--- _id: '7996' abstract: - lang: eng text: "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 \r\n 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.\r\nHere 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\r\nresonator which enabled gate reflectometry readout. The vanishing inter-dot transition signal, for increasing external magnetic field, revealed the spin nature of the measured quantity." alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Josip full_name: Kukucka, Josip id: 3F5D8856-F248-11E8-B48F-1D18A9856A87 last_name: Kukucka citation: ama: Kukucka J. Implementation of a hole spin qubit in Ge hut wires and dispersive spin sensing. 2020. doi:10.15479/AT:ISTA:7996 apa: Kukucka, J. (2020). Implementation of a hole spin qubit in Ge hut wires and dispersive spin sensing. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:7996 chicago: Kukucka, Josip. “Implementation of a Hole Spin Qubit in Ge Hut Wires and Dispersive Spin Sensing.” Institute of Science and Technology Austria, 2020. https://doi.org/10.15479/AT:ISTA:7996. ieee: J. Kukucka, “Implementation of a hole spin qubit in Ge hut wires and dispersive spin sensing,” Institute of Science and Technology Austria, 2020. ista: Kukucka J. 2020. Implementation of a hole spin qubit in Ge hut wires and dispersive spin sensing. Institute of Science and Technology Austria. mla: Kukucka, Josip. Implementation of a Hole Spin Qubit in Ge Hut Wires and Dispersive Spin Sensing. Institute of Science and Technology Austria, 2020, doi:10.15479/AT:ISTA:7996. short: J. Kukucka, Implementation of a Hole Spin Qubit in Ge Hut Wires and Dispersive Spin Sensing, Institute of Science and Technology Austria, 2020. date_created: 2020-06-22T09:22:23Z date_published: 2020-06-22T00:00:00Z date_updated: 2023-09-26T15:50:22Z day: '22' ddc: - '530' degree_awarded: PhD department: - _id: GeKa doi: 10.15479/AT:ISTA:7996 file: - access_level: closed checksum: 467e52feb3e361ce8cf5fe8d5c254ece content_type: application/x-zip-compressed creator: dernst date_created: 2020-06-22T09:22:04Z date_updated: 2020-07-14T12:48:07Z file_id: '7997' file_name: JK_thesis_latex_source_files.zip file_size: 392794743 relation: main_file - access_level: open_access checksum: 1de716bf110dbd77d383e479232bf496 content_type: application/pdf creator: dernst date_created: 2020-06-22T09:21:29Z date_updated: 2020-07-14T12:48:07Z file_id: '7998' file_name: PhD_thesis_JK_pdfa.pdf file_size: 28453247 relation: main_file file_date_updated: 2020-07-14T12:48:07Z has_accepted_license: '1' language: - iso: eng month: '06' oa: 1 oa_version: Published Version page: '178' publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '1328' relation: part_of_dissertation status: public - id: '7541' relation: part_of_dissertation status: public - id: '77' relation: part_of_dissertation status: public - id: '23' relation: part_of_dissertation status: public - id: '840' relation: part_of_dissertation status: public status: public supervisor: - first_name: Georgios full_name: Katsaros, Georgios id: 38DB5788-F248-11E8-B48F-1D18A9856A87 last_name: Katsaros orcid: 0000-0001-8342-202X title: Implementation of a hole spin qubit in Ge hut wires and dispersive spin sensing type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2020' ... --- _id: '8390' abstract: - lang: eng text: "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\r\nfor 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\r\ndissimilar domains. " acknowledged_ssus: - _id: CampIT - _id: ScienComp acknowledgement: Last but not least, I would like to acknowledge the support of the IST IT and scientific computing team for helping provide a great work environment. alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Amélie full_name: Royer, Amélie id: 3811D890-F248-11E8-B48F-1D18A9856A87 last_name: Royer orcid: 0000-0002-8407-0705 citation: ama: Royer A. Leveraging structure in Computer Vision tasks for flexible Deep Learning models. 2020. doi:10.15479/AT:ISTA:8390 apa: Royer, A. (2020). Leveraging structure in Computer Vision tasks for flexible Deep Learning models. