Yukawa couplings in SO(10) heterotic M-theory vacua

We demonstrate the existence of a class of N=1 supersymmetric nonperturbative vacua of Horava-Witten M-theory compactified on a torus fibered Calabi-Yau 3-fold Z with first homotopy group \pi_{1}(Z)= Z2, having the following properties: 1) SO(10) grand unification group, 2) net number of three generations of chiral fermions in the observable sector, and 3) potentially viable matter Yukawa couplings. These vacua correspond to semistable holomorphic vector bundles V_{Z} over Z having structure group SU(4)_C, and generically contain M5-branes in the bulk space. The nontrivial first homotopy group allows Wilson line breaking of the SO(10) symmetry. Additionally, we propose how the 11-dimensional Horava-Witten M-theory framework may be used to extend the perturbative calculation of the top quark Yukawa coupling in the realistic free-fermionic models to the nonperturbative regime. The basic argument being that the relevant coupling couples twisted-twisted-untwisted states and can be calculated at the level of the Z2 X Z2 orbifold without resorting to the full three generation models.Comment: 24 pages. Standard LaTe

Comparing TMS perturbations to occipital and parietal cortices in concurrent TMS-fMRI studies-Methodological considerations

Neglect and hemianopia are two neuropsychological syndromes that are associated with reduced awareness for visual signals in patients' contralesional hemifield. They offer the unique possibility to dissociate the contributions of retino-geniculate and retino-colliculo circuitries in visual perception. Yet, insights from patient fMRI studies are limited by heterogeneity in lesion location and extent, long-term functional reorganization and behavioural compensation after stroke. Transcranial magnetic stimulation (TMS) has therefore been proposed as a complementary method to investigate the effect of transient perturbations on functional brain organization. This concurrent TMS-fMRI study applied TMS perturbation to occipital and parietal cortices with the aim to 'mimick' neglect and hemianopia. Based on the challenges and interpretational limitations of our own study we aim to provide tutorial guidance on how future studies should compare TMS to primary sensory and association areas that are governed by distinct computational principles, neural dynamics and functional architecture

Social media analytics and the role of twitter in the 2014 South Africa general election: a case study

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science., University of the Witwatersrand, Johannesburg, 2018Social network sites such as Twitter have created vibrant and diverse communities in which users express their opinions and views on a variety of topics such as politics. Extensive research has been conducted in countries such as Ireland, Germany and the United States, in which text mining techniques have been used to obtain information from politically oriented tweets. The purpose of this research was to determine if text mining techniques can be used to uncover meaningful information from a corpus of political tweets collected during the 2014 South African General Election. The Twitter Application Programming Interface was used to collect tweets that were related to the three major political parties in South Africa, namely: the African National Congress (ANC), the Democratic Alliance (DA) and the Economic Freedom Fighters (EFF). The text mining techniques used in this research are: sentiment analysis, clustering, association rule mining and word cloud analysis. In addition, a correlation analysis was performed to determine if there exists a relationship between the total number of tweets mentioning a political party and the total number of votes obtained by that party. The VADER (Valence Aware Dictionary for sEntiment Reasoning) sentiment classifier was used to determine the public’s sentiment towards the three main political parties. This revealed an overwhelming neutral sentiment of the public towards the ANC, DA and EFF. The result produced by the VADER sentiment classifier was significantly greater than any of the baselines in this research. The K-Means cluster algorithm was used to successfully cluster the corpus of political tweets into political-party clusters. Clusters containing tweets relating to the ANC and EFF were formed. However, tweets relating to the DA were scattered across multiple clusters. A fairly strong relationship was discovered between the number of positive tweets that mention the ANC and the number of votes the ANC received in election. Due to the lack of data, no conclusions could be made for the DA or the EFF. The apriori algorithm uncovered numerous association rules, some of which were found to be interest- ing. The results have also demonstrated the usefulness of word cloud analysis in providing easy-to-understand information from the tweet corpus used in this study. This research has highlighted the many ways in which text mining techniques can be used to obtain meaningful information from a corpus of political tweets. This case study can be seen as a contribution to a research effort that seeks to unlock the information contained in textual data from social network sites.MT 201

Feature Augmentation for Improved Topic Modeling of Youtube Lecture Videos using Latent Dirichlet Allocation

Application of Topic Models in text mining of educational data and more specifically, the text data obtained from lecture videos, is an area of research which is largely unexplored yet holds great potential. This work seeks to find empirical evidence for an improvement in Topic Modeling by pre- extracting bigram tokens and adding them as additional features in the Latent Dirichlet Allocation (LDA) algorithm, a widely-recognized topic modeling technique. The dataset considered for analysis is a collection of transcripts of video lectures on Machine Learning scraped from YouTube. Using the cosine similarity distance measure as a metric, the experiment showed a statistically significant improvement in topic model performance against the baseline topic model which did not use extra features, thus confirming the hypothesis. By introducing explainable features before modeling and using deep learning based text representation only at the post-modeling evaluation stage, the overall model interpretability is retained. This empowers educators and researchers alike to not only benefit from the LDA model in their own fields but also to play a substantial role in eorts to improve model performance. It also sets the direction for future work which could use the feature augmented topic model as the input to other more common text mining tasks like document categorization and information retrieval

