Interactive Style Transfer for Data Visualization and Data Art

This thesis discusses Data Brushes, an interactive web application to explore neural style transfer using models trained on artistic data visualizations. The application invites casual creators to engage with deep convolutional neural networks to co-create custom artworks with a focus on style transfer networks created from canonical and contemporary works of data visualization and data art to demonstrate the versatility and flexibility of the algorithm. In addition to enabling a novel creative workflow, the process of interactively modifying an image via multiple style transfer networks reveals meaningful features encoded within the networks, and provides insight into the effects particular networks have on different images, or different regions within a single image. To evaluate Data Brushes, we gathered expert feedback from participants of a data science symposium and ran an observational study, finding that our application facilitates the creative exploration of neural style transfer for data art and enhances user intuition regarding the expressive range of style transfer features. This thesis explores both the practical uses of such tools for artists as Data Brushes and the interpretive uses of creating such venues for accessibility to computational art, remixing the purpose of data visualizations to be more than just graphical representations of information

Evaluation of Signal Processing Methods for Speech Enhancement

This thesis explores some of the main approaches to the problem of speech signal enhancement. Traditional signal processing techniques including spectral subtraction, Wiener filtering, and subspace methods are very widely used and can produce very good results, especially in the cases of constant ambient noise, or noise that is predictable over the course of the signal. We first study these methods and their results, and conclude with an analysis of the successes and failures of each. Comparisons are based on the effectiveness of the methods of removing disruptive noise, the speech quality and intelligibility of the enhanced signals, and whether or not they introduce some new artifacts into the signal. These characteristics are analyzed using the perceptual evaluation of speech quality (PESQ) measure, the segmental signal-to-noise ratio (SNR), the log likelihood ratio (LLR), and weighted spectral slope distance.Ope

RuleVis: Constructing Patterns and Rules for Rule-Based Models

We introduce RuleVis, a web-based application for defining and editing "correct-by-construction" executable rules that model biochemical functionality, which can be used to simulate the behavior of protein-protein interaction networks and other complex systems. Rule-based models involve emergent effects based on the interactions between rules, which can vary considerably with regard to the scale of a model, requiring the user to inspect and edit individual rules. RuleVis bridges the graph rewriting and systems biology research communities by providing an external visual representation of salient patterns that experts can use to determine the appropriate level of detail for a particular modeling context. We describe the visualization and interaction features available in RuleVisand provide a detailed example demonstrating how RuleVis can be used to reason about intracellular interactions