---
_id: '18110'
abstract:
- lang: eng
text: We study a chaotic particle-conserving kinetically constrained model, with
a single parameter which allows us to break reflection symmetry. Through extensive
numerical simulations we find that the domain wall state shows a variety of dynamical
behaviors from localization all the way to ballistic transport, depending on the
value of the reflection breaking parameter. Surprisingly, such anomalous behavior
is not mirrored in infinite-temperature dynamics, which appear to scale diffusively,
in line with expectations for generic interacting models. However, studying the
particle density gradient, we show that the lack of reflection symmetry affects
infinite-temperature dynamics, resulting in an asymmetric dynamical structure
factor. This is in disagreement with normal diffusion and suggests that the model
may also exhibit anomalous dynamics at infinite temperature in the thermodynamic
limit. Finally, we observe low-entangled eigenstates in the spectrum of the model,
a telltale sign of quantum many-body scars.
acknowledgement: "The authors acknowledge useful discussions with M. Serbyn, Z. Papic,
and A. Nunnenkamp. ´\r\nP.B. is supported by the Erwin Schrödinger Center for Quantum
Science & Technology (ESQ) of the Österreichische Akademie der Wissenschaften (ÖAW)
under the Discovery Grant. M.L. acknowledges support from the European Research
Council (ERC) under the European Union’s Horizon 2020 research and innovation programme
(Grant Agreement\r\nNo. 850899). The numerical simulations were performed using
the ITensor library [68] on the Vienna Scientific Cluster (VSC)."
article_number: L100304
article_processing_charge: No
article_type: letter_note
arxiv: 1
author:
- first_name: Pietro
full_name: Brighi, Pietro
id: 4115AF5C-F248-11E8-B48F-1D18A9856A87
last_name: Brighi
orcid: 0000-0002-7969-2729
- first_name: Marko
full_name: Ljubotina, Marko
id: F75EE9BE-5C90-11EA-905D-16643DDC885E
last_name: Ljubotina
orcid: 0000-0003-0038-7068
citation:
ama: Brighi P, Ljubotina M. Anomalous transport in the kinetically constrained quantum
East-West model. Physical Review B. 2024;110(10). doi:10.1103/PhysRevB.110.L100304
apa: Brighi, P., & Ljubotina, M. (2024). Anomalous transport in the kinetically
constrained quantum East-West model. Physical Review B. American Physical
Society. https://doi.org/10.1103/PhysRevB.110.L100304
chicago: Brighi, Pietro, and Marko Ljubotina. “Anomalous Transport in the Kinetically
Constrained Quantum East-West Model.” Physical Review B. American Physical
Society, 2024. https://doi.org/10.1103/PhysRevB.110.L100304.
ieee: P. Brighi and M. Ljubotina, “Anomalous transport in the kinetically constrained
quantum East-West model,” Physical Review B, vol. 110, no. 10. American
Physical Society, 2024.
ista: Brighi P, Ljubotina M. 2024. Anomalous transport in the kinetically constrained
quantum East-West model. Physical Review B. 110(10), L100304.
mla: Brighi, Pietro, and Marko Ljubotina. “Anomalous Transport in the Kinetically
Constrained Quantum East-West Model.” Physical Review B, vol. 110, no.
10, L100304, American Physical Society, 2024, doi:10.1103/PhysRevB.110.L100304.
short: P. Brighi, M. Ljubotina, Physical Review B 110 (2024).
corr_author: '1'
date_created: 2024-09-22T22:01:42Z
date_published: 2024-09-11T00:00:00Z
date_updated: 2025-09-08T09:49:29Z
day: '11'
department:
- _id: MaSe
doi: 10.1103/PhysRevB.110.L100304
ec_funded: 1
external_id:
arxiv:
- '2405.02102'
isi:
- '001361617100003'
intvolume: ' 110'
isi: 1
issue: '10'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2405.02102
month: '09'
oa: 1
oa_version: Preprint
project:
- _id: 23841C26-32DE-11EA-91FC-C7463DDC885E
call_identifier: H2020
grant_number: '850899'
name: 'Non-Ergodic Quantum Matter: Universality, Dynamics and Control'
publication: Physical Review B
publication_identifier:
eissn:
- 2469-9969
issn:
- 2469-9950
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Anomalous transport in the kinetically constrained quantum East-West model
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 110
year: '2024'
...
---
DOAJ_listed: '1'
_id: '18111'
abstract:
- lang: eng
text: Observations of tidal disruption events (TDEs) show signs of nitrogen enrichment
reminiscent of other astrophysical sources such as active galactic nuclei and
star-forming galaxies. Given that TDEs probe the gas from a single star, it is
possible to test whether the observed enrichment is consistent with expectations
from the CNO cycle by looking at the observed nitrogen/carbon (N/C) abundance
ratios. Given that ≈20% of solar-mass stars (and an even larger fraction of more
massive stars) live in close binaries, it is worthwhile to also consider what
TDEs from stars influenced by binary evolution would look like. We show here that
TDEs from stars stripped of their hydrogen-rich (and nitrogen-poor) envelopes
through previous binary-induced mass loss can produce much higher observable N/C
enhancements than even TDEs from massive stars. Additionally, we predict that
the time dependence of the N/C abundance ratio in the mass fallback rate of stripped
stars will follow the inverse behavior of main-sequence stars, enabling a more
accurate characterization of the disrupted star.
acknowledgement: "This work was performed in part at Aspen Center for Physics, which
is supported by National Science Foundation grant PHY-2210452. We thank the participants
and organizers of the summer Aspen 2023 workshop on “Stellar Interactions and the
Transients They Cause” for fruitful discussions. B.M. is grateful for support from
the Carnegie Theoretical Astrophysics\r\nCenter. M.G.-G. is grateful for the support
from Northwestern University’s Presidential Fellowship. E.R.-R. thanks the Heising-Simons
Foundation, NSF (AST-2150255 and AST2307710), Swift (80NSSC21K1409, 80NSSC19K1391),
and Chandra (22-0142) for support. "
article_number: L9
article_processing_charge: Yes
article_type: original
author:
- first_name: Brenna
full_name: Mockler, Brenna
last_name: Mockler
- first_name: Monica
full_name: Gallegos-Garcia, Monica
last_name: Gallegos-Garcia
- first_name: Ylva Louise Linsdotter
full_name: Götberg, Ylva Louise Linsdotter
id: d0648d0c-0f64-11ee-a2e0-dd0faa2e4f7d
last_name: Götberg
orcid: 0000-0002-6960-6911
- first_name: Jon M.
full_name: Miller, Jon M.
last_name: Miller
- first_name: Enrico
full_name: Ramirez-Ruiz, Enrico
last_name: Ramirez-Ruiz
citation:
ama: Mockler B, Gallegos-Garcia M, Götberg YLL, Miller JM, Ramirez-Ruiz E. Tidal
disruption events from stripped stars. Astrophysical Journal Letters. 2024;973(1).
doi:10.3847/2041-8213/ad6c34
apa: Mockler, B., Gallegos-Garcia, M., Götberg, Y. L. L., Miller, J. M., & Ramirez-Ruiz,
E. (2024). Tidal disruption events from stripped stars. Astrophysical Journal
Letters. IOP Publishing. https://doi.org/10.3847/2041-8213/ad6c34
chicago: Mockler, Brenna, Monica Gallegos-Garcia, Ylva Louise Linsdotter Götberg,
Jon M. Miller, and Enrico Ramirez-Ruiz. “Tidal Disruption Events from Stripped
Stars.” Astrophysical Journal Letters. IOP Publishing, 2024. https://doi.org/10.3847/2041-8213/ad6c34.
ieee: B. Mockler, M. Gallegos-Garcia, Y. L. L. Götberg, J. M. Miller, and E. Ramirez-Ruiz,
“Tidal disruption events from stripped stars,” Astrophysical Journal Letters,
vol. 973, no. 1. IOP Publishing, 2024.
ista: Mockler B, Gallegos-Garcia M, Götberg YLL, Miller JM, Ramirez-Ruiz E. 2024.
