Stoat trap tunnel location : GIS predictive modelling to identify the best tunnel location : a thesis submitted in fulfillment of the requirements for the degree of Master of Philosophy in Geographic Information Systems in Massey University

Stoats are recognised as one of the biggest threats to New Zealand's threatened species. They are difficult to control because of their biological characteristics. Currently trapping is the most common type of control technique that has a proven success rate. Research studies have shown that some traps catch more stoats than others. However the reason for this is not well documented. The effectiveness of a trap set is difficult to determine because not all trap locations are the same and not all people have the same ability to select the best location for a trap. This study uses GIS to spatially analyse stoat capture data from a control operation on Secretary Island in conjunction with commonly available vegetation, habitat, diet and home range spatial data to see if there are consistent patterns that could be used as variables in a model that would predict the best place to locate a stoat trap tunnel. The model would then be tested against a similar dataset from Resolution Island. The Department of Conservation supplied the stoat capture data from the control operations on both islands. Standard spatial analysis techniques were used to generate surfaces that combined the capture data with the vegetation, habitat, diet and home range surfaces to produce predictive surfaces. The key finding from the research was that it is possible to produce a predictive model, although one was not created because the spatial datasets were not of a high enough resolution to provide conclusive evidence that could be confidently used as a variable in a model. The spatial analysis also indicated that stoats on both islands were caught mainly in the warmer northwestern parts of the islands although the study could not determine why there was a preference for these areas. In rugged terrain like that found on both islands the location of the track network will influence where the majority of stoats will be caught

A complexity analysis of statistical learning algorithms

We apply information-based complexity analysis to support vector machine (SVM) algorithms, with the goal of a comprehensive continuous algorithmic analysis of such algorithms. This involves complexity measures in which some higher order operations (e.g., certain optimizations) are considered primitive for the purposes of measuring complexity. We consider classes of information operators and algorithms made up of scaled families, and investigate the utility of scaling the complexities to minimize error. We look at the division of statistical learning into information and algorithmic components, at the complexities of each, and at applications to support vector machine (SVM) and more general machine learning algorithms. We give applications to SVM algorithms graded into linear and higher order components, and give an example in biomedical informatics

Beard and Uber-Beard

Symposium: One-Hundred Years Later: Revisiting Charles Beard's An Economic Interpretation of the Constitution of the United State

Volunteer Management Capacity in America's Charities and Congregations

The volunteer management capacity study was completed in 2003 under contract with the Corporation for National and Community Service. Results are based on surveys of separate samples of U.S. charities and congregations. The study concludes that prevailing low levels of management capacity have not translated into widespread concern regarding the challenges of volunteer management. While charities and congregations need help in developing their capacity to recruit and manage volunteers, these organizations are nonetheless eager and prepared to take on additional volunteers. This conclusion has already been taken as validation by the federal government that the President's Call to Service need not be postponed due to an inability of organizations to take on new volunteers