A hybrid clustering and graph based algorithm for tagSNP selection

TagSNP selection, which aims to select a small subset of informative single nucleotide polymorphisms (SNPs) to represent the whole large SNP set, has played an important role in current genomic research. Not only can this cut down the cost of genotyping by filtering a large number of redundant SNPs, but also it can accelerate the study of genome-wide disease association. In this paper, we propose a new hybrid method called CMDStagger that combines the ideas of the clustering and the graph algorithm, to find the minimum set of tagSNPs. The proposed algorithm uses the information of the linkage disequilibrium association and the haplotype diversity to reduce the information loss in tagSNP selection, and has no limit of block partition. The approach is tested on eight benchmark datasets from Hapmap and chromosome 5q31. Experimental results show that the algorithm in this paper can reduce the selection time and obtain less tagSNPs with high prediction accuracy. It indicates that this method has better performance than previous ones.     

高压变频器在重钢步进梁式加热炉风机控制中的应用

本文介绍HARSVERT—A系列高压变频器在重钢宽厚板工程步进梁式加热炉风机功率控制中的应用,主要论述了所采用的高压变频器的原理及控制系统技术方案。     

校务系统中的Web服务应用

从拓展校务系统的角度,介绍了 Web服务在校务系统中的理论模型和实现方法.     

家庭理财系统的开发及实现

划分了家庭理财软件的功能模块,完成了数据库设计,并实现了家庭理财系统的建立.     

Applying deep learning in football ankle injury for value of high-power magnetic resonance bioimaging evaluation

The Journal of Supercomputing - This study was to evaluate the performance of magnetic resonance imaging (MRI) reconstruction algorithm based on convolutional neural network (CNN) in the diagnosis...     

A Survey on Long-Tailed Visual Recognition

The heavy reliance on data is one of the major reasons that currently limit the development of deep learning. Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality. The long-tailed phenomenon is prevalent due to the prevalence of power law in nature. In this case, the performance of deep learning models is often dominated by the head classes while the learning of the tail classes is severely underdeveloped. In order to learn adequately for all classes, many researchers have studied and preliminarily addressed the long-tailed problem. In this survey, we focus on the problems caused by long-tailed data distribution, sort out the representative long-tailed visual recognition datasets and summarize some mainstream long-tailed studies. Specifically, we summarize these studies into ten categories from the perspective of representation learning, and outline the highlights and limitations of each category. Besides, we have studied four quantitative metrics for evaluating the imbalance, and suggest using the Gini coefficient to evaluate the long-tailedness of a dataset. Based on the Gini coefficient, we quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade, and find that the long-tailed phenomenon is widespread and has not been fully studied. Finally, we provide several future directions for the development of long-tailed learning to provide more ideas for readers.

     

Optimizing Financial Engineering Time Indicator Using Bionics Computation Algorithm and Neural Network Deep Learning

Computational Economics - The present work aims to optimize the time index of financial engineering to improve the efficiency of financial decision-making. A Back Propagation Neural Network (BPNN)...     

Tensile Properties and Failure Mechanism of a New 3D Nonorthogonal Woven Composite Material

Tensile properties and failure mechanism of a newly developed three-dimensional (3D) woven composite material named 3D nonorthogonal woven composite are investigated in this paper. The microstructure of the composite is studied and the tensile properties are obtained by quasi-static tensile tests. The failure mechanism of specimen is discussed based on observation of the fracture surfaces via electron microscope. It is found that the specimens always split along the oblique yarns and produce typical v-shaped fracture surfaces. The representative volume cell (RVC) is established based on the microstructure. A finite element analysis is conducted with periodical boundary conditions. The finite element simulation results agree well with the experimental data. By analyzing deformation and stress distribution under different loading conditions, it is demonstrated that finite element model based on RVC is valid in predicting tensile properties of 3D nonorthogonal woven composites. Stress distribution shows that the oblique yarns and warp yarns oriented along the x direction carry primary load under x tension and that warp yarns bear primary load under y tension.