Virtual Reality (VR) offers the possibility to explore and interact with complex digital worlds, yet natural locomotion is constrained by the limits of physical space. Hyperbolic geometry provides a compelling solution by embedding infinite virtual environments within finite areas, creating novel opportunities for research and design. This thesis investigates how embodied training and haptic feedback can enhance navigation and user experience in such non-Euclidean spaces. Twenty-eight participants took part in a between-subjects user study, using Holonomy VR, a hyperbolic VR application instrumented with the SenseGlove Nova 1 for force feedback and vibrotactile interaction. Participants were trained with either a drag-based embodied interface or a conventional button-based control scheme before completing matched navigation tasks. Performance was measured through speed, path efficiency and sequencing, while user experience was assessed through established questionnaires and interaction behaviour. The study finds that embodied training affords a practical advantage in subsequent navigation, and that perceived engagement with haptic elements is a strong predictor of positive usability, beyond the effects of task duration alone. Together, these results demonstrate that embodied practice and meaningful tactile interaction can help users adapt more effectively to non-Euclidean environments, offering both methodological contributions for VR research and design implications for the creation of more intuitive and engaging virtual worlds.

Recent advances in generative AI have enabled high-quality video generation from text prompts. However, the majority of existing approaches rely exclusively on prompts, making it difficult for an artist to control the generated scene layout and motion. In this thesis, we propose a novel method for geometry-guided Text to Video generation. Our method takes as input an animated mesh sequence and a text prompt and generates a video following both the text prompt and input geometry. Our pipeline consists of two main stages: Firstly, we use an existing text-driven texture generation method to create an initial rough texture for the geometry. Next, a depth-conditioned T2I model is used to generate video frames following the guidance animation, using the generated texture to enforce temporal consistency across frames. By generating video frames rather than directly using the result of the texture generation, our method supports generating deformations from the guidance geometry and variable lighting and by using the texture for feature alignment, we acheive significantly stronger robustness to occlusions and camera motion than existing controllable video-generation approaches. We begin by identifying the failure modes of existing methods through a set of initial experiments, we then use these findings to propose our method and finally evaluate it through a series of comparisons and ablations.

Distinguishing between benign and malignant ovarian cysts is a challenging task that depends on subjective visual markers in ultrasound scans. Current manual methods remain prone to costly misdiagnoses and the application of these methods depend heavily on the clinician's level of expertise. Recent research demonstrates promising applications of Convolutional Neural Networks (CNNs) for ovarian tumor classification; however, we observed that their performance is limited when applied to a diverse and complex dataset. To address this, we propose, implement, and evaluate three improvements to a baseline classifier.

First, we use a deep learning-based approach to remove burned-in medical annotations and introduce a weighted mean squared error (MSE) loss to improve its effectiveness by emphasizing relevant regions. This aims to better recover the original image content prior to annotation and remove annotations which can act as confounders. Second, we enhance classification by fusing image features with two readily available clinical factors at an intermediate stage of the network. Third, and central to this study, we incorporate a segmentation path that acts as a regularizer, encouraging the shared encoder to learn lesion-specific features that benefit the classification head.

These three contributions are informed by domain-specific knowledge of ovarian lesions and collectively demonstrate promising directions for improving deep learning-based models in this setting.

Single-cell RNA sequencing (scRNAseq) is a measuring technique of gene expressions in single cells that has allowed researchers to tackle Alzheimer’s disease (AD) in many ways. Single-cell data has been joined with machine learning to classify brain cells as affected by AD. However, not much is known regarding the usage of such classification models in a spatial setting. This paper analyzes how models trained on scRNAseq data can be used to find AD properties of single cells when measuring them with spatially resolved transcriptomics. With that we study the hypothesis that cells labeled as affected by the disease should appear closer to amyloid plaques, than those that are unaffected. To find out if this holds, three models are used to classify single cells spatially and their predictions are analyzed. Two single-cell datasets are used for training, each giving a drastically different classification outcome. The models do not come to a consensus on the hypothesis’ validity either, as the analysis finds no significant correlation between the variables.

