As virtual reality (VR) technology becomes cheaper, higher-quality, and more widely available, it is seeing increasing use in a variety of applications including cultural heritage, real estate, and architecture. A common goal for all these applications is a compelling virtual recreation of a real place. Despite this, there has been very little research into how users perceive and experience such replicated spaces. This article reports the results from a series of three user studies investigating this topic. Results include that the scale of the room and large objects in it are most important for users to perceive the room as real and that non-physical behaviors such as objects floating in air are readily noticeable and have a negative effect even when the errors are small in scale.

Redirected walking techniques have the potential to provide natural locomotion while users experience large virtual environments. However, when using redirected walking in small physical workspaces, disruptive overt resets are often required. We describe the design of an educational virtual reality experience in which users physically walk through virtual tunnels representative of the World War I battle of Vauquois. Walking in only a 15- by 5-foot tracked space, users are redirected through subtle, narrative-driven resets to walk through a tunnel nearly 50 feet in length. This work contributes approaches and lessons that can be used to provide a seamless and natural virtual reality walking experience in highly constrained physical spaces.

We formulate conditions on a set of log-concave sequences, under which any linear combination of those sequences is log-concave, and further, of conditions under which linear combinations of log-concave sequences that have been transformed by convolution are log-concave. These conditions involve relations on sequences called synchronicity and ratio-dominance, and a characterization of some bivariate sequences as lexicographic. We are motivated by the 25-year-old conjecture that the genus distribution of every graph is log-concave. Although calculating genus distributions is NP-hard, they have been calculated explicitly for many graphs of tractable size, and the three conditions have been observed to occur in the partitioned genus distributions of all such graphs. They are used here to prove the log-concavity of the genus distributions of graphs constructed by iterative amalgamation of double-rooted graph fragments whose genus distributions adhere to these conditions, even though it is known that the genus polynomials of some such graphs have imaginary roots. A blend of topological and combinatorial arguments demonstrates that log-concavity is preserved through the iterations.

Understanding how resources of High Performance Compute platforms are utilized by applications both individually and as a composite is key to application and platform performance. Typical system monitoring tools do not provide sufficient fidelity while application profiling tools do not capture the complex interplay between applications competing for shared resources. To gain new insights, monitoring tools must run continuously, system wide, at frequencies appropriate to the metrics of interest while having minimal impact on application performance. We introduce the Lightweight Distributed Metric Service for scalable, lightweight monitoring of large scale computing systems and applications. We describe issues and constraints guiding deployment in Sandia National Laboratories' capacity computing environment and on the National Center for Supercomputing Applications' Blue Waters platform including motivations, metrics of choice, and requirements relating to the scale and specialized nature of Blue Waters. We address monitoring overhead and impact on application performance and provide illustrative profiling results.