Learning multidimensional joint control of a robot using receding horizon locally weighted regression

In this paper we explore the ability of a recent model-based learning technique Receding Horizon Locally Weighted Regression (RH-LWR) useful for learning temporally dependent systems. In particular this paper investigates the application of RH-LWR to learn control of Multiple-input Multiple-output robot systems. RH-LWR is demonstrated through learning joint velocity and position control of a three Degree of Freedom (DoF) rigid body robot.

Efficiency of research performance of Australian universities : a reappraisal using a bootstrap truncated regression approach

The motivation of the study stems from the results reported in the Excellence in Research for Australia (ERA) 2010 report. The report showed that only 12 universities performed research at or above international standards, of which, the Group of Eight (G8) universities filled the top eight spots. While performance of universities was based on number of research outputs, total amount of research income and other quantitative indicators, the measure of efficiency or productivity was not considered. The objectives of this paper are twofold. First, to provide a review of the research performance of 37 Australian universities using the data envelopment analysis (DEA) bootstrap approach of Simar and Wilson (2007). Second, to determine sources of productivity drivers by regressing the efficiency scores against a set of environmental variables.

The role of insulin and IGF2 signalling on metabolic pathways in prostate cancer progression

Prostate cancer (CaP) is the most commonly diagnosed cancer in males in Australia, North America, and Europe. If found early and locally confined, CaP can be treated with radical prostatectomy or radiation therapy; however, 25-40% patients will relapse and go on to advanced disease. The most common therapy in these cases is androgen deprivation therapy (ADT), which suppresses androgen production from the testis. Lack of the testicular androgen supply causes cells of the prostate to undergo apoptosis. However, in some cases the regression initially seen with ADT eventually gives way to a growth of a population of cancerous cells that no longer require testicular androgens. This phenotype is essentially fatal and is termed castrate resistant prostate cancer (CRPC). In addition to eventual regression, there are many undesirable side effects which accompany ADT, including development of a metabolic syndrome, which is defined by the U.S. National Library of Medicine as “a combination of medical disorders that increase the risk of developing cardiovascular disease and diabetes.” This project will focus on the effect of ADT induced hyperinsulinemia, as mimicked by treating androgen receptor positive CaP cells with insulin in a serum (hormone) deprived environment. While this side effect is not widely explored, in this thesis it is demonstrated for the first time that insulin upregulates pathways important to CaP progression. Our group has previously shown that during CaP progression, the enzymes necessary for de novo steroidogenesis are upregulated in the LNCaP xenograft model, total steroid levels are increased in tumours compared to pre castrate levels, and de novo steroidogenesis from radio-labelled acetate has been demonstrated. Because of the CaP dependence on AR for survival, we and other groups believe that CaP cells carry out de novo steroidogenesis to survive in androgen deprived conditions. Because (a) men on ADT often develop metabolic syndrome, and (b) men with lifestyle-induced obesity and hyperinsulinemia have worse prognosis and faster disease progression, and because (c) insulin causes steroidogenesis in other cell lines, the hypothesis that insulin may contribute to CaP progression through upregulation of steroidogenesis was explored. Insulin upregulates steroidogenesis enzymes at the mRNA level in three AR positive cell lines, as well as upregulating these enzymes at the protein level in two cell lines. It has also been demonstrated that insulin increases mitochondrial (functional) levels of steroid acute regulatory protein (StAR). Furthermore, insulin causes increased levels of total steroids in and induction of de novo steroid synthesis by insulin has been demonstrated at levels induced sufficient to activate AR. The effect of insulin analogs on CaP steroidogenesis in LNCaP and VCaP cells has also been investigated because epidemiological studies suggest that some of the analogs developed may have more cancer stimulatory effects than normal insulin. In this project, despite the signalling differences between glargine, X10, and insulin, these analogs did not appear to induce steroidogenesis any more potently that normal insulin. The effect of insulin of MCF7breast cancer cells was also investigated with results suggesting that breast cancer cells may be capable of de novo steroidogenesis, and that increase in estradiol production may be exacerbated by insulin. Insulin has also been long known to stimulate lipogenesis in the liver and adipocytes, and has been demonstrated to increase lipogenesis