Remembering the Peel Island Lazaret through re-imagining the Teerk Roo Ra forest

This paper discusses my current research which aims to re-member the site of the Peel Island Lazaret through re-imagining the Teerk Roo Ra forest as a series of animated artworks. Teerk Roo Ra National Park (formally known as Peel Island) is a small island in Moreton Bay, Queensland and is visible on the ferry journey from Cleveland to Stradbroke Island. The island has an intriguing history, and is the site of a former Lazaret and quarantine station. The Lazaret treated patients diagnosed with Hansen’s disease (or Leprosy), and operated between 1907 and 1959. In this paper I will discuss conceptions of the non-indigenous historical context of the Peel Island Lazaret and the notion of the liminal state (Turner,1967). Through this discussion conceptions of place from Australian cultural theorist Ross Gibson are also examined. The concept of two overlapping realms is then explored through the clues and shared stories about the people who inhabited the site. There is then an explanation of my own approach to re-member this place through re-imagining the forest that witnessed the events of the Lazaret. I then draw on theories of the uncanny from German Psychiatrist Ernst Jentsch, Austrian Neurologist Sigmund Freud and South African animation theorist Meg Rickards to argue that my experience of the forest of Teerk Roo Ra was an uncanny experience where two worlds or states of mind existed simultaneously and overlapped, causing a viscerally unsettling uncanny experience. Through an analysis of Czech Surrealist Animator Jan Švankmajer’s cinematic narrative Down to the cellar (1982), my creative work Structure #24(2011), and Australian Artist Patricia Piccinini’s cinematic artwork The Gathering (2007), I discuss the situation of the inanimate and the animate co-existing simultaneously. Using this approach I propose an understanding of the uncanny as an intellectual uncertainty as outlined by Jentsch (1906). I also develop the notion of the familiar being concealed and becoming unfamiliar through mimicry (Freud, 1919). These discussions form an introduction to my creative work Nocturne #5(2014) which re-members the forests of Teerk Roo Ra as an uncanny place primarily expressed through animation.

Promoting law student well-being – A developing program and the use of animation

It is widely acknowledged that student mental well-being is a critical factor in the tertiary student learning experience and is important to student learning success. The issue of student mental well-being also has implications for effective student transition out of university and into the world of work. It is therefore vital that intentional strategies are adopted by universities both within the formal curriculum, and outside it, to promote student well-being. This paper describes the ongoing development of the ‘I Belong in the LLB’ program at the Queensland University of Technology Law School, and the use of animation to engage students with the importance of mental health.

Structural adaptability in the system Bi2O3 --- WO3 : Higher members of the homologous series Bi2WnO3n+3

The ultramicrostructure of phases with n = 1, 2 and 3 in the hypothetical series Bi2WnO3n+3 has been investigated by high resolution electron microscopy and energy dispersive X-ray emission spectroscopy. For n = 1 and 2, well ordered phases with the predicted compositions have been obtained, but for n = 3, a severely disordered assemblage containing intergrowths of the two known structures and strips of the n = 3 member is produced. No evidence for ordered structures with n > 2 has yet been obtained.

Nocturne #39

Creative Work as part of the Nocturne series

Animated Mise-en-scene and aesthetic harmony: An expansion of the traditional principles of animation to 3D computer animation

3D Computer Graphics (CG) has become the dominant medium for modern animated feature films. It is widely understood that traditional principles of animation developed in the 1930s at the Walt Disney Studio remain applicable to this new medium and heavily influence the range of aesthetic motion styles in contemporary animation. Via a frame-by-frame textual analysis of four animated feature films, this thesis tests and confirms the validity of the principles of animation and expands upon them by reinterpreting the Disney principle of appeal as aesthetic harmony, which delineates the way in which character posing and transitions between poses contribute to the animated motion styles that animators work in today.

Clancestry Conversation Series

The Clancestry Conversation series forms part of QPAC's Clancestry Festival which is an annual celebration of the arts and cultural practices of the world's First Nations Peoples with a particular focus on Aboriginal and Torres Strait Islander peoples.

