@phdthesis{49, abstract = {Nowadays, quantum computation is receiving more and more attention as an alternative to the classical way of computing. For realizing a quantum computer, different devices are investigated as potential quantum bits. In this thesis, the focus is on Ge hut wires, which turned out to be promising candidates for implementing hole spin quantum bits. The advantages of Ge as a material system are the low hyperfine interaction for holes and the strong spin orbit coupling, as well as the compatibility with the highly developed CMOS processes in industry. In addition, Ge can also be isotopically purified which is expected to boost the spin coherence times. The strong spin orbit interaction for holes in Ge on the one hand enables the full electrical control of the quantum bit and on the other hand should allow short spin manipulation times. Starting with a bare Si wafer, this work covers the entire process reaching from growth over the fabrication and characterization of hut wire devices up to the demonstration of hole spin resonance. From experiments with single quantum dots, a large g-factor anisotropy between the in-plane and the out-of-plane direction was found. A comparison to a theoretical model unveiled the heavy-hole character of the lowest energy states. The second part of the thesis addresses double quantum dot devices, which were realized by adding two gate electrodes to a hut wire. In such devices, Pauli spin blockade was observed, which can serve as a read-out mechanism for spin quantum bits. Applying oscillating electric fields in spin blockade allowed the demonstration of continuous spin rotations and the extraction of a lower bound for the spin dephasing time. Despite the strong spin orbit coupling in Ge, the obtained value for the dephasing time is comparable to what has been recently reported for holes in Si. All in all, the presented results point out the high potential of Ge hut wires as a platform for long-lived, fast and fully electrically tunable hole spin quantum bits.}, author = {Watzinger, Hannes}, issn = {2663-337X}, pages = {77}, publisher = {Institute of Science and Technology Austria}, title = {{Ge hut wires - from growth to hole spin resonance}}, doi = {10.15479/AT:ISTA:th_1033}, year = {2018}, } @phdthesis{201, abstract = {We describe arrangements of three-dimensional spheres from a geometrical and topological point of view. Real data (fitting this setup) often consist of soft spheres which show certain degree of deformation while strongly packing against each other. In this context, we answer the following questions: If we model a soft packing of spheres by hard spheres that are allowed to overlap, can we measure the volume in the overlapped areas? Can we be more specific about the overlap volume, i.e. quantify how much volume is there covered exactly twice, three times, or k times? What would be a good optimization criteria that rule the arrangement of soft spheres while making a good use of the available space? Fixing a particular criterion, what would be the optimal sphere configuration? The first result of this thesis are short formulas for the computation of volumes covered by at least k of the balls. The formulas exploit information contained in the order-k Voronoi diagrams and its closely related Level-k complex. The used complexes lead to a natural generalization into poset diagrams, a theoretical formalism that contains the order-k and degree-k diagrams as special cases. In parallel, we define different criteria to determine what could be considered an optimal arrangement from a geometrical point of view. Fixing a criterion, we find optimal soft packing configurations in 2D and 3D where the ball centers lie on a lattice. As a last step, we use tools from computational topology on real physical data, to show the potentials of higher-order diagrams in the description of melting crystals. The results of the experiments leaves us with an open window to apply the theories developed in this thesis in real applications.}, author = {Iglesias Ham, Mabel}, issn = {2663-337X}, pages = {171}, publisher = {Institute of Science and Technology Austria}, title = {{Multiple covers with balls}}, doi = {10.15479/AT:ISTA:th_1026}, year = {2018}, } @phdthesis{68, abstract = {The most common assumption made in statistical learning theory is the assumption of the independent and identically distributed (i.i.d.) data. While being very convenient mathematically, it is often very clearly violated in practice. This disparity between the machine learning theory and applications underlies a growing demand in the development of algorithms that learn from dependent data and theory that can provide generalization guarantees similar to the independent situations. This thesis is dedicated to two variants of dependencies that can arise in practice. One is a dependence on the level of samples in a single learning task. Another dependency type arises in the multi-task setting when the tasks are dependent on each other even though the data for them can be i.i.d. In both cases we model the data (samples or tasks) as stochastic processes and introduce new algorithms for both settings that take into account and exploit the resulting dependencies. We prove the theoretical guarantees on the performance of the introduced algorithms under different evaluation criteria and, in addition, we compliment the theoretical study by the empirical one, where we evaluate some of the algorithms on two real world datasets to highlight their practical applicability.