Hippocampal models for simultaneous localisation and mapping on an autonomous robot

To navigate successfully in a novel environment a robot needs to be able to Simultaneously Localize And Map (SLAM) its surroundings. The most successful solutions to this problem so far have involved probabilistic algorithms, but there has been much promising work involving systems based on the workings of part of the rodent brain known as the hippocampus. In this paper we present a biologically plausible system called RatSLAM that uses competitive attractor networks to carry out SLAM in a probabilistic manner. The system can effectively perform parameter self-calibration and SLAM in onedimension. Tests in two dimensional environments revealed the inability of the RatSLAM system to maintain multiple pose hypotheses in the face of ambiguous visual input. These results support recent rat experimentation that suggest current competitive attractor models are not a complete solution to the hippocampal modelling problem.

Probabilistic visual recognition of artificial landmarks for simultaneous localization and mapping

Probabilistic robotics most often applied to the problem of simultaneous localisation and mapping (SLAM), requires measures of uncertainty to accompany observations of the environment. This paper describes how uncertainty can be characterised for a vision system that locates coloured landmarks in a typical laboratory environment. The paper describes a model of the uncertainty in segmentation, the internal cameral model and the mounting of the camera on the robot. It explains the implementation of the system on a laboratory robot, and provides experimental results that show the coherence of the uncertainty model.

The Desert of the Real in Spec Ops: The Line. A Study of Simultaneous Hyperreal Experiences by Protagonist and Player : A Study of Simultaneous Hyperreal Experiences by Protagonist and Player

As an anomaly on the market of military shooters of the 21st century, Spec Ops: The Line entails a journey of undetermined realities and modern warfare consequences. In this study, the narrative is analyzed from the perspective of Jean Baudrillard’s idea that simulations have replaced our conception of reality. Both the protagonist and the player of Spec Ops will unavoidably descend into a state of the hyperreal. They experience multiple possible realities within the game narrative and end up unable to comprehend what has transpired. The hyperreal is defined as the state in which it is impossible to discern reality from simulation. The simulation of reality has proliferated itself into being the reality, and the original has been lost. The excessive use of violence, direct approach of the player through a break with the 4th wall and a deceitful narrator contribute to this loss of reality within the game. Although the game represents simulacra, being a simulation in itself, the object of study is the coexisting state of hyperreal shared between protagonist and player when comprehending events in the game. In the end, neither part can understand or discern with any certainty what transpired within the game.

Estimating conditional volatility with neural networks

It is well known that one of the obstacles to effective forecasting of exchange rates is heteroscedasticity (non-stationary conditional variance). The autoregressive conditional heteroscedastic (ARCH) model and its variants have been used to estimate a time dependent variance for many financial time series. However, such models are essentially linear in form and we can ask whether a non-linear model for variance can improve results just as non-linear models (such as neural networks) for the mean have done. In this paper we consider two neural network models for variance estimation. Mixture Density Networks (Bishop 1994, Nix and Weigend 1994) combine a Multi-Layer Perceptron (MLP) and a mixture model to estimate the conditional data density. They are trained using a maximum likelihood approach. However, it is known that maximum likelihood estimates are biased and lead to a systematic under-estimate of variance. More recently, a Bayesian approach to parameter estimation has been developed (Bishop and Qazaz 1996) that shows promise in removing the maximum likelihood bias. However, up to now, this model has not been used for time series prediction. Here we compare these algorithms with two other models to provide benchmark results: a linear model (from the ARIMA family), and a conventional neural network trained with a sum-of-squares error function (which estimates the conditional mean of the time series with a constant variance noise model). This comparison is carried out on daily exchange rate data for five currencies.

Data visualization with simultaneous feature selection

Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches. Until now there has been no method of measuring feature saliency while training a data visualization model. We derive a generative topographic mapping (GTM) based data visualization approach which estimates feature saliency simultaneously with the training of the visualization model. The approach not only provides a better projection by modeling irrelevant features with a separate noise model but also gives feature saliency values which help the user to assess the significance of each feature. We compare the quality of projection obtained using the new approach with the projections from traditional GTM and self-organizing maps (SOM) algorithms. The results obtained on a synthetic and a real-life chemoinformatics dataset demonstrate that the proposed approach successfully identifies feature significance and provides coherent (compact) projections. © 2006 IEEE.

