---
OA_place: repository
OA_type: green
_id: '17426'
abstract:
- lang: eng
text: "The robustness of neural networks against input perturbations with bounded\r\nmagnitude
represents a serious concern in the deployment of deep learning\r\nmodels in safety-critical
systems. Recently, the scientific community has\r\nfocused on enhancing certifiable
robustness guarantees by crafting 1-Lipschitz\r\nneural networks that leverage
Lipschitz bounded dense and convolutional layers.\r\nAlthough different methods
have been proposed in the literature to achieve this\r\ngoal, understanding the
performance of such methods is not straightforward,\r\nsince different metrics
can be relevant (e.g., training time, memory usage,\r\naccuracy, certifiable robustness)
for different applications. For this reason,\r\nthis work provides a thorough
theoretical and empirical comparison between\r\nmethods by evaluating them in
terms of memory usage, speed, and certifiable\r\nrobust accuracy. The paper also
provides some guidelines and recommendations to\r\nsupport the user in selecting
the methods that work best depending on the\r\navailable resources. We provide
code at\r\nhttps://github.com/berndprach/1LipschitzLayersCompared."
acknowledgement: "This work was partially supported by project SERICS (PE00000014)
under the MUR National Recovery and Resilience Plan funded by the European Union
- NextGenerationEU.\r\n"
article_processing_charge: No
arxiv: 1
author:
- first_name: Bernd
full_name: Prach, Bernd
id: 2D561D42-C427-11E9-89B4-9C1AE6697425
last_name: Prach
- first_name: Fabio
full_name: Brau, Fabio
last_name: Brau
- first_name: Giorgio
full_name: Buttazzo, Giorgio
last_name: Buttazzo
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Prach B, Brau F, Buttazzo G, Lampert C. 1-Lipschitz layers compared: Memory,
speed, and certifiable robustness. In: Proceedings of the IEEE/CVF Conference
on Computer Vision and Pattern Recognition. Computer Vision Foundation; 2024:24574-24583.
doi:10.1109/CVPR52733.2024.02320'
apa: 'Prach, B., Brau, F., Buttazzo, G., & Lampert, C. (2024). 1-Lipschitz layers
compared: Memory, speed, and certifiable robustness. In Proceedings of the
IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 24574–24583).
Seattle, WA, United States: Computer Vision Foundation. https://doi.org/10.1109/CVPR52733.2024.02320'
chicago: 'Prach, Bernd, Fabio Brau, Giorgio Buttazzo, and Christoph Lampert. “1-Lipschitz
Layers Compared: Memory, Speed, and Certifiable Robustness.” In Proceedings
of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 24574–83.
Computer Vision Foundation, 2024. https://doi.org/10.1109/CVPR52733.2024.02320.'
ieee: 'B. Prach, F. Brau, G. Buttazzo, and C. Lampert, “1-Lipschitz layers compared:
Memory, speed, and certifiable robustness,” in Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern Recognition, Seattle, WA, United
States, 2024, pp. 24574–24583.'
ista: 'Prach B, Brau F, Buttazzo G, Lampert C. 2024. 1-Lipschitz layers compared:
Memory, speed, and certifiable robustness. Proceedings of the IEEE/CVF Conference
on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision
and Pattern Recognition, 24574–24583.'
mla: 'Prach, Bernd, et al. “1-Lipschitz Layers Compared: Memory, Speed, and Certifiable
Robustness.” Proceedings of the IEEE/CVF Conference on Computer Vision and
Pattern Recognition, Computer Vision Foundation, 2024, pp. 24574–83, doi:10.1109/CVPR52733.2024.02320.'
short: B. Prach, F. Brau, G. Buttazzo, C. Lampert, in:, Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern Recognition, Computer Vision Foundation,
2024, pp. 24574–24583.
conference:
end_date: 2024-06-22
location: Seattle, WA, United States
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2024-06-16
corr_author: '1'
das_tickbox: '0'
date_created: 2024-08-14T08:42:32Z
date_published: 2024-06-01T00:00:00Z
date_updated: 2026-10-02T11:16:04Z
day: '01'
department:
- _id: GradSch
- _id: ChLa
doi: 10.1109/CVPR52733.2024.02320
external_id:
arxiv:
- '2311.16833'
isi:
- '001344387500055'
fulldoi: https://doi.org/10.1109/CVPR52733.2024.02320
has_accepted_license: '1'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2311.16833
month: '06'
oa: 1
oa_version: Preprint
page: 24574-24583
publication: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern
Recognition
publication_status: published
publisher: Computer Vision Foundation
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/berndprach/1LipschitzLayersCompared
record:
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relation: dissertation_contains
status: public
researchdata_availability: no
status: public
supplementarymaterial: no
title: '1-Lipschitz layers compared: Memory, speed, and certifiable robustness'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
OA_place: repository
_id: '18874'
abstract:
- lang: eng
text: "Despite extensive research since the community learned about adversarial\r\nexamples
10 years ago, we still do not know how to train high-accuracy\r\nclassifiers that
are guaranteed to be robust to small perturbations of their\r\ninputs. Previous
works often argued that this might be because no classifier\r\nexists that is
robust and accurate at the same time. However, in computer\r\nvision this assumption
does not match reality where humans are usually accurate\r\nand robust on most
tasks of interest. We offer an alternative explanation and\r\nshow that in certain
settings robust generalization is only possible with\r\nunrealistically large
amounts of data. More precisely we find a setting where a\r\nrobust classifier
exists, it is easy to learn an accurate classifier, yet it\r\nrequires an exponential
amount of data to learn a robust classifier. Based on\r\nthis theoretical result,
we explore how well robust classifiers generalize on\r\ndatasets such as CIFAR-10.
We come to the conclusion that on this datasets, the\r\nlimitation of current
robust models also lies in the generalization, and that\r\nthey require a lot
of data to do well on the test set. We also show that the\r\nproblem is not in
the expressiveness or generalization capabilities of current\r\narchitectures,
and that there are low magnitude features in the data which are\r\nuseful for
non-robust generalization but are not available for robust\r\nclassifiers."
article_number: '2412.04245'
article_processing_charge: No
arxiv: 1
author:
- first_name: Bernd
full_name: Prach, Bernd
id: 2D561D42-C427-11E9-89B4-9C1AE6697425
last_name: Prach
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: Prach B, Lampert C. Intriguing properties of robust classification. arXiv.
doi:10.48550/arXiv.2412.04245
apa: Prach, B., & Lampert, C. (n.d.). Intriguing properties of robust classification.
arXiv. https://doi.org/10.48550/arXiv.2412.04245
chicago: Prach, Bernd, and Christoph Lampert. “Intriguing Properties of Robust Classification.”
ArXiv, n.d. https://doi.org/10.48550/arXiv.2412.04245.
ieee: B. Prach and C. Lampert, “Intriguing properties of robust classification,”
arXiv. .
ista: Prach B, Lampert C. Intriguing properties of robust classification. arXiv,
2412.04245.
mla: Prach, Bernd, and Christoph Lampert. “Intriguing Properties of Robust Classification.”
ArXiv, 2412.04245, doi:10.48550/arXiv.2412.04245.
short: B. Prach, C. Lampert, ArXiv (n.d.).
corr_author: '1'
das_tickbox: '0'
date_created: 2025-01-24T16:57:29Z
date_published: 2024-12-05T00:00:00Z
date_updated: 2026-10-02T11:15:43Z
day: '05'
department:
- _id: GradSch
- _id: ChLa
doi: 10.48550/arXiv.2412.04245
external_id:
arxiv:
- '2412.04245'
fulldoi: https://doi.org/10.48550/arXiv.2412.04245
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2412.04245
month: '12'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: draft
related_material:
record:
- id: '20455'
relation: later_version
status: public
- id: '19759'
relation: dissertation_contains
status: public
researchdata_availability: no
status: public
supplementarymaterial: yes
title: Intriguing properties of robust classification
type: preprint
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
OA_place: repository
_id: '17361'
abstract:
- lang: eng
text: We present symplectic structures on the shape space of unparameterized space
curves that generalize the classical Marsden-Weinstein structure. Our method integrates
the Liouville 1-form of the Marsden-Weinstein structure with Riemannian structures
that have been introduced in mathematical shape analysis. We also derive Hamiltonian
vector fields for several classical Hamiltonian functions with respect to these
new symplectic structures.
acknowledgement: The authors are grateful to Boris Khesin for valuable comments on
the MW symplectic structure and S. Ishida thanks Albert Chern for insightful discussions
on space curves and Chris Wojtan for his continuous support. M. Bauer was partially
supported by NSF grant DMS-1953244 and by the Binational Science Foundation (BSF).
S. Ishida was partially supported by ERC Consolidator Grant 101045083 “CoDiNA” funded
by the European Research Council. Some figures were generated by the software Houdini
and its education license was provided by SideFX.
article_processing_charge: No
arxiv: 1
author:
- first_name: Martin
full_name: Bauer, Martin
last_name: Bauer
- first_name: Sadashige
full_name: Ishida, Sadashige
id: 6F7C4B96-A8E9-11E9-A7CA-09ECE5697425
last_name: Ishida
orcid: 0000-0002-3121-3100
- first_name: Peter W.
full_name: Michor, Peter W.
last_name: Michor
citation:
ama: Bauer M, Ishida S, Michor PW. Symplectic structures on the space of space curves.
arXiv. doi:10.48550/arXiv.2407.19908
apa: Bauer, M., Ishida, S., & Michor, P. W. (n.d.). Symplectic structures on
the space of space curves. arXiv. https://doi.org/10.48550/arXiv.2407.19908
chicago: Bauer, Martin, Sadashige Ishida, and Peter W. Michor. “Symplectic Structures
on the Space of Space Curves.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2407.19908.
ieee: M. Bauer, S. Ishida, and P. W. Michor, “Symplectic structures on the space
of space curves,” arXiv. .
ista: Bauer M, Ishida S, Michor PW. Symplectic structures on the space of space
curves. arXiv, 10.48550/arXiv.2407.19908.
mla: Bauer, Martin, et al. “Symplectic Structures on the Space of Space Curves.”
