---
_id: '7896'
abstract:
- lang: eng
text: "A search problem lies in the complexity class FNP if a solution to the given
instance of the problem can be verified efficiently. The complexity class TFNP
consists of all search problems in FNP that are total in the sense that a solution
is guaranteed to exist. TFNP contains a host of interesting problems from fields
such as algorithmic game theory, computational topology, number theory and combinatorics.
Since TFNP is a semantic class, it is unlikely to have a complete problem. Instead,
one studies its syntactic subclasses which are defined based on the combinatorial
principle used to argue totality. Of particular interest is the subclass PPAD,
which contains important problems\r\nlike computing Nash equilibrium for bimatrix
games and computational counterparts of several fixed-point theorems as complete.
In the thesis, we undertake the study of averagecase hardness of TFNP, and in
particular its subclass PPAD.\r\nAlmost nothing was known about average-case hardness
of PPAD before a series of recent results showed how to achieve it using a cryptographic
primitive called program obfuscation.\r\nHowever, it is currently not known how
to construct program obfuscation from standard cryptographic assumptions. Therefore,
it is desirable to relax the assumption under which average-case hardness of PPAD
can be shown. In the thesis we take a step in this direction. First, we show that
assuming the (average-case) hardness of a numbertheoretic\r\nproblem related to
factoring of integers, which we call Iterated-Squaring, PPAD is hard-on-average
in the random-oracle model. Then we strengthen this result to show that the average-case
hardness of PPAD reduces to the (adaptive) soundness of the Fiat-Shamir Transform,
a well-known technique used to compile a public-coin interactive protocol into
a non-interactive one. As a corollary, we obtain average-case hardness for PPAD
in the random-oracle model assuming the worst-case hardness of #SAT. Moreover,
the above results can all be strengthened to obtain average-case hardness for
the class CLS ⊆ PPAD.\r\nOur main technical contribution is constructing incrementally-verifiable
procedures for computing Iterated-Squaring and #SAT. By incrementally-verifiable,
we mean that every intermediate state of the computation includes a proof of its
correctness, and the proof can be updated and verified in polynomial time. Previous
constructions of such procedures relied on strong, non-standard assumptions. Instead,
we introduce a technique called recursive proof-merging to obtain the same from
weaker assumptions. "
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Chethan
full_name: Kamath Hosdurg, Chethan
id: 4BD3F30E-F248-11E8-B48F-1D18A9856A87
last_name: Kamath Hosdurg
citation:
ama: Kamath Hosdurg C. On the average-case hardness of total search problems. 2020.
doi:10.15479/AT:ISTA:7896
apa: Kamath Hosdurg, C. (2020). On the average-case hardness of total search
problems. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:7896
chicago: Kamath Hosdurg, Chethan. “On the Average-Case Hardness of Total Search
Problems.” Institute of Science and Technology Austria, 2020. https://doi.org/10.15479/AT:ISTA:7896.
ieee: C. Kamath Hosdurg, “On the average-case hardness of total search problems,”
Institute of Science and Technology Austria, 2020.
ista: Kamath Hosdurg C. 2020. On the average-case hardness of total search problems.
Institute of Science and Technology Austria.
mla: Kamath Hosdurg, Chethan. On the Average-Case Hardness of Total Search Problems.
Institute of Science and Technology Austria, 2020, doi:10.15479/AT:ISTA:7896.
short: C. Kamath Hosdurg, On the Average-Case Hardness of Total Search Problems,
Institute of Science and Technology Austria, 2020.
date_created: 2020-05-26T14:08:55Z
date_published: 2020-05-25T00:00:00Z
date_updated: 2023-09-07T13:15:55Z
day: '25'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: KrPi
doi: 10.15479/AT:ISTA:7896
ec_funded: 1
file:
- access_level: open_access
checksum: b39e2e1c376f5819b823fb7077491c64
content_type: application/pdf
creator: dernst
date_created: 2020-05-26T14:08:13Z
date_updated: 2020-07-14T12:48:04Z
file_id: '7897'
file_name: 2020_Thesis_Kamath.pdf
file_size: 1622742
relation: main_file
- access_level: closed
checksum: 8b26ba729c1a85ac6bea775f5d73cdc7
content_type: application/x-zip-compressed
creator: dernst
date_created: 2020-05-26T14:08:23Z
date_updated: 2020-07-14T12:48:04Z
file_id: '7898'
file_name: Thesis_Kamath.zip
file_size: 15301529
relation: source_file
file_date_updated: 2020-07-14T12:48:04Z
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '05'
oa: 1
oa_version: Published Version
page: '126'
project:
- _id: 258C570E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '259668'
name: Provable Security for Physical Cryptography
- _id: 258AA5B2-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '682815'
name: Teaching Old Crypto New Tricks
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '6677'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Krzysztof Z
full_name: Pietrzak, Krzysztof Z
id: 3E04A7AA-F248-11E8-B48F-1D18A9856A87
last_name: Pietrzak
orcid: 0000-0002-9139-1654
title: On the average-case hardness of total search problems
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2020'
...
