Adding a receding horizon to Locally Weighted Regression for learning robot control

There have been notable advances in learning to control complex robotic systems using methods such as Locally Weighted Regression (LWR). In this paper we explore some potential limits of LWR for robotic applications, particularly investigating its application to systems with a long horizon of temporal dependence. We define the horizon of temporal dependence as the delay from a control input to a desired change in output. LWR alone cannot be used in a temporally dependent system to find meaningful control values from only the current state variables and output, as the relationship between the input and the current state is under-constrained. By introducing a receding horizon of the future output states of the system, we show that sufficient constraint is applied to learn good solutions through LWR. The new method, Receding Horizon Locally Weighted Regression (RH-LWR), is demonstrated through one-shot learning on a real Series Elastic Actuator controlling a pendulum.

Robust designs for Poisson regression models

We consider the problem of how to construct robust designs for Poisson regression models. An analytical expression is derived for robust designs for first-order Poisson regression models where uncertainty exists in the prior parameter estimates. Given certain constraints in the methodology, it may be necessary to extend the robust designs for implementation in practical experiments. With these extensions, our methodology constructs designs which perform similarly, in terms of estimation, to current techniques, and offers the solution in a more timely manner. We further apply this analytic result to cases where uncertainty exists in the linear predictor. The application of this methodology to practical design problems such as screening experiments is explored. Given the minimal prior knowledge that is usually available when conducting such experiments, it is recommended to derive designs robust across a variety of systems. However, incorporating such uncertainty into the design process can be a computationally intense exercise. Hence, our analytic approach is explored as an alternative.

Predicting residue-wise contact orders in proteins by support vector regression

Background The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Robust designs for Poisson regression models

We consider the problem of how to construct robust designs for Poisson regression models. An analytical expression is derived for robust designs for first-order Poisson regression models where uncertainty exists in the prior parameter estimates. Given certain constraints in the methodology, it may be necessary to extend the robust designs for implementation in practical experiments. With these extensions, our methodology constructs designs which perform similarly, in terms of estimation, to current techniques, and offers the solution in a more timely manner. We further apply this analytic result to cases where uncertainty exists in the linear predictor. The application of this methodology to practical design problems such as screening experiments is explored. Given the minimal prior knowledge that is usually available when conducting such experiments, it is recommended to derive designs robust across a variety of systems. However, incorporating such uncertainty into the design process can be a computationally intense exercise. Hence, our analytic approach is explored as an alternative.

Operational performance of low-cost carriers and international airlines : New evidence using a bootstrap truncated regression

Between 2001 and 2005, the US airline industry faced financial turmoil. At the same time, the European airline industry entered a period of substantive deregulation. This period witnessed opportunities for low-cost carriers to become more competitive in the market as a result of these combined events. To help assess airline performance in the aftermath of these events, this paper provides new evidence of technical efficiency for 42 national and international airlines in 2006 using the data envelopment analysis (DEA) bootstrap approach first proposed by Simar and Wilson (J Econ, 136:31-64, 2007). In the first stage, technical efficiency scores are estimated using a bootstrap DEA model. In the second stage, a truncated regression is employed to quantify the economic drivers underlying measured technical efficiency. The results highlight the key role played by non-discretionary inputs in measures of airline technical efficiency.

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.