Cultural Competency for Native Women at Southern Minnesotan Anti-Violence Advocacy Programs

Violence against Native American women is heavily documented within the state of Minnesota. However, there is limited research documenting the processes advocates use to help Native women. Though there has been an increase in organizations dedicated to addressing the intersections of race and gender-based violence, much is unclear regarding the extent to which different types of programming are implemented across the state. Thus, this research study examined the implementation of cultural competency, a type of anti-violence programming, by advocates at one organization in Southern Minnesota. I hypothesized that advocates at the organization would have limited resources for implementing cultural competency for Native women and would have varying knowledge of how to incorporate it into their advocacy practice. This study found that although knowledge of the history of violence against Native women played a part in a lack of cultural competency several other causes, such as funding and whiteness, defined advocates\u27 experience with cultural competency. Using the reflections from advocates, I proposed several processes for the decolonization of advocacy for Native women

The comprehensive home lesson book : in six parts. Part III

Containing lessons in Holy Scripture, Arithmetic, Spelling, Grammar, Geography, History, Poetry, and CompositionEntered at Stationers' HallIntended for standard III. New Code (1882)

Shape from Shading through Shape Evolution

In this paper, we address the shape-from-shading problem by training deep networks with synthetic images. Unlike conventional approaches that combine deep learning and synthetic imagery, we propose an approach that does not need any external shape dataset to render synthetic images. Our approach consists of two synergistic processes: the evolution of complex shapes from simple primitives, and the training of a deep network for shape-from-shading. The evolution generates better shapes guided by the network training, while the training improves by using the evolved shapes. We show that our approach achieves state-of-the-art performance on a shape-from-shading benchmark

Shape-Induced Frustration of Hexagonal Order in Polyhedral Colloids\ud

The effect of a nonspherical particle shape and shape polydispersity on the structure of densely packed hard colloidal particles was studied in real space by confocal microscopy. We show that the first layer at the wall of concentrated size-monodisperse but shape-polydisperse polyhedral colloids exhibits significant deviations from a hexagonal lattice. These deviations are identified as bond-orientational fluctuations which lead to percolating “mismatch lines.” While the shape-induced geometrical frustration of the hexagonal symmetry suppresses translational order, bond-orientational order is clearly retained, indicating a hexaticlike structure of the polyhedral colloid

Convergence in shape of Steiner symmetrizations

There are sequences of directions such that, given any compact set K in R^n, the sequence of iterated Steiner symmetrals of K in these directions converges to a ball. However examples show that Steiner symmetrization along a sequence of directions whose differences are square summable does not generally converge. (Note that this may happen even with sequences of directions which are dense in S^{n-1}.) Here we show that such sequences converge in shape. The limit need not be an ellipsoid or even a convex set. We also deal with uniformly distributed sequences of directions, and with a recent result of Klain on Steiner symmetrization along sequences chosen from a finite set of directions.Comment: 11 page

Shape Calculus for Shape Energies in Image Processing

Many image processing problems are naturally expressed as energy minimization or shape optimization problems, in which the free variable is a shape, such as a curve in 2d or a surface in 3d. Examples are image segmentation, multiview stereo reconstruction, geometric interpolation from data point clouds. To obtain the solution of such a problem, one usually resorts to an iterative approach, a gradient descent algorithm, which updates a candidate shape gradually deforming it into the optimal shape. Computing the gradient descent updates requires the knowledge of the first variation of the shape energy, or rather the first shape derivative. In addition to the first shape derivative, one can also utilize the second shape derivative and develop a Newton-type method with faster convergence. Unfortunately, the knowledge of shape derivatives for shape energies in image processing is patchy. The second shape derivatives are known for only two of the energies in the image processing literature and many results for the first shape derivative are limiting, in the sense that they are either for curves on planes, or developed for a specific representation of the shape or for a very specific functional form in the shape energy. In this work, these limitations are overcome and the first and second shape derivatives are computed for large classes of shape energies that are representative of the energies found in image processing. Many of the formulas we obtain are new and some generalize previous existing results. These results are valid for general surfaces in any number of dimensions. This work is intended to serve as a cookbook for researchers who deal with shape energies for various applications in image processing and need to develop algorithms to compute the shapes minimizing these energies

Shape Dynamics

Barbour's formulation of Mach's principle requires a theory of gravity to implement local relativity of clocks, local relativity of rods and spatial covariance. It turns out that relativity of clocks and rods are mutually exclusive. General Relativity implements local relativity of clocks and spatial covariance, but not local relativity of rods. It is the purpose of this contribution to show how Shape Dynamics, a theory that is locally equivalent to General Relativity, implements local relativity of rods and spatial covariance and how a BRST formulation, which I call Doubly General Relativity, implements all of Barbour's principles.Comment: 8 pages, LaTeX, based on a talk given at Relativity and Gravitation 100 years after Einstein in Prague, June 201

Perceptions of Safety within Residence Halls at a Midwestern College Campus

Sexual violence is a widespread issue in American society. Though sexual violence takes many forms, the topic of campus sexual violence is especially pressing as it has occupied a fair amount of controversy in American media. The experience of sexual violence for college students is often traumatic as they navigate through the administrative systems and criminal justice systems of their respective communities. From a feminist perspective, students that become victims of campus sexual violence are often met with institutional resistance and inadequacies. The purpose of this study was to assess college students’ feelings of safety in residence halls. This research surveyed students in introductory-level Gender and Women’s Studies courses at a Midwestern college campus and sought knowledge from its students on the topics of campus safety and sexual violence. The findings from this research help to critically address the climate of the college campus from the student\u27s perspective and addresses the intricacies of identities and how those identities shape the experiences of both safety and violence