---
_id: '7936'
abstract:
- lang: eng
text: 'State-of-the-art detection systems are generally evaluated on their ability
to exhaustively retrieve objects densely distributed in the image, across a wide
variety of appearances and semantic categories. Orthogonal to this, many real-life
object detection applications, for example in remote sensing, instead require
dealing with large images that contain only a few small objects of a single class,
scattered heterogeneously across the space. In addition, they are often subject
to strict computational constraints, such as limited battery capacity and computing
power.To tackle these more practical scenarios, we propose a novel flexible detection
scheme that efficiently adapts to variable object sizes and densities: We rely
on a sequence of detection stages, each of which has the ability to predict groups
of objects as well as individuals. Similar to a detection cascade, this multi-stage
architecture spares computational effort by discarding large irrelevant regions
of the image early during the detection process. The ability to group objects
provides further computational and memory savings, as it allows working with lower
image resolutions in early stages, where groups are more easily detected than
individuals, as they are more salient. We report experimental results on two aerial
image datasets, and show that the proposed method is as accurate yet computationally
more efficient than standard single-shot detectors, consistently across three
different backbone architectures.'
article_number: 1716-1725
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
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Royer A, Lampert C. Localizing grouped instances for efficient detection in
low-resource scenarios. In: IEEE Winter Conference on Applications of Computer
Vision. IEEE; 2020. doi:10.1109/WACV45572.2020.9093288'
apa: 'Royer, A., & Lampert, C. (2020). Localizing grouped instances for efficient
detection in low-resource scenarios. In IEEE Winter Conference on Applications
of Computer Vision. Snowmass Village, CO, United States: IEEE. https://doi.org/10.1109/WACV45572.2020.9093288'
chicago: Royer, Amélie, and Christoph Lampert. “Localizing Grouped Instances for
Efficient Detection in Low-Resource Scenarios.” In IEEE Winter Conference on
Applications of Computer Vision. IEEE, 2020. https://doi.org/10.1109/WACV45572.2020.9093288.
ieee: A. Royer and C. Lampert, “Localizing grouped instances for efficient detection
in low-resource scenarios,” in IEEE Winter Conference on Applications of Computer
Vision, Snowmass Village, CO, United States, 2020.
ista: 'Royer A, Lampert C. 2020. Localizing grouped instances for efficient detection
in low-resource scenarios. IEEE Winter Conference on Applications of Computer
Vision. WACV: Winter Conference on Applications of Computer Vision, 1716–1725.'
mla: Royer, Amélie, and Christoph Lampert. “Localizing Grouped Instances for Efficient
Detection in Low-Resource Scenarios.” IEEE Winter Conference on Applications
of Computer Vision, 1716–1725, IEEE, 2020, doi:10.1109/WACV45572.2020.9093288.
short: A. Royer, C. Lampert, in:, IEEE Winter Conference on Applications of Computer
Vision, IEEE, 2020.
conference:
end_date: 2020-03-05
location: ' Snowmass Village, CO, United States'
name: 'WACV: Winter Conference on Applications of Computer Vision'
start_date: 2020-03-01
date_created: 2020-06-07T22:00:53Z
date_published: 2020-03-01T00:00:00Z
date_updated: 2023-09-07T13:16:17Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/WACV45572.2020.9093288
external_id:
arxiv:
- '2004.12623'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2004.12623
month: '03'
oa: 1
oa_version: Preprint
publication: IEEE Winter Conference on Applications of Computer Vision
publication_identifier:
isbn:
- '9781728165530'
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
record:
- id: '8331'
relation: dissertation_contains
status: deleted
- id: '8390'
relation: dissertation_contains
status: public
scopus_import: 1
status: public
title: Localizing grouped instances for efficient detection in low-resource scenarios
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '7937'
abstract:
- lang: eng
text: 'Fine-tuning is a popular way of exploiting knowledge contained in a pre-trained
convolutional network for a new visual recognition task. However, the orthogonal
setting of transferring knowledge from a pretrained network to a visually different
yet semantically close source is rarely considered: This commonly happens with
real-life data, which is not necessarily as clean as the training source (noise,
geometric transformations, different modalities, etc.).To tackle such scenarios,
we introduce a new, generalized form of fine-tuning, called flex-tuning, in which
any individual unit (e.g. layer) of a network can be tuned, and the most promising
one is chosen automatically. In order to make the method appealing for practical
use, we propose two lightweight and faster selection procedures that prove to
be good approximations in practice. We study these selection criteria empirically
across a variety of domain shifts and data scarcity scenarios, and show that fine-tuning
individual units, despite its simplicity, yields very good results as an adaptation
technique. As it turns out, in contrast to common practice, rather than the last
fully-connected unit it is best to tune an intermediate or early one in many domain-
shift scenarios, which is accurately detected by flex-tuning.'
