Snapshots of complexity: using motion capture and principal component analysis to reconceptualise dance

This article brings together the disparate worlds of dance practice, motion capture and statistical analysis. Digital technologies such as motion capture offer dance artists new processes for recording and studying dance movement. Statistical analysis of these data can reveal hidden patterns in movement in ways that are semantically ‘blind’, and are hence able to challenge accepted culturo-physical ‘grammars’ of dance creation. The potential benefit to dance artists is to open up new ways of understanding choreographic movement. However, quantitative analysis does not allow for the uncertainty inherent in emergent, artistic practices such as dance. This article uses motion capture and principal component analysis (PCA), a common statistical technique in human movement recognition studies, to examine contemporary dance movement, and explores how this analysis might be interpreted in an artistic context to generate a new way of looking at the nature and role of movement patterning in dance creation.

Beyond the usual suspects: complexity of fragility and analytical framework

Development in so-called ‘fragile states’ has become a key priority for the international community over the past few years, but international actors have not yet adequately incorporated sufficiently nuanced understandings of fragility into policies or practices. The increasing proportion of the world’s poor living in fragile contexts, the depth of human need in these contexts, and the potential regional spillover implications of this fragility, all make this an urgent concern. This chapter examines this growing need and discusses the origins and methodological approach in this volume, before setting up the rest of the book with definitions and an analysis framework. The chapter concludes with a summary of the book chapters and contributions.

The diversity and complexity of settings and arrangements forming the ‘experienced environments’ for doctoral candidates : some implications for doctoral education

A significant feature of contemporary doctoral education is the continuing trend for research and research education to migrate beyond discipline-based institutional teaching and research structures. The result is a more diverse array of settings and arrangements for doctoral education linked to an increasingly global research enterprise. Recognising the complexity of what is a distributed environment challenges some commonly held assumptions about doctoral education and its practice. Drawing on data gathered in an Australian study of PhD programme development in Australia carried out in 2006–2009, the article describes the fluid and complex arrangements forming the ‘experienced environments’ for doctoral candidates, an environment that can afford them varying opportunities and challenges for completing their candidacy. Some implications for doctoral education are discussed.

Distribution Entropy (DistEn) : a complexity measure to detect arrhythmia from short length RR interval time series

Heart rate complexity analysis is a powerful non-invasive means to diagnose several cardiac ailments. Non-linear tools of complexity measurement are indispensable in order to bring out the complete non-linear behavior of Physiological signals. The most popularly used non-linear tools to measure signal complexity are the entropy measures like Approximate entropy (ApEn) and Sample entropy (SampEn). But, these methods become unreliable and inaccurate at times, in particular, for short length data. Recently, a novel method of complexity measurement called Distribution Entropy (DistEn) was introduced, which showed reliable performance to capture complexity of both short term synthetic and short term physiologic data. This study aims to i) examine the competence of DistEn in discriminating Arrhythmia from Normal sinus rhythm (NSR) subjects, using RR interval time series data; ii) explore the level of consistency of DistEn with data length N; and iii) compare the performance of DistEn with ApEn and SampEn. Sixty six RR interval time series data belonging to two groups of cardiac conditions namely `Arrhythmia' and `NSR' have been used for the analysis. The data length N was varied from 50 to 1000 beats with embedding dimension m = 2 for all entropy measurements. Maximum ROC area obtained using ApEn, SampEn and DistEn were 0.83, 0.86 and 0.94 for data length 1000, 1000 and 500 beats respectively. The results show that DistEn undoubtedly exhibits a consistently high performance as a classification feature in comparison with ApEn and SampEn. Therefore, DistEn shows a promising behavior as bio marker for detecting Arrhythmia from short length RR interval data.

A novel time computation model based on algorithm complexity for data intensive scientific workflow design and scheduling

Scientific workflow offers a framework for cooperation between remote and shared resources on a grid computing environment (GCE) for scientific discovery. One major function of scientific workflow is to schedule a collection of computational subtasks in well-defined orders for efficient outputs by estimating task duration at runtime. In this paper, we propose a novel time computation model based on algorithm complexity (termed as TCMAC model) for high-level data intensive scientific workflow design. The proposed model schedules the subtasks based on their durations and the complexities of participant algorithms. Characterized by utilization of task duration computation function for time efficiency, the TCMAC model has three features for a full-aspect scientific workflow including both dataflow and control-flow: (1) provides flexible and reusable task duration functions in GCE;(2) facilitates better parallelism in iteration structures for providing more precise task durations;and (3) accommodates dynamic task durations for rescheduling in selective structures of control flow. We will also present theories and examples in scientific workflows to show the efficiency of the TCMAC model, especially for control-flow. Copyright©2009 John Wiley & Sons, Ltd.

The impact of supply chain complexity on operational performance

Drawn on the System Complexity literature, this study investigated how supply chain complexity impacts firms’ operational performance and what role supply chain orientation plays in complexity-performance relationship. The study provided empirical evidence for the argument that dynamic complexity as opposed to structural complexity is more difficult for firms to effectively accommodate.

A bridge over troubling waters in education: the complexity of a "students as co-researchers" project

“Students as co-researchers” is a mode of engagement between students and teachers in school systems that has been likened to a bridge. This article explores the bridge metaphor with reference to one school’s experience of a students as co-researchers project involving students and teachers in the school and a university partner. We use the bridge metaphor, inspired by the imagist poet Ezra Pound, to explore particular challenges faced in this project, and to envision new modes of teacher/student relationships in education. We argue that the purpose of building such a bridge between students and teachers is not an instrumental one (to reach the other side), but rather that the bridge offers up zones of affective relational encounters between students and teachers.