Conceptualising neuroscience-based leadership behaviour

This thesis primarily focuses on conceptualising Neuroscience Based Leadership (NSBL) by providing a working definition of NSBL, describing the foundational concepts and core behaviours of neuroscience-based leadership (NSBL), and presenting a conceptual framework that integrates interdisciplinary perspectives on leadership behaviour. This was achieved by: 1. Reviewing existing relevant scientific literature and highlighting current knowledge gaps in the conceptualisations of NSBL using Leadership Behaviour, Social Cognitive Neuroscience (SCN), and Neuropsychotherapy (NP) 2. Conducting a small-scale research project using semi-structured, in-depth interviews with three neuroscientists who have employed neuroscience-based diagnostics in leadership development within a corporate context. This study’s key findings reveal key conceptual themes with the following theoretical propositions that underpin NSBL key behaviours: social safety is a primary operating principle; conscious thinking and nonconscious processes drive behaviour; nature-nurture dynamics influence behaviour; experienced-based neuroplasticity drives change; and overlapping large-scale brain networks enable information processing in the brain. 3. Designing and implementing a qualitative Delphi study involving 33 experienced professionals in NSBL to explore how NSBL is defined, conceptualise NSBL as a different domain of leadership behaviour, and provide descriptors of NSBL key behaviours 4. Adopting a case study approach involving an organisational psychologist experienced in Neuropsychotherapy and drawing on his views and experiences to produce a single-case study of NSBL within the context of organisational psychology and applied organisational neuroscience (AONS). 5. Undertaking a reflective and critical review of the four pieces of research and proposing a theoretical framework of NSBL, specifically within formal organisations, to inform, support, foster and develop future NSBL-based behaviour. The contribution of this study is broad in that it offers a working definition of neuroscience-based leadership and an interdisciplinary conceptual framework to guide practitioners and further research. This conceptual framework integrates theoretical propositions regarding leadership behaviour from Leadership Behaviour theory, Social Cognitive and Affective Neuroscience, and Neuropsychotherapy. The theoretical framework of NSBL addresses gaps in the literature by differentiating four domains of NSBL: stress resilience-focused core behaviours, affect and emotional-focused core behaviours, relationship-focused core behaviours, and task-focused core behaviours. It also provides neuroscientific concepts that underpin behaviour. The contribution to practice is that this study advances the understanding of how formal organisations can apply a neuroscientific lens to inform the design of leadership development interventions. This integrative, interdisciplinary theoretical framework can be used for leadership coaching at an individual level. At the group level, it can facilitate team building. It can provide a neuroscientific language for mental experience at an organisational level, thereby enhancing the explanatory power of concepts in leadership and organisational behaviour

Machine learning in astronomy

The search to find answers to the deepest questions we have about the Universe has fueled the collection of data for ever larger volumes of our cosmos. The field of supernova cosmology, for example, is seeing continuous development with upcoming surveys set to produce a vast amount of data that will require new statistical inference and machine learning techniques for processing and analysis. Distinguishing between real objects and artefacts is one of the first steps in any transient science pipeline and, currently, is still carried out by humans - often leading to hand scanners having to sort hundreds or thousands of images per night. This is a time-consuming activity introducing human biases that are extremely hard to characterise. To succeed in the objectives of future transient surveys, the successful substitution of human hand scanners with machine learning techniques for the purpose of this artefact-transient classification therefore represents a vital frontier. In this thesis we test various machine learning algorithms and show that many of them can match the human hand scanner performance in classifying transient difference g, r and i-band imaging data from the SDSS-II SN Survey into real objects and artefacts. Using principal component analysis and linear discriminant analysis, we construct a grand total of 56 feature sets with which to train, optimise and test a Minimum Error Classifier (MEC), a naive Bayes classifier, a k-Nearest Neighbours (kNN) algorithm, a Support Vector Machine (SVM) and the SkyNet artificial neural network

