3,002 research outputs found
Pathways to apprenticeships and traineeships for people from culturally and linguistically diverse backgrounds
Western Australia is experiencing a substantial shortage of skilled labour.
The State Government is committed to increasing participation in the workforce across the board, through schemes such as the Plans 4 Jobs, especially because of the current severe skills shortage in the economy.
The State Government is also committed, through the four principles of civic values, fairness, equality, and participation enshrined in the WA Charter of Multiculturalism, to promoting the full participation of all groups in all aspects of our community – social, economic, and civil – and to removing any systemic barriers to full participation. This commitment is also embodied in the multi‐departmental Policy Framework for Substantive Equality.
Aside from equity, denying equal opportunity also engenders resentment and hostility within our community, diminishes human value, and denies the state the full benefits of all its members’ talents. The cost of discrimination accrues to those who are denied opportunity and to the society which tolerates discrimination.
Members of Culturally and Linguistically Diverse (CaLD) communities in this state, in particular those from new and emerging communities, do not currently participate in apprenticeships and traineeships in significant numbers; this is regrettable and it is recommended that measures be taken to remedy this.
The Western Australian Department of Education and Training (DET) commissioned this report to investigate barriers to apprenticeships and traineeships confronting people from CaLD backgrounds, and to recommend strategies to improve their participation in apprenticeships and traineeships. The project was undertaken by the Australian Academy of Race Relations (AARR) at Murdoch University, for DET, during the period July – November 2005.
DET has many good programmes to promote apprenticeships and traineeships, including to members of CaLD communities. However, the array and complexity of these can be daunting, resulting in a lack of awareness of all the programmes available
A Bayesian spatio-temporal model of panel design data: airborne particle number concentration in Brisbane, Australia
This paper outlines a methodology for semi-parametric spatio-temporal
modelling of data which is dense in time but sparse in space, obtained from a
split panel design, the most feasible approach to covering space and time with
limited equipment. The data are hourly averaged particle number concentration
(PNC) and were collected, as part of the Ultrafine Particles from Transport
Emissions and Child Health (UPTECH) project. Two weeks of continuous
measurements were taken at each of a number of government primary schools in
the Brisbane Metropolitan Area. The monitoring equipment was taken to each
school sequentially. The school data are augmented by data from long term
monitoring stations at three locations in Brisbane, Australia.
Fitting the model helps describe the spatial and temporal variability at a
subset of the UPTECH schools and the long-term monitoring sites. The temporal
variation is modelled hierarchically with penalised random walk terms, one
common to all sites and a term accounting for the remaining temporal trend at
each site. Parameter estimates and their uncertainty are computed in a
computationally efficient approximate Bayesian inference environment, R-INLA.
The temporal part of the model explains daily and weekly cycles in PNC at the
schools, which can be used to estimate the exposure of school children to
ultrafine particles (UFPs) emitted by vehicles. At each school and long-term
monitoring site, peaks in PNC can be attributed to the morning and afternoon
rush hour traffic and new particle formation events. The spatial component of
the model describes the school to school variation in mean PNC at each school
and within each school ground. It is shown how the spatial model can be
expanded to identify spatial patterns at the city scale with the inclusion of
more spatial locations.Comment: Draft of this paper presented at ISBA 2012 as poster, part of UPTECH
projec
Large-scale Nonlinear Variable Selection via Kernel Random Features
We propose a new method for input variable selection in nonlinear regression.
The method is embedded into a kernel regression machine that can model general
nonlinear functions, not being a priori limited to additive models. This is the
first kernel-based variable selection method applicable to large datasets. It
sidesteps the typical poor scaling properties of kernel methods by mapping the
inputs into a relatively low-dimensional space of random features. The
algorithm discovers the variables relevant for the regression task together
with learning the prediction model through learning the appropriate nonlinear
random feature maps. We demonstrate the outstanding performance of our method
on a set of large-scale synthetic and real datasets.Comment: Final version for proceedings of ECML/PKDD 201
Geo-additive models of Childhood Undernutrition in three Sub-Saharan African Countries
We investigate the geographical and socioeconomic determinants of childhood undernutrition in Malawi, Tanzania and Zambia, three neighboring countries in Southern Africa using the 1992 Demographic and Health Surveys. We estimate models of undernutrition jointly for the three countries to explore regional patterns of undernutrition that transcend boundaries, while allowing for country-specific interactions. We use semiparametric models to flexibly model the effects of selected so-cioeconomic covariates and spatial effects. Our spatial analysis is based on a flexible geo-additive model using the district as the geographic unit of anal-ysis, which allows to separate smooth structured spatial effects from random effect. Inference is fully Bayesian and uses recent Markov chain Monte Carlo techniques. While the socioeconomic determinants generally confirm what is known in the literature, we find distinct residual spatial patterns that are not explained by the socioeconomic determinants. In particular, there appears to be a belt run-ning from Southern Tanzania to Northeastern Zambia which exhibits much worse undernutrition, even after controlling for socioeconomic effects. These effects do transcend borders between the countries, but to a varying degree. These findings have important implications for targeting policy as well as the search for left-out variables that might account for these residual spatial patterns
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Discovery of molecular subtypes in leiomyosarcoma through integrative molecular profiling.
