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Towards Intercontinental Teleoperation: A Cloud-Based Framework for Ultra-Remote Human-Robot Dual-Arm Motion Mapping 1

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16th International Conference on intelligent Robotics and Applications, ICIRA 2023

作者： Lv, Honghao Zhou, Huiying Wang, Ruohan Wu, Haiteng Pang, Zhibo Yang, Geng State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot Hangzhou Shenhao Technology Hangzhou China Department of Automation Technology ABB Corporate Research Vasteras Sweden Stockholm Sweden

ISBN: (数字)9789819964987

ISBN: (纸本)9789819964970

Teleoperated robots offer a unique combination of robotic strength and precision, coupled with human judgment and ingenuity, to enhance productivity and safety in complex, unstructured, and hazardous industrial environments. However, teleoperation over ultra-remote distances has posed a significant challenge, with control performance being compromised by the inherent trade-off between distance and latency. This study presents a novel cloud-based human-robot motion mapping framework for ultra-remote teleoperation. An intuitive dual-arm human-robot motion mapping framework based on capturing human motion has been implemented, and private network connectivity is established using the Google Cloud platform for the efficient transfer of control data required for human-robot motion mapping. Moreover, a novel feedforward control framework is proposed to mitigate the negative impact of high network latency on teleoperation control performance. To demonstrate the feasibility of the proposed approach, an intercontinental teleoperation system between China and Sweden was developed, and a series of practical applications were demonstrated by capturing the motion of an operator in Sweden to control the dual-arm robot located over 7800 km away in China. The proposed feedforward control framework for intercontinental teleoperation has the potential utility for improving human-robot interaction performance in ultra-remote teleoperation scenarios with high network latency. More demonstration videos can be found at https://***/intercontinental-teleoperation/. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Remote control

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A Robust Semantic Communication System for Image Transmission

A Robust Semantic Communication System for Image Transmissio...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Peng, Xiang Qin, Zhijin Tao, Xiaoming Lu, Jianhua Letaief, Khaled B. Tsinghua University Department of Electronic Engineering Beijing China Beijing China State Key Laboratory of Space Network and Communications Beijing China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798350351255

Semantic communications have gained significant attention as a promising approach to address the transmission bottleneck, especially with the continuous development of 6G techniques. Distinct from the well investigated physical channel impairments, this paper focuses on semantic impairments in images, particularly those arising from adversarial perturbations. Specifically, we propose a novel metric for quantifying the intensity of semantic impairment and develop a semantic impairment dataset. Furthermore, we introduce a deep learning enabled semantic communication system, termed as DeepSC-RI, to enhance the robustness of image transmission, which incorporates a multi-scale semantic extractor with a dual-branch architecture for extracting semantics with varying granularity, thereby improving the robustness of the system. The fine-grained branch incorporates a semantic importance evaluation module to identify and prioritize crucial semantics, while the coarse-grained branch adopts a hierarchical approach for capturing the robust semantics. These two streams of semantics are seamlessly integrated via an advanced cross-attention-based semantic fusion module. Experimental results demonstrate the superior performance of DeepSC-RI under various levels of semantic impairment intensity. © 2024 IEEE.

关键词： Image communication systems

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Holistic Correction with Object Prototype for Video Object Segmentation 39

Holistic Correction with Object Prototype for Video Object S...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Qiao, Shengye Xia, Changqun Liang, Yanjie Lan, Gongjin Li, Jia State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University Beijing China Pengcheng Laboratory Shenzhen China Department of Computer Science and Technology Southern University of Science and Technology Shenzhen China

ISBN: (纸本)157735897X

Recently, memory-based methods have achieved progress in semi-supervised video object segmentation. However, these methods still suffer from unstructured challenges, such as object transformations, occlusions and disappearance-reappearance. To this end, we propose a Holistic Correction Network (HCNet) to adaptively acquire concise object prototypes for holistic correction at semantic, spatial and temporal aspects. Specifically, an Adaptive Prototype Update module is firstly designed to construct multi-level core object representations by associating object variations in consecutive frames with segmentation quality assessment. Based on the updated object prototypes, Semantic, Spatial and Temporal Correction modules are respectively designed to enhance the object semantics in the entire frame, eliminate the incorrect semantic enhancement outside the object regions and calibrate the estimated object regions with temporal changes of objects. Through the holistic correction mechanism with effective object prototypes, our proposed HCNet can robustly and efficiently deal with diverse complex scenarios. Extensive and comprehensive experiments conducted on seven datasets demonstrate that our proposed HCNet can significantly improve the segmentation performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantic Segmentation

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Spatial adaptivity with boundary refinement for smoothed particle hydrodynamics fluid simulation

Spatial adaptivity with boundary refinement for smoothed par...

