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On Abstract Models in Cryptography and Their Applications
(2024)When talking about the security of a cryptographic scheme, researchers often model it as a game between a challenger and an adversary. Such a game gives a clear description of the ways we consider the adversary to interact with the scheme whose security we want to prove. A security reduction then only needs to implement these ways of interactions which we usually call ‘oracles’. However, the scheme may have some underlying building blocks, ...Doctoral Thesis -
A systematic analysis of the molecular patterns that define endothelial identities and states
(2024)Doctoral Thesis -
Identification and Characterization of the AGTR1 Regulatory Proteins MPP1 and DAPK1
(2024)Doctoral Thesis -
Two Algos, One Option: Impact of New Technology on Mispricing and Hedging Strategies
(2024)This thesis contains three studies on the impact of new technologies on financial markets. The first study investigates in an experiment whether algorithmic trading has an impact on the formation of asset price bubbles. It finds that especially market-maker algorithms lead to traded prices being closer to the fundamental values of the asset. The second study looks at whether markets with algorithmic trading can improve the aggregation of ...Doctoral Thesis -
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Towards the laser-based powder bed fusion of high performance oxide ceramics
(2024)The potential of laser-based powder bed fusion for engineering ceramics is highly promising, as it has the capability to create intricate near-net shapes in a single-step process. If implemented successfully, this additive manufacturing technology could be profoundly disruptive, reducing production costs, and increasing accessibility to engineered ceramics. However, the current adoption of this technology in industry is hindered by ...Doctoral Thesis -
Meta-Learning Priors from Limited Data: From Theory to Practice
(2024)Unlike machine learning systems, humans can learn new concepts from only a few examples and efficiently adapt to changing circumstances. Machine learning systems typically require magnitudes more data to learn similar concepts or adapt to changes. This is because they lack domain-specific prior knowledge (also known as inductive bias). To address these shortcomings, meta-learning aims to acquire domain-specific inductive bias in a ...Doctoral Thesis