Tidal disruption events from stripped stars. Astrophysical Journal Letters. 973(1),
L9.
mla: Mockler, Brenna, et al. “Tidal Disruption Events from Stripped Stars.” Astrophysical
Journal Letters, vol. 973, no. 1, L9, IOP Publishing, 2024, doi:10.3847/2041-8213/ad6c34.
short: B. Mockler, M. Gallegos-Garcia, Y.L.L. Götberg, J.M. Miller, E. Ramirez-Ruiz,
Astrophysical Journal Letters 973 (2024).
date_created: 2024-09-22T22:01:42Z
date_published: 2024-09-12T00:00:00Z
date_updated: 2025-09-08T09:48:50Z
day: '12'
ddc:
- '520'
department:
- _id: YlGo
doi: 10.3847/2041-8213/ad6c34
external_id:
isi:
- '001310592900001'
file:
- access_level: open_access
checksum: 050ddf873244839825714cca42b5d857
content_type: application/pdf
creator: dernst
date_created: 2024-09-30T08:54:26Z
date_updated: 2024-09-30T08:54:26Z
file_id: '18161'
file_name: 2024_AstrophysicalJourn_Mockler.pdf
file_size: 844227
relation: main_file
success: 1
file_date_updated: 2024-09-30T08:54:26Z
has_accepted_license: '1'
intvolume: ' 973'
isi: 1
issue: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '09'
oa: 1
oa_version: Published Version
publication: Astrophysical Journal Letters
publication_identifier:
eissn:
- 2041-8213
issn:
- 2041-8205
publication_status: published
publisher: IOP Publishing
quality_controlled: '1'
scopus_import: '1'
status: public
title: Tidal disruption events from stripped stars
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: 973
year: '2024'
...
---
_id: '18113'
abstract:
- lang: eng
text: 'The emergence of accurate open large language models (LLMs) has led to a
race towards performant quantization techniques which can enable their execution
on end-user devices. In this paper, we revisit the problem of “extreme” LLM compression—defined
as targeting extremely low bit counts, such as 2 to 3 bits per parameter—from
the point of view of classic methods in Multi-Codebook Quantization (MCQ). Our
algorithm, called AQLM, generalizes the classic Additive Quantization (AQ) approach
for information retrieval to advance the state-of-the-art in LLM compression,
via two innovations: 1) learned additive quantization of weight matrices in input-adaptive
fashion, and 2) joint optimization of codebook parameters across each transformer
blocks. Broadly, AQLM is the first scheme that is Pareto optimal in terms of accuracy-vs-model-size
when compressing to less than 3 bits per parameter, and significantly improves
upon all known schemes in the extreme compression (2bit) regime. In addition,
AQLM is practical: we provide fast GPU and CPU implementations of AQLM for token
generation, which enable us to match or outperform optimized FP16 implementations
for speed, while executing in a much smaller memory footprint.'
acknowledgement: "Authors would like to thank Ruslan Svirschevski for his help in
solving technical issues with AQLM and baselines. We also thank Tim Dettmers for
helpful discussions on the structure of weights in modern LLMs and size-accuracy
trade-offs. The authors would also like to thank Daniil Pavlov for his assistance
with CPU benchmarking. Finally, authors would like to thank the communities of ML
enthusiasts known as LocalLLaMA5 and Petals community on discord6\r\nfor the crowd
wisdom about running LLMs on consumer devices. Egiazarian Vage and Denis Kuznedelev
and Andrei Panferov were supported by the grant for research centers in the field
of AI provided by the Analytical Center for the Government of the Russian Federation
(ACRF) in\r\naccordance with the agreement on the provision of subsidies (identifier
of the agreement 000000D730321P5Q0002) and the agreement with HSE University No.
70-2021-00139."
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Vage
full_name: Egiazarian, Vage
last_name: Egiazarian
- first_name: Andrei
full_name: Panferov, Andrei
id: 2c18daae-4dbe-11ef-8491-98ce2d960f09
last_name: Panferov
- first_name: Denis
full_name: Kuznedelev, Denis
last_name: Kuznedelev
- first_name: Elias
full_name: Frantar, Elias
id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f
last_name: Frantar
- first_name: Artem
full_name: Babenko, Artem
last_name: Babenko
- 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: 'Egiazarian V, Panferov A, Kuznedelev D, Frantar E, Babenko A, Alistarh D-A.
Extreme compression of large language models via additive quantization. In: Proceedings
of the 41st International Conference on Machine Learning. Vol 235. ML Research
Press; 2024:12284-12303.'
apa: 'Egiazarian, V., Panferov, A., Kuznedelev, D., Frantar, E., Babenko, A., &
Alistarh, D.-A. (2024). Extreme compression of large language models via additive
quantization. In Proceedings of the 41st International Conference on Machine
Learning (Vol. 235, pp. 12284–12303). Vienna, Austria: ML Research Press.'
chicago: Egiazarian, Vage, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem
Babenko, and Dan-Adrian Alistarh. “Extreme Compression of Large Language Models
via Additive Quantization.” In Proceedings of the 41st International Conference
on Machine Learning, 235:12284–303. ML Research Press, 2024.
ieee: V. Egiazarian, A. Panferov, D. Kuznedelev, E. Frantar, A. Babenko, and D.-A.
Alistarh, “Extreme compression of large language models via additive quantization,”
in Proceedings of the 41st International Conference on Machine Learning,
Vienna, Austria, 2024, vol. 235, pp. 12284–12303.
ista: 'Egiazarian V, Panferov A, Kuznedelev D, Frantar E, Babenko A, Alistarh D-A.
2024. Extreme compression of large language models via additive quantization.
Proceedings of the 41st International Conference on Machine Learning. ICML: International
Conference on Machine Learning, PMLR, vol. 235, 12284–12303.'
mla: Egiazarian, Vage, et al. “Extreme Compression of Large Language Models via
Additive Quantization.” Proceedings of the 41st International Conference on
Machine Learning, vol. 235, ML Research Press, 2024, pp. 12284–303.
short: V. Egiazarian, A. Panferov, D. Kuznedelev, E. Frantar, A. Babenko, D.-A.
Alistarh, in:, Proceedings of the 41st International Conference on Machine Learning,
ML Research Press, 2024, pp. 12284–12303.
conference:
end_date: 2024-07-27
location: Vienna, Austria
name: 'ICML: International Conference on Machine Learning'
start_date: 2024-07-21
corr_author: '1'
date_created: 2024-09-22T22:01:43Z
date_published: 2024-09-01T00:00:00Z
date_updated: 2024-10-01T08:13:05Z
day: '01'
department:
- _id: DaAl
- _id: GradSch
external_id:
arxiv:
- '2401.06118'
intvolume: ' 235'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2401.06118'
month: '09'
oa: 1
oa_version: Preprint
page: 12284-12303
publication: Proceedings of the 41st International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Extreme compression of large language models via additive quantization
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 235
year: '2024'
...
---
_id: '18114'
abstract:
- lang: eng
text: This paper presents Mechanistic Neural Networks, a neural network design for
machine learning applications in the sciences. It incorporates a new Mechanistic
Block in standard architectures to explicitly learn governing differential equations
as representations, revealing the underlying dynamics of data and enhancing interpretability
and efficiency in data modeling. Central to our approach is a novel Relaxed Linear
Programming Solver (NeuRLP) inspired by a technique that reduces solving linear
ODEs to solving linear programs. This integrates well with neural networks and
surpasses the limitations of traditional ODE solvers enabling scalable GPU parallel
processing. Overall, Mechanistic Neural Networks demonstrate their versatility
for scientific machine learning applications, adeptly managing tasks from equation
discovery to dynamic systems modeling. We prove their comprehensive capabilities
in analyzing and interpreting complex scientific data across various applications,
showing significant performance against specialized state-of-the-art methods.
Source code is available at https://github.com/alpz/mech-nn.
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Adeel A
full_name: Pervez, Adeel A
id: fca6d90c-d47f-11ee-bc87-93ff51604981
last_name: Pervez
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Efstratios
full_name: Gavves, Efstratios
last_name: Gavves
citation:
ama: 'Pervez AA, Locatello F, Gavves E. Mechanistic neural networks for scientific
machine learning. In: Proceedings of the 41st International Conference on Machine
Learning. Vol 235. ML Research Press; 2024:40484-40501.'
apa: 'Pervez, A. A., Locatello, F., & Gavves, E. (2024). Mechanistic neural
networks for scientific machine learning. In Proceedings of the 41st International
Conference on Machine Learning (Vol. 235, pp. 40484–40501). Vienna, Austria:
ML Research Press.'
chicago: Pervez, Adeel A, Francesco Locatello, and Efstratios Gavves. “Mechanistic
Neural Networks for Scientific Machine Learning.” In Proceedings of the 41st
International Conference on Machine Learning, 235:40484–501. ML Research Press,
2024.
ieee: A. A. Pervez, F. Locatello, and E. Gavves, “Mechanistic neural networks for
scientific machine learning,” in Proceedings of the 41st International Conference
on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 40484–40501.
ista: 'Pervez AA, Locatello F, Gavves E. 2024. Mechanistic neural networks for scientific
machine learning. Proceedings of the 41st International Conference on Machine
Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235,
40484–40501.'
mla: Pervez, Adeel A., et al. “Mechanistic Neural Networks for Scientific Machine
Learning.” Proceedings of the 41st International Conference on Machine Learning,
vol. 235, ML Research Press, 2024, pp. 40484–501.
short: A.A. Pervez, F. Locatello, E. Gavves, in:, Proceedings of the 41st International
Conference on Machine Learning, ML Research Press, 2024, pp. 40484–40501.
conference:
end_date: 2024-07-27
location: Vienna, Austria
name: 'ICML: International Conference on Machine Learning'
start_date: 2024-07-21
date_created: 2024-09-22T22:01:43Z
date_published: 2024-09-01T00:00:00Z
date_updated: 2024-10-01T08:01:17Z
day: '01'
department:
- _id: FrLo
external_id:
arxiv:
- '2402.13077'
intvolume: ' 235'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2402.13077
month: '09'
oa: 1
oa_version: Published Version
page: 40484-40501
publication: Proceedings of the 41st International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/alpz/mech-nn
scopus_import: '1'
status: public
title: Mechanistic neural networks for scientific machine learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 235
year: '2024'
...
