Rigorous bounds on the nonlinear saturation of instabilities to parallel shear flows

A novel method is presented for obtaining rigorous upper bounds on the finite-amplitude growth of instabilities to parallel shear flows on the beta-plane. The method relies on the existence of finite-amplitude Liapunov (normed) stability theorems, due to Arnol'd, which are nonlinear generalizations of the classical stability theorems of Rayleigh and Fjørtoft. Briefly, the idea is to use the finite-amplitude stability theorems to constrain the evolution of unstable flows in terms of their proximity to a stable flow. Two classes of general bounds are derived, and various examples are considered. It is also shown that, for a certain kind of forced-dissipative problem with dissipation proportional to vorticity, the finite-amplitude stability theorems (which were originally derived for inviscid, unforced flow) remain valid (though they are no longer strictly Liapunov); the saturation bounds therefore continue to hold under these conditions.

Content validation for level of use of feature rich systems: a Delphi study of electronic medical records systems

Introduction. Feature usage is a pre-requisite to realising the benefits of investments in feature rich systems. We propose that conceptualising the dependent variable 'system use' as 'level of use' and specifying it as a formative construct has greater value for measuring the post-adoption use of feature rich systems. We then validate the content of the construct as a first step in developing a research instrument to measure it. The context of our study is the post-adoption use of electronic medical records (EMR) by primary care physicians. Method. Initially, a literature review of the empirical context defines the scope based on prior studies. Having identified core features from the literature, they are further refined with the help of experts in a consensus seeking process that follows the Delphi technique. Results.The methodology was successfully applied to EMRs, which were selected as an example of feature rich systems. A review of EMR usage and regulatory standards provided the feature input for the first round of the Delphi process. A panel of experts then reached consensus after four rounds, identifying ten task-based features that would be indicators of level of use. Conclusions. To study why some users deploy more advanced features than others, theories of post-adoption require a rich formative dependent variable that measures level of use. We have demonstrated that a context sensitive literature review followed by refinement through a consensus seeking process is a suitable methodology to validate the content of this dependent variable. This is the first step of instrument development prior to statistical confirmation with a larger sample.

A generalised Bayesian instrumental variable approach under student t-distributed errors with application

Bayesian analysis is given of an instrumental variable model that allows for heteroscedasticity in both the structural equation and the instrument equation. Specifically, the approach for dealing with heteroscedastic errors in Geweke (1993) is extended to the Bayesian instrumental variable estimator outlined in Rossi et al. (2005). Heteroscedasticity is treated by modelling the variance for each error using a hierarchical prior that is Gamma distributed. The computation is carried out by using a Markov chain Monte Carlo sampling algorithm with an augmented draw for the heteroscedastic case. An example using real data illustrates the approach and shows that ignoring heteroscedasticity in the instrument equation when it exists may lead to biased estimates.

Variable binding and coreference in sentence comprehension: evidence from eye movements

The hypothesis that pronouns can be resolved via either the syntax or the discourse representation has played an important role in linguistic accounts of pronoun interpretation (e.g. Grodzinsky & Reinhart, 1993). We report the results of an eye-movement monitoring study investigating the relative timing of syntactically-mediated variable binding and discourse-based coreference assignment during pronoun resolution. We examined whether ambiguous pronouns are preferentially resolved via either the variable binding or coreference route, and in particular tested the hypothesis that variable binding should always be computed before coreference assignment. Participants’ eye movements were monitored while they read sentences containing a pronoun and two potential antecedents, a c-commanding quantified noun phrase and a non c-commanding proper name. Gender congruence between the pronoun and either of the two potential antecedents was manipulated as an experimental diagnostic for dependency formation. In two experiments, we found that participants’ reading times were reliably longer when the linearly closest antecedent mismatched in gender with the pronoun. These findings fail to support the hypothesis that variable binding is computed before coreference assignment, and instead suggest that antecedent recency plays an important role in affecting the extent to which a variable binding antecedent is considered. We discuss these results in relation to models of memory retrieval during sentence comprehension, and interpret the antecedent recency preference as an example of forgetting over time.

