TY - THES AB - Quantum computation enables the execution of algorithms that have exponential complexity. This might open the path towards the synthesis of new materials or medical drugs, optimization of transport or financial strategies etc., intractable on even the fastest classical computers. A quantum computer consists of interconnected two level quantum systems, called qubits, that satisfy DiVincezo’s criteria. Worldwide, there are ongoing efforts to find the qubit architecture which will unite quantum error correction compatible single and two qubit fidelities, long distance qubit to qubit coupling and calability. Superconducting qubits have gone the furthest in this race, demonstrating an algorithm running on 53 coupled qubits, but still the fidelities are not even close to those required for realizing a single logical qubit. emiconductor qubits offer extremely good characteristics, but they are currently investigated across different platforms. Uniting those good characteristics into a single platform might be a big step towards the quantum computer realization. Here we describe the implementation of a hole spin qubit hosted in a Ge hut wire double quantum dot. The high and tunable spin-orbit coupling together with a heavy hole state character is expected to allow fast spin manipulation and long coherence times. Furthermore large lever arms, for hut wire devices, should allow good coupling to superconducting resonators enabling efficient long distance spin to spin coupling and a sensitive gate reflectometry spin readout. The developed cryogenic setup (printed circuit board sample holders, filtering, high-frequency wiring) enabled us to perform low temperature spin dynamics experiments. Indeed, we measured the fastest single spin qubit Rabi frequencies reported so far, reaching 140 MHz, while the dephasing times of 130 ns oppose the long decoherence predictions. In order to further investigate this, a double quantum dot gate was connected directly to a lumped element resonator which enabled gate reflectometry readout. The vanishing inter-dot transition signal, for increasing external magnetic field, revealed the spin nature of the measured quantity. AU - Kukucka, Josip ID - 7996 SN - 2663-337X TI - Implementation of a hole spin qubit in Ge hut wires and dispersive spin sensing ER - TY - THES AB - Deep neural networks have established a new standard for data-dependent feature extraction pipelines in the Computer Vision literature. Despite their remarkable performance in the standard supervised learning scenario, i.e. when models are trained with labeled data and tested on samples that follow a similar distribution, neural networks have been shown to struggle with more advanced generalization abilities, such as transferring knowledge across visually different domains, or generalizing to new unseen combinations of known concepts. In this thesis we argue that, in contrast to the usual black-box behavior of neural networks, leveraging more structured internal representations is a promising direction for tackling such problems. In particular, we focus on two forms of structure. First, we tackle modularity: We show that (i) compositional architectures are a natural tool for modeling reasoning tasks, in that they efficiently capture their combinatorial nature, which is key for generalizing beyond the compositions seen during training. We investigate how to to learn such models, both formally and experimentally, for the task of abstract visual reasoning. Then, we show that (ii) in some settings, modularity allows us to efficiently break down complex tasks into smaller, easier, modules, thereby improving computational efficiency; We study this behavior in the context of generative models for colorization, as well as for small objects detection. Secondly, we investigate the inherently layered structure of representations learned by neural networks, and analyze its role in the context of transfer learning and domain adaptation across visually dissimilar domains. AU - Royer, Amélie ID - 8390 SN - 2663-337X TI - Leveraging structure in Computer Vision tasks for flexible Deep Learning models ER - TY - THES AB - In this thesis we study certain mathematical aspects of evolution. The two primary forces that drive an evolutionary process are mutation and selection. Mutation generates new variants in a population. Selection chooses among the variants depending on the reproductive rates of individuals. Evolutionary processes are intrinsically random – a new mutation that is initially present in the population at low frequency can go extinct, even if it confers a reproductive advantage. The overall rate of evolution is largely determined by two quantities: the probability that an invading advantageous mutation spreads through the population (called fixation probability) and the time until it does so (called fixation time). Both those quantities crucially depend not only on the strength of the invading mutation but also on the population structure. In this thesis, we aim to understand how the underlying population structure affects the overall rate of evolution. Specifically, we study population structures that increase the fixation probability of advantageous mutants (called amplifiers of selection). Broadly speaking, our results are of three different types: We present various strong amplifiers, we identify regimes under which only limited amplification is feasible, and we propose population structures that provide different tradeoffs between high fixation probability and short fixation time. AU - Tkadlec, Josef ID - 7196 TI - A role of graphs in evolutionary processes ER - TY - THES AB - We present solutions to several problems originating from geometry and discrete mathematics: existence of equipartitions, maps without Tverberg multiple points, and inscribing quadrilaterals. Equivariant obstruction theory is the natural topological approach to these type of questions. However, for the specific problems we consider it had yielded only partial or no results. We get our results by complementing equivariant obstruction theory with other techniques from topology and geometry. AU - Avvakumov, Sergey ID - 8156 SN - 2663-337X TI - Topological methods in geometry and discrete mathematics ER - TY - THES AB - Fabrication of curved shells plays an important role in modern design, industry, and science. Among their remarkable properties are, for example, aesthetics of organic shapes, ability to evenly distribute loads, or efficient flow separation. They find applications across vast length scales ranging from sky-scraper architecture to microscopic devices. But, at the same time, the design of curved shells and their manufacturing process pose a variety of challenges. In this thesis, they are addressed from several perspectives. In particular, this thesis presents approaches based on the transformation of initially flat sheets into the target curved surfaces. This involves problems of interactive design of shells with nontrivial mechanical constraints, inverse design of complex structural materials, and data-driven modeling of delicate and time-dependent physical properties. At the same time, two newly-developed self-morphing mechanisms targeting flat-to-curved transformation are presented. In architecture, doubly curved surfaces can be realized as cold bent glass panelizations. Originally flat glass panels are bent into frames and remain stressed. This is a cost-efficient fabrication approach compared to hot bending, when glass panels are shaped plastically. However such constructions are prone to breaking during bending, and it is highly nontrivial to navigate the design space, keeping the panels fabricable and aesthetically pleasing at the same time. We introduce an interactive design system for cold bent glass façades, while previously even offline optimization for such scenarios has not been sufficiently developed. Our method is based on a deep learning approach providing quick and high precision estimation of glass panel shape and stress while handling the shape multimodality. Fabrication of smaller objects of scales below 1 m, can also greatly benefit from shaping originally flat sheets. In this respect, we designed new self-morphing shell mechanisms transforming from an initial flat state to a doubly curved state with high precision and detail. Our so-called CurveUps demonstrate the encodement of the geometric information into the shell. Furthermore, we explored the frontiers of programmable materials and showed how temporal information can additionally be encoded into a flat shell. This allows prescribing deformation sequences for doubly curved surfaces and, thus, facilitates self-collision avoidance enabling complex shapes and functionalities otherwise impossible. Both of these methods include inverse design tools keeping the user in the design loop. AU - Guseinov, Ruslan ID - 8366 KW - computer-aided design KW - shape modeling KW - self-morphing KW - mechanical engineering SN - 2663-337X TI - Computational design of curved thin shells: From glass façades to programmable matter ER -