Craft Education in the United Kingdom and the United States: A cross-cultural examination of ideals, approaches and solutions

At the conclusion of the Second World War both the United Kingdom and the United States experienced drastic changes in their building industries. As the construction industry progressed, the training systems for construction workers evolved to meet this new demand. This thesis argues that these changes have caused the UK and the US to face a perceived crisis in the training and supply of traditional craft workers. In both societies, different approaches have been taken to address these concerns, based on the evolving ethos of conservation theory in their respective cultures and their educational frameworks. The approaches taken can be seen as reflecting the evolution of conservation theory and practice in each society, which is often expressed through variations in perception of value, age, and methodology, as well as distinct differences in terminology. This thesis studies the progression of heritage craft training through the examination of historical evidence juxtaposed against ethnographic surveys of three generations of craft practitioners along with current educational providers. Using this evidence, this thesis examines the strengths and shortcomings of current heritage craft educational offerings in both networks through the opinions of both practitioners and educational providers using Actor-Network Theory methodology. It is from the triangulation of historical evidence, craft practitioner opinions, and educational provider experiences that this research proposes pathways to improve the educational offerings in both networks. This study argues that contrary to popular belief, the crisis in heritage craft training may be misdiagnosed, but significant improvements need to be made by both countries to enhance the visibility and delivery of the existing training opportunities. This thesis aims to inform our understanding of the progression of this under-studied sphere of the conservation industry in order to enrich future craft training practices

Frequency of Participation in an Employee Fitness Program and Health Care Expenditures

Regular physical activity is strongly linked to prevention of costly chronic health conditions. However, there has been limited examination of the impact that level of participation in physical activity promotion programs has on health care costs. This study examined a fitness reimbursement program (FRP) offered to small employers. FRP participants received $20 reimbursement every month they visited their fitness center ≥12 days. Visits were recorded electronically. Participants were assigned to 4 mutually exclusive cohorts by mean monthly fitness center visits: low (<4 visits); low-moderate (≥4 and <8 visits), high-moderate (≥8 and <12 visits), and high (≥12 visits, which qualified for reimbursement). Cohorts were matched by inverse propensity score weighting on demographic, health status, health care supply, and socioeconomic characteristics. Between-cohort differences in propensity score-weighted health care costs, starting from FRP program sign-up, were examined with a generalized linear model. Analyses were conducted with and without high-cost outliers during the pre- and post-FRP period. A total of 8723 participants (mean follow-up: 11.1 months) were identified during October 2010-June 2013. With high-cost outliers removed (n?=?226), a pattern of lower per-member-per-month health care costs was observed with increasing participation: compared with the low cohort, monthly savings were:$6.14 (2.6%) for low-moderate (P?=?0.60), $16.40 (6.9$20.01 (8.4%) for high (P?=?0.08). With high-cost outliers included, significant monthly cost savings were observed for the moderate-high ($43.52, P?Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140192/1/pop.2015.0102.pd

Non-Linear Temporal Subspace Representations for Activity Recognition

Representations that can compactly and effectively capture the temporal evolution of semantic content are important to computer vision and machine learning algorithms that operate on multi-variate time-series data. We investigate such representations motivated by the task of human action recognition. Here each data instance is encoded by a multivariate feature (such as via a deep CNN) where action dynamics are characterized by their variations in time. As these features are often non-linear, we propose a novel pooling method, kernelized rank pooling, that represents a given sequence compactly as the pre-image of the parameters of a hyperplane in a reproducing kernel Hilbert space, projections of data onto which captures their temporal order. We develop this idea further and show that such a pooling scheme can be cast as an order-constrained kernelized PCA objective. We then propose to use the parameters of a kernelized low-rank feature subspace as the representation of the sequences. We cast our formulation as an optimization problem on generalized Grassmann manifolds and then solve it efficiently using Riemannian optimization techniques. We present experiments on several action recognition datasets using diverse feature modalities and demonstrate state-of-the-art results.Comment: Accepted at the IEEE International Conference on Computer Vision and Pattern Recognition, CVPR, 2018. arXiv admin note: substantial text overlap with arXiv:1705.0858

Self-Calibration of Cameras with Euclidean Image Plane in Case of Two Views and Known Relative Rotation Angle

The internal calibration of a pinhole camera is given by five parameters that are combined into an upper-triangular $3\times 3$ calibration matrix. If the skew parameter is zero and the aspect ratio is equal to one, then the camera is said to have Euclidean image plane. In this paper, we propose a non-iterative self-calibration algorithm for a camera with Euclidean image plane in case the remaining three internal parameters --- the focal length and the principal point coordinates --- are fixed but unknown. The algorithm requires a set of $N \geq 7$ point correspondences in two views and also the measured relative rotation angle between the views. We show that the problem generically has six solutions (including complex ones). The algorithm has been implemented and tested both on synthetic data and on publicly available real dataset. The experiments demonstrate that the method is correct, numerically stable and robust.Comment: 13 pages, 7 eps-figure