Figure 1: A screenshot of the RuleVis interface, depicting a user in the process of building a rule, consisting of two patterns that describe the 'before' and 'after' states of the system (separated by an arrow). The editor panel (left) enables users to add and delete agents, sites, and links in the visualization in the display panel (right). The text and visualization are mirrored, and changes made to one representation are immediately reflected in the other. ABSTRACTWe 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 RuleVis and provide a detailed example demonstrating how RuleVis can be used to reason about intracellular interactions.

Figure 1: A series of style transfer data brushes applied to an image from Kelley's Airportraits project. a shows the original image without any styling, b shows the image styled with the Bruises brush (Lupi and King), c uses the Hennessy brush (Lupi, Maeda, and King), d is styled using our Flatland brush (generated from an image analyzed by Tufte), e is styled using a brush generated from one of Lupi's experimental scrapbook pieces, f showcases styling with the data brush based on Lupi's Data Items, and g demonstrates the result of layering multiple data brushes (including brushes created from visualizations by Minard).

No abstract

Bike sharing platforms are becoming increasingly common alternatives to public transportation in cities, improving accessibility to areas not reachable by bus, train, or tram. While this can be beneficial for improving city connectivity, it also increases the likelihood of biker related accidents and vehicle collisions, especially in areas where protected bike lanes and safety infrastructure are not already in place. We compare machine learning models to predict biker density at road intersections in the city of San Francisco, using publicly available trip data from the city's most widely used bikeshare service, Ford GoBike, evaluating our model performance by monitoring mean squared error. Alongside our predictive models we develop a heatmap visualization application to display our predictions, providing an additional mode of interaction for users to access the forecasted information. The intended usage of our work is to predict areas of highest biker density at different times so that drivers and bikers can experience improved shared road safety. The deployment of our models can also inform city planning and alternative public transportation development.

No abstract