article_number: 2180-2189
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
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Royer A, Lampert C. A flexible selection scheme for minimum-effort transfer
learning. In: 2020 IEEE Winter Conference on Applications of Computer Vision.
IEEE; 2020. doi:10.1109/WACV45572.2020.9093635'
apa: 'Royer, A., & Lampert, C. (2020). A flexible selection scheme for minimum-effort
transfer learning. In 2020 IEEE Winter Conference on Applications of Computer
Vision. Snowmass Village, CO, United States: IEEE. https://doi.org/10.1109/WACV45572.2020.9093635'
chicago: Royer, Amélie, and Christoph Lampert. “A Flexible Selection Scheme for
Minimum-Effort Transfer Learning.” In 2020 IEEE Winter Conference on Applications
of Computer Vision. IEEE, 2020. https://doi.org/10.1109/WACV45572.2020.9093635.
ieee: A. Royer and C. Lampert, “A flexible selection scheme for minimum-effort transfer
learning,” in 2020 IEEE Winter Conference on Applications of Computer Vision,
Snowmass Village, CO, United States, 2020.
ista: 'Royer A, Lampert C. 2020. A flexible selection scheme for minimum-effort
transfer learning. 2020 IEEE Winter Conference on Applications of Computer Vision.
WACV: Winter Conference on Applications of Computer Vision, 2180–2189.'
mla: Royer, Amélie, and Christoph Lampert. “A Flexible Selection Scheme for Minimum-Effort
Transfer Learning.” 2020 IEEE Winter Conference on Applications of Computer
Vision, 2180–2189, IEEE, 2020, doi:10.1109/WACV45572.2020.9093635.
short: A. Royer, C. Lampert, in:, 2020 IEEE Winter Conference on Applications of
Computer Vision, IEEE, 2020.
conference:
end_date: 2020-03-05
location: Snowmass Village, CO, United States
name: 'WACV: Winter Conference on Applications of Computer Vision'
start_date: 2020-03-01
date_created: 2020-06-07T22:00:53Z
date_published: 2020-03-01T00:00:00Z
date_updated: 2023-09-07T13:16:17Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/WACV45572.2020.9093635
external_id:
arxiv:
- '2008.11995'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/2008.11995
month: '03'
oa: 1
oa_version: Preprint
publication: 2020 IEEE Winter Conference on Applications of Computer Vision
publication_identifier:
isbn:
- '9781728165530'
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
record:
- id: '8331'
relation: dissertation_contains
status: deleted
- id: '8390'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: A flexible selection scheme for minimum-effort transfer learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '8092'
abstract:
- lang: eng
text: Image translation refers to the task of mapping images from a visual domain
to another. Given two unpaired collections of images, we aim to learn a mapping
between the corpus-level style of each collection, while preserving semantic content
shared across the two domains. We introduce xgan, a dual adversarial auto-encoder,
which captures a shared representation of the common domain semantic content in
an unsupervised way, while jointly learning the domain-to-domain image translations
in both directions. We exploit ideas from the domain adaptation literature and
define a semantic consistency loss which encourages the learned embedding to preserve
semantics shared across domains. We report promising qualitative results for the
task of face-to-cartoon translation. The cartoon dataset we collected for this
purpose, “CartoonSet”, is also publicly available as a new benchmark for semantic
style transfer at https://google.github.io/cartoonset/index.html.
