department of Molecular and Cell Biology University of California Berkeley United States Department of Electrical and Computer Engineering and of Computer Science Technion Israel Institute of Technology Israel

Image-to-image regression is an important learning task, used frequently in biological imaging. Current algorithms, however, do not generally offer statistical guarantees that protect against a model's mistakes and hallucinations. To address this, we develop uncertainty quantification techniques with rigorous statistical guarantees for image-to-image regression problems. In particular, we show how to derive uncertainty intervals around each pixel that are guaranteed to contain the true value with a user-specified confidence probability. Our methods work in conjunction with any base machine learning model, such as a neural network, and endow it with formal mathematical guarantees-regardless of the true unknown data distribution or choice of model. Furthermore, they are simple to implement and computationally inexpensive. We evaluate our procedure on three image-to-image regression tasks: quantitative phase microscopy, accelerated magnetic resonance imaging, and super-resolution transmission electron microscopy of a Drosophila melanogaster brain. Copyright © 2022, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Zero crosstalk in anisotropic TM leaky mode with subwavelength grating metamaterials

Zero crosstalk in anisotropic TM leaky mode with subwaveleng...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Md Faiyaz Kabir Md Borhan Mia Ishtiaque Ahmed Nafiz Jaidye Syed Z. Ahmed Sangsik Kim Department of Electrical and Computer Engineering Texas Tech University Lubbock Texas USA Department of Physics and Astronomy Texas Tech University Lubbock Texas USA School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea

We show that a TM leaky mode realized by subwavelength gratings (SWGs) can eliminate crosstalk between proximate waveguides. Oscillating fields i n t he SWGs allow coupling coefficient components to counteract each ot... 详细信息

ISBN: (纸本)9781957171258

关键词： Crosstalk Effective medium theory Electric fields Optical systems Subwavelength gratings Waveguides

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Strong Long-Wave Infrared Optical Response in a Topological Semiconductor with a Mexican Hat Band Structure

arXiv

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arXiv 2025年

作者： Polking, Mark J. Xu, Haowei Sankar, Raman Grossklaus, Kevin Li, Ju Advanced Materials and Microsystems Group Massachusetts Institute of Technology Lincoln Laboratory 02421 United States Department of Nuclear Science and Engineering Massachusetts Institute of Technology 02139 United States Institute of Physics Academia Sinica 11529 Taiwan Department of Electrical and Computer Engineering Tufts University 02155 United States Department of Materials Science and Engineering Massachusetts Institute of Technology 02139 United States

Light sources and photodetectors operating in the far- to mid-infrared (FIR/MIR) band (8-12 μm, 0.1-0.15 eV) remain relatively poorly developed compared to their counterparts operating in the visible and near-infrared ranges, despite extensive application potential for thermal imaging, standoff sensing, and other technologies. This is attributable in part to the lack of narrow-gap materials (10 larger than that of HgxCd1-xTe (MCT), the dominant material for FIR/MIR photodetectors. A previous theoretical investigation indicated that chalcogen p and metal d band inversion in this material creates a Mexican hat band structure (MHBS), which results in a dramatic increase in the joint density of states at the optical transition edge compared to typical semiconductors. This prediction is experimentally validated here using single-crystal specimens of Pb0.7Sn0.3Se measured using temperature-dependent spectroscopic ellipsometry over a wavelength range of 1.7-20 μm (0.73-0.062 eV). These measurements demonstrate a large enhancement in extinction coefficient and refractive index characteristic of a MHBS in the vicinity of the absorption edge, in agreement with theoretical predictions. The realization of topological semiconductors with a MHBS is expected to lead to high-efficiency detectors operating in the FIR/MID range. © 2025, CC BY.

关键词： Single crystals

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On Creativity and Open-Endedness

arXiv

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arXiv 2024年

作者： Soros, L.B. Adams, Alyssa M. Kalonaris, Stefano Witkowski, Olaf Guckelsberger, Christian Department of Computer Science Barnard College United States Riken AIP Japan Cross Labs Cross Compass Ltd. Japan Graduate School of Arts and Sciences University of Tokyo Japan Earth-Life Science Institute Tokyo Institute of Technology Japan Institute for Advanced Study United States Department of Computer Science Aalto University Finland School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

Artificial Life (ALife) as an interdisciplinary field draws inspiration and influence from a variety of perspectives. Scientific progress crucially depends, then, on concerted efforts to invite cross-disciplinary dialogue. The goal of this paper is to revitalize discussions of potential connections between the fields of Computational Creativity (CC) and ALife, focusing specifically on the concept of Open-Endedness (OE);the primary goal of CC is to endow artificial systems with creativity, and ALife has dedicated much research effort into studying and synthesizing OE and artificial innovation. However, despite the close proximity of these concepts, their use so far remains confined to their respective communities, and their relationship is largely unclear. We provide historical context for research in both domains, and review the limited work connecting research on creativity and OE explicitly. We then highlight specific questions to be investigated in future work, with the eventual goals of (i) decreasing conceptual ambiguity by highlighting similarities and differences between the concepts of OE and creativity, (ii) identifying synergy effects of a research agenda that encompasses both concepts, and (iii) establishing a dialogue between ALife and CC research. © 2024, CC BY.

