Self-efficacy in depression: Bridging the gap between competence and real-world functioning

Background: Depression is the largest contributing factor to years lost to disability, and symptom remission does not always result in functional improvement. Comprehensive analysis of functioning requires investigation both of the competence to perform behaviours, as well as actual performance in the real world. Further, two independent domains of functioning have been proposed: adaptive (behaviours conducive to daily living skills and independent functioning) and interpersonal (behaviours conducive to the successful initiation and maintenance of social relationships). To date, very little is known about the relationship between these constructs in depression, and the factors that may play a key role in the disparity between competence and real-world performance in adaptive and interpersonal functioning. Purpose: This study used a multidimensional (adaptive and interpersonal functioning), multi-level (competence and performance) approach to explore the potential discrepancy between competence and real-world performance in depression, specifically investigating whether self-efficacy (one’s beliefs of their capability to perform particular actions) predicts depressed individuals’ underperformance in the real world relative to their ability. A comparison sample of healthy participants was included to investigate the level of depressed individuals’ impairment, across variables, relative to healthy individuals. Method: Forty-two participants with depression and twenty healthy participants without history of, or current, psychiatric illness were recruited in the Kingston, Ontario community. Competence, self-efficacy, and real-world functioning all in both adaptive and interpersonal domains, and symptoms were assessed during a single-visit assessment. Results: Relative to healthy individuals, depressed individuals showed significantly poorer adaptive and interpersonal competence, adaptive and interpersonal functioning, and significantly lower self-efficacy for adaptive and interpersonal behaviours. Self-efficacy significantly predicted functional disability both in the domain of adaptive and interpersonal functioning. Interpersonal self-efficacy accounted for significant variance in the discrepancy between interpersonal competence and functioning. Conclusions: The current study provides the first data regarding relationships among competence, functioning, and self-efficacy in depression. Self-efficacy may play an important role in the deployment of functional skills in everyday life. This has implications for therapeutic interventions aimed at enhancing depressed individuals’ engagement in functional activities. There may be additional intrinsic or extrinsic factors that influence the relationships among competence and functioning in depression.

Linear Hyperspectral Unmixing Using L0-norm Approximations and Nonnegative Matrix Factorization

Spectral unmixing (SU) is a technique to characterize mixed pixels of the hyperspectral images measured by remote sensors. Most of the existing spectral unmixing algorithms are developed using the linear mixing models. Since the number of endmembers/materials present at each mixed pixel is normally scanty compared with the number of total endmembers (the dimension of spectral library), the problem becomes sparse. This thesis introduces sparse hyperspectral unmixing methods for the linear mixing model through two different scenarios. In the first scenario, the library of spectral signatures is assumed to be known and the main problem is to find the minimum number of endmembers under a reasonable small approximation error. Mathematically, the corresponding problem is called the $\ell_0$-norm problem which is NP-hard problem. Our main study for the first part of thesis is to find more accurate and reliable approximations of $\ell_0$-norm term and propose sparse unmixing methods via such approximations. The resulting methods are shown considerable improvements to reconstruct the fractional abundances of endmembers in comparison with state-of-the-art methods such as having lower reconstruction errors. In the second part of the thesis, the first scenario (i.e., dictionary-aided semiblind unmixing scheme) will be generalized as the blind unmixing scenario that the library of spectral signatures is also estimated. We apply the nonnegative matrix factorization (NMF) method for proposing new unmixing methods due to its noticeable supports such as considering the nonnegativity constraints of two decomposed matrices. Furthermore, we introduce new cost functions through some statistical and physical features of spectral signatures of materials (SSoM) and hyperspectral pixels such as the collaborative property of hyperspectral pixels and the mathematical representation of the concentrated energy of SSoM for the first few subbands. Finally, we introduce sparse unmixing methods for the blind scenario and evaluate the efficiency of the proposed methods via simulations over synthetic and real hyperspectral data sets. The results illustrate considerable enhancements to estimate the spectral library of materials and their fractional abundances such as smaller values of spectral angle distance (SAD) and abundance angle distance (AAD) as well.