检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

Unified Framework for Oculomotor Nerve Reconstruction: Tractography-Based Anatomical Assessment

引用

Journal of Neuroimaging 2025年第3期35卷

作者： Jiahao Huang Qingrun Zeng Ye Wu Jiawei Zhang Mengjun Li Lei Xie Mingchu Li Yuanjing Feng Institute of Information Processing and Automation College of Information Engineering Zhejiang University of Technology Hangzhou China Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Department of Radiology Second Xiangya Hospital Central South University Changsha China Department of Neurosurgery Capital Medical University Xuanwu Hospital Beijing China

Background and Purpose The oculomotor nerve (OCN) innervates the eye muscles and can be affected by inflammatory, compressive, and pathological conditions. Diffusion MRI (dMRI) tractography shows the potential ability to describe the trajectory of the OCN. However, reconstruction of the OCN in the cavernous sinus is still challenging due to the complex tissue environment at the skull base. Methods In this study, we integrated anatomical knowledge to propose a unified framework for OCN tractography, using 45 dMRI datasets from the Human Connectome Project subjects aged 22–36 years and data from four neurosurgical patients aged 41–53 years with visual behavior disorders. We first employed automatically labeled direct and indirect anatomical landmarks as reference locations for individualized tractography. Next, we compared five widely used cranial nerve reconstruction algorithms to assess the most suitable method for OCN reconstruction. Finally, we tested the combination of the unified framework and the optimized tractography method in tumor patients. Results We found that unscented Kalman filter (UKF)-2T and probabilistic tractography outperformed other methods in OCN fiber tractography, owing to their “step-by-step” fiber direction computation and multidirectional consideration, respectively. In neurosurgical patients, UKF-2T effectively reconstructed OCN fibers around lesions. Conclusion Our study provides valuable insights for researchers and clinicians in the diagnosis and treatment of OCN-related diseases and neurosurgeries.

关键词： diffusion magnetic resonance imaging Human Connectome Project neurosurgery oculomotor nerve tractography

来源：评论

学校读者我要写书评

暂无评论

衰亡链揭示互利共生生态系统中安全与崩溃的中间阶段（英文）

引用

Engineering 2024年第12期 89-98页

作者：王光炜刘雪明肖颖袁烨潘林强管晓宏高建喜张海涛 The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and Technology Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi'an Jiaotong University Tsinghua National Laboratory of Information Science and Technology Department of Automation Tsinghua University Department of Computer Science Rensselaer Polytechnic Institute

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities, such as overfishing and deforestation, and the effects of such damage on ecological stability are uncertain. Despite recent advances in experimental and theoretical studies on regime shifts and tipping points, theoretical tools for understanding the extinction chain, which is the sequence of species extinctions resulting from overexploitation, are still lacking, especially for large-scale nonlinear networked systems. In this study, we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and densities. We discovered five phases during the exploitation process: safe,partial extinction, bistable, tristable, and collapse, which enabled the optimal design of restoration strategies for degraded or collapsed systems. We validated our approach using a 20-year dataset from an eelgrass restoration project. Counterintuitively, we also found a specific region in the diagram spanning exploitation rates and competition intensities, where exploiting more species helps increase *** computational tool provides insights into harvesting, fishing, exploitation, or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Reliable Distributed Computing for Metaverse: A Hierarchical Game-Theoretic Approach

arXiv

引用

arXiv 2021年

作者： Jiang, Yuna Kang, Jiawen Niyato, Dusit Ge, Xiaohu Xiong, Zehui Miao, Chunyan Shen, Xuemin School of Electronic Information and Communications Huazhong University of Science and Technology Hubei Wuhan430074 China Shenzhen Huazhong University Science and Technology Research Institute Guangdong Shenzhen518063 China The School of Automation Guangdong University of Technology Guangdong Guangzhou510006 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

The metaverse is regarded as a new wave of technological transformation that provides a virtual space for people to interact through digital avatars. To achieve immersive user experiences in the metaverse, real-time rendering is the key technology. However, computing intensive tasks of real-time rendering from metaverse service providers cannot be processed efficiently on a single resource-limited mobile device. Alternatively, such mobile devices can offload the metaverse rendering tasks to other mobile devices by adopting the collaborative computing paradigm based on Coded Distributed Computing (CDC). Therefore, this paper introduces a hierarchical game-theoretic CDC framework for the metaverse services, especially for vehicular metaverse. In the framework, idle resources from vehicles, acting as CDC workers, are aggregated to handle intensive computation tasks in the vehicular metaverse. Specifically, in the upper layer, a miner coalition formation game is formulated based on a reputation metric to select reliable workers. To guarantee the reliable management of reputation values, the reputation values calculated based on the subjective logical model are maintained in a blockchain database. In the lower layer, a Stackelberg game based incentive mechanism is considered to attract reliable workers selected in the upper layer to participate in rendering tasks. The simulation results illustrate that the proposed framework is resistant to malicious workers. Compared with the best-effort worker selection scheme, the proposed scheme can improve the utility of metaverse service provider and average profit of CDC workers. Copyright © 2021, The Authors. All rights reserved.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Hyperchaos on the dynamics of memristive Tabu learning neuron model under influence of electromagnetic radiation: Application in biomedical data privacy

