Thesis (M. Com. (Business Management)) -- University of Limpopo, 2017 / The purpose of this study was to examine the determinants of the financial bootstrapping strategies used by rural based small, medium and micro enterprises (SMMEs). The study was guided by the following objectives, (1) To investigate the financial bootstrapping strategies used by rural entrepreneurs, (2) To examine the effect of the education of the owners on the financial bootstrapping strategies used by rural entrepreneurs, (3) To determine the effect of the age of the owners on the financial bootstrapping strategies used by rural entrepreneurs and (4) To investigate the effect of the gender of the owners on the financial bootstrapping strategies used by rural entrepreneurs. The research followed a quantitative research method with a descriptive research design. A sample of 104 rural SMMEs participated in the survey. Data was collected through the use of self-administered questionnaires in a survey. The participants in the study were rural SMME owners in Fetakgomo Municipality in the Limpopo province of South Africa. The study utilised the convenience and snowball sampling techniques to obtain data from the respondents. Data analysis included descriptive statistics, factor analysis, T-test and regression analysis. The Cronbach’s alpha was used to measure reliability. The results indicated that rural SMMEs have a low usage and adoption rate of bootstrapping finance. The research also found that rural SMMEs mainly utilised owner’s finance as the primary bootstrapping method. The results of the T-test showed significant differences between gender and bootstrapping methods as well as between the level of education and the bootstrapping methods used by rural SMMEs. However, there was no significant difference between age of the owners and bootstrapping methods used by rural SMMEs. Recommendations were ii proposed for the government to create an enabling environment for rural SMEs by embarking on a variety of strategies to raise awareness on the importance of bootstrapping finance. Rural SMME owners were recommended to enrol for a certificate course in financial management to sharpen their skills. Keywords: entrepreneurs’ characteristics, financial bootstrapping, rural area, SMMEs, South Africa.

The topic of my diploma thesis is the statistical evaluation of biological sequences with the help of phylogenic trees. In the theoretical part we will create a literary recherche of estimation methodology concerning the course of phylogeny on the basis of the similarity of biological sequences (DNA and proteins) and we will focus on the inaccuracies of the estimation, their causes and the possibilities of their elimination. Afterwards, we will compare the methods for the statistical evaluation of the correctness of the course of phylogeny. In the practical part of the thesis we will suggest algorithms that will be used for testing the correctness of the phylogenic trees on the basis of bootstrapping, jackknifing, OTU jackknifing and PTP test which are able to the capture phylogenic tree with the method neighbor joining from the biological sequences in FASTA code. It is also possible to change the distance model and the substitution matrix. To be able to use these algorithms for the statistical support of phylogenic trees we have to verify their right function. This verification will be evaluated on the theoretical sequences of the amino acids. For the verification of the correct function of the algorithms, we will carry out single statistical tests on real 10 sequences of mammalian ubiquitin. These results will be analysed and appropriately discussed.

This study investigates and evaluates how different portfolio optimization methods perform when varying assets and financial market scenarios. Methods included are mean variance, Conditional Value-at-Risk, utility based, risk factor based and Monte Carlo optimization. Market scenarios are represented by stagnating, bull and bear market data from the Bloomberg database. In order to perform robust optimizations resampling of the Bloomberg data has been done hundred times. The evaluation of the methods has been done with respect to selected ratios and two benchmark portfolios. Namely an equally weighted portfolio and an equally weighted risk contributions portfolio. The study found that mean variance and Conditional Value-at-Risk optimization performed best when using linear assets in all the investigated cases. Considering non-linear assets such as options an equally weighted portfolio performs best. / Den här studien undersöker och utvärderar hur olika portföljoptimeringsmetoder presterar med varierande finansiella tillgångsslag och marknadsscenarion. De metoder som har undersökts är: väntevärde-varians, villkorligt-värde-av-risk, nyttjande- och Monte Carlo baserad optimering. De marknadsscenarion som valts är: stagnerande, uppåt- samt nedåtgående scenarion där marknadsdata hämtats från Bloomberg för respektive tillgång. För att erhålla robusta optimeringsresultat har data omsamplats hundra gånger. Utvärderingen av metoderna har gjorts med avseende på utvalda indikatorer och två jämförelseportföljer, en likaviktad portfölj och en likariskviktad portfölj. Studien fann att portföljer genererade av väntevärde-varians och villkorligt-värde-av-risk optimering visade bäst prestanda, när linjära tillgångar använts i samtliga scenarion. När ickelinjära tillgångar såsom optioner har använts gav den likaviktade jämförelseportföljen bäst resultat i samtliga scenarion.

