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ID-Unet: Iterative soft and hard deformation for view synthesis

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arXiv 2021年

作者： Yin, Mingyu Sun, Li Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing Shanghi China Key Laboratory of Advanced Theory and Application in Statistics & Data Science East China Normal University Shanghai200241 China

View synthesis is usually done by an autoencoder, in which the encoder maps a source view image into a latent content code, and the decoder transforms it into a target view image according to the condition. However, the source contents are often not well kept in this setting, which leads to unnecessary changes during the view translation. Although adding skipped connections, like Unet, alleviates the problem, but it often causes the failure on the view conformity. This paper proposes a new architecture by performing the source-to-target deformation in an iterative way. Instead of simply incorporating the features from multiple layers of the encoder, we design soft and hard deformation modules, which warp the encoder features to the target view at different resolutions, and give results to the decoder to complement the details. Particularly, the current warping flow is not only used to align the feature of the same resolution, but also as an approximation to coarsely deform the high resolution feature. Then the residual flow is estimated and applied in the high resolution, so that the deformation is built up in the coarse-to-fine fashion. To better constrain the model, we synthesize a rough target view image based on the intermediate flows and their warped features. The extensive ablation studies and the final results on two different data sets show the effectiveness of the proposed model. https://***/MingyuY/Iterative-view-synthesis © 2021, CC BY.

关键词： Signal encoding

Semiparametric estimation for accelerated failure time mixture cure model allowing non-curable competing risk

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Statistical theory and Related Fields 2020年第1期4卷 97-108页

作者： Yijun Wang Jiajia Zhang Yincai Tang Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal UniversityShanghai200062People’s Republic of China Department of Epidemiology and Biostatistics University of South CarolinaColumbiaSCUSA

The mixture cure model is the most popular model used to analyse the major event with a potential cure *** in the real world there may exist a potential risk from other non-curable competing *** this paper,we study the accelerated failure time model with mixture cure model via kernel-based nonparametric maximum likelihood estimation allowing non-curable competing *** EM algorithm is developed to calculate the estimates for both the regression parameters and the unknown error densities,in which a kernel-smoothed conditional profile likelihood is maximised in the M-step,and the resulting estimates are *** performance is demonstrated through comprehensive simulation ***,the proposed method is applied to the colorectal clinical trial data.

关键词： AFT mixture cure model competing risk EM algorithm

On stochastic control problems with higher-order moments

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arXiv 2024年

作者： Wang, Yike Liu, Jingzhen Bensoussan, Alain Yiu, Ka-Fai Cedric Wei, Jiaqin School of Finance Chongqing Technology and Business University Chongqing400067 China China Institute for Actuarial Science Central University of Finance and Economics Beijing100081 China Naveen Jindal School of Management University of Texas at Dallas DallasTX75083 United States Department of Applied Mathematics The Hong Kong Polytechnic University Kowloon Hunghom Hong Kong Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOE School of Statistics East China Normal University Shanghai200241 China

In this paper, we focus on a class of time-inconsistent stochastic control problems, where the objective function includes the mean and several higher-order central moments of the terminal value of state. To tackle the time-inconsistency, we seek both the closed-loop and the open-loop Nash equilibrium controls as time-consistent solutions. We establish a partial differential equation (PDE) system for deriving a closed-loop Nash equilibrium control, which does not include the equilibrium value function and is different from the extended Hamilton-Jacobi-Bellman (HJB) equations as in Björk et al. (Finance Stoch. 21: 331-360, 2017). We show that our PDE system is equivalent to the extended HJB equations that seems difficult to be solved for our higher-order moment problems. In deriving an open-loop Nash equilibrium control, due to the non-separable higher-order moments in the objective function, we make some moment estimates in addition to the standard perturbation argument for developing a maximum principle. Then, the problem is reduced to solving a flow of forward-backward stochastic differential equations. In particular, we investigate linear controlled dynamics and some objective functions affine in the mean. The closed-loop and the open-loop Nash equilibrium controls are identical, which are independent of the state value, random path and the preference on the odd-order central moments. By sending the highest order of central moments to infinity, we obtain the time-consistent solutions to some control problems whose objective functions include some penalty functions for *** Codes Primary: 93E20, 91G80, Secondary: 91B08, 49N90 Copyright © 2024, The Authors. All rights reserved.

