Geometric approximations towards free specular comic shading

We extend the standard solution to comic rendering with a comic-style specular component. To minimise the computational overhead associated with this extension, we introduce two optimising approximations; the perspective correction angle and the vertex face-orientation measure. Both of these optimisations are generally applicable, but they are especially well suited for applications where a physically correct lighting simulation is not required. Using our optimisations we achieve performances comparable to the standard solution. As our approximations favour large models, we even outperform the standard approach for models consisting of 10,000 triangles or more, which we can render exceeding 40 frames per second, including the specular component.

Cutting angle method - a tool for constrained global optimization

Cutting angle method (CAM) is a deterministic global optimization technique applicable to Lipschitz functions f: Rn → R. The method builds a sequence of piecewise linear lower approximations to the objective function f. The sequence of solutions to these relaxed problems converges to the global minimum of f. This article adapts CAM to the case of linear constraints on the feasible domain. We show how the relaxed problems are modified, and how the numerical efficiency of solving these problems can be preserved. A number of numerical experiments confirms the improved numerical efficiency.

Methods of construction of OWA operators from data

This paper investigates the problem of obtaining the weights of the ordered weighted aggregation (OWA) operators from observations. The problem is formulated as a restricted least squares and uniform approximation problems. We take full advantage of the linearity of the problem. In the former case, a well known technique of non-negative least squares is used. In a case of uniform approximation, we employ a recently developed cutting angle method of global optimisation. Both presented methods give results superior to earlier approaches, and do not require complicated nonlinear constructions. Additional restrictions, such as degree of orness of the operator, can be easily introduced

Review of applications of the Cutting Angle methods

The theory of abstract convexity provides us with the necessary tools for building accurate one-sided approximations of functions. Cutting angle methods have recently emerged as a tool for global optimization of families of abstract convex functions. Their applicability have been subsequently extended to other problems, such as scattered data interpolation. This paper reviews three different applications of cutting angle methods, namely global optimization, generation of nonuniform random variates and multivatiate interpolation.

Modelling the public health consequences of cannabis use in Australia

Aims: To detail and validate a simulation model that describes the dynamics of cannabis use, including its probable causal relationships with schizophrenia, road traffic accidents (RTA) and heroin/poly-drug use (HPU).

Methods: A Markov model with 17 health-states was constructed. Annual cycles were used to simulate the initiation of cannabis use, progression in use, reduction and complete remission. The probabilities of transition between health-states were derived from observational data. Following 10-year-old Australian children for 90 years, the model estimated age-specific prevalence for cannabis use. By applying the relative risks according to the extent of cannabis use, the age-specific prevalence of schizophrenia and HPU, and the annual RTA incidence and fatality rate were also estimated. Predictive validity of the model was tested by comparing modelled outputs with data from other credible sources. Sensitivity and scenario analyses were conducted to evaluate technical validity and face validity.

Results: The estimated cannabis use prevalence in individuals aged 10-65 years was 12.2% which comprised 27.4% weekly and 18.0% daily users. The modelled prevalence and age profile were comparable to the reported cross-sectional data. The model also provided good approximations to the prevalence of schizophrenia (Modelled: 4.75/1,000 persons vs Observed: 4.6/1,000 persons), HPU (3.2/1,000 vs 3.1/1,000) and the RTA fatality rate (8.1 per 100,000 vs 8.2 per 100,000). Sensitivity analyses and scenario analysis provided expected and explainable trends.

Conclusions: The validated model provides a valuable tool to assess the likely effectiveness and cost-effectiveness of interventions designed to affect patterns of cannabis use. It can be updated as new data becomes available and/or applied to other countries.

Scales of policy intervention : problematising high poverty contexts in South Australia.

Education policy intervention for schools in high poverty neighbourhoods has focused on the capacity of local schools to make a difference and on the kinds of co-ordinated human services provision that might support individual families with “high needs”. In this paper I suggest that a more detailed analysis of “the problem” represented in such schools might yield a richer and more integrated policy approach. I use the notion of “scale”, arbitrary and imperfect approximations of spheres of activity, and apply it to a specific context in Adelaide, South Australia, to demonstrate the connections between the local school and factors which impinge on its capacities to make a positive difference. I suggest that the implication of the analysis is a more holistic approach to policy.

Analytic study on pure bending of metal sheets

In this work, analytical models of pure bending are developed to simulate a particular type of bend test and to determine possible errors arising from approximations used in analyzing experimental data. Analytical models proposed for steels include a theoretical solution of pure bending and a series of finite element models, based on the von Mises yield function, are subjected to different stress and strain conditions. The results show that for steel sheets the difference between measured and calculated results of the moment-curvature behaviour is small and the numerical results from the finite element models indicate that experimental results obtained from the test are acceptable in the range of the pure bending operation. Further for magnesium alloys, which exhibit unsymmetrical yielding, the algorithm of the yield function with a linear isotropic hardening model is implemented by programming a user subroutine in Abaqus for bending simulations of magnesium. The simulations using the proposed user subroutine extract better results than those using the von Mises yield function.