ArXiv, doi:10.48550/arXiv.2407.19908.
short: M. Bauer, S. Ishida, P.W. Michor, ArXiv (n.d.).
das_tickbox: '0'
date_created: 2024-08-01T06:34:08Z
date_published: 2024-07-29T00:00:00Z
date_updated: 2026-10-02T11:16:46Z
day: '29'
department:
- _id: GradSch
- _id: ChWo
doi: 10.48550/arXiv.2407.19908
external_id:
arxiv:
- '2407.19908'
fulldoi: https://doi.org/10.48550/arXiv.2407.19908
keyword:
- space of space curves
- symplectic stuctures
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2407.19908
month: '07'
oa: 1
oa_version: Preprint
project:
- _id: 34bc2376-11ca-11ed-8bc3-9a3b3961a088
grant_number: '101045083'
name: Computational Discovery of Numerical Algorithms for Animation and Simulation
of Natural Phenomena
publication: arXiv
publication_status: draft
related_material:
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relation: later_version
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researchdata_availability: no
status: public
supplementarymaterial: no
title: Symplectic structures on the space of space curves
type: preprint
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
OA_place: publisher
OA_type: hybrid
_id: '17219'
abstract:
- lang: eng
text: 'We introduce a multi-material non-manifold mesh-based surface tracking algorithm
that converts self-intersections into topological changes. Our algorithm generalizes
prior work on manifold surface tracking with topological changes: it preserves
surface features like mesh-based methods, and it robustly handles topological
changes like level set methods. Our method also offers improved efficiency and
robustness over the state of the art. We demonstrate the effectiveness of the
approach on a range of examples, including complex soap film simulations with
thousands of interacting bubbles, and boolean unions of non-manifold meshes consisting
of millions of triangles.'
acknowledgement: Peter Heiss-Synak helped conceive the project, helped formulate the
algorithm structure, contributed ideas and code to Sections 6 & 8, the mesh data
structure, algorithm robustness and benchmarks, helped write the paper, and provided
supervision and conceptual solutions throughout the project. Aleksei Kalinov contributed
ideas and code to Sections 7, 8.5, and 5, the sparse grid data structure, algorithm
robustness and benchmarks, optimized the performance, produced all results, most
figures, and the supplementary video, helped write the text, and provided conceptual
solutions throughout the project. Malina Strugaru helped implement the mesh data
structure and designed re-meshing operations for non-manifold triangle meshes. Arian
Etemadi developed early prototypes for ideas in Sections 8.1 and 8.3 and helped
write the paper. Huidong Yang developed early prototypes for isosurface extraction
and visualization. Chris Wojtan helped conceive the project, helped write the paper,
and provided supervision, prototype grid data structure code, and conceptual solutions
throughout the project. We thank the anonymous reviewers for their helpful comments,
the members of the Visual Computing Group at ISTA for their feedback, Christopher
Batty for discussions about LosTopos, and SideFX for the Houdini Education software
licenses. This research was funded in part by the European Union (ERC-2021-COG
101045083 CoDiNA).
article_number: '54'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Peter
full_name: Synak, Peter
id: 331776E2-F248-11E8-B48F-1D18A9856A87
last_name: Synak
- first_name: Aleksei
full_name: Kalinov, Aleksei
id: 44b7120e-eb97-11eb-a6c2-e1557aa81d02
last_name: Kalinov
orcid: 0000-0003-2189-3904
- first_name: Irina-Malina
full_name: Strugaru, Irina-Malina
id: 2afc607f-f128-11eb-9611-8f2a0dfcf074
last_name: Strugaru
- first_name: Arian
full_name: Etemadihaghighi, Arian
id: 36cea3aa-f38e-11ec-8ae0-c65ae6f6098f
last_name: Etemadihaghighi
- first_name: Huidong
full_name: Yang, Huidong
last_name: Yang
- first_name: Christopher J
full_name: Wojtan, Christopher J
id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87
last_name: Wojtan
orcid: 0000-0001-6646-5546
citation:
ama: Synak P, Kalinov A, Strugaru I-M, Etemadi A, Yang H, Wojtan C. Multi-material
mesh-based surface tracking with implicit topology changes. ACM Transactions
on Graphics. 2024;43(4). doi:10.1145/3658223
apa: Synak, P., Kalinov, A., Strugaru, I.-M., Etemadi, A., Yang, H., & Wojtan,
C. (2024). Multi-material mesh-based surface tracking with implicit topology changes.
ACM Transactions on Graphics. Association for Computing Machinery. https://doi.org/10.1145/3658223
chicago: Synak, Peter, Aleksei Kalinov, Irina-Malina Strugaru, Arian Etemadi, Huidong
Yang, and Chris Wojtan. “Multi-Material Mesh-Based Surface Tracking with Implicit
Topology Changes.” ACM Transactions on Graphics. Association for Computing
Machinery, 2024. https://doi.org/10.1145/3658223.
ieee: P. Synak, A. Kalinov, I.-M. Strugaru, A. Etemadi, H. Yang, and C. Wojtan,
“Multi-material mesh-based surface tracking with implicit topology changes,” ACM
Transactions on Graphics, vol. 43, no. 4. Association for Computing Machinery,
2024.
ista: Synak P, Kalinov A, Strugaru I-M, Etemadi A, Yang H, Wojtan C. 2024. Multi-material
mesh-based surface tracking with implicit topology changes. ACM Transactions on
Graphics. 43(4), 54.
mla: Synak, Peter, et al. “Multi-Material Mesh-Based Surface Tracking with Implicit
Topology Changes.” ACM Transactions on Graphics, vol. 43, no. 4, 54, Association
for Computing Machinery, 2024, doi:10.1145/3658223.
short: P. Synak, A. Kalinov, I.-M. Strugaru, A. Etemadi, H. Yang, C. Wojtan, ACM
Transactions on Graphics 43 (2024).
corr_author: '1'
das_tickbox: '0'
date_created: 2024-07-10T12:24:00Z
date_published: 2024-07-01T00:00:00Z
date_updated: 2026-10-02T11:19:25Z
day: '01'
ddc:
- '004'
department:
- _id: GradSch
- _id: ChWo
doi: 10.1145/3658223
external_id:
isi:
- '001289270900021'
file:
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content_type: application/pdf
creator: dernst
date_created: 2024-07-23T06:35:15Z
date_updated: 2024-07-23T06:35:15Z
file_id: '17317'
file_name: 2024_ACMToG_HeissSynak.pdf
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date_updated: 2025-11-11T09:50:52Z
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fulldoi: https://doi.org/10.1145/3658223
has_accepted_license: '1'
intvolume: ' 43'
isi: 1
issue: '4'
keyword:
- surface tracking
- topology change
- non- manifold meshes
- multi-material flows
- solid modeling
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-sa/4.0/
month: '07'
oa: 1
oa_version: Published Version
project:
- _id: 34bc2376-11ca-11ed-8bc3-9a3b3961a088
grant_number: '101045083'
name: Computational Discovery of Numerical Algorithms for Animation and Simulation
of Natural Phenomena
publication: ACM Transactions on Graphics
publication_identifier:
eissn:
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issn:
- 0730-0301
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
related_material:
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supplementarymaterial: no
title: Multi-material mesh-based surface tracking with implicit topology changes
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image: /images/cc_by_nc_sa.png
legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
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short: CC BY-NC-SA (4.0)
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...
---
OA_place: publisher
_id: '18301'
abstract:
- lang: eng
text: Physics simulation in computer graphics can bring triangle meshes into topologically
invalid states. The method in this thesis contributed to Heiss-Synak* and Kalinov*
et al. [2024] who devised a non-manifold hybrid surface tracker—a surface tracker
that repairs explicit non-manifold triangle meshes with the help of the implicit
domain. Specifically, this thesis provides an algorithm for filling the holes
that are left after removing problematic parts of the mesh.
alternative_title:
- ISTA Master's Thesis
article_processing_charge: No
author:
- first_name: Arian
full_name: Etemadihaghighi, Arian
id: 36cea3aa-f38e-11ec-8ae0-c65ae6f6098f
last_name: Etemadihaghighi
citation:
ama: Etemadi A. Filling the holes of non-manifold self-intersecting meshes for implicit
topology changes in surface tracking. 2024. doi:10.15479/at:ista:18301
apa: Etemadi, A. (2024). Filling the holes of non-manifold self-intersecting
meshes for implicit topology changes in surface tracking. Institute of Science
and Technology Austria. https://doi.org/10.15479/at:ista:18301
chicago: Etemadi, Arian. “Filling the Holes of Non-Manifold Self-Intersecting Meshes
for Implicit Topology Changes in Surface Tracking.” Institute of Science and Technology
Austria, 2024. https://doi.org/10.15479/at:ista:18301.
ieee: A. Etemadi, “Filling the holes of non-manifold self-intersecting meshes for
implicit topology changes in surface tracking,” Institute of Science and Technology
Austria, 2024.
ista: Etemadi A. 2024. Filling the holes of non-manifold self-intersecting meshes
for implicit topology changes in surface tracking. Institute of Science and Technology
Austria.
mla: Etemadi, Arian. Filling the Holes of Non-Manifold Self-Intersecting Meshes
for Implicit Topology Changes in Surface Tracking. Institute of Science and
Technology Austria, 2024, doi:10.15479/at:ista:18301.
short: A. Etemadi, Filling the Holes of Non-Manifold Self-Intersecting Meshes for
Implicit Topology Changes in Surface Tracking, Institute of Science and Technology
Austria, 2024.
corr_author: '1'
das_tickbox: '0'
date_created: 2024-10-11T19:52:20Z
date_published: 2024-10-15T00:00:00Z
date_updated: 2026-10-02T11:19:26Z
day: '15'
ddc:
- '000'
degree_awarded: MS
department:
- _id: GradSch
- _id: ChWo
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doi_confirm: '1'
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date_updated: 2024-10-24T14:34:42Z
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relation: main_file
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fulldoi: https://doi.org/10.15479/at:ista:18301
has_accepted_license: '1'
keyword:
- surface tracking
- non-manifold
- hole-filling
- topology change
- multi-material
- solid-modeling
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: '39'
publication_identifier:
issn:
- 2791-4585
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
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relation: part_of_dissertation
status: public
researchdata_availability: no
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
supplementarymaterial: no
title: Filling the holes of non-manifold self-intersecting meshes for implicit topology
changes in surface tracking
tmp:
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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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '12846'
abstract:
- lang: eng
text: We present a formula for the signed area of a spherical polygon via prequantization.
In contrast to the traditional formula based on the Gauss-Bonnet theorem that
requires measuring angles, the new formula mimics Green's theorem and is applicable
to a wider range of degenerate spherical curves and polygons.
acknowledgement: This work was funded by European Research Council Consolidator grant
101045083 CoDiNA and National Science Foundation CAREER award 2239062. Some figures
in the article were generated by the software Houdini and its education license
was provided by SideFX. The authors acknowledge anonymous referees for their reviews
and insightful suggestions, and Chris Wojtan for his continuous support through
discussions. The second author thanks Anna Sisak for a fruitful discussion on prequantum
bundles.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Albert
full_name: Chern, Albert
last_name: Chern
- first_name: Sadashige
full_name: Ishida, Sadashige
id: 6F7C4B96-A8E9-11E9-A7CA-09ECE5697425
last_name: Ishida
orcid: 0000-0002-3121-3100
citation:
ama: Chern A, Ishida S. Area formula for spherical polygons via prequantization.
SIAM Journal on Applied Algebra and Geometry. 2024;8(3):782-796. doi:10.1137/23M1565255
apa: Chern, A., & Ishida, S. (2024). Area formula for spherical polygons via
prequantization. SIAM Journal on Applied Algebra and Geometry. Society
for Industrial and Applied Mathematics. https://doi.org/10.1137/23M1565255
chicago: Chern, Albert, and Sadashige Ishida. “Area Formula for Spherical Polygons
via Prequantization.” SIAM Journal on Applied Algebra and Geometry. Society
for Industrial and Applied Mathematics, 2024. https://doi.org/10.1137/23M1565255.
ieee: A. Chern and S. Ishida, “Area formula for spherical polygons via prequantization,”
SIAM Journal on Applied Algebra and Geometry, vol. 8, no. 3. Society for
Industrial and Applied Mathematics, pp. 782–796, 2024.
ista: Chern A, Ishida S. 2024. Area formula for spherical polygons via prequantization.
