A multi-hypothesis topological SLAM approach for loop closing on edge-ordered graphs

We present a method for topological SLAM that specifically targets loop closing for edge-ordered graphs. Instead of using a heuristic approach to accept or reject loop closing, we propose a probabilistically grounded multi-hypothesis technique that relies on the incremental construction of a map/state hypothesis tree. Loop closing is introduced automatically within the tree expansion, and likely hypotheses are chosen based on their posterior probability after a sequence of sensor measurements. Careful pruning of the hypothesis tree keeps the growing number of hypotheses under control and a recursive formulation reduces storage and computational costs. Experiments are used to validate the approach.

Test of the enemy release hypothesis: the native magpie moth prefers a native fireweed (Senecio pinnatifolius) to its introduced congener (S.Madagascariensis)

Abstract The enemy release hypothesis predicts that native herbivores will either prefer or cause more damage to native than introduced plant species. We tested this using preference and performance experiments in the laboratory and surveys of leaf damage caused by the magpie moth Nyctemera amica on a co-occuring native and introduced species of fireweed (Senecio) in eastern Australia. In the laboratory, ovipositing females and feeding larvae preferred the native S. pinnatifolius over the introduced S. madagascariensis. Larvae performed equally well on foliage of S. pinnatifolius and S. madagascariensis: pupal weights did not differ between insects reared on the two species, but growth rates were significantly faster on S. pinnatifolius. In the field, foliage damage was significantly greater on native S. pinnatifolius than introduced S. madagascariensis. These results support the enemy release hypothesis, and suggest that the failure of native consumers to switch to introduced species contributes to their invasive success. Both plant species experienced reduced, rather than increased, levels of herbivory when growing in mixed populations, as opposed to pure stands in the field; thus, there was no evidence that apparent competition occurred.

Age-related maculopathy : linking aetiology and pathophysiological changes to the ischaemia hypothesis

Age-related maculopathy (ARM) has remained a challenging topic with respect to its aetiology, pathomechanisms, early detection and treatment since the late 19th century when it was first described as its own entity. ARM was previously considered an inflammatory disease, a degenerative disease, a tumor and as the result of choroidal hemodynamic disturbances and ischaemia. The latter processes have been repeatedly suggested to have a key role in its development and progression. In vivo experiments under hypoxic conditions could be models for the ischaemic deficits in ARM. Recent research has also linked ARM with gene polymorphisms. It is however unclear what triggers a person's gene susceptibility. In this manuscript, a linking hypothesis between aetiological factors including ischaemia and genetics and the development of early clinicopathological changes in ARM is proposed. New clinical psychophysical and electrophysiological tests are introduced that can detect ARM at an early stage. Models of early ARM based upon hemodynamic, photoreceptor and post-receptoral deficits are described and the mechanisms by which ischaemia may be involved as a final common pathway are considered. In neovascular age-related macular degeneration (neovascular AMD), ischaemia is thought to promote release of vascular endothelial growth factor (VEGF) which induces chorioretinal neovascularisation. VEGF is critical in the maintenance of the healthy choriocapillaris. In the final section of the manuscript the documentation of the effect of new anti-VEGF treatments on retinal function in neovascular AMD is critically viewed.

Stochastic modelling of financial time series with memory and multifractal scaling

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.

The ultra-low friction of the articular surface is pH-dependent and is built on a hydrophobic underlay including a hypothesis on joint lubrication mechanism

In this study, the influence of pH on interfacial energy distributed over the phospholipids-bilayer surface model and the effect of hydrophobicity on coefficient of friction (f) were investigated by using microelectrophoresis. An important clinical implication of deficiency in hydrophobicity is the loss of phospholipids that is readily observed in osteoarthritis joints. This paper establishes the influence of pH on interfacial energy upon an increase f, which might be associated with a decrease of hydrophobicity of the articular surface.

