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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Hao, Yijun Yang, Shusen Li, Fang Zhang, Yifan Zhao, Cong Ren, Xuebin Zhao, Peng Xu, Chenren Wang, Shibo Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Xi’an710049 China Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an710049 China Peking University School of Computer Science Beijing100871 China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University Xi'an710049 China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports 3.12x more demand when compared to related existing approaches. © 1983-2012 IEEE.

关键词： Network topology

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Face Reconstruction-Based Generalized Deepfake Detection Model with Residual Outlook Attention

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ACM Transactions on Multimedia Computing, Communications and Applications 2025年第4期21卷 1-19页

作者： Shi, Zenan Liu, Wenyu Chen, Haipeng College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

With the continuous development of deep counterfeiting technology, the information security in our daily life is under serious threat. While existing face forgery detection methods exhibit impressive accuracy when applied to datasets such as FaceForensics++ and Celeb-DF, they falter significantly when confronted with out-of-domain scenarios. This causes specialization of learned representations to known forgery patterns presented in the training set, rendering it difficult to detect forgeries with unknown patterns. To address this challenge, we propose a novel end-to-end Face Reconstruction-Based Generalized Deepfake Detection (FRG2D) model with Residual Outlook Attention (ROA), which emphasizes the robust visual representations of genuine faces and discerns the subtle differences between authentic and manipulated facial images. Our methodology entails reconstructing authentic face images using an encoder-decoder architecture based on U-net, facilitating a deeper understanding of disparities between genuine and manipulated facial images. Furthermore, we integrate the convolutional block attention module (CBAM) and channel attention block (CAB) to selectively focus the network's attention on salient features within real face images. Furthermore, we employ ROA to guide the network's focus towards precise features within manipulated facial images. Simultaneously, the computed reconstruction differences obtained through ROA serves as the ultimate representation fed into the classifier for face forgery detection. Both the reconstruction and classification learning processes are optimized end-to-end. Through extensive experimentation, our model demonstrated a substantial improvement in deepfake detection across unknown domains, while maintaining a high accuracy within the known domain. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Deep learning

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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Electrochemical extraction of strontium from molten salts using reactive zincand aluminum electrodes

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International Journal of Minerals,Metallurgy and Materials 2025年第4期32卷 892-901页

作者： Yongcheng Zhang Taiqi Yin Lei Zhang Xiaochen Zhang Tao Bo Xiaoli Tan Mei Li Wei Han Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China College of Materials Science and Chemical Engineering Harbin Engineering UniversityHarbin 150001China MOE Key Laboratory of Resources and Environmental System Optimization College of Environmental Science and EngineeringNorth China Electric Power UniversityBeijing 102206China Qianwan Institute of CNITECH Ningbo 315336China

Herein, the electrochemical behaviors of Sr on inert W electrode and reactive Zn/Al electrodes were systematically investig-ated in LiCl–KCl–SrCl2molten salts at 773 K using various electrochemical methods. The chemical reaction potentials of Li and Sr on re-active Zn/Al electrodes were determined. We observed that Sr could be extracted by decreasing the activity of the deposited metal Sr onthe reactive electrode, although the standard reduction potential of Sr(II)/Sr was more negative than that of Li(I)/Li. The electrochemicalextraction products of Sr on reactive Zn and Al electrodes were Zn13Sr and Al4Sr, respectively, with no codeposition of Li *** on the density functional theory calculations, both Zn13Sr and Al4Sr were identified as stable intermetallic compounds with Zn-/Al-rich phases. In LiCl–KCl molten salt containing 3wt% SrCl2, the coulombic efficiency of Sr in the Zn electrode was ~54%. The depolar-ization values for Sr on Zn and Al electrodes were 0.864 and 0.485 V, respectively, exhibiting a stronger chemical interaction between Znand Sr than between Al and Sr. This study suggests that using reactive electrodes can facilitate extraction of Sr accumulated while elec-trorefining molten salts, thereby enabling the purification and reuse of the salt and decreasing the volume of the nuclear waste.

关键词： strontium reductive extraction molten salt depolarization effect reactive electrode

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Histological Image Diagnosis of Breast Cancer Based on Multi-Attention Convolution Neural Network

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 91-106页

作者： XU Wangwang XU Liangfeng LIU Ninghui LU Na Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei230601China School of Computer and Information Hefei University of TechnologyHefei230601China First Affiliated Hospital of Anhui Medical University Hefei230022China

Breast cancer is a serious and high morbidity disease in women,and it is the main cause of cancer death in ***,getting tested and diagnosed early can reduce the risk of *** present,there are clinical examinations,imaging screening and biopsies,among which histopathological examination is the gold ***,the process is complicated and time-consuming,and misdiagnosis may *** paper puts forward a classification framework based on deep learning,introducing multi-attention mechanism,selecting kernel convolution instead of ordinary convolution,and using different weights and combinations to pay attention to the accuracy index and growth rate of the *** addition,we also compared the learning rate *** function can fine-tune the learning rate to achieve good performance,using label softening to reduce the loss error caused by model error recognition in the label,and assigning different category weights in the loss function to balance the positive and negative *** used the BreakHis data set to automatically classify histological images into benign and malignant,four categories and eight *** results showed that the accuracy of binary classifications ranged from 98.23%to 98.83%,and that of multiple classifications ranged from 97.89%to 98.11%.