Franklin Open

引用

Franklin Open 2025年 10卷

作者： Boya, Bertrand Frederick Boui A Kengne, Jacques Nanfak, Arnaud Muni, Sishu Shankar Nkapkop, Jean de Dieu Kenmoe, Germaine Djuidje Babenko, Lyudmila Klimentyevna Unité de Recherche d'Automatique et d'Informatique Appliquée (UR-AIA) IUT-FV Bandjoun University of Dschang Bandjoun P.O. Box 134 Cameroon Laboratory of Energy-Electric and Electronic Systems Department of Physics Faculty of Science University of Yaoundé I Yaoundé P.O. Box 812 Cameroon Department of Physics Faculty of Science University of Yaoundé 1 Yaoundé Cameroon Institute of Computer Technology and Information Security Southern Federal University P.O. Box 347922 Taganrog Russian Federation Laboratory for electronics electrical engineering automation and telecommunications National Higher Polytechnic School of Douala University of Douala Douala PO Box 2701 Cameroon School of Digital Sciences Digital University Kerala Technocity campus Kerala Mangalapuram India Indian Institute of Information Technology and Management Kerala Technopark road Kerala India Department of Electrical Engineering and Industrial Computing University Institute of Technology Douala P.O. Box 8698 Cameroon

This paper presents a novel biological neural networks based on memristive Tabu learning neuron (MTLN) model influenced by electromagnetic radiation. Despite the model having an unstable equilibrium plane, numerical investigation without the influence of electromagnetic radiation reveal that when increasing the memristor strength induces complex phenomena, including the coexistence of infinite bubbles of bifurcations, offset plane coexistence of various attractors. Furthermore, electromagnetic radiation can significantly impact the system's behavior, giving rise to hyperchaotic dynamics. A decent understanding is seen between the mathematical outcomes, approved through trial execution on an Arduino microcontroller-based. The insights gained from neural dynamics could pave the way for applications in neuromorphic computing, engineering, and brain-machine interfaces. Based on the complex dynamics of the model, a data privacy of biomedical data has been build and reveals good results, and enable to resist to robust attacks. © 2024 The Author(s)

关键词： Data privacy Electromagnetic radiation Hyperchaos Microcontroller design MTLN model Offset plane coexisting behaviors

来源：评论

学校读者我要写书评

暂无评论

A novel Skin lesion prediction and classification technique: ViT-GradCAM

引用

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) 2024年第9期30卷 e70040页

作者： Muhammad Shafiq Kapil Aggarwal Jagannathan Jayachandran Gayathri Srinivasan Rajasekhar Boddu Adugna Alemayehu School of Information Engineering Qujing Normal University Qujing China. Key Laboratory of Intelligent Sensor and System Design College of Information Engineering Qujing Normal University Qujing China. Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India. Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India. Department of AIML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India. Lecturer in Software Engineering Wachemo University Hosaina Ethiopia.

BACKGROUND:Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities. MATERIALS AND METHODS:In this research, we propose a new ViT Gradient-Weighted Class Activation Mapping (GradCAM) based architecture named ViT-GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance. RESULT:The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT-GradCAM obtains better and more accurate detection and classification than other state-of-the-art deep learning-based skin lesion detection models. The architecture of ViT-GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin-specific pathologies. CONCLUSION:This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.

关键词： Grad‐CAM Multi‐Headed Self attention (MSA) linear patch projection multilayer perceptron (MLP) skin lesion transformer encoder vision transformer (ViT)

来源：评论

学校读者我要写书评

暂无评论

Adjacency Constraint for Efficient Hierarchical Reinforcement Learning

arXiv

引用

arXiv 2021年

作者： Zhang, Tianren Guo, Shangqi Tan, Tian Hu, Xiaolin Chen, Feng The Department of Automation Tsinghua University Beijing100086 China The Beijing Innovation Center for Future Chip Beijing100086 China The LSBDPA Beijing Key Laboratory Beijing100084 China The Department of Civil and Environmental Engineering Stanford University Stanford CA94305 United States The Department of Computer Science and Technology Institute for Artificial Intelligence Beijing National Research Center for Information Science and Technology State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing100084 China

Goal-conditioned Hierarchical Reinforcement Learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training inefficiency as the action space of the high-level, i.e., the goal space, is large. Searching in a large goal space poses difficulty for both high-level subgoal generation and low-level policy learning. In this paper, we show that this problem can be effectively alleviated by restricting the high-level action space from the whole goal space to a k-step adjacent region of the current state using an adjacency constraint. We theoretically prove that in a deterministic Markov Decision Process (MDP), the proposed adjacency constraint preserves the optimal hierarchical policy, while in a stochastic MDP the adjacency constraint induces a bounded state-value suboptimality determined by the MDP’s transition structure. We further show that this constraint can be practically implemented by training an adjacency network that can discriminate between adjacent and non-adjacent subgoals. Experimental results on discrete and continuous control tasks including challenging simulated robot locomotion and manipulation tasks show that incorporating the adjacency constraint significantly boosts the performance of state-of-the-art goal-conditioned HRL approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A DC-Ka-Band 7-Bit Passive Attenuator with Capacitive-Compensation-Based Bandwidth Extension Technique in 55-nm CMOS

A DC-Ka-Band 7-Bit Passive Attenuator with Capacitive-Compen...