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:8390 chicago: Royer, Amélie. “Leveraging Structure in Computer Vision Tasks for Flexible Deep Learning Models.” Institute of Science and Technology Austria, 2020. https://doi.org/10.15479/AT:ISTA:8390. ieee: A. Royer, “Leveraging structure in Computer Vision tasks for flexible Deep Learning models,” Institute of Science and Technology Austria, 2020. ista: Royer A. 2020. Leveraging structure in Computer Vision tasks for flexible Deep Learning models. Institute of Science and Technology Austria. mla: Royer, Amélie. Leveraging Structure in Computer Vision Tasks for Flexible Deep Learning Models. Institute of Science and Technology Austria, 2020, doi:10.15479/AT:ISTA:8390. short: A. Royer, Leveraging Structure in Computer Vision Tasks for Flexible Deep Learning Models, Institute of Science and Technology Austria, 2020. date_created: 2020-09-14T13:42:09Z date_published: 2020-09-14T00:00:00Z date_updated: 2023-10-16T10:04:02Z day: '14' ddc: - '000' degree_awarded: PhD department: - _id: ChLa doi: 10.15479/AT:ISTA:8390 file: - access_level: open_access checksum: c914d2f88846032f3d8507734861b6ee content_type: application/pdf creator: dernst date_created: 2020-09-14T13:39:14Z date_updated: 2020-09-14T13:39:14Z file_id: '8391' file_name: 2020_Thesis_Royer.pdf file_size: 30224591 relation: main_file success: 1 - access_level: closed checksum: ae98fb35d912cff84a89035ae5794d3c content_type: application/x-zip-compressed creator: dernst date_created: 2020-09-14T13:39:17Z date_updated: 2020-09-14T13:39:17Z file_id: '8392' file_name: thesis_sources.zip file_size: 74227627 relation: main_file file_date_updated: 2020-09-14T13:39:17Z has_accepted_license: '1' language: - iso: eng license: https://creativecommons.org/licenses/by-nc-sa/4.0/ month: '09' oa: 1 oa_version: Published Version page: '197' publication_identifier: isbn: - 978-3-99078-007-7 issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '7936' relation: part_of_dissertation status: public - id: '7937' relation: part_of_dissertation status: public - id: '8193' relation: part_of_dissertation status: public - id: '8092' relation: part_of_dissertation status: public - id: '911' relation: part_of_dissertation status: public status: public supervisor: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 title: Leveraging structure in Computer Vision tasks for flexible Deep Learning models tmp: image: /images/cc_by_nc_sa.png legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) short: CC BY-NC-SA (4.0) type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2020' ... --- _id: '7196' abstract: - lang: eng text: '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.' alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Josef full_name: Tkadlec, Josef id: 3F24CCC8-F248-11E8-B48F-1D18A9856A87 last_name: Tkadlec orcid: 0000-0002-1097-9684 citation: ama: Tkadlec J. A role of graphs in evolutionary processes. 2020. doi:10.15479/AT:ISTA:7196 apa: Tkadlec, J. (2020). A role of graphs in evolutionary processes. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:7196 chicago: Tkadlec, Josef. “A Role of Graphs in Evolutionary Processes.” Institute of Science and Technology Austria, 2020. https://doi.org/10.15479/AT:ISTA:7196. ieee: J. Tkadlec, “A role of graphs in evolutionary processes,” Institute of Science and Technology Austria, 2020. ista: Tkadlec J. 2020. A role of graphs in evolutionary processes. Institute of Science and Technology Austria. mla: Tkadlec, Josef. A Role of Graphs in Evolutionary Processes. Institute of Science and Technology Austria, 2020, doi:10.15479/AT:ISTA:7196. short: J. Tkadlec, A Role of Graphs in Evolutionary Processes, Institute of Science and Technology Austria, 2020. date_created: 2019-12-20T12:26:36Z date_published: 2020-01-12T00:00:00Z date_updated: 2023-10-17T12:29:46Z day: '12' ddc: - '519' degree_awarded: PhD department: - _id: KrCh - _id: GradSch doi: 10.15479/AT:ISTA:7196 file: - access_level: closed checksum: 451f8e64b0eb26bf297644ac72bfcbe9 content_type: application/zip creator: jtkadlec date_created: 2020-01-12T11:49:49Z date_updated: 