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
- first_name: Konstantinos
full_name: Bousmalis, Konstantinos
last_name: Bousmalis
- first_name: Stephan
full_name: Gouws, Stephan
last_name: Gouws
- first_name: Fred
full_name: Bertsch, Fred
last_name: Bertsch
- first_name: Inbar
full_name: Mosseri, Inbar
last_name: Mosseri
- first_name: Forrester
full_name: Cole, Forrester
last_name: Cole
- first_name: Kevin
full_name: Murphy, Kevin
last_name: Murphy
citation:
ama: 'Royer A, Bousmalis K, Gouws S, et al. XGAN: Unsupervised image-to-image translation
for many-to-many mappings. In: Singh R, Vatsa M, Patel VM, Ratha N, eds. Domain
Adaptation for Visual Understanding. Springer Nature; 2020:33-49. doi:10.1007/978-3-030-30671-7_3'
apa: 'Royer, A., Bousmalis, K., Gouws, S., Bertsch, F., Mosseri, I., Cole, F., &
Murphy, K. (2020). XGAN: Unsupervised image-to-image translation for many-to-many
mappings. In R. Singh, M. Vatsa, V. M. Patel, & N. Ratha (Eds.), Domain
Adaptation for Visual Understanding (pp. 33–49). Springer Nature. https://doi.org/10.1007/978-3-030-30671-7_3'
chicago: 'Royer, Amélie, Konstantinos Bousmalis, Stephan Gouws, Fred Bertsch, Inbar
Mosseri, Forrester Cole, and Kevin Murphy. “XGAN: Unsupervised Image-to-Image
Translation for Many-to-Many Mappings.” In Domain Adaptation for Visual Understanding,
edited by Richa Singh, Mayank Vatsa, Vishal M. Patel, and Nalini Ratha, 33–49.
Springer Nature, 2020. https://doi.org/10.1007/978-3-030-30671-7_3.'
ieee: 'A. Royer et al., “XGAN: Unsupervised image-to-image translation for
many-to-many mappings,” in Domain Adaptation for Visual Understanding,
R. Singh, M. Vatsa, V. M. Patel, and N. Ratha, Eds. Springer Nature, 2020, pp.
33–49.'
ista: 'Royer A, Bousmalis K, Gouws S, Bertsch F, Mosseri I, Cole F, Murphy K. 2020.XGAN:
Unsupervised image-to-image translation for many-to-many mappings. In: Domain
Adaptation for Visual Understanding. , 33–49.'
mla: 'Royer, Amélie, et al. “XGAN: Unsupervised Image-to-Image Translation for Many-to-Many
Mappings.” Domain Adaptation for Visual Understanding, edited by Richa
Singh et al., Springer Nature, 2020, pp. 33–49, doi:10.1007/978-3-030-30671-7_3.'
short: A. Royer, K. Bousmalis, S. Gouws, F. Bertsch, I. Mosseri, F. Cole, K. Murphy,
in:, R. Singh, M. Vatsa, V.M. Patel, N. Ratha (Eds.), Domain Adaptation for Visual
Understanding, Springer Nature, 2020, pp. 33–49.
date_created: 2020-07-05T22:00:46Z
date_published: 2020-01-08T00:00:00Z
date_updated: 2023-09-07T13:16:18Z
day: '08'
department:
- _id: ChLa
doi: 10.1007/978-3-030-30671-7_3
editor:
- first_name: Richa
full_name: Singh, Richa
last_name: Singh
- first_name: Mayank
full_name: Vatsa, Mayank
last_name: Vatsa
- first_name: Vishal M.
full_name: Patel, Vishal M.
last_name: Patel
- first_name: Nalini
full_name: Ratha, Nalini
last_name: Ratha
external_id:
arxiv:
- '1711.05139'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1711.05139
month: '01'
oa: 1
oa_version: Preprint
page: 33-49
publication: Domain Adaptation for Visual Understanding
publication_identifier:
isbn:
- '9783030306717'
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '8331'
relation: dissertation_contains
status: deleted
- id: '8390'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: 'XGAN: Unsupervised image-to-image translation for many-to-many mappings'
type: book_chapter
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '7944'
abstract:
- lang: eng
text: "This thesis considers two examples of reconfiguration problems: flipping
edges in edge-labelled triangulations of planar point sets and swapping labelled
tokens placed on vertices of a graph. In both cases the studied structures – all
the triangulations of a given point set or all token placements on a given graph
– can be thought of as vertices of the so-called reconfiguration graph, in which
two vertices are adjacent if the corresponding structures differ by a single elementary
operation – by a flip of a diagonal in a triangulation or by a swap of tokens
on adjacent vertices, respectively. We study the reconfiguration of one instance
of a structure into another via (shortest) paths in the reconfiguration graph.\r\n\r\nFor
triangulations of point sets in which each edge has a unique label and a flip
transfers the label from the removed edge to the new edge, we prove a polynomial-time
testable condition, called the Orbit Theorem, that characterizes when two triangulations
of the same point set lie in the same connected component of the reconfiguration
graph. The condition was first conjectured by Bose, Lubiw, Pathak and Verdonschot.
