Stochastic Attribute-Value Grammars

Probabilistic analogues of regular and context-free grammars are well-known in computational linguistics, and currently the subject of intensive research. To date, however, no satisfactory probabilistic analogue of attribute-value grammars has been proposed: previous attempts have failed to define a correct parameter-estimation algorithm. In the present paper, I define stochastic attribute-value grammars and give a correct algorithm for estimating their parameters. The estimation algorithm is adapted from Della Pietra, Della Pietra, and Lafferty (1995). To estimate model parameters, it is necessary to compute the expectations of certain functions under random fields. In the application discussed by Della Pietra, Della Pietra, and Lafferty (representing English orthographic constraints), Gibbs sampling can be used to estimate the needed expectations. The fact that attribute-value grammars generate constrained languages makes Gibbs sampling inapplicable, but I show how a variant of Gibbs sampling, the Metropolis-Hastings algorithm, can be used instead.Comment: 23 pages, 21 Postscript figures, uses rotate.st

Parsing By Chunks

Introduction I begin with an intuition: when I read a sentence, I read it a chunk at a time. For example, the previous sentence breaks up something like this: (1) [I begin] [with an intuition]: [when I read] [a sentence], [I read it] [a chunk] [at a time] These chunks correspond in some way to prosodic patterns. It appears, for instance, that the strongest stresses in the sentence fall one to a chunk, and pauses are most likely to fall between chunks. Chunks also represent a grammatical watershed of sorts. The typical chunk consists of a single content word surrounded by a constellation of function words, matching a fixed template. A simple context-free grammar is quite adequate to describe the structure of chunks. By contrast, the relationships between chunks are mediated more by lexical selection than by rigid templates. Co-occurence of chunks is determined not just by their syntactic categories, but is sensitive to the precise words that head the

21st Century Church Leadership and Pastor Preparation

This study examined 21st-century American churches and the pastors who lead them to determine whether those pastors who attended seminaries, traditional universities, or denominational institutions were more academically prepared to grow church membership, increase weekly attendance, retain members, manage high-impact leadership teams, and engage in local church formation than were pastors who did not attend or complete any academic institution. The researcher examined the academic completion levels, leadership and church growth characteristics, and professional training of the participating pastors from six broadly defined categories: (a) pastors who participated in the 2014-2015 Outreach/LifeWay magazine survey; (b) pastors who participated in the 2014-2015 The Global Leader Summit-TGLS; (c) pastors of the 2014-2015 The Church Network (TCN) Certified Church Administrator (CCA) members; (d) pastors from the Church of the Nazarene K-Church project; (e) Best Christian Workplaces Institute (BCWI) respondent client pastors; and (f) pastors who were mentored under the leadership of a megachurch pastor located in the Midwest. The researcher compared the pastoral groups in leadership characteristics and identified their leadership commonalities. The research findings should assist pastors, clergy and denominational leaders, seminary faculty, church committees and administrators, to improve clergy recruitment, continuous learning initiatives and effective leadership development training for the 21st-century church

Interactions of Cardiomyocytes and Myofibroblasts: An Experimental and Theoretical Model Study

Cardiomyocytes and fibroblasts make up the majority of cells in natural myocardium. While cardiomyocytes are primarily responsible for the mechanical contraction, fibroblasts are responsible for maintaining the extracellular matrix and tissue compliance. In response to pathologies such as hypertension or infarction, fibroblasts in the heart can convert to myofibroblasts, a larger and more contractile phenotype between a fibroblast and a smooth muscle cell. Myofibroblasts are essential to wound healing, but can change the compliance and functioning of heart tissue and can produce pathological fibrosis, formation of excess fibrous connective tissue. In developing therapeutic approaches it is essential to understand how myofibroblasts modulate the electromechanical functions of fibrotic heart. In this dissertation, the problem is studied by developing computational and experimental models of heart muscle with randomly distributed varying ratios of myofibroblasts. The experimental model consists of engineered heart tissues assembled from embryonic cardiomyocytes and containing defined fractions of myofibroblasts randomly distributed throughout the tissue. The computational model is formulated at the cellular level taking into account individual cardiomyocytes and myofibroblasts to yield the pattern of impulse spread as modulated by the presence of myofibroblasts acting either as insulators or resistors. The excitatory impulse activates the contraction of individual viscoelastic cells that are mechanically linked to other cells and the extracellular matrix. The results give insight into the mechanical and electrical modulation of engineering heart tissue by myofibroblasts