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作者： Xu, Yanrui Song, Chongming Wang, Xiaokun Ban, Xiaojuan Wang, Jiamin Zhang, Yalan Chang, Jian School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science University of Science and Technology Beijing Beijing China National Centre for Computer Animation Bournemouth University Poole United Kingdom Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Perception and Control of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China

Fluid simulation is well-known for being visually stunning while computationally expensive. Spatial adaptivity can effectively ease the computational cost by discretizing the simulation space with varying resolutions. Adaptive methods nowadays mainly focus on the mechanism of refining the fluid surfaces to obtain more vivid splashes and wave effects. But such techniques hinder further performance gain under the condition where most of the vast fluid surface is tranquil. Moreover, energetic flow beneath the surface cannot be adequately captured with the interior of the fluid still being simulated under coarse discretization. This article proposes a novel boundary-distance based adaptive method for smoothed particle hydrodynamics fluid simulation. The signed-distance field constructed with respect to the coupling boundary is introduced to determine particle resolution in different spatial positions. The resolution is maximal within a specific distance to the boundary and decreases smoothly as the distance increases until a threshold is reached. The sizes of the particles are then adjusted towards the resolution via splitting and merging. Additionally, a wake flow preservation mechanism is introduced to keep the particle resolution at a high level for a period of time after a particle flows through the boundary object to prevent the loss of flow details. Experiments show that our method can refine fluid–solid coupling details more efficiently and effectively capture dynamic effects beneath the surface. © 2023 The Authors. computer Animation and Virtual Worlds published by John Wiley & Sons Ltd.

关键词： Hydrodynamics

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FULLY POLARIZED RADAR TARGET DETECTION AND WAVEFORM OPTIMIZATION UNDER RAINY CONDITION

FULLY POLARIZED RADAR TARGET DETECTION AND WAVEFORM OPTIMIZA...

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IET International Radar Conference 2023, IRC 2023

作者： Li, Xinda Cheng, Xu Ju, Xinjie Wang, Xuesong Li, Jianbing College of Electronic Science and Technology National University of Defense Technology Hunan Changsha410073 China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Hunan Changsha410073 China School of Computer Science and Engineering Wuhan Institute of Technology Wuhan430205 China Hubei Key Laboratory of Intelligent Robot Wuhan430205 China

ISBN: (纸本)9781839539954

Polarization and waveform optimization can significantly improve the detection ability of fully polarized radar. However, in practical scenarios, atmospheric particles frequently induce depolarization effects on radar waves, thereby influencing precise target detection, a phenomenon predominantly observed with raindrop particles. This paper establishes a target detection model for fully polarized radar in rainy condition. Subsequently, a joint transceiver iterative optimization algorithm is devised, utilizing the maximum output signal-to-interference plus noise ratio (SINR) of the system as the benchmark, and imposing dual constraints of energy and resemblance on the transmitting waveform. Ultimately, the effectiveness of the proposed method under rainy condition is validated through simulation, and recommendations for engineering applications are provided based on the analysis. © The Institution of Engineering & technology 2023.

关键词： Signal to noise ratio

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Global Stability of a Viral Infection Model with Defectively Infected Cells and Latent Age

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Chinese Annals of Mathematics,Series B 2024年第4期45卷 555-572页

作者： Jianquan LI Yuming CHEN Peijun ZHANG Dian ZHANG School of Computer Science Xijing UniversityXi’an 710123China Department of Mathematics Wilfrid Laurier UniversityWaterlooON N2L 3C5Canada Corresponding author.Xi’an Key Laboratory of Human-Machine Integration and Control Technology for Intelligent Rehabilitation Xijing UniversityXi’an 710123China Equal contributor.Department of Immunology Xi’an Medical UniversityXi’an 710021China

The authors propose and analyze a viral infection model with defectively infected cells and age of the latently infected *** existence of steady states is determined by the basic reproduction number of *** the Lyapunov's direct method,they establish a threshold dynamics of the model with the basic reproduction number of virus as the threshold *** achieve it,a novel procedure is *** novelties are *** one hand,the coefficients involved in the specific forms of the used Lyapunov functionals for the two feasible steady states are determined by the same set of *** the other hand,for the infection steady state,a new approach is proposed to check whether the derivative of the Lyapunov functional candidate along solutions is negative(semi-)definite or *** procedure not only simplifies the analysis but also exhibits the relationship between the two Lyapunov functionals for the two feasible steady ***,the procedure is expected to be applicable for other similar models.

