Effects Of A Relationship Strengthening Intervention On Quality Of Life Of Young Low-Income, Minority Parents

Partners in LIFE (PIL) is a 15-session relationship-based intervention delivered to young low-income minority parents (age 14-25). Fourty-nine parenting couples were recruited from urban pediatric clinics and randomized into PIL or a control group. PIL activities consisted of relationship strengthening (e.g., intimacy, empathy, communication, conflict resolution), parent education, and sexual health. The control group received only parent education. Participants were assessed at baselines and immediately after the intervention (4 months from baseline). Using within-groups analysis of variance (ANOVA) to account for the dependence of the data from time and dyad, we evaluated the effects of this intervention on physical and mental quality of life (QOL). There was a significant interaction of intervention * time * gender effect (p=0.003) for mental QOL. For males, PIL significantly increased mental QOL compared to controls (p=0.009); but for females the PIL and control did not differ (p=0.101). However, there was a dose response relationship for females (d=0.63). Females with \u3e 50% attendance of PIL sessions increased their mental QOL, while those with \u3c50% attendance had a decreased mental QOL. No intervention effects were observed on physical QOL. Our results provide preliminary evidence for the effectiveness of a relationship strengthening intervention for young low-income minority parents to improve mental quality of life

Global Geometric Conditions on Sensing Matrices for the Success of L1 Minimization Algorithm

Compressed Sensing concerns a new class of linear data acquisition protocols that are more efficient than the classical Shannon sampling theorem when targeting at signals with sparse structures. In this thesis, we study the stability of a Statistical Restricted Isometry Property and show how this property can be further relaxed while maintaining its sufficiency for the Basis Pursuit algorithm to recover sparse signals. We then look at the dictionary extension of Compressed Sensing where signals are sparse under a redundant dictionary and reconstruction is achieved by the $\ell_1$ synthesis method. By establishing a necessary and sufficient condition for the stability of $\ell_1$ synthesis, we are able to predict this algorithm's performances under different dictionaries. Last, we construct a class of deterministic sensing matrix for the Dirac-Fourier joint dictionary