关键词： Nash equilibrium

Kernel entropy estimation for long memory linear processes with infinite variance

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arXiv 2022年

作者： Liu, Hui Xu, Fangjun School of Statistics East China Normal University Shanghai200262 China 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 at NYU Shanghai 3663 Zhongshan Road North Shanghai200062 China

Let X = {Xn : n ∈ N} be a long memory linear process with innovations in the domain of attraction of an α-stable law (0 RR f 2(x) dx by using the kernel estimator X 2 Tn(hn) = n(n − 1)hn 1≤jn K (Xih−nXj) . The simulation study for long memory linear processes with symmetric α-stable innovations is also given. © 2022, CC BY-NC-SA.

关键词： Probability density function

Novel View Synthesis on Unpaired data by Conditional Deformable Variational Auto-Encoder 1

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16th European Conference on Computer Vision, ECCV 2020

作者： Yin, Mingyu Sun, Li Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University Shanghai200241 China

ISBN: (数字)9783030586041

ISBN: (纸本)9783030586034

Novel view synthesis often needs the paired data from both the source and target views. This paper proposes a view translation model under cVAE-GAN framework without requiring the paired data. We design a conditional deformable module (CDM) which uses the view condition vectors as the filters to convolve the feature maps of the main branch in VAE. It generates several pairs of displacement maps to deform the features, like the 2D optical flows. The results are fed into the deformed feature based normalization module (DFNM), which scales and offsets the main branch feature, given its deformed one as the input from the side branch. Taking the advantage of the CDM and DFNM, the encoder outputs a view-irrelevant posterior, while the decoder takes the code drawn from it to synthesize the reconstructed and the view-translated images. To further ensure the disentanglement between the views and other factors, we add adversarial training on the code. The results and ablation studies on MultiPIE and 3D chair datasets validate the effectiveness of the framework in cVAE and the designed module. © 2020, Springer Nature Switzerland AG.

关键词： Deformation

Differentially Private Precision Matrix Estimation

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Acta Mathematica Sinica,English Series 2020年第10期36卷 1107-1124页

作者： Wen Qing SU Xiao GUO Hai ZHANG School of Mathematics Northwest UniversityXi'an 710127P.R.China Faculty of Information Technology Macao University of Science and TechnologyMacao 999078P.R.China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(East China Normal University) Ministry of EducationShanghai 200062P.R.China

In this paper, we study the problem of precision matrix estimation when the dataset contains sensitive information. In the differential privacy framework, we develop a differentially private ridge estimator by perturbing the sample covariance matrix. Then we develop a differentially private graphical lasso estimator by using the alternating direction method of multipliers(ADMM) ***, we prove theoretical results showing that the differentially private ridge estimator for the precision matrix is consistent under fixed-dimension asymptotic, and establish a convergence rate of differentially private graphical lasso estimator in the Frobenius norm as both data dimension p and sample size n are allowed to grow. The empirical results that show the utility of the proposed methods are also provided.

关键词： Differential privacy graphical model ADMM algorithm

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

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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

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

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SSRN

SSRN 2023年

作者： Li, Mengtian Lin, Shaohui Wang, Zihan Shen, Yunhang Zhang, Baochang Ma, Lizhuang Visual Media Intelligent Processing Research Group Department of Film and Television Engineering Shanghai University Shanghai200072 China School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Youtu Lab Tencent 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. Ex-✩Fully documented templates are available in the elsarticle package on CTAN. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

HYPOTHESIS TEST ON A MIXTURE FORWARD-INCUBATION-TIME EPIDEMIC MODEL WITH application TO COVID-19 OUTBREAK

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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