Leiomyosarcoma (LMS) is a soft tissue tumor with a significant degree of morphologic and molecular heterogeneity. We used integrative molecular profiling to discover and characterize molecular subtypes of LMS. Gene expression profiling was performed on 51 LMS samples. Unsupervised clustering showed three reproducible LMS clusters. Array comparative genomic hybridization (aCGH) was performed on 20 LMS samples and showed that the molecular subtypes defined by gene expression showed distinct genomic changes. Tumors from the muscle-enriched cluster showed significantly increased copy number changes (P=0.04). A majority of the muscle-enriched cases showed loss at 16q24, which contains Fanconi anemia, complementation group A, known to have an important role in DNA repair, and loss at 1p36, which contains PRDM16, of which loss promotes muscle differentiation. Immunohistochemistry (IHC) was performed on LMS tissue microarrays (n=377) for five markers with high levels of messenger RNA in the muscle-enriched cluster (ACTG2, CASQ2, SLMAP, CFL2 and MYLK) and showed significantly correlated expression of the five proteins (all pairwise P<0.005). Expression of the five markers was associated with improved disease-specific survival in a multivariate Cox regression analysis (P<0.04). In this analysis that combined gene expression profiling, aCGH and IHC, we characterized distinct molecular LMS subtypes, provided insight into their pathogenesis, and identified prognostic biomarkers
Validation of nonlinear PCA
Linear principal component analysis (PCA) can be extended to a nonlinear PCA
by using artificial neural networks. But the benefit of curved components
requires a careful control of the model complexity. Moreover, standard
techniques for model selection, including cross-validation and more generally
the use of an independent test set, fail when applied to nonlinear PCA because
of its inherent unsupervised characteristics. This paper presents a new
approach for validating the complexity of nonlinear PCA models by using the
error in missing data estimation as a criterion for model selection. It is
motivated by the idea that only the model of optimal complexity is able to
predict missing values with the highest accuracy. While standard test set
validation usually favours over-fitted nonlinear PCA models, the proposed model
validation approach correctly selects the optimal model complexity.Comment: 12 pages, 5 figure
Aerial low-frequency hearing in captive and free-ranging harbour seals (Phoca vitulina) measured using auditory brainstem responses
The hearing sensitivity of 18 free-ranging and 10 captive harbour seals (Phoca vitulina) to aerial sounds was measured in the presence of typical environmental noise through auditory brainstem response measurements. A focus was put on the comparative hearing sensitivity at low frequencies. Low- and mid-frequency thresholds appeared to be elevated in both captive and free-ranging seals, but this is likely due to masking effects and limitations of the methodology used. The data also showed individual variability in hearing sensitivity with probable age-related hearing loss found in two old harbour seals. These results suggest that the acoustic sensitivity of free-ranging animals was not negatively affected by the soundscape they experienced in the wild
Implicitly Constrained Semi-Supervised Least Squares Classification
We introduce a novel semi-supervised version of the least squares classifier.
This implicitly constrained least squares (ICLS) classifier minimizes the
squared loss on the labeled data among the set of parameters implied by all
possible labelings of the unlabeled data. Unlike other discriminative
semi-supervised methods, our approach does not introduce explicit additional
assumptions into the objective function, but leverages implicit assumptions
already present in the choice of the supervised least squares classifier. We
show this approach can be formulated as a quadratic programming problem and its
solution can be found using a simple gradient descent procedure. We prove that,
in a certain way, our method never leads to performance worse than the
supervised classifier. Experimental results corroborate this theoretical result
in the multidimensional case on benchmark datasets, also in terms of the error
rate.Comment: 12 pages, 2 figures, 1 table. The Fourteenth International Symposium
on Intelligent Data Analysis (2015), Saint-Etienne, Franc
Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction
Deep learning for regression tasks on medical imaging data has shown
promising results. However, compared to other approaches, their power is
strongly linked to the dataset size. In this study, we evaluate
3D-convolutional neural networks (CNNs) and classical regression methods with
hand-crafted features for survival time regression of patients with high grade
brain tumors. The tested CNNs for regression showed promising but unstable
results. The best performing deep learning approach reached an accuracy of
51.5% on held-out samples of the training set. All tested deep learning
experiments were outperformed by a Support Vector Classifier (SVC) using 30
radiomic features. The investigated features included intensity, shape,
location and deep features. The submitted method to the BraTS 2018 survival
prediction challenge is an ensemble of SVCs, which reached a cross-validated
accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set,
and 42.9% on the testing set. The results suggest that more training data is
necessary for a stable performance of a CNN model for direct regression from
magnetic resonance images, and that non-imaging clinical patient information is
crucial along with imaging information.Comment: Contribution to The International Multimodal Brain Tumor Segmentation
(BraTS) Challenge 2018, survival prediction tas
A Simple Iterative Algorithm for Parsimonious Binary Kernel Fisher Discrimination
By applying recent results in optimization theory variously known as optimization transfer or majorize/minimize algorithms, an algorithm for binary, kernel, Fisher discriminant analysis is introduced that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution. The problem is converted into a smooth optimization that can be solved iteratively with no greater overhead than iteratively re-weighted least-squares. The result is simple, easily programmed and is shown to perform, in terms of both accuracy and parsimony, as well as or better than a number of leading machine learning algorithms on two well-studied and substantial benchmarks
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