33 resultados para Context data
Resumo:
ARK (‘Access Research Knowledge’) was set up with a single goal: to make social science information on Northern Ireland available to the widest possible audience. The most well-known and widely used part of the ARK resource is CAIN (Conflict Archive on the INternet), which is one of the largest on-line collections of source material and information and about the Northern Ireland conflict. The compilation of CAIN's new Remembering: Victims, Survivors and Commemoration section raised issues related to the sensitivity of the material, as it feeds into the fundamental debate on the legacy of the Northern Ireland conflict. It also fundamentally raises the question to what extent archiving is a neutral or political activity and necessitates a discourse on responsibility and ethics among social researchers. Experiences from the establishment of the Northern Ireland Qualitative Archive (NIQA) shed light on future possibilities with regard to qualitative archives on the Northern Ireland conflict.
Resumo:
The Internet provides a new tool to investigate old questions in experimental social psychology regarding Person x Context interaction. We examined the interaction of self-reported shyness and context on computer-mediated communication measures. Sixty female undergraduates unfamiliar were paired in dyads and engaged in a 10 min free chat conversation on the Internet with and without a live webcam. Free chat conversations were archived, transcripts were objectively coded for communication variables, and a linear mixed model used for data analysis of dyadic interaction was performed on each communication measure. As predicted, increases in self-reported shyness were significantly related to decreases in the number of prompted self-disclosures (after controlling for the number of opportunities to self-disclose) only in the webcam condition. Self-reported shyness was not related to the number of prompted self-disclosures in the no webcam condition, suggesting that shyness was context dependent. The present study appears to be the first to objectively code measures of Internet behaviour in relation to the study of personality in general and shyness in particular. Theoretical and clinical implications for understanding the contextual nature of shyness are discussed. (C) 2006 Elsevier Inc. All rights reserved.
Resumo:
Asking and answering certain types of questions are thought to develop thinking skills in all types of classrooms. Previous research has demonstrated that asking higher order questions and answering with elaborated responses are associated with high achievement in first, second, and foreign language contexts. Typically more attention is paid to question frequency or achievements inferred from individual performances than to the dialogues in which asking and answering occurs. This paper argues for a focus on the construction of responses in interaction as an alternative to the investigation of questions, effects of training or individual measurements of performance. Drawing on interactional data from an adult English as a Second Language classroom, it is argued that constructing an answer to a critical question appears to be a highly collaborative and evaluative affair. The thinking skills literature suggests that responding to higher order questions is an individual higher cognitive function, however it is argued in this paper that in attempting to construct evaluative answers language learners are involved not only in a cognitive task, which may or may not be helpful to language learning, but also in a complex social task in which perspectives need to be negotiated, stances taken and identities navigated. It is suggested that higher order thinking cannot be separated from the social and cultural knowledge through which it is brought into being. It is argued that any implementation of thinking skills in an English language teaching context ought to consider interpersonal and social aspects, particularly in intercultural settings.
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An optimal search theory, the so-called Levy-flight foraging hypothesis(1), predicts that predators should adopt search strategies known as Levy flights where prey is sparse and distributed unpredictably, but that Brownian movement is sufficiently efficient for locating abundant prey(2-4). Empirical studies have generated controversy because the accuracy of statistical methods that have been used to identify Levy behaviour has recently been questioned(5,6). Consequently, whether foragers exhibit Levy flights in the wild remains unclear. Crucially, moreover, it has not been tested whether observed movement patterns across natural landscapes having different expected resource distributions conform to the theory's central predictions. Here we use maximum-likelihood methods to test for Levy patterns in relation to environmental gradients in the largest animal movement data set assembled for this purpose. Strong support was found for Levy search patterns across 14 species of open-ocean predatory fish (sharks, tuna, billfish and ocean sunfish), with some individuals switching between Levy and Brownian movement as they traversed different habitat types. We tested the spatial occurrence of these two principal patterns and found Levy behaviour to be associated with less productive waters (sparser prey) and Brownian movements to be associated with productive shelf or convergence-front habitats (abundant prey). These results are consistent with the Levy-flight foraging hypothesis(1,7), supporting the contention(8,9) that organism search strategies naturally evolved in such a way that they exploit optimal Levy patterns.
