---
_id: '11473'
abstract:
- lang: eng
text: "The polaron model is a basic model of quantum field theory describing a single
particle\r\ninteracting with a bosonic field. It arises in many physical contexts.
We are mostly concerned\r\nwith models applicable in the context of an impurity
atom in a Bose-Einstein condensate as\r\nwell as the problem of electrons moving
in polar crystals.\r\nThe model has a simple structure in which the interaction
of the particle with the field is given\r\nby a term linear in the field’s creation
and annihilation operators. In this work, we investigate\r\nthe properties of
this model by providing rigorous estimates on various energies relevant to the\r\nproblem.
The estimates are obtained, for the most part, by suitable operator techniques
which\r\nconstitute the principal mathematical substance of the thesis.\r\nThe
first application of these techniques is to derive the polaron model rigorously
from first\r\nprinciples, i.e., from a full microscopic quantum-mechanical many-body
problem involving an\r\nimpurity in an otherwise homogeneous system. We accomplish
this for the N + 1 Bose gas\r\nin the mean-field regime by showing that a suitable
polaron-type Hamiltonian arises at weak\r\ninteractions as a low-energy effective
theory for this problem.\r\nIn the second part, we investigate rigorously the
ground state of the model at fixed momentum\r\nand for large values of the coupling
constant. Qualitatively, the system is expected to display\r\na transition from
the quasi-particle behavior at small momenta, where the dispersion relation\r\nis
parabolic and the particle moves through the medium dragging along a cloud of
phonons, to\r\nthe radiative behavior at larger momenta where the polaron decelerates
and emits free phonons.\r\nAt the same time, in the strong coupling regime, the
bosonic field is expected to behave purely\r\nclassically. Accordingly, the effective
mass of the polaron at strong coupling is conjectured to\r\nbe asymptotically
equal to the one obtained from the semiclassical counterpart of the problem,\r\nfirst
studied by Landau and Pekar in the 1940s. For polaron models with regularized
form\r\nfactors and phonon dispersion relations of superfluid type, i.e., bounded
below by a linear\r\nfunction of the wavenumbers for all phonon momenta as in
the interacting Bose gas, we prove\r\nthat for a large window of momenta below
the radiation threshold, the energy-momentum\r\nrelation at strong coupling is
indeed essentially a parabola with semi-latus rectum equal to the\r\nLandau–Pekar
effective mass, as expected.\r\nFor the Fröhlich polaron describing electrons
in polar crystals where the dispersion relation is\r\nof the optical type and
the form factor is formally UV–singular due to the nature of the point\r\ncharge-dipole
interaction, we are able to give the corresponding upper bound. In contrast to\r\nthe
regular case, this requires the inclusion of the quantum fluctuations of the phonon
field,\r\nwhich makes the problem considerably more difficult.\r\nThe results
are supplemented by studies on the absolute ground-state energy at strong coupling,\r\na
proof of the divergence of the effective mass with the coupling constant for a
wide class of\r\npolaron models, as well as the discussion of the apparent UV
singularity of the Fröhlich model\r\nand the application of the techniques used
for its removal for the energy estimates.\r\n"
acknowledged_ssus:
- _id: SSU
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Krzysztof
full_name: Mysliwy, Krzysztof
id: 316457FC-F248-11E8-B48F-1D18A9856A87
last_name: Mysliwy
citation:
ama: 'Mysliwy K. Polarons in Bose gases and polar crystals: Some rigorous energy
estimates. 2022. doi:10.15479/at:ista:11473'
apa: 'Mysliwy, K. (2022). Polarons in Bose gases and polar crystals: Some rigorous
energy estimates. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:11473'
chicago: 'Mysliwy, Krzysztof. “Polarons in Bose Gases and Polar Crystals: Some Rigorous
Energy Estimates.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:11473.'
ieee: 'K. Mysliwy, “Polarons in Bose gases and polar crystals: Some rigorous energy
estimates,” Institute of Science and Technology Austria, 2022.'