---
_id: '18115'
abstract:
- lang: eng
text: "We study the data selection problem, whose aim is to select a small representative
subset of data that can be used to efficiently train a machine learning model.
We present a new data selection approach based on k-means clustering and sensitivity
sampling. Assuming access to an embedding representation of the data with respect
to which the model loss is Holder continuous, our approach provably allows selecting
a set of “typical” k+1/ε2 elements whose average loss corresponds to the average
loss of the whole dataset, up to a multiplicative (1±ε)\r\n factor and an additive
ελΦk, where Φk represents the k-means cost for the input embeddings and λ is the
Holder constant. We furthermore demonstrate the performance and scalability of
our approach on fine-tuning foundation models and show that it outperforms state-of-the-art
methods. We also show how it can be applied on linear regression, leading to a
new sampling strategy that surprisingly matches the performance of leverage score
sampling, while being conceptually simpler and more scalable."
acknowledgement: "Monika Henzinger: This project has received funding from the European
Research Council (ERC) under the European Union’s Horizon 2020 research and innovation
programme (Grant agreement No. 101019564) and the Austrian Science Fund (FWF) grant
DOI 10.55776/Z422, grant DOI 10.55776/I5982, and grant DOI 10.55776/P33775 with
additional funding from the netidee SCIENCE Stiftung, 2020–2024. This work was partially
done while David Saulpic was at the Institute for Science and Technology, Austria
(ISTA). David Sauplic has received funding from the European Union’s Horizon 2020
research and innovation programme under the\r\nMarie Sklodowska-Curie grant agreement
No 101034413. Work was done while David Woodruff was visiting Google Research."
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Kyriakos
full_name: Axiotis, Kyriakos
last_name: Axiotis
- first_name: Vincent
full_name: Cohen-Addad, Vincent
last_name: Cohen-Addad
- first_name: Monika H
full_name: Henzinger, Monika H
id: 540c9bbd-f2de-11ec-812d-d04a5be85630
last_name: Henzinger
orcid: 0000-0002-5008-6530
- first_name: Sammy
full_name: Jerome, Sammy
last_name: Jerome
- first_name: Vahab
full_name: Mirrokni, Vahab
last_name: Mirrokni
- first_name: David
full_name: Saulpic, David
id: f8e48cf0-b0ff-11ed-b0e9-b4c35598f964
last_name: Saulpic
- first_name: David P.
full_name: Woodruff, David P.
last_name: Woodruff
- first_name: Michael
full_name: Wunder, Michael
last_name: Wunder
citation:
ama: 'Axiotis K, Cohen-Addad V, Henzinger M, et al. Data-efficient learning via
clustering-based sensitivity sampling: Foundation models and beyond. In: Proceedings
of the 41st International Conference on Machine Learning. Vol 235. ML Research
Press; 2024:2086-2107.'
apa: 'Axiotis, K., Cohen-Addad, V., Henzinger, M., Jerome, S., Mirrokni, V., Saulpic,
D., … Wunder, M. (2024). Data-efficient learning via clustering-based sensitivity
sampling: Foundation models and beyond. In Proceedings of the 41st International
Conference on Machine Learning (Vol. 235, pp. 2086–2107). Vienna, Austria:
ML Research Press.'
chicago: 'Axiotis, Kyriakos, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome,
Vahab Mirrokni, David Saulpic, David P. Woodruff, and Michael Wunder. “Data-Efficient
Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond.”
In Proceedings of the 41st International Conference on Machine Learning,
235:2086–2107. ML Research Press, 2024.'
ieee: 'K. Axiotis et al., “Data-efficient learning via clustering-based sensitivity
sampling: Foundation models and beyond,” in Proceedings of the 41st International
Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 2086–2107.'
ista: 'Axiotis K, Cohen-Addad V, Henzinger M, Jerome S, Mirrokni V, Saulpic D, Woodruff
DP, Wunder M. 2024. Data-efficient learning via clustering-based sensitivity sampling:
Foundation models and beyond. Proceedings of the 41st International Conference
on Machine Learning. ICML: International Conference on Machine Learning, PMLR,
vol. 235, 2086–2107.'
mla: 'Axiotis, Kyriakos, et al. “Data-Efficient Learning via Clustering-Based Sensitivity
Sampling: Foundation Models and Beyond.” Proceedings of the 41st International
Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 2086–107.'
short: K. Axiotis, V. Cohen-Addad, M. Henzinger, S. Jerome, V. Mirrokni, D. Saulpic,
D.P. Woodruff, M. Wunder, in:, Proceedings of the 41st International Conference
on Machine Learning, ML Research Press, 2024, pp. 2086–2107.
conference:
end_date: 2024-07-27
location: Vienna, Austria
name: 'ICML: International Conference on Machine Learning'
start_date: 2024-07-21
date_created: 2024-09-22T22:01:44Z
date_published: 2024-09-01T00:00:00Z
date_updated: 2025-04-14T13:50:50Z
day: '01'
department:
- _id: MoHe
ec_funded: 1
external_id:
arxiv:
- '2402.17327'
intvolume: ' 235'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2402.17327
month: '09'
oa: 1
oa_version: Published Version
page: 2086-2107
project:
- _id: bd9ca328-d553-11ed-ba76-dc4f890cfe62
call_identifier: H2020
grant_number: '101019564'
name: The design and evaluation of modern fully dynamic data structures
- _id: 34def286-11ca-11ed-8bc3-da5948e1613c
grant_number: Z00422
name: Efficient algorithms
- _id: bda196b2-d553-11ed-ba76-8e8ee6c21103
grant_number: I05982
name: Static and Dynamic Hierarchical Graph Decompositions
- _id: bd9e3a2e-d553-11ed-ba76-8aa684ce17fe
grant_number: P33775
name: Fast Algorithms for a Reactive Network Layer
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Proceedings of the 41st International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Data-efficient learning via clustering-based sensitivity sampling: Foundation
models and beyond'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 235
year: '2024'
...
---
_id: '18116'
abstract:
- lang: eng
text: 'As a staple of data analysis and unsupervised learning, the problem of private
clustering has been widely studied, under various privacy models. Centralized
differential privacy is the first of them, and the problem has also been studied
for the local and the shuffle variation. In each case, the goal is to design an
algorithm that computes privately a clustering, with the smallest possible error.
The study of each variation gave rise to new algorithm: the landscape of private
clustering algorithm is therefore quite intricate. In this paper, we show that
a 20 year-old algorithm can be slightly modified to work for any of those models.
This provides a unified picture: while matching almost all previously known results,
it allows us to improve some of them, and extend to a new privacy model, the continual
observation setting, where the input is changing over time and the algorithm must
output a new solution at each time step.'
acknowledgement: 'Monika Henzinger: This project has received funding from the European
Research Council (ERC) under the European Union’s Horizon 2020 research and innovation
programme (Grant agreement No. 101019564) and the Austrian Science Fund (FWF) grant
DOI 10.55776/Z422, grant DOI 10.55776/I5982, and grant DOI 10.55776/P33775 with
additional funding from the netidee SCIENCE Stiftung, 2020–2024.This work was partially
done while David Saulpic was at the Institute for Science and Technology, Austria
(ISTA). David Sauplic has received funding from the European Union’s Horizon 2020
research and innovation programme under the Marie Sklodowska-Curie grant agreement
No 101034413.'
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Max Dupré
full_name: La Tour, Max Dupré
last_name: La Tour
- first_name: Monika H
full_name: Henzinger, Monika H
id: 540c9bbd-f2de-11ec-812d-d04a5be85630
last_name: Henzinger
orcid: 0000-0002-5008-6530
- first_name: David
full_name: Saulpic, David
id: f8e48cf0-b0ff-11ed-b0e9-b4c35598f964
last_name: Saulpic
citation:
ama: 'La Tour MD, Henzinger M, Saulpic D. Making old things new: A unified algorithm
for differentially private clustering. In: Proceedings of the 41st International
Conference on Machine Learning. Vol 235. ML Research Press; 2024:12046-12086.'
apa: 'La Tour, M. D., Henzinger, M., & Saulpic, D. (2024). Making old things
new: A unified algorithm for differentially private clustering. In Proceedings
of the 41st International Conference on Machine Learning (Vol. 235, pp. 12046–12086).