Alzheimer's Disease (AD) is a complex heterogeneous disease and is the leading cause of dementia around the world. Treatment options remain limited and the underlying mechanisms are not yet fully understood. To get more insight on this celular level, single-cell gene expression data can be used. It has proven to be effective with machine learning for tasks like cell type classification. While prior studies have explored AD classification using scRNA-seq, this has only been a binary classification. Severity of AD is classified using multiple measures, ranging from cognitive ability scores, to neuro pathological measures. This research explores the possibility of expanding the binary prediction of AD by including these measures for AD severity. In addition, given that these measures are associated, we also investigate if Multi Task Learning (MTL) models can improve the predictions by learning multiple AD related data points. If successful, this approach can give additional analysis into key tasks, genes and/or cells (sub)types that drive the models, which would lead to more possibilities for personalized treatment options, alongside more insight into the development of AD in the brain. We used a three-layer neural network architecture alongside a translation from cellular level to individual level to make individual-level predictions. Results show that Cognitive Ability can be classified best, but overal performance is only slightly above Naive Bayes. Furthermore, MTL does not appear to have any measurable positive effect on scores compared to single task models. A link to the github repository is available at \url{https://github.com/WillemDieleman/ADseverityCSE3000}.

Alzheimer’s Disease is a complex neurodegenerative disorder marked by the abnormal build-up of proteins in the brain. As no cure currently exists, understanding the disease’s cellular mechanisms is essential for advancing diagnostics and treatment. To this end, single-cell RNA sequencing (scRNA-seq) is a method that offers detailed information about the gene activity of individual cells but lacks their spatial context. Conversely, spatial transcriptomics technology preserves the localization of the cells but provides more limited transcriptomic information. To resolve this, we provide a model that predicts a cell’s distance to pathology from single-cell RNA-sequencing data. Additionally, we identify APOE, LYVE1, and SLC17A7 as genes potentially associated with AD-related microglial clustering around plaques.

This study tackles an important issue in evaluating the reliability of confidence intervals in causal forests by examining how data characteristics and hyperparameters influence actual coverage rates compared to theoretical benchmarks. Using synthetic data sets with polynomial treatment effects, Sobol sampling, High-Dimensional Model Representation (HDMR), and comprehensive grid searches, the study assesses causal forest performance in different data contexts.

A primary discovery is the identification of a practical limit for reliable confidence interval coverage: When the sum of confounders and effect modifiers exceeds 4, coverage rates drop considerably below 80%, even for simple treatment effect functions. This limitation remains steady despite substantial increases in computational resources.

The examination of hyperparameters revealed that the most influential parameters are the maximum tree depth and the balance tolerance in splits, which demonstrate substantial changes in performance, both of which performed best at their maximums (unlimited and 0.5, respectively). Other key suggestions involve increasing the training data fraction per tree from 0.45 to 0.5, keeping the minimum impurity decrease threshold at 0.0, and utilizing at least ≈ 2400 trees to meet theoretical expectations.

In addition, this paper did not identify any noteworthy interaction between tree count and sample size. As a result, both of these characteristics can be optimized independently of each other.

These findings provide systematic guidelines for practitioners to assess when causal forest confidence intervals are reliable and how to optimize them, bridging the gap between theoretical guarantees and practical performance.

Causal inference, particularly the estimation of the Conditional Average Treatment Effects (CATE), is necessary for understanding the impact of interventions beyond simple predictions. This study analyzes the influence of key hyperparameter choices, specifically maximum tree depth and minimum leaf size, on the accuracy and generalization of CATE estimates derived from honest and adaptive causal trees. The research explores how these hyperparameters affect the bias-variance trade-off and the model's tendency to overfit or underfit across various simulated data scenarios and a real-world dataset.