ista: 'Mysliwy K. 2022. Polarons in Bose gases and polar crystals: Some rigorous
energy estimates. Institute of Science and Technology Austria.'
mla: 'Mysliwy, Krzysztof. Polarons in Bose Gases and Polar Crystals: Some Rigorous
Energy Estimates. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:11473.'
short: 'K. Mysliwy, Polarons in Bose Gases and Polar Crystals: Some Rigorous Energy
Estimates, Institute of Science and Technology Austria, 2022.'
date_created: 2022-06-30T12:15:03Z
date_published: 2022-07-01T00:00:00Z
date_updated: 2023-09-07T13:43:52Z
day: '01'
ddc:
- '515'
- '539'
degree_awarded: PhD
department:
- _id: GradSch
- _id: RoSe
doi: 10.15479/at:ista:11473
ec_funded: 1
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creator: kmysliwy
date_created: 2022-07-05T08:15:52Z
date_updated: 2022-07-05T08:17:12Z
file_id: '11487'
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language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: '138'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
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relation: part_of_dissertation
status: public
- id: '8705'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Robert
full_name: Seiringer, Robert
id: 4AFD0470-F248-11E8-B48F-1D18A9856A87
last_name: Seiringer
orcid: 0000-0002-6781-0521
title: 'Polarons in Bose gases and polar crystals: Some rigorous energy estimates'
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2022'
...
---
_id: '10799'
abstract:
- lang: eng
text: "Because of the increasing popularity of machine learning methods, it is becoming
important to understand the impact of learned components on automated decision-making
systems and to guarantee that their consequences are beneficial to society. In
other words, it is necessary to ensure that machine learning is sufficiently trustworthy
to be used in real-world applications. This thesis studies two properties of machine
learning models that are highly desirable for the\r\nsake of reliability: robustness
and fairness. In the first part of the thesis we study the robustness of learning
algorithms to training data corruption. Previous work has shown that machine learning
models are vulnerable to a range\r\nof training set issues, varying from label
noise through systematic biases to worst-case data manipulations. This is an especially
relevant problem from a present perspective, since modern machine learning methods
are particularly data hungry and therefore practitioners often have to rely on
data collected from various external sources, e.g. from the Internet, from app
users or via crowdsourcing. Naturally, such sources vary greatly in the quality
and reliability of the\r\ndata they provide. With these considerations in mind,
we study the problem of designing machine learning algorithms that are robust
to corruptions in data coming from multiple sources. We show that, in contrast
to the case of a single dataset with outliers, successful learning within this
model is possible both theoretically and practically, even under worst-case data
corruptions. The second part of this thesis deals with fairness-aware machine
learning. There are multiple areas where machine learning models have shown promising
results, but where careful considerations are required, in order to avoid discrimanative
decisions taken by such learned components. Ensuring fairness can be particularly
challenging, because real-world training datasets are expected to contain various
forms of historical bias that may affect the learning process. In this thesis
we show that data corruption can indeed render the problem of achieving fairness
impossible, by tightly characterizing the theoretical limits of fair learning
under worst-case data manipulations. However, assuming access to clean data, we
also show how fairness-aware learning can be made practical in contexts beyond
binary classification, in particular in the challenging learning to rank setting."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Nikola H
full_name: Konstantinov, Nikola H
id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87
last_name: Konstantinov
citation:
ama: Konstantinov NH. Robustness and fairness in machine learning. 2022. doi:10.15479/at:ista:10799
apa: Konstantinov, N. H. (2022). Robustness and fairness in machine learning.
Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10799
chicago: Konstantinov, Nikola H. “Robustness and Fairness in Machine Learning.”
Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:10799.
ieee: N. H. Konstantinov, “Robustness and fairness in machine learning,” Institute
of Science and Technology Austria, 2022.
ista: Konstantinov NH. 2022. Robustness and fairness in machine learning. Institute
of Science and Technology Austria.
mla: Konstantinov, Nikola H. Robustness and Fairness in Machine Learning.
Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:10799.
short: N.H. Konstantinov, Robustness and Fairness in Machine Learning, Institute
of Science and Technology Austria, 2022.
date_created: 2022-02-28T13:03:49Z
date_published: 2022-03-08T00:00:00Z
date_updated: 2023-10-17T12:31:54Z
day: '08'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: GradSch
- _id: ChLa
doi: 10.15479/at:ista:10799
ec_funded: 1
file:
- access_level: open_access
checksum: 626bc523ae8822d20e635d0e2d95182e
content_type: application/pdf
creator: nkonstan
date_created: 2022-03-06T11:42:54Z
date_updated: 2022-03-06T11:42:54Z
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date_created: 2022-03-06T11:42:57Z
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file_size: 22841103
relation: source_file
file_date_updated: 2022-03-10T12:11:48Z
has_accepted_license: '1'
keyword:
- robustness
- fairness
- machine learning
- PAC learning
- adversarial learning
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: '176'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication_identifier:
isbn:
- 978-3-99078-015-2
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
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relation: part_of_dissertation
status: public
- id: '10803'
relation: part_of_dissertation
status: public
- id: '10802'
relation: part_of_dissertation
status: public
- id: '6590'
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: Robustness and fairness in machine learning
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2022'
...
---
_id: '11626'
abstract:
- lang: eng
text: Plant growth and development is well known to be both, flexible and dynamic.
The high capacity for post-embryonic organ formation and tissue regeneration requires
tightly regulated intercellular communication and coordinated tissue polarization.
One of the most important drivers for patterning and polarity in plant development
is the phytohormone auxin. Auxin has the unique characteristic to establish polarized
channels for its own active directional cell to cell transport. This fascinating
phenomenon is called auxin canalization. Those auxin transport channels are characterized
by the expression and polar, subcellular localization of PIN auxin efflux carriers.
PIN proteins have the ability to dynamically change their localization and auxin
itself can affect this by interfering with trafficking. Most of the underlying
molecular mechanisms of canalization still remain enigmatic. What is known so
far is that canonical auxin signaling is indispensable but also other non-canonical
signaling components are thought to play a role. In order to shed light into the
mysteries auf auxin canalization this study revisits the branches of auxin signaling
in detail. Further a new auxin analogue, PISA, is developed which triggers auxin-like
responses but does not directly activate canonical transcriptional auxin signaling.
We revisit the direct auxin effect on PIN trafficking where we found that, contradictory
to previous observations, auxin is very specifically promoting endocytosis of
PIN2 but has no overall effect on endocytosis. Further, we evaluate which cellular
processes related to PIN subcellular dynamics are involved in the establishment
of auxin conducting channels and the formation of vascular tissue. We are re-evaluating
the function of AUXIN BINDING PROTEIN 1 (ABP1) and provide a comprehensive picture
about its developmental phneotypes and involvement in auxin signaling and canalization.
Lastly, we are focusing on the crosstalk between the hormone strigolactone (SL)
and auxin and found that SL is interfering with essentially all processes involved
in auxin canalization in a non-transcriptional manner. Lastly we identify a new
way of SL perception and signaling which is emanating from mitochondria, is independent
of canonical SL signaling and is modulating primary root growth.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Michelle C
full_name: Gallei, Michelle C
id: 35A03822-F248-11E8-B48F-1D18A9856A87
last_name: Gallei
orcid: 0000-0003-1286-7368
citation:
ama: Gallei MC. Auxin and strigolactone non-canonical signaling regulating development
in Arabidopsis thaliana. 2022. doi:10.15479/at:ista:11626
apa: Gallei, M. C. (2022). Auxin and strigolactone non-canonical signaling regulating
development in Arabidopsis thaliana. Institute of Science and Technology Austria.
https://doi.org/10.15479/at:ista:11626
chicago: Gallei, Michelle C. “Auxin and Strigolactone Non-Canonical Signaling Regulating
Development in Arabidopsis Thaliana.” Institute of Science and Technology Austria,
2022. https://doi.org/10.15479/at:ista:11626.