Vienna, Austria: ML Research Press.'
chicago: 'La Tour, Max Dupré, Monika Henzinger, and David Saulpic. “Making Old Things
New: A Unified Algorithm for Differentially Private Clustering.” In Proceedings
of the 41st International Conference on Machine Learning, 235:12046–86. ML
Research Press, 2024.'
ieee: 'M. D. La Tour, M. Henzinger, and D. Saulpic, “Making old things new: A unified
algorithm for differentially private clustering,” in Proceedings of the 41st
International Conference on Machine Learning, Vienna, Austria, 2024, vol.
235, pp. 12046–12086.'
ista: 'La Tour MD, Henzinger M, Saulpic D. 2024. Making old things new: A unified
algorithm for differentially private clustering. Proceedings of the 41st International
Conference on Machine Learning. ICML: International Conference on Machine Learning,
PMLR, vol. 235, 12046–12086.'
mla: 'La Tour, Max Dupré, et al. “Making Old Things New: A Unified Algorithm for
Differentially Private Clustering.” Proceedings of the 41st International Conference
on Machine Learning, vol. 235, ML Research Press, 2024, pp. 12046–86.'
short: M.D. La Tour, M. Henzinger, D. Saulpic, in:, Proceedings of the 41st International
Conference on Machine Learning, ML Research Press, 2024, pp. 12046–12086.
conference:
end_date: 2024-07-27
location: Vienna, Austria
name: 'ICML: International Conference on Machine Learning'
start_date: 2024-07-21
corr_author: '1'
date_created: 2024-09-22T22:01:44Z
date_published: 2024-09-01T00:00:00Z
date_updated: 2025-04-14T13:50:50Z
day: '01'
department:
- _id: MoHe
ec_funded: 1
external_id:
arxiv:
- '2406.11649'
intvolume: ' 235'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2406.11649
month: '09'
oa: 1
oa_version: Published Version
page: 12046-12086
project:
- _id: bd9ca328-d553-11ed-ba76-dc4f890cfe62
call_identifier: H2020
grant_number: '101019564'
name: The design and evaluation of modern fully dynamic data structures
- _id: 34def286-11ca-11ed-8bc3-da5948e1613c
grant_number: Z00422
name: Efficient algorithms
- _id: bda196b2-d553-11ed-ba76-8e8ee6c21103
grant_number: I05982
name: Static and Dynamic Hierarchical Graph Decompositions
- _id: bd9e3a2e-d553-11ed-ba76-8aa684ce17fe
grant_number: P33775
name: Fast Algorithms for a Reactive Network Layer
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Proceedings of the 41st International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Making old things new: A unified algorithm for differentially private clustering'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 235
year: '2024'
...
---
_id: '18117'
abstract:
- lang: eng
text: "We investigate parameter-efficient fine-tuning (PEFT) methods that can provide
good accuracy under limited computational and memory budgets in the context of
large language models (LLMs). We present a new PEFT method called Robust Adaptation
(RoSA) inspired by robust principal component analysis that jointly trains low-rank\r\n
and highly-sparse components on top of a set of fixed pretrained weights to efficiently
approximate the performance of a full-fine-tuning (FFT) solution. Across a series
of challenging generative tasks such as grade-school math and SQL query generation,
which require fine-tuning for good performance, we show that RoSA outperforms
LoRA, pure sparse fine-tuning, and alternative hybrid methods at the same parameter
budget, and can even recover the performance of FFT on some tasks. We provide
system support for RoSA to complement the training algorithm, specifically in
the form of sparse GPU kernels which enable memory- and computationally-efficient
training, and show that it is also compatible with low-precision base weights,
resulting in the first joint representation combining quantization, low-rank and
sparse approximations. Our code is available at https://github.com/IST-DASLab/RoSA."
acknowledgement: The authors would like to thank Eldar Kurtic for experimental support
and useful suggestions throughout the project
article_processing_charge: No
arxiv: 1
author:
- first_name: Mahdi
full_name: Nikdan, Mahdi
id: 66374281-f394-11eb-9cf6-869147deecc0
last_name: Nikdan
- first_name: Soroush
full_name: Tabesh, Soroush
id: 06000900-6068-11ef-8d61-c2472ef2e752
last_name: Tabesh
orcid: 0009-0003-4119-6281
- first_name: Elvir
full_name: Crncevic, Elvir
id: 41888001-440d-11ef-8299-d0e838b8185e
last_name: Crncevic
- 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: 'Nikdan M, Tabesh S, Crncevic E, Alistarh D-A. RoSA: Accurate parameter-efficient
fine-tuning via robust adaptation. In: Proceedings of the 41st International
Conference on Machine Learning. Vol 235. ML Research Press; 2024:38187-38206.'
apa: 'Nikdan, M., Tabesh, S., Crncevic, E., & Alistarh, D.-A. (2024). RoSA:
Accurate parameter-efficient fine-tuning via robust adaptation. In Proceedings
of the 41st International Conference on Machine Learning (Vol. 235, pp. 38187–38206).
Vienna, Austria: ML Research Press.'
chicago: 'Nikdan, Mahdi, Soroush Tabesh, Elvir Crncevic, and Dan-Adrian Alistarh.
“RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.” In Proceedings
of the 41st International Conference on Machine Learning, 235:38187–206. ML
Research Press, 2024.'
ieee: 'M. Nikdan, S. Tabesh, E. Crncevic, and D.-A. Alistarh, “RoSA: Accurate parameter-efficient
fine-tuning via robust adaptation,” in Proceedings of the 41st International
Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 38187–38206.'
ista: 'Nikdan M, Tabesh S, Crncevic E, Alistarh D-A. 2024. RoSA: Accurate parameter-efficient
fine-tuning via robust adaptation. Proceedings of the 41st International Conference
on Machine Learning. ICML: International Conference on Machine Learning vol. 235,
38187–38206.'
mla: 'Nikdan, Mahdi, et al. “RoSA: Accurate Parameter-Efficient Fine-Tuning via
Robust Adaptation.” Proceedings of the 41st International Conference on Machine
Learning, vol. 235, ML Research Press, 2024, pp. 38187–206.'
short: M. Nikdan, S. Tabesh, E. Crncevic, D.-A. Alistarh, in:, Proceedings of the
41st International Conference on Machine Learning, ML Research Press, 2024, pp.
38187–38206.
conference:
end_date: 2024-07-27
location: Vienna, Austria
name: 'ICML: International Conference on Machine Learning'
start_date: 2024-07-21
corr_author: '1'
date_created: 2024-09-22T22:01:44Z
date_published: 2024-09-01T00:00:00Z
date_updated: 2024-10-01T08:22:01Z
day: '01'
department:
- _id: DaAl
- _id: GradSch
external_id:
arxiv:
- '2401.04679'
intvolume: ' 235'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2401.04679
month: '09'
oa: 1
oa_version: Preprint
page: 38187-38206
publication: Proceedings of the 41st International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/IST-DASLab/RoSA
scopus_import: '1'
status: public
title: 'RoSA: Accurate parameter-efficient fine-tuning via robust adaptation'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 235
year: '2024'
...
---
_id: '18118'
abstract:
- lang: eng
text: We introduce a new framework for studying meta-learning methods using PAC-Bayesian
theory. Its main advantage over previous work is that it allows for more flexibility
in how the transfer of knowledge between tasks is realized. For previous approaches,
this could only happen indirectly, by means of learning prior distributions over
models. In contrast, the new generalization bounds that we prove express the process
of meta-learning much more directly as learning the learning algorithm that should
be used for future tasks. The flexibility of our framework makes it suitable to
analyze a wide range of meta-learning mechanisms and even design new mechanisms.
Other than our theoretical contributions we also show empirically that our framework
improves the prediction quality in practical meta-learning mechanisms.
