Texture analysis and classification using deterministic tourist walk

In this paper, we present a study on a deterministic partially self-avoiding walk (tourist walk), which provides a novel method for texture feature extraction. The method is able to explore an image on all scales simultaneously. Experiments were conducted using different dynamics concerning the tourist walk. A new strategy, based on histograms. to extract information from its joint probability distribution is presented. The promising results are discussed and compared to the best-known methods for texture description reported in the literature. (C) 2009 Elsevier Ltd. All rights reserved.

Texture analysis using graphs generated by deterministic partially self-avoiding walks

Texture is one of the most important visual attributes for image analysis. It has been widely used in image analysis and pattern recognition. A partially self-avoiding deterministic walk has recently been proposed as an approach for texture analysis with promising results. This approach uses walkers (called tourists) to exploit the gray scale image contexts in several levels. Here, we present an approach to generate graphs out of the trajectories produced by the tourist walks. The generated graphs embody important characteristics related to tourist transitivity in the image. Computed from these graphs, the statistical position (degree mean) and dispersion (entropy of two vertices with the same degree) measures are used as texture descriptors. A comparison with traditional texture analysis methods is performed to illustrate the high performance of this novel approach. (C) 2011 Elsevier Ltd. All rights reserved.

Density-Profile Processes Describing Biological Signaling Networks: Almost Sure Convergence to Deterministic Trajectories

We introduce jump processes in R(k), called density-profile processes, to model biological signaling networks. Our modeling setup describes the macroscopic evolution of a finite-size spin-flip model with k types of spins with arbitrary number of internal states interacting through a non-reversible stochastic dynamics. We are mostly interested on the multi-dimensional empirical-magnetization vector in the thermodynamic limit, and prove that, within arbitrary finite time-intervals, its path converges almost surely to a deterministic trajectory determined by a first-order (non-linear) differential equation with explicit bounds on the distance between the stochastic and deterministic trajectories. As parameters of the spin-flip dynamics change, the associated dynamical system may go through bifurcations, associated to phase transitions in the statistical mechanical setting. We present a simple example of spin-flip stochastic model, associated to a synthetic biology model known as repressilator, which leads to a dynamical system with Hopf and pitchfork bifurcations. Depending on the parameter values, the magnetization random path can either converge to a unique stable fixed point, converge to one of a pair of stable fixed points, or asymptotically evolve close to a deterministic orbit in Rk. We also discuss a simple signaling pathway related to cancer research, called p53 module.

Learning how to engage students online in hard times

In a context of financial restraint and enterprising university managers, teacher-researchers have reason to be sceptical about the trend towards online teaching and away from learning for its own sake. This article departs from both economic and technological determinism and turns instead to ideas about technology embedded in social and political institutions. Activity theory offers a useful means of analysing such embeddedness. Its Marxian assumptions about human nature specify a non-deterministic approach to technology. Its dynamic model of the subjects, tools, and objects of activity within a context of rules, a community, and a division of labour helps to specify aspects of the authors process of learning how to use electronic conferencing effectively. A full deployment of activity theory would also analyse the activity of students. Here the evidence comes mainly from the activity of researcher-teachers engaging greater activity among students. The numbers of students involved precludes reliable quantitative analysis but qualitative evidence from students does support conclusions about researcher-teachers learning how to make best use of electronic conferencing.

Global optimisation of neural networks using a deterministic hybrid approach

Selection of the topology of a neural network and correct parameters for the learning algorithm is a tedious task for designing an optimal artificial neural network, which is smaller, faster and with a better generalization performance. In this paper we introduce a recently developed cutting angle method (a deterministic technique) for global optimization of connection weights. Neural networks are initially trained using the cutting angle method and later the learning is fine-tuned (meta-learning) using conventional gradient descent or other optimization techniques. Experiments were carried out on three time series benchmarks and a comparison was done using evolutionary neural networks. Our preliminary experimentation results show that the proposed deterministic approach could provide near optimal results much faster than the evolutionary approach.

Projecting the trend in the Canadian gender wage gap 2001-2031: will an increase in female education acquisition and commitment be enough?

The paper projects the gender wage gap for 25-64 year-olds in Canada over the period 2001-2031. The empirical analysis uses the Survey of Labour and Income Dynamics together with Statistics Canada demographic projections. The methodology combines the population projections with assumptions relating to the evolution of educational attainment in order to first project the future distribution of human capital skills and, based on these projections, the future size of the gender wage gap. The projections suggest continued gender wage convergence produced by changing skills characteristics. However, a substantial pay gap will remain in 2031.

The successful school: a genuine trend or a statistical artifice?

Schools are increasingly being expected to make improvements based on data about students' learning outcomes. Such an expectation implies that principals, teachers and key personnel within systems can read and act upon the data available. There is evidence, however, that many people have poor understanding of statistical information, and that many factors inside and outside the school have an effect on students' outcomes. This study considers one primary school's data from statewide testing programs. Trends across time are considered as a basis for making judgments about the school's performance in improving students' learning outcomes in literacy and numeracy.

Deterministic versus stochastic quantitative microbial Risk Assessment models for wastewater irrigation of food crops

Reuse of wastewater to irrigate food crops is being practiced in many parts of the world and is becoming more commonplace as the competition for, and stresses on, freshwater resources intensify. But there are risks associated with wastewater irrigation, including the possibility of transmission of pathogens causing infectious disease, to both workers in the field and to consumers buying and eating produce irrigated with wastewater. To manage these risks appropriately we need objective and quantitative estimates of them. This is typically achieved through one of two modelling approaches: deterministic or stochastic. Each parameter in a deterministic model is represented by a single value, whereas in stochastic models probability functions are used. Stochastic models are theoretically superior because they account for variability and uncertainty, but they are computationally demanding and not readily accessible to water resource and public health managers. We constructed models to estimate risk of enteric virus infection arising from the consumption of wastewater-irrigated horticultural crops (broccoli, cucumber and lettuce), and compared the resultant levels of risk between the deterministic and stochastic approaches. Several scenarios were tested for each crop, accounting for different concentrations of enteric viruses and different lengths of environmental exposure (i.e. the time between the last irrigation event and harvest, when the viruses are liable to decay or inactivation). In most situations modelled the two approaches yielded similar estimates of risk (within 1 order-of-magnitude). The two methods diverged most markedly, up to around 2 orders-of-magnitude, when there was large uncertainty associated with the estimate of virus concentration and the exposure period was short (1 day). Therefore, in some circumstances deterministic modelling may offer water resource managers a pragmatic alternative to stochastic modelling, but its usefulness as a surrogate will depend upon the level of uncertainty in the model parameters.

Do permanent shocks explain income levels? a common cycle-common trend analysis of regional income levels for China

In this paper, we analyse per capita income levels of China's three main regions: the western region, the eastern region and the central region using common cycle and common trend tests. Our main contribution is that we impose the common cycle and common trend restrictions in decomposing shocks into permanent and transitory components. We find that: (i) there is evidence for two cointegrating relationships and one common cycle; and (ii) the variance decomposition analysis of shocks provides evidence that over short horizons, permanent shocks play a large role in explaining variations in regional per capita incomes.