---
_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
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- iso: eng
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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:
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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
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date_created: 2020-09-14T13:39:14Z
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date_created: 2020-09-14T13:39:17Z
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language:
- iso: eng
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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:
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relation: part_of_dissertation
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- 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:
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date_created: 2020-01-12T11:49:49Z
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creator: dernst
date_created: 2020-01-28T07:32:42Z
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file_name: 2020_Tkadlec_Thesis.pdf
file_size: 11670983
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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:
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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:
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creator: savvakum
date_created: 2020-07-27T12:44:51Z
date_updated: 2020-07-27T12:44:51Z
file_id: '8178'
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content_type: application/pdf
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oa: 1
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publication_identifier:
issn:
- 2663-337X
publication_status: published
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related_material:
record:
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- id: '8183'
relation: part_of_dissertation
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- 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:
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doi: 10.15479/AT:ISTA:8366
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file_size: 76207597
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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:
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relation: research_data
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relation: part_of_dissertation
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- id: '8562'
relation: part_of_dissertation
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- 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'
...