'n Ondersoek na die verband tussen leierskappotensiaal en sekere biografiese veranderlikes onder 'n groep universiteitstudente

The original publication is available at http://www.sajip.co.zaThe study investigates whether university students, classified as leaders and non-leaders differ with respect to their responses to a number of biographical items. The results indicate that the following biographical variables do differentiate between leaders and non-leaders: educational qualifications and occupation of father, number of times the family moved, class position and leader activities during high school, parents’ motivation of and interest in child, extent of freedom enjoyed during childhood, membership of social, religious and other organizations during high school, number of hobbies and extent of confidence experienced in strange situations. Not all the items were found to be equally relevant in die case of female and male leaders, however.Publisher's versio

The end of essentialist gods and Ubuntu: a feminist critical investigation

Abstract:The focus on god and Ubuntu constructs affirms the fact that people are natural social constructivists involved in a continuous process of conceptualising ideas that give meaning to their contexts. The juxtaposing of these two constructs extends what is known of new god ideas to that of Ubuntu or African ‘humanness.’ Whereas ideology criticism served as the broad overarching hermeneutical tool for this study, feminism was used as the specific interpretative framework to critically scrutinise mostly patriarchally biased conceptualisations of god and Ubuntu. In contrast to Western feminism, African womanism, with its emphasis on African Motherhood/Womanhood, contributed to a much needed contextual and culturally sensitive analysis of Ubuntu in particular. It was concluded that there are various god and Ubuntu constructs and that it is no longer tenable to claim a single (dogmatic/essentialist) god or a single (dogmatic) Ubuntu to structure reality meaningfully. ‘Truth’ lives only momentarily as history continues to unfold and people find new ways in their search for meaning

Out-of-Distribution Detection of Melanoma using Normalizing Flows

Generative modelling has been a topic at the forefront of machine learning research for a substantial amount of time. With the recent success in the field of machine learning, especially in deep learning, there has been an increased interest in explainable and interpretable machine learning. The ability to model distributions and provide insight in the density estimation and exact data likelihood is an example of such a feature. Normalizing Flows (NFs), a relatively new research field of generative modelling, has received substantial attention since it is able to do exactly this at a relatively low cost whilst enabling competitive generative results. While the generative abilities of NFs are typically explored, we focus on exploring the data distribution modelling for Out-of-Distribution (OOD) detection. Using one of the state-of-the-art NF models, GLOW, we attempt to detect OOD examples in the ISIC dataset. We notice that this model under performs in conform related research. To improve the OOD detection, we explore the masking methods to inhibit co-adaptation of the coupling layers however find no substantial improvement. Furthermore, we utilize Wavelet Flow which uses wavelets that can filter particular frequency components, thus simplifying the modeling process to data-driven conditional wavelet coefficients instead of complete images. This enables us to efficiently model larger resolution images in the hopes that it would capture more relevant features for OOD. The paper that introduced Wavelet Flow mainly focuses on its ability of sampling high resolution images and did not treat OOD detection. We present the results and propose several ideas for improvement such as controlling frequency components, using different wavelets and using other state-of-the-art NF architectures

Towards real-time 6D pose estimation of objects in single-view cone-beam X-ray

Deep learning-based pose estimation algorithms can successfully estimate the pose of objects in an image, especially in the field of color images. 6D Object pose estimation based on deep learning models for X-ray images often use custom architectures that employ extensive CAD models and simulated data for training purposes. Recent RGB-based methods opt to solve pose estimation problems using small datasets, making them more attractive for the X-ray domain where medical data is scarcely available. We refine an existing RGB-based model (SingleShotPose) to estimate the 6D pose of a marked cube from grayscale X-ray images by creating a generic solution trained on only real X-ray data and adjusted for X-ray acquisition geometry. The model regresses 2D control points and calculates the pose through 2D/3D correspondences using Perspective-n-Point(PnP), allowing a single trained model to be used across all supporting cone-beam-based X-ray geometries. Since modern X-ray systems continuously adjust acquisition parameters during a procedure, it is essential for such a pose estimation network to consider these parameters in order to be deployed successfully and find a real use case. With a 5-cm/5-degree accuracy of 93% and an average 3D rotation error of 2.2 degrees, the results of the proposed approach are comparable with state-of-the-art alternatives, while requiring significantly less real training examples and being applicable in real-time applications.Comment: Published at SPIE Medical Imaging 202

Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging

Data uncertainties, such as sensor noise or occlusions, can introduce irreducible ambiguities in images, which result in varying, yet plausible, semantic hypotheses. In Machine Learning, this ambiguity is commonly referred to as aleatoric uncertainty. Latent density models can be utilized to address this problem in image segmentation. The most popular approach is the Probabilistic U-Net (PU-Net), which uses latent Normal densities to optimize the conditional data log-likelihood Evidence Lower Bound. In this work, we demonstrate that the PU- Net latent space is severely inhomogenous. As a result, the effectiveness of gradient descent is inhibited and the model becomes extremely sensitive to the localization of the latent space samples, resulting in defective predictions. To address this, we present the Sinkhorn PU-Net (SPU-Net), which uses the Sinkhorn Divergence to promote homogeneity across all latent dimensions, effectively improving gradient-descent updates and model robustness. Our results show that by applying this on public datasets of various clinical segmentation problems, the SPU-Net receives up to 11% performance gains compared against preceding latent variable models for probabilistic segmentation on the Hungarian-Matched metric. The results indicate that by encouraging a homogeneous latent space, one can significantly improve latent density modeling for medical image segmentation.Comment: 12 pages incl. references, 11 figure

Aphrati and Kato Syme: Pottery, Continuity, and Cult in Late Archaic and Classical Crete

The analysis of ceramics from Aphrati sheds valuable new light on the history of this Cretan settlement and on its relationship with a nearby rural sanctuary at Kato Syme in the Late Archaic and Classical periods. It has long been held that Aphrati was deserted from ca. 600 to 400 B.C. A pottery deposit from the domestic quarter, however, now supports occupation of the city during this period. A ceramic classification system is presented and the morphological development and absolute chronology of several key shapes at Aphrati and Kato Syme are plotted. Historical implications of the ceramic evidence are also explored