Investigating the practicality of using radio tracking to determine home range and movements of Picathartidae

Radiotelemetry is an important tool used to aid the understanding and conservation of cryptic and rare birds. The two bird species of the family Picathartidae are little-known, secretive, forest-dwelling birds endemic to western and central Africa. In 2005, we conducted a radio-tracking trial of Grey-necked Picathartes Picathartes oreas in the Mbam Minkom Mountain Forest, southern Cameroon, using neck collar (two birds) and tail-mounted (four birds) transmitters to investigate the practicality of radio-tracking Picathartidae. Three birds with tail-mounted transmitters were successfully tracked with the fourth, though not relocated for radio tracking, resighted the following breeding season. Two of these were breeding birds that continued to provision young during radio tracking. One neck-collared bird was found dead three days after transmitter attachment and the other neither relocated nor resighted. As mortality in one bird was potentially caused by the neck collar transmitter we recommend tail-mounted transmitters in future radio-tracking studies of Picathartidae. Home ranges, shown using minimum convex polygon and kernel estimation methods, were generally small (<0.5 km(2)) and centred around breeding sites. A minimum of 60 fixes were found to be sufficient for home range estimation.

Will rising household incomes solve China's micronutrient deficiency problems?

Rapid economic growth in China has resulted in substantially improved household incomes. Diets have also changed, with a movement away from traditional foods and towards animal products and processed foods. Yet micronutrient deficiencies, particularly for calcium and vitamin A, are still widespread in China. In this research we model the determinants of the intakes of these micronutrients using household panel data, asking particularly whether continuing income increases are likely to cause the deficiencies to be overcome. Nonparametric kernel regressions and random effects panel regression models are employed. The results show a statistically significant but relatively small positive income effect on both nutrient intakes. The local availability of milk is seen to have a strong positive effect on intakes of both micronutrients. Thus, rather than relying on increasing incomes to overcome deficiencies, supplementary government policies, such as school milk programmes, may be warranted.

Adaptive approximate Bayesian computation

Sequential techniques can enhance the efficiency of the approximate Bayesian computation algorithm, as in Sisson et al.'s (2007) partial rejection control version. While this method is based upon the theoretical works of Del Moral et al. (2006), the application to approximate Bayesian computation results in a bias in the approximation to the posterior. An alternative version based on genuine importance sampling arguments bypasses this difficulty, in connection with the population Monte Carlo method of Cappe et al. (2004), and it includes an automatic scaling of the forward kernel. When applied to a population genetics example, it compares favourably with two other versions of the approximate algorithm.

Effect of fluid mechanical stresses and plasma constituents on aggregation of LDL

LDL aggregates when exposed to even moderate fluid mechanical stresses in the laboratory, yet its half-life in the circulation is 2-3 days, implying that little aggregation occurs. LDL may be protected from aggregation in vivo by components of plasma, or by a qualitative difference in flows. Previous studies have shown that HDL and albumin inhibit the aggregation induced by vortexing. Using a more reproducible method of inducing aggregation and assessing aggregation both spectrophotometrically and by sedimentation techniques, we showed that at physiological concentrations, albumin is the more effective inhibitor, and that aggregation is substantially but not completely inhibited in plasma. Heat denatured and fatty-acid-stripped albumin were more effective inhibitors than normal albumin, supporting the idea that hydrophobic interactions are involved. Aggregation of LDL in a model reproducing several aspects of flow in the circulation was 200-fold slower, but was still inhibited by HDL and albumin, suggesting similar mechanisms are involved. Within the sensitivity of our technique, LDL aggregation did not occur in plasma exposed to these flows.jlr Thus, as a result of the characteristics of blood flow and the inhibitory effects of plasma components, particularly albumin, LDL aggregation is unlikely to occur within the circulation.

Inverse problems in dynamic cognitive modeling

Inverse problems for dynamical system models of cognitive processes comprise the determination of synaptic weight matrices or kernel functions for neural networks or neural/dynamic field models, respectively. We introduce dynamic cognitive modeling as a three tier top-down approach where cognitive processes are first described as algorithms that operate on complex symbolic data structures. Second, symbolic expressions and operations are represented by states and transformations in abstract vector spaces. Third, prescribed trajectories through representation space are implemented in neurodynamical systems. We discuss the Amari equation for a neural/dynamic field theory as a special case and show that the kernel construction problem is particularly ill-posed. We suggest a Tikhonov-Hebbian learning method as regularization technique and demonstrate its validity and robustness for basic examples of cognitive computations.