Simultaneous chemical reaction and distillation of formaldehyde

The thesis is concerned with the development and testing of a mathematical model of a distillation process in which the components react chemically. The formaldehyde-methanol-water system was selected and only the reversible reactions between formaldehyde and water giving methylene glycol and between formaldehyde and methanol producing hemiformal were assumed to occur under the distillation conditions. Accordingly the system has been treated as a five component system. The vapour-liquid equilibrium calculations were performed by solving iteratively the thermodynamic relationships expressing the phase equilibria with the stoichiometric equations expressing the chemical equilibria. Using optimisation techniques, the Wilson single parameters and Henry's constants were calculated for binary systems containing formaldehyde which was assumed to be a supercritical component whilst Wilson binary parameters were calculated for the remaining binary systems. Thus the phase equilibria for the formaldehyde system could be calculated using these parameters and good accuracy was obtained when calculated values were compared with experimental values. The distillation process was modelled using the mass and energy balance equations together with the phase equilibria calculations. The plate efficiencies were obtained from a modified A.I.Ch.E. Bubble Tray method. The resulting equations were solved by an iterative plate to plate calculation based on the Newton Raphson method. Experiments were carried out in a 76mm I.D., eight sieve plate distillation column and the results were compared with the mathematical model calculations. Overall, good agreement was obtained but some discrepancies were observed in the concentration profiles and these may have been caused by the effect of limited physical property data and a limited understanding of the reactions mechanism. The model equations were solved in the form of modular computer programs. Although they were written to describe the steady state distillation with simultaneous chemical reaction of the formaldehyde system, the approach used may be of wider application.

Volatility changes in drachma exchange rates

In January 2001 Greece joined the eurozone. The aim of this article is to examine whether an intention to join the eurozone had any impact on exchange rate volatility. We apply the Iterated Cumulative Sum of Squares (ICSS) algorithm of Inclan and Tiao (1994) to a set of Greek drachma exchange rate changes. We find evidence to suggest that the unconditional volatility of the drachma exchange rate against the dollar, British pound, yen, German mark and ECU/Euro was nonstationary, exhibiting a large number of volatility changes prior to European Monetary Union (EMU) membership. We then use a news archive service to identify the events that might have caused exchange rate volatility to shift. We find that devaluation of the drachma increased exchange rate volatility but ERM membership and a commitment to joining the eurozone led to lower volatility. Our findings therefore suggest that a strong commitment to join the eurozone may be sufficient to reduce some exchange rate volatility which has implications for countries intending to join the eurozone in the future.

Explaining volatility and serial correlation in opening and closing returns:a study of the FT-30 components

In this paper the performance of opening and closing returns, for the components of the FT-30 will be studied. It will be shown that for these stocks opening returns have higher volatility and a greater tendency towards negative serial correlation than closing returns. Unlike previous studies this contrasting performance cannot solely be attributed to differences in the trading mechanism across the trading day. All the stocks used in our sample trade thought the day using a uniform trading mechanism. In this paper, we suggest that it is differences in the speed that closing and opening returns adjust to new information that causes differences in return performance. By estimating the Amihud and Mendelson (1987) [Amihud, Yakov, & Mendelson, Haim (1987). Trading mechanisms and stock returns: An empirical investigation, Journal of Finance, 62 533-553.] partial adjustment model with noise, we show that opening returns have a tendency towards over-reaction, while closing returns have a tendency towards under-reaction. We suggest that it is these differences that cause a substantial proportion (although not all) of the asymmetric return patterns associated with opening and closing returns. © 2005 Elsevier Inc. All rights reserved.

Volatility changes caused by the trading system:a Markov switching application

An expanding literature exists to suggest that the trading mechanism can influence the volatility of security returns. This study adds to this literature by examining the impact that the introduction of SETS, on the London Stock Exchange, had on the volatility of security returns. Using a Markov switching regime change model security volatility is categorized as being in a regime of either high or low volatility. It is shown that prior to the introduction of SETS securities tended to be in a low volatility regime. At the time SETS was introduced securities moved to a high volatility regime. This suggests that volatility increased when SETS was introduced.

Stochastic volatility and the goodness-of-fit of the Heston model

Recently, Drǎgulescu and Yakovenko proposed an analytical formula for computing the probability density function of stock log returns, based on the Heston model, which they tested empirically. Their research design inadvertently favourably biased the fit of the data to the Heston model, thus overstating their empirical results. Furthermore, Drǎgulescu and Yakovenko did not perform any goodness-of-fit statistical tests. This study employs a research design that facilitates statistical tests of the goodness-of-fit of the Heston model to empirical returns. Robustness checks are also performed. In brief, the Heston model outperformed the Gaussian model only at high frequencies and even so does not provide a statistically acceptable fit to the data. The Gaussian model performed (marginally) better at medium and low frequencies, at which points the extra parameters of the Heston model have adverse impacts on the test statistics. © 2005 Taylor & Francis Group Ltd.