引用

作者： Zhang, Zijiang Li, Nayu Gao, Huiyan Li, Min Wang, Shaogang Kuan, Yen-Cheng Song, Chunyi Yu, Xiaopeng Gu, Qun Jane Xu, Zhiwei Ocean College Institute of Marine Electronics and Intelligent Systems Zhejiang University Zhoushan China College of Information Science and Electronic Engineering Institute of VLSI Design Zhejiang University Hangzhou China Department of Electrical and Computer Engineering University of California at Davis DavisCA United States Engineering Research Center of Oceanic Sensing Technology and Equipment Ministry of Education Zhoushan316021 China Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province Zhoushan316021 China International College of Semiconductor Technology National Chiao Tung University Hsinchu30010 Taiwan

This article presents a 7-bit wideband passive attenuator with low insertion loss (IL) and high attenuation accuracy in 55-nm CMOS technology. The $\pi $ -type and bridge-T-type attenuation units utilize an effective capacitive compensation technique, whose bandwidth extension mechanism is detailed in a single-unit pole-zero analysis. The matching-induced performance deterioration is investigated to minimize amplitude and phase errors at the chip level. The fabricated attenuator demonstrates a 32.4-dB attenuation range with a 0.255-dB resolution and a 3.5-8.4-dB IL from dc to 32 GHz. The measured root-mean-square (rms) amplitude and phase errors are below 0.32 dB and 5.33°, respectively. The attenuator occupies a 0.054-mm2 core area and consumes negligible power. © 1963-2012 IEEE.

关键词： Insertion losses

来源：评论

学校读者我要写书评

暂无评论

Generating adjacency-constrained subgoals in hierarchical reinforcement learning 20

Generating adjacency-constrained subgoals in hierarchical re...

引用

Proceedings of the 34th International Conference on Neural information Processing systems

作者： Tianren Zhang Shangqi Guo Tian Tan Xiaolin Hu Feng Chen Department of Automation Tsinghua University Department of Civil and Environmental Engineering Stanford University Department of Computer Science and Technology Tsinghua University and Beijing National Research Center for Information Science and Technology and State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University and Beijing Innovation Center for Future Chip and LSBDPA Beijing Key Laboratory

ISBN: (纸本)9781713829546

Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training inefficiency as the action space of the high-level, i.e., the goal space, is often large. Searching in a large goal space poses difficulties for both high-level subgoal generation and low-level policy learning. In this paper, we show that this problem can be effectively alleviated by restricting the high-level action space from the whole goal space to a k-step adjacent region of the current state using an adjacency constraint. We theoretically prove that the proposed adjacency constraint preserves the optimal hierarchical policy in deterministic MDPs, and show that this constraint can be practically implemented by training an adjacency network that can discriminate between adjacent and non-adjacent subgoals. Experimental results on discrete and continuous control tasks show that incorporating the adjacency constraint improves the performance of state-of-the-art HRL approaches in both deterministic and stochastic environments.

关键词：

来源：评论

学校读者我要写书评

暂无评论

sCompile: Critical Path Identification and Analysis for Smart Contracts 21st

sCompile: Critical Path Identification and Analysis for Smar...

引用

21st International Conference on Formal Engineering Methods, ICFEM 2019

作者： Chang, Jialiang Gao, Bo Xiao, Hao Sun, Jun Cai, Yan Yang, Zijiang Department of Computer Science Western Michigan University KalamazooMI49009 United States Pillar of Information System Technology and Design Singapore University of Technology and Design Singapore Singapore School of Information Systems Singapore Management University Singapore Singapore State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030324087

Ethereum smart contracts are an innovation built on top of the blockchain technology, which provides a platform for automatically executing contracts in an anonymous, distributed, and trusted way. The problem is magnified by the fact that smart contracts, unlike ordinary programs, cannot be patched easily once deployed. It is important for smart contracts to be checked against potential vulnerabilities. In this work, we propose an alternative approach to automatically identify critical program paths (with multiple function calls including inter-contract function calls) in a smart contract, rank the paths according to their criticalness, discard them if they are infeasible or otherwise present them with user friendly warnings for user inspection. We identify paths which involve monetary transaction as critical paths, and prioritize those which potentially violate important properties. For scalability, symbolic execution techniques are only applied to top ranked critical paths. Our approach has been implemented in a tool called sCompile, which has been applied to 36,099 smart contracts. The experiment results show that sCompile is efficient, i.e., 5 s on average for one smart contract. Furthermore, we show that many known vulnerabilities can be captured if user inspects as few as 10 program paths generated by sCompile. Lastly, sCompile discovered 224 unknown vulnerabilities with a false positive rate of 15.4% before user inspection. © 2019, Springer Nature Switzerland AG.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：