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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

Frontiers of Computer science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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Slippery quartz surfaces for anti‐fouling optical windows

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Droplet 2023年第1期2卷 47-55页

作者： Minjing Li Tongzhen Yang Qing Yang Shaokun Wang Zheng Fang Yang Cheng Xun Hou Feng Chen School of Mechanical Engineering Xi'an Jiaotong UniversityXi'anChina State Key Laboratory for Manufacturing System Engineering and Shaanxi Key Laboratory of Photonics Technology for Information School of Electronic Science and EngineeringXi'an Jiaotong UniversityXi'anChina

The surface of camera‐based medical devices is easily smeared by blood and fog during the surgical procedure,causing visual field loss and bringing great distress to both doctors and *** this article,a slippery liquid‐infused porous surface(SLIPS)on a quartz window surface that can repel various liquids,especially blood droplets is reported.A femtosecond laser pulse train was used to create periodic microhole structures on the silica *** subsequent low surface energy treatment and lubricant infusion led to the successful preparation of a slippery *** blood‐repellent windows exhibited high transparency,great antifogging,and antibacterial *** addition,the slippery ability of the as‐prepared surface exhibited outstanding stability since the surface could withstand harsh treatments/environments,such as repeated pipette scratches and immersion in different pH *** as‐prepared millimeter‐sized quartz samples with SLIPS were attached to the endoscope lens as a protective coating and could maintain high visibility after repeated immersion in *** believe that the coating developed in this study will provide inspiration for the design of next‐generation endoscopes or other camera‐guided devices that will resist fouling,keep clear vision,and reduce operation time,thus offering great potential applications in lesion diagnosis and therapy.

关键词： quartz fouling visibility

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Representation learning: serial-autoencoder for personalized recommendation

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Frontiers of Computer science 2024年第4期18卷 61-72页

作者： Yi ZHU Yishuai GENG Yun LI Jipeng QIANG Xindong WU School of Information Engineering Yangzhou UniversityYangzhou 225127China Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education of the People’s Republic of China) Hefei University of TechnologyHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China

Nowadays,the personalized recommendation has become a research hotspot for addressing information *** this,generating effective recommendations from sparse data remains a ***,auxiliary information has been widely used to address data sparsity,but most models using auxiliary information are linear and have limited *** to the advantages of feature extraction and no-label requirements,autoencoder-based methods have become quite ***,most existing autoencoder-based methods discard the reconstruction of auxiliary information,which poses huge challenges for better representation learning and model *** address these problems,we propose Serial-Autoencoder for Personalized Recommendation(SAPR),which aims to reduce the loss of critical information and enhance the learning of feature ***,we first combine the original rating matrix and item attribute features and feed them into the first autoencoder for generating a higher-level representation of the ***,we use a second autoencoder to enhance the reconstruction of the data representation of the prediciton rating *** output rating information is used for recommendation *** experiments on the MovieTweetings and MovieLens datasets have verified the effectiveness of SAPR compared to state-of-the-art models.

关键词： personalized recommendation autoencoder representation learning collaborative filtering

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Hemilabileα-Diimine Nickel Catalyzed Olefin Polymerization

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Chinese Journal of Chemistry 2025年第5期43卷 517-523页

作者： Muhammad Asadullah Khan Yue Liu Wenmin Pang Ao Chen Min Chen CAS Key Laboratory of Soft Matter Chemistry Department of Polymer Science and EngineeringUniversity of Science and Technology of ChinaHefeiAnhui230026 China Institutes of Physical Science and Information Technology Anhui UniversityHefeiAnhui230601 China

Comprehensive Summary For coordination-insertion olefin polymerization,the development of novel transition-metal catalysts has drawn extensive attention in this *** this contribution,we designed a series of hemilabileα-diimine nickel catalysts bearing oxygen atom as neighboring *** steric hindrance and oxygen atom number of these nickel complexes(Ni1—Ni4)could be adjusted,which influenced ethylene(co)polymerization processes.

关键词： Homogeneous catalysis Olefin polymerization Nickel catalyst Hemilabile effects Catalytic activity Copolymers

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Designing Proportional-Integral Consensus Protocols for Second-Order Multi-Agent systems Using Delayed and Memorized State information

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 878-892页

作者： Honghai Wang Qing-Long Han the College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang 110819China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

This paper is concerned with consensus of a secondorder linear time-invariant multi-agent system in the situation that there exists a communication delay among the agents in the network.A proportional-integral consensus protocol is designed by using delayed and memorized state *** the proportional-integral consensus protocol,the consensus problem of the multi-agent system is transformed into the problem of asymptotic stability of the corresponding linear time-invariant time-delay *** that the location of the eigenvalues of the corresponding characteristic function of the linear time-invariant time-delay system not only determines the stability of the system,but also plays a critical role in the dynamic performance of the *** this paper,based on recent results on the distribution of roots of quasi-polynomials,several necessary conditions for Hurwitz stability for a class of quasi-polynomials are first *** allowable regions of consensus protocol parameters are *** necessary and sufficient conditions for determining effective protocol parameters are *** designed protocol can achieve consensus and improve the dynamic performance of the second-order multi-agent ***,the effects of delays on consensus of systems of harmonic oscillators/double integrators under proportional-integral consensus protocols are ***,some results on proportional-integral consensus are derived for a class of high-order linear time-invariant multi-agent systems.