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Multicore computational accelerators such as GPUs are now commodity components for highperformance computing at scale. While such accelerators have been studied in some detail as stand-alone computational engines, their integration in large-scale distributed systems raises new challenges and trade-offs. In this paper, we present an exploration of resource management alternatives for building asymmetric accelerator-based distributed systems. We present these alternatives in the context of a capabilities-aware framework for data-intensive computing, which uses an enhanced implementation of the MapReduce programming model for accelerator-based clusters, compared to the state of the art. The framework can transparently utilize heterogeneous accelerators for deriving high performance with low programming effort. Our work is the first to compare heterogeneous types of accelerators, GPUs and a Cell processors, in the same environment and the first to explore the trade-offs between compute-efficient and control-efficient accelerators on data-intensive systems. Our investigation shows that our framework scales well with the number of different compute nodes. Furthermore, it runs simultaneously on two different types of accelerators, successfully adapts to the resource capabilities, and performs 26.9% better on average than a static execution approach.
Resumo:
Psychological research into national identity has considered both the banal quality of nationalism alongside the active, strategic construction of national categories and boundaries. Less attention has been paid to the conflict between these processes for those whose claims to national identity may be problematic. In the present study, focus groups were conducted with 36 Roman Catholic adolescents living in border regions of Ireland, in which participants were asked to talk about their own and others’ Irish national identity. Discursive analysis of the data revealed that those in the Republic of Ireland strategically displayed their national identity as obvious and ‘banal’, while those in Northern Ireland proactively claimed their Irishness. Moreover, those in Northern Ireland displayed an assumption that their fellow Irish in the Republic shared their imperative to assert national identity, while those in the Republic actively distanced themselves from this version of Irishness. These results suggest that for dominant ethnic groups, ‘banality’ may itself provide a marker of national identity while paradoxically the proactive display of national identity undermines minority groups claims to national identity.
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The relationship between migration and age has long been established, and most recently, there have been calls for the inclusion of a life course perspective to migration research. In this paper, we explore Northern Ireland’s internal migration patterns, and in particular, we test for the importance of urban to rural migration at different stages of the life course. Data from the Northern Ireland Longitudinal Study are used for the first time to analyse urban–rural migration patterns. The resulting modelling demonstrates unique aspects of urban to rural migration within Northern Ireland, which up until now have gone largely
unreported. Results from logistic regression modelling suggest that there is an age selectivity to urban– rural mobility but not necessarily at the life course stages predicted from a review of the life course migration literature. Individuals in younger age groups (at the household and family formation stages of the life course) are most likely to make an urban to rural move in Northern Ireland, with a decline in the likelihood of this move type with age. Possible explanations are offered linked to Northern Ireland’s settlement hierarchy, rural planning policy, and family farming traditions. The findings challenge researchers to pay due attention to how migration processes may play out differently in varying geographical, social, and planning contexts and emphasise the importance of structural factors to explain migration patterns.
Resumo:
Background: Ineffective risk stratification can delay diagnosis of serious disease in patients with hematuria. We applied a systems biology approach to analyze clinical, demographic and biomarker measurements (n = 29) collected from 157 hematuric patients: 80 urothelial cancer (UC) and 77 controls with confounding pathologies.
Methods: On the basis of biomarkers, we conducted agglomerative hierarchical clustering to identify patient and biomarker clusters. We then explored the relationship between the patient clusters and clinical characteristics using Chi-square analyses. We determined classification errors and areas under the receiver operating curve of Random Forest Classifiers (RFC) for patient subpopulations using the biomarker clusters to reduce the dimensionality of the data.
Results: Agglomerative clustering identified five patient clusters and seven biomarker clusters. Final diagnoses categories were non-randomly distributed across the five patient clusters. In addition, two of the patient clusters were enriched with patients with ‘low cancer-risk’ characteristics. The biomarkers which contributed to the diagnostic classifiers for these two patient clusters were similar. In contrast, three of the patient clusters were significantly enriched with patients harboring ‘high cancer-risk” characteristics including proteinuria, aggressive pathological stage and grade, and malignant cytology. Patients in these three clusters included controls, that is, patients with other serious disease and patients with cancers other than UC. Biomarkers which contributed to the diagnostic classifiers for the largest ‘high cancer- risk’ cluster were different than those contributing to the classifiers for the ‘low cancer-risk’ clusters. Biomarkers which contributed to subpopulations that were split according to smoking status, gender and medication were different.
