京都大学 / 新制・課程博士 / 博士(理学) / 甲第22982号 / 理博第4659号 / 新制||理||1669(附属図書館) / 京都大学大学院理学研究科数学・数理解析専攻 / (主査)教授 熊谷 隆, 教授 長谷川 真人, 小澤 登高 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

Indiana University-Purdue University Indianapolis (IUPUI) / Efficient topic modeling is needed to support applications that aim at identifying main themes from a collection of documents. In this thesis, a reduced vector embedding representation and particle swarm optimization (PSO) are combined to develop a topic modeling strategy that is able to identify representative themes from a large collection of documents. Documents are encoded using a reduced, contextual vector embedding from a general-purpose pre-trained language model (sBERT). A modified PSO algorithm (pPSO) that tracks particle fitness on a dimension-by-dimension basis is then applied to these embeddings to create clusters of related documents. The proposed methodology is demonstrated on three datasets across different domains. The first dataset consists of posts from the online health forum r/Cancer. The second dataset is a collection of NY Times abstracts and is used to compare

This research investigates how immigrant entrepreneurs in the falafel business in Malmö position themselves in relation to the host society. Interviews with five immigrant entrepreneurs in the falafel trade were conducted to explore their life stories, business endeavours and their relations with the host society. The data was then analysed to establish the degree to which they feel embedded in different arenas of the host society and their society of origin. This study confirms the disadvantageous position of immigrants in Swedish society, though demonstrates the various strategies they utilise to improve their situation through entrepreneurship. The study, the first of its kind in Malmö, is important in the context of rising xenophobia in Sweden and segregation in the city.

We propose an e cient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHQ dataset as an example, we show results for image morphing, style transfer, and expression transfer. Studying the results of the embedding algorithm provides valuable insights into the structure of the StyleGAN latent space. We propose a set of experiments to test what class of images can be embedded, how they are embedded, what latent space is suitable for embedding, and if the embedding is semantically meaningful.

The stock market undergoes many fluctuations on a daily basis. These changes can be challenging to anticipate. Understanding such volatility are beneficial to investors as it empowers them to make inform decisions to avoid losses and invest when opportunities are predicted to earn funds. The objective of this research is to use text mining and data mining techniques to discover the relationship between news articles and stock prices fluctuations. There are a variety of sources for news articles, including Bloomberg, Google Finance, Yahoo Finance, Factiva, Thompson Routers, and Twitter. In our research, we use Factive and Intrinio news databases. These databases provide daily analytical articles about the general stock market, as well as daily changes in stock prices. The focus of this research is on understanding the news articles which influence stock prices. We believe that different types of stocks in the market behave differently, and news articles could provide indications on different stock price movements. The goal of this research is to create a framework that uses text mining and data mining algorithms to correlate different types of news articles with stock fluctuations to predict whether to “Buy”, “Sell”, or “Hold” a specific stock. We train Doc2Vec models on 1GB of financial news from Factiva to convert news articles into vectors of 100 dimensions. After preprocessing the data, including labeling and balancing the data, we build five predictive models, namely Neural Networks, SVM, Decision Tree, KNN, and Random Forest to predict stock movements (Buy, Sell, or Hold). We evaluate the performances of the predictive models in terms of accuracy and area under the ROC. We conclude that SVM provides the best performance among the five models to predict the stock movement.

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Objective: Child maltreatment (CM) is a widespread problem associated with poor mental and physical health outcomes. The underlying mechanisms of this link are not always well understood, however certain biological changes observed in maltreated individuals may play a role in connecting experience and outcome. This review specifically focuses on two markers of biological embedding, DNA methylation (DNAm) and telomere length (TL) in maltreated children and youth. As biomarker changes are not uniform among maltreated children, we additionally discuss biological and environmental resilience factors that may contribute to variability. Methods: We conducted a systematic review of Medline, Embase and PsycInfo databases for studies examining DNAm and/or TL in maltreated children and youth. Methodological quality of the included articles was assessed using the Scottish Intercollegiate Guidelines Network (SIGN) checklists for cohort studies and randomized control trials. Data extraction focused on various factors including population and CM (type, chronicity, severity, and duration) characteristics. Results: The initial search returned 1,688 non-duplicate results, with 417 full text articles reviewed. Twenty-six articles from 16 studies were ultimately included of which 8 examined telomere length and 18 examined DNA methylation. Conclusions: While some heterogeneity of findings was found, evidence supports differential changes in both biomarkers associated with CM. This review enhances understanding of the constellation of biological changes related to CM and consideration of the important role of resilience factors in mitigating risk. Elucidating these factors may highlight targets for future study and intervention development. / Thesis / Master of Science (MSc) / Child maltreatment is a serious problem linked to poor mental and physical health outcomes. The mechanisms of these links are not always clear, however biological changes observed in some maltreated individuals may play a role. Here, we systematically review literature related to two biomarkers of interest in maltreated children, telomere length and DNA methylation. Findings are varied; however, overall, they support an association between child maltreatment and changes in both biomarkers. We additionally discuss factors that may confer resilience related to these changes to highlight potential targets for future study and interventions.

Several linear embeddings of the logistic equation, xn+1=axn(1-xn) are considered, the goal being to establish a connection between linear embedding and the Poincaré Functional Equation. In particular, we consider linear embedding schemes in a classical Hardy space.