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Factorize international mobility on academic performance: a time-varying DID method examining over 7million Chinese scholars

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Journal of Informetrics 2025年第3期19卷

作者： Wen Lou Mingzhu Gao Department of Information Management School of Economics and Management East China Normal University Shanghai PR China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE (East China Normal University) Shanghai PR China

Objective International academic mobility of researchers provides them with access to global knowledge and innovation and shaping their research activities. This study seeks to examine the impact of international mobility on academic performance of researchers. Method We first visualized changes in researchers' academic performance before and after mobility. Then, we employed the Time-varying Difference-in-Differences (DID) model and Propensity Score Matching (PSM) methodology to uncover the impact of international mobility on researchers. Finally, we delved deeper into the relationship between international mobility patterns and academic performance. Results The visualized analysis results highlight substantial academic advancement in disciplines such as Biomedical and Life sciences. Notably, researchers returning to China after mobility exhibit the most significant impact on academic performance, followed by those engaged in multiple movements ('circulation') across countries. In contrast, the 'emigration' group, not returning to China after mobility, exhibits the lowest impact on academic performance. Conclusions These results emphasize how international academic mobility significantly boosts researchers' academic performance. Particularly, researchers returning to China after mobility demonstrate the most substantial impact on academic performance. However, the noticeable surge in researchers leaving China presents a challenge in retaining their expertise. Therefore, it's crucial to promote active participation in international mobility while developing strategies to retain expertise post-mobility, which is vital for the scientific development of China.

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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

Assessment of Effects of Age and Gender on the Incubation Period of COVID-19 with a Mixture Regression Model

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Journal of data science 2021年第1期19卷 p1-16页

作者： SIMING ZHENG JING QIN YONG ZHOU Academy of Mathematics and Systems Science University of Chinese Academy of Sciences Beijing China National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda Maryland USA Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOE Academy of Statistics and Interdisciplinary Sciences Faculty of Economics and Management East China Normal University Shanghai China

Following the outbreak of COVID-19, various containment measures have been taken, including the use of quarantine. At present, the quarantine period is the same for everyone, since it is implicitly assumed that the incubation period distribution of COVID-19 is the same regardless of age or gender. For testing the effects of age and gender on the incubation period of COVID-19, a novel two-component mixture regression model is proposed. An expectation-maximization (EM) algorithm is adopted to obtain estimates of the parameters of interest, and the simulation results show that the proposed method outperforms the simple regression method and has robustness. The proposed method is applied to a Zhejiang COVID-19 dataset, and it is found that age and gender statistically have no effect on the incubation period of COVID-19, which indicates that the quarantine measure currently in operation is reasonable. [ABSTRACT FROM AUTHOR]

关键词： EM algorithm incubation period length-biased data mixture model

A majorized-generalized alternating direction method of multipliers for convex composite programming

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arXiv 2021年

作者： Qin, Congying Xiao, Yunhai Li, Peili School of Mathematics and Statistics Henan University Kaifeng475000 China Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms Henan University Kaifeng475000 China School of Statistics Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE East China Normal University Shanghai200062 China

The linearly constrained convex composite programming problems whose objective function contains two blocks with each block being the form of nonsmooth+smooth arises frequently in multiple fields of applications. If both of the smooth terms are quadratic, this problem can be solved efficiently by using the symmetric Gaussian-Seidel (sGS) technique based proximal alternating direction method of multipliers (ADMM). However, in the non-quadratic case, the sGS technique can not be used any more, which leads to the separable structure of nonsmooth+smooth had to be ignored. In this paper, we present a generalized ADMM and particularly use a majorization technique to make the corresponding subproblems more amenable to efficient computations. Under some appropriate conditions, we prove its global convergence for the relaxation factor in (0, 2). We apply the algorithm to solve a kind of simulated convex composite optimization problems and a type of sparse inverse covariance matrix estimation problems which illustrates that the effectiveness of the algorithm are obvious. Copyright © 2021, The Authors. All rights reserved.

关键词： Covariance matrix

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

Simultaneous optimal control of directional missed discovery rates in data stream diagnosis

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A I I E Transactions

A I I E Transactions 2025年第4期57卷

作者： Yan He Yicheng Kang Dongdong Xiang Peihua Qiu a Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University Shanghai China b Department of Information Systems & Analytics Miami University OxfordOH USA c Department of Biostatistics The University of Florida Gainsville FL USA

High-dimensional data streams are ubiquitous in modern manufacturing, due to their ability to provide valuable information about the industrial system’s performance on a real-time basis. If a shift occurs in a production process, fault diagnosis based on the data streams is of critical importance for identifying the root cause. Existing methods have largely focused on controlling the total missed discovery rate without distinguishing missed signals for positive versus negative components of the shift vector. In practice, however, losses incurred from the two directional shifts can differ substantially, so it is desirable to constrain the proportions of missed signals for positive and negative components at two distinctive levels. In this article, we propose a fault classification procedure that controls the two proportions separately. By formulating the problem as Lagrangian multiplier optimization, we show that the proposed procedure is optimal in the sense that it minimizes the expected number of false discoveries. We also suggest an iterative adjustment algorithm that converges to the optimal Lagrangian parameters. The asymptotic optimality for the data-driven version of our procedure is also established. Theoretical justification and numerical comparison with state-of-the-art methods show that the proposed procedure works well in applications.

