Synthetic polymers are crucial in diverse industries, but current AI-driven design methodologies primarily target linear homopolymers, with limited emphasis on developing customized approaches for copolymers. To address this gap, we introduce a generative model for goal-directed synthetic copolymer design using reinforcement learning. Our model operates in a graph generation environment, facilitating efficient monomer unit design while incorporating domain-specific constraints to ensure high validity rates. In a case study optimizing for Hydrogen Evolution Rate (HER) and synthetic accessibility, our approach showcases the efficacy of reinforcement learning in advancing copolymer design. Furthermore, experimental results underscore the challenges in designing effective scoring functions due to the sparse nature of polymer datasets, emphasizing the need for robust property predictors in polymer design methodologies before integrating more complex generative models into the design process.

Mesh data is widely used in engineering for instance for simulations, CAD engineering and visualizations. The accuracy and quality of the meshes influence the reliability and validity of these processes. Besides manual modelling, scanning is becoming increasingly more common due to the increase in devices that have scanning capabilities. Unwanted noise is often present in scanned models. The process of mesh denoising is removing the unwanted noise whilst keeping the features of the mesh. These features are often anisotropic, e.g. sharp edges and corners.

The DeltaConv convolution is an anisotropic convolution, Wiersma et al. [24] show the advantage of using the anisotropic DeltaConv convolution for anisotropic tasks over other isotropic convolutions. In this thesis it is investigated if state-of-the-art mesh denoising can benefit from using the DeltaConv convolution. This is done by integrating the DeltaConv convolution in the Dual-DMP [6] algorithm, and tuning this network.

In this thesis we found that state-of-the-art mesh denoising can benefit from using the DeltaConv convolution. Due to the expressiveness of the DeltaConv convolution, objects with sharp features are denoised better than the state-of-the-art algorithms and on smooth meshes, DeltaConv works comparable to state-of-the-art algorithms.

Recent advancements in machine learning (ML) have shown promise in accelerating polymer discovery by aiding in tasks such as virtual screening via property prediction, and the design of new polymer materials with desired chemical properties. However, progress in polymer ML is hampered by the scarcity of high-quality, labelled datasets, which are necessary for training supervised ML models. In this work, we study the use of the very recent ’Joint Embedding Predictive Architecture’ (JEPA) type for self-supervised learning (SSL) on polymer molecular graphs, to understand whether pretraining with the proposed SSL strategy improves downstream performance when labelled data is scarce. By doing so, this study aims to shed light on this new family of architectures in the molecular graph domain and provide insights and directions for future research on JEPAs. Our experimental results indicate that JEPA self-supervised pretraining enhances downstream performance, particularly when labelled data is very scarce, achieving improvements across all tested datasets.

In the scientific community, a few prominent researchers, known as "superstars," receive most of the attention, citations, and resources. However, it is unclear whether they promote true innovation. This study replicates and extends previous work analyzing how superstars influence their collaborators, focusing on the field of computer science. Using the Semantic Scholar Academic Graph dataset, we confirm that while connected researchers in computer science tend to publish more and receive more citations, their ideas are often less innovative. Unlike in the work we replicate, however, we observe this effect even before dissociating the connected researchers from superstars. We also develop a new metric to further clarify the impact of superstars on research diversity. The findings provide insights into the role of superstars in scientific innovation.

Recent developments in the capability and availability of small internet of things devices has meant that networked medical devices, like networked implants and wearable monitors, have become more widespread. This data is invaluable for solving pressing global healthcare concerns, like eectively monitoring and treating heart patients. The European Union has announced plans to create an international collaborative network for sharing medical data. However, such a system will have to overcome some major unsolved issues regarding security and privacy. Citizens surveys have stressed the im-portance of privacy protection and transparency in recipients. Governments have appointed administrative bodies tasked with supervising the processing of personal data, or assuring healthcare quality. However, medical health-care providers have signalled concern with unrestricted governmental access to patient data. In this thesis, we propose a system for auditable medical data sharing compatible with privacy-preserving technologies. We demon-strate a method to securely generate encryption keys which are recoverable using an audit key.
We combine this with distributed key generation to cre-ate a board of trusted members, with each a share of the audit key. Board members can work together to collaboratively audit communication between healthcare providers and medical researchers. We demonstrate that the key generation is secure and ecient. We show that auditability is guaranteed under the assumptions that at least one of the communicating parties is hon-est. Our system bridges the gap between privacy-preserving medical data analysis and governing capabilities by assuring auditability without handing this power over to a single party. In real world scenarios, this system can be used to create international level of data sharing, as is explored for the European Health Data Space. The data inspection can be combined with already existing legislative power to detect fraudulent behavior and perform physical audits when required. The system can be extended to facilitate reproducible medical research.

Simulating lighting is one of the most important parts of rendering 3D scenes. While lighting coming directly from a light source is easy to simulate in real-time, indirect illumination is more difficult. One of the methods used to convincingly approximate indirect illumination is ambient occlusion, where the brightness of objects is reduced when there is geometry nearby, most visible in corners and crevices.

To render ambient occlusion in real-time often requires shortcuts that leave out some important lighting cues when geometry is not directly visible on screen. Our method aims to improve this specifically for character animation by pre-calculating the occlusion and storing it in a compact data format using cubemaps. This method has the potential to improve both visual quality and rendering performance.

Motivation: Many tumors show deficiencies in DNA damage repair. These deficiencies can play a role in the disease, but also expose vulnerabilities with therapeutic potential. Targeted treatments exploit specific repair deficiencies, for instance based on synthetic lethality. To decide which patients could benefit from such therapies requires the ability to determine the repair deficiency status of a tumor. It has been suggested that mutational signatures could be better predictors of DNA repair deficiency than loss of function in select genes. However, current models for prediction of repair deficiency rely on mutational signatures extracted using unsupervised learning techniques. As a result, the signatures are not optimized to discriminate between repair deficiency status or pathway. We argue that the supervised learning of mutational signatures guided by repair deficiency status could enable the identification of signatures that are predictive of repair deficiency, and capture underlying mechanisms of DNA repair.
Results: We propose S-NMF, a supervised non-negative matrix factorization method, which jointly optimizes two objectives: (1) learning of signatures shared across tumor samples using NMF, and (2) learning of signatures predictive of repair deficiency using logistic regression. We apply S-NMF to mutation profiles of human induced pluripotent cell lines carrying knockouts of genes involved in three DNA repair pathways: homologous recombination, base excision repair, and mismatch repair. We show that S-NMF achieves high prediction accuracy (0.971) and learns signatures that better distinguish the repair deficiency of a sample. Signatures extracted by S-NMF are similar to cancer-related signatures associated with the same repair deficiency. Additionally, S-NMF can capture signatures of deficiencies affecting distinct subpathways within a main repair pathway (e.g. OGG1 and UNG mechanisms in base excision repair).