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Multiobjective Resource Allocation for Cloud-Edge–Terminal Collaboration

IEEE Internet of Things Journal 2025年第11期12卷 17024-17033页

作者： Liu, Xin Wang, Zhaokun Zhang, Chunqing Cao, Bin Fridenfalk, Mikael Kumar Singh, Amit Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment Tianjin300401 China Uppsala University Department of Game Design Uppsala751 05 Sweden National Institute of Technology Patna Department of Computer Science Patna800005 India

This article proposes a cloud-edge–terminal collaborative resource allocation architecture that efficiently allocates resources. Traditional resource allocation often focuses solely on optimizing delay and service cost, making it less suitable for intensive real-world scenarios. In response, a comprehensive multiobjective resource allocation model is developed, encompassing delay, service cost, load balancing, and resource utilization. This article proposes a diversity-filling large-scale multiobjective evolutionary algorithm based on generative adversarial networks (DFGAN-LSMOEA). The Otsu-based grouping method in DFGAN-LSMOEA is employed to group decision variables and improve optimization performance. Compared with state-of-the-art algorithms, the proposed method validates its effectiveness and advantages in the applications, particularly when handling high-dimensional decision variables and dynamic demands. © 2014 IEEE.

关键词： Generative adversarial networks

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Dynamics of synthetic yeast chromosome evolution shaped by hierarchical chromatin organization

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National science Review 2023年第5期10卷 240-250页

作者： Sijie Zhou Yi Wu Yu Zhao Zhen Zhang Limin Jiang Lin Liu Yan Zhang Jijun Tang Ying-Jin Yuan Frontiers Science Center for Synthetic Biology (Ministry of Education) Tianjin University Key Laboratory of Systems Bioengineering(Ministry of Education) School of Chemical Engineering and TechnologyTianjin University Institute for Systems Genetics NYU Langone Health School of Computer Science and Technology College of Intelligence and ComputingTianjin University Epigenetic Group Frasergen Bioinformatics Co.Ltd. Department of Computer Science University of South Carolina

Synthetic genome evolution provides a dynamic approach for systematically and straightforwardly exploring evolutionary *** Chromosome Rearrangement and Modification by LoxP-mediated Evolution(SCRaMbLE) is an evolutionary system intrinsic to the synthetic yeast genome that can rapidly drive structural ***,we detect over 260 000 rearrangement events after the SCRaMbLEing of a yeast strain harboring 5.5 synthetic yeast chromosomes(synⅡ,synⅢ,synⅤ,circular synⅥ,synⅨR and synⅩ).Remarkably,we find that the rearrangement events exhibit a specific landscape of *** further reveal that the landscape is shaped by the combined effects of chromatin accessibili and spatial contact *** rearrangements tend to occur in 3D spatially proximal and chromatin-accessible *** enormous numbers of rearrangements mediated by SCRaMbLE provide a driving force to potentiate directed genome evolution,and the investigation of the rearrangement landscape offers mechanistic insights into the dynamics of genome evolution.

关键词： synthetic biology genome rearrangement hierarchical chromatin organization synthetic yeast genome SCRaMbLE Saccharomyces cerevisiae

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Tjong:A transformer‐based Mahjong AI via hierarchical decision‐making and fan backward

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CAAI Transactions on Intelligence technology 2024年第4期9卷 982-995页

作者： Xiali Li Bo Liu Zhi Wei Zhaoqi Wang Licheng Wu School of Information and Engineering Minzu University of ChinaBeijingChina Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of ChinaBeijingChina Department of Computer Science New Jersey Institute of TechnologyNewarkNew JerseyUSA

Mahjong,a complex game with hidden information and sparse rewards,poses significant *** Mahjong AIs require substantial hardware resources and extensive datasets to enhance AI *** authors propose a transformer‐based Mahjong AI(Tjong)via hierarchical decision‐*** utilising self‐attention mechanisms,Tjong effectively captures tile patterns and game dynamics,and it decouples the decision pro-cess into two distinct stages:action decision and tile *** design reduces de-cision complexity ***,a fan backward technique is proposed to address the sparse rewards by allocating reversed rewards for actions based on winning *** consists of 15M parameters and is trained using approximately 0.5 M data over 7 days of supervised learning on a single server with 2 *** action decision achieved an accuracy of 94.63%,while the claim decision attained 98.55%and the discard decision reached 81.51%.In a tournament format,Tjong outperformed AIs(CNN,MLP,RNN,ResNet,VIT),achieving scores up to 230%higher than its ***-more,after 3 days of reinforcement learning training,it ranked within the top 1%on the leaderboard on the Botzone platform.

