Patterns of faunal change at an upper Cambrian trilobite extinction event, Nolichucky Formation, Tennessee and Virginia

During the Upper Cambrian there were three mass extinctions, each of which eliminated at least half of the trilobite families living in North American shelf seas. The Nolichucky Formation preserves the record of one of these extinction events at the base of the Steptoean Stage. Sixty-six trilobite collections were made from five sections In Tennessee and Virginia. The lower Steptoean faunas are assigned to one low diversity, Aphelaspis-dominated biofacies, which can be recognized in several other parts of North America. In Tennessee, the underlying upper Marjuman strata contain two higher diversity biofacies, the Coosella-Glaphyraspis Biofacies and the Tricrepicephalus-Norwoodiid Biofacies. At least four different biofacies are present in other parts of North America: the Crepicephalus -Lonchocephalus Biofacies, the Kingstonia Biofacies, the Cedaria Biofacies, and the Uncaspis Biofacies. A new, species-based zonation for the Nolichucky Formation imcludes five zones, three of which are new. These zones are the Crepicephalus Zone, the Coosella perplexa Zone, the Aphelaspis buttsi Zone, the A. walcotti Zone and the A. tarda Zone. The Nolichucky Formation was deposited within a shallow shelf basin and consists largely of subtidal shales with stormgenerated carbonate interbeds. A relative deepening is recorded In the Nolichucky Formation near the extinction, and is indicated In some sections by the appearance of shale-rich, distal storm deposits above a carbonate-rich, more proximal storm deposit sequence. A comparable deepening-upward sequence occurs near the extinction in the Great Basin of southwestern United States and in central Texas, and this suggests a possible eustatic control. In other parts of North America, the extinction IS recorded In a variety of environmental settings that range from near-shore to slope. In shelf environments, there is a marked decrease in diversity, and a sharp reduction in biofacies differentiation. Although extinctions do take place in slope environments, there IS no net reduction in diversity because of the immigration of several new taxa.

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Slide - Celebrity Ice Wine Event

A slide from the Inniskillin Celebrity Ice Wine event featuring: Michael Burgess, Ron Barbaro; Jonathan Welsh.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Major Sport Event Operational Planning Issues and Strategies: A Multi-Case Delphi Study

This study examined the operational planning, implementation and execution issues of major sport events, as well as the mitigation and management strategies used to address these issues, with the aim of determining best practices in sport event operational planning. The three Research Questions were: 1) What can previous major sport events provide to guide the operational management of future events? 2) What are the operational issues that arise in the planning and execution of a major sport event, how are they mitigated and what are the strategies used to deal with these issues? 3) What are the best practices for sport event operational planning and how can these practices aid future events? Data collection involved a modified Delphi technique that consisted of one round of in-depth interviews followed by two rounds of questionnaires. Both data collection and analysis were guided by an adaptation of the work of Parent, Rouillard & Leopkey (2011) with a focus on previously established issue and strategy categories. The results provided a list of Top 26 Prominent Issues and Top 17 Prominent Strategies with additional issue-strategy links that can be used to aid event managers producing future major sport events. The following issue categories emerged as having had the highest impact on previous major sport events that participants had managed: timing, funding and knowledge management. In addition, participants used strategies from the following categories most frequently: other, formalized agreements and communication.