An adaptive and dynamic dimensionality reduction method for high-dimensional indexing

The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approach to overcome degradation in performance with respect to increasing dimensions is to reduce the dimensionality of the original dataset before constructing the index. However, identifying the correlation among the dimensions and effectively reducing them are challenging tasks. In this paper, we present an adaptive Multi-level Mahalanobis-based Dimensionality Reduction (MMDR) technique for high-dimensional indexing. Our MMDR technique has four notable features compared to existing methods. First, it discovers elliptical clusters for more effective dimensionality reduction by using only the low-dimensional subspaces. Second, data points in the different axis systems are indexed using a single B+-tree. Third, our technique is highly scalable in terms of data size and dimension. Finally, it is also dynamic and adaptive to insertions. An extensive performance study was conducted using both real and synthetic datasets, and the results show that our technique not only achieves higher precision, but also enables queries to be processed efficiently. Copyright Springer-Verlag 2005

Self-efficacy and task performance: An investigation from two levels of analysis and specificity

This research adopts a resource allocation theoretical framework to generate predictions regarding the relationship between self-efficacy and task performance from two levels of analysis and specificity. Participants were given multiple trials of practice on an air traffic control task. Measures of task-specific self-efficacy and performance were taken at repeated intervals. The authors used multilevel analysis to demonstrate dynamic main effects, dynamic mediation and dynamic moderation. As predicted, the positive effects of overall task specific self-efficacy and general self-efficacy on task performance strengthened throughout practice. In line with these dynamic main effects, the effect of general self-efficacy was mediated by overall task specific self-efficacy; however this pattern emerged over time. Finally, changes in task specific self-efficacy were negatively associated with changes in performance at the within-person level; however this effect only emerged towards the end of practice for individuals with high levels of overall task specific self-efficacy. These novel findings emphasise the importance of conceptualising self-efficacy within a multi-level and multi-specificity framework and make a significant contribution to understanding the way this construct relates to task performance.

On aggregation issues in spatial data management

Large amounts of information can be overwhelming and costly to process, especially when transmitting data over a network. A typical modern Geographical Information System (GIS) brings all types of data together based on the geographic component of the data and provides simple point-and-click query capabilities as well as complex analysis tools. Querying a Geographical Information System, however, can be prohibitively expensive due to the large amounts of data which may need to be processed. Since the use of GIS technology has grown dramatically in the past few years, there is now a need more than ever, to provide users with the fastest and least expensive query capabilities, especially since an approximated 80 % of data stored in corporate databases has a geographical component. However, not every application requires the same, high quality data for its processing. In this paper we address the issues of reducing the cost and response time of GIS queries by preaggregating data by compromising the data accuracy and precision. We present computational issues in generation of multi-level resolutions of spatial data and show that the problem of finding the best approximation for the given region and a real value function on this region, under a predictable error, in general is "NP-complete.

10,000 Steps Rockhampton: A Whole Community Approach to Increasing Levels of Physical Activity

10,000 Steps Rockhampton is a multi-strategy health promotion program which aims to develop sustainable community-based strategies to increase physical activity.The central coordinating focus of the project is the use of pedometers to raise awareness of and provide motivation for physical activity, around the theme of '10,000 steps/day - Every step counts.' To date, five key strategies have been implemented: (1) a media-based awareness raising campaign; (2) promotion of physical activity by health professionals; (3) improving social support for physical activity through group-based programs; (4) working with local council to improve environmental support for physical activity; and (5) establishment of a ‘micro-grants’ fund to which community groups could apply for assistance with small, innovative physical activity enhancing projects. Strategies were introduced on a rolling basis beginning in February 2002 with 'layering' of interventions designed to address the multi-level individual social and environmental determinants of physical activity. The project was quasi-experimental in design, involving collection of baseline and two year follow-up data from community based surveys in Rockhampton and in a matched regional Queensland town. In August 2001,the baseline CATI survey (N=1281)found that 47.9% of men and 33.0% of women were meeting the national guidelines for physical activity. In August 2002, a smaller survey (N=400) found an increase in activity levels among women (39.7% active) but not in men (48.5%). Data from the two year follow up survey, to be conducted in August 2003, will be presented, with discussion of the major successes and challenges of this landmark physical activity intervention. Acknowledgement: This project is supported by a grant from Health Promotion Queensland

Predictors of young dating adults' inclination to engage in extradyadic sexual activities: A multi-perspective study

Using a multi-perspective vignette design, we explored predictors of young peoples' (N = 119) propensity to engage in unfaithful activities while dating. Demographic measures, a datding investment model, and measures of functional and dysfunctional impulsivity were used to predict inclination to engage in each of two extradyadic activities (kissing and sexual activity). The results of moderated multiple regression analyses revealed that a respondent's number of sexual partners, level of dysfunctional impulsivity, satisfaction with current relationship, and quality of relationship alternatives significantly predicted inclination to engage in both of the extradyadic activities. Consistent with previous findings, gender only showed significant predictive value in relation to extradyadic sex inclination. Moreover, the association between sex, love, and marriage interacted with gender in the prediction of both extradyadic activities and interacted with commitment in the prediction of extradyadic sex inclination. Suggestions for future research in this area are offered in light of these new findings.