ieee: M. C. Gallei, “Auxin and strigolactone non-canonical signaling regulating
development in Arabidopsis thaliana,” Institute of Science and Technology Austria,
2022.
ista: Gallei MC. 2022. Auxin and strigolactone non-canonical signaling regulating
development in Arabidopsis thaliana. Institute of Science and Technology Austria.
mla: Gallei, Michelle C. Auxin and Strigolactone Non-Canonical Signaling Regulating
Development in Arabidopsis Thaliana. Institute of Science and Technology Austria,
2022, doi:10.15479/at:ista:11626.
short: M.C. Gallei, Auxin and Strigolactone Non-Canonical Signaling Regulating Development
in Arabidopsis Thaliana, Institute of Science and Technology Austria, 2022.
date_created: 2022-07-20T11:21:53Z
date_published: 2022-07-20T00:00:00Z
date_updated: 2023-11-07T08:20:13Z
day: '20'
ddc:
- '575'
degree_awarded: PhD
department:
- _id: GradSch
- _id: JiFr
doi: 10.15479/at:ista:11626
ec_funded: 1
file:
- access_level: open_access
checksum: bd7ac35403cf5b4b2607287d2a104b3a
content_type: application/pdf
creator: mgallei
date_created: 2022-07-25T09:08:47Z
date_updated: 2022-07-25T09:08:47Z
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date_updated: 2022-07-25T09:39:58Z
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date_created: 2022-07-25T09:09:32Z
date_updated: 2022-07-25T09:39:58Z
description: This is the print version of the thesis including the full appendix
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content_type: application/pdf
creator: mgallei
date_created: 2022-07-25T11:48:45Z
date_updated: 2022-07-25T11:48:45Z
file_id: '11650'
file_name: Thesis_Gallei_Appendix.pdf
file_size: 15435966
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file_date_updated: 2022-07-25T11:48:45Z
has_accepted_license: '1'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: '248'
project:
- _id: 261099A6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '742985'
name: Tracing Evolution of Auxin Transport and Polarity in Plants
publication_identifier:
isbn:
- 978-3-99078-019-0
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '8931'
relation: part_of_dissertation
status: public
- id: '9287'
relation: part_of_dissertation
status: public
- id: '7142'
relation: part_of_dissertation
status: public
- id: '7465'
relation: part_of_dissertation
status: public
- id: '8138'
relation: part_of_dissertation
status: public
- id: '6260'
relation: part_of_dissertation
status: public
- id: '10411'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Jiří
full_name: Friml, Jiří
id: 4159519E-F248-11E8-B48F-1D18A9856A87
last_name: Friml
orcid: 0000-0002-8302-7596
- first_name: Eva
full_name: Benková, Eva
id: 38F4F166-F248-11E8-B48F-1D18A9856A87
last_name: Benková
orcid: 0000-0002-8510-9739
- first_name: Eilon
full_name: Shani, Eilon
last_name: Shani
title: Auxin and strigolactone non-canonical signaling regulating development in Arabidopsis
thaliana
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2022'
...
---
_id: '12358'
abstract:
- lang: eng
text: "The complex yarn structure of knitted and woven fabrics gives rise to both
a mechanical and\r\nvisual complexity. The small-scale interactions of yarns colliding
with and pulling on each\r\nother result in drastically different large-scale
stretching and bending behavior, introducing\r\nanisotropy, curling, and more.
