S4E3 : What is AI and what roles does it play in our lives?

Artificial Intelligence, or AI, sounds like a futuristic concept from science fiction movies, but is very much with us in the present day. We interact with this emerging technology on a daily basis when we apply for jobs, order groceries, access our bank accounts, apply for a loan and scroll through social media. In Episode 3 of Season 4 of “The Maine Question,” we examine AI, how it improves our lives and how it can cause problems. Penny Rheingans, director of the University of Maine’s School of Computing and Information Science, and Roy Turner, a UMaine associate professor of computer science, help us unravel the fascinating and complicated story of AI

NIH-NSF Visualization Research Challenges Report

Engineering and Applied Science

Graphical Perception of Continuous Quantitative Maps: the Effects of Spatial Frequency and Colormap Design

Continuous 'pseudocolor' maps visualize how a quantitative attribute varies smoothly over space. These maps are widely used by experts and lay citizens alike for communicating scientific and geographical data. A critical challenge for designers of these maps is selecting a color scheme that is both effective and aesthetically pleasing. Although there exist empirically grounded guidelines for color choice in segmented maps (e.g., choropleths), continuous maps are significantly understudied, and their color-coding guidelines are largely based on expert opinion and design heuristics--many of these guidelines have yet to be verified experimentally. We conducted a series of crowdsourced experiments to investigate how the perception of continuous maps is affected by colormap characteristics and spatial frequency (a measure of data complexity). We find that spatial frequency significantly impacts the effectiveness of color encodes, but the precise effect is task-dependent. While rainbow schemes afforded the highest accuracy in quantity estimation irrespective of spatial complexity, divergent colormaps significantly outperformed other schemes in tasks requiring the perception of high-frequency patterns. We interpret these results in relation to current practices and devise new and more granular guidelines for color mapping in continuous maps

Opacity-modulating Triangular Textures for Irregular Surfaces

Many scientific and medical visualization techniques produce irregular surfaces whose shape and structure need to be understood. Examples include tissue and tumor boundaries in medical imaging, molecular surfaces and force thresholds in chemical and pharmaceutical applications, and isosurfaces in a wide range of 3D domains. The 3D shape of such surfaces can be particularly difficult to interpret because of the unfamiliar, irregular shapes, the potential concavities and bulges, and the lack of parallel lines and right angles to provide perspective depth cues. Attempts to display multiple irregular surfaces by making some or all of them transparent further complicates the problem. Texture can provide valuable cues to aid in the interpretation of irregular surfaces. Opacity-modulating textures offer a mechanism for the display of multiple surfaces without the extreme loss of clarity of multiple transparent surfaces. This paper presents a method for creating simple repeating textures and mapping them onto irregular surfaces

Expressive volume rendering

Accurately and automatically conveying the structure of a volume model is a problem not fully solved by existing volume rendering approaches. Physics-based volume rendering approaches create images that may match the appearance of translucent materials in nature, but may not embody important structural details. Transfer function approaches allow flexible design of the volume appearance, but generally require substantial hand tuning for each new data set in order to be effective. We have introduced the volume illustration approach, combining the familiarity of a physics-based illumination model with the ability to enhance important features using non-photorealistic rendering techniques. Since features to be enhanced are defined on the basis of higherorder model characteristics rather than volume sample value, the application of volume illustration techniques requires less manual tuning than the design of a good transfer function. Volume illustration provides a flexible unified framework for enhancing structural perception of volume models through the amplification of features, the addition of illumination effects, and the application of procedural textures. Volume illustration works on both presampled and procedurally defined volume models, enabling a range of image styles from practical technical illustrations to more abstract painterly effects