Dielectric anisotropy and relaxation studies of the homologous series of N-(4-n-alkyloxy-2-hydroxy-benzylidene)-4-carbethoxyaniline

Dielectric properties of the homologous series of newly synthesized nonchiral compounds N-(4-n-alkyloxy-2-hydroxy-benzylidene)-4-carbethoxyaniline, (n = 6, 8, 10, 12) having wide temperature range (∼60°C) smectic A (SmA) phase, have been studied by the impedance spectroscopy in the frequency range of 100 Hz to 1 MHz. Measurements have been carried out for two principal alignments (planar as well as homeotropic) of the SmA phase. Dielectric anisotropy (Δε' = ε'∥ - ε'⊥) for all the members of the series has been found to be negative for the whole temperature range of SmA phase. Magnitude of the dielectric anisotropy (|Δε'|) has been found to decrease with the number of alkyl chains. Relaxation frequencies corresponding to the rotation of the individual molecules about their short axes, lie below 1 MHz and obey the Arrhenius law by which activation energies have been determined. However, the relaxation frequencies corresponding to the rotation of the molecules about their short axes apparently lie above 10 MHz.

Kernels for large margin time-series classification

In this paper we propose a novel family of kernels for multivariate time-series classification problems. Each time-series is approximated by a linear combination of piecewise polynomial functions in a Reproducing Kernel Hilbert Space by a novel kernel interpolation technique. Using the associated kernel function a large margin classification formulation is proposed which can discriminate between two classes. The formulation leads to kernels, between two multivariate time-series, which can be efficiently computed. The kernels have been successfully applied to writer independent handwritten character recognition.

Forecasting day-ahead electricity load using a multiple equation time series approach

The quality of short-term electricity load forecasting is crucial to the operation and trading activities of market participants in an electricity market. In this paper, it is shown that a multiple equation time-series model, which is estimated by repeated application of ordinary least squares, has the potential to match or even outperform more complex nonlinear and nonparametric forecasting models. The key ingredient of the success of this simple model is the effective use of lagged information by allowing for interaction between seasonal patterns and intra-day dependencies. Although the model is built using data for the Queensland region of Australia, the method is completely generic and applicable to any load forecasting problem. The model’s forecasting ability is assessed by means of the mean absolute percentage error (MAPE). For day-ahead forecast, the MAPE returned by the model over a period of 11 years is an impressive 1.36%. The forecast accuracy of the model is compared with a number of benchmarks including three popular alternatives and one industrial standard reported by the Australia Energy Market Operator (AEMO). The performance of the model developed in this paper is superior to all benchmarks and outperforms the AEMO forecasts by about a third in terms of the MAPE criterion.

Improving GPS time series for geodynamic studies

Accurate and stable time series of geodetic parameters can be used to help in understanding the dynamic Earth and its response to global change. The Global Positioning System, GPS, has proven to be invaluable in modern geodynamic studies. In Fennoscandia the first GPS networks were set up in 1993. These networks form the basis of the national reference frames in the area, but they also provide long and important time series for crustal deformation studies. These time series can be used, for example, to better constrain the ice history of the last ice age and the Earth s structure, via existing glacial isostatic adjustment models. To improve the accuracy and stability of the GPS time series, the possible nuisance parameters and error sources need to be minimized. We have analysed GPS time series to study two phenomena. First, we study the refraction in the neutral atmosphere of the GPS signal, and, second, we study the surface loading of the crust by environmental factors, namely the non-tidal Baltic Sea, atmospheric load and varying continental water reservoirs. We studied the atmospheric effects on the GPS time series by comparing the standard method to slant delays derived from a regional numerical weather model. We have presented a method for correcting the atmospheric delays at the observational level. The results show that both standard atmosphere modelling and the atmospheric delays derived from a numerical weather model by ray-tracing provide a stable solution. The advantage of the latter is that the number of unknowns used in the computation decreases and thus, the computation may become faster and more robust. The computation can also be done with any processing software that allows the atmospheric correction to be turned off. The crustal deformation due to loading was computed by convolving Green s functions with surface load data, that is to say, global hydrology models, global numerical weather models and a local model for the Baltic Sea. The result was that the loading factors can be seen in the GPS coordinate time series. Reducing the computed deformation from the vertical time series of GPS coordinates reduces the scatter of the time series; however, the long term trends are not influenced. We show that global hydrology models and the local sea surface can explain up to 30% of the GPS time series variation. On the other hand atmospheric loading admittance in the GPS time series is low, and different hydrological surface load models could not be validated in the present study. In order to be used for GPS corrections in the future, both atmospheric loading and hydrological models need further analysis and improvements.