Contextually Learnt Detection of Unusual Motion-Based Behaviour in Crowded Public Spaces

In this paper we are interested in analyzing behaviour in crowded publicplaces at the level of holistic motion. Our aim is to learn, without user input, strong scene priors or labelled data, the scope of ‘‘normal behaviour’’ for a particular scene and thus alert to novelty in unseen footage. The first contribution is a low-level motion model based on what we term tracklet primitives, which are scenespecific elementary motions. We propose a clustering-based algorithm for tracklet estimation from local approximations to tracks of appearance features. This is followed by two methods for motion novelty inference from tracklet primitives: (a) an approach based on a non-hierarchial ensemble of Markov chains as a means of capturing behavioural characteristics at different scales, and (b) a more flexible alternative which exhibits a higher generalizing power by accounting for constraints introduced by intentionality and goal-oriented planning of human motion in a particular scene. Evaluated on a 2 h long video of a busy city marketplace, both algorithms are shown to be successful at inferring unusual behaviour, the latter model achieving better performance for novelties at a larger spatial scale.

Influence of orientation on twin nucleation and growth at low strains in a magnesium alloy

The resolved shear stress is believed to play an important role in twin formation. The present study tests this idea for an extruded magnesium alloy by examining "tension" twinning in different grain orientations. Electron backscatter diffraction analysis is employed for alloy AZ31 tested in compression along the extrusion axis to strains between 0.008 and 0.015. For heavily twinned grains, it is seen that twinning occurs on 2.3 twin systems per grain on average. The active systems are also most commonly those with, or very near to, the highest Schmid factor. The most active system in multiply twinned grains accounts on average for ∼0.6 of the twinning events. In addition, it is found that the twin habit plane falls within 6° of the K1 plane. Orientations with the highest Schmid factors (0.45-0.5) for twinning display twin aspect ratios greater by ∼40% and twin number densities greater by ∼10 times than orientations with maximum Schmid factors for twinning of 0.15-0.2. Thus the Schmid factor for twinning is seen to affect nucleation more than thickening in the present material. Viscoplastic crystal plasticity simulations are employed to obtain approximations for the resolved shear stress. Both the twin aspect ratio and number density correlate quite well with this term. The effect of the former can be assumed to be linear and that of the latter follows a power law with exponent ∼13. Increased aspect ratios and number densities are seen at low Schmid factors and this may relate to stress fluctuations, caused most probably in the present material by the stress fields at the tips of blocked twins. Overall, it is evident that the dominance of twinning on high Schmid factor systems is preserved at the low strains examined in the present work, despite the stress fluctuations known to be present. © 2014 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

New tools for understanding the local asymptotic power of panel unit root tests

Abstract Motivated by the previously documented discrepancy between actual and predicted power, the present paper provides new tools for analyzing the local asymptotic power of panel unit root tests. These tools are appropriate in general when considering panel data with a dominant autoregressive root of the form ^ρi=1+^ciN-^κT-^τ, where i=1,...,N indexes the cross-sectional units, T is the number of time periods and ^ci is a random local-to-unity parameter. A limit theory for the sample moments of such panel data is developed and is shown to involve infinite-order series expansions in the moments of ^ci, in which existing theories can be seen as mere first-order approximations. The new theory is applied to study the asymptotic local power functions of some known test statistics for a unit root. These functions can be expressed in terms of the expansions in the moments of ^ci, and include existing local power functions as special cases. Monte Carlo evidence is provided to suggest that the new results go a long way toward bridging the gap between actual and predicted power.

Small-variance asymptotics for bayesian nonparametric models with constraints

The users often have additional knowledge when Bayesian nonparametric models (BNP) are employed, e.g. for clustering there may be prior knowledge that some of the data instances should be in the same cluster (must-link constraint) or in different clusters (cannot-link constraint), and similarly for topic modeling some words should be grouped together or separately because of an underlying semantic. This can be achieved by imposing appropriate sampling probabilities based on such constraints. However, the traditional inference technique of BNP models via Gibbs sampling is time consuming and is not scalable for large data. Variational approximations are faster but many times they do not offer good solutions. Addressing this we present a small-variance asymptotic analysis of the MAP estimates of BNP models with constraints. We derive the objective function for Dirichlet process mixture model with constraints and devise a simple and efficient K-means type algorithm. We further extend the small-variance analysis to hierarchical BNP models with constraints and devise a similar simple objective function. Experiments on synthetic and real data sets demonstrate the efficiency and effectiveness of our algorithms.

Statistical modelling of artificial neural network for sorting temporally synchronous spikes

Artificial neural network (ANN) models are able to predict future events based on current data. The usefulness of an ANN lies in the capacity of the model to learn and adjust the weights following previous errors during training. In this study, we carefully analyse the existing methods in neuronal spike sorting algorithms. The current methods use clustering as a basis to establish the ground truths, which requires tedious procedures pertaining to feature selection and evaluation of the selected features. Even so, the accuracy of clusters is still questionable. Here, we develop an ANN model to specially address the present drawbacks and major challenges in neuronal spike sorting. New enhancements are introduced into the conventional backpropagation ANN for determining the network weights, input nodes, target node, and error calculation. Coiflet modelling of noise is employed to enhance the spike shape features and overshadow noise. The ANN is used in conjunction with a special spiking event detection technique to prioritize the targets. The proposed enhancements are able to bolster the training concept, and on the whole, contributing to sorting neuronal spikes with close approximations.