关键词： Viral infection model Basic reproduction number Equilibrium Global stability Lyapunov direct method Age structure

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Large Language Models as Evolutionary Optimizers 13

Large Language Models as Evolutionary Optimizers

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Liu, Shengcai Chen, Caishun Qu, Xinghua Tang, Ke Ong, Yew-Soon Southern University of Science and Technology Centre for Frontier Ai Research A∗star Department of Computer Science and Engineering China Centre for Frontier Ai Research A∗star Singapore Tianqiao & Chrissy Chen Institute China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering China Nanyang Technological University Centre for Frontier Ai Research A∗star College of Computing and Data Science Singapore

ISBN: (纸本)9798350308365

Evolutionary algorithms (EAs) have achieved remarkable success in tackling complex combinatorial optimization problems. However, EAs often demand carefully-designed operators with the aid of domain expertise to achieve satisfactory performance. In this work, we present the first study on large language models (LLMs) as evolutionary combinatorial optimizers. The main advantage is that it requires minimal domain knowledge and human efforts, as well as no additional training of the model. This approach is referred to as LLM-driven EA (LMEA). Specifically, in each generation of the evolutionary search, LMEA instructs the LLM to select parent solutions from current population, and perform crossover and mutation to generate offspring solutions. Then, LMEA evaluates these new solutions and include them into the population for the next generation. LMEA is equipped with a self-adaptation mechanism that controls the temperature of the LLM. This enables it to balance between exploration and exploitation and prevents the search from getting stuck in local optima. We investigate the power of LMEA on the classical traveling salesman problems (TSPs) widely used in combinatorial optimization research. Notably, the results show that LMEA performs competitively to traditional heuristics in finding high-quality solutions on TSP instances with up to 20 nodes. Additionally, we also study the effectiveness of LLM-driven crossover/mutation and the self- adaptation mechanism in evolutionary search. In summary, our results reveal the great potentials of LLMs as evolutionary optimizers for solving combinatorial problems. We hope our research shall inspire future explorations on LLM-driven EAs for complex optimization challenges. © 2024 IEEE.

关键词： Traveling salesman problem

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Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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Why go full? elevating federated learning through partial network updates 24

Why go full? elevating federated learning through partial ne...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Haolin Wang Xuefeng Liu Jianwei Niu Wenkai Guo Shaojie Tang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China and Zhongguancun Laboratory Beijing China Center for AI Business Innovation Department of Management Science and Systems University at Buffalo Buffalo New York

ISBN: (纸本)9798331314385

Federated learning is a distributed machine learning paradigm designed to protect user data privacy, which has been successfully implemented across various scenarios. In traditional federated learning, the entire parameter set of local models is updated and averaged in each training round. Although this full network update method maximizes knowledge acquisition and sharing for each model layer, it prevents the layers of the global model from cooperating effectively to complete the tasks of each client, a challenge we refer to as layer mismatch. This mismatch problem recurs after every parameter averaging, consequently slowing down model convergence and degrading overall performance. To address the layer mismatch issue, we introduce the FedPart method, which restricts model updates to either a single layer or a few layers during each communication round. Furthermore, to maintain the efficiency of knowledge acquisition and sharing, we develop several strategies to select trainable layers in each round, including sequential updating and multi-round cycle training. Our theoretical analysis and experimental results show that the FedPart method significantly outperforms traditional full-network update strategies, achieving faster convergence, greater accuracy, and reduced communication and computational overhead.

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High Efficiency and Brightness Quantum Rods Light Emitting Diode

High Efficiency and Brightness Quantum Rods Light Emitting D...

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International Symposium, Seminar, and Exhibition, Display Week 2024

作者： Liao, Zebing Prodanov, Maksym F. Mallem, Kumar Kang, Chengbin Song, Jianxin Bhadra, Debjyoti Srivastava, Abhishek K. State Key Laboratory on Advanced Displays and Optoelectronics Technologies and Centre for Display Research Department of Electronics and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong

Despite substantial efforts, the maximum external quantum efficiency (EQE) of red, green, and blue quantum dots remains close to 25%, limited by the outcoupling efficiency of specific device optical structures. Further improvements have primarily focused on optimizing light management. In this study, we employed rodshaped nanocrystals instead of sphere-like quantum dots as the light-emitting layer. The theoretical device outcoupling efficiency significantly increased to nearly 41% due to the intrinsic horizontal dipole alignment within the quantum rod film, nearly doubling the efficiency compared to quantum dot light-emitting diodes (QDLEDs). As a result, we achieved record-high EQEs of 14.1% for green quantum rod light-emitting diodes, with maximum brightness exceeding 150,000 cd/m2, respectively. Our research demonstrates the immense potential of QRLEDs for display and lighting applications, paving a new way for the commercialization of nanocrystal particles. © 2024, John Wiley and Sons Inc. All rights reserved.

关键词： Semiconductor quantum dots

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