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Dimensional-noise-aware battery lifetime prediction via an EM-TLS framework

Progress in Natural science:Materials International 2025年第1期35卷 146-155页

作者： Chenlong Yu Ting Lu Guohua Liu Xiaoang Zhai Wuyan Deng Jiayu Wan Yang Liu Xin Li School of Computer Science and Technology Donghua University Future Battery Research Center Global Institute of Future Technology Shanghai Jiaotong University Data Science Research Center Duke Kunshan University

Machine learning has been massively utilized to construct data-driven solutions for predicting the lifetime of rechargeable batteries in recent years, which project the physical measurements obtained during the early charging/discharging cycles to the remaining useful lifetime. While most existing techniques train the prediction model through minimizing the prediction error only, the errors associated with the physical measurements can also induce negative impact to the prediction accuracy. Although total-least-squares(TLS) regression has been applied to address this issue, it relies on the unrealistic assumption that the distributions of measurement errors on all input variables are equivalent, and cannot appropriately capture the practical characteristics of battery degradation. In order to tackle this challenge, this work intends to model the variations along different input dimensions, thereby improving the accuracy and robustness of battery lifetime prediction. In specific, we propose an innovative EM-TLS framework that enhances the TLS-based prediction to accommodate dimension-variate errors, while simultaneously investigating the distributions of them using expectation-maximization(EM). Experiments have been conducted to validate the proposed method based on the data of commercial Lithium-Ion batteries, where it reduces the prediction error by up to 29.9 % compared with conventional TLS. This demonstrates the immense potential of the proposed method for advancing the R&D of rechargeable batteries.

关键词： Battery lifetime prediction Dimension-variate measurement error Energy storage Expectation-maximization Total-least-squares regression

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Vital nodes identification method integrating degree centrality and cycle ratio

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Chinese Physics B 2025年第3期34卷 45-62页

作者： Yu Zhao Bo Yang Data Science Research Center Kunming University of Science and TechnologyKunming 650500China Faculty of Science Kunming University of Science and TechnologyKunming 650500China

Identifying vital nodes is one of the core issues of network science,and is crucial for epidemic prevention and control,network security maintenance,and biomedical research and *** this paper,a new vital nodes identification method,named degree and cycle ratio(DC),is proposed by integrating degree centrality(weightα)and cycle ratio(weight 1-α).The results show that the dynamic observations and weightαare nonlinear and non-monotonicity(i.e.,there exists an optimal valueα^(*)forα),and that DC performs better than a single index in most *** to the value ofα^(*),networks are classified into degree-dominant networks(α^(*)>0.5)and cycle-dominant networks(α^(*)<0.5).Specifically,in most degree-dominant networks(such as Chengdu-BUS,Chongqing-BUS and Beijing-BUS),degree is dominant in the identification of vital nodes,but the identification effect can be improved by adding cycle structure information to the *** most cycle-dominant networks(such as Email,Wiki and Hamsterster),the cycle ratio is dominant in the identification of vital nodes,but the effect can be notably enhanced by additional node degree ***,interestingly,in Lancichinetti-Fortunato-Radicchi(LFR)synthesis networks,the cycle-dominant network is observed.

关键词： cycle ratio percolation epidemic spreading targeted immunization

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Communication-Efficient Federated Learning for Decision Trees

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5478-5492页

作者： Zhao, Shuo Zhu, Zikun Li, Xin Chen, Ying-Chi Duke Kunshan University Data Science Research Center Jiangsu Kunshan215316 China

The increasing concerns about data privacy and security have driven the emergence of federated learning, which preserves privacy by collaborative learning across multiple clients without sharing their raw data. In this article, we propose a communication-efficient federated learning algorithm for decision trees (DTs), referred to as FL-DT. The key idea is to exchange the statistics of a small number of features among the server and all clients, enabling identification of the optimal feature to split each DT node without compromising privacy. To efficiently find the splitting feature based on the partially available information at each DT node, a novel formulation is derived to estimate the lower and upper bounds of Gini indexes of all features by solving a sequence of mixed-integer convex programming problems. Our experimental results based on various public datasets demonstrate that FL-DT can reduce the communication overhead substantially without surrendering any classification accuracy, compared to other conventional methods. © 2024 IEEE.

