While the visualization of statistical data tends to a mature technology, the visualization of textual data is still in its infancy, especially for the artistic text. Due to the fact that visualization of artistic text is valuable and attractive in both art and information science, we attempt to realize this tentative idea in this article. We propose the Generative Adversarial Network based Artistic Textual Visualization (GAN-ATV) which can create paintings after analyzing the semantic content of existing poems. Our GAN-ATV consists of two main sections: natural language analysis section and visual information synthesis section. In natural language analysis section, we use Bag-of-Word (BoW) feature descriptors and a two-layer network to mine and analyze the high-level semantic information from poems. In visual information synthesis section, we design a cross-modal semantic understanding module and integrate it with Generative Adversarial Network (GAN) to create paintings, whose content are corresponding to the original poems. Moreover, in order to train our GAN-ATV and verify its performance, we establish a cross-modal artistic dataset named "Cross-Art". In the Cross-Art dataset, there are six topics and each topic has their corresponding paintings and poems. The experimental results on Cross-Art dataset are shown in this article.
The Wiener polarity index W P (G) of a graph G is the number of unordered pairs of vertices {u, v} where the distance between u and v is 3. In this paper, we determine the third smallest Wiener polarity index of unicyclic graphs. Moreover, the corresponding extremal graphs are characterized.
In this paper, we examine the impact of Covid-19 on the Chinese logistics industry, using both qualitative and quantitative analysis to examine the performance of the top five logistics companies in the months following the initial outbreak in early 2020. The top five companies are Shunfeng, Yuantong, Zhongtong, Shentong, and Yunda. Using a linear regression model and a questionnaire of 251 individuals, we analyze the strengths and weaknesses of the top five companies. We then conclude with our financial recommendations for the five companies, based on our analysis.
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