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A model-free feature extraction procedure for interval-valued time series prediction

Soft Computing 2025年第6期29卷 2727-2741页

作者： Tian, Wan Qin, Zhongfeng Hu, Tao School of Economics and Management Beihang University Beijing100191 China Beijing100191 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education Shanghai China School of Mathematical Sciences Capital Normal University Beijing100048 China

We present a novel feature extraction procedure to predict interval-valued time series by combing transfer learning and imaging approaches. Initially, we represent interval-valued time series as a bivariate point-valued time series, and then transform each time series into images by employing imaging approaches such as recurrence plot, gramian angular summation/difference field, and Markov transition field, thereby obtaining an image dataset containing four classes. Based on this dataset, we train multiple candidate feature extraction networks, specifically ResNet with varying layers. Then we choose the penultimate layer of the feature extraction networks to extract the most relevant features from the images. To formulate prediction, we integrate the extracted features into a traditional prediction model. The proposed method are evaluated on three data-generating processes as well as the S&P 500 index, and the experimental results demonstrate a notable improvement in prediction performance compared to existing methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Feature Selection

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FrDiff: Framelet-based Conditional Diffusion Model for Multispectral and Panchromatic Image Fusion

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Junkang Fang, Faming Wang, Tingting Zhang, Guixu Song, Haichuan East China Norm University School of Computer Science and Technology Shanghai200062 China Ministry of Education Key Laboratory of Advanced Theory and Application in Statistics and Data Science China

The process of fusing low-resolution multispectral (LRMS) and high-resolution panchromatic (PAN) imagery, commonly referred to as pansharpening, is intended to generate high-resolution multispectral (HRMS) imagery. Typically, most pre-existing pansharpening frameworks mainly emphasize the straightforward learning of the mapping relationship among PAN and LRMS images to HRMS images. However, a key limitation of these frameworks is their potential overemphasis on spatial information, particularly the enhancement of low-frequency components. As a result, such an oversight potentially hinders the model's ability to simultaneously restore both spectral and spatial details. To address this issue, we propose a novel pansharpening model based on the denoising diffusion probabilistic model (DDPM), dubbed FrDiff. Specifically, we build a framelet-based conditional diffusion model that leverages the generative power of diffusion models to produce more refine results. Different from conventional methods directly inferring HRMS images, our strategy is designed to project their framelet coefficients, utilizing the available PAN and LRMS images as resources. This approach enables the separation of high-frequency and low-frequency components through framelet transformation, which are subsequently recombined to create a novel set of conditional embeddings that feed into the diffusion process. At the same time, the powerful predictive power of the diffusion model is exploited to simultaneously recover the high-frequency and low-frequency components of the HRMS. Moreover, we introduce a framelet-oriented cross-attention module dedicated to honing spectral fidelity. This module is crucial for improving the spectral precision of the HRMS images, ensuring a balanced emphasis on both spatial and spectral enhancements. Quantitative and qualitative experiments on multiple benchmark datasets demonstrate that the proposed method achieves more robustness and high-quality results than other sta

关键词： Convolutional neural networks

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Escalating variability of daily sea temperature decreases phytoplankton spring blooms

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Continental Shelf Research 2025年 290卷

作者： Sun, Zixuan Du, Yan Xiang, Dongdong Chongben Honors College Ocean University of China Qingdao 266100 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science School of Statistics East China Normal University Shanghai 200062 China

Increases in sea surface temperature (SST) and its variability (SSTV) in the context of climate change have a great impact on marine phytoplankton productivity, but their combined effects on phytoplankton biomass, especially at a daily scale, have been poorly assessed. Here, we used an extended autoregressive (EAR) model to assess the different roles of SST and SSTV on phytoplankton spring blooms (PSB) in the Yellow Sea, based on 25 years (1998–2022) of daily-scale high-resolution chlorophyll-a (Chl-a) data from satellites. The annual patterns of PSB indices (start, peak, and duration) clearly exhibited years which were weak (low peak: Chl-a <0.80 mg/m³;short duration: <15 days) and strong (high peak: Chl-a >1.0 mg/m³;long duration: >60 days). The EAR model detected the significant effects of SST and SSTV on PSB indices, with SSTV being the more important determinant. SST is highly related to the timing of PSB start and end, with the average SST of 9.5 ± 0.7 °C at the start and 13.2 ± 1.95 °C at the end. The amplitude of SSTV was negatively correlated with the daily increase in Chl-a, highlighting the role of SSTV in modulating the magnitude of PSB. At low SST, the deepening of the mixed layer prolonged the time for phytoplankton to equilibrate nutrient and light requirements, whereas at low SSTV, the rate of environmental change within the mixed layer was reduced, allowing phytoplankton to adapt to the environment more quickly. Since the accumulation of phytoplankton biomass depends largely on daily acclimated growth, our results suggest that increasing daily temperature anomalies and warming in future are detrimental to phytoplankton biomass accumulation, and may reduce the magnitude of PSB in mid-to high-latitude seas. © 2025 Elsevier Ltd