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Mode-Symmetry-Assisted Optical Pulling by Bound States in the Continuum

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Physical Review Letters 2024年第25期132卷 253802-253802页

作者： Hang Li Yongyin Cao Rui Feng Bojian Shi Yuzhi Shi Yang Chen Dongliang Gao Tongtong Zhu Donghua Tang Fangkui Sun Cheng-Wei Qiu Weiqiang Ding Institute of Advanced Photonics School of Physics Institute of Precision Optical Engineering School of Physics Science and Engineering Chinese Academy of Sciences Key Laboratory of Mechanical Behavior and Design of Materials Department of Precision Machinery and Precision Instrumentation School of Physical Science and Technology Collaborative Innovation Center of Suzhou Nano Science and Technology School of Optoelectronic Engineering and Instrumentation Science Department of Physics School of Science Department of Electrical and Computer Engineering Collaborative Innovation Center of Extreme Optics

Aside from optical pushing and trapping that have been implemented successfully, the transportation of objects backward to the source by the optical pulling forces (OPFs) has attracted tremendous attention, which was usually achieved by increasing the forward momentum of light. However, the limited momentum transfer between light and object greatly constrains the amplitudes of OPFs. Here, we present a mechanism to generate strong interactions between object and background through the bound states in the continuums, which can generate large OPFs without increasing the forward momentum of light. The underlying physics is the extraction of momentum from the designed background lattice units assisted by mode symmetry. This work paves the way for extraordinary optical manipulations and shows great potential for exploring the momenta of light in media.

关键词： Mechanical effects of light on material media Nanophotonics Photonic crystals

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FedRC: A Rapid-Converged Hierarchical Federated Learning Framework in Street Scene Semantic Understanding

arXiv

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arXiv 2024年

作者： Kou, Wei-Bin Lin, Qingfeng Tang, Ming Wang, Shuai Zhu, Guangxu Wu, Yik-Chung Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Shenzhen Research Institute of Big Data Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Street Scene Semantic Understanding (denoted as TriSU) is a crucial but complex task for world-wide distributed autonomous driving (AD) vehicles (e.g., Tesla). Its inference model faces poor generalization issue due to inter-city domain-shift. Hierarchical Federated Learning (HFL) offers a potential solution for improving TriSU model generalization, but suffers from slow convergence rate because of vehicles' surrounding heterogeneity across cities. Going beyond existing HFL works that have deficient capabilities in complex tasks, we propose a rapid-converged heterogeneous HFL framework (FedRC) to address the inter-city data heterogeneity and accelerate HFL model convergence rate. In our proposed FedRC framework, both single RGB image and RGB dataset are modelled as Gaussian distributions in HFL aggregation weight design. This approach not only differentiates each RGB sample instead of typically equalizing them, but also considers both data volume and statistical properties rather than simply taking data quantity into consideration. Extensive experiments on the TriSU task using across-city datasets demonstrate that FedRC converges faster than the state-of-the-art benchmark by 38.7%, 37.5%, 35.5%, and 40.6% in terms of mIoU, mPrecision, mRecall, and mF1, respectively. Furthermore, qualitative evaluations in the CARLA simulation environment confirm that the proposed FedRC framework delivers top-tier performance. © 2024, CC BY-NC-ND.

关键词： Semantics

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Large-Scale Electromagnetic Structure for Ambient RF Energy Harvesting

Large-Scale Electromagnetic Structure for Ambient RF Energy ...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Charles Ng Chi-Yuk Chiu Ross Murch Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Institute for Advanced Study The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

This research explores the integration of the Internet of Things (IoT) within a large-scale electromagnetic (EM) structure powered by innovative ambient radio frequency (RF) energy harvesting. The proposed planar Yagi antenna, designed for the GSM-900 frequency range, serves as a foundational element. Simulations reveal its efficiency, achieving a maximum gain of 5.9 dBi within the 930 to 980 MHz band. Building upon this, an 8×4 array of 32 planar Yagi antennas is devised to increase signal strength, aiming for approximately 20 dB gain. A phased-array approach with 60-degree phase shifts enhances adaptability, allowing beam tilting away from the wall. Simulations confirm a 34-degree tilt, validating the array's performance. The large-scale EM structure, with its array configuration and beamforming capabilities, presents significant promise for applications requiring heightened signal strength and adaptability. Prototyping for IoT sensor systems is underway, marking a step toward interconnected, sustainable, and intelligent environments.

关键词： Radio frequency Adaptation models Array signal processing Adaptive arrays Sensor systems Energy harvesting Structural beams

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Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving

arXiv

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arXiv 2024年

作者： Kou, Wei-Bin Lin, Qingfeng Tang, Ming Ye, Rongguang Wang, Shuai Zhu, Guangxu Wu, Yik-Chung The Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Shenzhen Research Institute of Big Data Shenzhen China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Street Scene Semantic Understanding (denoted as TriSU) is a complex task for autonomous driving (AD). However, inference model trained from data in a particular geographical region faces poor generalization when applied in other regions due to inter-city data domain-shift. Hierarchical Federated Learning (HFL) offers a potential solution for improving TriSU model generalization by collaborative privacy-preserving training over distributed datasets from different cities. Unfortunately, it suffers from slow convergence because data from different cities are with disparate statistical properties. Going beyond existing HFL methods, we propose a Gaussian heterogeneous HFL algorithm (FedGau) to address inter-city data heterogeneity so that convergence can be accelerated. In the proposed FedGau algorithm, both single RGB image and RGB dataset are modelled as Gaussian distributions for aggregation weight design. This approach not only differentiates each RGB image by respective statistical distribution, but also exploits the statistics of dataset from each city in addition to the conventionally considered data volume. With the proposed approach, the convergence is accelerated by 35.5%-40.6% compared to existing state-of-the-art (SOTA) HFL methods. On the other hand, to reduce the involved communication resource, we further introduce a novel performance-aware adaptive resource scheduling (AdapRS) policy. Unlike the traditional static resource scheduling policy that exchanges a fixed number of models between two adjacent aggregations, AdapRS adjusts the number of model aggregation at different levels of HFL so that unnecessary communications are minimized. Extensive experiments demonstrate that AdapRS saves 29.65% communication overhead compared to conventional static resource scheduling policy while maintaining almost the same performance. © 2024, CC BY-NC-ND.

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