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

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

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

FUSE Detection of Galactic OVI Emission in the Halo above the Perseus Arm

Background observations obtained with the Far Ultraviolet Spectroscopic Explorer (FUSE) toward l=95.4, b=36.1 show OVI 1032,1038 in emission. This sight line probes a region of stronger-than-average soft X-ray emission in the direction of high-velocity cloud Complex C above a part of the disk where Halpha filaments rise into the halo. The OVI intensities, 1600+/-300 ph/s/cm^2/sr (1032A) and 800+/-300 ph/s/cm^2/sr (1038A), are the lowest detected in emission in the Milky Way to date. A second sight line nearby (l=99.3, b=43.3) also shows OVI 1032 emission, but with too low a signal-to-noise ratio to obtain reliable measurements. The measured intensities, velocities, and FWHMs of the OVI doublet and the CII* line at 1037A are consistent with a model in which the observed emission is produced in the Galactic halo by hot gas ejected by supernovae in the Perseus arm. An association of the observed gas with Complex C appears unlikely.Comment: accepted for publication in ApJL, 11 pages including 3 figure

Hierarchical Temporal Representation in Linear Reservoir Computing

Recently, studies on deep Reservoir Computing (RC) highlighted the role of layering in deep recurrent neural networks (RNNs). In this paper, the use of linear recurrent units allows us to bring more evidence on the intrinsic hierarchical temporal representation in deep RNNs through frequency analysis applied to the state signals. The potentiality of our approach is assessed on the class of Multiple Superimposed Oscillator tasks. Furthermore, our investigation provides useful insights to open a discussion on the main aspects that characterize the deep learning framework in the temporal domain.Comment: This is a pre-print of the paper submitted to the 27th Italian Workshop on Neural Networks, WIRN 201

Macrospin limit and configurational anisotropy in nanoscale Permalloy triangles

In Permalloy submicron triangles, configurational anisotropy - a higher-order form of shape anisotropy - yields three equivalent easy axes, imposed by the structures' symmetry order. Supported by micromagnetic simulations, an experimental method was devised to evaluate the nanostructure dimensions for which a Stoner-Wohlfarth type of reversal could be used to describe this particular magnetic anisotropy. In this regime, a straightforward procedure using an in-plane rotating field allowed us to quantify experimentally the six-fold anisotropy fields for triangles of different thicknesses and sizes

Detection of Cherenkov light from air showers with Geiger-APDs

We have detected Cherenkov light from air showers with Geiger-mode APDs (G-APDs). G-APDs are novel semiconductor photon-detectors, which offer several advantages compared to conventional photomultiplier tubes in the field of ground-based gamma-ray astronomy. In a field test with the MAGIC telescope we have tested the efficiency of a G-APD / light catcher setup to detect Cherenkov light from air showers. We estimate a detection efficiency, which is 60% higher than the efficiency of a MAGIC camera pixel. Ambient temperature dark count rates of the tested G-APDs are below the rates of the night sky light background. According to these recent tests G-APDs promise a major progress in ground-based gamma-ray astronomy.Comment: 4 pages, 5 figures, to appear in the proceedings of the 30th International Cosmic Ray Conference, Merida, July 200