Memory Augmented Control Networks

Planning problems in partially observable environments cannot be solved directly with convolutional networks and require some form of memory. But, even memory networks with sophisticated addressing schemes are unable to learn intelligent reasoning satisfactorily due to the complexity of simultaneously learning to access memory and plan. To mitigate these challenges we introduce the Memory Augmented Control Network (MACN). The proposed network architecture consists of three main parts. The first part uses convolutions to extract features and the second part uses a neural network-based planning module to pre-plan in the environment. The third part uses a network controller that learns to store those specific instances of past information that are necessary for planning. The performance of the network is evaluated in discrete grid world environments for path planning in the presence of simple and complex obstacles. We show that our network learns to plan and can generalize to new environments

Healthy Universities: Concept, Model and Framework for Applying the Healthy Settings Approach within Higher Education in England

As part of a Department of Health funded project, the University of Central Lancashire (UCLan) – working with Manchester Metropolitan University – was commissioned by the Royal Society for Public Health (RSPH), to: - articulate a model for Healthy Universities whereby the healthy settings approach is applied within the higher education sector - produce recommendations for the development and operationalisation of a National Healthy Universities Framework for England - to ensure effective co-ordination of initiatives and propose next steps for progressing the Healthy Universities agenda. In fulfilment of these objectives, this report provides a background to Healthy Universities, outlines the project implementation process, presents a model, discusses the key dimensions for consideration in formulating a framework, and makes recommendations for taking things forward

CAST – City analysis simulation tool: an integrated model of land use, population, transport and economics

The paper reports on research into city modelling based on principles of Science of Complexity. It focuses on integration of major processes in cities, such as economics, land use, transport and population movement. This is achieved using an extended Cellular Automata model, which allows cells to form networks, and operate on individual financial budgets. There are 22 cell types with individual processes in them. The formation of networks is based on supply and demand mechanisms for products, skills, accommodation, and services. Demand for transport is obtained as an emergent property of the system resulting from the network connectivity and relevant economic mechanisms. Population movement is a consequence of mechanisms in the housing and skill markets. Income and expenditure of cells are self-regulated through market mechanisms and changing patterns of land use are a consequence of collective interaction of all mechanisms in the model, which are integrated through emergence