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arXiv 2022年

作者： Wang, Chunlin Li, Pengfei Liu, Yukun Zhou, Xiao-Hua Qin, Jing Department of Statistics and Data Science School of Economics Wang Yanan Institute for Studies in Economics MOE Key Lab of Econometrics Fujian Key Lab of Statistics Xiamen University Xiamen China Department of Statistics and Actuarial Sciences University of Waterloo Canada Key Laboratory of Advanced Theory and Application in Statistics and Data Science - MOE School of Statistics East China Normal University Shanghai China Department of Biostatistics School of Public Health Peking University Beijing China National Institute of Allergy and Infectious Diseases National Institutes of Health United States

The distribution of the incubation period of the novel coronavirus disease that emerged in 2019 (COVID-19) has crucial clinical implications for understanding this disease and devising effective disease-control measures. Qin et al. (2020) designed a cross-sectional and forward follow-up study to collect the duration times between a specific observation time and the onset of COVID-19 symptoms for a number of individuals. They further proposed a mixture forward-incubation-time epidemic model, which is a mixture of an incubation-period distribution and a forward time distribution, to model the collected duration times and to estimate the incubation-period distribution of COVID-19. In this paper, we provide sufficient conditions for the identifiability of the unknown parameters in the mixture forward-incubation-time epidemic model when the incubation period follows a two-parameter distribution. Under the same setup, we propose a likelihood ratio test (LRT) for testing the null hypothesis that the mixture forward-incubation-time epidemic model is a homogeneous exponential distribution. The testing problem is non-regular because a nuisance parameter is present only under the alternative. We establish the limiting distribution of the LRT and identify an explicit representation for it. The limiting distribution of the LRT under a sequence of local alternatives is also obtained. Our simulation results indicate that the LRT has desirable type I errors and powers, and we analyze a COVID-19 outbreak dataset from China to illustrate the usefulness of the LRT. Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

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Mean-variance portfolio selection with dynamic attention behavior in a hidden Markov model

arXiv

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arXiv 2022年

作者： Zhang, Yu Jin, Zhuo Wei, Jiaqin Yin, George Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University Shanghai200062 China Department of Actuarial Studies and Business Analytics Macquarie University NSW2109 Australia Department of Mathematics University of Connecticut StorrsCT06269-1009 United States

In this paper, we study closed-loop equilibrium strategies for mean-variance portfolio selection problem in a hidden Markov model with dynamic attention behavior. In addition to the investment strategy, the investor’s attention to news is introduced as a control of the accuracy of the news signal process. The objective is to find equilibrium strategies by numerically solving an extended HJB equation by using Markov chain approximation method. An iterative algorithm is constructed and its convergence is established. Numerical examples are also provided to illustrate the results. Copyright © 2022, The Authors. All rights reserved.

关键词： Hidden Markov models

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CLIP-Driven Open-Vocabulary 3D Scene Graph Generation via Cross-Modality Contrastive Learning

CLIP-Driven Open-Vocabulary 3D Scene Graph Generation via Cr...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lianggangxu Chen Xuejiao Wang Jiale Lu Shaohui Lin Changbo Wang Gaoqi He School of Computer Science and Technology East China Normal University Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China Chongqing Key Laboratory of Precision Optics Chongqing Institute of East China Normal University Chongqing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

3D Scene Graph Generation (3DSGG) aims to classify objects and their predicates within 3D point cloud scenes. However, current 3DSGG methods struggle with two main challenges. 1) The dependency on labor-intensive ground-truth annotations. 2) Closed-set classes training hampers the recognition of novel objects and predicates. Addressing these issues, our idea is to extract cross-modality features by CLIP from text and image data naturally related to 3D point clouds. Cross-modality features are used to train a robust 3D scene graph (3DSG)feature extractor. Specifically, we propose a novel Cross-Modality Contrastive Learning 3DSGG (CCL-3DSGG) method. Firstly, to align the text with 3DSG, the text is parsed into word level that are consistent with the 3DSG annotation. To enhance robustness during the alignment, adjectives are exchanged for different objects as negative samples. Then, to align the image with 3DSG, the camera view is treated as a positive sample and other views as negatives. Lastly, the recognition of novel object and predicate classes is achieved by calculating the cosine similarity between prompts and 3DSG features. Our rigorous experiments confirm the superior open-vocabulary capability and applicability of CCL-3DSGG in real-world contexts.

关键词： Point cloud compression Training Computer vision Three-dimensional displays Annotations Contrastive learning Feature extraction

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Cross Attention Based Style Distribution for Controllable Person Image Synthesis

arXiv

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arXiv 2022年

作者： Zhou, Xinyue Yin, Mingyu Chen, Xinyuan Sun, Li Gao, Changxin Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University Shanghai China Shanghai AI Laboratory Shanghai China Huazhong University of Science and Technology Wuhan China

Controllable person image synthesis task enables a wide range of applications through explicit control over body pose and appearance. In this paper, we propose a cross attention based style distribution module that computes between the source semantic styles and target pose for pose transfer. The module intentionally selects the style represented by each semantic and distributes them according to the target pose. The attention matrix in cross attention expresses the dynamic similarities between the target pose and the source styles for all semantics. Therefore, it can be utilized to route the color and texture from the source image, and is further constrained by the target parsing map to achieve a clearer objective. At the same time, to encode the source appearance accurately, the self attention among different semantic styles is also added. The effectiveness of our model is validated quantitatively and qualitatively on pose transfer and virtual try-on tasks. Codes are available at https://***/xyzhouo/CASD. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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NOVEL ENSEMBLE ALGORITHMS FOR RANDOM TWO-DOMAIN PARABOLIC PROBLEMS

arXiv

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arXiv 2023年

作者： Sun, Yizhong Wang, Jiangshan Zheng, Haibiao 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 Shanghai China

In this paper, three efficient ensemble algorithms are proposed for fast-solving the random fluid-fluid interaction model. Such a model can be simplified as coupling two heat equations with random diffusion coefficients and a friction parameter due to its complexity and uncertainty. We utilize the Monte Carlo method for the coupled model with random inputs to derive some deterministic fluid-fluid numerical models and use the ensemble idea to realize the fast computation of multiple problems. Our remarkable feature of these algorithms is employing the same coefficient matrix for multiple linear systems, significantly reducing the computational cost. By data-passing partitioned techniques, we can decouple the numerical models into two smaller sub-domain problems and achieve parallel computation. Theoretically, we derive that both algorithms are unconditionally stable and convergent. Finally, numerical experiments are conducted not only to support the theoretical results but also to validate the exclusive feature of the proposed algorithms. © 2023, CC BY-NC-SA.

关键词： Numerical models

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Robust Online Detection in Serially Correlated Directed Network

arXiv

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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

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

arXiv

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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

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

arXiv

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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

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

arXiv

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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)

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

arXiv

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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)

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