检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Zhu, Fenghao Wang, Xinquan Li, Xinyi Zhang, Maojun Chen, Yixuan Huang, Chongwen Yang, Zhaohui Chen, Xiaoming Zhang, Zhaoyang Jin, Richeng Huang, Yongming Feng, Wei Yang, Tingting Bai, Baoming Gao, Feifei Yang, Kun Liu, Yuanwen Muhaidat, Sami Yuen, Chau Huang, Kaibin Wong, Kai-Kit Niyato, Dusit Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Automation Southeast University Nanjing210096 China Tsinghua University Beijing100084 China Pengcheng Laboratory Shenzhen518066 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China Suzhou215163 China The University of Hong Kong Hong Kong KU 6G Research Center Computer and Communication Engineering Khalifa University Abu Dhabi127788 United Arab Emirates School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is the wireless large AI model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, elucidating its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges hindering the practical implementation of WLAM and discuss pivotal future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Sorting

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

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When Brain-inspired AI Meets AGI

arXiv

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arXiv 2023年

作者： Zhao, Lin Zhang, Lu Wu, Zihao Chen, Yuzhong Dai, Haixing Yu, Xiaowei Liu, Zhengliang Zhang, Tuo Hu, Xintao Jiang, Xi Li, Xiang Zhu, Dajiang Shen, Dinggang Liu, Tianming School of Computing The University of Georgia Athens30602 United States Department of Computer Science and Engineering The University of Texas at Arlington Arlington76019 United States MOE Key Laboratory for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu611731 China School of Automation Northwestern Polytechnical University Xi’an710072 China Department of Radiology Massachusetts General Hospital Harvard Medical School Boston02115 United States School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200230 China Shanghai Clinical Research and Trial Center Shanghai201210 China

Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do. To achieve this, AGI researchers draw inspiration from the human brain and seek to replicate its principles in intelligent machines. Brain-inspired artificial intelligence is a field that has emerged from this endeavor, combining insights from neuroscience, psychology, and computer science to develop more efficient and powerful AI systems. In this article, we provide a comprehensive overview of brain-inspired AI from the perspective of AGI. We begin with the current progress in brain-inspired AI and its extensive connection with AGI. We then cover the important characteristics for both human intelligence and AGI (e.g., scaling, multimodality, and reasoning). We discuss important technologies toward achieving AGI in current AI systems, such as in-context learning and prompt tuning. We also investigate the evolution of AGI systems from both algorithmic and infrastructural perspectives. Finally, we explore the limitations and future of AGI. © 2023, CC BY.

关键词： Brain

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Camera Frame Misalignment in a Teleoperated Eye-in-Hand Robot: Effects and a Simple Correction Method

arXiv

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arXiv 2021年

作者： Wu, Liao Yu, Fangwen Do, Thanh Nho Wang, Jiaole The School of Mechanical and Manufacturing Engineering University of New South Wales Sydney Australia The Center for Brain Inspired Computing Research The Department of Precision Instrument Tsinghua University Beijing China University of New South Wales Sydney Australia The School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China

Misalignment between the camera frame and the operator frame is commonly seen in a teleoperated system and usually degrades the operation performance. The effects of such misalignment have not been fully investigated for eye-in-hand systems - systems that have the camera (eye) mounted to the end-effector (hand) to gain compactness in confined spaces such as in endoscopic surgery. This paper provides a systematic study on the effects of the camera frame misalignment in a teleoperated eye-in-hand robot and proposes a simple correction method in the view display. A simulation is designed to compare the effects of the misalignment under different conditions. Users are asked to move a rigid body from its initial position to the specified target position via teleoperation, with different levels of misalignment simulated. It is found that misalignment between the input motion and the output view is much more difficult to compensate by the operators when it is in the orthogonal direction (∼40s) compared with the opposite direction (∼20s). An experiment on a real concentric tube robot with an eye-in-hand configuration is also conducted. Users are asked to telemanipulate the robot to complete a pick-and-place task. Results show that with the correction enabled, there is a significant improvement in the operation performance in terms of completion time (mean 40.6%, median 38.6%), trajectory length (mean 34.3%, median 28.1%), difficulty (50.5%), unsteadiness (49.4%), and mental stress (60.9%). A video demonstrating the experiments can be found at https://***/EQILlwoS_Cw. Copyright © 2021, The Authors. All rights reserved.

