Drumming toward communitas : a case study of facilitated recreational music making and the Arthurian method

The phenomenon of communitas has been described as a moment 'in and out of time' in which a collective of individuals may be experienced by one as equal and individuated in an environment stripped of structural attributes (Turner, 1 969). In these moments, emotional bonds form and an experience of perceived 'oneness' and synergy may be described. As a result of the perceived value of these experiences, it has been suggested by Sharpe (2005) that more clearly understanding how this phenomenon may be purposefully facilitated would be beneficial for leisure service providers. Consequently, the purpose of this research endeavor was to examine the ways in which a particular leisure service provider systematically employs specific methods and sets specific parameters with the intention of guiding participants toward experiences associated with communitas or "shared spirit" as described by the organization. A qualitative case study taking a phenomenological approach was employed in order to capture the depth and complexity of both the phenomenon and the purposefiil negotiation of experiences in guiding participants toward this phenomenon. The means through which these experiences were intentionally facilitated was recreational music making in a group drumming context. As such, an organization which employs specific methods of rhythm circle facilitation as well as trains other facilitators all over the world was chosen purposely for their recognition as the most respectable and credible in this field. The specific facilitator was chosen based on high recommendation by the organization due to her level of experience and expertise. Two rhythm circles were held, and participants were chosen randomly by the facilitator. Data was collected through observation in the first circle and participant- observation in the second, as well as through focus groups with circle participants. Interviews with the facilitator were held both initially to gain broad understanding of concepts and phenomenon as well as after each circle to reflect on each circle specifically. Data was read repeatedly to drawn out patterns which emerged and were coded and organized accordingly. It was found that this specific process or system of implementation lead to experiences associated with communitas by participants. In order to more clearly understand this process and the ways in which experiences associated with communitas manifest as a result of deliberate facilitator actions, these objective facilitator actions were plotted along a continuum relating to subjective participant experiences. These findings were then linked to the literature with regards to specific characteristics of communitas. In so doing, the intentional manifestation of these experiences may be more clearly understood for ftiture facilitators in many contexts. Beyond this, findings summarized important considerations with regards to specific technical and communication competencies which were found to be essential to fostering these experiences for participants within each group. Findings surrounding the maintenance of a fluid negotiation of certain transition points within a group rhythm event overall were also highlighted, and this fluidity was found to be essential to the experience of absorption and engagement in the activity and experience. Emergent themes of structure, control, and consciousness have been presented as they manifested and were found to affect experiences within this study. Discussions surrounding the ethics and authenticity of these particular methods and their implementation has also been generated throughout. In conclusion, there was a breadth as well as depth of knowledge found in unpacking this complex process of guiding individuals toward experiences associated with communitas. The implications of these findings contribute in broadening the current theoretical as well as practical understanding as to how certain intentional parameters may be set and methods employed which may lead to experiences of communitas, and as well contribute a greater knowledge to conceptualizing the manifestation of these experiences when broken down.

Primary structure and expression studies of the dihydrofolate reductase gene (DFRI) of Saccharomyces cerevisiae

The nucleotide sequence of a genomic DNA fragment thought previously to contain the dihydrofolate reductase gene (DFR1) of Saccharomyces cerevisiae by genetic criteria was determined. This DNA fragment of 1784' basepairs contains a large open reading frame from position 800 to 1432, which encodes a enzyme with a predicted molecular weight of 24,229.8 Daltons. Analysis of the amino acid sequence of this protein revealed that the yeast polypep·tide contained 211 amino acids, compared to the 186 residues commonly found in the polypeptides of other eukaryotes. The difference in size of the gene product can be attributed mainly to an insert in the yeast gene. Within this region, several consensus sequences required for processing of yeast nuclear and class II mitochondrial introns were identified, but appear not sufficient for the RNA splicing. The primary structure of the yeast DHFR protein has considerable sequence homology with analogous polypeptides from other organisms, especially in the consensus residues involved in cofactor and/or inhibitor binding. Analysis of the nucleotide sequence also revealed the presence of a number of canonical sequences identified in yeast as having some function in the regulation of gene expression. These include UAS elements (TGACTC) required for tIle amino acid general control response, and "TATA H boxes as well as several consensus sequences thought to be required for transcriptional termination and polyadenylation. Analysis of the codon usage of the yeast DFRl coding region revealed a codon bias index of 0.0083. this valve very close to zero suggestes 3 that the gene is expressed at a relatively low level under normal physiological conditions. The information concerning the organization of the DFRl were used to construct a variety of fusions of its 5' regulatory region with the coding region of the lacZ gene of E. coli. Some of such fused genes encoded a fusion product that expressed in E.coli and/or in yeast under the control of the 5' regulatory elements of the DFR1. Further studies with these fusion constructions revealed that the beta-galactosidase activity encoded on multicopy plasmids was stimulated transiently by prior exposure of yeast host cells to UV light. This suggests that the yeast PFRl gene is indu.ced by UV light and nlay in1ply a novel function of DHFR protein in the cellular responses to DNA damage. Another novel f~ature of yeast DHFR was revealed during preliminary studies of a diploid strain containing a heterozygous DFRl null allele. The strain was constructed by insertion of a URA3 gene within the coding region of DFR1. Sporulation of this diploid revealed that meiotic products segregated 2:0 for uracil prototrophy when spore clones were germinated on medium supplemented with 5-formyltetrahydrofolate (folinic acid). This finding suggests that, in addition to its catalytic activity, the DFRl gene product nlay play some role in the anabolisln of folinic acid. Alternatively, this result may indicate that Ura+ haploid segregants were inviable and suggest that the enzyme has an essential cellular function in this species.

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.