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Constrained Multi-objective Optimization Based on Dynamic Priority and Cooperative Offspring Generation

IEEE Transactions on Evolutionary Computation 2025年

作者： He, Zhihui Wang, Feng Li, Bingdong Zhou, Aimin Wuhan University School of Computer Science Hubei 430072 China East China Normal University Shanghai Frontiers Science Center of Molecule Intelligent Syntheses Shanghai Institute of AI for Education School of Computer Science and Technology Shanghai200062 China Ministry of Education Key Laboratory of Advanced Theory and Application in Statistics and Data Science China

As the number and complexity of constraints in constrained multi-objective optimization problems (CMOPs) increase, the performance of existing constrained multi-objective evolutionary algorithms (CMOEAs) declines significantly. A novel idea is to sequentially address each constraint based on priority, effectively reducing the complexity of CMOPs. However, in these algorithms, the constraint-handling priority is determined statically in the initial stage. This may lead to inappropriate determination of constraint-handling priority since accurately estimating the constraint landscape in the initial stage is quite challenging. Moreover, these algorithms tackle constraints separately, neglecting the potential for inter-constraint cooperation and thus compromising their efficiency in constraint handling. Thus, we propose a constrained multi-objective evolutionary algorithm based on dynamic priority and cooperative offspring generation called DPCMOEA. Firstly, the constraint-handling priority is determined dynamically by the estimated inconsistency degree (EID) between the Pareto fronts of the candidate constraints and the current population. Secondly, computational resources are automatically allocated to each constraint according to EID based constraint relationship analysis. Finally, a new offspring generation strategy based on constraint cooperation is designed to enhance the quality of new solutions. Experimental results on six CMOP test suites demonstrate that DPCMOEA outperforms six state-of-the-art algorithms. © 1997-2012 IEEE.

关键词： Optimization algorithms

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High-dimensional inference for single-index model with latent factors

arXiv

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arXiv 2025年

作者： Shi, Yanmei Hao, Meiling Tang, Yanlin Lian, Heng Guo, Xu School of Statistics Beijing Normal University Beijing China School of Statistics University of International Business and Economics Beijing China Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOE School of Statistics East China Normal University Shanghai China Department of Mathematics City University of Hong Kong Hong Kong

Models with latent factors recently attract a lot of attention. However, most investigations focus on linear regression models and thus cannot capture nonlinearity. To address this issue, we propose a novel Factor Augmented Single-Index Model. We first address the concern whether it is necessary to consider the augmented part by introducing a score-type test statistic. Compared with previous test statistics, our proposed test statistic does not need to estimate the high-dimensional regression coefficients, nor high-dimensional precision matrix, making it simpler in implementation. We also propose a Gaussian multiplier bootstrap to determine the critical value. The validity of our procedure is theoretically established under suitable conditions. We further investigate the penalized estimation of the regression model. With estimated latent factors, we establish the error bounds of the estimators. Lastly, we introduce debiased estimator and construct confidence interval for individual coefficient based on the asymptotic normality. No moment condition for the error term is imposed for our proposal. Thus our procedures work well when random error follows heavy-tailed distributions or when outliers are present. We demonstrate the finite sample performance of the proposed method through comprehensive numerical studies and its application to an FRED-MD macroeconomics dataset. Copyright © 2025, The Authors. All rights reserved.

关键词： Random errors

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Semisupervised D-Learning for Optimal Individualized Treatment Regimes

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Stat 2025年第2期14卷

作者： Li, Xintong Zhang, Shuyi Zhou, Yong School of Statistics East China Normal University Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE East China Normal University Shanghai China Academy of Statistics and Interdisciplinary Sciences East China Normal University Shanghai China

In precision medicine, linear treatment decision classes have attracted widespread attention due to their simple structures and good interpretability. However, linear decision models may often be misspecified in practical applications. To address this issue, we develop an imputation-based semisupervised D-learning method that leverages information from unlabelled data to enhance the efficiency of estimating optimal individualized treatment regimes (ITRs), especially when the linear decision model is misspecified. Specifically, we estimate the imputation function using a projection-based dimension reduction approach, adjust for the bias in imputation estimates via a residual refitting step, and estimate the decision function based on the debiased imputation function. To mitigate potential bias due to overfitting, cross-validation is incorporated. Theoretical results show that the semisupervised D-learning method achieves (Formula presented.) -consistent parameter estimates with asymptotic normality. Numerical experiments on both simulated and real datasets demonstrate the superior performance of our proposed approach. © 2025 John Wiley & Sons Ltd.