Conclusions: The systems biology approach applied in this study allowed the hematuric patients to cluster naturally on the basis of the heterogeneity within their biomarker data, into five distinct risk subpopulations. Our findings highlight an approach with the promise to unlock the potential of biomarkers. This will be especially valuable in the field of diagnostic bladder cancer where biomarkers are urgently required. Clinicians could interpret risk classification scores in the context of clinical parameters at the time of triage. This could reduce cystoscopies and enable priority diagnosis of aggressive diseases, leading to improved patient outcomes at reduced costs. © 2013 Emmert-Streib et al; licensee BioMed Central Ltd.
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Web sites that rely on databases for their content are now ubiquitous. Query result pages are dynamically generated from these databases in response to user-submitted queries. Automatically extracting structured data from query result pages is a challenging problem, as the structure of the data is not explicitly represented. While humans have shown good intuition in visually understanding data records on a query result page as displayed by a web browser, no existing approach to data record extraction has made full use of this intuition. We propose a novel approach, in which we make use of the common sources of evidence that humans use to understand data records on a displayed query result page. These include structural regularity, and visual and content similarity between data records displayed on a query result page. Based on these observations we propose new techniques that can identify each data record individually, while ignoring noise items, such as navigation bars and adverts. We have implemented these techniques in a software prototype, rExtractor, and tested it using two datasets. Our experimental results show that our approach achieves significantly higher accuracy than previous approaches. Furthermore, it establishes the case for use of vision-based algorithms in the context of data extraction from web sites.
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High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.
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Prader-Willi syndrome (PWS) and Fragile X syndrome (FraX) are associated with distinctive cognitive and behavioural profiles. We examined whether repetitive behaviours in the two syndromes were associated with deficits in specific executive functions. PWS, FraX, and typically developing (TD) children were assessed for executive functioning using the Test of Everyday Attention for Children and an adapted Simon spatial interference task. Relative to the TD children, children with PWS and FraX showed greater costs of attention switching on the Simon task, but after controlling for intellectual ability, these switching deficits were only significant in the PWS group. Children with PWS and FraX also showed significantly increased preference for routine and differing profiles of other specific types of repetitive behaviours. A measure of switch cost from the Simon task was positively correlated to scores on preference for routine questionnaire items and was strongly associated with scores on other items relating to a preference for predictability. It is proposed that a deficit in attention switching is a component of the endophenotypes of both PWS and FraX and is associated with specific behaviours. This proposal is discussed in the context of neurocognitive pathways between genes and behaviour.
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Understanding how invasive species spread is of particular concern in the current era of globalisation and rapid environmental change. The occurrence of super-diffusive movements within the context of Lévy flights has been discussed with respect to particle physics, human movements, microzooplankton, disease spread in global epidemiology and animal foraging behaviour. Super-diffusive movements provide a theoretical explanation for the rapid spread of organisms and disease, but their applicability to empirical data on the historic spread of organisms has rarely been tested. This study focuses on the role of long-distance dispersal in the invasion dynamics of aquatic invasive species across three contrasting areas and spatial scales: open ocean (north-east Atlantic), enclosed sea (Mediterranean) and an island environment (Ireland). Study species included five freshwater plant species, Azolla filiculoides, Elodea canadensis, Lagarosiphon major, Elodea nuttallii and Lemna minuta; and ten species of marine algae, Asparagopsis armata, Antithamnionella elegans, Antithamnionella ternifolia, Codium fragile, Colpomenia peregrina, Caulerpa taxifolia, Dasysiphonia sp., Sargassum muticum, Undaria pinnatifida and Womersleyella setacea. A simulation model is constructed to show the validity of using historical data to reconstruct dispersal kernels. Lévy movement patterns similar to those previously observed in humans and wild animals are evident in the re-constructed dispersal pattern of invasive aquatic species. Such patterns may be widespread among invasive species and could be exacerbated by further development of trade networks, human travel and environmental change. These findings have implications for our ability to predict and manage future invasions, and improve our understanding of the potential for spread of organisms including infectious diseases, plant pests and genetically modified organisms.
Resumo:
Recent advances in hardware development coupled with the rapid adoption and broad applicability of cloud computing have introduced widespread heterogeneity in data centers, significantly complicating the management of cloud applications and data center resources. This paper presents the CACTOS approach to cloud infrastructure automation and optimization, which addresses heterogeneity through a combination of in-depth analysis of application behavior with insights from commercial cloud providers. The aim of the approach is threefold: to model applications and data center resources, to simulate applications and resources for planning and operation, and to optimize application deployment and resource use in an autonomic manner. The approach is based on case studies from the areas of business analytics, enterprise applications, and scientific computing.