Distribution, Hybridization, and Taxonomic Status of Two-lined Salamanders (\u3ci\u3eEurycea bislineata\u3c/i\u3e complex) in Virginia and West Virginia

We used three diagnostic protein markers to examine salamanders of the Eurycea bislineata complex at 80 localities in Virginia and West Virginia. Two groups were strongly differentiated and met at a narrow contact zone. Rare hybridization was observed as well as limited introgression up to 5 km north and 10 km south of the contact zone. At the contact zone, 1% F1, 2% F2, 32% backcross, and 66% parental genotypes were observed. This pattern of parapatric distribution with limited hybridization and introgression argues for the recognition of Eurycea bislineata and E. cirrigera as separate species

GIS for the Bartlett Hills Association: Increasing Knowledge to Enhance Land Management Practices

The natural world has many unique areas and when there is no management conducted on these areas, they become lost to time. The 800-acre property owned by the Bartlett Hills Association (BHA) in western Iowa is no different. The loess hills are deposits from glaciers 12,000 to 30,000 years ago, and over the last several decades there has been no management of these unique hills on the property. The BHA has established management objectives that will bring its forest back to a sustainable forest and its members want to use GIS to enhance their knowledge. There are currently trails inside the property, but they are at risk for soil erosion and maintenance is difficult to conduct. By creating a model within Model Builder in ArcGIS Desktop 10.2, suitable areas for new trail construction can be located. In addition, several maps and a 3D terrain model were produced to represent different characteristics of the property and provide new knowledge of the property. With the use of spatial analysis and cartographic skills, the BHA can learn more about its unique property and manage the property for future generations

Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization

Image-based camera relocalization is an important problem in computer vision and robotics. Recent works utilize convolutional neural networks (CNNs) to regress for pixels in a query image their corresponding 3D world coordinates in the scene. The final pose is then solved via a RANSAC-based optimization scheme using the predicted coordinates. Usually, the CNN is trained with ground truth scene coordinates, but it has also been shown that the network can discover 3D scene geometry automatically by minimizing single-view reprojection loss. However, due to the deficiencies of the reprojection loss, the network needs to be carefully initialized. In this paper, we present a new angle-based reprojection loss, which resolves the issues of the original reprojection loss. With this new loss function, the network can be trained without careful initialization, and the system achieves more accurate results. The new loss also enables us to utilize available multi-view constraints, which further improve performance.Comment: ECCV 2018 Workshop (Geometry Meets Deep Learning

Modeling Micro-Porous Surfaces for Secondary Electron Emission Control to Suppress Multipactor

This work seeks to understand how the topography of a surface can be engineered to control secondary electron emission (SEE) for multipactor suppression. Two unique, semi-empirical models for the secondary electron yield (SEY) of a micro-porous surface are derived and compared. The first model is based on a two-dimensional (2D) pore geometry. The second model is based on a three-dimensional (3D) pore geometry. The SEY of both models is shown to depend on two categories of surface parameters: chemistry and topography. An important parameter in these models is the probability of electron emissions to escape the surface pores. This probability is shown by both models to depend exclusively on the aspect ratio of the pore (the ratio of the pore height to the pore diameter). The increased accuracy of the 3D model (compared to the 2D model) results in lower electron escape probabilities with the greatest reductions occurring for aspect ratios less than two. In order to validate these models, a variety of micro-porous gold surfaces were designed and fabricated using photolithography and electroplating processes. The use of an additive metal-deposition process (instead of the more commonly used subtractive metal-etch process) provided geometrically ideal pores which were necessary to accurately assess the 2D and 3D models. Comparison of the experimentally measured SEY data with model predictions from both the 2D and 3D models illustrates the improved accuracy of the 3D model. For a micro-porous gold surface consisting of pores with aspect ratios of two and a 50% pore density, the 3D model predicts that the maximum total SEY will be one. This provides optimal engineered surface design objectives to pursue for multipactor suppression using gold surfaces

Semantically Informed Multiview Surface Refinement

We present a method to jointly refine the geometry and semantic segmentation of 3D surface meshes. Our method alternates between updating the shape and the semantic labels. In the geometry refinement step, the mesh is deformed with variational energy minimization, such that it simultaneously maximizes photo-consistency and the compatibility of the semantic segmentations across a set of calibrated images. Label-specific shape priors account for interactions between the geometry and the semantic labels in 3D. In the semantic segmentation step, the labels on the mesh are updated with MRF inference, such that they are compatible with the semantic segmentations in the input images. Also, this step includes prior assumptions about the surface shape of different semantic classes. The priors induce a tight coupling, where semantic information influences the shape update and vice versa. Specifically, we introduce priors that favor (i) adaptive smoothing, depending on the class label; (ii) straightness of class boundaries; and (iii) semantic labels that are consistent with the surface orientation. The novel mesh-based reconstruction is evaluated in a series of experiments with real and synthetic data. We compare both to state-of-the-art, voxel-based semantic 3D reconstruction, and to purely geometric mesh refinement, and demonstrate that the proposed scheme yields improved 3D geometry as well as an improved semantic segmentation

Efeito de mecanismos sulcadores de solo em semeadoras para plantio direto em pequenas unidades produtivas.

bitstream/CNPT-2010/40328/1/p-bp55.pd