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Robust Online Detection in Serially Correlated Directed Network

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arXiv 2021年

作者： Yu, Miaomiao Zhou, Yuhao Tsung, Fugee Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOE Academy of Statistics and Interdisciplinary Sciences East China Normal University Shanghai China Department of Statistics and Operations Research University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Industrial Engineering and Decision Analytics Hong Kong University of Science and Technology Kowloon Hong Kong

As the complexity of production processes increases, the diversity of data types drives the development of network monitoring technology. This paper mainly focuses on an online algorithm to detect serially correlated directed networks robustly and sensitively. First, we consider a transition probability matrix to resolve the double correlation of primary data. Further, since the sum of each row of the transition probability matrix is one, it standardizes the data, facilitating subsequent modeling. Then we extend the spring length based method to the multivariate case and propose an adaptive cumulative sum (CUSUM) control chart on the strength of a weighted statistic to monitor directed networks. This novel approach assumes only that the process observation is associated with nearby points without any parametric time series model, which is in line with reality. Simulation results and a real example from metro transportation demonstrate the superiority of our design. Copyright © 2021, The Authors. All rights reserved.

关键词： Digital storage

Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning

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arXiv 2023年

作者： Ma, Chaofan Xu, Qisen Wang, Xiangfeng Jin, Bo Zhang, Xiaoyun Wang, Yanfeng Zhang, Ya The Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China The MOE Key Laboratory for Advanced Theory and Application in Statistics and Data Science School of Computer Science and Technology East China Normal University Shanghai200062 China The Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

Interactive segmentation has recently been explored to effectively and efficiently harvest high-quality segmentation masks by iteratively incorporating user hints. While iterative in nature, most existing interactive segmentation methods tend to ignore the dynamics of successive interactions and take each interaction independently. We here propose to model iterative interactive image segmentation with a Markov decision process (MDP) and solve it with reinforcement learning (RL) where each voxel is treated as an agent. Considering the large exploration space for voxel-wise prediction and the dependence among neighboring voxels for the segmentation tasks, multi-agent reinforcement learning is adopted, where the voxel-level policy is shared among agents. Considering that boundary voxels are more important for segmentation, we further introduce a boundary-aware reward, which consists of a global reward in the form of relative cross-entropy gain, to update the policy in a constrained direction, and a boundary reward in the form of relative weight, to emphasize the correctness of boundary predictions. To combine the advantages of different types of interactions, i.e., simple and efficient for point-clicking, and stable and robust for scribbles, we propose a supervoxel-clicking based interaction design. Experimental results on four benchmark datasets have shown that the proposed method significantly outperforms the state-of-the-arts, with the advantage of fewer interactions, higher accuracy, and enhanced robustness. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction

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arXiv 2022年

作者： Dang, Yuchen Chen, Ziqi Li, Heng Shu, Hai Center for Data Science New York University New YorkNY United States Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University Shanghai China Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Province Shenzhen China Department of Biostatistics School of Global Public Health New York University New YorkNY United States

Solar activity has significant impacts on human activities and health. One most commonly used measure of solar activity is the sunspot number. This paper compares three important non-deep learning models, four popular deep learning models, and their five ensemble models in forecasting sunspot numbers. In particular, we propose an ensemble model called XGBoost-DL, which uses XGBoost as a two-level nonlinear ensemble method to combine the deep learning models. Our XGBoost-DL achieves the best forecasting performance (RMSE = 25.70 and MAE = 19.82) in the comparison, outperforming the best non-deep learning model SARIMA (RMSE = 54.11 and MAE = 45.51), the best deep learning model Informer (RMSE = 29.90 and MAE = 22.35) and the NASA's forecast (RMSE = 48.38 and MAE = 38.45). Our XGBoost-DL forecasts a peak sunspot number of 133.47 in May 2025 for Solar Cycle 25 and 164.62 in November 2035 for Solar Cycle 26, similar to but later than the NASA's at 137.7 in October 2024 and 161.2 in December 2034. An open-source Python package of our XGBoost-DL for the sunspot number prediction is available at https://***/yd1008/ts_ensemble_sunspot. Copyright © 2022, The Authors. All rights reserved.

