Statistical Mechanics of Semi-Supervised Clustering in Sparse Graphs

We theoretically study semi-supervised clustering in sparse graphs in the presence of pairwise constraints on the cluster assignments of nodes. We focus on bi-cluster graphs, and study the impact of semi-supervision for varying constraint density and overlap between the clusters. Recent results for unsupervised clustering in sparse graphs indicate that there is a critical ratio of within-cluster and between-cluster connectivities below which clusters cannot be recovered with better than random accuracy. The goal of this paper is to examine the impact of pairwise constraints on the clustering accuracy. Our results suggests that the addition of constraints does not provide automatic improvement over the unsupervised case. When the density of the constraints is sufficiently small, their only impact is to shift the detection threshold while preserving the criticality. Conversely, if the density of (hard) constraints is above the percolation threshold, the criticality is suppressed and the detection threshold disappears.Comment: 8 pages, 4 figure

A Review of Web-Based Job Advertisements for Australian Event Management Positions

Strong growths in the Australian event management industry, ongoing technological changes and the internationalisation of the market place has spurred the need for appropriately educated and trained event managers and for a re-evaluation of educational and job training curriculum to meet these new challenges. In order for Australia to position itself as a world leader in event management, it is important to provide consistent high professional standards and event managers that not only meet, but exceed the demands of the industry. While there is some literature that focuses on the tourism and leisure job market (Crossley, 1992; Keung & Pine, 2000), and a small but developing literature base that focuses on event management training (Harris & Jago, 1999; Hawkins & Goldbatt, 1995) relatively little consideration has been given to a national agenda for event management skilling. To provide an indication of current employer requirements, a nationwide study of job advertisements in event management has commenced. The aims of the study are to further the understanding of the educational needs and training requirements of the industry; to ascertain the learned skills and personal attributes sought from event managers; to determine the compatibility of industry demands with current educational and vocational provisions; and to suggest post-secondary institutional avenues through which event management education and training needs can be pursued. This is an ongoing study and it is hoped that it will contribute towards a broad scale understanding of the event management job market. More importantly however, it can be used as the basis for curriculum evaluation and training needs, and create a better understanding and compatibility between event management education and industry practice. This paper reports the preliminary results from a content analysis of approximately 100 web-based job advertisements. Email alert accounts were established with several search engines to gather a sample of event management related job advertisements from around Australia. An analytical framework was devised for the analysis of the advertisements themselves. The results reveal several interesting trends including the geographical concentration of the event management job market, the range of industries that require event management specialists or event management skills, and a series of required skills and key attributes of event managers. The results of this study establish a platform from which to develop a classification of event management skills required by the industry

Family history of cancer and head and neck cancer survival

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137774/1/lary26524_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137774/2/lary26524.pd

The Iterative Signature Algorithm for the analysis of large scale gene expression data

We present a new approach for the analysis of genome-wide expression data. Our method is designed to overcome the limitations of traditional techniques, when applied to large-scale data. Rather than alloting each gene to a single cluster, we assign both genes and conditions to context-dependent and potentially overlapping transcription modules. We provide a rigorous definition of a transcription module as the object to be retrieved from the expression data. An efficient algorithm, that searches for the modules encoded in the data by iteratively refining sets of genes and conditions until they match this definition, is established. Each iteration involves a linear map, induced by the normalized expression matrix, followed by the application of a threshold function. We argue that our method is in fact a generalization of Singular Value Decomposition, which corresponds to the special case where no threshold is applied. We show analytically that for noisy expression data our approach leads to better classification due to the implementation of the threshold. This result is confirmed by numerical analyses based on in-silico expression data. We discuss briefly results obtained by applying our algorithm to expression data from the yeast S. cerevisiae.Comment: Latex, 36 pages, 8 figure

Designing and managing music festival experiences to enhance attendees’ psychological and social benefits

Attendance and participation at popular music festivals has become an important and increasingly common experience for people in many Western societies, yet little is known about the kinds of benefits visitors perceive they gain as a result of attending. This research explores attendees’ perceptions of the psychological and social benefits associated with their attendance of the Woodford Folk Music Festival in Queensland (Australia). Based upon the research findings, music festival management strategies are suggested to improve the design of festival experiences to better cater to the artistic, musical, social and psychological needs of attendees thereby increasing the impact and depth of the experience

Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data

Determining the functional structure of biological networks is a central goal of systems biology. One approach is to analyze gene expression data to infer a network of gene interactions on the basis of their correlated responses to environmental and genetic perturbations. The inferred network can then be analyzed to identify functional communities. However, commonly used algorithms can yield unreliable results due to experimental noise, algorithmic stochasticity, and the influence of arbitrarily chosen parameter values. Furthermore, the results obtained typically provide only a simplistic view of the network partitioned into disjoint communities and provide no information of the relationship between communities. Here, we present methods to robustly detect coregulated and functionally enriched gene communities and demonstrate their application and validity for Escherichia coli gene expression data. Applying a recently developed community detection algorithm to the network of interactions identified with the context likelihood of relatedness (CLR) method, we show that a hierarchy of network communities can be identified. These communities significantly enrich for gene ontology (GO) terms, consistent with them representing biologically meaningful groups. Further, analysis of the most significantly enriched communities identified several candidate new regulatory interactions. The robustness of our methods is demonstrated by showing that a core set of functional communities is reliably found when artificial noise, modeling experimental noise, is added to the data. We find that noise mainly acts conservatively, increasing the relatedness required for a network link to be reliably assigned and decreasing the size of the core communities, rather than causing association of genes into new communities.Comment: Due to appear in PLoS Computational Biology. Supplementary Figure S1 was not uploaded but is available by contacting the author. 27 pages, 5 figures, 15 supplementary file