Community in Tension (CiT)

The development and availability of Information Communication Technology (ICT) impacts many sectors yet a digital divide is still present amongst citizens in communities. Not only is there a digital divide evident but also many other factors that causes tension in communities. This paper defines a Community in Tension (CiT) as a community where the wellbeing of its citizens is being threatened. This provides an opportunity to use these available ICTs in communities and have it locally appropriated to empower the citizens and stabilise these communities

An application of stochastic interest rates models in life assurance

Although assurance companies are pooling many risks, the law of large numbers does not fully apply. This leaves the companies with a possibility of insolvency and a corresponding need for contingency reserves which are matters of serious concern. In this thesis we derive some fundamental results that are useful when the time comes to set contingency reserves or to assess solvency. We use a model where both the mortality and the interest rates are random variables. We choose to model the force of interest by the Ornstein-Uhlenbeck process. For temporary assurances and endowment assurances we derive an efficient recursive method to find the first three moments of the present value of a portfolio of identical policies. We then use these moments to approximate accurately the distribution of the present value of such a portfolio, firstly when the number of policies in the portfolio tends to infinity, and secondly, for a portfolio of finite size.Formation de Chercheurs et l'Aide à la Recherche (FCAR

Simultaneous determination of in situ vertical transitions of color, pore-water metals, and visualization of infaunal activity in marine sediments

The vertical color transition from brown to gray-green in marine sediments is linked to the Fe redox boundary and is commonly used as a proxy for biogeochemical state. We combine time-lapse sediment profile imaging with diffusive gradient thin (DGT) gels to obtain simultaneous in situ measurements of sediment color profiles, pore-water Fe and Mn profiles, and qualitative estimates of faunal activity at the Oyster Ground and North Dogger (North Sea). Analysis of Fe and Mn profiles using generalized additive modeling reveals that high variability between profiles within the sites makes it difficult to determine any intersite differences in trace metal behavior. At the Oyster Ground, the depth of sediment color transition (4.78 +/- 0.76 cm) was not significantly different from the Fe redox boundary (7.67 +/- 4.04 cm). At the North Dogger, there was a significant discrepancy between the depth of the sediment color transition (2.86 +/- 0.78 cm) and the Fe redox boundary (10.17 +/- 1.04 cm), which most likely results from high sulfate reduction rates at the North Dogger, leading to complexation of reduced iron to a form not available to the DGT technique. The differences in the coupling of sediment color and the Fe redox boundary between stations is likely to be related to variations in recent infaunal bioturbation activity, rather than variations in sediment source or fundamental differences in bulk sediment chemistry. Our results highlight the importance of the infaunal community in mediating Fe and Mn cycles, which are key pathways in the degradation of organic matter, and suggest that descriptions of bulk chemistry alone may be insufficient to understand the dynamics of biogeochemical cycling

Fitness Biasing for Evolving an Xpilot Combat Agent

In this paper we present an application of Fitness Biasing, a type of Punctuated Anytime Learning, for learning autonomous agents in the space combat game Xpilot. Fitness Biasing was originally developed as a means of linking the model to the actual robot in evolutionary robotics. We use fitness biasing with a standard genetic algorithm to learn control programs for a video game agent in real-time. Xpilot-AI, an Xpilot add-on designed for testing learning systems, is used to evolve the controller in the background while periodic checks in normal game play are used to compensate for errors produced by running the system at a high frame rate. The resultant learned controllers are comparable to our best hand-coded Xpilot-AI bots, display complex behavior that resemble human strategies, and are capable of adapting to a changing enemy in real-time

Evolving Expert Agent Parameters for Capture the Flag Agent in Xpilot

Xpilot is an open source, 2d space combat game. Xpilot-AI allows a programmer to write scripts that control an agent playing a game of Xpilot. It provides a reasonable environment for testing learning systems for autonomous agents, both video game agents and robots. In previous work, a wide range of techniques have been used to develop controllers that are focused on the combat skills for an Xpilot agent. In this research, a Genetic Algorithm (GA) was used to evolve the parameters for an expert agent solving the more challenging problem of capture the flag

Learning Area Coverage for a Self-Sufficient Colony Robot

It is advantageous for colony robots to be autonomous and self-sufficient. This requires them to perform their duties while maintaining enough energy to operate. Previously, we reported the equipping of power storage for legged robots with high capacitance capacitors, the configuration of one of these robots to effectively use its power storage in a colony recharging system, and the learning of a control program that enabled the robot to navigate to a charging station in simulation. In this work, we report the learning of a control program that allowed the simulated robot to perform area coverage in a self-sufficient framework that made available the best pre-learned navigation behavior module.

Learning Navigation for Recharging a Self-Sufficient Colony Robot

It is advantageous for colony robots to be autonomous and self-sufficient. This requires them to perform their duties while maintaining enough energy to operate. Previously, we reported the equipping of power storage for legged robots with high capacitance capacitors, the configuration of one of these robots to effectively use its power storage in a colony recharging system, and the learning of a control program that enabled the robot to navigate to a charging station in simulation. In this work, we report the learning of a control program that allowed the simulated robot to perform area coverage in a self-sufficient framework that made available the best pre-learned navigation behavior module