SIAM Journal on Applied Algebra and Geometry. 8(3), 782–796.
mla: Chern, Albert, and Sadashige Ishida. “Area Formula for Spherical Polygons via
Prequantization.” SIAM Journal on Applied Algebra and Geometry, vol. 8,
no. 3, Society for Industrial and Applied Mathematics, 2024, pp. 782–96, doi:10.1137/23M1565255.
short: A. Chern, S. Ishida, SIAM Journal on Applied Algebra and Geometry 8 (2024)
782–796.
corr_author: '1'
das_tickbox: '0'
date_created: 2023-04-18T19:16:06Z
date_published: 2024-09-23T00:00:00Z
date_updated: 2026-10-02T11:17:04Z
day: '23'
ddc:
- '516'
department:
- _id: GradSch
- _id: ChWo
doi: 10.1137/23M1565255
external_id:
arxiv:
- '2303.14555'
isi:
- '001342265800009'
fulldoi: https://doi.org/10.1137/23M1565255
has_accepted_license: '1'
intvolume: ' 8'
isi: 1
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2303.14555
month: '09'
oa: 1
oa_version: Preprint
page: 782-796
project:
- _id: 34bc2376-11ca-11ed-8bc3-9a3b3961a088
grant_number: '101045083'
name: Computational Discovery of Numerical Algorithms for Animation and Simulation
of Natural Phenomena
publication: SIAM Journal on Applied Algebra and Geometry
publication_identifier:
eissn:
- 2470-6566
publication_status: published
publisher: Society for Industrial and Applied Mathematics
quality_controlled: '1'
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researchdata_availability: no
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status: public
supplementarymaterial: no
title: Area formula for spherical polygons via prequantization
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: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 8
year: '2024'
...
---
OA_place: publisher
_id: '17485'
abstract:
- lang: eng
text: "Large language models (LLMs) have made tremendous progress in the past few
years, from being able to generate coherent text to matching or surpassing humans
in a wide variety of creative, knowledge or reasoning tasks. Much of this can
be attributed to massively increased scale, both in the size of the model as well
as the amount of training data, from 100s of millions to 100s of billions, or
even trillions. This trend is expected to continue, which, although exciting,
also raises major practical concerns. Already today's 100+ billion parameter LLMs
require top-of-the-line hardware just to run. Hence, it is clear that sustaining
these developments will require significant efficiency advances.\r\n\r\nHistorically,
one of the most practical ways of improving model efficiency has been compression,
especially in the form of sparsity or quantization. While this has been studied
extensively in the past, existing accurate methods are all designed for models
around 100 million parameters; scaling them up to ones literally 1000x larger
is highly challenging. In this thesis, we introduce a new unified sparsification
and quantization approach OBC, which through additional algorithmic enhancements
leads to GPTQ and SparseGPT, the first techniques fast and accurate enough to
compress 100+ billion parameter models to 4- or even 3-bit precision and 50% weight-sparsity,
respectively. Additionally, we show how weight-only quantizion does not just bring
space savings but also up to 4.5x faster generation speed, via custom GPU kernels.\r\n\r\nIn
fact, we show for the first time that it is possible to develop an FP16 times
INT4 mixed-precision matrix multiplication kernel, called Marlin, which comes
close to simultaneously maximizing both memory and compute utilization, making
weight-only quantization highly practical even for multi-user serving. Further,
we demonstrate that GPTQ can be scaled to widely overparametrized trillion-parameter
models, where extreme sub-1-bit compression rates can be achieved without any
inference slow-down, by co-designing a bespoke entropy coding scheme together
with an efficient kernel.\r\n\r\nFinally, we also study compression from the perspective
of someone with access to massive amounts of compute resources for training large
models completely from scratch. Here the key questions evolve around the joint
scaling behavior between compression, model size, and amount of training data
used. Based on extensive experimental results for both vision and text models,
we introduce the first scaling law which accurately captures the relationship
between weight-sparsity, number of non-zero weights and data. This further allows
us to characterize the optimal sparsity, which we find to increase the longer
a fixed cost model is being trained.\r\n\r\nOverall, this thesis presents contributions
to three different angles of large model efficiency: affordable but accurate algorithms,
highly efficient systems implementations, and fundamental scaling laws for compressed
training."
acknowledged_ssus:
- _id: ScienComp
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Elias
full_name: Frantar, Elias
id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f
last_name: Frantar
citation:
ama: 'Frantar E. Compressing large neural networks: Algorithms, systems and scaling
laws. 2024. doi:10.15479/at:ista:17485'
apa: 'Frantar, E. (2024). Compressing large neural networks: Algorithms, systems
and scaling laws. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:17485'
chicago: 'Frantar, Elias. “Compressing Large Neural Networks: Algorithms, Systems
and Scaling Laws.” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:17485.'
ieee: 'E. Frantar, “Compressing large neural networks: Algorithms, systems and scaling
laws,” Institute of Science and Technology Austria, 2024.'
ista: 'Frantar E. 2024. Compressing large neural networks: Algorithms, systems and
scaling laws. Institute of Science and Technology Austria.'
mla: 'Frantar, Elias. Compressing Large Neural Networks: Algorithms, Systems
and Scaling Laws. Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:17485.'
short: 'E. Frantar, Compressing Large Neural Networks: Algorithms, Systems and Scaling
Laws, Institute of Science and Technology Austria, 2024.'
corr_author: '1'
das_tickbox: '0'
date_created: 2024-09-02T11:01:48Z
date_published: 2024-09-05T00:00:00Z
date_updated: 2026-10-02T11:19:46Z
day: '05'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: GradSch
- _id: DaAl
doi: 10.15479/at:ista:17485
doi_confirm: '1'
ec_funded: 1
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date_created: 2024-09-06T16:24:59Z
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file_id: '17880'
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language:
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month: '09'
oa: 1
oa_version: Published Version
page: '129'
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
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
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- id: '17087'
relation: part_of_dissertation
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- id: '14458'
relation: part_of_dissertation
status: public
- id: '18061'
relation: part_of_dissertation
status: public
- id: '18062'
relation: part_of_dissertation
status: public
researchdata_availability: no
status: public
supervisor:
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
supplementarymaterial: no
title: 'Compressing large neural networks: Algorithms, systems and scaling laws'
type: dissertation
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
_id: '18062'
abstract:
- lang: eng
text: 'We explore the impact of parameter sparsity on the scaling behavior of Transformers
trained on massive datasets (i.e., "foundation models"), in both vision and language
domains. In this setting, we identify the first scaling law describing the relationship
between weight sparsity, number of non-zero parameters, and amount of training
data, which we validate empirically across model and data scales; on ViT/JFT-4B
and T5/C4. These results allow us to characterize the "optimal sparsity", the
sparsity level which yields the best performance for a given effective model size
and training budget. For a fixed number of non-zero parameters, we identify that
the optimal sparsity increases with the amount of data used for training. We also
extend our study to different sparsity structures (such as the hardware-friendly
n:m pattern) and strategies (such as starting from a pretrained dense model).
Our findings shed light on the power and limitations of weight sparsity across
various parameter and computational settings, offering both theoretical understanding
and practical implications for leveraging sparsity towards computational efficiency
improvements. We provide pruning and scaling law fitting code at: github.com/google-research/jaxpruner/tree/main/jaxpruner/projects/bigsparse.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Elias
full_name: Frantar, Elias
id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f
last_name: Frantar
- first_name: Carlos Riquelme
full_name: Ruiz, Carlos Riquelme
last_name: Ruiz
- first_name: Neil
full_name: Houlsby, Neil
last_name: Houlsby
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
- first_name: Utku
full_name: Evci, Utku
last_name: Evci
citation:
ama: 'Frantar E, Ruiz CR, Houlsby N, Alistarh D-A, Evci U. Scaling laws for sparsely-connected
foundation models. In: The Twelfth International Conference on Learning Representations.
; 2024.'
apa: Frantar, E., Ruiz, C. R., Houlsby, N., Alistarh, D.-A., & Evci, U. (2024).
Scaling laws for sparsely-connected foundation models. In The Twelfth International
Conference on Learning Representations. Vienna, Austria.
chicago: Frantar, Elias, Carlos Riquelme Ruiz, Neil Houlsby, Dan-Adrian Alistarh,
and Utku Evci. “Scaling Laws for Sparsely-Connected Foundation Models.” In The
Twelfth International Conference on Learning Representations, 2024.
ieee: E. Frantar, C. R. Ruiz, N. Houlsby, D.-A. Alistarh, and U. Evci, “Scaling
laws for sparsely-connected foundation models,” in The Twelfth International
Conference on Learning Representations, Vienna, Austria, 2024.
ista: 'Frantar E, Ruiz CR, Houlsby N, Alistarh D-A, Evci U. 2024. Scaling laws for
sparsely-connected foundation models. The Twelfth International Conference on
Learning Representations. ICLR: International Conference on Learning Representations.'
mla: Frantar, Elias, et al. “Scaling Laws for Sparsely-Connected Foundation Models.”
The Twelfth International Conference on Learning Representations, 2024.
short: E. Frantar, C.R. Ruiz, N. Houlsby, D.-A. Alistarh, U. Evci, in:, The Twelfth
International Conference on Learning Representations, 2024.
conference:
end_date: 2024-05-07
location: Vienna, Austria
name: 'ICLR: International Conference on Learning Representations'
start_date: 2024-05-07
corr_author: '1'
das_tickbox: '0'
date_created: 2024-09-13T10:31:08Z
date_published: 2024-01-16T00:00:00Z
date_updated: 2026-10-02T11:19:46Z
day: '16'
ddc:
- '000'
department:
- _id: DaAl
external_id:
arxiv:
- '2309.08520'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=i9K2ZWkYIP
month: '01'
oa: 1
oa_version: Published Version
publication: The Twelfth International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
related_material:
record:
- id: '17485'
relation: dissertation_contains
status: public
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: Scaling laws for sparsely-connected foundation models
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
_id: '18061'
abstract:
- lang: eng
text: 'Mixture-of-Experts (MoE) architectures offer a general solution to the high
inference costs of large language models (LLMs) via sparse routing, bringing faster
and more accurate models, at the cost of massive parameter counts. For example,
the SwitchTransformer-c2048 model has 1.6 trillion parameters, requiring 3.2TB
of accelerator memory to run efficiently, which makes practical deployment challenging
and expensive. In this paper, we present a solution to this memory problem, in
form of a new compression and execution framework called QMoE. Specifically, QMoE
consists of a scalable algorithm which accurately compresses trillion-parameter
MoEs to less than 1 bit per parameter, in a custom format co-designed with bespoke
GPU decoding kernels to facilitate efficient end-to-end compressed inference,
with minor runtime overheads relative to uncompressed execution. Concretely, QMoE
can compress the 1.6 trillion parameter SwitchTransformer-c2048 model to less
than 160GB (20x compression, 0.8 bits per parameter) at only minor accuracy loss,
in less than a day on a single GPU. This enables, for the first time, the execution
of a trillion-parameter model on affordable commodity hardware, like a single
server with 4x NVIDIA A6000 or 8x NVIDIA 3090 GPUs, at less than 5% runtime overhead
relative to ideal uncompressed inference. The anonymized code is available at:
github.com/mlsys24-qmoe/qmoe.'