Population structure of Bactrocera dorsalis s.s., B. papayae and B. philippinensis (Diptera: Tephritidae) in southeast Asia: evidence for a single species hypothesis using mitochondrial DNA and wingshape data

Background Bactrocera dorsalis s.s. is a pestiferous tephritid fruit fly distributed from Pakistan to the Pacific, with the Thai/Malay peninsula its southern limit. Sister pest taxa, B. papayae and B. philippinensis, occur in the southeast Asian archipelago and the Philippines, respectively. The relationship among these species is unclear due to their high molecular and morphological similarity. This study analysed population structure of these three species within a southeast Asian biogeographical context to assess potential dispersal patterns and the validity of their current taxonomic status. Results Geometric morphometric results generated from 15 landmarks for wings of 169 flies revealed significant differences in wing shape between almost all sites following canonical variate analysis. For the combined data set there was a greater isolation-by-distance (IBD) effect under a ‘non-Euclidean’ scenario which used geographical distances within a biogeographical ‘Sundaland context’ (r2 = 0.772, P < 0.0001) as compared to a ‘Euclidean’ scenario for which direct geographic distances between sample sites was used (r2 = 0.217, P < 0.01). COI sequence data were obtained for 156 individuals and yielded 83 unique haplotypes with no correlation to current taxonomic designations via a minimum spanning network. BEAST analysis provided a root age and location of 540kya in northern Thailand, with migration of B. dorsalis s.l. into Malaysia 470kya and Sumatra 270kya. Two migration events into the Philippines are inferred. Sequence data revealed a weak but significant IBD effect under the ‘non-Euclidean’ scenario (r2 = 0.110, P < 0.05), with no historical migration evident between Taiwan and the Philippines. Results are consistent with those expected at the intra-specific level. Conclusions Bactrocera dorsalis s.s., B. papayae and B. philippinensis likely represent one species structured around the South China Sea, having migrated from northern Thailand into the southeast Asian archipelago and across into the Philippines. No migration is apparent between the Philippines and Taiwan. This information has implications for quarantine, trade and pest management.

Challenging the female underperformance hypothesis

In their recent review of prior studies examining firm performance, Klapper and Parker (2010, p.7) conclude that “women entrepreneurs tend to underperform relative to their male counterparts.” However, Robb and Watson (2011) argue that much of this prior research is based on inappropriate performance measures and/or does not adequately control (due to data limitations) for important demographic differences. Given the conflicting findings reported in the literature, the aim of this study is to replicate the study by Robb and Watson (2011) to see if their findings can be generalized to another geographical location. Our results, based on an analysis of 209 female-owned and 263 male-owned young Australian firms, confirm those of Robb and Watson (2011). We believe that this outcome should help dispel the female underperformance myth; which if left unchallenged could result in inappropriate policy decisions and, more importantly, could discourage women from establishing new ventures.

The creative instability hypothesis

This paper provides an analysis of why many ‘stars’ tend to fade away rather than enjoying ongoing branding advantages from their reputations. We propose a theory of market overshooting in creative industries that is based on Schumpeterian competition between producers to maintain the interest of boundedly rational fans. As creative producers compete by offering further artistic novelty, this escalation of product complexity eventually leads to overshooting. We propose this as a theory of endogenous cycles in the creative industries.

The cancer stem-cell hypothesis : its emerging role in lung cancer biology and its relevance for future therapy

The cancer stem-cell (CSC) hypothesis suggests that there is a small subset of cancer cells that are responsible for tumor initiation and growth, possessing properties such as indefinite self-renewal, slow replication, intrinsic resistance to chemotherapy and radiotherapy, and an ability to give rise to differentiated progeny. Through the use of xenotransplantation assays, putative CSCs have been identified in many cancers, often identified by markers usually expressed in normal stem cells. This is also the case in lung cancer, and the accumulated data on side population cells, CD133, CD166, CD44 and ALDH1 are beginning to clarify the true phenotype of the lung cancer stem cell. Furthermore, it is now clear that many of the pathways of normal stem cells, which guide cellular proliferation, differentiation, and apoptosis are also prominent in CSCs; the Hedgehog (Hh), Notch, and Wnt signaling pathways being notable examples. The CSC hypothesis suggests that there is a small reservoir of cells within the tumor, which are resistant to many standard therapies, and can give rise to new tumors in the form of metastases or relapses after apparent tumor regression. Therapeutic interventions that target CSC pathways are still in their infancy and clinical data of their efficacy remain limited. However Smoothened inhibitors, gamma-secretase inhibitors, anti-DLL4 antagonists, Wnt antagonists, and CBP/β-catenin inhibitors have all shown promising anticancer effects in early studies. The evidence to support the emerging picture of a lung cancer CSC phenotype and the development of novel therapeutic strategies to target CSCs are described in this review.

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.