检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Mao, Shanjun Huang, Xiao Zhang, Chenyang Chen, Runjiu Diao, Yizhu Li, Zongjin Wang, Qingzhe Tang, Shan Guo, Shuixia College of Finance and Statistics Hunan University Changsha410079 China School of Statistics and Mathematics Central University of Finance and Economics Beijing100081 China MOE-LCSM School of Mathematics and Statistics Hunan Normal University Changsha410081 China Key Laboratory of Applied Statistics and Data Science Hunan Normal University College of Hunan Province Changsha410081 China

Motivation: Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Due to the lack of comprehensive integration of spatial and temporal dimensions of brain gene expression data, previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of the mechanisms underlying brain development or disorders associated with abnormal brain development, such as Alzheimer’s disease (AD), and to identify potential determinants of response. Results: In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees, which can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity problems. In this paper, we focus on two datasets: the AD dataset includes gene expression data from early, middle, and late stages, while the brain development dataset spans fetal development to adulthood. The results show that the model framework in this paper can effectively analyze the genes affecting brain development and AD progression in different brain regions and different stages, which is consistent with some existing studies, and can provide some insights into the process of brain development and abnormal brain development behavior. Availability: STW-MD is available at. Copyright © 2023, The Authors. All rights reserved.

关键词： Brain

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Using Information-Theoretic Approaches and Multi-Source data to Explore Inland Arid Region Hydrological Model Systems

SSRN

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SSRN 2023年

作者： Bao, Qingling Ding, Jianli Wang, Jingjie Han, Lijing Key Laboratory of Smart City and Environment Modeling of Higher Education Institute College of resources and Environment Science Xingjiang University Urumqi800046 China Big Data Intelligence Analytics Center of Xinjiang Social Economy College of Statistics and Data Science Xinjiang University of Finance & Economics Urumqi800046 China

Assessing and improving the realistic simulation of water fluxes in each component of the hydrological model is of practical importance for the accurate portrayal of hydrological systems in inland arid regions. Many hydrological modeling studies focused on simulations driven by a single meteorological data source and are evaluated using only classical statistical metrics, thus providing limited insight into model performance at the process level. This study analyzed and compared the applicability of three different meteorologically driven data interpolation methods and satellite reanalysis data in hydrological modeling of arid inland watersheds using the Variable Infiltration Capacity (VIC) model. An information-theoretic-based approach (transfer entropy) was used to quantify the information transfer between hydrological model subsystems for different model settings and combinations of meteorologically driven data. The results showed that (1) Water fluxes simulated by satellite-driven data were more accurately portrayed spatially than interpolated data but did not enhance the runoff simulation. (2) Precipitation and snow water equivalents in the seasonal water balance dominated in the spring and winter months, with runoff and evapotranspiration increasing in the summer and fall. (3) The information transfer rate of each water flux subsystem was lowest between rainfall and snow water equivalent and highest between runoff and soil moisture. (4) The transfer entropy revealed more about the information transfer of individual water fluxes of runoff on the time scale than the Pearson correlation coefficient. (5) The causal relationship between runoff and evapotranspiration and soil moisture was more obvious, while the relationship with snow water equivalent was less obvious. © 2023, The Authors. All rights reserved.

关键词： Runoff

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Hypergraph-enhanced Dual Semi-supervised Graph Classification

arXiv

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arXiv 2024年

作者： Ju, Wei Mao, Zhengyang Yi, Siyu Qin, Yifang Gu, Yiyang Xiao, Zhiping Wang, Yifan Luo, Xiao Zhang, Ming School of Computer Science National Key Laboratory for Multimedia Information Processing Anker Embodied AI Lab Peking University China School of Statistics and Data Science Nankai University China Department of Computer Science University of California Los Angeles United States School of Information Technology & Management University of International Business and Economics China