关键词： Solar energy

Fractional stochastic wave equation driven by a Gaussian noise rough in space

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arXiv 2019年

作者： Song, Jian Song, Xiaoming Xu, Fangjun School of Mathematics Shandong University Jinan Shandong250100 China Department of Mathematics Drexel University PhiladelphiaPA19104 United States Key Laboratory of Advanced Theory and Application in Statistics and Data Science - MOE School of Statistics East China Normal University Shanghai200062 China NYU-ECNU Institute of Mathematical Sciences NYU Shanghai Shanghai200062 China

In this article, we consider fractional stochastic wave equations on R driven by a multiplicative Gaussian noise which is white/colored in time and has the covariance of a fractional Brownian motion with Hurst parameter H ∈ (1/4, 1/2) in space. We prove the existence and uniqueness of the mild Skorohod solution, establish lower and upper bounds for the p-th moment of the solution for all p ≥ 2, and obtain the Hölder continuity in time and space variables for the *** Codes 60H07, 60H15, 60G15 Copyright © 2019, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks

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arXiv 2020年

作者： Shu, Hai Shi, Ronghua Jia, Qiran Zhu, Hongtu Chen, Ziqi Department of Biostatistics School of Global Public Health New York University New YorkNY United States School of Mathematics and Statistics Central South University Changsha China Departments of Biostatistics Statistics Computer Science and Genetics The University of North Carolina at Chapel Hill Chapel HillNC United States Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University Shanghai China

Deep neural networks (DNNs) have achieved great success in image classification, but can be very vulnerable to adversarial attacks with small perturbations to images. To improve adversarial image generation for DNNs, we develop a novel method, called mFI-PSO, which utilizes a Manifoldbased First-order Influence measure for vulnerable image and pixel selection and the Particle Swarm Optimization for various objective functions. Our mFI-PSO can thus effectively design adversarial images with flexible, customized options on the number of perturbed pixels, the misclassification probability, and the targeted incorrect class. Experiments demonstrate the flexibility and effectiveness of our mFI-PSO in adversarial attacks and its appealing advantages over some popular methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

Optimal subsampling for the Cox proportional hazards model with massive survival data

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arXiv 2023年

作者： Qiao, Nan Li, Wangcheng Xiao, Feng Lin, Cunjie Zhou, Yong Center for Applied Statistics Renmin University of China Beijing100872 China School of Statistics Renmin University of China Beijing100872 China School of Statistics Beijing Normal University Beijing100875 China Sichuan Rural Credit Union Chengdu61000 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE Shanghai200062 China Academy of Statistics and Interdisciplinary Sciences East China Normal University Shanghai200062 China

The use of massive survival data has become common in survival analysis. In this study, a subsampling algorithm is proposed for the Cox proportional hazards model with time-dependent covariates when the sample is extraordinarily large but computing resources are relatively limited. A subsample estimator is developed by maximizing the weighted partial likelihood;it is shown to have consistency and asymptotic normality. By minimizing the asymptotic mean squared error of the subsample estimator, the optimal subsampling probabilities are formulated with explicit expressions. Simulation studies show that the proposed method can satisfactorily approximate the estimator of the full dataset. The proposed method is then applied to corporate loan and breast cancer datasets, with different censoring rates, and the outcomes confirm its practical advantages. Copyright © 2023, The Authors. All rights reserved.

关键词： Mean square error

Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization

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arXiv 2024年

作者： Li, Mengtian Lin, Shaohui Wang, Zihan Shen, Yunhang Zhang, Baochang Ma, Lizhuang Shanghai University China Shanghai Engineering Research Center of Motion Picture Special Effects China East China Normal University China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China Beihang University China Tencent Youtu Lab China