While simulating cloth as individual yarns can reproduce this\r\ncomplexity and
match the quality of real fabric, it may be too computationally expensive for\r\nlarge
fabrics. On the other hand, continuum-based approaches do not need to discretize
the\r\ncloth at a stitch-level, but it is non-trivial to find a material model
that would replicate the\r\nlarge-scale behavior of yarn fabrics, and they discard
the intricate visual detail. In this thesis,\r\nwe discuss three methods to try
and bridge the gap between small-scale and large-scale yarn\r\nmechanics using
numerical homogenization: fitting a continuum model to periodic yarn simulations,
adding mechanics-aware yarn detail onto thin-shell simulations, and quantitatively\r\nfitting
yarn parameters to physical measurements of real fabric.\r\nTo start, we present
a method for animating yarn-level cloth effects using a thin-shell solver.\r\nWe
first use a large number of periodic yarn-level simulations to build a model of
the potential\r\nenergy density of the cloth, and then use it to compute forces
in a thin-shell simulator. The\r\nresulting simulations faithfully reproduce expected
effects like the stiffening of woven fabrics\r\nand the highly deformable nature
and anisotropy of knitted fabrics at a fraction of the cost of\r\nfull yarn-level
simulation.\r\nWhile our thin-shell simulations are able to capture large-scale
yarn mechanics, they lack\r\nthe rich visual detail of yarn-level simulations.
Therefore, we propose a method to animate\r\nyarn-level cloth geometry on top
of an underlying deforming mesh in a mechanics-aware\r\nfashion in real time.
Using triangle strains to interpolate precomputed yarn geometry, we are\r\nable
to reproduce effects such as knit loops tightening under stretching at negligible
cost.\r\nFinally, we introduce a methodology for inverse-modeling of yarn-level
mechanics of cloth,\r\nbased on the mechanical response of fabrics in the real
world. We compile a database from\r\nphysical tests of several knitted fabrics
used in the textile industry spanning diverse physical\r\nproperties like stiffness,
nonlinearity, and anisotropy. We then develop a system for approximating these
mechanical responses with yarn-level cloth simulation, using homogenized\r\nshell
models to speed up computation and adding some small-but-necessary extensions
to\r\nyarn-level models used in computer graphics.\r\n"
acknowledged_ssus:
- _id: SSU
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Georg
full_name: Sperl, Georg
id: 4DD40360-F248-11E8-B48F-1D18A9856A87
last_name: Sperl
citation:
ama: 'Sperl G. Homogenizing yarn simulations: Large-scale mechanics, small-scale
detail, and quantitative fitting. 2022. doi:10.15479/at:ista:12103'
apa: 'Sperl, G. (2022). Homogenizing yarn simulations: Large-scale mechanics,
small-scale detail, and quantitative fitting. Institute of Science and Technology
Austria. https://doi.org/10.15479/at:ista:12103'
chicago: 'Sperl, Georg. “Homogenizing Yarn Simulations: Large-Scale Mechanics, Small-Scale
Detail, and Quantitative Fitting.” Institute of Science and Technology Austria,
2022. https://doi.org/10.15479/at:ista:12103.'
ieee: 'G. Sperl, “Homogenizing yarn simulations: Large-scale mechanics, small-scale
detail, and quantitative fitting,” Institute of Science and Technology Austria,
2022.'
ista: 'Sperl G. 2022. Homogenizing yarn simulations: Large-scale mechanics, small-scale
detail, and quantitative fitting. Institute of Science and Technology Austria.'
mla: 'Sperl, Georg. Homogenizing Yarn Simulations: Large-Scale Mechanics, Small-Scale
Detail, and Quantitative Fitting. Institute of Science and Technology Austria,
2022, doi:10.15479/at:ista:12103.'
short: 'G. Sperl, Homogenizing Yarn Simulations: Large-Scale Mechanics, Small-Scale
Detail, and Quantitative Fitting, Institute of Science and Technology Austria,
2022.'
date_created: 2023-01-24T10:49:46Z
date_published: 2022-09-22T00:00:00Z
date_updated: 2024-02-28T12:57:46Z
day: '22'
ddc:
- '000'
- '620'
degree_awarded: PhD
department:
- _id: GradSch
- _id: ChWo
doi: 10.15479/at:ista:12103
ec_funded: 1
file:
- access_level: open_access
checksum: 083722acbb8115e52e3b0fdec6226769
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creator: cchlebak
date_created: 2023-01-25T12:04:41Z
date_updated: 2023-02-02T09:29:57Z
description: 'This is the main PDF file of the thesis. File size: 105 MB'
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date_created: 2023-02-02T09:33:37Z
date_updated: 2023-02-02T09:33:37Z
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has_accepted_license: '1'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: '138'
project:
- _id: 2533E772-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '638176'
name: Efficient Simulation of Natural Phenomena at Extremely Large Scales
publication_identifier:
isbn:
- 978-3-99078-020-6
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
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relation: part_of_dissertation
status: public
- id: '8385'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Christopher J
full_name: Wojtan, Christopher J
id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87
last_name: Wojtan
orcid: 0000-0001-6646-5546
title: 'Homogenizing yarn simulations: Large-scale mechanics, small-scale detail,
and quantitative fitting'
type: dissertation
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year: '2022'
...