Effect of substitution on molecular conformation and packing features in a series of aryl substituted ethyl-6-methyl-4-phenyl-2-thioxo-1,2,3,4-tetrahydropyrimidine-5-carboxylates

The supramolecular structures of eight aryl protected ethyl-6-methyl-4-phenyl-2-thioxo-1,2,3,4 tetrahydropyrimidine-5-carboxyl ates were analyzed in order to understand the effect of variations in functional groups on molecular geometry, conformation and packing of molecules in the crystalline lattice. It is observed that the existence of a short intra-molecular C-H center dot center dot center dot pi interaction between the aromatic hydrogen of the aryl ring with the isolated double bond of the six-membered tetrahydropyrimidine ring is a key feature which imparts additional stability to the molecular conformation in the solid state. The compounds pack via the cooperative involvement of both N-H center dot center dot center dot S=C and N-H center dot center dot center dot O=C intermolecular dimers forming a sheet like structure. In addition, weak C-H center dot center dot center dot O and C-H center dot center dot center dot pi intermolecular interactions provide additional stability to the crystal packing.

Diagnostic Tests Based on Quantile Residuals for Nonlinear Time Series Models

This thesis studies quantile residuals and uses different methodologies to develop test statistics that are applicable in evaluating linear and nonlinear time series models based on continuous distributions. Models based on mixtures of distributions are of special interest because it turns out that for those models traditional residuals, often referred to as Pearson's residuals, are not appropriate. As such models have become more and more popular in practice, especially with financial time series data there is a need for reliable diagnostic tools that can be used to evaluate them. The aim of the thesis is to show how such diagnostic tools can be obtained and used in model evaluation. The quantile residuals considered here are defined in such a way that, when the model is correctly specified and its parameters are consistently estimated, they are approximately independent with standard normal distribution. All the tests derived in the thesis are pure significance type tests and are theoretically sound in that they properly take the uncertainty caused by parameter estimation into account. -- In Chapter 2 a general framework based on the likelihood function and smooth functions of univariate quantile residuals is derived that can be used to obtain misspecification tests for various purposes. Three easy-to-use tests aimed at detecting non-normality, autocorrelation, and conditional heteroscedasticity in quantile residuals are formulated. It also turns out that these tests can be interpreted as Lagrange Multiplier or score tests so that they are asymptotically optimal against local alternatives. Chapter 3 extends the concept of quantile residuals to multivariate models. The framework of Chapter 2 is generalized and tests aimed at detecting non-normality, serial correlation, and conditional heteroscedasticity in multivariate quantile residuals are derived based on it. Score test interpretations are obtained for the serial correlation and conditional heteroscedasticity tests and in a rather restricted special case for the normality test. In Chapter 4 the tests are constructed using the empirical distribution function of quantile residuals. So-called Khmaladze s martingale transformation is applied in order to eliminate the uncertainty caused by parameter estimation. Various test statistics are considered so that critical bounds for histogram type plots as well as Quantile-Quantile and Probability-Probability type plots of quantile residuals are obtained. Chapters 2, 3, and 4 contain simulations and empirical examples which illustrate the finite sample size and power properties of the derived tests and also how the tests and related graphical tools based on residuals are applied in practice.

Studies on Binary Time Series Models with Applications to Empirical Macroeconomics and Finance

This thesis studies binary time series models and their applications in empirical macroeconomics and finance. In addition to previously suggested models, new dynamic extensions are proposed to the static probit model commonly used in the previous literature. In particular, we are interested in probit models with an autoregressive model structure. In Chapter 2, the main objective is to compare the predictive performance of the static and dynamic probit models in forecasting the U.S. and German business cycle recession periods. Financial variables, such as interest rates and stock market returns, are used as predictive variables. The empirical results suggest that the recession periods are predictable and dynamic probit models, especially models with the autoregressive structure, outperform the static model. Chapter 3 proposes a Lagrange Multiplier (LM) test for the usefulness of the autoregressive structure of the probit model. The finite sample properties of the LM test are considered with simulation experiments. Results indicate that the two alternative LM test statistics have reasonable size and power in large samples. In small samples, a parametric bootstrap method is suggested to obtain approximately correct size. In Chapter 4, the predictive power of dynamic probit models in predicting the direction of stock market returns are examined. The novel idea is to use recession forecast (see Chapter 2) as a predictor of the stock return sign. The evidence suggests that the signs of the U.S. excess stock returns over the risk-free return are predictable both in and out of sample. The new "error correction" probit model yields the best forecasts and it also outperforms other predictive models, such as ARMAX models, in terms of statistical and economic goodness-of-fit measures. Chapter 5 generalizes the analysis of univariate models considered in Chapters 2 4 to the case of a bivariate model. A new bivariate autoregressive probit model is applied to predict the current state of the U.S. business cycle and growth rate cycle periods. Evidence of predictability of both cycle indicators is obtained and the bivariate model is found to outperform the univariate models in terms of predictive power.