Facilitating Multilingual Tutorials at the University of the Free State

Conducting undergraduate studies in the English language, while only a small minority of students speak English at home, poses many problems to learning in the South African context. This article explores how restrictive language policies may influence proper learning and impact negatively on the self-understanding of students. It also explores how multilingualism could help to reduce the continued reliance on English, without doing away with English in its entirety. This is especially relevant in light of English and other colonial languages still being perceived as “languages of power” (Stroud & Kerfoot, 2013, p. 403). Therefore, attention is given to the link between language and power, especially in light of languages often being used to implement, display and preserve power. Language use in the classroom, especially with regard to codeswitching (also called translanguaging), is discussed. Finally, it explores the success that was achieved during multilingual tutorial sessions. In the tutorials, students were encouraged to explore the course work in their native languages, thereby internalising it and getting a better understanding thereof

Machine Learning Classification of SDSS Transient Survey Images

We show that multiple machine learning algorithms can match human performance in classifying transient imaging data from the Sloan Digital Sky Survey (SDSS) supernova survey into real objects and artefacts. This is a first step in any transient science pipeline and is currently still done by humans, but future surveys such as the Large Synoptic Survey Telescope (LSST) will necessitate fully machine-enabled solutions. Using features trained from eigenimage analysis (principal component analysis, PCA) of single-epoch g, r and i-difference images, we can reach a completeness (recall) of 96 per cent, while only incorrectly classifying at most 18 per cent of artefacts as real objects, corresponding to a precision (purity) of 84 per cent. In general, random forests performed best, followed by the k-nearest neighbour and the SkyNet artificial neural net algorithms, compared to other methods such as na\"ive Bayes and kernel support vector machine. Our results show that PCA-based machine learning can match human success levels and can naturally be extended by including multiple epochs of data, transient colours and host galaxy information which should allow for significant further improvements, especially at low signal-to-noise.Comment: 14 pages, 8 figures. In this version extremely minor adjustments to the paper were made - e.g. Figure 5 is now easier to view in greyscal

The diagnostic patterns of the iso-enitmes of serum non-specific alkaline phosphatase

No Abstract

Performance limits of information engines

We review recent studies of a colloidal information engine that consists of a bead in water and held by an optical trap. The bead is ratcheted upward without any apparent external work, by taking advantage of favorable thermal fluctuations. Much of the previous work on such engines aimed to show that accounting for information-processing costs can reconcile the observed motion with the second law of thermodynamics. By contrast, we focus on the factors that limit the performance of such engines by optimizing variously the upward velocity, rate of gravitational free-energy extraction, or ability to track a trajectory. We then consider measurement noise, which degrades engine performance. A naive use of noisy measurements in the feedback algorithm leads to a phase transition at finite signal-to-noise ratio: below the transition, the engine no longer functions. A more sophisticated, `Bayesian' algorithm eliminates the phase transition and improves performance. Finally, operating the information engine in a nonequilibrium environment with extra force fluctuations can enhance the performance by orders of magnitude, even to the point where the energy extracted exceeds that needed to run the information processing. Autonomous implementations of an information engine in such environments could be powered entirely by the additional energy of the bath.Comment: 25 pages, 10 figure

Large deviations of the stochastic area for linear diffusions

The area enclosed by the two-dimensional Brownian motion in the plane was studied by L\'evy, who found the characteristic function and probability density of this random variable. For other planar processes, in particular ergodic diffusions described by linear stochastic differential equations (SDEs), only the expected value of the stochastic area is known. Here, we calculate the generating function of the stochastic area for linear SDEs, which can be related to the integral of the angular momentum, and extract from the result the large deviation functions characterising the dominant part of its probability density in the long-time limit, as well as the effective SDE describing how large deviations arise in that limit. In addition, we obtain the asymptotic mean of the stochastic area, which is known to be related to the probability current, and the asymptotic variance, which is important for determining from observed trajectories whether or not a diffusion is reversible. Examples of reversible and irreversible linear SDEs are studied to illustrate our results.Comment: v1: 13 pages, 7 figures; v2: minor errors corrected; v3: minor edits, close to published versio