The results reveal that optimal hyperparameter configurations are dependent on the data characteristics, such as dimensionality, noise levels, and the complexity of the true causal effects. Honest causal trees demonstrate a better performance in high-dimensional and noisy environments due to their effective variance control. Conversely, in simpler, low-noise settings or complex CATE structures, adaptive causal trees or baseline models frequently achieve better results by reducing bias. The study also highlights the challenges of using moderately sized datasets, where the sample splitting limitations can lead to higher estimation errors. This work provides thorough suggestions for hyperparameter selection, emphasizing the fact that tuning based on the underlying characteristics of the data is needed for achieving the best CATE estimates possible.

Estimating the Conditional Average Treatment Effect (CATE) with neural networks adapted for causal inference, like TARNet, is a promising approach, yet the impact of model architecture on performance remains underexplored.
This paper systematically investigates how the depth and width of TARNet affect the CATE estimation in diverse simulated data environments. The research investigates two central questions: how TARNet's performance varies across data regimes (e.g., confounding strength, sample size), and how its optimal architecture changes in response to these conditions.
A comprehensive set of simulation-based experiments is conducted using the CATENets framework, isolating and varying factors such as sample size, feature dimensionality, confounding strength, and the presence of noise. The results demonstrate that deeper architectures generally yield better performance in complex or high-dimensional scenarios, whereas narrower networks are preferable in small-sample or high-noise settings due to their regularizing effect. Furthermore, the findings suggest that there is no universally optimal architecture. The best configuration depends on the specific characteristics of the data. The study concludes with practical recommendations for architecture selection based on the experiments conducted.

Causal Multi-task Gaussian Processes (CMGPs) provide a Bayesian approach for estimating in-
dividualized treatment effects by modeling potential outcomes as correlated functions. However,
they struggle under high-dimensionality and treatment imbalance, leading to overfitting and unre-
liable uncertainty estimates. This study examines two failure modes: poor generalization in high-
dimensional spaces and overconfident predictions in low-overlap regions. To address these, two
data-aware enhancements are proposed: an overlap-adaptive kernel that scales similarity based on
local treatment density, and a regularized prior that down-weights unstable features using marginal
treatment effect variance. Evaluations on synthetic data and the IHDP benchmark show improved
effect estimation, credible interval calibration, and robustness in challenging settings. These find-
ings highlight practical strategies for enhancing CMGPs in real-world causal inference tasks.

Reflectance fields have a limited number of discrete lighting directions that can be used for lighting design. Multiple methods for interpolating these reflectance fields have been proposed to get an approximation of the missing lighting directions. However, these methods can struggle with specular highlights, because they are more sensitive to changes in lighting direction. We propose a method that solves this problem by representing the specular highlights as Gaussians and interpolating them, framing the problem as a Lagrangian formulation. We interpolate these Gaussians by optimizing their parameters twice: first for each lighting direction, then for every neighboring lighting direction. By doing this, we can efficiently interpolate specular highlights in real-time. This enables the designer to design the specular highlights and their movements with immediate feedback.

The swift growth of artificial intelligence has led to the development of large language models, revolutionising various scientific domains and professional fields. This research explores collaborative, cooperative, and competitive game designs, that enhance knowledge elicitation using LLMs in games with a purpose. Through a systematic literature review using the PRISMA workflow, we identify that collaborative and cooperative game designs are more effective for knowledge elicitation than competitive designs. We also address the potential and limitations of incorporating LLMs into GWAPs, such as their use as Non-Player Characters to create engaging interactions. This study provides design principles for GWAPs leveraging LLMs, offering insights for researchers and developers, and discusses ethical considerations and future research directions.

The process of knowledge elicitation is crucial to the field of artificial intelligence because of the lack of data on commonsense knowledge. This paper explores the potential of using large language models (LLM) to enhance knowledge elicitation in games with a purpose (GWAP). By analyzing the capability of LLMs to play games and generate game content, this research shows how LLMs can increase accessibility, efficiency and engagement in GWAPs for knowledge elicitation. The findings show that LLMs can play games with great performance and show much promise in generating game content that facilitates knowledge elicitation. Through a comprehensive literature survey, this research highlights potential ways of enhancing knowledge elicitation in GWAPs using LLMs and offers recommendations for future research in this field.