Adaptive nonlinear equalizer using a mixture of gaussians based on-line density estimator

This paper introduces a new adaptive nonlinear equalizer relying on a radial basis function (RBF) model, which is designed based on the minimum bit error rate (MBER) criterion, in the system setting of the intersymbol interference channel plus a co-channel interference. Our proposed algorithm is referred to as the on-line mixture of Gaussians estimator aided MBER (OMG-MBER) equalizer. Specifically, a mixture of Gaussians based probability density function (PDF) estimator is used to model the PDF of the decision variable, for which a novel on-line PDF update algorithm is derived to track the incoming data. With the aid of this novel on-line mixture of Gaussians based sample-by-sample updated PDF estimator, our adaptive nonlinear equalizer is capable of updating its equalizer’s parameters sample by sample to aim directly at minimizing the RBF nonlinear equalizer’s achievable bit error rate (BER). The proposed OMG-MBER equalizer significantly outperforms the existing on-line nonlinear MBER equalizer, known as the least bit error rate equalizer, in terms of both the convergence speed and the achievable BER, as is confirmed in our simulation study

A radial basis function network classifier to maximise leave-one-out mutual information

tWe develop an orthogonal forward selection (OFS) approach to construct radial basis function (RBF)network classifiers for two-class problems. Our approach integrates several concepts in probabilisticmodelling, including cross validation, mutual information and Bayesian hyperparameter fitting. At eachstage of the OFS procedure, one model term is selected by maximising the leave-one-out mutual infor-mation (LOOMI) between the classifier’s predicted class labels and the true class labels. We derive theformula of LOOMI within the OFS framework so that the LOOMI can be evaluated efficiently for modelterm selection. Furthermore, a Bayesian procedure of hyperparameter fitting is also integrated into theeach stage of the OFS to infer the l2-norm based local regularisation parameter from the data. Since eachforward stage is effectively fitting of a one-variable model, this task is very fast. The classifier construc-tion procedure is automatically terminated without the need of using additional stopping criterion toyield very sparse RBF classifiers with excellent classification generalisation performance, which is par-ticular useful for the noisy data sets with highly overlapping class distribution. A number of benchmarkexamples are employed to demonstrate the effectiveness of our proposed approach.

Evaluation of milk compositional variables on coagulation properties using partial least squares

The aim of this study was to investigate the effects of numerous milk compositional factors on milk coagulation properties using Partial Least Squares (PLS). Milk from herds of Jersey and Holstein-Friesian cattle was collected across the year and blended (n=55), to maximize variation in composition and coagulation. The milk was analysed for casein, protein, fat, titratable acidity, lactose, Ca2+, urea content, micelles size, fat globule size, somatic cell count and pH. Milk coagulation properties were defined as coagulation time, curd firmness and curd firmness rate measured by a controlled strain rheometer. The models derived from PLS had higher predictive power than previous models demonstrating the value of measuring more milk components. In addition to the well-established relationships with casein and protein levels, CMS and fat globule size were found to have as strong impact on all of the three models. The study also found a positive impact of fat on milk coagulation properties and a strong relationship between lactose and curd firmness, and urea and curd firmness rate, all of which warrant further investigation due to current lack of knowledge of the underlying mechanism. These findings demonstrate the importance of using a wider range of milk compositional variable for the prediction of the milk coagulation properties, and hence as indicators of milk suitability for cheese making.

Plane wave discontinuous Galerkin methods: exponential convergence of the hp-version

We consider the two-dimensional Helmholtz equation with constant coefficients on a domain with piecewise analytic boundary, modelling the scattering of acoustic waves at a sound-soft obstacle. Our discretisation relies on the Trefftz-discontinuous Galerkin approach with plane wave basis functions on meshes with very general element shapes, geometrically graded towards domain corners. We prove exponential convergence of the discrete solution in terms of number of unknowns.

A Posteriori analysis of discontinuous Galerkin schemes for systems of hyperbolic conservation laws

In this work we construct reliable a posteriori estimates for some semi- (spatially) discrete discontinuous Galerkin schemes applied to nonlinear systems of hyperbolic conservation laws. We make use of appropriate reconstructions of the discrete solution together with the relative entropy stability framework, which leads to error control in the case of smooth solutions. The methodology we use is quite general and allows for a posteriori control of discontinuous Galerkin schemes with standard flux choices which appear in the approximation of conservation laws. In addition to the analysis, we conduct some numerical benchmarking to test the robustness of the resultant estimator.

