No description available.

Breastfeeding is recommended as the optimal source of nutrition for newborns for the first 6 months of life and beyond with the addition of complementary foods. While breastfeeding initiation rates have been increasing, duration rates remain a concern as many women prematurely discontinue due to difficulties encountered rather than maternal choice. In addition, there is a sizable gap between rates of exclusive breastfeeding and current recommendations. Targeting modifiable variables that may be amenable to intervention is one strategy to improve breastfeeding outcomes. One such modifiable variable is breastfeeding self-efficacy. Although research has clearly shown that breastfeeding self-efficacy is predictive of breastfeeding duration and exclusivity, it is unknown whether it can be enhanced to improve breastfeeding outcomes. The purpose of this pilot randomized controlled trial was to examine the feasibility and compliance of a newly developed trial protocol and the acceptability of an intervention to increase breastfeeding self-efficacy in the immediate postpartum period. Secondary outcomes included determining whether there were any trends between groups related to breastfeeding self-efficacy, duration, and exclusivity. Participants included 150 primiparous mothers who were breastfeeding their healthy, full-term infants. Eligible and consenting mothers were randomized to either a control group (standard postpartum care) or an intervention group (standard postpartum care plus the self-efficacy intervention). Participants allocated to the intervention group received three individualized, self-efficacy enhancing sessions with the researcher; two sessions were conducted in hospital, and one was administered via telephone 1 week following hospital discharge. A research assistant blinded to group allocation collected outcome data at 4 and 8 weeks postpartum. The results suggested that the administration of the intervention was feasible and that there was a high degree of protocol compliance; the majority of participants reported that the intervention was beneficial. Secondary outcomes identified that there was a trend among participants in the intervention group to have improved breastfeeding outcomes, including higher rates of breastfeeding self-efficacy, duration, and exclusivity at 4 and 8 weeks postpartum. Preliminary evidence also suggested that the self-efficacy intervention may have assisted to decrease perceptions of insufficient milk supply among the intervention group participants. Overall, the findings from this pilot trial indicated that a larger trial is warranted.

One of the most fundamental computational problems in biology is that of inferring evolutionary histories of groups of species from sequence data. Such evolutionary histories, known as phylogenies are usually represented as binary trees where leaves represent extant species, whereas internal nodes represent their shared ancestors. As the amount of sequence data available to biologists increases, very fast phylogenetic reconstruction algorithms are becoming necessary. Currently, large sequence alignments can contain up to hundreds of thousands of sequences, making traditional methods, such as Neighbor Joining, computationally prohibitive. To address this problem, we have developed three novel fast phylogenetic algorithms. The first algorithm, QTree, is a quartet-based heuristic that runs in O(n log n) time. It is based on a theoretical algorithm that reconstructs the correct tree, with high probability, assuming every quartet is inferred correctly with constant probability. The core of our algorithm is a balanced search tree structure that enables us to locate an edge in the tree in O(log n) time. Our algorithm is several times faster than all the current methods, while its accuracy approaches that of Neighbour Joining. The second algorithm, LSHTree, is the first sub-quadratic time algorithm with theoretical performance guarantees under a Markov model of sequence evolution. Our new algorithm runs in O(n^{1+γ(g)} log^2 n) time, where γ is an increasing function of an upper bound on the mutation rate along any branch in the phylogeny, and γ(g) < 1 for all g. For phylogenies with very short branches, the running time of our algorithm is close to linear. In experiments, our prototype implementation was more accurate than the current fast algorithms, while being comparably fast. In the final part of this thesis, we apply the algorithmic framework behind LSHTree to the problem of placing large numbers of short sequence reads onto a fixed phylogenetic tree. Our initial results in this area are promising, but there are still many challenges to be resolved.

No description available.

Diss. (sammanfattning) Uppsala : Univ., 2003. / Härtill 5 uppsatser.

Thesis (Ph. D.)--University of Washington, 2008. / Vita. Includes bibliographical references (p. 86-93).

Source: Dissertation Abstracts International, Volume: 69-10, Section: B, page: 5863. Adviser: Lemuel Moye. Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (leaves 99-106).

Diss. med. dent. Zürich. / Literaturverz.