in breast cancer cells; therefore, investigation of the effect of insulin on lipogenesis, which is a hallmark of aggressive cancers, was investigated. In CaP progression sterol regulatory element binding protein (SREBP) is dysregulated and upregulates fatty acid synthase (FASN), acetyl CoA-carboxylase, and other lipogenesis genes. SREBP is important for steroidogenesis and in this project has been shown to be upregulated by insulin in CaP cells. Fatty acid synthesis provides building blocks of membrane growth, provides substrates for acid oxidation, the main energy source for CaP cells, provides building blocks for anti-apoptotic and proinflammatory molecules, and provides molecules that stimulate steroidogenesis. In this project it has been shown that insulin upregulates FASN and ACC, which synthesize fatty acids, as well as upregulating hormone sensitive lipase (HSL), diazepam-binding inhibitor (DBI), and long-chain acyl-CoA synthetase 3 (ACSL3), which contribute to lipid activation of steroidogenesis. Insulin also upregulates total lipid levels and de novo lipogenesis, which can be suppressed by inhibition of the insulin receptor (INSR). The fatty acids synthesized after insulin treatment are those that have been associated with CaP; furthermore, microarray data suggests insulin may upregulate fatty acid biosynthesis, metabolism and arachidonic acid metabolism pathways, which have been implicated in CaP growth and survival. Pharmacological agents used to treat patients with hyperinsulinemia/ hyperlipidemia have gained much interest in regards to CaP risk and treatment; however, the scientific rationale behind these clinical applications has not been examined. This thesis explores whether the use of metformin or simvastatin would decrease either lipogenesis or steroidogenesis or both in CaP cells. Simvastatin is a 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) inhibitor, which blocks synthesis of cholesterol, the building block of steroids/ androgens. It has also been postulated to down regulate SREBP in other metabolic disorders. It has been shown in this thesis, in LNCaP cells, that simvastatin inhibited and decreased insulin induced steroidogenesis and lipogenesis, respectively, but increased these pathways in the absence of insulin. Conversely, metformin, which activates AMP-activated protein kinase (AMPK) to shut down lipogenesis, cholesterol synthesis, and protein synthesis, highly suppresses both steroidogenesis and lipogenesis in the presence and absence of insulin. Lastly, because it has been demonstrated to increase steroidogenesis in other cell lines, and because the elucidation of any factors affecting steroidogenesis is important to understanding CaP, the effect of IGF2 on steroidogenesis in CaP cells was investigated. In patient samples, as men progress to CRPC, IGF2 mRNA and the protein levels of the receptors it may signal through are upregulated. It has also been demonstrated that IGF2 upregulates steroidogenic enzymes at both the mRNA and protein levels in LNCaP cells, increases intracellular and secreted steroid/androgen levels in LNCaPs to levels sufficient to stimulate the AR, and upregulated de novo steroidogenesis in LNCaPs and VCaPs. As well, inhibition of INSR and insulin-like growth factor 1 receptor (IGF1R), which IGF2 signals through, suggests that induction of steroidogenesis may be occurring predominantly through IGF1R. In summary, this project has illuminated for the first time that insulin is likely to play a large role in cancer progression, through upregulation of the steroidogenesis and lipogenesis pathways at the mRNA and protein levels, and production levels, and demonstrates a novel role for IGF-II in CaP progression through stimulation of steroidogenesis. It has also been demonstrated that metformin and simvastatin drugs may be useful in suppressing the insulin induction of these pathways. This project affirms the pathways by which ADT- induced metabolic syndrome may exacerbate CaP progression and strongly suggests that the monitoring and modulation of the metabolic state of CaP patients could have a strong impact on their therapeutic outcomes.

A hybrid approach to combining CART and logistic regression for stock ranking

The benefits of applying tree-based methods to the purpose of modelling financial assets as opposed to linear factor analysis are increasingly being understood by market practitioners. Tree-based models such as CART (classification and regression trees) are particularly well suited to analysing stock market data which is noisy and often contains non-linear relationships and high-order interactions. CART was originally developed in the 1980s by medical researchers disheartened by the stringent assumptions applied by traditional regression analysis (Brieman et al. [1984]). In the intervening years, CART has been successfully applied to many areas of finance such as the classification of financial distress of firms (see Frydman, Altman and Kao [1985]), asset allocation (see Sorensen, Mezrich and Miller [1996]), equity style timing (see Kao and Shumaker [1999]) and stock selection (see Sorensen, Miller and Ooi [2000])...

