“Do You Want to See My Hornpipe?”: The Traditional Irish Dancing Body at Play in the Work of Jean Butler and Colin Dunne.

This chapter considers the radical re-imaginings of traditional Irish step dance in the recent works of Jean Butler and Colin Dunne. In Butler's Does She Take Sugar (2007) and Dunne's Out of Time (2008), the Irish step dancing body is separated from its historical roots in nationalism, from the exhibitionism required by the competitive form, and from the spectacularization of the commercialized theatrical format. In these works, which are both solo pieces performed by the choreographers themselves, the traditional form undergoes a critical interrogation in which the dancers attempt to depart from the determinacy of the traditional technique, while acknowledging its formation of their corporealities; the Irish step dance technique becomes a springboard for creative experimentation. In order to consider the importance of the creative potential revealed by these works, this chapter will contextualize them within the dance background from which they emerged, outlining the history of competitive step dancing in Ireland, the "modernization" of traditional Irish dance with the emergence of Riverdance (1994), and the experiments of Ireland's national folk theatre, Siamsa Tíre.

Laughter Type Recognition from Whole Body Motion

Despite the importance of laughter in social interactions it remains little studied in affective computing. Respiratory, auditory, and facial laughter signals have been investigated but laughter-related body movements have received almost no attention. The aim of this study is twofold: first an investigation into observers' perception of laughter states (hilarious, social, awkward, fake, and non-laughter) based on body movements alone, through their categorization of avatars animated with natural and acted motion capture data. Significant differences in torso and limb movements were found between animations perceived as containing laughter and those perceived as nonlaughter. Hilarious laughter also differed from social laughter in the amount of bending of the spine, the amount of shoulder rotation and the amount of hand movement. The body movement features indicative of laughter differed between sitting and standing avatar postures. Based on the positive findings in this perceptual study, the second aim is to investigate the possibility of automatically predicting the distributions of observer's ratings for the laughter states. The findings show that the automated laughter recognition rates approach human rating levels, with the Random Forest method yielding the best performance.

Digital pathology and image analysis in tissue biomarker research

Digital pathology and the adoption of image analysis have grown rapidly in the last few years. This is largely due to the implementation of whole slide scanning, advances in software and computer processing capacity and the increasing importance of tissue-based research for biomarker discovery and stratified medicine. This review sets out the key application areas for digital pathology and image analysis, with a particular focus on research and biomarker discovery. A variety of image analysis applications are reviewed including nuclear morphometry and tissue architecture analysis, but with emphasis on immunohistochemistry and fluorescence analysis of tissue biomarkers. Digital pathology and image analysis have important roles across the drug/companion diagnostic development pipeline including biobanking, molecular pathology, tissue microarray analysis, molecular profiling of tissue and these important developments are reviewed. Underpinning all of these important developments is the need for high quality tissue samples and the impact of pre-analytical variables on tissue research is discussed. This requirement is combined with practical advice on setting up and running a digital pathology laboratory. Finally, we discuss the need to integrate digital image analysis data with epidemiological, clinical and genomic data in order to fully understand the relationship between genotype and phenotype and to drive discovery and the delivery of personalized medicine.

Perception and Automatic Recognition of Laughter from Whole-Body Motion: Continuous and Categorical Perspectives

Despite its importance in social interactions, laughter remains little studied in affective computing. Intelligent virtual agents are often blind to users’ laughter and unable to produce convincing laughter themselves. Respiratory, auditory, and facial laughter signals have been investigated but laughter-related body movements have received less attention. The aim of this study is threefold. First, to probe human laughter perception by analyzing patterns of categorisations of natural laughter animated on a minimal avatar. Results reveal that a low dimensional space can describe perception of laughter “types”. Second, to investigate observers’ perception of laughter (hilarious, social, awkward, fake, and non-laughter) based on animated avatars generated from natural and acted motion-capture data. Significant differences in torso and limb movements are found between animations perceived as laughter and those perceived as non-laughter. Hilarious laughter also differs from social laughter. Different body movement features were indicative of laughter in sitting and standing avatar postures. Third, to investigate automatic recognition of laughter to the same level of certainty as observers’ perceptions. Results show recognition rates of the Random Forest model approach human rating levels. Classification comparisons and feature importance analyses indicate an improvement in recognition of social laughter when localized features and nonlinear models are used.

