GOLF IN IRELAND: A STATISTICAL ANALYSIS OF PARTICIPATION. ESRI RESEARCH SERIES NUMBER 63 MAY 2017

This report, commissioned by the Confederation of Golf in Ireland, provides evidence relating to the level of participation in golf in Ireland and the factors that underpin it. Four data sources are used – three from the Republic of Ireland and one from Northern Ireland. In addition, the report reviews evidence for the positive contribution made by golf to physical activity, health and wellbeing. The analysis assesses patterns of participation in golf over time and by social group, as well as exploring people’s motivations and patterns of playing. The analysis examines both active participation in golf, i.e. physically playing the game, and social participation in the form of club membership and attendance at events. The primary purpose is to provide helpful evidence for the various organisations involved in managing and administering golf in Ireland. Below we summarise some of the key findings and policy implications. Additional findings, details and discussion of policy implications are to be found in the body of the report

Dirichlet belief networks for topic structure learning

Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures. Although several deep models have been proposed to learn better topic proportions of documents, how to leverage the benefits of deep structures for learning word distributions of topics has not yet been rigorously studied. Here we propose a new multi-layer generative process on word distributions of topics, where each layer consists of a set of topics and each topic is drawn from a mixture of the topics of the layer above. As the topics in all layers can be directly interpreted by words, the proposed model is able to discover interpretable topic hierarchies. As a self-contained module, our model can be flexibly adapted to different kinds of topic models to improve their modelling accuracy and interpretability. Extensive experiments on text corpora demonstrate the advantages of the proposed model.Comment: accepted in NIPS 201

Epistemic Game Master: A referee for GDL-III Games

General Game Playing is the field of Artificial Intelligence that designs agents that are able to understand game rules written in Game Description Language and use them to play those games effectively. A General Game Playing system uses a Game Master, or referee, to control games and players. With the introduction of the latest extension of GDL, the GDL-III enabled to describe epistemic games. However, the complexity of the state space of these new games became in such way large that is impossible for both the players and the manager to reason precisely about GDL-III games. One way to approach this problem is to use an approximative approach, such as model-sampling. This dissertation shows a Game Master that is able to understand and control games in GDL-III and its players, by using model-sampling to sample possible game states. With the development of this Game Master, players can be developed to be able to play GDL-III games without human intervention. Throughout this dissertation, we present details of our developed solution, how we manage to make the Game Master understand a GDL-III game and how we implemented model sampling. Furthermore, we show that our solution, however approximative, has the same capabilities of an non approximative approach while given enough resources. We show how the Game Master timely scales with increasingly bigger epistemic games

Revealing Robust Oil and Gas Company Macro-Strategies using Deep Multi-Agent Reinforcement Learning

The energy transition potentially poses an existential risk for major international oil companies (IOCs) if they fail to adapt to low-carbon business models. Projections of energy futures, however, are met with diverging assumptions on its scale and pace, causing disagreement among IOC decision-makers and their stakeholders over what the business model of an incumbent fossil fuel company should be. In this work, we used deep multi-agent reinforcement learning to solve an energy systems wargame wherein players simulate IOC decision-making, including hydrocarbon and low-carbon investments decisions, dividend policies, and capital structure measures, through an uncertain energy transition to explore critical and non-linear governance questions, from leveraged transitions to reserve replacements. Adversarial play facilitated by state-of-the-art algorithms revealed decision-making strategies robust to energy transition uncertainty and against multiple IOCs. In all games, robust strategies emerged in the form of low-carbon business models as a result of early transition-oriented movement. IOCs adopting such strategies outperformed business-as-usual and delayed transition strategies regardless of hydrocarbon demand projections. In addition to maximizing value, these strategies benefit greater society by contributing substantial amounts of capital necessary to accelerate the global low-carbon energy transition. Our findings point towards the need for lenders and investors to effectively mobilize transition-oriented finance and engage with IOCs to ensure responsible reallocation of capital towards low-carbon business models that would enable the emergence of fossil fuel incumbents as future low-carbon leaders

International Conference on NeuroRehabilitation 2012

This volume 3, number 2 gathers a set of articles based on the most outstanding research on accessibility and disability issues that was presented in the International Conference on NeuroRehabilitation 2012 (ICNR).The articles’ research present in this number is centred on the analysis and/or rehabilitation of body impairment most due to brain injury and neurological disorders.JACCES thanks the collaboration of the ICNR members and the research authors and reviewers that have collaborated for making possible that issue