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Hossein
full_name: Zakerinia, Hossein
id: 653bd8b6-f394-11eb-9cf6-c0bbf6cd78d4
last_name: Zakerinia
- first_name: Amin
full_name: Behjati, Amin
last_name: Behjati
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Zakerinia H, Behjati A, Lampert C. More flexible PAC-Bayesian meta-learning
by learning learning algorithms. In: Proceedings of the 41st International
Conference on Machine Learning. Vol 235. ML Research Press; 2024:58122-58139.'
apa: 'Zakerinia, H., Behjati, A., & Lampert, C. (2024). More flexible PAC-Bayesian
meta-learning by learning learning algorithms. In Proceedings of the 41st International
Conference on Machine Learning (Vol. 235, pp. 58122–58139). Vienna, Austria:
ML Research Press.'
chicago: Zakerinia, Hossein, Amin Behjati, and Christoph Lampert. “More Flexible
PAC-Bayesian Meta-Learning by Learning Learning Algorithms.” In Proceedings
of the 41st International Conference on Machine Learning, 235:58122–39. ML
Research Press, 2024.
ieee: H. Zakerinia, A. Behjati, and C. Lampert, “More flexible PAC-Bayesian meta-learning
by learning learning algorithms,” in Proceedings of the 41st International
Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 58122–58139.
ista: 'Zakerinia H, Behjati A, Lampert C. 2024. More flexible PAC-Bayesian meta-learning
by learning learning algorithms. Proceedings of the 41st International Conference
on Machine Learning. ICML: International Conference on Machine Learning, PMLR,
vol. 235, 58122–58139.'
mla: Zakerinia, Hossein, et al. “More Flexible PAC-Bayesian Meta-Learning by Learning
Learning Algorithms.” Proceedings of the 41st International Conference on Machine
Learning, vol. 235, ML Research Press, 2024, pp. 58122–39.
short: H. Zakerinia, A. Behjati, C. Lampert, in:, Proceedings of the 41st International
Conference on Machine Learning, ML Research Press, 2024, pp. 58122–58139.
conference:
end_date: 2024-07-27
location: Vienna, Austria
name: 'ICML: International Conference on Machine Learning'
start_date: 2024-07-21
corr_author: '1'
date_created: 2024-09-22T22:01:45Z
date_published: 2024-09-01T00:00:00Z
date_updated: 2024-10-01T09:30:03Z
day: '01'
department:
- _id: ChLa
external_id:
arxiv:
- '2402.04054'
intvolume: ' 235'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2402.04054'
month: '09'
oa: 1
oa_version: Published Version
page: 58122-58139
publication: Proceedings of the 41st International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: More flexible PAC-Bayesian meta-learning by learning learning algorithms
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 235
year: '2024'
...
---
_id: '18120'
abstract:
- lang: eng
text: In practice, training using federated learning can be orders of magnitude
slower than standard centralized training. This severely limits the amount of
experimentation and tuning that can be done, making it challenging to obtain good
performance on a given task. Server-side proxy data can be used to run training
simulations, for instance for hyperparameter tuning. This can greatly speed up
the training pipeline by reducing the number of tuning runs to be performed overall
on the true clients. However, it is challenging to ensure that these simulations
accurately reflect the dynamics of the real federated training. In particular,
the proxy data used for simulations often comes as a single centralized dataset
without a partition into distinct clients, and partitioning this data in a naive
way can lead to simulations that poorly reflect real federated training. In this
paper we address the challenge of how to partition centralized data in a way that
reflects the statistical heterogeneity of the true federated clients. We propose
a fully federated, theoretically justified, algorithm that efficiently learns
the distribution of the true clients and observe improved server-side simulations
when using the inferred distribution to create simulated clients from the centralized
data.
acknowledgement: 'We would like to thank: Mona Chitnis and everyone in the Private
Federated Learning team at Apple for their help and support throughout the entire
project; Audra McMillan, Martin Pelikan, Anosh Raj and Barry Theobold for feedback
on the initial versions of the paper; and Christoph Lampert for valuable feedback
on the paper structure and suggestions for additional experiments.'
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Jonathan A
full_name: Scott, Jonathan A
id: e499926b-f6e0-11ea-865d-9c63db0031e8
last_name: Scott
- first_name: Áine
full_name: Cahill, Áine
last_name: Cahill
citation:
ama: 'Scott JA, Cahill Á. Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials.
In: Proceedings of the 41st International Conference on Machine Learning.
Vol 235. ML Research Press; 2024:44012-44037.'
apa: 'Scott, J. A., & Cahill, Á. (2024). Improved modelling of federated datasets
using mixtures-of-Dirichlet-multinomials. In Proceedings of the 41st International
Conference on Machine Learning (Vol. 235, pp. 44012–44037). Vienna, Austria:
ML Research Press.'
chicago: Scott, Jonathan A, and Áine Cahill. “Improved Modelling of Federated Datasets
Using Mixtures-of-Dirichlet-Multinomials.” In Proceedings of the 41st International
Conference on Machine Learning, 235:44012–37. ML Research Press, 2024.
ieee: J. A. Scott and Á. Cahill, “Improved modelling of federated datasets using
mixtures-of-Dirichlet-multinomials,” in Proceedings of the 41st International
Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 44012–44037.
ista: 'Scott JA, Cahill Á. 2024. Improved modelling of federated datasets using
mixtures-of-Dirichlet-multinomials. Proceedings of the 41st International Conference
on Machine Learning. ICML: International Conference on Machine Learning, PMLR,
vol. 235, 44012–44037.'
mla: Scott, Jonathan A., and Áine Cahill. “Improved Modelling of Federated Datasets
Using Mixtures-of-Dirichlet-Multinomials.” Proceedings of the 41st International
Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 44012–37.
short: J.A. Scott, Á. Cahill, in:, Proceedings of the 41st International Conference
on Machine Learning, ML Research Press, 2024, pp. 44012–44037.
conference:
end_date: 2024-07-27
location: Vienna, Austria
name: 'ICML: International Conference on Machine Learning'
start_date: 2024-07-21
corr_author: '1'
date_created: 2024-09-22T22:01:45Z
date_published: 2024-09-01T00:00:00Z
date_updated: 2026-03-03T08:20:56Z
day: '01'
department:
- _id: ChLa
external_id:
arxiv:
- '2406.02416'
intvolume: ' 235'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2406.02416
month: '09'
oa: 1
oa_version: Preprint
page: 44012-44037
publication: Proceedings of the 41st International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
record:
- id: '21198'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 235
year: '2024'
...
---
_id: '18121'
abstract:
- lang: eng
text: It is known that sparsity can improve interpretability for deep neural networks.
However, existing methods in the area either require networks that are pre-trained
with sparsity constraints, or impose sparsity after the fact, altering the network’s
general behavior. In this paper, we demonstrate, for the first time, that sparsity
can instead be incorporated into the interpretation process itself, as a sample-specific
preprocessing step. Unlike previous work, this approach, which we call SPADE,
does not place constraints on the trained model and does not affect its behavior
during inference on the sample. Given a trained model and a target sample, SPADE
uses sample-targeted pruning to provide a "trace" of the network’s execution on
the sample, reducing the network to the most important connections prior to computing
an interpretation. We demonstrate that preprocessing with SPADE significantly
increases the accuracy of image saliency maps across several interpretability
methods. Additionally, SPADE improves the usefulness of neuron visualizations,
aiding humans in reasoning about network behavior. Our code is available at https://github.com/IST-DASLab/SPADE.
acknowledged_ssus:
- _id: ScienComp
acknowledgement: The authors would like to thank Stephen Casper and Tony Wang for
their feedback on this work, and Eldar Kurtic for his advice on aspects of the project.
This research was supported by the Scientific Service Units (SSU) of IST Austria
through resources provided by Scientific Computing (SciComp). EI was supported in
part by the FWF DK VGSCO, grant agreement number W1260-N35.
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Arshia Soltani
full_name: Moakhar, Arshia Soltani
last_name: Moakhar
- first_name: Eugenia B
full_name: Iofinova, Eugenia B
id: f9a17499-f6e0-11ea-865d-fdf9a3f77117
last_name: Iofinova
orcid: 0000-0002-7778-3221
- 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: 'Moakhar AS, Iofinova EB, Frantar E, Alistarh D-A. SPADE: Sparsity-guided debugging
for deep neural networks. In: Proceedings of the 41st International Conference
on Machine Learning. Vol 235. ML Research Press; 2024:45955-45987.'
apa: 'Moakhar, A. S., Iofinova, E. B., Frantar, E., & Alistarh, D.-A. (2024).
SPADE: Sparsity-guided debugging for deep neural networks. In Proceedings of
the 41st International Conference on Machine Learning (Vol. 235, pp. 45955–45987).
