Morningness-eveningness orientation and attitude change:evidence for greater systematic processing and attitude change at optimal time-of-day

This study examines the relationship between morningness-eveningness orientation and time-of day on attitude change, and tests the hypothesis that people will be more persuaded when tested at their optimal time-of-day (i.e., morning for M-types and evening for E-types) than non-optimal time-of-day (i.e., evening for M-Types and morning for E-types). Two hundred and twenty participants read a message that contained either strong vs. weak quality counter-attitudinal arguments (anti-voluntary euthanasia) in the morning (9.00. a.m.) or in the evening (7.00. p.m.). When tested at their respective optimal time-of-day (for both M- and E-types) there was a reliable difference in attitude change between the strong vs. weak messages (indicating message processing had occurred) while there was no difference between strong vs. weak messages when tested at their non-optimal time-of-day. In addition, the amount of message-congruent thinking mediated the attitude change. The results show that M- and E-types pay greater attention to and elaborate on a persuasive message at their optimal time-of-day, and this leads to increased attitude change, compared to those tested at their non-optimal time-of-day. © 2012.

Understanding the role of self-identity in habitual risky behaviours: pedestrian road-crossing decisions across the lifespan

Self-identity as a careful pedestrian has not been fully considered in previous work on predicting intention to cross the road, or actual crossing behaviour, in non-optimal situations. Evidence suggests that self-identity may be a better predictor than attitudes in situations where decision-making styles have become habitual ways to respond. This study compared contributions of self-identity and attitudes to the prediction of intentions in two situations differing in level of habitual crossing expectation, and to crossing behaviour. Three hundred and sixty-two adults (17–92 years) completed a questionnaire measuring self-identity, attitudes, intentions, experience, social identity variables (e.g. age, gender) and personal limitations (mobility). Two hundred and five participants also completed a road-crossing simulation. Self-identity and attitude were both shown as significant independent predictors of intention in both situations. However, self-identity was less effective as a predictor in the higher risk scenario, where intention to perform the behaviour was lower, and for participants aged >75 years who had lower intention across scenarios. Self-identity strongly predicted intention to cross, which in turn predicted behaviour, but self-identity did not directly predict behaviour. Self-identity was strongly predicted by age. Implications for theories of compensation in older age and for design and targeting of pedestrian safety education are discussed.

Data visualisation with missing data: A non-linear approach

Exploratory analysis of data in all sciences seeks to find common patterns to gain insights into the structure and distribution of the data. Typically visualisation methods like principal components analysis are used but these methods are not easily able to deal with missing data nor can they capture non-linear structure in the data. One approach to discovering complex, non-linear structure in the data is through the use of linked plots, or brushing, while ignoring the missing data. In this technical report we discuss a complementary approach based on a non-linear probabilistic model. The generative topographic mapping enables the visualisation of the effects of very many variables on a single plot, which is able to incorporate far more structure than a two dimensional principal components plot could, and deal at the same time with missing data. We show that using the generative topographic mapping provides us with an optimal method to explore the data while being able to replace missing values in a dataset, particularly where a large proportion of the data is missing.

Optimal geographic diversification and firm performance:evidence from the U.K.

This paper examines the relationship between multinationality and firm performance. The analysis is based on a sample of over 400 UK multinationals, and encompasses both service sector and manufacturing sector multinationals. This paper confirms the non-linear relationship between performance and multinationality that is reported elsewhere in the literature, but offers further analysis of this relationship. Specifically, by correcting for endogeneity in the investment decision, and for shocks in productivity across countries, the paper demonstrates that the returns to multinationality are greater than those that have been reported elsewhere, and persist to higher degrees of international diversification.

Using non-parametric search algorithms to forecast daily excess stock returns

Are the learning procedures of genetic algorithms (GAs) able to generate optimal architectures for artificial neural networks (ANNs) in high frequency data? In this experimental study,GAs are used to identify the best architecture for ANNs. Additional learning is undertaken by the ANNs to forecast daily excess stock returns. No ANN architectures were able to outperform a random walk,despite the finding of non-linearity in the excess returns. This failure is attributed to the absence of suitable ANN structures and further implies that researchers need to be cautious when making inferences from ANN results that use high frequency data.

Non-aqueous silicone elastomer gels as a vaginal microbicide delivery system for the HIV-1 entry inhibitor maraviroc

Aqueous semi-solid polymeric gels, such as those based on hydroxyethylcellulose (HEC) and polyacrylic acid (e.g. Carbopol®), have a long history of use in vaginal drug delivery. However, despite their ubiquity, they often provide sub-optimal clinical performance, due to poor mucosal retention and limited solubility for poorly water-soluble actives. These issues are particularly pertinent for vaginal HIV microbicides, since many lead candidates are poorly water-soluble and where a major goal is the development of a coitally independent, once daily gel product. In this study, we report the use of a non-aqueous silicone elastomer gel for vaginal delivery of the HIV-1 entry inhibitor maraviroc. In vitro rheological, syringeability and retention studies demonstrated enhanced performance for silicone gels compared with a conventional aqueous HEC gel, while testing of the gels in the slug model confirmed a lack of mucosal irritancy. Pharmacokinetic studies following single dose vaginal administration of a maraviroc silicone gel in rhesus macaques showed higher and sustained MVC levels in vaginal fluid, vaginal tissue and plasma compared with a HEC gel containing the same maraviroc loading. The results demonstrate that non-aqueous silicone gels have potential as a formulation platform for coitally independent vaginal HIV microbicides.

