desirable properties in terms of privacy needs of users, parallelization of the algorithm as well as collaborative filtering as a service. We evaluate the algorithm on data provided by WikiLens. This data is a cross-domain data set as it contains ratings on items from a vast array of categories. Evaluation

of Knowledge Based Service Systems and demonstrate that the way to make an economic, managerial, organizational, industrial, computer system smarter, which means that the system evolves by adapting to the needs and changing conditions thereby reducing the mismatch and loss of resources, is to consider such a

Abstract. Learning ranking functions is crucial for solving many problems, ranging from document retrieval to building recommendation systems based on an individual user’s preferences or on collaborative filtering. Learning-to-rank is particularly necessary for adaptive or personalizable tasks

then extend this approach to the collaborative case, where a set of simultaneously coded signals share the same sparsity pattern at the higher (group) level, but not necessarily at the lower (inside the group) level, obtaining the collaborative HiLasso model (C-HiLasso). Such signals then share the same

Abstract. Learning ranking functions is crucial for solving many prob-lems, ranging from document retrieval to building recommendation sys-tems based on an individual user’s preferences or on collaborative filter-ing. Learning-to-rank is particularly necessary for adaptive or person-alizable tasks

effects. Unfortunately, faculty often have reservations about adopting active learning. These reservations include loss of content coverage, possible loss of control over the class, and possible failure of the activities. Tanner (2009) addressed a number of these. Similarly, several dysfunctional

m]]l]]s¶D]]¿÷mB]iv]b]oD]m¶¨]iv]§]iv]r]j]t¿rv]]irj]]t]]m] / | ap]]r¿]ÎNy]s¶D]]mb¶r]ix} Û]Ix]]rd]mb]} p—N]t]o%ism] in]ty]m] / || Û]Is]¡uÎc]rN]]riv]nd]p]*N]m]st¶ I always bow to Śri ̄ Śāradāmbā, the limitless ocean of the nectar of compassion, who bears a rosary, a vessel of nectar, the symbol of knowledge and a book in Her lotus hands. Dedicated to the Lotus Feet of the revered Sadguru. This thesis presents a new active mask (AM) framework and an algorithm for segmenta-tion of digital images, particularly those of punctate patterns from fluorescence microscopy. Fluorescence microscopy has greatly facilitated the task of understanding complex sys-tems at cellular and molecular levels in recent years. Segmentation, an important yet dif-ficult problem, is often the first processing step following acquisition. Our team previously demonstrated that a stochastic active contour based algorithm together with the concept of

First and foremost, I would like to thank my advisor, Prof. Jennifer E. Michaels. I am most appreciative of your guidance over the last four years, from the basics of elastic waves to both short- and long-term career advice. I have grown a tremendous amount, both profes-sionally and personally, and am deeply grateful for your mentorship, patience, and support. I would also like to thank my Ph.D. committee for their valuable insight and sugges-

, which can efficiently exploit the sparse nature of local intensity variations of images. However, existing wavelet frame systems lack the capability of exploiting another important image prior, i.e., the self-recursion of local image structures in both spatial and scale domain. Such a self

technological advances have led to the addition of Fourier-domain optical detection in OCT, enabling significantly faster acquisition speeds with minimal corresponding loss of sensitivity. Employing a method referred to as spectral-domain OCT (SD-OCT) (33-35), the same broadband light source was employed