Investigation of stock price network based on time series analysis and complex network

Complex network is now widely used in a series of disciplines such as biology, physics, mathematics, sociology and so on. In this paper, we construct the stock price trend network based on the knowledge of complex network, and then propose a method based on information entropy to divide the stock network into some communities, that is, a gathering study of stock price trend. We construct time series networks for each stock in Chinese A-share market based on time series network model, and then use these networks to divide the stock market into communities. We find that the average trend of stocks in the same community is the same as the trend of market value weighting, but the average trend of stocks in different communities is quite different and the sequence correlation is low. This conclusion shows that stocks in the same community share the same price trend, while the stock trend in different communities varies. This paper is a successful application of complex network and information entropy in stock trend analysis, which mainly includes two contributions. First, the success of the visibility graph algorithm provides a new perspective for enriching stock price trend modeling. Second, our conclusion proves that the clustering based on information entropy theory is effective, which provides a new method for further research on stock price trend, portfolio construction and stock return prediction.

Five-weight codes from three-valued correlation of M-sequences

<p style='text-indent:20px;'>In this paper, for each of six families of three-valued <inline-formula><tex-math id="M1">\begin{document}$ m $\end{document}</tex-math></inline-formula>-sequence correlation, we construct an infinite family of five-weight codes from trace codes over the ring <inline-formula><tex-math id="M2">\begin{document}$ R = \mathbb{F}_2+u\mathbb{F}_2 $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M3">\begin{document}$ u^2 = 0. $\end{document}</tex-math></inline-formula> The trace codes have the algebraic structure of abelian codes. Their Lee weight distribution is computed by using character sums. Their support structure is determined. An application to secret sharing schemes is given. The parameters of the binary image are <inline-formula><tex-math id="M4">\begin{document}$ [2^{m+1}(2^m-1),4m,2^{m}(2^m-2^r)] $\end{document}</tex-math></inline-formula> for some explicit <inline-formula><tex-math id="M5">\begin{document}$ r. $\end{document}</tex-math></inline-formula></p>

CDA-Net for Automatic Prostate Segmentation in MR Images

Automatic and accurate prostate segmentation is an essential prerequisite for assisting diagnosis and treatment, such as guiding biopsy procedures and radiation therapy. Therefore, this paper proposes a cascaded dual attention network (CDA-Net) for automatic prostate segmentation in MRI scans. The network includes two stages of RAS-FasterRCNN and RAU-Net. Firstly, RAS-FasterRCNN uses improved FasterRCNN and sequence correlation processing to extract regions of interest (ROI) of organs. This ROI extraction serves as a hard attention mechanism to focus the segmentation of the subsequent network on a certain area. Secondly, the addition of residual convolution block and self-attention mechanism in RAU-Net enables the network to gradually focus on the area where the organ exists while making full use of multiscale features. The algorithm was evaluated on the PROMISE12 and ASPS13 datasets and presents the dice similarity coefficient of 92.88% and 92.65%, respectively, surpassing the state-of-the-art algorithms. In a variety of complex slice images, especially for the base and apex of slice sequences, the algorithm also achieved credible segmentation performance.