关键词： Fault classification high-dimensional data large-scale testing Markov models post-signal diagnostics quality control

The Conditional Mutual Information-Based Single-Sample Network Biomarker Approach Reveals the Critical Transition Moments of Disease Progression

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SSRN

SSRN 2025年

作者： Zhang, Lantian Hu, Fuyan Chi, Qingjia Chen, Bifeng The Department of Statistics School of Mathematics and Statistics Wuhan University of Technology 122 Luoshi Road Hubei Wuhan China Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics Department of Mechanics and Engineering Structure Wuhan University of Technology 122 Luoshi Road Hubei Wuhan China The Department of Biological Science and Technology School of Chemistry Chemical Engineering and Life Sciences Wuhan University of Technology 122 Luoshi Road Hubei Wuhan China

During the progression of complex diseases, it is common to observe that the deterioration of the condition does not follow a smooth trajectory, and there is often a significant and critical transition from one state to another. Finding these critical transitions is of significant importance in the clinical treatment of cancer. In this study, we propose a novel computational approach, called the conditional mutual information-based single-sample network biomarker (CMISNB), which can reveal the critical transition moments of disease progression using only a single sample (https://***/ZLTSKY/CMISNB). By analyzing disease data from mouse acute lung injury, colon cancer, hepatocellular liver cancer, lung adenocarcinoma, and endometrial cancer, we validated the effectiveness of the CMISNB method during identifying critical transition moments in disease development. In particular, the CMISNB approach facilitates the discovery of new prognostic biomarkers and can provide personalized diagnoses for individuals. © 2025, The Authors. All rights reserved.

关键词： Lung cancer

Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler

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arXiv 2023年

作者： Lin, Shaohui Huang, Wenxuan Xie, Jiao Zhang, Baochang Shen, Yunhang Yu, Zhou Han, Jungong Doermann, David The School of Computer Science and Technology East China Normal University Shanghai China The Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China The Department of Automation School of Aerospace Engineering Xiamen University Xiamen China The Institute of Artificial Intelligence Beihang University Beijing China Zhongguancun Laboratory Beijing China The Youtu Lab Tencent Shanghai China The School of Statistics East China Normal University Shanghai China The Department of Computer Science University of Sheffield United Kingdom The University at Buffalo BuffaloNY United States

Filter pruning simultaneously accelerates the computation and reduces the memory overhead of CNNs, which can be effectively applied to edge devices and cloud services. In this paper, we propose a novel Knowledge-driven Differential Filter Sampler (KDFS) with Masked Filter Modeling (MFM) framework for filter pruning, which globally prunes the redundant filters based on the prior knowledge of a pre-trained model in a differential and non-alternative optimization. Specifically, we design a differential sampler with learnable sampling parameters to build a binary mask vector for each layer, determining whether the corresponding filters are redundant. To learn the mask, we introduce masked filter modeling to construct PCA-like knowledge by aligning the intermediate features from the pre-trained teacher model and the outputs of the student decoder taking sampling features as the input. The mask and sampler are directly optimized by the Gumbel-Softmax Straight-Through Gradient Estimator in an end-to-end manner in combination with global pruning constraint, MFM reconstruction error, and dark knowledge. Extensive experiments demonstrate the proposed KDFS’s effectiveness in compressing the base models on various datasets. For instance, the pruned ResNet-50 on ImageNet achieves 55.36% computation reduction, and 42.86% parameter reduction, while only dropping 0.35% Top-1 accuracy, significantly outperforming the state-of-the-art methods. The code is available at https://***/Osilly/KDFS. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

An efficient semismooth Newton method for adaptive sparse signal recovery problems

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arXiv 2021年

作者： Ding, Yanyun Zhang, Haibin Li, Peili Xiao, Yunhai Department of Operations Research and Information Engineering Beijing University of Technology Beijing100124 China School of Statistics Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE East China Normal University Shanghai200062 China School of Mathematics and Statistics Henan University Kaifeng475000 China

We know that compressive sensing can establish stable sparse recovery results from highly undersampled data under a restricted isometry property condition. In reality, however, numerous problems are coherent, and vast majority conventional methods might work not so well. Recently, it was shown that using the difference between l1- and l2-norm as a regularization always has superior performance. In this paper, we propose an adaptive lp-l1−2 model where the lp-norm with p ≥ 1 measures the data fidelity and the l1−2-term measures the sparsity. This proposed model has the ability to deal with different types of noises and extract the sparse property even under high coherent condition. We use a proximal majorization-minimization technique to handle the nonconvex regularization term and then employ a semismooth Newton method to solve the corresponding convex relaxation subproblem. We prove that the sequence generated by the semismooth Newton method admits fast local convergence rate to the subproblem under some technical assumptions. Finally, we do some numerical experiments to demonstrate the superiority of the proposed model and the progressiveness of the proposed algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Compressed sensing