2020-07-14T12:47:52Z file_id: '7255' file_name: thesis.zip file_size: 21100497 relation: source_file - access_level: open_access checksum: d8c44cbc4f939c49a8efc9d4b8bb3985 content_type: application/pdf creator: dernst date_created: 2020-01-28T07:32:42Z date_updated: 2020-07-14T12:47:52Z file_id: '7367' file_name: 2020_Tkadlec_Thesis.pdf file_size: 11670983 relation: main_file file_date_updated: 2020-07-14T12:47:52Z has_accepted_license: '1' language: - iso: eng month: '01' oa: 1 oa_version: Published Version page: '144' publication_identifier: eissn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '7210' relation: dissertation_contains status: public - id: '5751' relation: dissertation_contains status: public - id: '7212' relation: dissertation_contains status: public status: public supervisor: - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X title: A role of graphs in evolutionary processes type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2020' ... --- _id: '8156' abstract: - lang: eng text: '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.' alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Sergey full_name: Avvakumov, Sergey id: 3827DAC8-F248-11E8-B48F-1D18A9856A87 last_name: Avvakumov citation: ama: Avvakumov S. Topological methods in geometry and discrete mathematics. 2020. doi:10.15479/AT:ISTA:8156 apa: Avvakumov, S. (2020). Topological methods in geometry and discrete mathematics. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:8156 chicago: Avvakumov, Sergey. “Topological Methods in Geometry and Discrete Mathematics.” Institute of Science and Technology Austria, 2020. https://doi.org/10.15479/AT:ISTA:8156. ieee: S. Avvakumov, “Topological methods in geometry and discrete mathematics,” Institute of Science and Technology Austria, 2020. ista: Avvakumov S. 2020. Topological methods in geometry and discrete mathematics. Institute of Science and Technology Austria. mla: Avvakumov, Sergey. Topological Methods in Geometry and Discrete Mathematics. Institute of Science and Technology Austria, 2020, doi:10.15479/AT:ISTA:8156. short: S. Avvakumov, Topological Methods in Geometry and Discrete Mathematics, Institute of Science and Technology Austria, 2020. date_created: 2020-07-23T09:51:29Z date_published: 2020-07-24T00:00:00Z date_updated: 2023-12-18T10:51:01Z day: '24' ddc: - '514' degree_awarded: PhD department: - _id: UlWa doi: 10.15479/AT:ISTA:8156 file: - access_level: closed content_type: application/zip creator: savvakum date_created: 2020-07-27T12:44:51Z date_updated: 2020-07-27T12:44:51Z file_id: '8178' file_name: source.zip file_size: 1061740 relation: source_file - access_level: open_access content_type: application/pdf creator: savvakum date_created: 2020-07-27T12:46:53Z date_updated: 2020-07-27T12:46:53Z file_id: '8179' file_name: thesis_pdfa.pdf file_size: 1336501 relation: main_file success: 1 file_date_updated: 2020-07-27T12:46:53Z has_accepted_license: '1' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: '119' publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '8182' relation: part_of_dissertation status: public - id: '8183' relation: part_of_dissertation status: public - id: '8185' relation: part_of_dissertation status: public - id: '8184' relation: part_of_dissertation status: public - id: '6355' relation: part_of_dissertation status: public - id: '75' relation: part_of_dissertation status: public status: public supervisor: - first_name: Uli full_name: Wagner, Uli id: 36690CA2-F248-11E8-B48F-1D18A9856A87 last_name: Wagner orcid: 0000-0002-1494-0568 title: Topological methods in geometry and discrete mathematics type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2020' ... --- _id: '8366' abstract: - lang: eng text: "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\r\nthe 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.\r\nIn 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\r\nnontrivial 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\r\nand high precision estimation of glass panel shape and stress while handling the shape\r\nmultimodality.