关键词： Cameras

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High-Speed Low-Cost Line-Field Spectral-Domain Optical Coherence Tomography for Industrial Applications

SSRN

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SSRN 2024年

作者： Yang, Xingyu Zhang, Zijian Li, Xinhua Lin, Hungyen Lawman, Samuel Stoyanov, Stoyan Tilford, Timothy Hu, Yihua Shen, Yaochun Zheng, Yalin Department of Electrical Engineering and Electronics University of Liverpool LiverpoolL69 3GJ United Kingdom Department of Eye and Vision Sciences University of Liverpool LiverpoolL7 8TX United Kingdom Department of Automation Tsinghua University Beijing100084 China School of Engineering Lancaster University LancasterLA1 4YW United Kingdom School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom Department of Engineering King’s College London LondonWC2R 2LS United Kingdom Liverpool Centre for Cardiovascular Science University of Liverpool Liverpool Heart & Chest Hospital LiverpoolL7 8TX United Kingdom St Paul’s Eye Unit Liverpool University Hospitals NHS Foundation Trust LiverpoolL7 8XP United Kingdom

The applications of Optical Coherence Tomography (OCT) systems have been extensively explored in clinical settings and have attracted growing interest in other fields. Factors such as speed and cost are often prioritized over high resolution and power efficiency in industrial applications. Modern OCT systems typically operate at the speed of 40k A-scans/s and are priced at least $50k. We report a line-field OCT with a high imaging speed of 430k A-scans/s, by operating a 2D camera in 8-bit mode, at a cost of $3.2k. The reported OCT system provides an axial resolution of 9 µm in air and achieves a maximum on-axis signal-to-noise ratio and sensitivity of 68.8 dB and 88.8 dB, respectively. We demonstrate the practical use of the system by measuring, visualizing, and segmenting samples of Printed Circuit Boards, pharmaceutical tablets, and pea seeds. © 2024, The Authors. All rights reserved.

关键词： Optical tomography

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Theoretical foundations of studying criticality in the brain

arXiv

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arXiv 2023年

作者： Tian, Yang Tan, Zeren Hou, Hedong Li, Guoqi Cheng, Aohua Qiu, Yike Weng, Kangyu Chen, Chun Sun, Pei Department of Psychology Tsinghua Laboratory of Brain and Intelligence Tsinghua University Beijing100084 China Laboratory of Advanced Computing and Storage Central Research Institute 2012 Laboratories Huawei Technologies Co. Ltd. Beijing100084 China Institute for Interdisciplinary Information Science Tsinghua University Beijing100084 China UFR de Mathématiques Université de Paris Paris75013 France Institute of Automation Chinese Academy of Science Beijing100190 China University of Chinese Academy of Science Beijing100049 China Tsien Excellence in Engineering Program School of Aerospace Engineering Tsinghua University Beijing100084 China

Criticality is hypothesized as a physical mechanism underlying efficient transitions between cortical states and remarkable information processing capacities in the brain. While considerable evidence generally supports this hypothesis, non-negligible controversies persist regarding the ubiquity of criticality in neural dynamics and its role in information processing. Validity issues frequently arise during identifying potential brain criticality from empirical data. Moreover, the functional benefits implied by brain criticality are frequently misconceived or unduly generalized. These problems stem from the non-triviality and immaturity of the physical theories that analytically derive brain criticality and the statistic techniques that estimate brain criticality from empirical data. To help solve these problems, we present a systematic review and reformulate the foundations of studying brain criticality, i.e., ordinary criticality (OC), quasi-criticality (qC), self-organized criticality (SOC), and self-organized quasi-criticality (SOqC), using the terminology of neuroscience. We offer accessible explanations of the physical theories and statistic techniques of brain criticality, providing step-by-step derivations to characterize neural dynamics as a physical system with avalanches. We summarize error-prone details and existing limitations in brain criticality analysis and suggest possible solutions. Moreover, we present a forward-looking perspective on how optimizing the foundations of studying brain criticality can deepen our understanding of various neuroscience questions. © 2023, CC BY.