Vienna, Austria: ML Research Press.'
chicago: 'Moakhar, Arshia Soltani, Eugenia B Iofinova, Elias Frantar, and Dan-Adrian
Alistarh. “SPADE: Sparsity-Guided Debugging for Deep Neural Networks.” In Proceedings
of the 41st International Conference on Machine Learning, 235:45955–87. ML
Research Press, 2024.'
ieee: 'A. S. Moakhar, E. B. Iofinova, E. Frantar, and D.-A. Alistarh, “SPADE: Sparsity-guided
debugging for deep neural networks,” in Proceedings of the 41st International
Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 45955–45987.'
ista: 'Moakhar AS, Iofinova EB, Frantar E, Alistarh D-A. 2024. SPADE: Sparsity-guided
debugging for deep neural networks. Proceedings of the 41st International Conference
on Machine Learning. ICML: International Conference on Machine Learning, PMLR,
vol. 235, 45955–45987.'
mla: 'Moakhar, Arshia Soltani, et al. “SPADE: Sparsity-Guided Debugging for Deep
Neural Networks.” Proceedings of the 41st International Conference on Machine
Learning, vol. 235, ML Research Press, 2024, pp. 45955–87.'
short: A.S. Moakhar, E.B. Iofinova, E. Frantar, D.-A. Alistarh, in:, Proceedings
of the 41st International Conference on Machine Learning, ML Research Press, 2024,
pp. 45955–45987.
conference:
end_date: 2024-07-27
location: Vienna, Austria
name: 'ICML: International Conference on Machine Learning'
start_date: 2024-07-21
corr_author: '1'
date_created: 2024-09-22T22:01:46Z
date_published: 2024-09-01T00:00:00Z
date_updated: 2025-04-25T10:32:05Z
day: '01'
department:
- _id: DaAl
external_id:
arxiv:
- '2310.04519'
intvolume: ' 235'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2310.04519
month: '09'
oa: 1
oa_version: Preprint
page: 45955-45987
project:
- _id: 9B9290DE-BA93-11EA-9121-9846C619BF3A
grant_number: W1260-N35
name: Vienna Graduate School on Computational Optimization
publication: Proceedings of the 41st International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/IST-DASLab/SPADE
scopus_import: '1'
status: public
title: 'SPADE: Sparsity-guided debugging for deep neural networks'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 235
year: '2024'
...
---
_id: '18132'
abstract:
- lang: eng
text: "In this thesis, we are dealing with both arithmetic and geometric problems
coming from the\r\nstudy of rational points with a particular focus on function
fields over finite fields:\r\n(1) Using the circle method we produce upper bounds
for the number of rational points of\r\nbounded height on diagonal cubic surfaces
and fourfolds over Fq(t). This is based on\r\njoint work with Leonhard Hochfilzer.\r\n(2)
We study rational points on smooth complete intersections X defined by cubic and\r\nquadratic
hypersurfaces over Fq(t). We refine the Farey dissection of the “unit square”\r\ndeveloped
by Vishe [202] and use the circle method with a Kloosterman refinement to\r\nestablish
an asymptotic formula for the number of rational points of bounded height on\r\nX
when dim(X) ≥ 23. Under the same hypotheses, we also verify weak approximation.\r\n(3)
In joint work with Hochfilzer, we obtain upper bounds for the number of rational
points of\r\nbounded height on del Pezzo surfaces of low degree over any global
field. Our approach\r\nis to take hyperplane sections, which reduces the problem
to uniform estimates for the\r\nnumber of rational points on curves.\r\n(4) We
develop a version of the circle method capable of counting Fq-points on jet schemes\r\nof
moduli spaces of rational curves on hypersurfaces. Combining this with a spreading\r\nout
argument and a result of Mustaţă [150], this allows us to show that these moduli\r\nspaces
only have canonical singularities under suitable assumptions on the degree and
the\r\ndimension.\r\nIn addition, we give an overview of guiding questions and
conjectures in the field of rational\r\npoints and explain the basic mechanism
underlying the circle method.\r\n"
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Jakob
full_name: Glas, Jakob
id: d6423cba-dc74-11ea-a0a7-ee61689ff5fb
last_name: Glas
citation:
ama: Glas J. Counting rational points over function fields. 2024. doi:10.15479/at:ista:18132
apa: Glas, J. (2024). Counting rational points over function fields. Institute
of Science and Technology Austria. https://doi.org/10.15479/at:ista:18132
chicago: Glas, Jakob. “Counting Rational Points over Function Fields.” Institute
of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:18132.
ieee: J. Glas, “Counting rational points over function fields,” Institute of Science
and Technology Austria, 2024.
ista: Glas J. 2024. Counting rational points over function fields. Institute of
Science and Technology Austria.
mla: Glas, Jakob. Counting Rational Points over Function Fields. Institute
of Science and Technology Austria, 2024, doi:10.15479/at:ista:18132.
short: J. Glas, Counting Rational Points over Function Fields, Institute of Science
and Technology Austria, 2024.
corr_author: '1'
date_created: 2024-09-23T18:58:08Z
date_published: 2024-09-23T00:00:00Z
date_updated: 2025-04-15T08:05:40Z
day: '23'
ddc:
- '512'
degree_awarded: PhD
department:
- _id: GradSch
- _id: TiBr
doi: 10.15479/at:ista:18132
file:
- access_level: closed
checksum: 2f8cf5cefdab108b1979caa8146cae9a
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creator: jglas
date_created: 2024-09-23T18:49:22Z
date_updated: 2024-09-23T18:49:22Z
file_id: '18133'
file_name: PhDthesis (3).zip
file_size: 5382106
relation: source_file
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creator: jglas
date_created: 2024-09-25T14:08:57Z
date_updated: 2024-09-25T14:08:57Z
file_id: '18140'
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file_size: 2380127
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success: 1
file_date_updated: 2024-09-25T14:08:57Z
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc/4.0/
month: '09'
oa: 1
oa_version: Published Version
page: '195'
project:
- _id: bd8a4fdc-d553-11ed-ba76-80a0167441a3
grant_number: P36278
name: Rational curves via function field analytic number theory
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '18293'
relation: part_of_dissertation
status: public
- id: '18294'
relation: part_of_dissertation
status: public
- id: '18295'
relation: part_of_dissertation
status: public
- id: '18173'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Timothy D
full_name: Browning, Timothy D
id: 35827D50-F248-11E8-B48F-1D18A9856A87
last_name: Browning
orcid: 0000-0002-8314-0177
title: Counting rational points over function fields
tmp:
image: /images/cc_by_nc.png
legal_code_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode
name: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
short: CC BY-NC (4.0)
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2024'
...
---
_id: '18135'
abstract:
- lang: eng
text: "This thesis consists of two separate parts. In the first part we consider
a dilute Fermi gas interacting through a repulsive interaction in dimensions $d=1,2,3$.
Our focus is mostly on the physically most relevant dimension $d=3$ \r\nand the
setting of a spin-polarized (equivalently spinless) gas, where the Pauli exclusion
principle plays a key role. We show that, at zero temperature, the ground state
energy density of the interacting spin-polarized gas differs (to leading order)
from that of the free (i.e. non-interacting) gas by a term of order $a_p^d\\rho^{2+2/d}$
\ with $a_p$ the $p$-wave scattering length of the repulsive interaction and $\\rho$
the density. Further, we extend this to positive temperature and show that the
pressure of an interacting spin-polarized gas differs from that of the free gas
by a now temperature dependent term, again of order $a_p^d\\rho^{2+2/d}$. Lastly,
we consider the setting of a spin-$\\frac{1}{2}$ Fermi gas in $d=3$ dimensions
and show that here, as an upper bound, the ground state energy density differs
from that of the free system by a term of order $a_s \\rho^2$ with an error smaller
than $a_s \\rho^2 (a_s\\rho^{1/3})^{1-\\eps}$ for any $\\eps > 0$, where $a_s$
is the $s$-wave scattering length of the repulsive interaction. \r\n\r\nThese
asymptotic formulas complement the similar formulas in the literature for the
dilute Bose and spin-$\\frac{1}{2}$ Fermi gas, where the ground state energies
or pressures differ from that of the corresponding free systems by a term of order
$a_s \\rho^2$ in dimension $d=3$. In the spin-polarized setting, the corrections,
of order $a_p^3\\rho^{8/3}$ in dimension $d=3$, are thus much smaller and requires
a more delicate analysis.\r\n\r\nIn the second part of the thesis we consider
the Bardeen--Cooper--Schrieffer (BCS) theory of superconductivity and in particular
its associated critical temperature and energy gap. We prove that the ratio of
the zero-temperature energy gap and critical temperature $\\Xi(T=0)/T_c$ approaches
a universal constant $\\pi e^{-\\gamma}\\approx 1.76$ in both the limit of high
density in dimension $d=3$ and in the limit of weak coupling in dimensions $d=1,2$.
This complements the proofs in the literature of this universal behaviour in the
limit of weak coupling or low density in dimension $d=3$. Secondly, we prove that
the ratio of the energy gap at positive temperature and critical temperature $\\Xi(T)/T_c$
approaches a universal function of the relative temperature $T/T_c$ in the limit
of weak coupling in dimensions $d=1,2,3$."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Asbjørn Bækgaard
full_name: Lauritsen, Asbjørn Bækgaard
id: e1a2682f-dc8d-11ea-abe3-81da9ac728f1
last_name: Lauritsen
orcid: 0000-0003-4476-2288
citation:
ama: Lauritsen AB. Energies of dilute Fermi gases and universalities in BCS theory.