Probabilistic ideas and tools have recently begun to permeate into several fields where they had traditionally not played a major role, including fields such as numerical linear algebra and optimization. One of the key ways in which these ideas influence these fields is via the development and analysis of randomized algorithms for solving standard and new problems of these fields. Such methods are typically easier to analyze, and often lead to faster and/or more scalable and versatile methods in practice. This thesis explores the design and analysis of new randomized iterative methods for solving linear systems and inverting matrices. The methods are based on a novel sketch-and-project framework. By sketching we mean, to start with a difficult problem and then randomly generate a simple problem that contains all the solutions of the original problem. After sketching the problem, we calculate the next iterate by projecting our current iterate onto the solution space of the sketched problem. The starting point for this thesis is the development of an archetype randomized method for solving linear systems. Our method has six different but equivalent interpretations: sketch-and-project, constrain-and-approximate, random intersect, random linear solve, random update and random fixed point. By varying its two parameters – a positive definite matrix (defining geometry), and a random matrix (sampled in an i.i.d. fashion in each iteration) – we recover a comprehensive array of well known algorithms as special cases, including the randomized Kaczmarz method, randomized Newton method, randomized coordinate descent method and random Gaussian pursuit. We also naturally obtain variants of all these methods using blocks and importance sampling. However, our method allows for a much wider selection of these two parameters, which leads to a number of new specific methods. We prove exponential convergence of the expected norm of the error in a single theorem, from which existing complexity results for known variants can be obtained. However, we also give an exact formula for the evolution of the expected iterates, which allows us to give lower bounds on the convergence rate. We then extend our problem to that of finding the projection of given vector onto the solution space of a linear system. For this we develop a new randomized iterative algorithm: stochastic dual ascent (SDA). The method is dual in nature, and iteratively solves the dual of the projection problem. The dual problem is a non-strongly concave quadratic maximization problem without constraints. In each iteration of SDA, a dual variable is updated by a carefully chosen point in a subspace spanned by the columns of a random matrix drawn independently from a fixed distribution. The distribution plays the role of a parameter of the method. Our complexity results hold for a wide family of distributions of random matrices, which opens the possibility to fine-tune the stochasticity of the method to particular applications. We prove that primal iterates associated with the dual process converge to the projection exponentially fast in expectation, and give a formula and an insightful lower bound for the convergence rate. We also prove that the same rate applies to dual function values, primal function values and the duality gap. Unlike traditional iterative methods, SDA converges under virtually no additional assumptions on the system (e.g., rank, diagonal dominance) beyond consistency. In fact, our lower bound improves as the rank of the system matrix drops. By mapping our dual algorithm to a primal process, we uncover that the SDA method is the dual method with respect to the sketch-and-project method from the previous chapter. Thus our new more general convergence results for SDA carry over to the sketch-and-project method and all its specializations (randomized Kaczmarz, randomized coordinate descent ... etc.). When our method specializes to a known algorithm, we either recover the best known rates, or improve upon them. Finally, we show that the framework can be applied to the distributed average consensus problem to obtain an array of new algorithms. The randomized gossip algorithm arises as a special case. In the final chapter, we extend our method for solving linear system to inverting matrices, and develop a family of methods with specialized variants that maintain symmetry or positive definiteness of the iterates. All the methods in the family converge globally and exponentially, with explicit rates. In special cases, we obtain stochastic block variants of several quasi-Newton updates, including bad Broyden (BB), good Broyden (GB), Powell-symmetric-Broyden (PSB), Davidon-Fletcher-Powell (DFP) and Broyden-Fletcher-Goldfarb-Shanno (BFGS). Ours are the first stochastic versions of these updates shown to converge to an inverse of a fixed matrix. Through a dual viewpoint we uncover a fundamental link between quasi-Newton updates and approximate inverse preconditioning. Further, we develop an adaptive variant of the randomized block BFGS (AdaRBFGS), where we modify the distribution underlying the stochasticity of the method throughout the iterative process to achieve faster convergence. By inverting several matrices from varied applications, we demonstrate that AdaRBFGS is highly competitive when compared to the well established Newton-Schulz and approximate preconditioning methods. In particular, on large-scale problems our method outperforms the standard methods by orders of magnitude. The development of efficient methods for estimating the inverse of very large matrices is a much needed tool for preconditioning and variable metric methods in the big data era.