Effectiveness of national elite sport policies : a multidimensional approach applied to the case of Flanders

While the results of nations in international sport competitions are most often used as an evaluation of effectiveness of elite sport policies, they do not take into account the long-term duration of an athletic career, nor the many confounding variables influencing international success. This paper argues that output evaluation is a one-sided approach to policy assessment. It applies a multidimensional approach to the measurement of the effectiveness of elite sports policy evaluation (meso-level) by examining a four-year cycle of elite sport policies in Flanders. This study endeavors to advance the development of a framework to assess effectiveness of elite sport policies of nations. Data were collected at multiple points of the input-throughput-output and feedback cycle. It was found that in spite of the increasing elite sport expenditures in Flanders (inputs), and notwithstanding the development of the throughputs (processes), this has not as yet lead to acceptable results (outputs) at an international level.

Multidimensional evaluation framework for virtual environment efficacy assessment

This research is related to the user-centred design and use of Virtual Environment (VE) training systems. A multidimensional user-centred systematic training evaluation framework that combines ideas from human-computer interaction, training, education and psychology was proposed, which contributes to better design and evaluation of VE user interfaces.

Multidimensional correlates of preschool children's physical activity

This thesis identified a number of factors which are associated with preschool children’s physical activity and were previously unexplored. The findings are important as they help identify possible targets for interventions to support healthy levels of physical activity in young children.

Accounting for the multidimensional nature of the relationship between adult obesity and socio-economic status : the French second National Individual Survey on Food Consumption (INCA 2) dietary survey (2006–07)

The objective was to study the multidimensional nature of the relationship between adult obesity (OB) and socio-economic status (SES),
using comprehensive indices of SES taken separately or synthesised in an overall index. A nationally representative sample of adults aged
18–79 years was taken from the French second National Individual Survey on Food Consumption (INCA 2) dietary survey (2006–07).
Weight and height were measured and OB defined as BMI $ 30 kg/m². SES variables were reported in questionnaires and included
occupation, education and characteristics of household wealth. Composite indices of SES (household wealth and overall SES indices)
were computed by correspondence analysis, and relationships with OB were investigated with logistic regression analysis. In total, 11·8
(95% CI 10·1, 13·4) % of French adults were obese, without significant difference by sex. While no significant relationship was observed
in men, all SES indicators were inversely correlated to OB in women. Both education and the household wealth index were retained in the
stepwise multivariate model, confirming that different socio-economic variables are not necessarily proxies of each other regarding the OB
issue. On the other hand, ‘controlling for SES’ while including several measures of SES in multivariate models may lead to collinearity, and
thus over-adjustment. A more integrative approach may be to derive a synthetic index by including the SES factors available in a given
study. Beyond this methodological perspective, understanding how OB is related to the different dimensions of SES should help to
target the more vulnerable groups and increase the effectiveness of prevention.

Edge detection is a projection

A desirable property of any edge detector is that it be a projection in the mathematical sense, that is, that when it is applied to its own output it produces no further change. This report examines the behaviour of some conventional and some new operators when applied to line-drawings. The Marr-Hildreth and some gradient operators are among the conventional operators examined. Also a class of energy feature detectors is explored. It is shown that the energy feature detector is a true projection and does not proliferate edges when applied to a line-drawing, whereas several of the conventional operators do.

Exploiting side information in locality preserving projection

Even if the class label information is unknown, side information represents some equivalence constraints between pairs of patterns, indicating whether pairs originate from the same class. Exploiting side information, we develop algorithms to preserve both the intra-class and inter-class local structures. This new type of locality preserving projection (LPP), called LPP with side information (LPPSI), preserves the data's local structure in the sense that the close, similar training patterns will be kept close, whilst the close but dissimilar ones are separated. Our algorithms balance these conflicting requirements, and we further improve this technique using kernel methods. Experiments conducted on popular face databases demonstrate that the proposed algorithm significantly outperforms LPP. Further, we show that the performance of our algorithm with partial side information (that is, using only small amount of pair-wise similarity/dissimilarity information during training) is comparable with that when using full side information. We conclude that exploiting side information by preserving both similar and dissimilar local structures of the data significantly improves performance.