关键词： Brain

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Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT

arXiv

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arXiv 2023年

作者： Xiao, Zhenxiang Chen, Yuzhong Zhang, Lu Yao, Junjie Wu, Zihao Yu, Xiaowei Pan, Yi Zhao, Lin Ma, Chong Liu, Xinyu Liu, Wei Li, Xiang Yuan, Yixuan Shen, Dinggang Zhu, Dajiang Liu, Tianming Jiang, Xi School of Life Science and Technology University of Electronic Science and Technology of China Chengdu611731 China Department of Computer Science and Engineering The University of Texas at Arlington Arlington76019 United States School of Computing The University of Georgia Athens30602 United States Glasgow College University of Electronic Science and Technology of China Chengdu611731 China School of Automation Northwestern Polytechnical University Xi’an710072 China Department of Electronic Engineering Chinese University of Hong Kong Hong Kong Department of Radiotion Oncology Mayo Clinic Arizona United States Department of Radiology Massachusetts General Hospital Harvard Medical School Boston02115 United States School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200230 China Shanghai Clinical Research and Trial Center Shanghai201210 China

Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downstream tasks. In this paper, we focus on adapting prompt design based on instruction tuning into a visual transformer model for image classification which we called Instruction-ViT. The key idea is to implement multi-modal prompts (text or image prompt) related to category information to guide the fine-tuning of the model. Based on the experiments of several image captionining tasks, the performance and domain adaptability were improved. Our work provided an innovative strategy to fuse multi-modal prompts with better performance and faster adaptability for visual classification models. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Hybrid Reinforcement Learning-based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum computing

arXiv

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arXiv 2025年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Chen, Mingzhe Kim, Dong In Shen, Xuemin College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Exploiting quantum computing at the mobile edge holds immense potential for facilitating large-scale network design, processing multimodal data, optimizing resource management, and enhancing network security. In this paper, we propose a pioneering paradigm of mobile edge quantum computing (MEQC) that integrates quantum computing capabilities into classical edge computing servers that are proximate to mobile devices. To conceptualize the MEQC, we first design an MEQC system, where mobile devices can offload classical and quantum computation tasks to edge servers equipped with classical and quantum computers. We then formulate the hybrid classical-quantum computation offloading problem whose goal is to minimize system cost in terms of latency and energy consumption. To solve the offloading problem efficiently, we propose a hybrid discrete-continuous multi-agent reinforcement learning algorithm to learn long-term sustainable offloading and partitioning strategies. Finally, numerical results demonstrate that the proposed algorithm can reduce the MEQC system cost by up to 30% compared to existing baselines. © 2025, CC BY.

关键词： Qubits

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A quantum convolutional neural network for image classification

arXiv

引用

arXiv 2021年

作者： Lü, Yanxuan Gao, Qing Lü, Jinhu Ogorzalek, MacIej Zheng, Jin School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Information Technologies Jagiellonian University Kraków30-348 Poland

Artificial neural networks have achieved great success in many fields ranging from image recognition to video understanding. However, its high requirements for computing and memory resources have limited further development on processing big data with high dimensions. In recent years, advances in quantum computing show that building neural networks on quantum processors is a potential solution to this problem. In this paper, we propose a novel neural network model named Quantum Convolutional Neural Network (QCNN), aiming at utilizing the computing power of quantum systems to accelerate classical machine learning tasks. The designed QCNN is based on implementable quantum circuits and has a similar structure as classical convolutional neural networks. Numerical simulation results on the MNIST dataset demonstrate the effectiveness of our model. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Hierarchical Learning for IRS-Assisted MEC Systems with Rate-Splitting Multiple Access

arXiv

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arXiv 2024年

作者： Wu, Yinyu Zhang, Xuhui Ren, Jinke Shen, Yanyan Yang, Bo Wang, Shuqiang Guan, Xinping Niyato, Dusit The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong518055 China The University of Chinese Academy of Sciences Beijing100049 China The Shenzhen Future Network of Intelligence Institute The School of Science and Engineering The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen University of Advanced Technology Guangdong518055 China The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) systems have shown notable improvements in efficiency, such as reduced latency, higher data rates, and better energy efficiency. However, the resource competition among users will lead to uneven allocation, increased latency, and lower throughput. Fortunately, the rate-splitting multiple access (RSMA) technique has emerged as a promising solution for managing interference and optimizing resource allocation in MEC systems. This paper studies an IRS-assisted MEC system with RSMA, aiming to jointly optimize the passive beamforming of the IRS, the active beamforming of the base station, the task offloading allocation, the transmit power of users, the ratios of public and private information allocation, and the decoding order of the RSMA to minimize the average delay from a novel uplink transmission perspective. Since the formulated problem is non-convex and the optimization variables are highly coupled, we propose a hierarchical deep reinforcement learning-based algorithm to optimize both continuous and discrete variables of the problem. Additionally, to better extract channel features, we design a novel network architecture within the policy and evaluation networks of the proposed algorithm, combining convolutional neural networks and densely connected convolutional network for feature extraction. Simulation results indicate that the proposed algorithm not only exhibits excellent convergence performance but also outperforms various benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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