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The interquartile range (IQR) is used to describe the spread of a distribution. In an introductory statistics course, the IQR might be introduced as simply the “range within which the middle half of the data points lie.” In other words, it is the distance between the two quartiles, IQR = Q3 - Q1. We will compute the population IQR, the expected value, and the variance of the sample IQR for various continuous distributions. In addition, a bootstrap confidence interval for the population IQR will be evaluated.

<p>Building a robust deep neural network (DNN) framework turns out to be a very difficult task as adaptive attacks are developed that break a robust DNN strategy. In this work we first study the bootstrap distribution of DNN weights and biases. We bootstrap three DNN models: a simple three layer convolutional neural network (CNN), VGG16 with 13 convolutional layers and 3 fully connected layers, and Inception v3 with 42 layers. Both VGG16 and Inception v3 are trained on CIFAR10 in order for bootstrapping networks to converge. We then compare the bootstrap NN parameter distributions with those from training DNN with different random initial seeds. We discover that the bootstrap DNN parameter distributions change as the DNN model size increases. And the bootstrap DNN parameter distributions are very close to those obtained from training with different random initial seeds. The bootstrap DNN parameter distributions are used to create a graybox defense strategy. We randomize a certain percentage of the weights of the first convolutional layers of a DNN model, and create a random ensemble of DNNs. Based on one trained DNN, we have infinitely many random DNN ensembles. The adaptive attacks lose the target. A random DNN ensemble is resilient to the adversarial attacks and maintains performance on clean data.</p>

The Bag of Little Bootstraps (BLB) was introduced to make the bootstrap method more computationally efficient when used on massive data samples. Since its introduction, a broad spectrum of research on the application of the BLB has been made. However, while the BLB has shown promising results that can be used for logistic regression, these results have been for well-balanced data. There is, therefore, an obvious need for further research into how the BLB performs when the dependent variable is unbalanced and whether possible performance issues can be remedied through methods such as Firths's Penalized Maximum Likelihood Estimation (PMLE). This thesis shows that the dependent variable's imbalances severely affect the BLB's performance when applied in logistic regression. Further, this thesis also shows that PMLE produces mixed and unreliable results when used to remedy the drops in performance.

RNA-Seq is an increasingly popular approach to transcriptome profiling that uses the capabilities of next generation sequencing technologies and provides better measurement of levels of transcripts and their isoforms. In this thesis, we apply RNA-Seq protocol and transcriptome quantification to estimate gene expression and pathway activity levels. We present a novel method, called IsoDE, for differential gene expression analysis based on bootstrapping. In the first version of IsoDE, we compared the tool against four existing methods: Fisher's exact test, GFOLD, edgeR and Cuffdiff on RNA-Seq datasets generated using three different sequencing technologies, both with and without replicates. We also introduce the second version of IsoDE which runs 10 times faster than the first implementation due to some in-memory processing applied to the underlying gene expression frequencies estimation tool and we also perform more optimization on the analysis. The second part of this thesis presents a set of tools to differentially analyze metabolic pathways from RNA-Seq data. Metabolic pathways are series of chemical reactions occurring within a cell. We focus on two main problems in metabolic pathways differential analysis, namely, differential analysis of their inferred activity level and of their estimated abundance. We validate our approaches through differential expression analysis at the transcripts and genes levels and also through real-time quantitative PCR experiments. In part Four, we present the different packages created or updated in the course of this study. We conclude with our future work plans for further improving IsoDE 2.0.

