Constructions of health, weight and bodily appearance among Indo-Fijian women across three generations : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Psychology at Massey University

Discursive constructions of a 'thin ideal' body shape today have often associated the slender body to the idea of a 'healthy weight' and physical beauty. While idealised notions of the feminine figure have trended from the curvaceous body to the thin ideal within western societies, for women from non-western cultures living in a western milieu, research in this area is limited. Culturally derived understandings about health, weight and bodily appearances affects the ways in which women construct idealised notions of body shape. This thesis explored constructions of health, weight and bodily appearances among Indo-Fijian women across three generations. Six focus group discussions were held with a total of 24 women spanning three generations, where four women participated in each group. Focus group discussions were taped, transcribed and analysed based on the principles of Foucauldian discourse analysis. The analysis revealed that idealised notions of health, weight and bodily appearances were constituted as representations of the body as healthy and feminine among lndo­-Fijian women across all three generations. The body as healthy was understood in terms of eating practices and physical activity. Eating practices were further negotiated as notions of diet, illness and weight, and in turn shaped the way in which women across three generations constructed the body as healthy. The body as feminine was understood as a way of exercising femininity and, discussed within understandings of physical appearance and slenderness. Across each generation, women discussed ideas about idealised notions of the body shape in culturally specific ways. Therefore, all participants drew on particular cultural and social practices of negotiating health, weight and bodily appearances as Indo-Fijian women living in New Zealand. It is concluded that the construction of societal idealised notions of body shape is not static, but rather contingent upon the context in which women live; therefore shaped and reshaped within interactions with dominant discourses of health, biomedicine and culture to construct idealised notions of the feminine body shape

LS-CS-residual (LS-CS): Compressive Sensing on Least Squares Residual

We consider the problem of recursively and causally reconstructing time sequences of sparse signals (with unknown and time-varying sparsity patterns) from a limited number of noisy linear measurements. The sparsity pattern is assumed to change slowly with time. The idea of our proposed solution, LS-CS-residual (LS-CS), is to replace compressed sensing (CS) on the observation by CS on the least squares (LS) residual computed using the previous estimate of the support. We bound CS-residual error and show that when the number of available measurements is small, the bound is much smaller than that on CS error if the sparsity pattern changes slowly enough. We also obtain conditions for "stability" of LS-CS over time for a signal model that allows support additions and removals, and that allows coefficients to gradually increase (decrease) until they reach a constant value (become zero). By "stability", we mean that the number of misses and extras in the support estimate remain bounded by time-invariant values (in turn implying a time-invariant bound on LS-CS error). The concept is meaningful only if the bounds are small compared to the support size. Numerical experiments backing our claims are shown.Comment: Accepted (with mandatory minor revisions) to IEEE Trans. Signal Processing. 12 pages, 5 figure

Analyzing Least Squares and Kalman Filtered Compressed Sensing

In recent work, we studied the problem of causally reconstructing time sequences of spatially sparse signals, with unknown and slow time-varying sparsity patterns, from a limited number of linear "incoherent" measurements. We proposed a solution called Kalman Filtered Compressed Sensing (KF-CS). The key idea is to run a reduced order KF only for the current signal's estimated nonzero coefficients' set, while performing CS on the Kalman filtering error to estimate new additions, if any, to the set. KF may be replaced by Least Squares (LS) estimation and we call the resulting algorithm LS-CS. In this work, (a) we bound the error in performing CS on the LS error and (b) we obtain the conditions under which the KF-CS (or LS-CS) estimate converges to that of a genie-aided KF (or LS), i.e. the KF (or LS) which knows the true nonzero sets.Comment: Proc. IEEE Intl. Conf. Acous. Speech Sig. Proc. (ICASSP), 200

Exact Reconstruction Conditions for Regularized Modified Basis Pursuit

In this correspondence, we obtain exact recovery conditions for regularized modified basis pursuit (reg-mod-BP) and discuss when the obtained conditions are weaker than those for modified-CS or for basis pursuit (BP). The discussion is also supported by simulation comparisons. Reg-mod-BP provides a solution to the sparse recovery problem when both an erroneous estimate of the signal's support, denoted by $T$, and an erroneous estimate of the signal values on $T$ are available.Comment: 17 page