2024. doi:10.15479/at:ista:18135
apa: Lauritsen, A. B. (2024). Energies of dilute Fermi gases and universalities
in BCS theory. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:18135
chicago: Lauritsen, Asbjørn Bækgaard. “Energies of Dilute Fermi Gases and Universalities
in BCS Theory.” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:18135.
ieee: A. B. Lauritsen, “Energies of dilute Fermi gases and universalities in BCS
theory,” Institute of Science and Technology Austria, 2024.
ista: Lauritsen AB. 2024. Energies of dilute Fermi gases and universalities in BCS
theory. Institute of Science and Technology Austria.
mla: Lauritsen, Asbjørn Bækgaard. Energies of Dilute Fermi Gases and Universalities
in BCS Theory. Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:18135.
short: A.B. Lauritsen, Energies of Dilute Fermi Gases and Universalities in BCS
Theory, Institute of Science and Technology Austria, 2024.
corr_author: '1'
date_created: 2024-09-24T10:56:25Z
date_published: 2024-09-23T00:00:00Z
date_updated: 2025-09-30T10:21:40Z
day: '23'
ddc:
- '515'
- '539'
degree_awarded: PhD
department:
- _id: GradSch
- _id: RoSe
doi: 10.15479/at:ista:18135
ec_funded: 1
file:
- access_level: open_access
checksum: c7bc3b31e430d57c65393051ca439575
content_type: application/pdf
creator: alaurits
date_created: 2024-09-26T13:11:24Z
date_updated: 2024-09-26T13:11:24Z
file_id: '18147'
file_name: Lauritsen-thesis-final.pdf
file_size: 3648831
relation: main_file
success: 1
- access_level: closed
checksum: 39f6b1b7f83e25a3bf9f933f1ea0bc06
content_type: application/x-zip-compressed
creator: alaurits
date_created: 2024-09-26T13:12:55Z
date_updated: 2024-09-26T13:12:55Z
file_id: '18148'
file_name: Lauritsen-thesis-source.zip
file_size: 1625888
relation: source_file
file_date_updated: 2024-09-26T13:12:55Z
has_accepted_license: '1'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: '353'
project:
- _id: bda63fe5-d553-11ed-ba76-a16e3d2f256b
grant_number: I06427
name: Mathematical Challenges in BCS Theory of Superconductivity
- _id: 25C6DC12-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '694227'
name: Analysis of quantum many-body systems
publication_identifier:
isbn:
- 978-3-99078-042-8
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '11732'
relation: part_of_dissertation
status: public
- id: '14542'
relation: part_of_dissertation
status: public
- id: '14931'
relation: part_of_dissertation
status: public
- id: '18107'
relation: part_of_dissertation
status: public
- id: '17240'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Robert
full_name: Seiringer, Robert
id: 4AFD0470-F248-11E8-B48F-1D18A9856A87
last_name: Seiringer
orcid: 0000-0002-6781-0521
title: Energies of dilute Fermi gases and universalities in BCS theory
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2024'
...
---
_id: '18155'
abstract:
- lang: eng
text: We study the classical problem of verifying programs with respect to formal
specifications given in the linear temporal logic (LTL). We first present novel
sound and complete witnesses for LTL verification over imperative programs. Our
witnesses are applicable to both verification (proving) and refutation (finding
bugs) settings. We then consider LTL formulas in which atomic propositions can
be polynomial constraints and turn our focus to polynomial arithmetic programs,
i.e. programs in which every assignment and guard consists only of polynomial
expressions. For this setting, we provide an efficient algorithm to automatically
synthesize such LTL witnesses. Our synthesis procedure is both sound and semi-complete.
Finally, we present experimental results demonstrating the effectiveness of our
approach and that it can handle programs which were beyond the reach of previous
state-of-the-art tools.
acknowledgement: This work was supported in part by the ERC-2020-CoG 863818 (FoRM-SMArt)
and the Hong Kong Research Grants Council ECS Project Number 26208122.
alternative_title:
- LNCS
article_processing_charge: Yes (in subscription journal)
arxiv: 1
author:
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Amir Kafshdar
full_name: Goharshady, Amir Kafshdar
id: 391365CE-F248-11E8-B48F-1D18A9856A87
last_name: Goharshady
orcid: 0000-0003-1702-6584
- first_name: Ehsan
full_name: Goharshady, Ehsan
last_name: Goharshady
- first_name: Mehrdad
full_name: Karrabi, Mehrdad
id: 67638922-f394-11eb-9cf6-f20423e08757
last_name: Karrabi
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
citation:
ama: 'Chatterjee K, Goharshady AK, Goharshady E, Karrabi M, Zikelic D. Sound and complete
witnesses for template-based verification of LTL properties on polynomial programs.
In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in
Artificial Intelligence and Lecture Notes in Bioinformatics). Vol 14933. Springer
Nature; 2024:600-619. doi:10.1007/978-3-031-71162-6_31'
apa: 'Chatterjee, K., Goharshady, A. K., Goharshady, E., Karrabi, M., & Zikelic,
D. (2024). Sound and complete witnesses for template-based verification of LTL
properties on polynomial programs. In Lecture Notes in Computer Science (including
subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(Vol. 14933, pp. 600–619). Milan, Italy: Springer Nature. https://doi.org/10.1007/978-3-031-71162-6_31'
chicago: Chatterjee, Krishnendu, Amir Kafshdar Goharshady, Ehsan Goharshady, Mehrdad
Karrabi, and Dorde Zikelic. “Sound and Complete Witnesses for Template-Based Verification
of LTL Properties on Polynomial Programs.” In Lecture Notes in Computer Science
(Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics), 14933:600–619. Springer Nature, 2024. https://doi.org/10.1007/978-3-031-71162-6_31.
ieee: K. Chatterjee, A. K. Goharshady, E. Goharshady, M. Karrabi, and D. Zikelic,
“Sound and complete witnesses for template-based verification of LTL properties
on polynomial programs,” in Lecture Notes in Computer Science (including subseries
Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
Milan, Italy, 2024, vol. 14933, pp. 600–619.
ista: 'Chatterjee K, Goharshady AK, Goharshady E, Karrabi M, Zikelic D. 2024. Sound
and complete witnesses for template-based verification of LTL properties on polynomial
programs. Lecture Notes in Computer Science (including subseries Lecture Notes
in Artificial Intelligence and Lecture Notes in Bioinformatics). FM: Formal Methods,
LNCS, vol. 14933, 600–619.'
mla: Chatterjee, Krishnendu, et al. “Sound and Complete Witnesses for Template-Based
Verification of LTL Properties on Polynomial Programs.” Lecture Notes in Computer
Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture
Notes in Bioinformatics), vol. 14933, Springer Nature, 2024, pp. 600–19, doi:10.1007/978-3-031-71162-6_31.
short: K. Chatterjee, A.K. Goharshady, E. Goharshady, M. Karrabi, D. Zikelic, in:,
Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial
Intelligence and Lecture Notes in Bioinformatics), Springer Nature, 2024, pp.
600–619.
conference:
end_date: 2024-09-13
location: Milan, Italy
name: 'FM: Formal Methods'
start_date: 2024-09-09
corr_author: '1'
date_created: 2024-09-29T22:01:37Z
date_published: 2024-09-11T00:00:00Z
date_updated: 2025-09-08T09:51:34Z
day: '11'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.1007/978-3-031-71162-6_31
ec_funded: 1
external_id:
arxiv:
- '2403.05386'
isi:
- '001336893300031'
file:
- access_level: open_access
checksum: 223845be9e754681ee218866827c95e7
content_type: application/pdf
creator: dernst
date_created: 2024-10-01T09:56:54Z
date_updated: 2024-10-01T09:56:54Z
file_id: '18165'
file_name: 2024_LNCS_Chatterjee.pdf
file_size: 650495
relation: main_file
success: 1
file_date_updated: 2024-10-01T09:56:54Z
has_accepted_license: '1'
intvolume: ' 14933'
isi: 1
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: 600-619
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: Lecture Notes in Computer Science (including subseries Lecture Notes
in Artificial Intelligence and Lecture Notes in Bioinformatics)
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783031711619'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Sound and complete witnesses for template-based verification of LTL properties
on polynomial programs
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: 14933
year: '2024'
...
---
_id: '18156'
abstract:
- lang: eng
text: Privately counting distinct elements in a stream is a fundamental data analysis
problem with many applications in machine learning. In the turnstile model, Jain
et al. [NeurIPS2023] initiated the study of this problem parameterized by the
maximum flippancy of any element, i.e., the number of times that the count of
an element changes from 0 to above 0 or vice versa. They give an item-level (ε,δ)-differentially
private algorithm whose additive error is tight with respect to that parameterization.