Detection of signals corrupted by the combination of Gaussian and Non-Gaussian noise

The detection of signals in the presence of noise is one of the most basic and important problems encountered by communication engineers. Although the literature abounds with analyses of communications in Gaussian noise, relatively little work has appeared dealing with communications in non-Gaussian noise. In this thesis several digital communication systems disturbed by non-Gaussian noise are analysed. The thesis is divided into two main parts. In the first part, a filtered-Poisson impulse noise model is utilized to calulate error probability characteristics of a linear receiver operating in additive impulsive noise. Firstly the effect that non-Gaussian interference has on the performance of a receiver that has been optimized for Gaussian noise is determined. The factors affecting the choice of modulation scheme so as to minimize the deterimental effects of non-Gaussian noise are then discussed. In the second part, a new theoretical model of impulsive noise that fits well with the observed statistics of noise in radio channels below 100 MHz has been developed. This empirical noise model is applied to the detection of known signals in the presence of noise to determine the optimal receiver structure. The performance of such a detector has been assessed and is found to depend on the signal shape, the time-bandwidth product, as well as the signal-to-noise ratio. The optimal signal to minimize the probability of error of; the detector is determined. Attention is then turned to the problem of threshold detection. Detector structure, large sample performance and robustness against errors in the detector parameters are examined. Finally, estimators of such parameters as. the occurrence of an impulse and the parameters in an empirical noise model are developed for the case of an adaptive system with slowly varying conditions.

Non-linear hierarchical visualisation

This thesis applies a hierarchical latent trait model system to a large quantity of data. The motivation for it was lack of viable approaches to analyse High Throughput Screening datasets which maybe include thousands of data points with high dimensions. High Throughput Screening (HTS) is an important tool in the pharmaceutical industry for discovering leads which can be optimised and further developed into candidate drugs. Since the development of new robotic technologies, the ability to test the activities of compounds has considerably increased in recent years. Traditional methods, looking at tables and graphical plots for analysing relationships between measured activities and the structure of compounds, have not been feasible when facing a large HTS dataset. Instead, data visualisation provides a method for analysing such large datasets, especially with high dimensions. So far, a few visualisation techniques for drug design have been developed, but most of them just cope with several properties of compounds at one time. We believe that a latent variable model (LTM) with a non-linear mapping from the latent space to the data space is a preferred choice for visualising a complex high-dimensional data set. As a type of latent variable model, the latent trait model can deal with either continuous data or discrete data, which makes it particularly useful in this domain. In addition, with the aid of differential geometry, we can imagine the distribution of data from magnification factor and curvature plots. Rather than obtaining the useful information just from a single plot, a hierarchical LTM arranges a set of LTMs and their corresponding plots in a tree structure. We model the whole data set with a LTM at the top level, which is broken down into clusters at deeper levels of t.he hierarchy. In this manner, the refined visualisation plots can be displayed in deeper levels and sub-clusters may be found. Hierarchy of LTMs is trained using expectation-maximisation (EM) algorithm to maximise its likelihood with respect to the data sample. Training proceeds interactively in a recursive fashion (top-down). The user subjectively identifies interesting regions on the visualisation plot that they would like to model in a greater detail. At each stage of hierarchical LTM construction, the EM algorithm alternates between the E- and M-step. Another problem that can occur when visualising a large data set is that there may be significant overlaps of data clusters. It is very difficult for the user to judge where centres of regions of interest should be put. We address this problem by employing the minimum message length technique, which can help the user to decide the optimal structure of the model. In this thesis we also demonstrate the applicability of the hierarchy of latent trait models in the field of document data mining.

Addressing missing data in geochemistry: a non-linear approach

Exploratory analysis of petroleum geochemical data seeks to find common patterns to help distinguish between different source rocks, oils and gases, and to explain their source, maturity and any intra-reservoir alteration. However, at the outset, one is typically faced with (a) a large matrix of samples, each with a range of molecular and isotopic properties, (b) a spatially and temporally unrepresentative sampling pattern, (c) noisy data and (d) often, a large number of missing values. This inhibits analysis using conventional statistical methods. Typically, visualisation methods like principal components analysis are used, but these methods are not easily able to deal with missing data nor can they capture non-linear structure in the data. One approach to discovering complex, non-linear structure in the data is through the use of linked plots, or brushing, while ignoring the missing data. In this paper we introduce a complementary approach based on a non-linear probabilistic model. Generative topographic mapping enables the visualisation of the effects of very many variables on a single plot, while also dealing with missing data. We show how using generative topographic mapping also provides an optimal method with which to replace missing values in two geochemical datasets, particularly where a large proportion of the data is missing.

Sparse signal representation by adaptive non-uniform B-spline dictionaries on a compact interval

Non-uniform B-spline dictionaries on a compact interval are discussed in the context of sparse signal representation. For each given partition, dictionaries of B-spline functions for the corresponding spline space are built up by dividing the partition into subpartitions and joining together the bases for the concomitant subspaces. The resulting slightly redundant dictionaries are composed of B-spline functions of broader support than those corresponding to the B-spline basis for the identical space. Such dictionaries are meant to assist in the construction of adaptive sparse signal representation through a combination of stepwise optimal greedy techniques.

Non-Gaussian error probability in optical soliton transmission

We find the probability distribution of the fluctuating parameters of a soliton propagating through a medium with additive noise. Our method is a modification of the instanton formalism (method of optimal fluctuation) based on a saddle-point approximation in the path integral. We first solve consistently a fundamental problem of soliton propagation within the framework of noisy nonlinear Schrödinger equation. We then consider model modifications due to in-line (filtering, amplitude and phase modulation) control. It is examined how control elements change the error probability in optical soliton transmission. Even though a weak noise is considered, we are interested here in probabilities of error-causing large fluctuations which are beyond perturbation theory. We describe in detail a new phenomenon of soliton collapse that occurs under the combined action of noise, filtering and amplitude modulation. © 2004 Elsevier B.V. All rights reserved.