Undergraduate computer science programs have increasingly adopted introductory sequences with multiple entry points in order to accommodate students arriving with varying degrees of prior experience. This paper examines student experiences at the critical stage when multiple introductory tracks merge: the convergence course. We find that students from all tracks arrive with similar levels of enthusiasm and positive attitudes towards collaborative learning. By the end of the convergence course, group differences in self-efficacy and a sense of belonging in the CS community have begun to ease. Importantly, students with the least pre-college CS experience enjoy the most significant gains in self-efficacy. However, curricular techniques intended to integrate these student populations appear insufficient alone to close the achievement gap between these groups, suggesting that alternate approaches may be necessary to supplement.

Film directors are masters at controlling what we look at when we watch a film. However, there have been few quantitative studies of how gaze responds to cinematographic conventions thought to influence attention. We have collected and are releasing a dataset designed to help investigate eye movements in response to higher level features such as faces, dialogue, camera movements, image composition, and edits. The dataset, which will be released to the community, includes gaze information for 21 viewers watching 15 clips from live action 2D films, which have been hand annotated for high level features. This work has implications for the media studies, display technology, immersive reality, and human cognition.

Recent advances in personalized displays now allow for the delivery of high-fidelity content only to the most sensitive regions of the visual field, a process referred to as foveation [Guenter et al. 2012]. Because foveated systems require accurate knowledge of gaze location, attentional synchrony is particularly relevant: this is observed when multiple viewers attend to the same image region concurrently.

We introduce a fast tile-based method for adaptive two-dimensional sampling with user-specified spectral properties. At the core of our approach is a deterministic, hierarchical construction of self-similar, equi-area, tri-hex tiles whose centroids have a spatial distribution free of spurious spectral peaks. A lookup table of sample points, computed offline using any existing point set optimizer to shape the samples' Fourier spectrum, is then used to populate the tiles. The result is a linear-time, adaptive, and high-quality sampling of arbitrary density functions that conforms to the desired spectral distribution, achieving a speed improvement of several orders of magnitude over current spectrum-controlled sampling methods.

Skilled artists, using traditional media or modern computer painting tools, can create a variety of expressive styles that are very appealing in still images, but have been unsuitable for animation. The key difficulty is that existing techniques lack adequate temporal coherence to animate these styles effectively. Here we augment the range of practical animation styles by extending the guided texture synthesis method of Image Analogies [Hertzmann et al. 2001] to create temporally coherent animation sequences. To make the method art directable, we allow artists to paint portions of keyframes that are used as constraints. The in-betweens calculated by our method maintain stylistic continuity and yet change no more than necessary over time.

Patterns with pleasing structure are common in art, video games, and virtual worlds. We describe a method for synthesizing new patterns of tiles on a regular grid that are similar in appearance to a set of example patterns. Exemplars are used both to specify valid tile arrangements and to emphasize multi-tile structures. We model a pattern as a probabilistic graphical model called a factor graph . Factors represent the hard logical constraints between tiles, the soft statistical relationships that determine style, and the local dependencies between tiles at neighboring sites. We describe a simple method for learning factor functions from a small exemplar. We then synthesize new patterns through a stochastic search method that is inspired by MC-SAT. Efficient synthesis is challenging because of the combination of hard and soft constraints. Our synthesis algorithm, called BlockSS, scales linearly with the number of tiles and the hardness of the problem. We use our technique to model building facades, cities, and decorative patterns.

We present a fast, scalable algorithm to generate high-quality blue noise point distributions of arbitrary density functions. At its core is a novel formulation of the recently-introduced concept of capacity-constrained Voronoi tessellation as an optimal transport problem. This insight leads to a continuous formulation able to enforce the capacity constraints exactly, unlike previous work. We exploit the variational nature of this formulation to design an efficient optimization technique of point distributions via constrained minimization in the space of power diagrams. Our mathematical, algorithmic, and practical contributions lead to high-quality blue noise point sets with improved spectral and spatial properties.