Preliminary evaluation of electroencephalographic entrainment using thalamocortical modelling

Abstract: The concept of linked oscillators in biological control systems has long been established. Frequency entrainment is a predominant explanation behind many biological rhythms. In this paper a preliminary examination of electroencephalographic entrainment is made to survey the possibility and methods of achieving signal entrainment at the highest level of neurological organization and function. A model of the thalamocortical system is employed to generate simulated electroencephalographic signals and is tested in various configurations in the search for entrainment under very simple conditions. Additionally, an analysis of the coupled Van der Pol model of the circadian rhythm controller is performed to identify the possibility of affecting that system with a drastically different coupling input signal. We were able to conclude that overall signal shape can have a significant impact on the entrainment characteristics of the system. Due to the nature of the underlying mathematical structure of the model, by examining the circadian rhythm controller, we found that it is unsuitable for entrainment to an incident entraining signal of much higher frequency.     

Combining feature spaces for classification

In this paper we offer a variational Bayes approximation to the multinomial probit model for basis expansion and kernel combination. Our model is well-founded within a hierarchical Bayesian framework and is able to instructively combine available sources of information for multinomial classification. The proposed framework enables informative integration of possibly heterogeneous sources in a multitude of ways, from the simple summation of feature expansions to weighted product of kernels, and it is shown to match and in certain cases outperform the well-known ensemble learning approaches of combining individual classifiers. At the same time the approximation reduces considerably the CPU time and resources required with respect to both the ensemble learning methods and the full Markov chain Monte Carlo, Metropolis-Hastings within Gibbs solution of our model. We present our proposed framework together with extensive experimental studies on synthetic and benchmark datasets and also for the first time report a comparison between summation and product of individual kernels as possible different methods for constructing the composite kernel matrix.     

Enhancing the semantics of UML association redefinition

Association redefinition is a UML construct that permits us to define an association end more specifically in a particular context. Concretely, it allows specifying some additional participation and cardinality constraints on the association. Association refinements, which have been studied and used by many authors in conceptual modelling languages prior to UML, are closely related to association redefinitions. They also permit to refine the ends of an association adding participation and cardinality constraints. In this paper, we analyze and compare the semantics of both concepts and propose to extend the semantics of association redefinitions in UML to cover all the constraints that may be expressed by association refinements in other conceptual modelling languages. Additionally, we present how to integrate previous results on validation of association refinements to UML and how to generate code for a relational technology platform. Finally, we provide a prototype tool to verify the feasibility of the approach.     

Formal modelling of organisational goals based on performance indicators

Every organisation exists or is created for the achievement of one or more goals. To ensure continued success, the organisation should monitor its performance with respect to the formulated goals. In practice the performance of an organisation is often evaluated by estimating its performance indicators. In most existing approaches for organisation modelling the relation between performance indicators and goals remains implicit. This paper proposes a formal framework for modelling goals based on performance indicators and defines mechanisms for establishing goal satisfaction, which enable evaluation of organisational performance. Methodological and analysis issues related to goals are also discussed in the paper. The described framework is a part of a general framework for organisation modelling and analysis.     

Model-based cognitive diagnosis

This paper considers the problem of cognitive diagnosis as an instance of general diagnosis, as studied in artificial intelligence. Cognitive diagnosis is the process of inferring a cognitive state from observations of performance. It is thus a key component of any system which attempts to build a dynamic model of the user of that system. Many issues in cognitive diagnosis, previously discussed informally, are mapped onto formal techniques, with consequent increased clarity and rigour. But it is concluded that the general theories for diagnosis must be broadened to fully encompass the problems of cognitive diagnosis.     

The limits of path error-neglecting in straight lane driving

Abstract

It is argued that driving cannot simply be considered as a permanent, closed-loop task. Time-to-line-crossing (TLC) is used as a measure to quantify the potential role of visual open-loop and path-error-neglecting strategies. Basically, TLC represents the time available for a driver to neglect path errors until the moment at which any part of the vehicle reaches one of the lane boundaries. The strategy adopted by drivers during error-neglecting should be represented in terms of decision rules, describing how drivers switch from error-neglecting to error-correcting when approaching the edge of a lane. The experiment to be presented in this paper was designed to provide these rules for a straight lane-keeping task. Drivers were instructed to neglect the vehicle path error and to switch to error-correcting only at that moment when the vehicle heading could still comfortably be corrected to prevent a crossing of the lane boundary. The results show that the lateral distance from the lane boundary at which drivers switch to error-correction increases about linearly with the lateral approach speed. This mechanism results in an approximately constant TLC (time) distance at the moment of decision: this result being consistent over a broad range of speeds.     

Probabilistic characterisation of model error using Gaussian mixture model—With application to Charpy impact energy prediction for alloy steel

A novel approach to characterise the model prediction errors using a Gaussian mixture model is proposed. The motivation for this work lies behind many data models that are developed through prediction error minimisation with the assumption of a normal noise distribution. When the noise is non-normal, which may often be the case in complicated data modelling scenarios, the model prediction errors may contain rich information, which can be further exploited for model refinement and improvement. The key contents presented in this paper include: choosing the relevant variables to form the error data, optimising the number of Gaussian components required for the error data modelling, and fitting the Gaussian mixture parameters using an expectation-maximisation algorithm. Application of the proposed method for further model improvement, within the framework of hybrid deterministic/stochastic modelling, is also discussed. Preliminary results on the real industrial Charpy impact energy data for heat-treated steels show its effectiveness for model error characterisation, and the potential for model performance improvement in terms of prediction accuracy as well as providing accurate prediction confidence intervals.