关键词： decision making deep learning deep neural networks

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SoloGAN: Multi-domain Multimodal Unpaired Image-to-Image Translation via a Single Generative Adversarial Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第5期3卷 722-737页

作者： Huang, Shihua He, Cheng Cheng, Ran Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

Despite significant advances in image-to-image (I2I) translation with generative adversarial networks (GANs), it remains challenging to effectively translate an image to a set of diverse images in multiple target domains using a pair of generator and discriminator. Existing multimodal I2I translation methods adopt multiple domain-specific content encoders for different domains, where each domain-specific content encoder is trained with images from the same domain only. Nevertheless, we argue that the content (domain-invariance) features should be learned from images among all of the domains. Consequently, each domain-specific content encoder of existing schemes fails to extract the domain-invariant features efficiently. To address this issue, we present a flexible and general SoloGAN model for efficient multimodal I2I translation among multiple domains with unpaired data. In contrast to existing methods, the SoloGAN algorithm uses a single projection discriminator with an additional auxiliary classifier and shares the encoder and generator for all domains. As such, the SoloGAN model can be trained effectively with images from all domains so that the domain-invariance content representation can be efficiently extracted. Qualitative and quantitative results over a wide range of datasets against several counterparts and variants of the SoloGAN model demonstrate the merits of the method, especially for challenging I2I translation tasks, i.e., tasks that involve extreme shape variations or need to keep the complex backgrounds unchanged after translations. Furthermore, we demonstrate the contribution of each component using ablation studies. © 2020 IEEE.

关键词： Generative adversarial networks

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Integrated Clustering and Routing Design and Triangle Path Optimization for UAV-Assisted Wireless Sensor Networks

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China Communications 2024年第4期21卷 178-192页

作者： Shao Liwei Qian Liping Wu Mengru Wu Yuan College of Information Engineering Zhejiang University of TechnologyHangzhou 310014China State Key Laboratory of Internet of Things for Smart City University of MacaoMacaoChina Department of Computer and Information Science University of MacaoMacaoChina

With the development of the Internet of Things(IoT),it requires better performance from wireless sensor networks(WSNs),such as larger coverage,longer lifetime,and lower ***,a large amount of data generated from monitoring and long-distance transmission places a heavy burden on sensor nodes with the limited battery *** this,we investigate an unmanned aerial vehicles assisted mobile wireless sensor network(UAV-assisted WSN)to prolong the network lifetime in this ***,we use UAVs to assist the WSN in collecting *** the current UAV-assisted WSN,the clustering and routing schemes are determined ***,such a separate consideration might not maximize the lifetime of the whole WSN due to the mutual coupling of clustering and *** efficiently prolong the lifetime of the WSN,we propose an integrated clustering and routing scheme that jointly optimizes the clustering and routing *** the whole network space,it is intractable to efficiently obtain the optimal integrated clustering and routing ***,we propose the Monte-Las search strategy based on Monte Carlo and Las Vegas ideas,which can generate the chain matrix to guide the algorithm to find the solution *** point-to-point collection leads to long collection paths,so a triangle optimization strategy is then proposed that finds a compromise path to shorten the collection path based on the geometric distribution and energy of sensor *** avoid the coverage hole caused by the death of sensor nodes,the deployment of mobile sensor nodes and the preventive mechanism design are *** emergency data transmission mechanism is further proposed to reduce the latency of collecting the latency-sensitive data due to the absence of *** with the existing schemes,the proposed scheme can prolong the lifetime of the UAVassisted WSN at least by 360%,and shorten the collection path of UAVs by 56.24%.

关键词： Monte-Las search strategy triangle path optimization unmanned aerial vehicles wireless sensor networks

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Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge Computing

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IEEE Transactions on Vehicular technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Min Northeastern University School of Computer Science and Engineering The Engineering Research Center of Security Technology of Complex Network System Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system *** first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multiper-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Critical Path Optimization for Logic Netlists 9

Critical Path Optimization for Logic Netlists

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9th International Conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Yu, Xuewen Huang, Pengcheng Chen, Haiyan Chen, Wei School of Computer National University of Defense Technology Key Laboratory of Advanced Microprocessors Chips and Systems Changsha China School of Computer National University of Defense Technology Department of Computer Science Changsha China

ISBN: (纸本)9798331509453

It is of great concern for high performance microprocessors to optimize logic delay and improve performance in the semi-custom design flow based on commercial standard cell library. To solve this issue, the paper proposes an automated method to optimize the delay of critical path in any block. Firstly, we construct the logic cone for critical paths based on the logic netlist obtained from semi-custom design flow. Secondly, the algorithm conducts large-scale graph partitioning on the logic cone with appropriate constraints. Thirdly, using two-level optimization, we perform logic restructuring on sub graphs resulting from partitioning to get a set of new logic cells. Finally, employing a match algorithm, we pick out high-reusable restructured logic cells for full-custom circuit implementation and replace corresponding circuit structures with them in the logic netlist. The proposed algorithm picks out 5 logic cells for restructuring. Experimental results demonstrate that the approach achieves a reduction in the logic levels and logic delay can be reduced by about 10% after replacing the corresponding structures in the logic netlist. The work effectively reduces critical path delay, which has the potential to enhance the performance of microprocessors. © 2024 IEEE.