Semi-supervised learning (SSL), thanks to the significant reduction of data annotation costs, has been an active research topic for large-scale 3D scene understanding. However, the existing SSL-based methods suffer from severe training bias, mainly due to class imbalance and long-tail distributions of the point cloud data. As a result, they lead to a biased prediction for the tail class segmentation. In this paper, we introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively. In particular, we first employ two-round pseudo-label generation to select unlabeled points across head-to-tail classes. We further introduce multi-class imbalanced focus loss to adaptively pay more attention to feature learning across head-to-tail classes. We fix the backbone parameters after feature learning and retrain the classifier using ground-truth points to update its parameters. Extensive experiments demonstrate the effectiveness of our method outperforming previous state-of-the-art methods on both indoor and outdoor 3D point cloud datasets (i.e., S3DIS, ScanNet-V2, Semantic3D, and SemanticKITTI) using 1% and 1pt evaluation. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

ENSEMBLE DOMAIN DECOMPOSITION ALGORITHM FOR THE FULLY-MIXED RANDOM STOKES-DARCY MODEL WITH THE BEAVERS-JOSEPH INTERFACE CONDITIONS

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arXiv 2022年

作者： Shi, Feng Sun, Yizhong Zheng, Haibiao College of Science Harbin Institute of Technology Shenzhen China School of Mathematical Sciences East China Normal University Shanghai China School of Mathematical Sciences East China Normal University Shanghai Key Laboratory of Pure Mathematics and Mathematical Practice Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University Shanghai China

In this paper, an efficient ensemble domain decomposition algorithm is proposed for fast solving the fully-mixed random Stokes-Darcy model with the physically realistic Beavers-Joseph (BJ) interface conditions. We utilize the Monte Carlo method for the coupled model with random inputs to derive some deterministic Stokes-Darcy numerical models and use the idea of the ensemble to realize the fast computation of multiple problems. One remarkable feature of the algorithm is that multiple linear systems share a common coefficient matrix in each deterministic numerical model, which significantly reduces the computational cost and achieves comparable accuracy with the traditional methods. Moreover, by domain decomposition, we can decouple the Stokes-Darcy system into two smaller sub-physics problems naturally. Both mesh-dependent and mesh-independent convergence rates of the algorithm are rigorously derived by choosing suitable Robin parameters. Optimized Robin parameters are derived and analyzed to accelerate the convergence of the proposed algorithm. Especially, for small hydraulic conductivity in practice, the almost optimal geometric convergence can be obtained by finite element discretization. Finally, two groups of numerical experiments are conducted to validate the exclusive features of the proposed algorithm. © 2022, CC BY-NC-ND.

关键词： Numerical models

On the estimation of high-dimensional integrated covariance matrices based on high-frequency data with multiple transactions

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arXiv 2019年

作者： Wang, Moming Xia, Ningning Zhou, Yong School of Statistics and Management Shanghai University of Finance and Economics 777 Guo Ding Road Shanghai200433 China School of Statistics and Management Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics 777 Guo Ding Road Shanghai200433 China Faculty of Economics and Management East China Normal University Shanghai200241 China Academy of Mathematics and Systems Science Chinese Academy of Science Beijing100190 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOE Academy of Statistics and Interdisciplinary Sciences School of Statistics East China Normal University Shanghai200062 China

High-frequency data in financial markets often include multiple transactions at each recording time due to the mechanism of recording. Using random matrix theory, this paper considers the estimation of integrated covariance (ICV) matrices of high-dimensional diffusion processes based on multiple high-frequency observations. We start by studying the estimator, the time-variation adjusted realized covariance (TVA) matrix proposed by Zheng and Li (2011), without microstructure noise. We show that in the high-dimensional case, for a class C of diffusion processes, the limiting spectral distribution (LSD) of the averaged TVA depends not only on that of the ICV but also on the number of multiple transactions at each recording time. However, in practice, observed prices are always contaminated by market microstructure noise. Thus, we also study the limiting behavior of pre-averaging averaged TVA matrices based on noisy multiple observations. We show that for processes in class C, the pre-averaging averaged TVA has two desirable properties: it eliminates the effects of microstructure noise and multiple transactions, and its LSD depends solely on that of the ICV matrix. Further, three types of nonlinear shrinkage estimators of the ICV matrix are proposed based on high-frequency noisy multiple observations. Simulation studies support our theoretical results and demonstrate the finite sample performance of the proposed estimators. Finally, high-frequency portfolio strategies are evaluated under these estimators in real data analysis. Copyright © 2019, The Authors. All rights reserved.

关键词： Covariance matrix