Selecting algorithms for Earth observation of climate within the European Space Agency Climate Change initiative: introduction to a special issue

This special issue is focused on the assessment of algorithms for the observation of Earth’s climate from environ- mental satellites. Climate data records derived by remote sensing are increasingly a key source of insight into the workings of and changes in Earth’s climate system. Producers of data sets must devote considerable effort and expertise to maximise the true climate signals in their products and minimise effects of data processing choices and changing sensors. A key choice is the selection of algorithm(s) for classification and/or retrieval of the climate variable. Within the European Space Agency Climate Change Initiative, science teams undertook systematic assessment of algorithms for a range of essential climate variables. The papers in the special issue report some of these exercises (for ocean colour, aerosol, ozone, greenhouse gases, clouds, soil moisture, sea surface temper- ature and glaciers). The contributions show that assessment exercises must be designed with care, considering issues such as the relative importance of different aspects of data quality (accuracy, precision, stability, sensitivity, coverage, etc.), the availability and degree of independence of validation data and the limitations of validation in characterising some important aspects of data (such as long-term stability or spatial coherence). As well as re- quiring a significant investment of expertise and effort, systematic comparisons are found to be highly valuable. They reveal the relative strengths and weaknesses of different algorithmic approaches under different observa- tional contexts, and help ensure that scientific conclusions drawn from climate data records are not influenced by observational artifacts, but are robust.

Membrane-initiated actions of estrogens in neuroendocrinology: emerging principles

Hormonal ligands for the nuclear receptor superfamily have at least two interacting mechanisms of action: 1) classical transcriptional regulation of target genes (genomic mechanisms); and 2) nongenomic actions that are initiated at the cell membrane, which could impact transcription. Although transcriptional mechanisms are increasingly well understood, membrane-initiated actions of these ligands are incompletely understood. Historically, this has led to a considerable divergence of thought in the molecular endocrine field. We have attempted to uncover principles of hormone action that are relevant to membrane-initiated actions of estrogens. There is evidence that the membrane-limited actions of hormones, particularly estrogens, involve the rapid activation of kinases and the release of calcium. Membrane actions of estrogens, which activate these rapid signaling cascades, can also potentiate nuclear transcription. These signaling cascades may occur in parallel or in series but subsequently converge at the level of modification of transcriptionally relevant molecules such as nuclear receptors and/or coactivators. In addition, other hormones or neurotransmitters may also activate cascades to crosstalk with estrogen receptor-mediated transcription. The idea of synergistic coupling between membrane-initiated and genomic actions of hormones fundamentally revises the paradigms of cell signaling in neuroendocrinology.

Non-genomic actions of estrogens and their interaction with genomic actions in the brain

Ligands for the nuclear receptor superfamily have at least two mechanisms of action: (a) classical transcriptional regulation of target genes (genomic mechanisms); and (b) non-genomic actions, which are initiated at the cell membrane, which could also impact transcription. Though transcriptional mechanisms are increasingly well understood, membrane-initiated actions of these ligands are incompletely understood. This has led to considerable debate over the physiological relevance of membrane-initiated actions of hormones versus genomic actions of hormones, with genomic actions predominating in the endocrine field. There is good evidence that the membrane-limited actions of hormones, particularly estrogens, involve the rapid activation of kinases and the release of calcium and that these are linked to physiologically relevant scenarios in the brain. We show evidence in this review, that membrane actions of estrogens, which activate these rapid signaling cascades, can also potentiate nuclear transcription in both the central nervous system and in non-neuronal cell lines. We present a theoretical scenario which can be used to understand this phenomenon. These signaling cascades may occur in parallel or in series but subsequently, converge at the modification of transcriptionally relevant molecules such as nuclear receptors and/or coactivators. In addition, other non-cognate hormones or neurotransmitters may also activate cascades to crosstalk with estrogen receptor-mediated transcription, though the relevance of this is less clear. The idea that coupling between membrane-initiated and genomic actions of hormones is a novel idea in neuroendocrinology and provides us with a unified view of hormone action in the central nervous system.