As many entities aim to participate in the ongoing AI race to gain competitive advantages, there is a risk of creating knowledge gaps by overlooking fundamental steps in the research and development processes. This paper aims to bridge the knowledge gap in the domain of large language model (LLM) integrations for knowledge elicitation by performing a systematic literature review using the PRISMA workflow. Through an analysis of 17 research papers, this study identifies new directions, including tools for education, knowledge curation, and factual information. Additionally, the research highlights key benefits and concerns associated with each direction, providing further understanding of the potential and challenges of LLM integrations for knowledge elicitation.

This research investigates the types of knowledge that can be elicited through the integration of Large Language Models (LLMs) into Games With A Purpose (GWAPs). By using a literature survey using the PRISMA framework, we synthesize findings from different studies to find patterns and gaps in the existing research. The survey focuses on the utilization of LLMs within GWAPs and examines how these models diversify the types of knowledge elicited. The findings indicate that LLMs significantly enhance the knowledge elicitation capabilities of GWAPs, transforming them into more interactive and effective tools. We found that different types of knowledge can be elicited, such as contextual insights, factual information, semantic associations and experimental knowledge.

Live-cell imaging captures dynamic cellular behaviors and aims to maximize both spatial and temporal resolution while minimizing sample damage, enabling advancements in fundamental cell biology. However, spatial resolution is limited by the diffraction barrier of optical lenses, which prevents the visualization of many subcellular structures. Single-molecule localization microscopy (SMLM) overcomes this barrier, achieving resolutions as fine as 10 nm, but it typically requires millions of frames and higher illumination, which reduces temporal resolution and can damage the sample. Super-resolution Optical Fluctuation Imaging (SOFI) operates at lower illumination levels and requires hundreds of frames, leveraging the statistical relationships of blinking fluorophores to achieve n-fold spatial resolution based on the nth-order SOFI calculation. Despite its benefits, SOFI still demands too many frames and involves extensive post-processing, making it impractical for real-time live-cell imaging. Without real-time imaging, researchers are unable to make immediate decisions, ultimately costing valuable time for the researchers.

To address this limitation, we introduce a supervised deep learning model designed to accelerate second-order SOFI. Our model reconstructs super-resolved second-order SOFI images using just 20 frames, compared to the hundreds typically required, while maintaining a 2-fold improvement in spatial resolution and showing minimal background artifacts. We demonstrate that after being trained on real fixed-cell (static) mitochondria data, the model is able to reconstruct super-resolved images in a dynamic environment by moving the microscope stage. The model achieves real-time temporal resolutions of up to 4.85 fps, unlocking new possibilities for real-time studies of live-cell dynamics.

Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge owing to the constrained operational environment, which requires low latency and high energy efficacy. Previous benchmarks have provided limited insights into power consumption and latency. However, this study introduces algorithmic metrics that capture the potential and limitations of neural decoders for closed-loop intra-cortical brain-computer interfaces in the context of energy and hardware constraints. This study benchmarks common decoding methods for predicting a primate’s finger kinematics from the motor cortex and explores their suitability for low latency and high energy efficient neural decoding. The study found that ANN-based decoders provide superior decoding accuracy, requiring high latency and many operations to effectively decode neural signals. Spiking neural networks have emerged as a solution, bridging this gap by achieving competitive decoding performance within sub-10ms while utilizing a fraction of computational resources. These distinctive advantages of neuromorphic spiking neural networks make them highly suitable for the challenging closed-loop neural modulation environment. Their capacity to balance decoding accuracy and operational efficiency offers immense potential in reshaping the landscape of neural decoders, fostering greater understanding, and opening new frontiers in closed-loop intra-cortical human-machine interaction.