An Historical and Theological Analysis of Campus Crusade for Christ's Evangelistic Practice in Two American University Contexts

This dissertation, an exercise in practical theology, consists of a critical conversation between the evangelistic practice of Campus Crusade for Christ in two American university contexts, Bryan Stone's ecclesiologically grounded theology of evangelism, and William Abraham's eschatologically grounded theology of evangelism. It seeks to provide these evangelizing communities several strategic proposals for a more ecclesiologically and eschatologically grounded practice of evangelism within a university context. The current literature on evangelism is long on evangelistic strategy and activity, but short on theological analysis and reflection. This study focuses on concrete practices, but is grounded in a thick description of two particular contexts (derived from qualitative research methods) and a theological analysis of the ecclesiological and eschatological beliefs embedded within their evangelistic activities. The dissertation provides an historical overview of important figures, ideas, and events that helped mold the practice of evangelism inherited by the two ministries of this study, beginning with the famous Haystack Revival on Williams College in 1806. Both ministries, Campus Crusade for Christ at Bowling Green State University (Ohio) and at Washington State University, inherited an evangelistic practice sorely infected with many of the classic distortions that both Abraham and Stone attempt to correct. Qualitative research methods detail the direction that Campus Crusade for Christ at Bowling Green State University (Ohio) and Washington State University have taken the practice of evangelism they inherited. Applying the analytical categories that emerge from a detailed summary of Stone and Abraham to qualitative data of these two ministries reveals several ways evangelism has morphed in a manner sympathetic to Stone's insistence that the central logic of evangelism is the embodied witness of the church. The results of this analysis reveal the subversive and pervasive influence of modernity on these evangelizing communities—an influence that warrants several corrective strategic proposals including: 1) re-situating evangelism within a reading of the biblical narrative that emphasizes the present, social, public, and realized nature of the gospel of the kingdom of God rather than simply its future, personal, private, and unrealized dimensions; 2) clarifying the nature of the evangelizing communities and their relationship to the church; and 3) emphasizing the virtues that characterize a new evangelistic exemplar who is incarnational, intentional, humble, and courageous.

A Catalogue of the Alpha Chapter Boston University Graduates of the School of Theology 1850-1905

A listing of graduate of Boston University School of Theology and predecessor school. Arranged by class year, alphabetical by last name and geographically by region.

Library Technology Services at Boston University: an un-roadmap

A working paper for discussion

Fast Learning VIEWNET Architectures for Recognizing 3-D Objects from Multiple 2-D Views

The recognition of 3-D objects from sequences of their 2-D views is modeled by a family of self-organizing neural architectures, called VIEWNET, that use View Information Encoded With NETworks. VIEWNET incorporates a preprocessor that generates a compressed but 2-D invariant representation of an image, a supervised incremental learning system that classifies the preprocessed representations into 2-D view categories whose outputs arc combined into 3-D invariant object categories, and a working memory that makes a 3-D object prediction by accumulating evidence from 3-D object category nodes as multiple 2-D views are experienced. The simplest VIEWNET achieves high recognition scores without the need to explicitly code the temporal order of 2-D views in working memory. Working memories are also discussed that save memory resources by implicitly coding temporal order in terms of the relative activity of 2-D view category nodes, rather than as explicit 2-D view transitions. Variants of the VIEWNET architecture may also be used for scene understanding by using a preprocessor and classifier that can determine both What objects are in a scene and Where they are located. The present VIEWNET preprocessor includes the CORT-X 2 filter, which discounts the illuminant, regularizes and completes figural boundaries, and suppresses image noise. This boundary segmentation is rendered invariant under 2-D translation, rotation, and dilation by use of a log-polar transform. The invariant spectra undergo Gaussian coarse coding to further reduce noise and 3-D foreshortening effects, and to increase generalization. These compressed codes are input into the classifier, a supervised learning system based on the fuzzy ARTMAP algorithm. Fuzzy ARTMAP learns 2-D view categories that are invariant under 2-D image translation, rotation, and dilation as well as 3-D image transformations that do not cause a predictive error. Evidence from sequence of 2-D view categories converges at 3-D object nodes that generate a response invariant under changes of 2-D view. These 3-D object nodes input to a working memory that accumulates evidence over time to improve object recognition. ln the simplest working memory, each occurrence (nonoccurrence) of a 2-D view category increases (decreases) the corresponding node's activity in working memory. The maximally active node is used to predict the 3-D object. Recognition is studied with noisy and clean image using slow and fast learning. Slow learning at the fuzzy ARTMAP map field is adapted to learn the conditional probability of the 3-D object given the selected 2-D view category. VIEWNET is demonstrated on an MIT Lincoln Laboratory database of l28x128 2-D views of aircraft with and without additive noise. A recognition rate of up to 90% is achieved with one 2-D view and of up to 98.5% correct with three 2-D views. The properties of 2-D view and 3-D object category nodes are compared with those of cells in monkey inferotemporal cortex.