}, author = {Zimin, Alexander}, issn = {2663-337X}, pages = {92}, publisher = {Institute of Science and Technology Austria}, title = {{Learning from dependent data}}, doi = {10.15479/AT:ISTA:TH1048}, year = {2018}, } @phdthesis{83, abstract = {A proof system is a protocol between a prover and a verifier over a common input in which an honest prover convinces the verifier of the validity of true statements. Motivated by the success of decentralized cryptocurrencies, exemplified by Bitcoin, the focus of this thesis will be on proof systems which found applications in some sustainable alternatives to Bitcoin, such as the Spacemint and Chia cryptocurrencies. In particular, we focus on proofs of space and proofs of sequential work. Proofs of space (PoSpace) were suggested as more ecological, economical, and egalitarian alternative to the energy-wasteful proof-of-work mining of Bitcoin. However, the state-of-the-art constructions of PoSpace are based on sophisticated graph pebbling lower bounds, and are therefore complex. Moreover, when these PoSpace are used in cryptocurrencies like Spacemint, miners can only start mining after ensuring that a commitment to their space is already added in a special transaction to the blockchain. Proofs of sequential work (PoSW) are proof systems in which a prover, upon receiving a statement x and a time parameter T, computes a proof which convinces the verifier that T time units had passed since x was received. Whereas Spacemint assumes synchrony to retain some interesting Bitcoin dynamics, Chia requires PoSW with unique proofs, i.e., PoSW in which it is hard to come up with more than one accepting proof for any true statement. In this thesis we construct simple and practically-efficient PoSpace and PoSW. When using our PoSpace in cryptocurrencies, miners can start mining on the fly, like in Bitcoin, and unlike current constructions of PoSW, which either achieve efficient verification of sequential work, or faster-than-recomputing verification of correctness of proofs, but not both at the same time, ours achieve the best of these two worlds.}, author = {Abusalah, Hamza M}, issn = {2663-337X}, pages = {59}, publisher = {Institute of Science and Technology Austria}, title = {{Proof systems for sustainable decentralized cryptocurrencies}}, doi = {10.15479/AT:ISTA:TH_1046}, year = {2018}, } @phdthesis{197, abstract = {Modern computer vision systems heavily rely on statistical machine learning models, which typically require large amounts of labeled data to be learned reliably. Moreover, very recently computer vision research widely adopted techniques for representation learning, which further increase the demand for labeled data. However, for many important practical problems there is relatively small amount of labeled data available, so it is problematic to leverage full potential of the representation learning methods. One way to overcome this obstacle is to invest substantial resources into producing large labelled datasets. Unfortunately, this can be prohibitively expensive in practice. In this thesis we focus on the alternative way of tackling the aforementioned issue. We concentrate on methods, which make use of weakly-labeled or even unlabeled data. Specifically, the first half of the thesis is dedicated to the semantic image segmentation task. We develop a technique, which achieves competitive segmentation performance and only requires annotations in a form of global image-level labels instead of dense segmentation masks. Subsequently, we present a new methodology, which further improves segmentation performance by leveraging tiny additional feedback from a human annotator. By using our methods practitioners can greatly reduce the amount of data annotation effort, which is required to learn modern image segmentation models. In the second half of the thesis we focus on methods for learning from unlabeled visual data. We study a family of autoregressive models for modeling structure of natural images and discuss potential applications of these models. Moreover, we conduct in-depth study of one of these applications, where we develop the state-of-the-art model for the probabilistic image colorization task.}, author = {Kolesnikov, Alexander}, issn = {2663-337X}, pages = {113}, publisher = {Institute of Science and Technology Austria}, title = {{Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images}}, doi = {10.15479/AT:ISTA:th_1021}, year = {2018}, }