关键词： breast cancer deep learning attentional mechanism classification diagnosis

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Distributed Leader-Following Formation Control of Networked Mobile Robots via Global Orientation Estimation

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 2135-2150页

作者： Wang, Siqi Wang, Heng Che, Weiwei Li, Qing University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing100083 China Northeastern University College of Information Science and Engineering Shenyang110819 China

This article proposes a novel distributed leader-following formation control strategy for multiple networked mobile robots, utilizing the relative position measurements among robots. In particular, the leader is assumed to have the same kinematics as the followers and all the robots do not rely on orientation measurements. Firstly, a global orientation estimation law is proposed in the sense that the followers' orientation information is estimated in the leader's reference frame, only based on the leader's orientation estimation and relative bearing information. Secondly, since the leader is not directly connected to all the followers, a new distributed state observer is designed for each follower to estimate the leader's states. Especially, the designed observer not only removes the algebraic loops issue but also eliminates the requirement for the leader's acceleration information. Furthermore, a distributed formation control law is proposed by incorporating the previous estimations, and it is proved that the closed-loop system consisting of the observer and controller is asymptotically stable. Finally, simulation results validate the effectiveness and superiority of the proposed method. © 2013 IEEE.

关键词： Observers Formation control Estimation Robot sensing systems Position measurement Robot kinematics Mobile robots Topology Decentralized control Accuracy

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Learning-based Bundling Strategy for Two Products under Uncertain Consumer's Valuations

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IEEE Transactions on engineering Management 2025年 72卷 1970-1982页

作者： Fang, Lang Tang, Jiafu Pan, Zhendong Dongbei University of Finance and Economics School of Management Science & Engineering Liaoning Key Laboratory of Big Data Management and Optimization Decision Dalian116025 China

Should a firm engage in bundling to boost revenue when consumer's valuations of products are heterogeneous and uncertain? In recent years, technological advances have made it possible for firms to use large amounts of available data to make decisions under demand-side information uncertainty. However, it remains unclear exactly how they can dynamically optimize bundling and pricing decisions by learning from uncertain consumer's valuations. To answer this question, we study the bundling and pricing decisions of a monopoly firm offering a basic product and a premium product over a finite T periods. We first analyze the equilibrium outcomes of pure component strategies (PCS) and pure bundling strategies (PBS) under deterministic consumer's valuations. We then introduce a learning-based bundling strategy (LBBS) framework to make decisions dynamically over time. It employs Thompson Sampling to estimate the fractions of low-valuation consumers (LVC) of two products, allowing the firm to adjust its decisions based on updated beliefs about consumer's valuation distributions. We demonstrate the robust performance of the LBBS and show the interesting findings. That is, the fraction of LVC of premium product, the ratio between high- and low-valuation (RHL) of basic product, the ratio of low-valuation (RLV) and the ratio of high-valuation (RHV) of two products wield significant influence on the adoption of bundling strategy and its possibility. These findings offer practical guidance to firm's practitioners regarding when and which PBS to adopt and how to improve PBS decisions according to the fractions of LVC of products. We also extend the model to uniform distribution of consumer's valuations and correlated consumer's valuations to test our results. © 1988-2012 IEEE.

关键词： Cost accounting Pricing Training data mining Artificial intelligence Uncertainty Profitability Supply chain management Sustainable development Surveys

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Two efficient beamforming methods for hybrid IRS-aided AF relay wireless networks

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Science China(Information Sciences) 2025年第4期68卷 363-379页

作者： Xuehui WANG Qingbo LI Wen ZHU Feng SHU Mengxing HUANG Fuhui ZHOU Riqing CHEN Cunhua PAN Yongpeng WU Jiangzhou WANG School of Mathematics and Statistics Hainan Normal University Key Laboratory of Data Science and Smart Education Ministry of Education Hainan Normal University School of Information and Communication Engineering Hainan University Collaborative Innovation Center of Information Technology Hainan University School of Electronic and Optical Engineering Nanjing University of Science and Technology College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Digital Fujian Institute of Big Data for Agriculture Fujian Agriculture and Forestry University National Mobile Communications Research Laboratory Southeast University Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Jiao Tong University School of Engineering University of Kent