Probability density function estimation using orthogonal forward regression

Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density estimates. The proposed algorithm incrementally minimises a leave-one-out test error score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights are finally updated using the multiplicative nonnegative quadratic programming algorithm, which has the ability to reduce the model size further. Except for the kernel width, the proposed algorithm has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Two examples are used to demonstrate the ability of this regression-based approach to effectively construct a sparse kernel density estimate with comparable accuracy to that of the full-sample optimised Parzen window density estimate.

Parallel Monte Carlo approach for integration of the rendering equation

This paper is addressed to the numerical solving of the rendering equation in realistic image creation. The rendering equation is integral equation describing the light propagation in a scene accordingly to a given illumination model. The used illumination model determines the kernel of the equation under consideration. Nowadays, widely used are the Monte Carlo methods for solving the rendering equation in order to create photorealistic images. In this work we consider the Monte Carlo solving of the rendering equation in the context of the parallel sampling scheme for hemisphere. Our aim is to apply this sampling scheme to stratified Monte Carlo integration method for parallel solving of the rendering equation. The domain for integration of the rendering equation is a hemisphere. We divide the hemispherical domain into a number of equal sub-domains of orthogonal spherical triangles. This domain partitioning allows to solve the rendering equation in parallel. It is known that the Neumann series represent the solution of the integral equation as a infinity sum of integrals. We approximate this sum with a desired truncation error (systematic error) receiving the fixed number of iteration. Then the rendering equation is solved iteratively using Monte Carlo approach. At each iteration we solve multi-dimensional integrals using uniform hemisphere partitioning scheme. An estimate of the rate of convergence is obtained using the stratified Monte Carlo method. This domain partitioning allows easy parallel realization and leads to convergence improvement of the Monte Carlo method. The high performance and Grid computing of the corresponding Monte Carlo scheme are discussed.

Orthogonal-least-squares regression: A unified approach for data modelling

A unified approach is proposed for data modelling that includes supervised regression and classification applications as well as unsupervised probability density function estimation. The orthogonal-least-squares regression based on the leave-one-out test criteria is formulated within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic data-modelling approach for constructing parsimonious kernel models with excellent generalisation capability. (C) 2008 Elsevier B.V. All rights reserved.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

On the spectra of certain integro-differential-delay problems with applications in neurodynamics

We investigate the spectrum of certain integro-differential-delay equations (IDDEs) which arise naturally within spatially distributed, nonlocal, pattern formation problems. Our approach is based on the reformulation of the relevant dispersion relations with the use of the Lambert function. As a particular application of this approach, we consider the case of the Amari delay neural field equation which describes the local activity of a population of neurons taking into consideration the finite propagation speed of the electric signal. We show that if the kernel appearing in this equation is symmetric around some point a= 0 or consists of a sum of such terms, then the relevant dispersion relation yields spectra with an infinite number of branches, as opposed to finite sets of eigenvalues considered in previous works. Also, in earlier works the focus has been on the most rightward part of the spectrum and the possibility of an instability driven pattern formation. Here, we numerically survey the structure of the entire spectra and argue that a detailed knowledge of this structure is important within neurodynamical applications. Indeed, the Amari IDDE acts as a filter with the ability to recognise and respond whenever it is excited in such a way so as to resonate with one of its rightward modes, thereby amplifying such inputs and dampening others. Finally, we discuss how these results can be generalised to the case of systems of IDDEs.

Probability density estimation with tunable kernels using orthogonal forward regression

A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and diagonal covariance matrix, by minimizing a leave-one-out test criterion. The kernel mixing weights of the constructed sparse density estimate are finally updated using the multiplicative nonnegative quadratic programming algorithm to ensure the nonnegative and unity constraints, and this weight-updating process additionally has the desired ability to further reduce the model size. The proposed tunable-kernel model has advantages, in terms of model generalization capability and model sparsity, over the standard fixed-kernel model that restricts kernel centers to the training data points and employs a single common kernel variance for every kernel. On the other hand, it does not optimize all the model parameters together and thus avoids the problems of high-dimensional ill-conditioned nonlinear optimization associated with the conventional finite mixture model. Several examples are included to demonstrate the ability of the proposed novel tunable-kernel model to effectively construct a very compact density estimate accurately.