In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unlabeled graphs. Despite the promising capability of graph neural networks (GNNs), they typically require a large number of costly labeled graphs, while a wealth of unlabeled graphs fail to be effectively utilized. Moreover, GNNs are inherently limited to encoding local neighborhood information using message-passing mechanisms, thus lacking the ability to model higher-order dependencies among nodes. To tackle these challenges, we propose a Hypergraph-Enhanced DuAL framework named HEAL for semi-supervised graph classification, which captures graph semantics from the perspective of the hypergraph and the line graph, respectively. Specifically, to better explore the higher-order relationships among nodes, we design a hypergraph structure learning to adaptively learn complex node dependencies beyond pairwise relations. Meanwhile, based on the learned hypergraph, we introduce a line graph to capture the interaction between hyperedges, thereby better mining the underlying semantic structures. Finally, we develop a relational consistency learning to facilitate knowledge transfer between the two branches and provide better mutual guidance. Extensive experiments on real-world graph datasets verify the effectiveness of the proposed method against existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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A Two-Step Test for the Two-Sample Problem of Processes of Ornstein-Uhlenbeck Type

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Journal of Systems science & Complexity 2020年第6期33卷 2067-2079页

作者： WANG Yinfeng TANG Yanlin ZHANG Xinsheng School of Statistics and Mathematics Shanghai Lirin University of Acounting and FinanceShanghaiChina Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal UniversityShanghaiChina Department of Statistics Fudan UniversityShanghaiChina

The authors propose a two-step test for the two-sample problem of processes of OrnsteinUhlenbeck type. In the first step, the authors test the equality of correlation structures, based on the least square estimators of the correlation parameters, and the test statistic follows the standard normal distribution. If the null hypothesis is not rejected in the first step, the authors consider a second step to test the equality of marginal distributions, based on the weighted deviation of the empirical characteristic functions;the test statistic has a complicated asymptotic distribution, so that sequential bootstrap method is applied to reach a temporary decision. Simulation studies and real data analysis suggest that the proposed approach performs well in finite samples.

关键词： Empirical characteristic functions hypothesis testing process of Ornstein-Uhlenbeck type sequential Bootstrap

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Deep Generative Network for Image Inpainting with Gradient Semantics and Spatial-Smooth Attention

SSRN

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SSRN 2023年

作者： Sheng, Ziqi Xu, Wenbo Lin, Cong Lu, Wei School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China Applied Laboratory of Dig Data and Education Statistics School of Statistics and Mathematics Guangdong University of Finance and Economics Guangzhou510006 China

As a powerful means of tampering in image content security area, image inpainting based on deep generative networks can yield visually appealing outputs but often produces ambiguous artifacts, particularly in boundary and high semantic areas. To address this issue, a novel end-to-end network with gradient semantics and spatial-smooth attention (GS-SSA) is proposed, which combines a gradient learning network and an image inpainting network. The gradient learning network is meant to properly anticipate the gradient semantics in the hole region and get a complete gradient semantic map. The image inpainting network utilizes the complete gradient semantic map to better repair the missing pixels and obtain the final inpainting results. Moreover, spatial-smooth attention is introduced into the image inpainting network, considering the spatial structural relations and the surrounding information in the hole region respectively. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Hypergraph-enhanced dual semi-supervised graph classification 24

Hypergraph-enhanced dual semi-supervised graph classificatio...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Wei Ju Zhengyang Mao Siyu Yi Yifang Qin Yiyang Gu Zhiping Xiao Yifan Wang Xiao Luo Ming Zhang School of Computer Science National Key Laboratory for Multimedia Information Processing Peking University-Anker Embodied AI Lab Peking University China School of Statistics and Data Science Nankai University China Department of Computer Science University of California Los Angeles School of Information Technology & Management University of International Business and Economics China

关键词：

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Discovering the relationship between the number of film review topics and box office with NLP techniques 11

Discovering the relationship between the number of film revi...

