4,873 research outputs found
TECHNOLOGICAL CHANGE AND LABOR'S RELATIVE SHARE: THE MECHANIZATION OF U.S. COTTON PRODUCTION
Research and Development/Tech Change/Emerging Technologies,
A novel synthetic approach to the fasicularin family of marine alkaloids
This thesis is divided into four chapters. The first chapter comprises an introduction to the fasicularin family of marine alkaloids, and a review of previous total syntheses of the members of this class of compounds. This chapter also covers the previous work by Craig et al. on the synthesis of (±)-lepadiformine, and how this influenced our initial approach to the enantioselective synthesis of (–)-lepadiformine. The second chapter details the first part of our investigation into the synthesis of these compounds, including short reviews of diastereoselective aziridination protocols developed by Yoon et al. and Sharpless et al. This section includes the results of this investigation, and discusses the successes and failures of this approach. The third chapter concerns our revised second-generation strategy for the synthesis of (–)-lepadiformine, and includes a review of the decarboxylative Claisen rearrangement, and diastereoselective variants thereof. This section also covers our results and discussion for this revised synthesis. The fourth chapter contains experimental details and characterisation data for all compounds synthesised.Open Acces
Emergent Behaviour in Game AI: A Genetic Programming and CNN-based Approach to Intelligent Agent Design
Emergent behaviour is behaviour that arises from the interactions between the individual components of a system, rather than being explicitly programmed or designed. In this work, genetic programming is used to evolve an adaptive game AI, also known as an intelligent agent, whose job is to capture up to twenty-five prey agents in a simulated pursuit environment. For a pursuit game, the fitness score tallies each prey the predator captured during a run. The fitness is then used to evaluate each agent and choose who moves forward in the evolutionary process. A problem with only choosing the best performing agents is that genetic diversity becomes lost along the way, which can result in monotonous behaviour. Diverse behaviour helps agents perform under situations of uncertainty and creates more interesting computer opponents in video games, as it encourages the agent to explore different possibilities and adapt to changing circumstances. Inspired by the works of Cowan and Pozzuoli in diversifying agent behaviour, and Chen’s work in L-system tree evaluation, a convolutional neural network is introduced to automatically model the behaviour of each agent, something previously done manually. This involves training the convolutional neural network on a large data set of behaviours exhibited by the agents, which take the form of image-based traces. The resulting model is then used to detect interesting emergent behaviour. In the first set of experiments, the convolutional neural network is trained and tested on several sets of traces, then the performance of each run is analyzed. Results show that the convolutional neural network is capable of identifying 6 emergent behaviours with 98% accuracy. The second set of experiments combine genetic programming and the convolutional neural network in order to produce unique and interesting intelligent agents, as well as target specific behaviours. Results show that the system is able to evolve more innovative and effective agents that can operate in complex environments and could be extended to perform a wide range of tasks
Indications of Social Class Differences based on the Archeological Evidence for Occupational Specialization among the Classic Maya at Tikal, Guatemala.
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Ancient Maya houses and their identification: An evaluation of architectural groups at Tikal and inferences regarding their functions.
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The development of polity in Mesoamérica as interpreted through the evolution of plaza plans: suggested influences of the Central Mexican highlands on the Maya lowlands.
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