关键词： D-learning least square estimation precision medicine semisupervised inference

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Single-cell technologies: current and near future

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science China(Life sciences) 2025年第1期68卷 1-4页

作者： Chenfei Wang Qi Liu Xiaohui Fan Tieliu Shi KeyLaboratory of Spine and Spinal CordInjury Repairand Regeneration Ministry of EducationOrthopedics DepartmentTongji HospitalBioinformatics DepartmentSchoolofLifeSciencesandTechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(MOE) School of StatisticsEast China Normal UniversityShanghai 200062China

Single-cell technologies enable the indepth exploration of multiple biological hierarchies at the scale of individual cells,which have deepened our knowledge of cellular diversity,tissue organization,and overall organism function(Sun et al.,2024).In 2009,single-cell RNA sequencing(scRNA-seq)was first developed as a powerful tool to dissect gene expression and uncover transcriptional *** the rapid advancement of technology,single-cell approaches have expanded to encompass other omics,such as genomics,epigenomics,proteomics,and ***,multimodal technologies including spatially resolved transcriptomics and clustered regularly interspaced short palindromic repeats(CRISPR)screening at the single-cell level further advanced our ability to comprehensively understand cellular ***-cell methods provide deeper insights into disease mechanisms across various conditions,including cancer,developmental disorders,and aging-associated *** this issue,we focus on topics related to single-cell methods,covering the following four aspects:(i)single-cell multi-omic sequencing technology;(ii)single-cell spatial technology;(iii)singlecell CRISPR screening technology;(iv)applications of single-cell technology.

关键词： expanded enable insight

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Single-cell omics: experimental workflow, data analyses and applications

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science China(Life sciences) 2025年第1期68卷 5-102页

作者： Fengying Sun Haoyan Li Dongqing Sun Shaliu Fu Lei Gu Xin Shao Qinqin Wang Xin Dong Bin Duan Feiyang Xing Jun Wu Minmin Xiao Fangqing Zhao Jing-Dong J.Han Qi Liu Xiaohui Fan Chen Li Chenfei Wang Tieliu Shi Department of Clinical Laboratory the Affiliated Wuhu Hospital of East China Normal University(The Second People’s Hospital of Wuhu City)Wuhu 241000China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration(Tongji University) Ministry of EducationOrthopaedic DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 201210China Center for Single-cell Omics School of Public HealthShanghai Jiao Tong University School of MedicineShanghai 200025China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Beijing Institutes of Life Science Chinese Academy of SciencesBeijing 100101China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology(CQB)Peking UniversityBeijing 100871China Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases Women’s HospitalZhejiang University School of MedicineHangzhou 310006China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal U

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular *** exploration of how the genomes orchestrate the formation and maintenance of each cell,and control the cellular phenotypes of various organismsis,is both captivating and *** the inception of the first single-cell RNA technology,technologies related to single-cell sequencing have experienced rapid advancements in recent *** technologies have expanded horizontally to include single-cell genome,epigenome,proteome,and metabolome,while vertically,they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR ***-cell omics represent a groundbreaking advancement in the biomedical field,offering profound insights into the understanding of complex diseases,including ***,we comprehensively summarize recent advances in single-cell omics technologies,with a specific focus on the methodology *** overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

关键词： single-cell sequencing genome epigenome proteomics metabolomics multimodal spatial transcriptomics CRISPR screening

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Incomplete Multi-View Clustering via Multi-Level Contrastive Learning

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IEEE Transactions on Knowledge and data Engineering 2025年

作者： Yin, Jun Wang, Pei Sun, Shiliang Zheng, Zhonglong Shanghai Maritime University College of Information Engineering Shanghai 201306 China Shanghai Jiao Tong University Department of Automation Shanghai 200240 China Ministry of Education Key Laboratory of Advanced Theory and Application in Statistics and Data Science Shanghai 200062 China Zhejiang Normal University School of Computer Science Zhejiang Jinhua 321004 China

Although significant progress has been made in multi-view learning over the past few decades, it remains challenging, especially in the context of incomplete multi-view clustering, where modeling complex correlations among different views and handling missing data are key difficulties. In this paper, we propose a novel incomplete multi-view clustering network to address the aforementioned issue, named Incomplete Multi-view Clustering via Multi-level Contrastive Learning (IMC-MCL). Specifically, the proposed model aims to minimize the conditional entropy between views to recover missing data by dual prediction strategy. Moreover, the approach learns multi-level features, including latent, high-level and semantic features, with the goal of satisfying both reconstruction and consistency objectives in distinct feature spaces. Specifically, latent features are utilized to accomplish the reconstruction objective, while high-level features and semantic labels are employed to achieve the two consistency goals through contrastive learning. This framework enables the exploration of shared semantics within high-level features and achieves clustering assignment using semantic features. Extensive experiments have shown that the proposed approach outperforms other state-of-the-art incomplete multi-view clustering methods on seven challenging datasets. © 1989-2012 IEEE.