Owing to its ability to mitigate the double-fading effect by amplifying the reflected signal, the active intelligent reflecting surface(IRS) has garnered significant attention. In this paper, an amplify-and-forward(AF) relay network assisted by a hybrid IRS consisting of both passive and active units is developed. A signal-to-noise ratio(SNR) maximization problem is formulated, where the AF relay beamforming matrix and the hybrid IRS reflecting coefficient matrices for two-time slots need to be optimized. To address the SNR maximization problem, this paper proposes both a high-performance(HP) method and a low-complexity(LC) method. The HP method is based on the semidefinite relaxation and fractional programming(SDR-FP)algorithm, with rank-1 solutions obtained through Gaussian randomization. For the LC method, the amplification coefficient of each active IRS element is assumed to be equal. The SNR maximization problem is then addressed using the whitening filter,generalized power iteration, and generalized Rayleigh-Ritz(WF-GPI-GRR) approach. Simulation results show that compared with the benchmarks, such as the passive IRS-aided AF relay network, the proposed HP-SDR-FP and WF-GPI-GRR methods achieve significant rate improvements. In particular, the HP-SDR-FP and WF-GPI-GRR methods yield more than a 135.0%rate gain when the transmit power Ps of the source is 10 dBm. Furthermore, the proposed HP-SDR-FP method outperforms the WF-GPI-GRR method in terms of rate performance.

关键词： double-fading intelligent reflecting surface active elements passive elements hybrid IRS AF relay

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A level set model with shape prior constraint for intervertebral disc MRI image segmentation

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Multimedia Tools and Applications 2025年第9期84卷 6755-6783页

作者： Tian, Zhuangzhou Wang, Shengsheng College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

Accurate intervertebral disc image segmentation is necessary for further treatment. However, existing methods are difficult to segment due to the intensity inhomogeneity of intervertebral disc MRI images and the similarity in intensity between the intervertebral disc and the surrounding regions. This paper proposes a novel level set model by introducing a shape prior constraint for automatic and accurate segmentation. We utilize a detection model and intensity change to obtain rectangle prior information, which is used to define a shape constraint term. Then, combining data and regularizing terms construct an energy function and employ the steepest descent method minimization to get segmentation results. Finally, we apply the proposed new method to segment intervertebral disc images and get promising results. Extensive experiments and comparisons show that our model is more efficient and accurate. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image segmentation

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Liquid metal assistant self-propagating high-temperature synthesis of S-containing high-entropy MAX-phase materials

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材料科学技术（英文版） 2025年第6期209卷 1-8页

作者： Donglong Bai Qiang Wang Bin Deng Yang Li Ao Huang Zitong Cheng Yun Zhao Jing Li Wei Yao Jianguang Xu School of Materials and Energy Shanghai Key Laboratory of Engineering Materials Application and EvaluationShanghai Polytechnic UniversityShanghai 201209China Shanghai Thermophysical Properties Big Data Professional Technical Service Platform Shanghai Engineering Research Center of Advanced Thermal Functional MaterialsShanghai 201209China School of Materials Science and Engineering Yancheng Institute of TechnologyYancheng 224000China School of Materials and Energy Shanghai Key Laboratory of Engineering Materials Application and EvaluationShanghai Polytechnic UniversityShanghai 201209China

Due to their high-entropy effects,the high-entropy(HE)MAX-phase materials improve the comprehen-sive performance of MAX phases,opening up more possibilities for practical engineering ***,it is still challenging to obtain S-containing high-entropy MAX phases because of the high volatilization behavior of sulfur,suffering from issues such as high reaction temperature and long re-action time of traditional synthesis *** paper proposes a novel process named as liquid metal assistant self-propagating high-temperature synthesis(LMA-SHS)for efficient synthesis of high-purity S-containing high-entropy MAX-phase ***-melting-point metal(Sn or In)has been introduced into the raw mixture and melted into a liquid phase during the early stage of the SHS *** serv-ing as a"binder"between transition metal atoms of the M-site due to the negative mixing enthalpy,this liquid phase can accelerate mass and heat transfer during the SHS process,ensuring a uniform solid solution of each element and realizing the synthesis of high-purity(TiNbVZr)2SC in an extremely short *** synthesis method for high-entropy MAX-phase materials developed in this study,i.e.,LMA-SHS,showing very short reaction time,low energy consumption,high yield,and low cost,has the promise to be a general energy-and resource-efficient route towards high-purity HE materials.

关键词： High-entropy S-containing MAX phases Liquid metal LMA-SHS

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