6,716 research outputs found

    Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse

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    This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses. This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups. In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena

    k-Means

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    2023-2024 Boise State University Undergraduate Catalog

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    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    Concepts in low-cost and flow NMR

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    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    Designing similarity functions

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    The concept of similarity is important in many areas of cognitive science, computer science, and statistics. In machine learning, functions that measure similarity between two instances form the core of instance-based classifiers. Past similarity measures have been primarily based on simple Euclidean distance. As machine learning has matured, it has become obvious that a simple numeric instance representation is insufficient for most domains. Similarity functions for symbolic attributes have been developed, and simple methods for combining these functions with numeric similarity functions were devised. This sequence of events has revealed three important issues, which this thesis addresses. The first issue is concerned with combining multiple measures of similarity. There is no equivalence between units of numeric similarity and units of symbolic similarity. Existing similarity functions for numeric and symbolic attributes have no common foundation, and so various schemes have been devised to avoid biasing the overall similarity towards one type of attribute. The similarity function design framework proposed by this thesis produces probability distributions that describe the likelihood of transforming between two attribute values. Because common units of probability are employed, similarities may be combined using standard methods. It is empirically shown that the resulting similarity functions treat different attribute types coherently. The second issue relates to the instance representation itself. The current choice of numeric and symbolic attribute types is insufficient for many domains, in which more complicated representations are required. For example, a domain may require varying numbers of features, or features with structural information. The framework proposed by this thesis is sufficiently general to permit virtually any type of instance representation-all that is required is that a set of basic transformations that operate on the instances be defined. To illustrate the framework’s applicability to different instance representations, several example similarity functions are developed. The third, and perhaps most important, issue concerns the ability to incorporate domain knowledge within similarity functions. Domain information plays an important part in choosing an instance representation. However, even given an adequate instance representation, domain information is often lost. For example, numeric features that are modulo (such as the time of day) can be perfectly represented as a numeric attribute, but simple linear similarity functions ignore the modulo nature of the attribute. Similarly, symbolic attributes may have inter-symbol relationships that should be captured in the similarity function. The design framework proposed by this thesis allows domain information to be captured in the similarity function, both in the transformation model and in the probability assigned to basic transformations. Empirical results indicate that such domain information improves classifier performance, particularly when training data is limited

    Using collections to explore the evolution of plant associated lifestyles in the Ascomycota

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    The Ascomycota form the largest phylum in the fungal kingdom and show a wide diversity of lifestyles, some involving beneficial or harmful associations with plants. Distinguishing between fungal endophytes – species which live asymptomatically in plant tissues – and plant pathogens is of major significance to economic and ecological issues relating to plant health. Evolutionary genomics methods can provide insight into the genetic determinants of these lifestyles, and collections can act as an invaluable source of material to enable such analyses. As endophytes are comparatively poorly studied, comparing plant associated lifestyles in the Ascomycota first requires novel endophyte discovery. In this thesis, I have demonstrated the unexplored promise of Kew’s Millennium Seed Bank for isolating viable fungal endophytes and, in the process, highlighted the potential issues of overlooking the seed microbiome in the seed banking practice. I then performed whole genome sequencing, assembly and annotation of novel endophytic Fusarium strains for a case-study exploring lifestyle evolution in the genus. The distribution of lifestyles across the phylogeny; similarity of gene repertoires; and patterns of codon optimisation suggested that Fusarium taxa have a shared capacity for pathogenicity/endophytism. Exploring to what extent these results are common to different lineages of the Ascomycota requires the generation of new genomic resources for endophytes at large. Consequently, I sequenced, assembled and annotated genomes for a further 15 endophyte strains from CABI’s collections, which spanned 8 families and 5 orders and additionally represent the first assembly for the genus and/or species for 7 of the strains. Together, this thesis demonstrates the value of existing plant and fungal collections for producing material and data to explore the pathogenic-mutualistic spectrum in plant associated ascomycetes

    The molecular athlete: exercise physiology from mechanisms to medals

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    Human skeletal muscle demonstrates remarkable plasticity, adapting to numerous external stimuli including the habitual level of contractile loading. Accordingly, muscle function and exercise capacity encompass a broad spectrum, from inactive individuals with low levels of endurance and strength, to elite athletes who produce prodigious performances underpinned by pleiotropic training-induced muscular adaptations. Our current understanding of the signal integration, interpretation and output coordination of the cellular and molecular mechanisms that govern muscle plasticity across this continuum is incomplete. As such, training methods and their application to elite athletes largely rely on a "trial and error" approach with the experience and practices of successful coaches and athletes often providing the bases for "post hoc" scientific enquiry and research. This review provides a synopsis of the morphological and functional changes along with the molecular mechanisms underlying exercise adaptation to endurance- and resistance-based training. These traits are placed in the context of innate genetic and inter-individual differences in exercise capacity and performance, with special considerations given to the ageing athletes. Collectively, we provide a comprehensive overview of skeletal muscle plasticity in response to different modes of exercise, and how such adaptations translate from "molecules to medals"

    Where is the learning between young people, teachers, and professional musicians? A study of learning cultures within three music education partnership projects in England.

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    Music education partnership projects (MEPPs) between schools and music organisations are a familiar form of enrichment which can open up new creative pathways. While professional musician involvement in education settings is not new, partnerships have become increasingly important. Despite the prevalence of and investment in partnership initiatives, there is limited research that explores participants’ experiences of learning in these contexts. Barriers include: a lack of communication and reflective practice; a culture of ‘victory narratives’; limited youth voice and competing partner agendas. Against this backdrop, social practices within MEPPs and the impact of MEPPs on learning is under researched, creating a cycle whereby learning, and how best to facilitate it, is commonly overlooked. In order to develop a richer understanding of learning within this phenomenon this research asks: where is the learning between young people, teachers, and professional musicians during MEPPs? To explore this further, research centred on a qualitative multiple case study of three MEPPs. MEPP1 aimed to support the development of a new school choir in a primary school while supporting one teacher’s choir leadership skills. MEPP2 and MEPP3 centred on young people composing music in collaboration with professional musicians. All three MEPPs culminated in sharing events in prestigious concert halls. Data were obtained through participant observations, document analysis, and semi-structured interviews with children and young people (YP), teachers, musicians, music organisation learning and participation (L&P) staff, and staff from partnering sponsors/charities. Following this, four elite interviews with leaders from Arts Council England, Youth Music, Arts Connect and one Music Education Hub (MEH) were conducted to gain broader perspectives on partnership working. The concept ‘learning cultures’, in other words, social practices through which people learn, supports analysis of MEPP participants’ learning. This is theoretically underpinned by Bourdieu’s concepts of habitus, capital, and field, which permits understandings of learning within MEPPs as influenced by multiple structural, contextual, and individual factors. The need for this theoretical approach is amplified in the context of MEPPs which, being at intersection of the music education and professional music fields, accommodate multiple institutions and individuals as well as multiple motivations, goals, and values. Key aspects which impact learning cultures within MEPPs include teacher identity, power relations, knowledge integration, access to authentic learning environments, legacy, communication, roles, and contextual awareness. There is a general consensus that practices of performing in prestigious venues and practices of modelling professional musicians are key benefits of MEPPs. Drawing on the empirical findings, this study concludes with a discussion on how to build effective learning cultures in future MEPPs. Altogether it is hoped that this study will inform efficacy in music education partnership working, raise awareness of the multidimensional nature of learning within MEPPs, and contribute to growing international research on collaborative music projects in schools
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