关键词： Convex optimization

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Overview and New Opportunities for Multi-Source data Assimilation

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Journal of Meteorological research 2025年第1期39卷 1-25页

作者： Xiaolei ZOU Joint Center of Data Assimilation for Research and Application Nanjing University of Information Science&TechnologyNanjing210044China

The world witnessed an accelerated development of various types of meteorological observing technology,an evolution of numerical weather prediction(NWP)models from single atmospheric component to coupled multi-components of the earth system,as well as the multi graphics processing unit technology in computer sciences,a new era for rapidly advancing data assimilation science and technology development has *** multi-source data assimilation is important not only for NWP but also for further understanding of global and regional weather *** article firstly selectively reviews past methods of multi-source data *** opportunities are then discussed for future development of data assimilation system framework,for innovative uses of high-resolution observations,and for applications of artificial intelligence machine learning in meteorological data assimilation.

关键词： multi-source observations data assimilation innovative use of high-resolution observations

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GOES-16 ABI Brightness Temperature Observations Capturing Vortex Rossby Wave Signals during Rapid Intensification of Hurricane Irma(2017)

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Journal of Meteorological research 2024年第4期38卷 768-783页

作者： Yanyang HU Xiaolei ZOU Joint Center of Data Assimilation for Research and Application Nanjing University of Information Science&TechnologyNanjing 210044

Geostationary Operational Environmental Satellite-16(GOES-16) Advanced Baseline Imager(ABI) observations of brightness temperature(TB) are used to examine the temporal evolutions of convection-affected structures of Hurricane Irma(2017) during its rapid intensification(RI) period from 0600 to 1800 UTC 4 September *** ABI observations reveal that both an elliptical eye and a spiral rainband that originated from Irma's eyewall obviously exhibit wavenumber-2 TB *** elliptical eye underwent a counterclockwise rotation at a mean speed of a wavenumber-2 vortex Rossby edge wave from 0815 to 1005 UTC 4 *** the following about 2 hours(1025–1255 UTC 4 September),an inner spiral rainband originated from the eyewall and propagated at a phase speed that approximates the vortex Rossby wave(VRW) phase speed calculated from the aircraft reconnaissance *** the RI period of Irma,ABI TB observations show an on–off occurrence of low TB intrusions into the eye,accompanying a phase lock of eyewall TB asymmetries of wavenumbers 1 and 2 and an outward propagation of VRW-like inner spiral rainbands from the *** phase lock leads to an energy growth of Irma's eyewall *** the eye remained clear from 1415 to 1725 UTC 4 September,an inner spiral rainband that originated from a large convective area also had a VRW-like outward propagation,which is probably due to a vertical tilt of *** study suggests a potential link between convection sensitive GOES imager observations and hurricane dynamics.

关键词： Geostationary Operational Environmental Satellite(GOES)imager brightness temperature(TB) hurricane vortex Rossby wave(VRW)

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Attributing Mental States to Non-Embodied Autonomous Systems: A Systematic Review

Attributing Mental States to Non-Embodied Autonomous Systems...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Wischnewski, Magdalena Research Center Trustworthy Data Science and Security Dortmund Germany

ISBN: (纸本)9798400713958

Rapidly evolving, non-embodied autonomous agents, oftentimes powered by AI, have been shown to trigger mental state attributions by users. However, unlike embodied agents, attributions to non-embodied autonomous agents are not caused by physical presence but by language or abstract interfaces. To better understand this fast-growing field, the present article presents a systematic literature review of mental state attribution to non-embodied autonomous agents. Based on a literature body of 400+ papers, 26 relevant publications were identified, providing an overview of the communities studying mental state attributions, the study’s design choices, and findings of these efforts. The findings indicate that (1) the HCI community is less involved in the study of mental state attributions, (2) a lacking validity of the measures limits the current measurement of mental state attributions, (3) there is a skew towards chat- and voice bots, and (4) mental state attributions increased vulnerabilities of users and shifts in accountability. © 2025 Copyright held by the owner/author(s).

