Daybreak in Livingstonia; the story of the Livingstonia mission, British Central Africa, by James W. Jack.

http://www.archive.org/details/daybreakinliving011984mbp

Jam To-morrow and Jam Yesterday, but Never Jam To-day: The of Theology Libraries Planning the Twenty-first Century

The future of theology libraries is far from clear. Since the nineteenth century, theology libraries have evolved to support the work of theological education. This article briefly reviews the development of theology libraries in North America and examines the contextual changes impacting theology libraries today. Three significant factors that will shape theology libraries in the coming decade are collaborative models of pedagogy and scholarship, globalization and rapid changes in information technology, and changes in the nature of scholarly publishing including the digitization of information. A large body of research is available to assist those responsible for guiding the direction of theology libraries in the next decade, but there are significant gaps in what we know about the impact of technology on how people use information that must be filled in order to provide a solid foundation for planning.

open source spirituality: turning the world upside down

Sermon preached at Boston University School of Theology during Wednesday Chapel on October 24, 2007

OpenBU: Institutional Repository Procedures Manual

(adapted from the DSpace Procedures Manual developed by Kalamazoo College Digital Archive)

Library as Agent of [Re]Contextualization

Paper presented at the Digital Humanities 2009 conference in College Park, Maryland.

Collection Development and the Value of the Library

This is a draft 2 of a discussion paper written for Boston University Libraries

Services to support BU Authors with Open Access Issues

A working paper written for Boston University Libraries to foster discussion about how to provide better support for BU faculty authors.

Library Technology Services at Boston University: an un-roadmap

A working paper for discussion

Default ARTMAP

The default ARTMAP algorithm and its parameter values specified here define a ready-to-use general-purpose neural network system for supervised learning and recognition.

Information Fusion and Hierarchical Knowledge Discovery by ARTMAP Neural Networks

Mapping novel terrain from sparse, complex data often requires the resolution of conflicting information from sensors working at different times, locations, and scales, and from experts with different goals and situations. Information fusion methods help resolve inconsistencies in order to distinguish correct from incorrect answers, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods developed here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an objects class is car, vehicle, or man-made. Underlying relationships among objects are assumed to be unknown to the automated system of the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchial knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples.

Self-Organizing Hierarchical Knowledge Discovery by an ARTMAP Image Fusion System

Classifying novel terrain or objects front sparse, complex data may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods described here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among objects are assumed to be unknown to the automated system or the human user. The ARTMAP information fusion system used distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchical knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships.

Biologically Inspired Approaches to Automated Feature Extraction and Target Recognition

Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions. Vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically cornbine botton-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multispectral images illustrate road completion across occlusions in a cluttered scene and information fusion from incorrect labels that are simultaneously inconsistent and correct. The CNS Vision and Technology Labs (cns.bu.edulvisionlab and cns.bu.edu/techlab) are further integrating science and technology through analysis, testing, and development of cognitive and neural models for large-scale applications, complemented by software specification and code distribution.

CEDI: A Neural Model of Colour Vision, with Applications to Image Processing and Classification

Air Force Office of Scientific Research (F49620-01-1-0423); National Geospatial-Intelligence Agency (NMA 201-01-1-2016); National Science Foundation (SBE-035437, DEG-0221680); Office of Naval Research (N00014-01-1-0624)

Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks

Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors woring at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when eveidence variously suggests that and object's class is car, truck, or airplane. The methods described her address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the autonomated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierachical knowlege structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to image domain.

Default ARTMAP 2

Default ARTMAP combines winner-take-all category node activation during training , distributed activation during testing, and a set of default parameter values that define a ready-to-use, general-purpose neural network system for supervised learning and recognition. Winner-take-all ARTMAP learning is designed so that each input would make a correct prediction if re-presented immediately after its training presentation, passing the "next-input test." Distributed activation has been shown to improve test set prediction on many examples, but an input that made a correct winner-take-all prediction during training could make a different prediction with distributed activation. Default ARTMAP 2 introduces a distributed next-input test during training. On a number of benchmarks, this additional feature of the default system increases accuracy without significantly decreasing code compression. This paper includes a self-contained default ARTMAP 2 algorithm for implementation.