1988 DWC Membership and Mailing Lists

Membership list of DWC members, 198

Number of detected DEGs as a function of sample size.

<p>(a): <b>TALL</b> versus <b>TEL</b>; (b) <b>HYPERDIP</b> versus <b>TEL</b>. Total number of genes is . <i>t</i>-test and Bonferroni procedure are applied. Adjusted -value threshold: 0.05.</p

Number of true (a,c) and false (b,d) positives as functions of effect size (SIMU1).

<p>Total number of genes is . Total number of truly differentially expressed genes is , where and are the numbers of up- and down-regulated genes, respectively. The sample size is . <i>t</i>-test and Bonferroni procedure are applied. Adjusted -value threshold: 0.05.</p

Overview of our art generation process.

We first initialize the parameters of the particle system and the canvas and then continuously update the color and position of each particle and the canvas to create art.</p

Mean aesthetic ratings and standard deviations for the images in each IS.

Mean aesthetic ratings and standard deviations for the images in each IS.</p

The loading matrix of the principal component analysis.

Each element represents: how much each original variable contributed to the corresponding principal component. (XLSX)</p

Perlin noise flow field.

The gray arrows represent the directions of the force field. The movement of a particle in each frame is affected by the underlying force. The red arrow shows the movement direction of the particle in frame t+1.</p

Regression subset selection results.

Panels (a) and (b) show the AIC and adjusted R2 values for each regression model, respectively. The top row in each panel indicates the regression model that performed best.</p

Spearman coefficients and corresponding 95% confidence intervals(CI) for the correlations between time and ratings according to the image features.

Spearman coefficients and corresponding 95% confidence intervals(CI) for the correlations between time and ratings according to the image features.</p

Fixed-effects panel regression model.

Several automated aesthetic assessment models were developed to assist artists in producing artwork with high aesthetic appeal. However, most of them focused on static visual art, such as photographs and paintings, and evaluations of dynamic art received less attention. Dynamic visual art, especially computer-based art, includes diverse forms of artistic expression and can enhance an audience’s aesthetic experience. A model for evaluating dynamic visual art can provide valuable feedback and guidance for improving artistic skills and creativity, thereby benefiting audiences. In this study, we created eight generative artworks with dynamic art forms based on a commonly used method. We established a time-dependent model to predict the aesthetics based on visual features. We quantified the artworks according to selected image features and found that these features could effectively capture the characteristics of the changing visual forms during the generation process. To explore the effects of time-varying features on aesthetic appeal, we built a panel regression model and found that the aesthetic appeal of the generated artworks was significantly affected by their skewness of the luminance distribution, vertical symmetry, and mean hue value. Furthermore, our study demonstrated that the aesthetic appeal of dynamic generative artworks could be predicted by integrating image features into the temporal domain.</div