\r\nFabrication 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\r\ninto 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.\r\nBoth of these methods include inverse design tools keeping the user in the design loop." acknowledged_ssus: - _id: M-Shop - _id: ScienComp acknowledgement: "During the work on this thesis, I received substantial support from IST Austria’s scientific service units. A big thank you to Todor Asenov and other Miba Machine Shop team members for their help with fabrication of experimental prototypes. In addition, I would like to thank Scientific Computing team for the support with high performance computing.\r\nFinancial support was provided by the European Research Council (ERC) under grant agreement No 715767 - MATERIALIZABLE: Intelligent fabrication-oriented Computational Design and Modeling, which I gratefully acknowledge." alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Ruslan full_name: Guseinov, Ruslan id: 3AB45EE2-F248-11E8-B48F-1D18A9856A87 last_name: Guseinov orcid: 0000-0001-9819-5077 citation: ama: 'Guseinov R. Computational design of curved thin shells: From glass façades to programmable matter. 2020. doi:10.15479/AT:ISTA:8366' apa: 'Guseinov, R. (2020). Computational design of curved thin shells: From glass façades to programmable matter. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:8366' chicago: 'Guseinov, Ruslan. “Computational Design of Curved Thin Shells: From Glass Façades to Programmable Matter.” Institute of Science and Technology Austria, 2020. https://doi.org/10.15479/AT:ISTA:8366.' ieee: 'R. Guseinov, “Computational design of curved thin shells: From glass façades to programmable matter,” Institute of Science and Technology Austria, 2020.' ista: 'Guseinov R. 2020. Computational design of curved thin shells: From glass façades to programmable matter. Institute of Science and Technology Austria.' mla: 'Guseinov, Ruslan. Computational Design of Curved Thin Shells: From Glass Façades to Programmable Matter. Institute of Science and Technology Austria, 2020, doi:10.15479/AT:ISTA:8366.' short: 'R. Guseinov, Computational Design of Curved Thin Shells: From Glass Façades to Programmable Matter, Institute of Science and Technology Austria, 2020.' date_created: 2020-09-10T16:19:55Z date_published: 2020-09-21T00:00:00Z date_updated: 2024-02-21T12:44:29Z day: '21' ddc: - '000' degree_awarded: PhD department: - _id: BeBi doi: 10.15479/AT:ISTA:8366 ec_funded: 1 file: - access_level: open_access checksum: f8da89553da36037296b0a80f14ebf50 content_type: application/pdf creator: rguseino date_created: 2020-09-10T16:11:49Z date_updated: 2020-09-10T16:11:49Z file_id: '8367' file_name: thesis_rguseinov.pdf file_size: 70950442 relation: main_file success: 1 - access_level: closed checksum: e8fd944c960c20e0e27e6548af69121d content_type: application/x-zip-compressed creator: rguseino date_created: 2020-09-11T09:39:48Z date_updated: 2020-09-16T15:11:01Z file_id: '8374' file_name: thesis_source.zip file_size: 76207597 relation: source_file file_date_updated: 2020-09-16T15:11:01Z has_accepted_license: '1' keyword: - computer-aided design - shape modeling - self-morphing - mechanical engineering language: - iso: eng month: '09' oa: 1 oa_version: Published Version page: '118' project: - _id: 24F9549A-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '715767' name: 'MATERIALIZABLE: Intelligent fabrication-oriented Computational Design and Modeling' publication_identifier: isbn: - 978-3-99078-010-7 issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '7151' relation: research_data status: deleted - id: '7262' relation: part_of_dissertation status: public - id: '8562' relation: part_of_dissertation status: public - id: '1001' relation: part_of_dissertation status: public - id: '8375' relation: research_data status: public status: public supervisor: - first_name: Bernd full_name: Bickel, Bernd id: 49876194-F248-11E8-B48F-1D18A9856A87 last_name: Bickel orcid: 0000-0001-6511-9385 title: 'Computational design of curved thin shells: From glass façades to programmable matter' type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2020' ...