article_processing_charge: No
author:
- first_name: Elias
full_name: Frantar, Elias
id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f
last_name: Frantar
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Frantar E, Alistarh D-A. QMoE: Sub-1-bit compression of trillion parameter
models. In: Proceedings of Machine Learning and Systems. Vol 6. ; 2024.'
apa: 'Frantar, E., & Alistarh, D.-A. (2024). QMoE: Sub-1-bit compression of
trillion parameter models. In Proceedings of Machine Learning and Systems
(Vol. 6). Santa Clara, CA, United States.'
chicago: 'Frantar, Elias, and Dan-Adrian Alistarh. “QMoE: Sub-1-Bit Compression
of Trillion Parameter Models.” In Proceedings of Machine Learning and Systems,
Vol. 6, 2024.'
ieee: 'E. Frantar and D.-A. Alistarh, “QMoE: Sub-1-bit compression of trillion parameter
models,” in Proceedings of Machine Learning and Systems, Santa Clara, CA,
United States, 2024, vol. 6.'
ista: 'Frantar E, Alistarh D-A. 2024. QMoE: Sub-1-bit compression of trillion parameter
models. Proceedings of Machine Learning and Systems. MLSys: Machine Learning and
Systems vol. 6.'
mla: 'Frantar, Elias, and Dan-Adrian Alistarh. “QMoE: Sub-1-Bit Compression of Trillion
Parameter Models.” Proceedings of Machine Learning and Systems, vol. 6,
2024.'
short: E. Frantar, D.-A. Alistarh, in:, Proceedings of Machine Learning and Systems,
2024.
conference:
end_date: 2024-05-16
location: Santa Clara, CA, United States
name: 'MLSys: Machine Learning and Systems'
start_date: 2024-05-13
corr_author: '1'
das_tickbox: '0'
date_created: 2024-09-13T10:01:38Z
date_published: 2024-05-01T00:00:00Z
date_updated: 2026-10-02T11:19:46Z
day: '01'
ddc:
- '000'
department:
- _id: DaAl
intvolume: ' 6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.mlsys.org/paper_files/paper/2024/hash/c74b624843218d9b6713fcf299d6d5e4-Abstract-Conference.html
month: '05'
oa: 1
oa_version: Published Version
publication: Proceedings of Machine Learning and Systems
publication_status: published
quality_controlled: '1'
related_material:
record:
- id: '17485'
relation: dissertation_contains
status: public
researchdata_availability: yes
status: public
supplementarymaterial: no
title: 'QMoE: Sub-1-bit compression of trillion parameter models'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 6
year: '2024'
...
---
OA_place: publisher
_id: '17490'
abstract:
- lang: eng
text: "Deep learning is essential in numerous applications nowadays, with many recent
advancements made possible by training very large models. Despite their broad
applicability, training neural networks is often time-intensive, and it is usually
impractical to manage large models and datasets on a single machine. To address
these issues, distributed deep learning training has become increasingly important.
However, distributed training requires synchronization among nodes, and the mini-batch
stochastic gradient descent algorithm places a significant load on network connections.
A possible solution to tackle the synchronization bottleneck is to reduce a message
size by lossy compression.\r\n\r\nIn this thesis, we investigate systems and algorithmic
approaches to communication compression during training. From the systems perspective,
we demonstrate that a common approach of expensive hardware overprovisioning can
be replaced through a thorough system design. We introduce a framework that introduces
efficient software support for compressed communication in machine learning applications,
applicable to both multi-GPU single-node training and larger-scale multi-node
training. Our framework integrates with popular ML frameworks, providing up to
3x speedups for multi-GPU nodes based on commodity hardware and order-of-magnitude
improvements in the multi-node setting, with negligible impact on accuracy.\r\n\r\nAlso,
we consider an application of our framework to different communication schemes,
such as Fully Sharded Data Parallel. We provide strong convergence guarantees
for the compression in such a setup. Empirical validation shows that our method
preserves model accuracy for GPT-family models with up to 1.3 billion parameters,
while completely removing the communication bottlenecks of non-compressed alternatives,
providing up to 2.2x speedups end-to-end.\r\n\r\nFrom the algorithmic side, we
propose a general framework that dynamically adjusts the degree of compression
across a model's layers during training. This approach enhances overall compression
and results in significant speedups without compromising accuracy. Our algorithm
utilizes an adaptive algorithm that automatically selects the optimal compression
parameters for model layers, ensuring the best compression ratio while adhering
to an error constraint. Our method is effective across all existing families of
compression methods. It achieves up to 2.5x faster training and up to a 5x improvement
in compression compared to efficient implementations of current approaches. Additionally,
LGreCo can complement existing adaptive algorithms.\r\n"
acknowledged_ssus:
- _id: ScienComp
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Ilia
full_name: Markov, Ilia
id: D0CF4148-C985-11E9-8066-0BDEE5697425
last_name: Markov
citation:
ama: 'Markov I. Communication-efficient distributed training of deep neural networks :
An algorithms and systems perspective. 2024. doi:10.15479/at:ista:17490'
apa: 'Markov, I. (2024). Communication-efficient distributed training of deep
neural networks : An algorithms and systems perspective. Institute of Science
and Technology Austria. https://doi.org/10.15479/at:ista:17490'
chicago: 'Markov, Ilia. “Communication-Efficient Distributed Training of Deep Neural
Networks : An Algorithms and Systems Perspective.” Institute of Science and Technology
Austria, 2024. https://doi.org/10.15479/at:ista:17490.'
ieee: 'I. Markov, “Communication-efficient distributed training of deep neural networks :
An algorithms and systems perspective,” Institute of Science and Technology Austria,
2024.'
ista: 'Markov I. 2024. Communication-efficient distributed training of deep neural
networks : An algorithms and systems perspective. Institute of Science and Technology
Austria.'
mla: 'Markov, Ilia. Communication-Efficient Distributed Training of Deep Neural
Networks : An Algorithms and Systems Perspective. Institute of Science and
Technology Austria, 2024, doi:10.15479/at:ista:17490.'
short: 'I. Markov, Communication-Efficient Distributed Training of Deep Neural Networks :
An Algorithms and Systems Perspective, Institute of Science and Technology Austria,
2024.'
corr_author: '1'
das_tickbox: '0'
date_created: 2024-09-04T08:51:11Z
date_published: 2024-09-04T00:00:00Z
date_updated: 2026-10-02T11:20:06Z
day: '04'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: GradSch
- _id: DaAl
doi: 10.15479/at:ista:17490
doi_confirm: '1'
ec_funded: 1
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language:
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month: '09'
oa: 1
oa_version: Published Version
page: '102'
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '14461'
relation: part_of_dissertation
status: public
- id: '12780'
relation: part_of_dissertation
status: public
- id: '17456'
relation: part_of_dissertation
status: public
researchdata_availability: no
status: public
supervisor:
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
supplementarymaterial: no
title: 'Communication-efficient distributed training of deep neural networks : An
algorithms and systems perspective'
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
_id: '17456'
abstract:
- lang: eng
text: "Data-parallel distributed training of deep neural networks (DNN) has gained
very widespread adoption, but can still experience communication bottlenecks.
To address this issue, entire families of compression mechanisms have been developed,
including quantization, sparsification, and low-rank approximation, some of which
are seeing significant practical adoption. Despite this progress, almost all known
compression schemes apply compression uniformly across DNN layers, although layers
are heterogeneous in terms of parameter count and their impact on model accuracy.In
this work, we provide a general framework for adapting the degree of compression
across the model's layers dynamically during training, improving the overall compression,
while leading to substantial speedups, without sacrificing accuracy. Our framework,
called L-GreCo, is based on an adaptive algorithm, which automatically picks the
optimal compression parameters for model layers guaranteeing the best compression
ratio while satisfying an error constraint. Extensive experiments over image classification
and language modeling tasks shows that L-GreCo is effective across all existing
families of compression methods, and achieves up to 2.5\r\n×\r\n training speedup
and up to 5\r\n×\r\n compression improvement over efficient implementations of
existing approaches, while recovering full accuracy. Moreover, L-GreCo is complementary
to existing adaptive algorithms, improving their compression ratio by 50\\% and
practical throughput by 66\\%. An anonymized implementation is available at https://github.com/LGrCo/L-GreCo."
article_processing_charge: No
arxiv: 1
author:
- first_name: Ilia
full_name: Markov, Ilia
id: D0CF4148-C985-11E9-8066-0BDEE5697425
last_name: Markov
- first_name: Kaveh
full_name: Alimohammadi, Kaveh
last_name: Alimohammadi
- first_name: Elias
full_name: Frantar, Elias
id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f
last_name: Frantar
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Markov I, Alimohammadi K, Frantar E, Alistarh D-A. L-GreCo: Layerwise-adaptive
gradient compression for efficient data-parallel deep learning. In: Gibbons P,
Pekhimenko G, De Sa C, eds. Proceedings of Machine Learning and Systems .
Vol 6. Association for Computing Machinery; 2024.'
apa: 'Markov, I., Alimohammadi, K., Frantar, E., & Alistarh, D.-A. (2024). L-GreCo:
Layerwise-adaptive gradient compression for efficient data-parallel deep learning.
In P. Gibbons, G. Pekhimenko, & C. De Sa (Eds.), Proceedings of Machine
Learning and Systems (Vol. 6). Athens, Greece: Association for Computing
Machinery.'
chicago: 'Markov, Ilia, Kaveh Alimohammadi, Elias Frantar, and Dan-Adrian Alistarh.