---
_id: '10759'
abstract:
- lang: eng
text: In this Thesis, I study composite quantum impurities with variational techniques,
both inspired by machine learning as well as fully analytic. I supplement this
with exploration of other applications of machine learning, in particular artificial
neural networks, in many-body physics. In Chapters 3 and 4, I study quasiparticle
systems with variational approach. I derive a Hamiltonian describing the angulon
quasiparticle in the presence of a magnetic field. I apply analytic variational
treatment to this Hamiltonian. Then, I introduce a variational approach for non-additive
systems, based on artificial neural networks. I exemplify this approach on the
example of the polaron quasiparticle (Fröhlich Hamiltonian). In Chapter 5, I continue
using artificial neural networks, albeit in a different setting. I apply artificial
neural networks to detect phases from snapshots of two types physical systems.
Namely, I study Monte Carlo snapshots of multilayer classical spin models as well
as molecular dynamics maps of colloidal systems. The main type of networks that
I use here are convolutional neural networks, known for their applicability to
image data.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Wojciech
full_name: Rzadkowski, Wojciech
id: 48C55298-F248-11E8-B48F-1D18A9856A87
last_name: Rzadkowski
orcid: 0000-0002-1106-4419
citation:
ama: Rzadkowski W. Analytic and machine learning approaches to composite quantum
impurities. 2022. doi:10.15479/at:ista:10759
apa: Rzadkowski, W. (2022). Analytic and machine learning approaches to composite
quantum impurities. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10759
chicago: Rzadkowski, Wojciech. “Analytic and Machine Learning Approaches to Composite
Quantum Impurities.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:10759.
ieee: W. Rzadkowski, “Analytic and machine learning approaches to composite quantum
impurities,” Institute of Science and Technology Austria, 2022.
ista: Rzadkowski W. 2022. Analytic and machine learning approaches to composite
quantum impurities. Institute of Science and Technology Austria.
mla: Rzadkowski, Wojciech. Analytic and Machine Learning Approaches to Composite
Quantum Impurities. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:10759.
short: W. Rzadkowski, Analytic and Machine Learning Approaches to Composite Quantum
Impurities, Institute of Science and Technology Austria, 2022.
date_created: 2022-02-16T13:27:37Z
date_published: 2022-02-21T00:00:00Z
date_updated: 2024-02-28T13:01:59Z
day: '21'
ddc:
- '530'
degree_awarded: PhD
department:
- _id: GradSch
- _id: MiLe
doi: 10.15479/at:ista:10759
ec_funded: 1
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creator: wrzadkow
date_created: 2022-02-21T13:58:16Z
date_updated: 2022-02-22T07:20:12Z
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creator: wrzadkow
date_created: 2022-02-21T14:02:54Z
date_updated: 2022-02-21T14:02:54Z
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file_name: Rzadkowski_thesis_final.pdf
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file_date_updated: 2022-02-22T07:20:12Z
has_accepted_license: '1'
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
page: '120'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '10762'
relation: part_of_dissertation
status: public
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relation: part_of_dissertation
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relation: part_of_dissertation
status: public
- id: '415'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Mikhail
full_name: Lemeshko, Mikhail
id: 37CB05FA-F248-11E8-B48F-1D18A9856A87
last_name: Lemeshko
orcid: 0000-0002-6990-7802
title: Analytic and machine learning approaches to composite quantum impurities
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2022'
...