关键词： contrastive learning deep clustering incomplete multi-view clustering Representation learning

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Equilibrium Reinsurance Strategy and Mean Residual Life Function

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Acta Mathematicae Applicatae Sinica 2024年第3期40卷 758-777页

作者： Dan-ping LI Lv CHEN Lin-yi QIAN Wei WANG School of Statistics Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOEEast China Normal UniversityShanghai 200062China Academy of Statistics and Interdisciplinary Sciences Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOEEast China Normal UniversityShanghai 200062China School of Mathematics and Statistics Ningbo UniversityNingbo 315211China

In this paper,we analyze the relationship between the equilibrium reinsurance strategy and the tail of the distribution of the *** Mean Residual Life(MRL)has a close relationship with the tail of the distribution,we consider two classes of risk distributions,Decreasing Mean Residual Life(DMRL)and Increasing Mean Residual Life(IMRL)distributions,which can be used to classify light-tailed and heavy-tailed distributions,*** assume that the underlying risk process is modelled by the classical CramérLundberg model *** the mean-variance criterion,by solving the extended Hamilton-Jacobi-Bellman equation,we derive the equilibrium reinsurance strategy for the insurer and the reinsurer under DMRL and IMRL,***,we analyze how to choose the reinsurance premium to make the insurer and the reinsurer agree with the same reinsurance *** find that under the case of DMRL,if the distribution and the risk aversions satisfy certain conditions,the insurer and the reinsurer can adopt a reinsurance premium to agree on a reinsurance strategy,and under the case of IMRL,the insurer and the reinsurer can only agree with each other that the insurer do not purchase the reinsurance.

关键词： mean residual life excess-of-loss reinsurance insurer reinsurer stochastic control

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Functional Multiple-Outcome Model in application to Multivariate Growth Curves of Infant data

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Journal of Systems science & Complexity 2021年第4期34卷 1555-1577页

作者： YAN Xingyu ZHOU Yingchun PU Xiaolong ZHAO Peng School of Mathematics and Statistics Jiangsu Normal UniversityXuzhou 221116China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal UniversityShanghai 200062China

Motivated by a medical study that attempts to analyze the relationship between growth curves and other variables and to measure the association among multiple growth curves,the authors develop a functional multiple-outcome model to decompose the total variation of multiple functional outcomes into variation explained by independent variables with time-varying coefficient functions,by latent factors and by *** latent factors are the hidden common factors that influence the multiple outcomes and are found through the combined functional principal component analysis *** the coefficients of the latent factors one may further explore the association of the multiple *** method is applied to the multivariate growth data of infants in a real medical study in Shanghai and produces interpretable *** rates for the proposed estimates of the varying coefficient and covariance functions of the model are derived under mild conditions.

关键词： Convergence rate functional data functional principal components growth curve multiple outcomes

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The Conditional Mutual Information-Based Single-Sample Network Biomarker Approach Reveals the Critical Transition Moments of Disease Progression

SSRN

引用

SSRN 2025年

作者： Zhang, Lantian Hu, Fuyan Chi, Qingjia Chen, Bifeng The Department of Statistics School of Mathematics and Statistics Wuhan University of Technology 122 Luoshi Road Hubei Wuhan China Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics Department of Mechanics and Engineering Structure Wuhan University of Technology 122 Luoshi Road Hubei Wuhan China The Department of Biological Science and Technology School of Chemistry Chemical Engineering and Life Sciences Wuhan University of Technology 122 Luoshi Road Hubei Wuhan China

During the progression of complex diseases, it is common to observe that the deterioration of the condition does not follow a smooth trajectory, and there is often a significant and critical transition from one state to another. Finding these critical transitions is of significant importance in the clinical treatment of cancer. In this study, we propose a novel computational approach, called the conditional mutual information-based single-sample network biomarker (CMISNB), which can reveal the critical transition moments of disease progression using only a single sample (https://***/ZLTSKY/CMISNB). By analyzing disease data from mouse acute lung injury, colon cancer, hepatocellular liver cancer, lung adenocarcinoma, and endometrial cancer, we validated the effectiveness of the CMISNB method during identifying critical transition moments in disease development. In particular, the CMISNB approach facilitates the discovery of new prognostic biomarkers and can provide personalized diagnoses for individuals. © 2025, The Authors. All rights reserved.

关键词： Lung cancer

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Integrated Square Error of Hazard Rate Estimation for Survival data with Missing Censoring Indicators

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Journal of Systems science & Complexity 2021年第2期34卷 735-758页

作者： ZOU Yuye FAN Guoliang ZHANG Riquan School of Economics and Management Shanghai Maritime UniversityShanghai 201306China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal UniversityShanghai 200062China

The problem of hazard rate estimation under right-censored assumption has been investigated *** square error(ISE)of estimation is one of the most widely accepted measurements of the global performance for nonparametric kernel *** there are no results available for ISE of hazard rate estimation under right-censored model with censoring indicators missing at random(MAR)so *** paper constructs an imputation estimator of the hazard rate function and establish asymptotic normality of the ISE for the kernel hazard rate estimator with censoring indicators *** the same time,an asymptotic representation of the mean integrated square error(MISE)is also *** finite sample behavior of the estimator is investigated via one simple simulation.

关键词： Asymptotic normality integrated square error missing at random right-censored model

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