“L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient Data-Parallel
Deep Learning.” In Proceedings of Machine Learning and Systems , edited
by P. Gibbons, G. Pekhimenko, and C. De Sa, Vol. 6. Association for Computing
Machinery, 2024.'
ieee: 'I. Markov, K. Alimohammadi, E. Frantar, and D.-A. Alistarh, “L-GreCo: Layerwise-adaptive
gradient compression for efficient data-parallel deep learning,” in Proceedings
of Machine Learning and Systems , Athens, Greece, 2024, vol. 6.'
ista: 'Markov I, Alimohammadi K, Frantar E, Alistarh D-A. 2024. L-GreCo: Layerwise-adaptive
gradient compression for efficient data-parallel deep learning. Proceedings of
Machine Learning and Systems . MLSys: Machine Learning and Systems vol. 6.'
mla: 'Markov, Ilia, et al. “L-GreCo: Layerwise-Adaptive Gradient Compression for
Efficient Data-Parallel Deep Learning.” Proceedings of Machine Learning and
Systems , edited by P. Gibbons et al., vol. 6, Association for Computing Machinery,
2024.'
short: I. Markov, K. Alimohammadi, E. Frantar, D.-A. Alistarh, in:, P. Gibbons,
G. Pekhimenko, C. De Sa (Eds.), Proceedings of Machine Learning and Systems ,
Association for Computing Machinery, 2024.
conference:
end_date: 2024-04-22
location: Athens, Greece
name: 'MLSys: Machine Learning and Systems'
start_date: 2024-04-22
corr_author: '1'
das_tickbox: '0'
date_created: 2024-08-22T08:29:25Z
date_published: 2024-04-01T00:00:00Z
date_updated: 2026-10-02T11:20:05Z
day: '01'
ddc:
- '000'
department:
- _id: DaAl
editor:
- first_name: P.
full_name: Gibbons, P.
last_name: Gibbons
- first_name: G.
full_name: Pekhimenko, G.
last_name: Pekhimenko
- first_name: C.
full_name: De Sa, C.
last_name: De Sa
external_id:
arxiv:
- '2210.17357'
intvolume: ' 6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.mlsys.org/paper_files/paper/2024/hash/9069a8976ff06f6443e7f4172990a580-Abstract-Conference.html
month: '04'
oa: 1
oa_version: Published Version
publication: 'Proceedings of Machine Learning and Systems '
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
related_material:
record:
- id: '17490'
relation: dissertation_contains
status: public
researchdata_availability: no
status: public
supplementarymaterial: no
title: 'L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel
deep learning'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 6
year: '2024'
...
---
OA_place: publisher
_id: '18766'
abstract:
- lang: eng
text: Poxviruses are large pleomorphic double-stranded DNA viruses that include
well known members such as variola virus, the causative agent of smallpox, Mpox
virus, as well as Vaccinia virus (VACV), which serves as a vaccination strain
for formerly mentioned viruses. VACV is a valuable model for studying large pleomorphic
DNA viruses in general and poxviruses specifically, as many features, such as
core morphology and structural proteins, are well conserved within this family.
Despite decades of research, our understanding of the structural components and
proteins that comprise the poxvirus core in mature virions remains limited. Although
major core proteins were identified via indirect experimental evidence, the core's
complexity, with its large size, structure and number of involved proteins, has
hindered efforts to achieve high-resolution insights and to define the roles of
the individual proteins. The specific protein composition of the core's individual
layers, including the palisade layer and the inner core wall, has remained unclear.
In this study, we have merged multiple approaches, including single particle cryo
electron microscopy of purified virus cores, cryo-electron tomography and subtomogram
averaging of mature virions and molecular modeling to elucidate the structural
determinants of the VACV core. Due to the lack of experimentally derived structures,
either in situ or reconstituted in vitro, we used Alphafold to predict models
of the putative major core protein candidates, A10, 23k, A3, A4, and L4. Our results
show that the VACV core is composed of several layers with varying local symmetries,
forming more intricate interactions than observed previously. This allowed us
to identify several molecular building blocks forming the viral core lattice.
In particular, we identified trimers of protein A10 as a major core structure
that forms the palisade layer of the viral core. Additionally, we revealed that
six petals of a flower shaped core pore within the core wall are composed of A10
trimers. Furthermore, we obtained a cryo-EM density for the inner core wall that
could potentially accommodate an A3 dimer. Integrating descriptions of protein
interactions from previous studies enabled us to provide a detailed structural
model of the poxvirus core wall, and our findings indicate that the interactions
within A10 trimers are likely consistent across orthopox- and parapoxviruses.
This combined application of cryo-SPA and cryo-ET can help overcome obstacles
in studying complex virus structures in the future, including their key assembly
proteins, interactions, and the formation into a core lattice. Our work provides
important fundamental new insights into poxvirus core architecture, also considering
the recent re-emergence of poxviruses.
acknowledged_ssus:
- _id: EM-Fac
- _id: LifeSc
- _id: ScienComp
acknowledgement: "This work was funded by the Austrian Science Fund (FWF) grant P31445
and ISTA. I\r\nwould like to express my gratitude to the Scientific Service Units,
particularly the Lab\r\nSupport Facility, the Scientific Computing Facility and
the Electron Microscopy Facility\r\nfor their tremendous support. I want to especially
thank Alois for assisting me with the\r\ninstallation of countless new software
and for troubleshooting cluster issues. A special\r\nthanks goes to Valentin for
his outstanding support in cryo-EM data acquisition and\r\nhis ongoing help in improving
the process to ensure that I obtained the best possible\r\ndata from my sample."
alternative_title:
- ISTA thesis
article_processing_charge: No
author:
- first_name: Julia
full_name: Datler, Julia
id: 3B12E2E6-F248-11E8-B48F-1D18A9856A87
last_name: Datler
orcid: 0000-0002-3616-8580
citation:
ama: Datler J. Elucidating the structural determinants of the poxvirus core using
multi-modal cryo-EM. 2024. doi:10.15479/at:ista:18766
apa: Datler, J. (2024). Elucidating the structural determinants of the poxvirus
core using multi-modal cryo-EM. Institute of Science and Technology Austria.
https://doi.org/10.15479/at:ista:18766
chicago: Datler, Julia. “Elucidating the Structural Determinants of the Poxvirus
Core Using Multi-Modal Cryo-EM.” Institute of Science and Technology Austria,
2024. https://doi.org/10.15479/at:ista:18766.
ieee: J. Datler, “Elucidating the structural determinants of the poxvirus core using
multi-modal cryo-EM,” Institute of Science and Technology Austria, 2024.
ista: Datler J. 2024. Elucidating the structural determinants of the poxvirus core
using multi-modal cryo-EM. Institute of Science and Technology Austria.
mla: Datler, Julia. Elucidating the Structural Determinants of the Poxvirus Core
Using Multi-Modal Cryo-EM. Institute of Science and Technology Austria, 2024,
doi:10.15479/at:ista:18766.
short: J. Datler, Elucidating the Structural Determinants of the Poxvirus Core Using
Multi-Modal Cryo-EM, Institute of Science and Technology Austria, 2024.
corr_author: '1'
das_tickbox: '1'
date_created: 2025-01-07T10:23:12Z
date_published: 2024-12-30T00:00:00Z
date_updated: 2026-10-02T11:21:19Z
day: '30'
ddc:
- '570'
degree_awarded: PhD
department:
- _id: GradSch
- _id: FlSc
doi: 10.15479/at:ista:18766
doi_confirm: '1'
file:
- access_level: closed
checksum: 3e51cab327c754045c3d29c1a50cc9a9
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creator: jstanger
date_created: 2025-01-07T12:15:11Z
date_updated: 2025-01-07T12:15:11Z
file_id: '18769'
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file_size: 38814932
relation: source_file
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checksum: 22fabe5b97950bf852212f6edb555173
content_type: application/pdf
creator: jstanger
date_created: 2025-01-07T12:15:14Z
date_updated: 2025-01-07T12:15:14Z
file_id: '18770'
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success: 1
file_date_updated: 2025-01-07T12:15:14Z
fulldoi: https://doi.org/10.15479/at:ista:18766
has_accepted_license: '1'
keyword:
- cryo-EM
- cryo-ET
- cryo-SPA
- Structural Virology
- Poxvirus
- Vaccinia Virus
- Structural Biology
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: '106'
project:
- _id: 26736D6A-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P31445
name: Structural conservation and diversity in retroviral capsid
publication_identifier:
isbn:
- 978-3-99078-049-7
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '12334'
relation: part_of_dissertation
status: public
- id: '14979'
relation: part_of_dissertation
status: public
researchdata_availability: yes
status: public
supervisor:
- first_name: Florian KM
full_name: Schur, Florian KM
id: 48AD8942-F248-11E8-B48F-1D18A9856A87
last_name: Schur
orcid: 0000-0003-4790-8078
supplementarymaterial: no
title: Elucidating the structural determinants of the poxvirus core using multi-modal
cryo-EM
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: dissertation
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
APC_amount: 11700 EUR
OA_place: publisher
OA_type: hybrid
_id: '14979'
abstract:
- lang: eng
text: Poxviruses are among the largest double-stranded DNA viruses, with members
such as variola virus, monkeypox virus and the vaccination strain vaccinia virus
(VACV). Knowledge about the structural proteins that form the viral core has remained
sparse. While major core proteins have been annotated via indirect experimental
evidence, their structures have remained elusive and they could not be assigned
to individual core features. Hence, which proteins constitute which layers of
the core, such as the palisade layer and the inner core wall, has remained enigmatic.
Here we show, using a multi-modal cryo-electron microscopy (cryo-EM) approach
in combination with AlphaFold molecular modeling, that trimers formed by the cleavage
product of VACV protein A10 are the key component of the palisade layer. This
allows us to place previously obtained descriptions of protein interactions within
the core wall into perspective and to provide a detailed model of poxvirus core
architecture. Importantly, we show that interactions within A10 trimers are likely
generalizable over members of orthopox- and parapoxviruses.
acknowledged_ssus:
- _id: ScienComp
- _id: LifeSc
- _id: EM-Fac
acknowledgement: "We thank A. Bergthaler (Research Center for Molecular Medicine of
the Austrian Academy of Sciences) for providing VACV WR. We thank A. Nicholas and
his team at the ISTA proteomics facility, and S. Elefante at the ISTA Scientific
Computing facility for their support. We also thank F. Fäßler, D. Porley, T. Muthspiel
and other members of the Schur group for support and helpful discussions. We also
thank D. Castaño-Díez for support with Dynamo. We thank D. Farrell for his help
optimizing the Rosetta protocol to refine the atomic model into the cryo-EM map
with symmetry.\r\n\r\nF.K.M.S. acknowledges support from ISTA and EMBO. F.K.M.S.
also received support from the Austrian Science Fund (FWF) grant P31445. This publication
has been made possible in part by CZI grant DAF2021-234754 and grant https://doi.org/10.37921/812628ebpcwg
from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community
Foundation (funder https://doi.org/10.13039/100014989) awarded to F.K.M.S.\r\n\r\nThis
research was also supported by the Scientific Service Units (SSUs) of ISTA through
resources provided by Scientific Computing (SciComp), the Life Science Facility
(LSF), and the Electron Microscopy Facility (EMF). We also acknowledge the use of
COSMIC45 and Colabfold46."