关键词： agent artificial intelligence autonomous mental states mind perception non-embodied

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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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science China(Information sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

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Resource investment for DDoS attack resistant SDN: a practical assessment

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science China(Information sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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A Facial Expression Recognition Method Integrating Uncertainty Estimation and Active Learning

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Computers, Materials & Continua 2024年第10期81卷 533-548页

作者： Yujian Wang Jianxun Zhang Renhao Sun School of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China Data Space Research Institute of Hefei Comprehensive National Science Center Hefei230088China

The effectiveness of facial expression recognition(FER)algorithms hinges on the model’s quality and the availability of a substantial amount of labeled expression ***,labeling large datasets demands significant human,time,and financial *** active learning methods have mitigated the dependency on extensive labeled data,a cold-start problem persists in small to medium-sized expression recognition *** issue arises because the initial labeled data often fails to represent the full spectrum of facial expression *** paper introduces an active learning approach that integrates uncertainty estimation,aiming to improve the precision of facial expression recognition regardless of dataset scale *** method is divided into two primary ***,the model undergoes self-supervised pre-training using contrastive learning and uncertainty estimation to bolster its feature extraction ***,the model is fine-tuned using the prior knowledge obtained from the pre-training phase to significantly improve recognition *** the pretraining phase,the model employs contrastive learning to extract fundamental feature representations from the complete unlabeled *** features are then weighted through a self-attention mechanism with rank ***,data from the low-weighted set is relabeled to further refine the model’s feature extraction *** pre-trained model is then utilized in active learning to select and label information-rich samples more *** results demonstrate that the proposed method significantly outperforms existing approaches,achieving an improvement in recognition accuracy of 5.09%and 3.82%over the best existing active learning methods,Margin,and Least Confidence methods,respectively,and a 1.61%improvement compared to the conventional segmented active learning method.

关键词： Expression recognition active learning self-supervised learning uncertainty estimation

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Enhancing cut selection through reinforcement learning

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science China Mathematics 2024年第6期67卷 1377-1394页

作者： Shengchao Wang Liang Chen Lingfeng Niu Yu-Hong Dai Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing100190China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing100190China

With the rapid development of artificial intelligence in recent years,applying various learning techniques to solve mixed-integer linear programming(MILP)problems has emerged as a burgeoning research *** from constructing end-to-end models directly,integrating learning approaches with some modules in the traditional methods for solving MILPs is also a promising *** cutting plane method is one of the fundamental algorithms used in modern MILP solvers,and the selection of appropriate cuts from the candidate cuts subset is crucial for enhancing *** to the reliance on expert knowledge and problem-specific heuristics,classical cut selection methods are not always transferable and often limit the scalability and generalizability of the cutting plane *** provide a more efficient and generalizable strategy,we propose a reinforcement learning(RL)framework to enhance cut selection in the solving process of ***,we design feature vectors to incorporate the inherent properties of MILP and computational information from the solver and represent MILP instances as bipartite ***,we choose the weighted metrics to approximate the proximity of feasible solutions to the convex hull and utilize the learning method to determine the weights assigned to each ***,a graph convolutional neural network is adopted with a self-attention mechanism to predict the value of weighting ***,we transform the cut selection process into a Markov decision process and utilize RL method to train the *** experiments are conducted based on a leading open-source MILP solver *** on both general and specific datasets validate the effectiveness and efficiency of our proposed approach.

关键词： reinforcement learning mixed-integer linear programming cutting plane method cut selection

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