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On construction of strong orthogonal arrays and column-orthogonal strong orthogonal arrays of strength two plus

science China Mathematics 2025年第5期68卷 1219-1242页

作者： Bochuan Jiang Yaping Wang Fasheng Sun School of Mathematics and Statistics Beijing Jiaotong University Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of EducationSchool of Statistics East China Normal University Key Laboratory for Applied Statistics of Ministry of Education and School of Mathematics and Statistics Northeast Normal University

Computer experiments require space-filling designs with good low-dimensional projection *** orthogonal arrays are a type of space-filling design that provides better stratifications in low dimensions than ordinary orthogonal arrays. In this paper, we address the problem of constructing strong orthogonal arrays and column-orthogonal strong orthogonal arrays of strength two plus. Existing methods typically rely on regular designs or specific nonregular designs as base orthogonal arrays, limiting the sizes of the final ***, we propose two general methods that are easy to implement and applicable to a wide range of base orthogonal arrays. These methods produce space-filling designs that can accommodate a large number of factors,provide significant flexibility in terms of run sizes, and possess appealing low-dimensional projection ***, these designs are ideal for computer experiments.

关键词： computer experiment difference scheme strong orthogonal array column-orthogonality space-filling property

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Byzantine-robust distributed support vector machine

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science China Mathematics 2025年第3期68卷 707-728页

作者： Xiaozhou Wang Weidong Liu Xiaojun Mao School of Statistics East China Normal UniversityShanghai 200062China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(Ministry of Education) East China Normal UniversityShanghai 200062China School of Mathematical Sciences Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Artificial Intelligence(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Scientific and Engineering Computing(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China

The development of information technology brings diversification of data sources and large-scale data sets and calls for the exploration of distributed learning algorithms. In distributed systems, some local machines may behave abnormally and send arbitrary information to the central machine(known as Byzantine failures), which can invalidate the distributed algorithms based on the assumption of faultless systems. This paper studies Byzantine-robust distributed algorithms for support vector machines(SVMs) in the context of binary classification. Despite a vast literature on Byzantine problems, much less is known about the theoretical properties of Byzantine-robust SVMs due to their unique challenges. In this paper, we propose two distributed gradient descent algorithms for SVMs. The median and trimmed mean operations in aggregation can effectively defend against Byzantine failures. Theoretically, we show the convergence of the proposed estimators and provide the statistical error rates. After a certain number of iterations, our estimators achieve near-optimal rates. Simulation studies and real data analysis are conducted to demonstrate the performance of the proposed Byzantine-robust distributed algorithms.

关键词： Byzantine robustness convergence distributed learning support vector machine

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Customized priors for enhanced multispectral and hyperspectral image sharpening

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Information Fusion 2025年 122卷

作者： Zhang, Junkang Zhang, Haoyang Wang, Tingting Fang, Faming Zhang, Guixu Xu, Ming School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Shanghai China

In recent years, deep unfolding (DU) models have gained significant traction in multi-source image fusion applications, including remote sensing pansharpening and the integration of multispectral and hyperspectral images, owing to their enhanced interpretability. However, despite embedding physical degradation models, these DU models still rely on implicit learning for their priors, which can impede interpretability. In this paper, we present a novel image fusion model based on maximum a posterior (MAP) framework. This model enhances interpretability and fusion performance by incorporating two innovative prior modules. The first module employs codebook to capture the spectral traits of the target image, subsequently integrating these insights within our DU framework. The second module specializes in uncovering the intrinsic correlations between multi-source images, ensuring that the fusion process effectively leverages the rich, diverse information encoded within each modality. Specifically, we have effectively combined the self-similarity and cross-modal similarity paradigms into a unified cross image prior module. To counteract the redundancy present in images, we have integrated an agent mechanism that significantly simplifies the computational load of our prior module. Quantitative and qualitative experiments on multiple benchmark datasets demonstrate that the proposed method achieves more robustness and high-quality results than other state-of-the-art sharpening methods. © 2025 Elsevier B.V.

关键词： Miocene

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Knowledge Transfer Across Modalities for Weakly Supervised Point Cloud Semantic Segmentation

Knowledge Transfer Across Modalities for Weakly Supervised P...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zihan Wang Yunhang Shen Mengtian Li Ke Li Xing Sun Shaohui Lin Lizhuang Ma East China Normal University Tencent Youtu Lab Shanghai University Key Laboratory of Advanced Theory and Application in Statistics and Data Science

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current weakly supervised point cloud semantic segmentation struggles with insufficient utilization of limited annotations in unimodal representation learning due to the sparse and textureless nature of point clouds. In this work, we leverage cross-modality information by transferring knowledge from image and text sources to the point cloud network. The intuition is that images contribute rich texture, color, and discriminative information, complementing point clouds to boost semantic segmentation performance. To reduce extensive computational resources for cross-modality fusion, we introduce the Multi-Scale Deformable Knowledge Transfer, an innovative training scheme that optimizes and extends the one-to-one mapping to flexible one-to-many relations between multi-modal data. Furthermore, we employ pre-trained image-text models to generate pseudo labels for point clouds and construct positive and negative samples for semantic contrastive regularization, facilitating the full exploitation of unlabeled data. The experimental results evaluated on SemanticKITTI and nuScenes demonstrate substantial improvements, achieving an average gain of 3.8% over the previous weakly supervised methods, and comparable performances to fully supervised approaches.

关键词： Point cloud compression Training Representation learning Weak supervision Three-dimensional displays Semantic segmentation Semantics Signal processing Speech processing Knowledge transfer

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Estimation and inference of high-dimensional partially linear regression models with latent factors

arXiv

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arXiv 2025年

作者： Shi, Yanmei Hao, Meiling Tang, Yanlin 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

In this paper, we introduce a novel high-dimensional Factor-Adjusted sparse Partially Linear regression Model (FAPLM), to integrate the linear effects of high-dimensional latent factors with the nonparametric effects of low-dimensional covariates. The proposed FAPLM combines the interpretability of linear models, the flexibility of nonparametric models, with the ability to effectively capture the dependencies among high-dimensional covariates. We develop a penalized estimation approach for the model by leveraging B-spline approximations and factor analysis techniques. Theoretical results establish error bounds for the estimators, aligning with the minimax rates of standard Lasso problems. To assess the significance of the linear component, we introduce a factor-adjusted projection debiased procedure and employ the Gaussian multiplier bootstrap method to derive critical values. Theoretical guarantees are provided under regularity conditions. Comprehensive numerical experiments validate the finite-sample performance of the proposed method. Its successful application to a birth weight dataset, the motivating example for this study, highlights both its effectiveness and practical relevance. Copyright © 2025, The Authors. All rights reserved.

关键词： Factor analysis

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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 Shanghai 200062 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 network diffusion model framelet transform pansharpening

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

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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 Beijing China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education Shanghai China Key Laboratory of Complex System Analysis Management and Decision (Beihang University) Beijing China School of Mathematical Sciences Capital Normal University Beijing 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.

关键词： Feature extraction Imaging Interval-valued time series Transfer learning

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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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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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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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