Human Perception of Laughter from Context-free Whole Body Motion Dynamic Stimuli

Laughter is a ubiquitous social signal in human interactions yet it remains understudied from a scientific point of view. The need to understand laughter and its role in human interactions has become more pressing as the ability to create conversational agents capable of interacting with humans has come closer to a reality. This paper reports on three aspects of the human perception of laughter when context has been removed and only the body information from the laughter episode remains. We report on ability to categorise the laugh type and the sex of the laugher; the relationship between personality factors with laughter categorisation and perception; and finally the importance of intensity in the perception and categorisation of laughter.

The idea, the machine, and the art: word and image in the 21st century

Taking in recent advances in neuroscience and digital technology, Gander and Garland assess the state of the inter-arts in America and the Western world, exploring and questioning the primacy of affect in an increasingly hypertextual everyday environment. In this analysis they signal a move beyond W. J. T. Mitchell’s coinage of the ‘imagetext’ to an approach that centres the reader-viewer in a recognition, after John Dewey, of ‘art as experience’. New thinking in cognitive and computer sciences about the relationship between the body and the mind challenges any established definitions of ‘embodiment’, ‘materiality’, ‘virtuality’ and even ‘intelligence, they argue, whilst ‘Extended Mind Theory’, they note, marries our cognitive processes with the material forms with which we engage, confirming and complicating Marshall McLuhan’s insight, decades ago, that ‘all media are “extensions of man”’. In this chapter, Gander and Garland open paths and suggest directions into understandings and critical interpretations of new and emerging imagetext worlds and experiences.

Importance of risk factor management in diabetic patients and reduction in Stage B heart failure

BACKGROUND: A number of studies have demonstrated the presence of a diabetic cardiomyopathy, increasing the risk of heart failure development in this population. Improvements in present-day risk factor control may have modified the risk of diabetes-associated cardiomyopathy.

AIM: We sought to determine the contemporary impact of diabetes mellitus (DM) on the prevalence of cardiomyopathy in at-risk patients with and without adjustment for risk factor control.

DESIGN: A cross-sectional study in a population at risk for heart failure.

METHODS: Those with diabetes were compared to those with other cardiovascular risk factors, unmatched, matched for age and gender and then matched for age, gender, body mass index, systolic blood pressure and low density lipoprotein cholesterol.

RESULTS: In total, 1399 patients enrolled in the St Vincent's Screening to Prevent Heart Failure (STOP-HF) cohort were included. About 543 participants had an established history of DM. In the whole sample, Stage B heart failure (asymptomatic cardiomyopathy) was not found more frequently among the diabetic cohort compared to those without diabetes [113 (20.8%) vs. 154 (18.0%), P = 0.22], even when matched for age and gender. When controlling for these risk factors and risk factor control Stage B was found to be more prevalent in those with diabetes [88 (22.2%)] compared to those without diabetes [65 (16.4%), P = 0.048].

CONCLUSION: In this cohort of patients with established risk factors for Stage B heart failure superior risk factor management among the diabetic population appears to dilute the independent diabetic insult to left ventricular structure and function, underlining the importance and benefit of effective risk factor control in this population on cardiovascular outcomes.

Deep Corals, Deep Learning: Moving the Deep Net Towards Real-Time Image Annotation

The mismatch between human capacity and the acquisition of Big Data such as Earth imagery undermines commitments to Convention on Biological Diversity (CBD) and Aichi targets. Artificial intelligence (AI) solutions to Big Data issues are urgently needed as these could prove to be faster, more accurate, and cheaper. Reducing costs of managing protected areas in remote deep waters and in the High Seas is of great importance, and this is a realm where autonomous technology will be transformative.

‘Do you want to see my hornpipe?’: the traditional Irish dancing body at play in the work of Jean Butler and Colin Dunne’, in Anthony Shay and Barbara Sellers-Young (eds.), The Oxford Handbook of Dance and Ethnicity, Oxford: Oxford University Press, 2016, pp.41-65.

This chapter considers the radical reimaginings of traditional Irish step dance in the recent works of Jean Butler and Colin Dunne, in which the Irish step-dancing body is separated from its historical roots in nationalism, from the exhibitionism required by the competitive form, and from the spectacularization of the commercialized theatrical format. In these works the traditional form undergoes a critical interrogation in which the dancers attempt to depart from the determinacy of the traditional technique, while acknowledging its formation of their corporealities; the Irish step-dance technique becomes a springboard for creative experimentation. To consider the importance of the creative potential revealed by these works, this chapter contextualizes them within the dance background from which they emerged, outlining the history of competitive step dancing in Ireland, the “modernization” of traditional Irish dance with the emergence of Riverdance (1994), and the experiments of Ireland’s national folk theater, Siamsa Tíre.