关键词： Chlorophyll-a Extended autoregressive model Phytoplankton acclimated growth Phytoplankton biomass Yellow sea

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Damage characteristics of low-velocity impact of hybrid laminates made of thick- and thin-plies

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Fuhe Cailiao Xuebao/Acta Materiae Compositae Sinica 2025年第5期42卷 2595-2606页

作者： Zheng, Kaidong Chen, Hongda Cai, Wei Cao, Dongfeng Hu, Haixiao Li, Ruiqi Li, Shuxin National Energy Key Laboratory for New Hydrogen-ammonia Energy Technologies Foshan Xianhu Laboratory Foshan528200 China State Key Laboratory of Materials Synthesis and Processing Wuhan University of Technology Wuhan430070 China Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics Wuhan University of Technology Wuhan430070 China Institute of Advanced Materials and Manufacturing Technology Wuhan University of Technology Wuhan430070 China

The ply level hybridization design employs multiple thin layers instead of a single thick layer, resulting in an increased complexity of the interface. To investigate the hybrid effect between thick and thin plies of laminated composites subjected to low-velocity impact loading, two hybrid laminates were designed based on the quasi-isotropic stacking sequence. The low-velocity impact (LVI) tests including the baseline laminate and hybrid laminates were carried out. The ultrasonic C-scan and de-ply technology were respectively used to make a nondestructive and destructive detection on laminates with impact damages. Impact damages were qualitatively and quantitatively evaluated based on the detection results. Subsequently, the performances and failure modes of compression after impact (CAI) were analyzed. Experimental results show that hybrid design of thick- and thin- plies makes full use of the damage suppression characteristics of thin-ply composite, improves the impact damage resistance of composite structures, reduces the projected delamination area and total interface delamination area, shortens the distance between the largest single delamination and the neutral layer, and significantly improves the CAI strength of composite structures. The outputs of this experimental research serves as an indication for the optimal design and safety evaluation of hybrid structures. © 2025 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.

关键词： Delamination

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Simultaneous topology and fiber path optimization for variable stiffness Double-Double laminates with strength control

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Computer Methods in Applied Mechanics and Engineering 2025年 442卷

作者： Wang, Dan Zhong, Yucheng Rosen, David W. Narayanaswamy, Sridhar 1 Fusionopolis Way #16-16 Connexis Singapore138632 Singapore Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics Wuhan University of Technology Wuhan430070 China

Variable stiffness laminates offer the advantage of tailoring structural performance by adjusting in-plane stiffness through curved fiber paths. Additionally, material distribution at the structural level can further fine-tune performance by varying the topology. If both the structural topology and curved fiber paths are optimized together, super-efficient composite laminates can be achieved. In this paper, a simultaneous topology and fiber path optimization method based on a coarse background mesh is proposed for variable stiffness Double-Double (DD) laminates with strength control. Firstly, elemental pseudo densities and nodal fiber orientations are selected as design variables to control topology and curved fiber paths, respectively. Due to the reduced design redundancy in DD laminates, only two independent fiber orientations are necessary for each node of a coarse background mesh. This ensures global fiber path continuity through interpolation of elemental values. Additionally, it helps decouple fiber path generation from the computationally expensive finite element analysis, significantly reducing the number of design variables and related constraints. Secondly, an optimization model is developed to maximize the laminate strength while satisfying weight and compliance constraints. The nested p-norm of Tsai-Hill failure indices is used to aggregate stresses across different elements and layers, enhancing the overall calculation efficiency. Additionally, minimum angle difference constraints are incorporated between the two groups of DD angles to ensure the laminate's ability to resist secondary loads, thereby improving structural integrity. Notably, the proposed framework is the first to address the simultaneous optimization of both topology and curved fiber paths with strength considerations for multi-layer composite laminates. Sensitivities for both topology and fiber orientation design variables are efficiently calculated by solving a single adjoint problem, s

关键词： Mesh generation

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

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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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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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