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Julia
full_name: Datler, Julia
id: 3B12E2E6-F248-11E8-B48F-1D18A9856A87
last_name: Datler
orcid: 0000-0002-3616-8580
- first_name: Jesse
full_name: Hansen, Jesse
id: 1063c618-6f9b-11ec-9123-f912fccded63
last_name: Hansen
orcid: 0000-0001-7967-2085
- first_name: Andreas
full_name: Thader, Andreas
id: 3A18A7B8-F248-11E8-B48F-1D18A9856A87
last_name: Thader
- first_name: Alois
full_name: Schlögl, Alois
id: 45BF87EE-F248-11E8-B48F-1D18A9856A87
last_name: Schlögl
orcid: 0000-0002-5621-8100
- first_name: Lukas W
full_name: Bauer, Lukas W
id: 0c894dcf-897b-11ed-a09c-8186353224b0
last_name: Bauer
- first_name: Victor-Valentin
full_name: Hodirnau, Victor-Valentin
id: 3661B498-F248-11E8-B48F-1D18A9856A87
last_name: Hodirnau
orcid: 0000-0003-3904-947X
- first_name: Florian KM
full_name: Schur, Florian KM
id: 48AD8942-F248-11E8-B48F-1D18A9856A87
last_name: Schur
orcid: 0000-0003-4790-8078
citation:
ama: Datler J, Hansen J, Thader A, et al. Multi-modal cryo-EM reveals trimers of
protein A10 to form the palisade layer in poxvirus cores. Nature Structural
& Molecular Biology. 2024;31:1114-1123. doi:10.1038/s41594-023-01201-6
apa: Datler, J., Hansen, J., Thader, A., Schlögl, A., Bauer, L. W., Hodirnau, V.-V.,
& Schur, F. K. (2024). Multi-modal cryo-EM reveals trimers of protein A10
to form the palisade layer in poxvirus cores. Nature Structural & Molecular
Biology. Springer Nature. https://doi.org/10.1038/s41594-023-01201-6
chicago: Datler, Julia, Jesse Hansen, Andreas Thader, Alois Schlögl, Lukas W Bauer,
Victor-Valentin Hodirnau, and Florian KM Schur. “Multi-Modal Cryo-EM Reveals Trimers
of Protein A10 to Form the Palisade Layer in Poxvirus Cores.” Nature Structural
& Molecular Biology. Springer Nature, 2024. https://doi.org/10.1038/s41594-023-01201-6.
ieee: J. Datler et al., “Multi-modal cryo-EM reveals trimers of protein A10
to form the palisade layer in poxvirus cores,” Nature Structural & Molecular
Biology, vol. 31. Springer Nature, pp. 1114–1123, 2024.
ista: Datler J, Hansen J, Thader A, Schlögl A, Bauer LW, Hodirnau V-V, Schur FK.
2024. Multi-modal cryo-EM reveals trimers of protein A10 to form the palisade
layer in poxvirus cores. Nature Structural & Molecular Biology. 31, 1114–1123.
mla: Datler, Julia, et al. “Multi-Modal Cryo-EM Reveals Trimers of Protein A10 to
Form the Palisade Layer in Poxvirus Cores.” Nature Structural & Molecular
Biology, vol. 31, Springer Nature, 2024, pp. 1114–23, doi:10.1038/s41594-023-01201-6.
short: J. Datler, J. Hansen, A. Thader, A. Schlögl, L.W. Bauer, V.-V. Hodirnau,
F.K. Schur, Nature Structural & Molecular Biology 31 (2024) 1114–1123.
corr_author: '1'
das_tickbox: '1'
dataavailabilitystatement: "The electron microscopy density maps of the A10 trimer
and the hexameric flower-shaped pore, the subtomogram average of the palisade layer
and representative tomograms for complete viruses as well as isolated cores have
been deposited in the Electron Microscopy Data Bank under accession codes: EMD-17410,
EMD-17411, EMD-17412, EMD-17413, EMD-17414 and EMD-18452.\r\n\r\nThe refined model
of the A10 trimer has been deposited in the Protein Data Bank accession code: PDB
8P4K.\r\n\r\nThe UniProt codes of VACV core proteins used for structure prediction
are: A10 (P16715), A3 (P06440), A4 (P29191) and L4 (P03295).\r\n\r\nGenBank Protein
IDs of variola virus A10 (ABF23487.1), monkeypox virus A10 (YP_010377118.1), rabbitpox
virus A10 (AAS49831.1), cowpox virus A10 (ADZ29251.1), ectromelia virus A10 (NP_671631.1),
orf virus P4a (AY386264.1), Amsacta moorei entomopoxvirus AMV139 (NP_064921.1) and
Melanoplus sanguinipes entomopoxvirus putative core protein P4a homolog (AF063866.1)
were used for protein sequence alignment. Source data for MS experiments are provided
as Supplementary Information accompanying this manuscript."
date_created: 2024-02-12T09:59:45Z
date_published: 2024-07-01T00:00:00Z
date_updated: 2026-10-02T11:21:19Z
day: '01'
ddc:
- '570'
department:
- _id: FlSc
- _id: ScienComp
- _id: EM-Fac
doi: 10.1038/s41594-023-01201-6
external_id:
isi:
- '001158144600002'
pmid:
- '38316877'
file:
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checksum: bda7bf65d81455480efaed8ca293b0db
content_type: application/pdf
creator: dernst
date_created: 2024-07-22T11:27:22Z
date_updated: 2024-07-22T11:27:22Z
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file_name: 2024_NatureStrucBio_Datler.pdf
file_size: 17485494
relation: main_file
success: 1
file_date_updated: 2024-07-22T11:27:22Z
fulldoi: https://doi.org/10.1038/s41594-023-01201-6
has_accepted_license: '1'
intvolume: ' 31'
isi: 1
keyword:
- Molecular Biology
- Structural Biology
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 1114-1123
pmid: 1
project:
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call_identifier: FWF
grant_number: P31445
name: Structural conservation and diversity in retroviral capsid
publication: Nature Structural & Molecular Biology
publication_identifier:
eissn:
- 1545-9985
issn:
- 1545-9993
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- description: News on ISTA Website
relation: press_release
url: https://ista.ac.at/en/news/down-to-the-core-of-poxviruses/
record:
- id: '18766'
relation: dissertation_contains
status: public
researchdata_availability: yes
scopus_import: '1'
status: public
supplementarymaterial: yes
title: Multi-modal cryo-EM reveals trimers of protein A10 to form the palisade layer
in poxvirus cores
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: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 31
year: '2024'
...
---
OA_place: publisher
_id: '17850'
abstract:
- lang: eng
text: "Understanding the relationship between a given phenotype and its underlying
genotype or genotypes is one of the most pressing challenges of biology, as it
lies at the heart of not only basic understanding of evolutionary theory, but
also of practical applications in medicine and bioengineering. Understanding this
relationship is complicated by the ubiquitous phenomenon of epistasis, wherein
mutation effects are dependent on their genetic context. Fitness landscapes —
representations of phenotype as a function of genotype — are being increasingly
used as a tool to study the effects and interactions of thousands of mutations,
but are experimentally limited to exploring a small fraction of a protein’s theoretical
sequence space. Furthermore, not all regions of said sequence space are necessarily
equally informative. Thus, gene selection for landscape surveys should be carefully
considered in order to maximize the usable output of necessarily limited data.\r\n\r\nIn
this work, we analyzed the fitness landscapes of orthologous green fluorescent
proteins from four different species, by systematically measuring the phenotype,
fluorescence, of tens of thousands of mutant genotypes from each protein. These
landscapes were highly heterogeneous, with some genes being mutationally robust
and displaying epistasis only rarely, and others being highly epistatic and mutationally
fragile. We used this data to train machine learning models to predict fluorescence
from genotype. Although the training data contained almost exclusively genotypes
with less than 3% sequence divergence from the original wild-type sequences, we
were able to create novel, functional genotypes with up to 20% sequence divergence.
Counterintuitively however, genes with high mutational robustness and rare epistasis
were more difficult to introduce large numbers of mutations into, not less. This
represents the first study of large-scale fitness landscapes of a protein family,
and provides insights into how to approach future landscape surveys and their
applications in novel protein design."
acknowledged_ssus:
- _id: Bio
- _id: LifeSc
- _id: ScienComp
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Louisa
full_name: Gonzalez Somermeyer, Louisa
id: 4720D23C-F248-11E8-B48F-1D18A9856A87
last_name: Gonzalez Somermeyer
orcid: 0000-0001-9139-5383
citation:
ama: Gonzalez Somermeyer L. Fitness landscapes of orthologous green fluorescent
proteins. 2024. doi:10.15479/at:ista:17850
apa: Gonzalez Somermeyer, L. (2024). Fitness landscapes of orthologous green
fluorescent proteins. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:17850
chicago: Gonzalez Somermeyer, Louisa. “Fitness Landscapes of Orthologous Green Fluorescent
Proteins.” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:17850.
ieee: L. Gonzalez Somermeyer, “Fitness landscapes of orthologous green fluorescent
proteins,” Institute of Science and Technology Austria, 2024.
ista: Gonzalez Somermeyer L. 2024. Fitness landscapes of orthologous green fluorescent
proteins. Institute of Science and Technology Austria.
mla: Gonzalez Somermeyer, Louisa. Fitness Landscapes of Orthologous Green Fluorescent
Proteins. Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:17850.
short: L. Gonzalez Somermeyer, Fitness Landscapes of Orthologous Green Fluorescent
Proteins, Institute of Science and Technology Austria, 2024.
corr_author: '1'
das_tickbox: '0'
date_created: 2024-09-06T12:57:44Z
date_published: 2024-09-06T00:00:00Z
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language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: '89'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 26580278-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '771209'
name: Characterizing the fitness landscape on population and global scales
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
link:
- relation: software
url: https://github.com/aequorea238/Orthologous_GFP_Fitness_Peaks
record:
- id: '11448'
relation: part_of_dissertation
status: public
researchdata_availability: no
status: public
supervisor:
- first_name: Fyodor
full_name: Kondrashov, Fyodor
id: 44FDEF62-F248-11E8-B48F-1D18A9856A87
last_name: Kondrashov
orcid: 0000-0001-8243-4694
supplementarymaterial: no
title: Fitness landscapes of orthologous green fluorescent proteins
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: dissertation
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
_id: '14901'
abstract:
- lang: eng
text: Global services like navigation, communication, and Earth observation have
increased dramatically in the 21st century due to advances in outer space industries.
But as orbits become increasingly crowded with both satellites and inevitable
space debris pollution, continued operations become endangered by the heightened
risks of debris collisions in orbit. Kessler Syndrome is the term for when a critical
threshold of orbiting debris triggers a runaway positive feedback loop of debris
collisions, creating debris congestion that can render orbits unusable. As this
potential tipping point becomes more widely recognized, there have been renewed
calls for debris mitigation and removal. Here, we combine complex systems and
social-ecological systems approaches to study how these efforts may affect space
debris accumulation and the likelihood of reaching Kessler Syndrome. Specifically,
we model how debris levels are affected by future launch rates, cleanup activities,
and collisions between extant debris. We contextualize and interpret our dynamic
model within a discussion of existing space debris governance and other social,
economic, and geopolitical factors that may influence effective collective management
of the orbital commons. In line with previous studies, our model finds that debris
congestion may be reached in less than 200 years, though a holistic management
strategy combining removal and mitigation actions can avoid such outcomes while
continuing space activities. Moreover, although active debris removal may be particularly
effective, the current lack of market and governance support may impede its implementation.
