Fast and deep deformation approximations

Character rigs are procedural systems that compute the shape of an animated character for a given pose. They can be highly complex and must account for bulges, wrinkles, and other aspects of a character's appearance. When comparing film-quality character rigs with those designed for real-time applications, there is typically a substantial and readily apparent difference in the quality of the mesh deformations. Real-time rigs are limited by a computational budget and often trade realism for performance. Rigs for film do not have this same limitation, and character riggers can make the rig as complicated as necessary to achieve realistic deformations. However, increasing the rig complexity slows rig evaluation, and the animators working with it can become less efficient and may experience frustration. In this paper, we present a method to reduce the time required to compute mesh deformations for film-quality rigs, allowing better interactivity during animation authoring and use in real-time games and applications. Our approach learns the deformations from an existing rig by splitting the mesh deformation into linear and nonlinear portions. The linear deformations are computed directly from the transformations of the rig's underlying skeleton. We use deep learning methods to approximate the remaining nonlinear portion. In the examples we show from production rigs used to animate lead characters, our approach reduces the computational time spent on evaluating deformations by a factor of 5×-10×. This significant savings allows us to run the complex, film-quality rigs in real-time even when using a CPU-only implementation on a mobile device

The Singing Insects of Michigan

Excerpt: The so-called singing insects are all those that make loud, rhythmical noises. They include members of three groups of Orthoptera (Gryllidae, Tettigoniidae, and Acridoidea) and one family of Homoptera (Cicadidae). There are about 300 noisy species in these four groups in eastern North America, perhaps a thousand in all of North America, and 25-30 thousand in the entire world. Only about 1000 of the world species have been studied in any detail, mostly in North America, Europe, Japan, and Australia

Word Adjacency Graph Modeling: Separating Signal From Noise in Big Data

There is a need to develop methods to analyze Big Data to inform patient-centered interventions for better health outcomes. The purpose of this study was to develop and test a method to explore Big Data to describe salient health concerns of people with epilepsy. Specifically, we used Word Adjacency Graph modeling to explore a data set containing 1.9 billion anonymous text queries submitted to the ChaCha question and answer service to (a) detect clusters of epilepsy-related topics, and (b) visualize the range of epilepsy-related topics and their mutual proximity to uncover the breadth and depth of particular topics and groups of users. Applied to a large, complex data set, this method successfully identified clusters of epilepsy-related topics while allowing for separation of potentially non-relevant topics. The method can be used to identify patient-driven research questions from large social media data sets and results can inform the development of patient-centered interventions

The evolution of genitalia and mating behavior in crickets (Gryllidae) and other Orthoptera

http://deepblue.lib.umich.edu/bitstream/2027.42/56377/1/MP133.pd

Boundedness properties of fermionic operators

The fermionic second quantization operator $d\Gamma(B)$ is shown to be bounded by a power $N^{s/2}$ of the number operator $N$ given that the operator $B$ belongs to the $r$-th von Neumann-Schatten class, $s=2(r-1)/r$. Conversely, number operator estimates for $d\Gamma(B)$ imply von Neumann-Schatten conditions on $B$. Quadratic creation and annihilation operators are treated as well.Comment: 15 page