School of Mathematics and Statistics - Research Publications
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ItemPregnant women maintain body temperatures within safe limits during moderate-intensity aqua-aerobic classes conducted in pools heated up to 33 degrees Celsius: an observational study(AUSTRALIAN PHYSIOTHERAPY ASSOC, 2015-10)QUESTION: What is the body temperature response of healthy pregnant women exercising at moderate intensity in an aqua-aerobics class where the water temperature is in the range of 28 to 33 degrees Celsius, as typically found in community swimming pools? DESIGN: An observational study. PARTICIPANTS: One hundred and nine women in the second and third trimester of pregnancy who were enrolled in a standardised aqua-aerobics class. OUTCOME MEASURES: Tympanic temperature was measured at rest pre-immersion (T1), after 35minutes of moderate-intensity aqua-aerobic exercise (T2), after a further 10minutes of light exercise while still in the water (T3) and finally on departure from the facility (T4). The range of water temperatures in seven indoor community pools was 28.8 to 33.4 degrees Celsius. RESULTS: Body temperature increased by a mean of 0.16 degrees Celsius (SD 0.35, p<0.001) at T2, was maintained at this level at T3 and had returned to pre-immersion resting values at T4. Regression analysis demonstrated that the temperature response was not related to the water temperature (T2 r = -0.01, p = 0.9; T3 r = -0.02, p=0.9; T4 r=0.03, p=0.8). Analysis of variance demonstrated no difference in body temperature response between participants when grouped in the cooler, medium and warmer water temperatures (T2 F=0.94, p=0.40; T3 F=0.93, p=0.40; T4 F=0.70, p=0.50). CONCLUSIONS: Healthy pregnant women maintain body temperatures within safe limits during moderate-intensity aqua-aerobic exercise conducted in pools heated up to 33 degrees Celsius. The study provides evidence to inform guidelines for safe water temperatures for aqua-aerobic exercise during pregnancy.
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ItemA Bayesian method for comparing and combining binary classifiers in the absence of a gold standard.(Springer Science and Business Media LLC, 2012-07-27)BACKGROUND: Many problems in bioinformatics involve classification based on features such as sequence, structure or morphology. Given multiple classifiers, two crucial questions arise: how does their performance compare, and how can they best be combined to produce a better classifier? A classifier can be evaluated in terms of sensitivity and specificity using benchmark, or gold standard, data, that is, data for which the true classification is known. However, a gold standard is not always available. Here we demonstrate that a Bayesian model for comparing medical diagnostics without a gold standard can be successfully applied in the bioinformatics domain, to genomic scale data sets. We present a new implementation, which unlike previous implementations is applicable to any number of classifiers. We apply this model, for the first time, to the problem of finding the globally optimal logical combination of classifiers. RESULTS: We compared three classifiers of protein subcellular localisation, and evaluated our estimates of sensitivity and specificity against estimates obtained using a gold standard. The method overestimated sensitivity and specificity with only a small discrepancy, and correctly ranked the classifiers. Diagnostic tests for swine flu were then compared on a small data set. Lastly, classifiers for a genome-wide association study of macular degeneration with 541094 SNPs were analysed. In all cases, run times were feasible, and results precise. The optimal logical combination of classifiers was also determined for all three data sets. Code and data are available from http://bioinformatics.monash.edu.au/downloads/. CONCLUSIONS: The examples demonstrate the methods are suitable for both small and large data sets, applicable to the wide range of bioinformatics classification problems, and robust to dependence between classifiers. In all three test cases, the globally optimal logical combination of the classifiers was found to be their union, according to three out of four ranking criteria. We propose as a general rule of thumb that the union of classifiers will be close to optimal.
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ItemSemiglobal Practical Stability of a Class of Parameterized Networked Control Systems(IEEE, 2012-01-01)This paper studies a class of parameterized networked control systems that are designed via an emulation procedure. In the first step, a controller is designed ignoring network so that semiglobal practical stability is achieved for the closed-loop. In the second step, it is shown that if the same controller is emulated and implemented over a large class of networks, then the networked control system is also semiglobally practically asymptotically stable; in this case, the controller parameter needs to be sufficiently small and communication bandwidth sufficiently high.
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ItemAn exponential filter model predicts lightness illusions(FRONTIERS MEDIA SA, 2015-06-24)Lightness, or perceived reflectance of a surface, is influenced by surrounding context. This is demonstrated by the Simultaneous Contrast Illusion (SCI), where a gray patch is perceived lighter against a black background and vice versa. Conversely, assimilation is where the lightness of the target patch moves toward that of the bounding areas and can be demonstrated in White's effect. Blakeslee and McCourt (1999) introduced an oriented difference-of-Gaussian (ODOG) model that is able to account for both contrast and assimilation in a number of lightness illusions and that has been subsequently improved using localized normalization techniques. We introduce a model inspired by image statistics that is based on a family of exponential filters, with kernels spanning across multiple sizes and shapes. We include an optional second stage of normalization based on contrast gain control. Our model was tested on a well-known set of lightness illusions that have previously been used to evaluate ODOG and its variants, and model lightness values were compared with typical human data. We investigate whether predictive success depends on filters of a particular size or shape and whether pooling information across filters can improve performance. The best single filter correctly predicted the direction of lightness effects for 21 out of 27 illusions. Combining two filters together increased the best performance to 23, with asymptotic performance at 24 for an arbitrarily large combination of filter outputs. While normalization improved prediction magnitudes, it only slightly improved overall scores in direction predictions. The prediction performance of 24 out of 27 illusions equals that of the best performing ODOG variant, with greater parsimony. Our model shows that V1-style orientation-selectivity is not necessary to account for lightness illusions and that a low-level model based on image statistics is able to account for a wide range of both contrast and assimilation effects.