Research into these critical dynamics and the multi-faceted variables that influence
debris outcomes can support policymakers in curating impactful governance strategies
and realistic transition pathways to sustaining debris-free orbits. Overall, our
study is useful for communicating about space debris sustainability in policy
and education settings by providing an exploration of policy portfolio options
supported by a simple and clear social-ecological modeling approach.
acknowledgement: The authors would like to thank the special issue co-editors, Marco
Janssen and Xiao-Shan Yap, and the anonymous reviewers for their comments that helped
improve the manuscript. The paper also benefited from suggestions by other author
participants in this special issue. We would also like to thank the 2022 Santa Fe
Institute Complex Systems Summer School for providing space to initiate this study.
article_processing_charge: Yes
article_type: original
author:
- first_name: Keiko
full_name: Nomura, Keiko
last_name: Nomura
- first_name: Simon
full_name: Rella, Simon
id: B4765ACA-AA38-11E9-AC9A-0930E6697425
last_name: Rella
- first_name: Haily
full_name: Merritt, Haily
last_name: Merritt
- first_name: Mathieu
full_name: Baltussen, Mathieu
last_name: Baltussen
- first_name: Darcy
full_name: Bird, Darcy
last_name: Bird
- first_name: Annika
full_name: Tjuka, Annika
last_name: Tjuka
- first_name: Dan
full_name: Falk, Dan
last_name: Falk
citation:
ama: Nomura K, Rella S, Merritt H, et al. Tipping points of space debris in low
earth orbit. International Journal of the Commons. 2024;18(1). doi:10.5334/ijc.1275
apa: Nomura, K., Rella, S., Merritt, H., Baltussen, M., Bird, D., Tjuka, A., &
Falk, D. (2024). Tipping points of space debris in low earth orbit. International
Journal of the Commons. Ubiquity Press. https://doi.org/10.5334/ijc.1275
chicago: Nomura, Keiko, Simon Rella, Haily Merritt, Mathieu Baltussen, Darcy Bird,
Annika Tjuka, and Dan Falk. “Tipping Points of Space Debris in Low Earth Orbit.”
International Journal of the Commons. Ubiquity Press, 2024. https://doi.org/10.5334/ijc.1275.
ieee: K. Nomura et al., “Tipping points of space debris in low earth orbit,”
International Journal of the Commons, vol. 18, no. 1. Ubiquity Press, 2024.
ista: Nomura K, Rella S, Merritt H, Baltussen M, Bird D, Tjuka A, Falk D. 2024.
Tipping points of space debris in low earth orbit. International Journal of the
Commons. 18(1).
mla: Nomura, Keiko, et al. “Tipping Points of Space Debris in Low Earth Orbit.”
International Journal of the Commons, vol. 18, no. 1, Ubiquity Press, 2024,
doi:10.5334/ijc.1275.
short: K. Nomura, S. Rella, H. Merritt, M. Baltussen, D. Bird, A. Tjuka, D. Falk,
International Journal of the Commons 18 (2024).
das_tickbox: '0'
date_created: 2024-01-30T11:58:02Z
date_published: 2024-01-11T00:00:00Z
date_updated: 2026-10-02T11:22:03Z
day: '11'
ddc:
- '550'
department:
- _id: GradSch
- _id: GaTk
doi: 10.5334/ijc.1275
file:
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success: 1
file_date_updated: 2024-02-05T10:06:35Z
fulldoi: https://doi.org/10.5334/ijc.1275
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intvolume: ' 18'
issue: '1'
keyword:
- Sociology and Political Science
language:
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month: '01'
oa: 1
oa_version: Published Version
publication: International Journal of the Commons
publication_identifier:
issn:
- 1875-0281
publication_status: published
publisher: Ubiquity Press
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: yes
title: Tipping points of space debris in low earth orbit
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: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 18
year: '2024'
...
---
OA_place: publisher
_id: '18076'
abstract:
- lang: eng
text: "The new era of Ge has opened up new possibilities in quantum computing. The
maturity of Ge\r\nspin qubits is unquestioned, while hybrid semiconductor-superconductor
Ge circuits are on track\r\nto enter the game. Gate-tunable transmons (gatemons)
employing semiconductor Josephson\r\njunctions have recently emerged as building
blocks for such hybrid quantum circuits. In this\r\nthesis, we present a gatemon
fabricated in planar Germanium. We induce superconductivity\r\nin a two-dimensional
hole gas by evaporating aluminum atop a thin spacer, which separates\r\nthe superconductor
from the Ge quantum well. The Josephson junction is then integrated\r\ninto an
Xmon circuit and capacitively coupled to a transmission line resonator. We showcase\r\nthe
qubit tunability in a broad frequency range with resonator and two-tone spectroscopy.\r\nTime-domain
characterizations reveal energy relaxation and coherence times up to 75 ns. Our\r\nresults,
combined with the recent advances in the spin qubit field, pave the way towards
novel\r\nhybrid and protected qubits in a group IV, CMOS-compatible material."
acknowledged_ssus:
- _id: NanoFab
- _id: M-Shop
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Oliver
full_name: Sagi, Oliver
id: 71616374-A8E9-11E9-A7CA-09ECE5697425
last_name: Sagi
citation:
ama: Sagi O. Hybrid circuits on planar Germanium. 2024. doi:10.15479/at:ista:18076
apa: Sagi, O. (2024). Hybrid circuits on planar Germanium. Institute of Science
and Technology Austria. https://doi.org/10.15479/at:ista:18076
chicago: Sagi, Oliver. “Hybrid Circuits on Planar Germanium.” Institute of Science
and Technology Austria, 2024. https://doi.org/10.15479/at:ista:18076.
ieee: O. Sagi, “Hybrid circuits on planar Germanium,” Institute of Science and Technology
Austria, 2024.
ista: Sagi O. 2024. Hybrid circuits on planar Germanium. Institute of Science and
Technology Austria.
mla: Sagi, Oliver. Hybrid Circuits on Planar Germanium. Institute of Science
and Technology Austria, 2024, doi:10.15479/at:ista:18076.
short: O. Sagi, Hybrid Circuits on Planar Germanium, Institute of Science and Technology
Austria, 2024.
corr_author: '1'
das_tickbox: '0'
date_created: 2024-09-16T12:58:36Z
date_published: 2024-09-18T00:00:00Z
date_updated: 2026-10-02T11:22:29Z
day: '18'
ddc:
- '539'
degree_awarded: PhD
department:
- _id: GradSch
- _id: GeKa
doi: 10.15479/at:ista:18076
doi_confirm: '1'
ec_funded: 1
file:
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date_updated: 2024-09-18T14:13:01Z
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file_size: 86679095
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success: 1
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creator: osagi
date_created: 2024-09-18T14:14:02Z
date_updated: 2024-09-19T09:20:33Z
file_id: '18094'
file_name: Thesis_OliverSagi.zip
file_size: 172098524
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fulldoi: https://doi.org/10.15479/at:ista:18076
has_accepted_license: '1'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: '111'
project:
- _id: bd8bd29e-d553-11ed-ba76-f0070d4b237a
grant_number: P36507
name: Merging spin and superconducting qubits in planar Ge
- _id: c0977eea-5a5b-11eb-8a69-a862db0cf4d1
grant_number: I05060
name: High impedance circuit quantum electrodynamics with hole spins
- _id: 262116AA-B435-11E9-9278-68D0E5697425
name: Hybrid Semiconductor - Superconductor Quantum Devices
- _id: 237E5020-32DE-11EA-91FC-C7463DDC885E
call_identifier: H2020
grant_number: '862046'
name: TOPOLOGICALLY PROTECTED AND SCALABLE QUANTUM BITS
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '17202'
relation: part_of_dissertation
status: public
researchdata_availability: no
status: public
supervisor:
- first_name: Georgios
full_name: Katsaros, Georgios
id: 38DB5788-F248-11E8-B48F-1D18A9856A87
last_name: Katsaros
orcid: 0000-0001-8342-202X
supplementarymaterial: yes
title: Hybrid circuits on planar Germanium
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
_id: '17170'
abstract:
- lang: eng
text: "In this article we extend and strengthen the seminal work by Niyogi, Smale,
and Weinberger on the learning of the homotopy type from a sample of an underlying
space. In their work, Niyogi, Smale, and Weinberger studied samples of C² manifolds
with positive reach embedded in ℝ^d. We extend their results in the following
ways: - As the ambient space we consider both ℝ^d and Riemannian manifolds with
lower bounded sectional curvature. - In both types of ambient spaces, we study
sets of positive reach - a significantly more general setting than C² manifolds
- as well as general manifolds of positive reach. - The sample P of a set (or
a manifold) \U0001D4AE of positive reach may be noisy. We work with two one-sided
Hausdorff distances - ε and δ - between P and \U0001D4AE. We provide tight bounds
in terms of ε and δ, that guarantee that there exists a parameter r such that
the union of balls of radius r centred at the sample P deformation-retracts to
\U0001D4AE. We exhibit their tightness by an explicit construction. We carefully
distinguish the roles of δ and ε. This is not only essential to achieve tight
bounds, but also sensible in practical situations, since it allows one to adapt
the bound according to sample density and the amount of noise present in the sample
separately."
acknowledgement: "This research has been supported by the European Research Council
(ERC), grant No. 788183, by the Wittgenstein Prize, Austrian Science Fund (FWF),
grant No. Z 342-N31, and by the DFG Collaborative Research Center TRR 109, Austrian
Science Fund (FWF), grant No. I 02979-N35.\r\nWintraecken, Mathijs: Supported by
the European Union’s Horizon 2020 research and innovation programme under the Marie
Skłodowska-Curie grant agreement No. 754411, the Austrian science fund (FWF) grant
No. M-3073, and the welcome package from IDEX of the Université Côte d'Azur."
alternative_title:
- LIPIcs
article_processing_charge: No
arxiv: 1
author:
- first_name: Dominique
full_name: Attali, Dominique
last_name: Attali
- first_name: Hana
full_name: Kourimska, Hana
id: D9B8E14C-3C26-11EA-98F5-1F833DDC885E
last_name: Kourimska
orcid: 0000-0001-7841-0091
- first_name: Christopher D
full_name: Fillmore, Christopher D
id: 35638A5C-AAC7-11E9-B0BF-5503E6697425
last_name: Fillmore
- first_name: Ishika
full_name: Ghosh, Ishika
id: ee449b28-344d-11ef-a6d5-9ca430e9e9ff
last_name: Ghosh
- first_name: André
full_name: Lieutier, André
last_name: Lieutier
- first_name: Elizabeth R
full_name: Stephenson, Elizabeth R
id: 2D04F932-F248-11E8-B48F-1D18A9856A87
last_name: Stephenson
orcid: 0000-0002-6862-208X
- first_name: Mathijs
full_name: Wintraecken, Mathijs
id: 307CFBC8-F248-11E8-B48F-1D18A9856A87
last_name: Wintraecken
orcid: 0000-0002-7472-2220
citation:
ama: 'Attali D, Kourimska H, Fillmore CD, et al. Tight bounds for the learning of
homotopy à la Niyogi, Smale, and Weinberger for subsets of euclidean spaces and
of Riemannian manifolds. In: 40th International Symposium on Computational
Geometry. Vol 293. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2024:11:1-11:19.
doi:10.4230/LIPIcs.SoCG.2024.11'
apa: 'Attali, D., Kourimska, H., Fillmore, C. D., Ghosh, I., Lieutier, A., Stephenson,
E. R., & Wintraecken, M. (2024). Tight bounds for the learning of homotopy
à la Niyogi, Smale, and Weinberger for subsets of euclidean spaces and of Riemannian
manifolds. In 40th International Symposium on Computational Geometry (Vol.