Evolutionary Decomposition of Complex Design Spaces

This dissertation investigates the support of conceptual engineering design through the decomposition of multi-dimensional search spaces into regions of high performance. Such decomposition helps the designer identify optimal design directions by the elimination of infeasible or undesirable regions within the search space. Moreover, high levels of interaction between the designer and the model increases overall domain knowledge and significantly reduces uncertainty relating to the design task at hand. The aim of the research is to develop the archetypal Cluster Oriented Genetic Algorithm (COGA) which achieves search space decomposition by using variable mutation (vmCOGA) to promote diverse search and an Adaptive Filter (AF) to extract solutions of high performance [Parmee 1996a, 1996b]. Since COGAs are primarily used to decompose design domains of unknown nature within a real-time environment, the elimination of apriori knowledge, speed and robustness are paramount. Furthermore COGA should promote the in-depth exploration of the entire search space, sampling all optima and the surrounding areas. Finally any proposed system should allow for trouble free integration within a Graphical User Interface environment. The replacement of the variable mutation strategy with a number of algorithms which increase search space sampling are investigated. Utility is then increased by incorporating a control mechanism that maintains optimal performance by adapting each algorithm throughout search by means of a feedback measure based upon population convergence. Robustness is greatly improved by modifying the Adaptive Filter through the introduction of a process that ensures more accurate modelling of the evolving population. The performance of each prospective algorithm is assessed upon a suite of two-dimensional test functions using a set of novel performance metrics. A six dimensional test function is also developed where the areas of high performance are explicitly known, thus allowing for evaluation under conditions of increased dimensionality. Further complexity is introduced by two real world models described by both continuous and discrete parameters. These relate to the design of conceptual airframes and cooling hole geometries within a gas turbine. Results are promising and indicate significant improvement over the vmCOGA in terms of all desired criteria. This further supports the utilisation of COGA as a decision support tool during the conceptual phase of design.British Aerospace plc, Warton and Rolls Royce plc, Filto

Automatic identification of physical activity intensity and modality from the fusion of accelerometry and heart rate data

Background: Physical activity (PA) is essential to prevent and to treat a variety of chronic diseases. The automated detection and quantification of PA over time empowers lifestyle interventions, facilitating reliable exercise tracking and data-driven counseling. Methods: We propose and compare various combinations of machine learning (ML) schemes for the automatic classification of PA from multi-modal data, simultaneously captured by a biaxial accelerometer and a heart rate (HR) monitor. Intensity levels (low/moderate/vigorous) were recognized, as well as for vigorous exercise, its modality (sustained aerobic/resistance/mixed). In total, 178.63 h of data about PA intensity (65.55% low/18.96% moderate/15.49% vigorous) and 17.00 h about modality were collected in two experiments: one in free-living conditions, another in a fitness center under controlled protocols. The structure used for automatic classification comprised: a) definition of 42 time-domain signal features, b) dimensionality reduction, c) data clustering, and d) temporal filtering to exploit time redundancy by means of a Hidden Markov Model (HMM). Four dimensionality reduction techniques and four clustering algorithms were studied. In order to cope with class imbalance in the dataset, a custom performance metric was defined to aggregate recognition accuracy, precision and recall. Results: The best scheme, which comprised a projection through Linear Discriminant Analysis (LDA) and k-means clustering, was evaluated in leave-one-subject-out cross-validation; notably outperforming the standard industry procedures for PA intensity classification: score 84.65%, versus up to 63.60%. Errors tended to be brief and to appear around transients. Conclusions: The application of ML techniques for pattern identification and temporal filtering allowed to merge accelerometry and HR data in a solid manner, and achieved markedly better recognition performances than the standard methods for PA intensity estimation

Sparse algorithms for EEG source localization

Source localization using EEG is important in diagnosing various physiological and psychiatric diseases related to the brain. The high temporal resolution of EEG helps medical professionals assess the internal physiology of the brain in a more informative way. The internal sources are obtained from EEG by an inversion process. The number of sources in the brain outnumbers the number of measurements. In this article, a comprehensive review of the state of the art sparse source localization methods in this field is presented. A recently developed method, certainty based reduced sparse solution (CARSS), is implemented and is examined. A vast comparative study is performed using a sixty four channel setup involving two source spaces. The first source space has 5004 sources and the other has 2004 sources. Four test cases with one, three, five, and seven simulated active sources are considered. Two noise levels are also being added to the noiseless data. The CARSS is also evaluated. The results are examined. A real EEG study is also attempted.Comment: Published in Medical & Biological Engineering & Computing, Springer on Oct 02, 202