Quantile regression without the curse of unsmoothness

We consider quantile regression models and investigate the induced smoothing method for obtaining the covariance matrix of the regression parameter estimates. We show that the difference between the smoothed and unsmoothed estimating functions in quantile regression is negligible. The detailed and simple computational algorithms for calculating the asymptotic covariance are provided. Intensive simulation studies indicate that the proposed method performs very well. We also illustrate the algorithm by analyzing the rainfall–runoff data from Murray Upland, Australia.

Therapeutic immunisation of rabbits with cottontail rabbit papillomavirus (CRPV) virus-like particles (VLP) induces regression of established papillomas

There is overwhelming evidence that persistent infection with high-risk human papillomaviruses (HR-HPV) is the main risk factor for invasive cancer of the cervix. Due to this global public health burden, two prophylactic HPV L1 virus-like particles (VLP) vaccines have been developed. While these vaccines have demonstrated excellent type-specific prevention of infection by the homologous vaccine types (high and low risk HPV types), no data have been reported on the therapeutic effects in people already infected with the low-risk HPV type. In this study we explored whether regression of CRPV-induced papillomas could be achieved following immunisation of out-bred New Zealand White rabbits with CRPV VLPs. Rabbits immunised with CRPV VLPs had papillomas that were significantly smaller compared to the negative control rabbit group (P ≤ 0.05). This data demonstrates the therapeutic potential of PV VLPs in a well-understood animal model with potential important implications for human therapeutic vaccination for low-risk HPVs. © 2008 Govan et al; licensee BioMed Central Ltd.

Regression and hypothesis tests for multivariate GNSS state time series

A satellite based observation system can continuously or repeatedly generate a user state vector time series that may contain useful information. One typical example is the collection of International GNSS Services (IGS) station daily and weekly combined solutions. Another example is the epoch-by-epoch kinematic position time series of a receiver derived by a GPS real time kinematic (RTK) technique. Although some multivariate analysis techniques have been adopted to assess the noise characteristics of multivariate state time series, statistic testings are limited to univariate time series. After review of frequently used hypotheses test statistics in univariate analysis of GNSS state time series, the paper presents a number of T-squared multivariate analysis statistics for use in the analysis of multivariate GNSS state time series. These T-squared test statistics have taken the correlation between coordinate components into account, which is neglected in univariate analysis. Numerical analysis was conducted with the multi-year time series of an IGS station to schematically demonstrate the results from the multivariate hypothesis testing in comparison with the univariate hypothesis testing results. The results have demonstrated that, in general, the testing for multivariate mean shifts and outliers tends to reject less data samples than the testing for univariate mean shifts and outliers under the same confidence level. It is noted that neither univariate nor multivariate data analysis methods are intended to replace physical analysis. Instead, these should be treated as complementary statistical methods for a prior or posteriori investigations. Physical analysis is necessary subsequently to refine and interpret the results.

Gradual training of cascaded shape regression for facial landmark localization and pose estimation

Facial landmarks play an important role in face recognition. They serve different steps of the recognition such as pose estimation, face alignment, and local feature extraction. Recently, cascaded shape regression has been proposed to accurately locate facial landmarks. A large number of weak regressors are cascaded in a sequence to fit face shapes to the correct landmark locations. In this paper, we propose to improve the method by applying gradual training. With this training, the regressors are not directly aimed to the true locations. The sequence instead is divided into successive parts each of which is aimed to intermediate targets between the initial and the true locations. We also investigate the incorporation of pose information in the cascaded model. The aim is to find out whether the model can be directly used to estimate head pose. Experiments on the Annotated Facial Landmarks in the Wild database have shown that the proposed method is able to improve the localization and give accurate estimates of pose.

Technical efficiency of mainstream airlines and low-cost carriers : New evidence using bootstrap data envelopment analysis truncated regression

Between 2001 and 2005, the US airline industry faced financial turmoil while the European airline industry entered a period of substantive deregulation. Consequently, this opened up opportunities for low-cost carriers to become more competitive in the market. To assess airline performance and identify the sources of efficiency in the immediate aftermath of these events, we employ a bootstrap data envelopment analysis truncated regression approach. The results suggest that at the time the mainstream airlines needed to significantly reorganize and rescale their operations to remain competitive. In the second-stage analysis, the results indicate that private ownership, status as a low-cost carrier, and improvements in weight load contributed to better organizational efficiency.