引用

11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Li, Bo Dai, Wei Liu, Shang Shi, Yong School of Finance and Economics Wuhan City Vocational College No. 10 Yezhihu West Road Hongshan District Wuhan430064 China PKU-WUHAN Institute for Artificial Intelligence No. 770 Gaoxin Avenue Hongshan District Wuhan430070 China School of Economics Shanghai University 99 Shangda Road BaoShan District Shanghai200444 China School of Economics and Management University of Chinese Academy of Sciences No. 3 Zhongguancun South Yitiao Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 Zhongguancun East Road Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences No. 80 Zhongguancun East Road Beijing100190 China

Numerous movie reviews on the internet can be used to analyze audience experiences of movies and provide inspiration for the operation and production of movies. In this work, we integrate and match the review data on Douban and the box office data on the website ***, and utilize the topic model in natural language processing to determine the number of each movie review topics, then establish an regression equation to analyze the relationship between the number of topics and movie box office. Our analysis shows a significant positive correlation between film box office and the number of movie review topics, and we have provided an explanation for this. Finally, we also analyzed the practical application value of this work. © 2024 The Authors.

关键词： Natural language processing systems

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Modelling Correlation Matrices in Multivariate Dyadic data: Latent Variable Models for Intergenerational Exchanges of Family Support

arXiv

引用

arXiv 2022年

作者： Zhang, Siliang Kuha, Jouni Steele, Fiona Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University China Department of Statistics London School of Economics & Political Science United Kingdom

We define a model for the joint distribution of multiple continuous latent variables which includes a model for how their correlations depend on explanatory variables. This is motivated by and applied to social scientific research questions in the analysis of intergenerational help and support within families, where the correlations describe reciprocity of help between generations and complementarity of different kinds of help. We propose an MCMC procedure for estimating the model which maintains the positive definiteness of the implied correlation matrices, and describe theoretical results which justify this approach and facilitate efficient implementation of it. The model is applied to data from the UK Household Longitudinal Study to analyse exchanges of practical and financial support between adult individuals and their non-coresident parents. Copyright © 2022, The Authors. All rights reserved.

关键词： Covariance matrix

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CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense

arXiv

引用

arXiv 2024年

作者： Zhang, Mingkun Bi, Keping Chen, Wei Chen, Quanrun Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology CAS China Key Laboratory of Network Data Science and Technology Institute of Computing Technology CAS China School of Statistics University of International Business and Economics China CAS stands for Chinese Academy of Sciences China

Despite ongoing efforts to defend neural classifiers from adversarial attacks, they remain vulnerable, especially to unseen attacks. In contrast, humans are difficult to be cheated by subtle manipulations, since we make judgments only based on essential factors. Inspired by this observation, we attempt to model label generation with essential label-causative factors and incorporate label-non-causative factors to assist data generation. For an adversarial example, we aim to discriminate the perturbations as non-causative factors and make predictions only based on the label-causative factors. Concretely, we propose a casual diffusion model (CausalDiff) that adapts diffusion models for conditional data generation and disentangles the two types of casual factors by learning towards a novel casual information bottleneck objective. Empirically, CausalDiff has significantly outperformed state-of-the-art defense methods on various unseen attacks, achieving an average robustness of 86.39% (+4.01%) on CIFAR-10, 56.25% (+3.13%) on CIFAR-100, and 82.62% (+4.93%) on GTSRB (German Traffic Sign Recognition Benchmark). The code is available at https://***/TMLResearchGroup-CAS/CausalDiff. Copyright © 2024, The Authors. All rights reserved.

关键词： Traffic signs

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Wellposedness and averaging principle for conditional distribution dependent SDEs driven by standard Brownian motions and fractional Brownian motions

arXiv

引用

arXiv 2025年

作者： Tan, Li Wang, Shengrong School of Statistics and Data Science Jiangxi University of Finance and Economics Jiangxi Nanchang330013 China Key Laboratory of Data Science in Finance and Economics Jiangxi University of Finance and Economics Jiangxi Nanchang330013 China

In this paper, we study a conditional distribution dependent stochastic differential equations driven by standard Brownian motion and fractional Brownian motion with Hurst exponent H > 21 simultaneously. First, the existence and uniqueness of the equation is established by the fixed point theorem. Then, we show that the solutions of conditional distribution dependent stochastic differential equations can be approximated by the solutions of the associated averaged distribution dependent stochastic differential equations. Copyright © 2025, The Authors. All rights reserved.

关键词： Brownian movement

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