Random Neural Networks and Optimisation

In this thesis we introduce new models and learning algorithms for the Random Neural Network (RNN), and we develop RNN-based and other approaches for the solution of emergency management optimisation problems. With respect to RNN developments, two novel supervised learning algorithms are proposed. The first, is a gradient descent algorithm for an RNN extension model that we have introduced, the RNN with synchronised interactions (RNNSI), which was inspired from the synchronised firing activity observed in brain neural circuits. The second algorithm is based on modelling the signal-flow equations in RNN as a nonnegative least squares (NNLS) problem. NNLS is solved using a limited-memory quasi-Newton algorithm specifically designed for the RNN case. Regarding the investigation of emergency management optimisation problems, we examine combinatorial assignment problems that require fast, distributed and close to optimal solution, under information uncertainty. We consider three different problems with the above characteristics associated with the assignment of emergency units to incidents with injured civilians (AEUI), the assignment of assets to tasks under execution uncertainty (ATAU), and the deployment of a robotic network to establish communication with trapped civilians (DRNCTC). AEUI is solved by training an RNN tool with instances of the optimisation problem and then using the trained RNN for decision making; training is achieved using the developed learning algorithms. For the solution of ATAU problem, we introduce two different approaches. The first is based on mapping parameters of the optimisation problem to RNN parameters, and the second on solving a sequence of minimum cost flow problems on appropriately constructed networks with estimated arc costs. For the exact solution of DRNCTC problem, we develop a mixed-integer linear programming formulation, which is based on network flows. Finally, we design and implement distributed heuristic algorithms for the deployment of robots when the civilian locations are known or uncertain

N-body realizations of cuspy dark matter haloes

We describe an algorithm for generating equilibrium initial conditions for numerical experiments with dark matter haloes. Our haloes are modelled using a general form for the mass density p{r), making it possible to represent most of the popular density profiles in the literature. The finite mass 7-models and the cuspy density profiles found in recent high-resolution cosmological TV-body simulations having a density power-law fall-off at large distances proportional to are included as special cases. The algorithm calculates the phase-space distribution function of each model assuming spherical symmetry and either an isotropic velocity dispersion tensor or an anisotropic velocity dispersion tensor of the type proposed by Osipkov and Merritt. The particle velocities are assigned according to the exact velocity distribution, making this method ideal for experiments requiring a high degree of stability. Numerical tests confirm that the resulting models are highly stable. This approach is motivated by the instabilities that arise when a local Maxwellian velocity distribution is adopted. For example, after approximating the velocity distribution by a Gaussian we show that a Hernquist halo with an initial r(^-1) density cusp immediately develops a constant density core. Moreover, after a single crossing time the orbital anisotropy has evolved over the entire system. Previous studies that use this approximation to construct halo or galaxy models could be compromised by this behaviour. Using the derived distribution functions we show the exact 1-d velocity distributions and we compare them with the Gaussian velocity distributions with the same second moment for different distances from the halo centre. We show that instabilities arise because a Gaussian velocity distribution is a very poor approximation to the true velocity distribution of particles. We also perform a series of numerical simulations evolving several dark matter halo models in isolation, with the intention of checking the stability of the initialization procedure in both configuration and velocity space. A subset of the models are evolved under the assumption that the velocity distribution at any given point is a Gaussian and the time evolution of the density profiles and velocity structure is monitored. Finally, a number of applications are discussed, including issues of relaxation in dark matter haloes as well as mergers of haloes in scattering experiments

The Origins of Ethnolinguistic Diversity: Theory and Evidence

This research examines theoretically and empirically the economic origins of ethnolinguistic diversity. The empirical analysis constructs detailed data on the distribution of land qualtiy and elevation across contiguous regions, virtual and real countries, and shows that variation in elevation and land quality has contributed significantly to the emergence and persistence of ethnic fractionalization. The empirical and historical evidence support the theoretical analysis, according to which heterogenous land endowments generated region specific human capital, liminting population mobility and leading to the formation of localized ethnicities and languages. The research contributes to the understanding of the emergence of ethnicities and languages. The research contributes to the understanding of the emergence of ethnicities and their spatial distribution and offers a distinction between the natural, georgraphically driven, versus the artificial, man-made, components of contemporary ethnic diversity.Ethnic Diversity, Geography, Technological Process, Human Capital, Colonization.

Nonlinear causality testing with stepwise multivariate filtering

This study explores the direction and nature of causal linkages among six currencies denoted relative to United States dollar (USD), namely Euro (EUR), Great Britain Pound (GBP), Japanese Yen (JPY), Swiss Frank (CHF), Australian Dollar (AUD) and Canadian Dollar (CAD). These are the most liquid and widely traded currency pairs in the world and make up about 90% of total Forex trading worldwide. The data covers the period 3/20/1987-11/14/2007, including the Asian crisis, the dot-com bubble and the period just before the outbreak of the US subprime crisis. The objective of the paper is to test for the existence of both linear and nonlinear causal relationships among these currency markets. The modified Baek-Brock test for nonlinear non-causality is applied on the currency return time series as well as the linear Granger test. Further to the classical pairwise analysis causality testing is conducted in a multivariate formulation, to correct for the effects of the other variables. A new stepwise multivariate filtering approach is implemented. To check if any of the observed causality is strictly nonlinear, the nonlinear causal relationships of VAR/VECM filtered residuals are also examined. Finally, the hypothesis of nonlinear non-causality is investigated after controlling for conditional heteroskedasticity in the data using GARCH-BEKK, CCC-GARCH and DCC-GARCH models. Significant nonlinear causal linkages persisted even after multivariate GARCH filtering. This indicates that if nonlinear effects are accounted for, neither FX market leads or lags the other consistently and currency returns may exhibit statistically significant higher-order moments and asymmetries.nonparametric Granger causality; filtering; multivariate GARCH models; spillovers

Hybrid linear programming to estimate CAP impacts of flatter rates and environmental top-ups

This paper examines evolutions of the Common Agricultural Policy (CAP) decoupling regime and their impacts on Greek arable agriculture. Policy analysis is performed by using mathematical programming tools. Taking into account increasing uncertainty, we assume that farmers perceive gross margin in intervals rather than as expected crisp values. A bottom-up hybrid model accommodates both profit maximizing and risk prudent attitudes in order to accurately assess farmers’ response. Marginal changes to crop plans are expected so that flatter single payment rates cause significant changes in incomes and subsidies. Nitrogen reduction incentives result in moderate changes putting their effectiveness in question.Interval Linear Programming, Min-Max Regret, Common Agricultural Policy, Arable cropping, Greece