Denna kandidatuppsats syftar till att undersöka hur småföretags strategiorientering påverkar det val av alternativa finansiella bootstrappingmetoder, som småföretag väljer att tillämpa. Småföretagare motsvarar 96 procent av samtliga svenska verksamma företag och utgör därmed en betydande roll i samhället. Detta för att de ses som samhällsbärare, då fyra av fem arbetstillfällen skapas av svenska småföretag och bidrar vidare till välfärd. Trots deras betydande roll i samhället möts de av finansiella barriärer som leder till att småföretag hamnar i ett så kallat finansiellt gap. För att ta sig ur det finansiella gapet, tvingas småföretagare finna alternativa finansieringsmetoder. De alternativa finansieringsmetoderna benämns finansiella bootstrappingmetoder, vilka har kommit att bli avgörande för småföretags överlevnad. Småföretags strategiorientering anses påverka valet av finansiella boostrappingmetoder. Således omfattas studien av småföretags strategiorientering, vilka har identifierats utifrån Miles och Snows typologi. En kvantitativ forskningsansats med en positivistisk forskningsfilosofi har legat till grund för studien vilket möjliggör att resultatet ska kunna generaliseras. Studiens enkätundersökning omfattar 84 svenska småföretag som bidragit till studiens resultat. Slutligen är studiens resultat att prospektörer är positivt relaterade till användningen av de ägarrelaterade- och minimerande bootstrappingmetoderna. Medan försvarare är positivt relaterade till användningen av den relationsorienterade bootstrappingmetoden. / This bachelor thesis aims to explore how strategy orientations influence the choice of financial bootstrapping methods among small firms. Small firms represent 96 percent of all Swedish operating companies and thus represent a significant role in the society. This because they contribute to welfare, since four out of five jobs are created by Swedish small firms. Despite their significant role in the society, they are faced with financial barriers that lead to small firms ending up in a so called financial gap. To get out of the financial gap, small firms are forced to find alternative financing methods. The alternative financing methods are called financial bootstrapping methods, which have become crucial for the survival of small firms. The strategy orientation in small firms is considered to affect the choise of financial bootstrapping methods. Thus, the study covers small business strategy orientation which has been identified based on Miles and Snow’s typology. A quantitative research approach with a positivist research philosophy has been the bases of the study which enables the result to be generalized. The survey included 84 Swedish small firms that contributed to the results of this study. Finally, the results of this study are that prospectors are positively related to the use of the owner-related and minimizing bootstrapping methods. While defenders are positively related to the use of the relationship-oriented bootstrapping method.

Nous proposons donc un modèle original d'apprentissage constructiviste adapté pour un système d'AmI. Ce modèle repose sur une approche décentralisée, permettant de multiples implémentations convenant à un environnement hétérogène. Dans les environnements réels continus sans modélisation à priori, se pose la question de la modélisation des structures élémentaires de représentation et particulièrement le problème d'amorçage de l'apprentissage sensorimoteur (comme décrit par [Kuipers06]). Dans le cadre du modèle général proposé, nous explicitons ce problème particulier et proposons de le traiter comme une forme d'auto-organisation modélisée par un système multi-agent. Cette approche permet de construire des motifs d'interaction élémentaires à partir des seules données brutes, sur lesquels peut reposer la construction d'une représentation plus élaborée (voir [Mazac14]). Nous présentons enfin une série d'expérimentations illustrant la résolution de ce problème d'amorçage : tout d'abord grâce à un environnement simulé, qui permet de maitriser les régularités de l'environnement et autorise des expérimentations rapides ; ensuite en implémentant ce système d'apprentissage au sein d'un environnement d'AmI réel. Pour cela le modèle est intégré dans le système d'AmI développé par l'entreprise partenaire de cette thèse CIFRE. Puis nous présentons une possible application industrielle des résultats de cette première étape implémentée d'amorçage de l'apprentissage sensorimoteur. Nous concluons par l'analyse des résultats et des perspectives de ce type d'approche pour l'AmI et l'application en général de l'IA aux systèmes réels en environnements continus / The theory of cognitive development from Jean Piaget (1923) is a constructivist perspective of learning that has substantially influenced cognitive science domain. Within AI, lots of works have tried to take inspiration from this paradigm since the beginning of the discipline. Indeed it seems that constructivism is a possible trail in order to overcome the limitations of classical techniques stemming from cognitivism or connectionism and create autonomous agents, fitted with strong adaptation ability within their environment, modelled on biological organisms. Potential applications concern intelligent agents in interaction with a complex environment, with objectives that cannot be predefined. Like robotics, Ambient Intelligence (AmI) is a rich and ambitious paradigm that represents a high complexity challenge for AI. In particular, as a part of constructivist theory, the agent has to build a representation of the world that relies on the learning of sensori-motor patterns starting from its own experience only. This step is difficult to set up for systems in continuous environments, using raw data from sensors without a priori modelling.With the use of multi-agent systems, we investigate the development of new techniques in order to adapt constructivist approach of learning on actual cases. Therefore, we use ambient intelligence as a reference domain for the application of our approach