High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional dat...High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data.Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many highdimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures.展开更多
Three-dimensional( 3 D) fabric composite is a newly developed sandwich structure,consisting of two identical parallel fabric decks woven integrally and mechanically together by means of vertical woven fabrics. In this...Three-dimensional( 3 D) fabric composite is a newly developed sandwich structure,consisting of two identical parallel fabric decks woven integrally and mechanically together by means of vertical woven fabrics. In this paper,six types of 3 D fabric sandwich composites were developed in terms of compressive and flexural properties as a function of pile height( 10, 20 and30 mm) and pile distance( 16, 24 and 32 mm) in pile structures. The mechanical characteristics and the damage modes of the 3 D fabric sandwich composites under compressive and flexural load conditions were investigated. Besides,the influence of pile height and pile distance on the 3 D fabric sandwich composites mechanical properties was analyzed. The results showed that the compressive properties decreased with the increase of the pile height and the pile distance. Flexural properties increased with the increase of pile height, while decreased with the increase of pile distance.展开更多
Although there are many studies involving influence of runaway truck entry speed and longitudinal grade on stopping distance,focusing on aggregate properties is scarce.This paper investigates the influence of the aggr...Although there are many studies involving influence of runaway truck entry speed and longitudinal grade on stopping distance,focusing on aggregate properties is scarce.This paper investigates the influence of the aggregate properties such as types of aggregate and river gravel radius on stopping distance through numerical analysis of particle flow code in two dimensions(PFC2D).The software is used to generate stopping distance data for two aggregate types and four group gravel radii under various approaching speeds and grades.The generated data are compared with the testing results of full-scale arrester bed.The simulated finding of this paper implies that types of aggregates and river gravel radii have a significant impact on the stopping distance for runaway truck on escape ramps.展开更多
基金supported by National Natural Science Foundation of China(Grant Nos.11401497 and 11301435)the Fundamental Research Funds for the Central Universities(Grant No.T2013221043)+3 种基金the Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry,the Fundamental Research Funds for the Central Universities(Grant No.20720140034)National Institute on Drug Abuse,National Institutes of Health(Grant Nos.P50 DA036107 and P50 DA039838)National Science Foundation(Grant No.DMS1512422)The content is solely the responsibility of the authors and does not necessarily represent the official views of National Institute on Drug Abuse, National Institutes of Health, National Science Foundation or National Natural Science Foundation of China
文摘High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data.Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many highdimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures.
基金National Key R&D Program of China(Nos.2016YFB0303104,2018YFC0810306)Jiangsu Overseas Visiting Scholar Program for University Prominent Younge Middle-aged Teachers and PresidentsSix Talent Peaks Project in Jiangsu Province,China(No.XCL-061)
文摘Three-dimensional( 3 D) fabric composite is a newly developed sandwich structure,consisting of two identical parallel fabric decks woven integrally and mechanically together by means of vertical woven fabrics. In this paper,six types of 3 D fabric sandwich composites were developed in terms of compressive and flexural properties as a function of pile height( 10, 20 and30 mm) and pile distance( 16, 24 and 32 mm) in pile structures. The mechanical characteristics and the damage modes of the 3 D fabric sandwich composites under compressive and flexural load conditions were investigated. Besides,the influence of pile height and pile distance on the 3 D fabric sandwich composites mechanical properties was analyzed. The results showed that the compressive properties decreased with the increase of the pile height and the pile distance. Flexural properties increased with the increase of pile height, while decreased with the increase of pile distance.
文摘Although there are many studies involving influence of runaway truck entry speed and longitudinal grade on stopping distance,focusing on aggregate properties is scarce.This paper investigates the influence of the aggregate properties such as types of aggregate and river gravel radius on stopping distance through numerical analysis of particle flow code in two dimensions(PFC2D).The software is used to generate stopping distance data for two aggregate types and four group gravel radii under various approaching speeds and grades.The generated data are compared with the testing results of full-scale arrester bed.The simulated finding of this paper implies that types of aggregates and river gravel radii have a significant impact on the stopping distance for runaway truck on escape ramps.