关键词： Consensus protocol Hurwitz stability multi-agent systems quasi-polynomials time delay

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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A Survey of Edge Caching:Key Issues and Challenges

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Tsinghua science and technology 2024年第3期29卷 818-842页

作者： Hanwen Li Mingtao Sun Fan Xia Xiaolong Xu Muhammad Bilal School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 262700China Reading Academy Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China Department of Computer Engineering Hankuk University of Foreign StudiesYongin-si 17035Republic of Korea

With the rapid development of mobile communication technology and intelligent applications,the quantity of mobile devices and data traffic in networks have been growing exponentially,which poses a great burden to networks and brings huge challenge to servicing user *** caching,which utilizes the storage and computation resources of the edge to bring resources closer to end users,is a promising way to relieve network burden and enhance user *** this paper,we aim to survey the edge caching techniques from a comprehensive and systematic *** first present an overview of edge caching,summarizing the three key issues regarding edge caching,i.e.,where,what,and how to cache,and then introducing several significant caching *** then carry out a detailed and in-depth elaboration on these three issues,which correspond to caching locations,caching objects,and caching strategies,*** particular,we innovate on the issue“what to cache”,interpreting it as the classification of the“caching objects”,which can be further classified into content cache,data cache,and service ***,we discuss several open issues and challenges of edge caching to inspire future investigations in this research area.

关键词： edge caching edge computing caching location caching object caching strategy 5G networkarchitecture Internet of Things(IoT)

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Structure-performance relationship between denitration performance and catalytic interface morphologies of MnCeO_(x)/P84 catalytic filters

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Journal of Rare Earths 2025年第3期43卷 516-525,I0004页

作者： Bo Yang Yujie Lei Maosen Ni Liuying Wang Qiong Huang Mindong Chen Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution ControlSchool of Environmental Science and EngineeringNanjing University of Information Science&TechnologyNanjing210044China

MnCeO_(x)/P84 catalytic filters with spherical,flower-like,cubic and rod-like catalytic interfaces were synthesized respectively,and their catalytic activities in the NH_(3)-SCR reaction were *** MnCeO_(x)/P84 catalytic filter with spherical catalytic interfaces(recorded as S-MnCeO_(x)/P84)exhibits the best catalytic denitration *** NO_(x)removal efficiency of S-MnCeO_(x)/P84 reaches the highest value of 98.6%at 160℃when the catalyst loading is 100 g/m^(2).At the same time,S-MnCeO_(x)/P84 exhibits good SO_(2)resistance and stability,achieving a NO_(x)removal rate of 83%at 190℃with 30 ppm SO_(2).The characterization results illustrate that the MnCeO_x active component in S-MnCeO_(x)/P84 is present in weak crystalline states,tightly wrapped around the surface of the filter fiber,and uniformly dispersed,and the mesopore is the main pore structure of the S-MnCeO_(x)/P84,which can provide a channel for the catalytic reaction to *** the same time,transmission electron microscopy(TEM)characterization shows that y-MnO_(2)is the main form of MnO_(2)in the S-MnCeO_(x)/*** analysis of H_(2)temperature programmed reduction(H_(2)-TPR).NH_(3)temperature programmed desorption(NH_(3)-TPD)and in-situ diffuse reflectance infrared spectra(DRIFTS)show that S-MnCeO_(x)/P84 has good redox ability at 100-200℃and has abundant Lewis acid sites and Bronsteds acid sites,which provides an important guarantee for its superior low-temperature NH_(3)-SCR denitration performance.

关键词： Catalytic filter Nitrogen oxides(NO_x) Denitration Catalytic interface Rare earths

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Deep Learning and Machine Learning Algorithms to Predict Lung Cancer 2

Deep Learning and Machine Learning Algorithms to Predict Lun...

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2nd International Conference on Emerging Trends in information technology and engineering, ic-ETITE 2024

作者： Gowthami, K. Kamalakannan, J. School of Computer Science Engineering and Information System Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Lung cancer is the most deadly illness for patients, and it is an incurable condition. Patient survival rates are greatly increased by early cancer prognosis and identification. The Computed Tomography (CT) scan is an important imaging tool for a lung cancer diagnosis. On the other hand, a manual CT scan inspection is laborious and prone to mistakes. Lung cancer can be impulsively recognized by utilizing an image processing technique of Computer-Aided Diagnosis (CAD). In recent years, various researchers identified various ways for accurate prediction by Using techniques for deep learning (DL) and machine learning (ML). This research represents a number of methodologies like Lung cancer disease prediction analysis makes use of Artificial Neural Network (ANN), Logistic Regression (LR), Support Vector Machine (SVM), K-means clustering, Convolutional Neural Network (CNN) Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). © 2024 IEEE.

关键词： Diseases

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UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 430-445页

作者： Jiawen Kang Junlong Chen Minrui Xu Zehui Xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie IEEE School of Automation Guangdong University of TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing based on IoT Technology Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Pillar of Information Systems Technology and Design Singapore University of Technology and DesignSingaporeSingapore College of Communication Engineering Army Engineering University of PLANanjing 210007China National Natural Science Foundation of China Beijing 100085China Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach

关键词： Avatar blockchain metaverses multi-agent deep reinforcement learning transformer UAVs

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