293, p. 11:1-11:19). Athens, Greece: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.
https://doi.org/10.4230/LIPIcs.SoCG.2024.11'
chicago: Attali, Dominique, Hana Kourimska, Christopher D Fillmore, Ishika Ghosh,
André Lieutier, Elizabeth R Stephenson, and Mathijs Wintraecken. “Tight Bounds
for the Learning of Homotopy à La Niyogi, Smale, and Weinberger for Subsets of
Euclidean Spaces and of Riemannian Manifolds.” In 40th International Symposium
on Computational Geometry, 293:11:1-11:19. Schloss Dagstuhl - Leibniz-Zentrum
für Informatik, 2024. https://doi.org/10.4230/LIPIcs.SoCG.2024.11.
ieee: D. Attali et al., “Tight bounds for the learning of homotopy à la Niyogi,
Smale, and Weinberger for subsets of euclidean spaces and of Riemannian manifolds,”
in 40th International Symposium on Computational Geometry, Athens, Greece,
2024, vol. 293, p. 11:1-11:19.
ista: 'Attali D, Kourimska H, Fillmore CD, Ghosh I, Lieutier A, Stephenson ER, Wintraecken
M. 2024. Tight bounds for the learning of homotopy à la Niyogi, Smale, and Weinberger
for subsets of euclidean spaces and of Riemannian manifolds. 40th International
Symposium on Computational Geometry. SoCG: Symposium on Computational Geometry,
LIPIcs, vol. 293, 11:1-11:19.'
mla: Attali, Dominique, et al. “Tight Bounds for the Learning of Homotopy à La Niyogi,
Smale, and Weinberger for Subsets of Euclidean Spaces and of Riemannian Manifolds.”
40th International Symposium on Computational Geometry, vol. 293, Schloss
Dagstuhl - Leibniz-Zentrum für Informatik, 2024, p. 11:1-11:19, doi:10.4230/LIPIcs.SoCG.2024.11.
short: D. Attali, H. Kourimska, C.D. Fillmore, I. Ghosh, A. Lieutier, E.R. Stephenson,
M. Wintraecken, in:, 40th International Symposium on Computational Geometry, Schloss
Dagstuhl - Leibniz-Zentrum für Informatik, 2024, p. 11:1-11:19.
conference:
end_date: 2024-06-14
location: Athens, Greece
name: 'SoCG: Symposium on Computational Geometry'
start_date: 2024-06-11
das_tickbox: '0'
date_created: 2024-06-25T11:45:58Z
date_published: 2024-06-06T00:00:00Z
date_updated: 2026-10-02T11:23:43Z
day: '06'
ddc:
- '516'
department:
- _id: GradSch
- _id: HeEd
doi: 10.4230/LIPIcs.SoCG.2024.11
ec_funded: 1
external_id:
arxiv:
- '2206.10485'
file:
- access_level: open_access
checksum: 6a2ddc8b51aa58f197a8b294750f1f8d
content_type: application/pdf
creator: cfillmor
date_created: 2024-06-25T11:47:26Z
date_updated: 2024-06-25T11:47:26Z
file_id: '17171'
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success: 1
file_date_updated: 2024-06-25T11:47:26Z
fulldoi: https://doi.org/10.4230/LIPIcs.SoCG.2024.11
has_accepted_license: '1'
intvolume: ' 293'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
page: 11:1-11:19
project:
- _id: 266A2E9E-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '788183'
name: Alpha Shape Theory Extended
- _id: 268116B8-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z00342
name: Mathematics, Computer Science
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
- _id: 2561EBF4-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: I02979-N35
name: Persistence and stability of geometric complexes
- _id: fc390959-9c52-11eb-aca3-afa58bd282b2
grant_number: M03073
name: Learning and triangulating manifolds via collapses
publication: 40th International Symposium on Computational Geometry
publication_identifier:
eissn:
- 1868-8969
isbn:
- '9783959773164'
publication_status: published
publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: Tight bounds for the learning of homotopy à la Niyogi, Smale, and Weinberger
for subsets of euclidean spaces and of Riemannian manifolds
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: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 293
year: '2024'
...
---
APC_amount: 6828 EUR
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '17202'
abstract:
- lang: eng
text: Gate-tunable transmons (gatemons) employing semiconductor Josephson junctions
have recently emerged as building blocks for hybrid quantum circuits. In this
study, we present a gatemon fabricated in planar Germanium. We induce superconductivity
in a two-dimensional hole gas by evaporating aluminum atop a thin spacer, which
separates the superconductor from the Ge quantum well. The Josephson junction
is then integrated into an Xmon circuit and capacitively coupled to a transmission
line resonator. We showcase the qubit tunability in a broad frequency range with
resonator and two-tone spectroscopy. Time-domain characterizations reveal energy
relaxation and coherence times up to 75 ns. Our results, combined with the recent
advances in the spin qubit field, pave the way towards novel hybrid and protected
qubits in a group IV, CMOS-compatible material.
acknowledged_ssus:
- _id: ScienComp
- _id: M-Shop
- _id: NanoFab
acknowledgement: "We acknowledge Lucas Casparis, Jeroen Danon, Valla Fatemi, Morten
Kjaergard and Javad Shabani for their valuable insights and comments. This research
was supported by the Scientific Service Units of ISTA through resources provided
by the MIBA Machine Shop\r\nand the Nanofabrication facility. This research and
related results were made possible with the support of the NOMIS Foundation and
the FWF Projects with DOI:10.55776/I5060 and DOI:10.55776/P36507. We also acknowledge
the NextGenerationEU PRIN project\r\n2022A8CJP3 (GAMESQUAD) for partial financial
support."
article_number: '6400'
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Oliver
full_name: Sagi, Oliver
id: 71616374-A8E9-11E9-A7CA-09ECE5697425
last_name: Sagi
- first_name: Alessandro
full_name: Crippa, Alessandro
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- first_name: Marco
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last_name: Baghumyan
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last_name: Fabris
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full_name: Kapoor, Lucky
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last_name: Kapoor
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last_name: Hassani
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citation:
ama: Sagi O, Crippa A, Valentini M, et al. A gate tunable transmon qubit in planar
Ge. Nature Communications. 2024;15. doi:10.1038/s41467-024-50763-6
apa: Sagi, O., Crippa, A., Valentini, M., Janik, M., Baghumyan, L., Fabris, G.,
… Katsaros, G. (2024). A gate tunable transmon qubit in planar Ge. Nature Communications.
Springer Nature. https://doi.org/10.1038/s41467-024-50763-6
chicago: Sagi, Oliver, Alessandro Crippa, Marco Valentini, Marian Janik, Levon Baghumyan,
Giorgio Fabris, Lucky Kapoor, et al. “A Gate Tunable Transmon Qubit in Planar
Ge.” Nature Communications. Springer Nature, 2024. https://doi.org/10.1038/s41467-024-50763-6.
ieee: O. Sagi et al., “A gate tunable transmon qubit in planar Ge,” Nature
Communications, vol. 15. Springer Nature, 2024.
ista: Sagi O, Crippa A, Valentini M, Janik M, Baghumyan L, Fabris G, Kapoor L, Hassani
F, Fink JM, Calcaterra S, Chrastina D, Isella G, Katsaros G. 2024. A gate tunable
transmon qubit in planar Ge. Nature Communications. 15, 6400.
mla: Sagi, Oliver, et al. “A Gate Tunable Transmon Qubit in Planar Ge.” Nature
Communications, vol. 15, 6400, Springer Nature, 2024, doi:10.1038/s41467-024-50763-6.
short: O. Sagi, A. Crippa, M. Valentini, M. Janik, L. Baghumyan, G. Fabris, L. Kapoor,
F. Hassani, J.M. Fink, S. Calcaterra, D. Chrastina, G. Isella, G. Katsaros, Nature
Communications 15 (2024).
corr_author: '1'
das_tickbox: '1'
dataavailabilitystatement: All experimental data included in this work will be available
at the ISTA Research Data Repository
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doi: 10.1038/s41467-024-50763-6
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and Technology Austria. https://doi.org/10.15479/AT:ISTA:17196
chicago: Sagi, Oliver. “A Gate-Tunable Transmon in Planar Ge.” Institute of Science
and Technology Austria, 2024. https://doi.org/10.15479/AT:ISTA:17196.
ieee: O. Sagi, “A gate-tunable transmon in planar Ge.” Institute of Science and
Technology Austria, 2024.
ista: Sagi O. 2024. A gate-tunable transmon in planar Ge, Institute of Science and
Technology Austria, 10.15479/AT:ISTA:17196.
mla: Sagi, Oliver. A Gate-Tunable Transmon in Planar Ge. Institute of Science
and Technology Austria, 2024, doi:10.15479/AT:ISTA:17196.
short: O. Sagi, (2024).
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abstract:
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text: The TIR1/AFB–Aux/IAA–ARF canonical auxin signaling pathway is widely accepted
to (de)active transcriptional regulation, thus controlling auxin-associated developmental
processes. However, the theme of a rapid auxin response has emerged since the
2018 Auxins and Cytokinin in Plant Development conference. To date, a few signaling
components have been identified to mediate both slow and rapid auxin responses,
which unveils the complexity of auxin signaling.
acknowledgement: "We thank other lab members for their critical comments on this manuscript.
We also thank the editor and reviewers for their constructive comments to improve
our manuscript. We apologize to authors whose important work we could not include
due to space limitations.\r\nThis work is supported by funding from Jiangxi Agricultural
University (9232308314) and the Science and Technology Department of Jiangxi Province
(20223BCJ25037) to HBH, and the Science and Technology Department of Jiangxi Province
(20202ACB215002) to SYP."
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author:
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full_name: Zhang, Zilin
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ama: Zhang Z, Chen H, Peng S, Han H. Slow and rapid auxin responses in Arabidopsis.
Journal of Experimental Botany. 2024;75(18). doi:10.1093/jxb/erae246
apa: Zhang, Z., Chen, H., Peng, S., & Han, H. (2024). Slow and rapid auxin responses
in Arabidopsis. Journal of Experimental Botany. Oxford University Press.
https://doi.org/10.1093/jxb/erae246
chicago: Zhang, Zilin, Huihuang Chen, Shuaiying Peng, and Huibin Han. “Slow and
Rapid Auxin Responses in Arabidopsis.” Journal of Experimental Botany.
Oxford University Press, 2024. https://doi.org/10.1093/jxb/erae246.
ieee: Z. Zhang, H. Chen, S. Peng, and H. Han, “Slow and rapid auxin responses in
Arabidopsis,” Journal of Experimental Botany, vol. 75, no. 18. Oxford University
Press, 2024.
ista: Zhang Z, Chen H, Peng S, Han H. 2024. Slow and rapid auxin responses in Arabidopsis.
Journal of Experimental Botany. 75(18), erae246.
mla: Zhang, Zilin, et al. “Slow and Rapid Auxin Responses in Arabidopsis.” Journal
of Experimental Botany, vol. 75, no. 18, erae246, Oxford University Press,
2024, doi:10.1093/jxb/erae246.
short: Z. Zhang, H. Chen, S. Peng, H. Han, Journal of Experimental Botany 75 (2024).
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