Sting jets in simulations of a real cyclone by two mesoscale models

The existence of sting jets as a potential source of damaging surface winds during the passage of extratropical cyclones has recently been recognized However, there are still very few published studies on the subject Furthermore, although ills known that other models are capable of reproducing sting jets, in the published literature only one numerical model [the Met Office Unified Model (MetUM)] has been used to numerically analyze these phenomena This article alms to improve our understanding of the processes that contribute to the development of sting jets and show that model differences affect the evolution of modeled sting jets A sting jet event during the passage of a cyclone over the United Kingdom on 26 February 2002 has been simulated using two mesoscale models namely the MetUM and the Consortium for Small Scale Modeling (COSMO) model to compare their performance Given the known critical importance of vertical resolution in the simulation of sting jets the vertical resolution of both models has been enhanced with respect to their operational versions Both simulations have been verified against surface measurements of maximum gusts, satellite imagery and Met Office operational synoptic analyses, as well as operational analyses from the ECMWF It is shown that both models are capable of reproducing sting jets with similar, though not identical. features Through the comparison of the results from these two models, the relevance of physical mechanisms, such as evaporative cooling and the release of conditional symmetric instability, in the generation and evolution of sting jets is also discussed

Computer generation and quantitative morphometric analysis of virtual neurons

An important goal in computational neuroanatomy is the complete and accurate simulation of neuronal morphology. We are developing computational tools to model three-dimensional dendritic structures based on sets of stochastic rules. This paper reports an extensive, quantitative anatomical characterization of simulated motoneurons and Purkinje cells. We used several local and global algorithms implemented in the L-Neuron and ArborVitae programs to generate sets of virtual neurons. Parameters statistics for all algorithms were measured from experimental data, thus providing a compact and consistent description of these morphological classes. We compared the emergent anatomical features of each group of virtual neurons with those of the experimental database in order to gain insights on the plausibility of the model assumptions, potential improvements to the algorithms, and non-trivial relations among morphological parameters. Algorithms mainly based on local constraints (e.g., branch diameter) were successful in reproducing many morphological properties of both motoneurons and Purkinje cells (e.g. total length, asymmetry, number of bifurcations). The addition of global constraints (e.g., trophic factors) improved the angle-dependent emergent characteristics (average Euclidean distance from the soma to the dendritic terminations, dendritic spread). Virtual neurons systematically displayed greater anatomical variability than real cells, suggesting the need for additional constraints in the models. For several emergent anatomical properties, a specific algorithm reproduced the experimental statistics better than the others did. However, relative performances were often reversed for different anatomical properties and/or morphological classes. Thus, combining the strengths of alternative generative models could lead to comprehensive algorithms for the complete and accurate simulation of dendritic morphology.

The Prony fitting method for digital deconvolution problem

The Prony fitting theory is applied in this paper to solve the deconvolution problem. There are two cases in deconvolution in which unstable solution is easy to appear. They are: (1)the frequency band of known kernel is more narraw than that of the unknown kernel; (2) there exists noise. These two cases are studied thoroughly and the effectiveness of Prony fitting method is showed. Finally, this method is simulated in computer. The simulation results are compared with those obtained by using FFT method directly.

Generalised Dirichelt-to-Neumann map in time dependent domains

We study the heat, linear Schrodinger and linear KdV equations in the domain l(t) < x < ∞, 0 < t < T, with prescribed initial and boundary conditions and with l(t) a given differentiable function. For the first two equations, we show that the unknown Neumann or Dirichlet boundary value can be computed as the solution of a linear Volterra integral equation with an explicit weakly singular kernel. This integral equation can be derived from the formal Fourier integral representation of the solution. For the linear KdV equation we show that the two unknown boundary values can be computed as the solution of a system of linear Volterra integral equations with explicit weakly singular kernels. The derivation in this case makes crucial use of analyticity and certain invariance properties in the complex spectral plane. The above Volterra equations are shown to admit a unique solution.