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ItemComplex cells decrease errors for the Muller-Lyer illusion in a model of the visual ventral stream(FRONTIERS RESEARCH FOUNDATION, 2014-09-24)To improve robustness in object recognition, many artificial visual systems imitate the way in which the human visual cortex encodes object information as a hierarchical set of features. These systems are usually evaluated in terms of their ability to accurately categorize well-defined, unambiguous objects and scenes. In the real world, however, not all objects and scenes are presented clearly, with well-defined labels and interpretations. Visual illusions demonstrate a disparity between perception and objective reality, allowing psychophysicists to methodically manipulate stimuli and study our interpretation of the environment. One prominent effect, the Müller-Lyer illusion, is demonstrated when the perceived length of a line is contracted (or expanded) by the addition of arrowheads (or arrow-tails) to its ends. HMAX, a benchmark object recognition system, consistently produces a bias when classifying Müller-Lyer images. HMAX is a hierarchical, artificial neural network that imitates the "simple" and "complex" cell layers found in the visual ventral stream. In this study, we perform two experiments to explore the Müller-Lyer illusion in HMAX, asking: (1) How do simple vs. complex cell operations within HMAX affect illusory bias and precision? (2) How does varying the position of the figures in the input image affect classification using HMAX? In our first experiment, we assessed classification after traversing each layer of HMAX and found that in general, kernel operations performed by simple cells increase bias and uncertainty while max-pooling operations executed by complex cells decrease bias and uncertainty. In our second experiment, we increased variation in the positions of figures in the input images that reduced bias and uncertainty in HMAX. Our findings suggest that the Müller-Lyer illusion is exacerbated by the vulnerability of simple cell operations to positional fluctuations, but ameliorated by the robustness of complex cell responses to such variance.
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ItemThe Muller-Lyer Illusion in a Computational Model of Biological Object Recognition(PUBLIC LIBRARY SCIENCE, 2013-02-15)Studying illusions provides insight into the way the brain processes information. The Müller-Lyer Illusion (MLI) is a classical geometrical illusion of size, in which perceived line length is decreased by arrowheads and increased by arrowtails. Many theories have been put forward to explain the MLI, such as misapplied size constancy scaling, the statistics of image-source relationships and the filtering properties of signal processing in primary visual areas. Artificial models of the ventral visual processing stream allow us to isolate factors hypothesised to cause the illusion and test how these affect classification performance. We trained a feed-forward feature hierarchical model, HMAX, to perform a dual category line length judgment task (short versus long) with over 90% accuracy. We then tested the system in its ability to judge relative line lengths for images in a control set versus images that induce the MLI in humans. Results from the computational model show an overall illusory effect similar to that experienced by human subjects. No natural images were used for training, implying that misapplied size constancy and image-source statistics are not necessary factors for generating the illusion. A post-hoc analysis of response weights within a representative trained network ruled out the possibility that the illusion is caused by a reliance on information at low spatial frequencies. Our results suggest that the MLI can be produced using only feed-forward, neurophysiological connections.
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ItemA minimum weight labelling method for determination of a shortest route in a non-directed network(Springer Science and Business Media LLC, 2013-01-01)
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ItemA new Bayesian approach for determining the number of components in a finite mixture(SPRINGER-VERLAG ITALIA SRL, 2015-08)
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ItemA Note on the Cops and Robber Game on Graphs Embedded in Non-Orientable Surfaces(Springer Science and Business Media LLC, 2014-01-01)
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ItemBayesian Estimation for Diagnostic Testing of Biosecurity Risk Material in the Absence of a Gold Standard when Test Data are Incomplete(SPRINGER, 2015-09)Diagnostic testing is used by biosecurity officers for the detection and identification of plant and animal pathogens, often informing high-consequence decisions such as restricting the entry of trade goods. It is rare that such tests can be considered gold standards; however, uncertainty can be reduced by using the results of other tests, measuring performance on samples of known status and incorporating prior knowledge from expert judgement. This article presents an extension to the methods of Joseph et al. (Am J Epidemiol 141:263–272, 1995), and Dendukuri and Joseph (Biometrics 57:158–167, 2001) for Bayesian estimation in the absence of a gold standard test, which allows for the use of incomplete test data. This extension is demonstrated with a novel application: the case study of myrtle rust from Holliday et al. (Plant Dis 97:828–834, 2013), which involves samples from potential biosecurity risk material on importation pathways to Australia. The samples were tested at two laboratories, and prior estimates for pathway prevalence were obtained by expert elicitation. The Bayesian estimation was based on a model with and without covariances for the test results to assess the assumption of conditional independence. The results show that pathogen prevalence, diagnostic sensitivity and diagnostic specificity can be estimated using all available data even where some samples have been subject to only one of two available tests. The results also indicate the importance of consideration of the assumption of conditional independence. The findings enable diagnostic testing laboratories and decision makers to make use of all test results and to explicitly incorporate prior knowledge to estimate pathogen prevalence and test accuracy.