In the United States, institutional investors submit 13F reports to SEC every quarter, disclosing the number of shares they own. However, investors may delay filing these reports to conceal their strategies. In this study, data from 13F filings were enriched with additional information such as stock prices and industries to create a bipartite, dynamic, and rich-attributed graph. The changes in network metrics, edge counts, and motif counts were visualized, highlighting crises in the relevant time period. The study found that clustering coefficient, significant position changes, late-filed 13F reports, and specific motif counts of graph shows significant changes during market crashes.

Date: 18.01.2024 / 10:00 Place: A-212

Cancers of unknown primary (CUP) pose treatment challenges due to unidentified primary sites, affecting survival rates significantly. Traditional methods falter in pinpointing origins. Gene expression-based studies offer promise; machine learning models trained on annotated cancer data achieve remarkable accuracy, surpassing conventional methods. Our study, utilizing three machine learning models trained on TCGA data, attained competitive accuracy, around 96%.

Date: 18.01.2024 / 11:00 Place: A-108

This thesis applies machine learning to predict outcomes of men’s singles tennis matches from 2009-2022, utilizing a standardized data mining framework, namely SRP-CRISP-DM, for replicable results. Employing six feature extraction techniques, three models, and two feature selection methods with time-based cross-validation and hyperparameter tuning, the Extreme Gradient Boosting model emerged as the top performer, scoring a Brier score of 0.1913 and an accuracy of 70.5%, with bookmakers' odds as the top predictive feature.

Date: 07.12.2023 Place: A-212

With the help of large language models, prompt engineering enables easy access to vast knowledge for various applications. However, limited research has been done on multi-hop question answering using this approach. This thesis introduces a new semi-automatic prompting method for answering two-hop questions. The method involves creating a prompt with automatically selected examples by grouping answer-named entities from the training set and using a chain-of-thought principle. The results demonstrate comparable performance to fine-tuned models on the MuSiQue dataset. Ablation studies further validate the effectiveness of each component in the proposed method. The approach has the potential to be applied to more complex multi-hop question-answering systems while upholding performance on par with other state-of-the-art techniques.

Date: 07.09.2023/13:00 Place: A-212

Crude oil prices are very volatile as they depend on many factors. Traders in this market, therefore need to constantly monitor different factors affecting the price, which can lead to information asymmetry for individual traders. Therefore, this thesis presents a comprehensive decision support system incorporating fundamental, technical, and sentiment analysis to support the decisions of crude oil traders.

Date: 06.09.2023 / 15:30 Place: A-212

Traditionally, football coaches make this decision by analyzing players' match and training performances and by analyzing the data of the opposing team. In this thesis, a new solution to the team selection problem is proposed with a data-driven approach by using the match data of the players and teams, grouping the players based on their positions and roles, considering the opposing team, tactical formation and environmental factors.

Date: 05.09.2023 / 13:00 Place: A-212

The study aimed to classify task difficulties using wavelet transform images of EEG signals and deep learning models. The EfficientNet-B0 model achieved the highest accuracy, but its performance varied significantly across individuals and task difficulties, indicating limited generalizability. The study suggests a need for further research to improve model generalizability, optimize performance, and validate the models on larger, more diverse datasets.

Date: 06.09.2023 Place: MODSIMMER Meeting Room

New technologies in distributed energy systems offer solutions for managing demand and generation variability in the grid. Trade between small grids allows cost reduction and system flexibility. This thesis proposes Multi-Agent Reinforcement Learning model for short-term energy trading among peers. It integrates short-term forecasts for load, generation, and price, leading to more accurate decision-making. Real-world data simulations demonstrate improved efficiency and stability compared to rule-based agents. This approach empowers prosumers to respond effectively to dynamic energy markets, enhancing grid reliability, energy efficiency, and sustainability.

Date: 07.09.2023 / 11:30 Place: -

Despite the high mortality and morbidity rates associated with alcohol use disorder (AUD) worldwide, the incomplete understanding of its complex molecular mechanism is the biggest limitation in providing successful treatments to patients. Monozygotic twin studies have found that AUD is about 50-60% heritable, and they have also revealed that environmental factors and epigenetic mechanisms play a role in the etiology of this disorder. In this study, we investigated excessive alcohol consumption related DNA methylation changes in four brain regions and blood in wistar rats to contribute to understanding to the understanding of the etiology of AUD.

Date: 04.09.2023 / 09:30 Place: B-116

This study explores the transformative potential of virtual reality in laparoscopic surgical training. Traditional box trainer implemented as a VR simulation, eliminating the need for physical tools and improved with incorporating feedback from surgeons and participant questionnaires. Results revealed that the VR version, equipped with tutorials, haptic feedback, and assisted grabbing physics, was more usable and accepted. Kinematic analysis showed similarities to physical training, and physiological monitoring revealed increased heart rate and reduced heart rate variability during tasks. This multimodal approach highlights VR's potential to enhance laparoscopic surgical skill development, providing an immersive and realistic training experience.

Date: 01.09.2023 / 10:00 Place: A-212