Robust learning of discriminative projection for multicategory classification on the Stiefel manifold

Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in pose, illumination, and facial expression. To address this problem, we propose a framework formulated under statistical learning theory that facilitates robust learning of a discriminative projection. Dimensionality reduction using the projection matrix is combined with a linear classifier in the regularized framework of lasso regression. The projection matrix in conjunction with the classifier parameters are then found by solving an optimization problem over the Stiefel manifold. The experimental results on standard face databases suggest that the proposed method outperforms some recent regularized techniques when the number of training samples is small.

Ridge regression for two dimensional locality preserving projection

Two Dimensional Locality Preserving Projection (2D-LPP) is a recent extension of LPP, a popular face recognition algorithm. It has been shown that 2D-LPP performs better than PCA, 2D-PCA and LPP. However, the computational cost of 2D-LPP is high. This paper proposes a novel algorithm called Ridge Regression for Two Dimensional Locality Preserving Projection (RR- 2DLPP), which is an extension of 2D-LPP with the use of ridge regression. RR-2DLPP is comparable to 2DLPP in performance whilst having a lower computational cost. The experimental results on three benchmark face data sets - the ORL, Yale and FERET databases - demonstrate the effectiveness and efficiency of RR-2DLPP compared with other face recognition algorithms such as PCA, LPP, SR, 2D-PCA and 2D-LPP.

A multidimensional framework for embedded academic skill development : transition pedagogy in social work

As a consequence of the widening participation agenda, student cohorts in Australian higher education are becoming increasingly diverse. While diversity is often characterised by a focus on culture or ethnicity, this variability also independently exists in regard to competence in academic skills (Dillon, 2007). Successfully developing discipline-specific academic skills is crucial to a student’s learning, progress and attainment in higher education. The growing recognition that students are entering Australian universities with varying levels of academic preparedness as a result of the widening participation agenda has made effective academic skill support even more important, since ‘access without a reasonable chance of success is an empty promise’ (International Associations of Universities, 2008, p. 1).

Duration and persistence in multidimensional deprivation : methodology and Australian application

This article extends the recent literature on static multidimensional deprivation to propose dynamic deprivation measures that incorporate both the persistence and duration of deprivation across multiple dimensions. The article then illustrates the usefulness of the extension by applying it to Australian panel data for the recent period, 2001–2008. The empirical application exploits the subgroup decomposability of the deprivation measures to identify the subgroups that are more deprived than others. The proposed measure is also decomposable by dimensions and is used to identify the dimensions where deprivation is more persistent. The comparison between the subgroups shows that the divide between homeowners and non-homeowners is one of the sharpest, with the latter suffering much more deprivation than the former. The results are robust to alternative schemes for weighting and aggregating the dimensions as well as to the choice of model parameters.

Dimensionality reduction of protein mass spectrometry data using random projection

Protein mass spectrometry (MS) pattern recognition has recently emerged as a new method for cancer diagnosis. Unfortunately, classification performance may degrade owing to the enormously high dimensionality of the data. This paper investigates the use of Random Projection in protein MS data dimensionality reduction. The effectiveness of Random Projection (RP) is analyzed and compared against Principal Component Analysis (PCA) by using three classification algorithms, namely Support Vector Machine, Feed-forward Neural Networks and K-Nearest Neighbour. Three real-world cancer data sets are employed to evaluate the performances of RP and PCA. Through the investigations, RP method demonstrated better or at least comparable classification performance as PCA if the dimensionality of the projection matrix is sufficiently large. This paper also explores the use of RP as a pre-processing step prior to PCA. The results show that without sacrificing classification accuracy, performing RP prior to PCA significantly improves the computational time.

The determination of bioactives using multidimensional separation and detection systems

This project was focused on the development of platform technology for the separation and detection of complex samples. The samples investigated included roasted coffee beans, opiate alkaloids and amino acids.