The Role of Feminine and Masculine Norms in College Women's Alcohol Use

Current literature suggests not only that men and women can conform to both feminine and masculine norms, but that women who adhere to certain masculine norms may be at greater risk for problematic alcohol use. This study examined conformity to both masculine and feminine norms, and how conformity to distinct norms influenced heavy episodic drinking and alcohol-related problems among a sample of underage college women (N= 645). Results demonstrated that the masculine norms risk-taking and emotional control were associated with increased HED, while the masculine norm power over women was associated with a decrease in HED. Traditional feminine norms, including modesty and sexual fidelity, were associated with a decrease in HED and alcohol-related problems. The feminine norm relational was associated with increased HED, while the norms thinness and appearance were associated with increased alcohol-related problems. The study’s theoretical and clinical implications are discussed.

Tensor Completion for Multidimensional Inverse Problems with Applications to Magnetic Resonance Relaxometry

This thesis deals with tensor completion for the solution of multidimensional inverse problems. We study the problem of reconstructing an approximately low rank tensor from a small number of noisy linear measurements. New recovery guarantees, numerical algorithms, non-uniform sampling strategies, and parameter selection algorithms are developed. We derive a fixed point continuation algorithm for tensor completion and prove its convergence. A restricted isometry property (RIP) based tensor recovery guarantee is proved. Probabilistic recovery guarantees are obtained for sub-Gaussian measurement operators and for measurements obtained by non-uniform sampling from a Parseval tight frame. We show how tensor completion can be used to solve multidimensional inverse problems arising in NMR relaxometry. Algorithms are developed for regularization parameter selection, including accelerated k-fold cross-validation and generalized cross-validation. These methods are validated on experimental and simulated data. We also derive condition number estimates for nonnegative least squares problems. Tensor recovery promises to significantly accelerate N-dimensional NMR relaxometry and related experiments, enabling previously impractical experiments. Our methods could also be applied to other inverse problems arising in machine learning, image processing, signal processing, computer vision, and other fields.