We additionally provide a polynomial time algorithm that computes a reconfiguring
flip sequence, if it exists. Our proof of the Orbit Theorem uses topological properties
of a certain high-dimensional cell complex that has the usual reconfiguration
graph as its 1-skeleton.\r\n\r\nIn the context of token swapping on a tree graph,
we make partial progress on the problem of finding shortest reconfiguration sequences.
We disprove the so-called Happy Leaf Conjecture and demonstrate the importance
of swapping tokens that are already placed at the correct vertices. We also prove
that a generalization of the problem to weighted coloured token swapping is NP-hard
on trees but solvable in polynomial time on paths and stars."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Zuzana
full_name: Masárová, Zuzana
id: 45CFE238-F248-11E8-B48F-1D18A9856A87
last_name: Masárová
orcid: 0000-0002-6660-1322
citation:
ama: Masárová Z. Reconfiguration problems. 2020. doi:10.15479/AT:ISTA:7944
apa: Masárová, Z. (2020). Reconfiguration problems. Institute of Science
and Technology Austria. https://doi.org/10.15479/AT:ISTA:7944
chicago: Masárová, Zuzana. “Reconfiguration Problems.” Institute of Science and
Technology Austria, 2020. https://doi.org/10.15479/AT:ISTA:7944.
ieee: Z. Masárová, “Reconfiguration problems,” Institute of Science and Technology
Austria, 2020.
ista: Masárová Z. 2020. Reconfiguration problems. Institute of Science and Technology
Austria.
mla: Masárová, Zuzana. Reconfiguration Problems. Institute of Science and
Technology Austria, 2020, doi:10.15479/AT:ISTA:7944.
short: Z. Masárová, Reconfiguration Problems, Institute of Science and Technology
Austria, 2020.
date_created: 2020-06-08T00:49:46Z
date_published: 2020-06-09T00:00:00Z
date_updated: 2023-09-07T13:17:37Z
day: '09'
ddc:
- '516'
- '514'
degree_awarded: PhD
department:
- _id: HeEd
- _id: UlWa
doi: 10.15479/AT:ISTA:7944
file:
- access_level: open_access
checksum: df688bc5a82b50baee0b99d25fc7b7f0
content_type: application/pdf
creator: zmasarov
date_created: 2020-06-08T00:34:00Z
date_updated: 2020-07-14T12:48:05Z
file_id: '7945'
file_name: THESIS_Zuzka_Masarova.pdf
file_size: 13661779
relation: main_file
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checksum: 45341a35b8f5529c74010b7af43ac188
content_type: application/zip
creator: zmasarov
date_created: 2020-06-08T00:35:30Z
date_updated: 2020-07-14T12:48:05Z
file_id: '7946'
file_name: THESIS_Zuzka_Masarova_SOURCE_FILES.zip
file_size: 32184006
relation: source_file
file_date_updated: 2020-07-14T12:48:05Z
has_accepted_license: '1'
keyword:
- reconfiguration
- reconfiguration graph
- triangulations
- flip
- constrained triangulations
- shellability
- piecewise-linear balls
- token swapping
- trees
- coloured weighted token swapping
language:
- iso: eng
license: https://creativecommons.org/licenses/by-sa/4.0/
month: '06'
oa: 1
oa_version: Published Version
page: '160'
publication_identifier:
isbn:
- 978-3-99078-005-3
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '7950'
relation: part_of_dissertation
status: public
- id: '5986'
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
- first_name: Herbert
full_name: Edelsbrunner, Herbert
id: 3FB178DA-F248-11E8-B48F-1D18A9856A87
last_name: Edelsbrunner
orcid: 0000-0002-9823-6833
title: Reconfiguration problems
tmp:
image: /images/cc_by_sa.png
legal_code_url: https://creativecommons.org/licenses/by-sa/4.0/legalcode
name: Creative Commons Attribution-ShareAlike 4.0 International Public License (CC
BY-SA 4.0)
short: CC BY-SA (4.0)
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2020'
...