Many existing hydrological modelling procedures do not make best use of available information, resulting in non-minimal uncertainties in model structure and parameters, and a lack of detailed information regarding model behaviour. A framework is required that balances the level of model complexity supported by the available data with the level of performance suitable for the desired application...

Recent research has demonstrated the great capability of deep belief networks for solving a variety of visual recognition tasks. However, primary focus has been on modelling higher level visual features and later stages of visual processing found in the brain. Lower level processes such as those found in the retina have gone ignored. In this paper, we address this issue and demonstrate how the ...

This paper proposes a new abstract framework for modelling interactions among agents in multi-agent organizations. The proposed model -the model of interaction categories, or MIgories exhibits compositionality of interactions as well as emergence of behavior that is not explicitly designed at the organizational level. The proposed framework is expressive enough to model some of the commonly obs...

Recent advances in high throughput data acquisition and data storage technologies call for designing distributed agents that are able to learn This paper proposes a new abstract framework for modelling interactions among agents in multi-agent organizations. The proposed model – the model of interaction categories, or MIgories exhibits compositionality of interactions as well as emergence of beh...

In this report we present a network-level multi-core energy model and a software development process workflow that allows software developers to estimate the energy consumption of multi-core embedded programs. This work focuses on a high performance, cache-less and timing predictable embedded processor architecture, XS1. Prior modelling work is improved to increase accuracy, then extended to be...

modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...