Counting the number of shortest paths on a grid is a simple procedure with close ties to Pascal’s triangle. We show how path counting can be used to select relatively direct grid paths for AI-related applications involving navigation through spatial environments. Typical implementations of Dijkstra’s algorithm and A* prioritize grid moves in an arbitrary manner, producing paths which stray conspicuously far from line-of-sight trajectories. We find that by counting the number of paths which traverse each vertex, then selecting the vertices with the highest counts, one obtains a path that is reasonably direct in practice and can be improved by refining the grid resolution. Central Dijkstra and Central A* are introduced as the basic methods for computing these central grid paths. Theoretical analysis reveals that the proposed grid-based navigation approach is related to an existing grid-based visibility approach, and establishes that central grid paths converge on clear sightlines as the grid spacing approaches zero. A more general property, that central paths converge on direct paths, is formulated as a conjecture.

An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction. These principles concern coherence, personalization, signaling, segmenting, multimedia, spatial contiguity, and pretraining. Principles of self-explanation and interactivity are also applied. Four experiments on the Mammography Problem showed that these principles help participants answer the questions at significantly improved rates. Nonetheless, in novel interactivity conditions, performance was lowered suggesting that more interaction can add more difficulty for participants. Overall, a leap forward in accuracy was found, with more than twice the participant accuracy of previous work. This indicates that an instructional approach to improving human performance in Bayesian inference is a promising direction.

User-authored annotations of data can support analysts in the activity of hypothesis generation and sensemaking, where it is not only critical to document key observations, but also to communicate insights between analysts. We present annotation graphs, a dynamic graph visualization that enables meta-analysis of data based on user-authored annotations. The annotation graph topology encodes annotation semantics, which describe the content of and relations between data selections, comments, and tags. We present a mixed-initiative approach to graph layout that integrates an analyst's manual manipulations with an automatic method based on similarity inferred from the annotation semantics. Various visual graph layout styles reveal different perspectives on the annotation semantics. Annotation graphs are implemented within C8, a system that supports authoring annotations during exploratory analysis of a dataset. We apply principles of Exploratory Sequential Data Analysis (ESDA) in designing C8, and further link these to an existing task typology in the visualization literature. We develop and evaluate the system through an iterative user-centered design process with three experts, situated in the domain of analyzing HCI experiment data. The results suggest that annotation graphs are effective as a method of visually extending user-authored annotations to data meta-analysis for discovery and organization of ideas.

The differential diagnosis of hereditary disorders is a challenging task for clinicians due to the heterogeneity of phenotypes that can be observed in patients. Existing clinical tools are often text-based and do not emphasize consistency, completeness, or granularity of phenotype reporting. This can impede clinical diagnosis and limit their utility to genetics researchers. Herein, we present PhenoBlocks, a novel visual analytics tool that supports the comparison of phenotypes between patients, or between a patient and the hallmark features of a disorder. An informal evaluation of PhenoBlocks with expert clinicians suggested that the visualization effectively guides the process of differential diagnosis and could reinforce the importance of complete, granular phenotypic reporting.

We present Mimic, an input capture and visual analytics system that records online user behavior to facilitate the discovery of micro-interactions that may affect problem understanding and decision making. As aggregate statistics and visualizations can mask important behaviors, Mimic can help interaction designers to improve the usability of their designs by going beyond aggregates to examine many individual user sessions in detail. To test Mimic, we replicate a recent crowd-sourcing experiment to better understand why participants consistently perform poorly in answering a canonical conditional probability question called the Mammography Problem. To analyze the micro-interactions, the Mimic web application is used to play back user sessions collected through remote logging of client-side events. We use Mimic to demonstrate the value of using advanced visual interfaces to interactively study interaction data. In the Mammography Problem, issues like user confusion, low confidence, and divided-attention were found based on participants changing their answers, doing repeated scrolling, and overestimating a base rate. Mimic shows how helpful detailed observational data can be and how important the careful design of micro-interactions is in helping users to successfully understand a problem, find a solution, and achieve their goals.

This article presents an algorithm for learning hatching styles from line drawings. An artist draws a single hatching illustration of a 3D object. Her strokes are analyzed to extract the following per-pixel properties: hatching level (hatching, cross-hatching, or no strokes), stroke orientation, spacing, intensity, length, and thickness. A mapping is learned from input geometric, contextual, and shading features of the 3D object to these hatching properties, using classification, regression, and clustering techniques. Then, a new illustration can be generated in the artist's style, as follows. First, given a new view of a 3D object, the learned mapping is applied to synthesize target stroke properties for each pixel. A new illustration is then generated by synthesizing hatching strokes according to the target properties.

We describe a new way to render 3D scenes in a variety of non-photorealistic styles, based on patterns whose structure and motion are defined in 2D. In doing so, we sacrifice the ability of patterns that wrap onto 3D surfaces to convey shape through their structure and motion. In return, we gain several advantages, chiefly that 2D patterns are more visually abstract - a quality often sought by artists, which explains their widespread use in hand-drawn images. Extending such styles to 3D graphics presents a challenge: how should a 2D pattern move? Our solution is to transform it each frame by a 2D similarity transform that closely follows the underlying 3D shape. The resulting motion is often surprisingly effective, and has a striking cartoon quality that matches the visual style.

Abstract We present a synthesis technique that can automatically generate stroke patterns based on a user‐specified reference pattern. Our method is an extension of texture synthesis techniques to vector‐based patterns. Such an extension requires (a) an analysis of the pattern properties to extract meaningful pattern elements (defined as clusters of strokes) and (b) a synthesis algorithm based on similarities in the detected stroke clusters. Our method is based on results from human vision research concerning perceptual organization. The resulting synthesized patterns effectively reproduce the properties of the input patterns, and can be used to fill both 1D paths and 2D regions. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Color, shading, shadowing, and texture I.3.4 [Computer Graphics]: Paint systems