关键词： computer circuits

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Attention-Based Deep Learning Model for Image Desaturation of SDO/AIA

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Research in Astronomy and Astrophysics 2023年第8期23卷 92-102页

作者： Xinze Zhang Long Xu Zhixiang Ren Xuexin Yu Jia Li State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng Laboratory Shenzhen 518000China Department of Automation Tsinghua UniversityBeijing 100084China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

The Atmospheric Imaging Assembly(AIA)onboard the Solar Dynamics Observatory(SDO)captures full-disk solar images in seven extreme ultraviolet wave *** a violent solar flare occurs,incoming photoflux may exceed the threshold of an optical imaging system,resulting in regional saturation/overexposure of ***,the lost signal can be partially retrieved from non-local unsaturated regions of an image according to scattering and diffraction principle,which is well consistent with the attention mechanism in deep ***,an attention augmented convolutional neural network(AANet)is proposed to perform image desaturation of SDO/AIA in this *** is built on a U-Net backbone network with partial convolution and adversarial *** addition,a lightweight attention model,namely criss-cross attention,is embedded between each two convolution layers to enhance the backbone *** results validate the superiority of the proposed AANet beyond state-of-the-arts from both quantitative and qualitative comparisons.

关键词： techniques image processing-Sun atmosphere-Sun flares

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Designing artificial ion channels with strict K+/Na+ selectivity toward next-generation electric-eel-mimetic ionic power generation

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National science Review 2023年第12期10卷 193-202页

作者： Jipeng Li Linhan Du Xian Kong Jianzhong Wu Diannan Lu Lei Jiang Wei Guo State Key Laboratory of Marine Resource Utilization in South China Sea School of Materials Science and Engineering Hainan University Department of Chemical Engineering Tsinghua University South China Advanced Institute for Soft Matter Science and Technology Guangdong Provincial Key Laboratory of Functional and Intelligent Hybrid Materials and Devices School of Emergent Soft MatterSouth China University of Technology Department of Chemical and Environmental Engineering University of California Research Institute for Frontier Science Beihang University Center for Quantum Physics and Intelligent Sciences Department of Physics Capital Normal University

A biological potassium channel is> 1000 times more permeable to K+than to Na+and exhibits a giant permeation rate of ～10～8 ions/*** is a great challenge to construct artificial potassium channels with such high sele ctivity and ion conduction ***,we unveil a long-overlo oked structural feature that underpins the ultra-high K+/Na+*** carrying out massive molecular dynamics simulation for ion transport through carbonyl-oxygen-modified bi-layer graphene nanopores,we find that the twisted carbonyl rings enable strict potassium selectivity with a dynamic K+/Na+selectivity ratio of 1295 and a K+conduction rate of 3.5 × 10～7 ions/s,approaching those of the biological ***,atomic trajectories of K+permeation events suggest a dual-ion transport mode,*** like-charged potassium ions are successively captured by the nanopores in the graphene bi-layer and are interconnected by sharing one or two interlayer water *** dual-ion behavior allows rapid release of the exiting potassium ion via a soft knock-on mechanism,which has previously been found only in biological ion *** a proof-of-concept utilization of this discovery,we propose a novel way for ionic power generation by mixing KCl and NaCl solutions through the bi-layer graphene nanopores,termed potassium-permselectivity enabled osmotic power generation（PoPee-OP G）.Theoretically,the biomimetic device achieves a very high power density of> 1000 W/m2with graphene sheets of <1% *** study provides a blueprint for artificial potassium channels and thus paves the way toward next-generation electric-eel-mimetic ionic power generation.

关键词： artificial ion channel bi-layer graphene dehydrated ion transport bio-inspired materials energy conversion

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Strategic Gradient Transmission With Targeted Privacy-Awareness in Model Training: A Stackelberg Game Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4635-4648页

作者： Sun, Hezhe Wang, Yufei Yang, Huiwen Huo, Kaixuan Li, Yuzhe Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China Liaoning ATS Intelligent Transportation Technology Company Ltd. Research Institute Shenyang110004 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong999077 Hong Kong

Privacy-aware machine learning paradigms have sparked widespread concern due to their ability to safeguard the local privacy of data owners, preventing the leakage of private information to untrustworthy platforms or malicious third parties. This article focuses on characterizing the interactions between the learner and the data owner within this privacy-aware training process. Here, the data owner hesitates to transmit the original gradient to the learner due to potential cybersecurity issues, such as gradient leakage and membership inference. To address this concern, we propose a Stackelberg game framework that models the training process. In this framework, the data owner's objective is not to maximize the discrepancy between the learner's obtained gradient and the true gradient but rather to ensure that the learner obtains a gradient closely resembling one deliberately designed by the data owner, while the learner's objective is to recover the true gradient as accurately as possible. We derive the optimal encoder and decoder using mismatched cost functions and characterize the equilibrium for specific cases, balancing model accuracy and local privacy. Numerical examples illustrate the main results, and we conclude with expanding discussions to suggest future investigations into reliable countermeasure designs. © 2020 IEEE.

关键词： Game theory

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