In this work, we show that a very simple algorithm based on the sparse vector
technique achieves a tight additive error for item-level (ε,δ)-differential privacy
and item-level ε-differential privacy with regards to a different parameterization,
namely the sum of all flippancies. Our second result is a bound which shows that
for a large class of algorithms, including all existing differentially private
algorithms for this problem, the lower bound from item-level differential privacy
extends to event-level differential privacy. This partially answers an open question
by Jain et al. [NeurIPS2023].
acknowledgement: "Monika Henzinger: This project has received funding from the European
Research Council\r\n(ERC) under the European Union’s Horizon 2020 research and innovation
programme (MoDynStruct,No. 101019564) and the Austrian Science Fund (FWF) grant
DOI 10.55776/Z422, grant DOI 10.55776/I5982, and grant DOI 10.55776/P33775 with
additional funding from the netidee SCIENCE Stiftung, 2020–2024.\r\nTeresa Anna
Steiner: Supported by a research grant (VIL51463) from VILLUM FONDEN."
alternative_title:
- LIPIcs
article_number: '40'
article_processing_charge: No
arxiv: 1
author:
- first_name: Monika H
full_name: Henzinger, Monika H
id: 540c9bbd-f2de-11ec-812d-d04a5be85630
last_name: Henzinger
orcid: 0000-0002-5008-6530
- first_name: A. R.
full_name: Sricharan, A. R.
last_name: Sricharan
- first_name: Teresa Anna
full_name: Steiner, Teresa Anna
last_name: Steiner
citation:
ama: 'Henzinger M, Sricharan AR, Steiner TA. Private counting of distinct elements
in the turnstile model and extensions. In: International Conference on Approximation
Algorithms for Combinatorial Optimization Problems . Vol 317. Schloss Dagstuhl
- Leibniz-Zentrum für Informatik; 2024. doi:10.4230/LIPIcs.APPROX/RANDOM.2024.40'
apa: 'Henzinger, M., Sricharan, A. R., & Steiner, T. A. (2024). Private counting
of distinct elements in the turnstile model and extensions. In International
Conference on Approximation Algorithms for Combinatorial Optimization Problems
(Vol. 317). London, United Kingdom: Schloss Dagstuhl - Leibniz-Zentrum für
Informatik. https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2024.40'
chicago: Henzinger, Monika, A. R. Sricharan, and Teresa Anna Steiner. “Private Counting
of Distinct Elements in the Turnstile Model and Extensions.” In International
Conference on Approximation Algorithms for Combinatorial Optimization Problems
, Vol. 317. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2024. https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2024.40.
ieee: M. Henzinger, A. R. Sricharan, and T. A. Steiner, “Private counting of distinct
elements in the turnstile model and extensions,” in International Conference
on Approximation Algorithms for Combinatorial Optimization Problems , London,
United Kingdom, 2024, vol. 317.
ista: 'Henzinger M, Sricharan AR, Steiner TA. 2024. Private counting of distinct
elements in the turnstile model and extensions. International Conference on Approximation
Algorithms for Combinatorial Optimization Problems . APPROX: Conference on Approximation
Algorithms for Combinatorial Optimization Problems, LIPIcs, vol. 317, 40.'
mla: Henzinger, Monika, et al. “Private Counting of Distinct Elements in the Turnstile
Model and Extensions.” International Conference on Approximation Algorithms
for Combinatorial Optimization Problems , vol. 317, 40, Schloss Dagstuhl -
Leibniz-Zentrum für Informatik, 2024, doi:10.4230/LIPIcs.APPROX/RANDOM.2024.40.
short: M. Henzinger, A.R. Sricharan, T.A. Steiner, in:, International Conference
on Approximation Algorithms for Combinatorial Optimization Problems , Schloss
Dagstuhl - Leibniz-Zentrum für Informatik, 2024.
conference:
end_date: 2024-08-30
location: London, United Kingdom
name: 'APPROX: Conference on Approximation Algorithms for Combinatorial Optimization
Problems'
start_date: 2024-08-27
corr_author: '1'
date_created: 2024-09-29T22:01:38Z
date_published: 2024-09-16T00:00:00Z
date_updated: 2025-12-02T13:47:16Z
day: '16'
ddc:
- '000'
department:
- _id: MoHe
doi: 10.4230/LIPIcs.APPROX/RANDOM.2024.40
ec_funded: 1
external_id:
arxiv:
- '2408.11637'
isi:
- '001545634500040'
file:
- access_level: open_access
checksum: c08b41c896e4d8c69570044808b40e0b
content_type: application/pdf
creator: dernst
date_created: 2024-10-01T10:07:14Z
date_updated: 2024-10-01T10:07:14Z
file_id: '18166'
file_name: 2024_LIPICs_HenzingerM.pdf
file_size: 973917
relation: main_file
success: 1
file_date_updated: 2024-10-01T10:07:14Z
has_accepted_license: '1'
intvolume: ' 317'
isi: 1
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
project:
- _id: bd9ca328-d553-11ed-ba76-dc4f890cfe62
call_identifier: H2020
grant_number: '101019564'
name: The design and evaluation of modern fully dynamic data structures
- _id: 34def286-11ca-11ed-8bc3-da5948e1613c
grant_number: Z00422
name: Efficient algorithms
- _id: bda196b2-d553-11ed-ba76-8e8ee6c21103
grant_number: I05982
name: Static and Dynamic Hierarchical Graph Decompositions
- _id: bd9e3a2e-d553-11ed-ba76-8aa684ce17fe
grant_number: P33775
name: Fast Algorithms for a Reactive Network Layer
publication: 'International Conference on Approximation Algorithms for Combinatorial
Optimization Problems '
publication_identifier:
isbn:
- '9783959773485'
issn:
- 1868-8969
publication_status: published
publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
quality_controlled: '1'
scopus_import: '1'
status: public
title: Private counting of distinct elements in the turnstile model and extensions
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 317
year: '2024'
...
---
_id: '18158'
abstract:
- lang: eng
text: "We study the geometry of Poisson point processes from the point of view of
optimal transport and Ricci lower bounds. We construct a Riemannian structure
on the space of point processes and the associated distance W that corresponds
to the Benamou–Brenier variational formula. Our main tool is a non-local continuity
equation formulated with the difference operator. The closure of the domain of
the relative entropy is a complete geodesic space, when endowed with \r\nW. The
geometry of this non-local infinite-dimensional space is analogous to that of
spaces with positive Ricci curvature. Among others: (a) the Ornstein–Uhlenbeck
semi-group is the gradient flow of the relative entropy; (b) the Poisson space
has an entropic Ricci curvature bounded from below by 1; (c) W satisfies an HWI
inequality."
- lang: fre
text: "Nous étudions la géométrie des processus ponctuels de Poisson à travers le
prisme du transport optimal et de la minoration de la courbure de Ricci. Nous
construisons une structure\r\nriemannienne sur l’espace des processus ponctuels
et la distance associée W qui concorde avec la formulation variationnelle de Benamou–Brenier.
Notre analyse repose sur une équation de continuité non locale définie à l’aide
de l’opérateur de différence. La fermeture du domaine de l’entropie relative,
équipé de W, est un espace géodésique complet. La géométrie de cet espace non
local et de dimension infinie est analogue à celle des espaces à courbure de Ricci
strictement positive. Entre autres : (a) le semi-groupe d’Ornstein–Uhlenbeck est
le flot du gradient de l’entropie relative ; (b) l’espace de Poisson a une courbure
de Ricci entropique minorée par 1 ; (c) W satisfait une inégalité HWI."
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Lorenzo
full_name: Dello Schiavo, Lorenzo
id: ECEBF480-9E4F-11EA-B557-B0823DDC885E
last_name: Dello Schiavo
orcid: 0000-0002-9881-6870
- first_name: Ronan
full_name: Herry, Ronan
last_name: Herry
- first_name: Kohei
full_name: Suzuki, Kohei
last_name: Suzuki
citation:
ama: Dello Schiavo L, Herry R, Suzuki K. Wasserstein geometry and Ricci curvature
bounds for Poisson spaces. Journal de l’Ecole Polytechnique - Mathematiques.
2024;11:957-1010. doi:10.5802/jep.270
apa: Dello Schiavo, L., Herry, R., & Suzuki, K. (2024). Wasserstein geometry
and Ricci curvature bounds for Poisson spaces. Journal de l’Ecole Polytechnique
- Mathematiques. Ecole Polytechnique. https://doi.org/10.5802/jep.270
chicago: Dello Schiavo, Lorenzo, Ronan Herry, and Kohei Suzuki. “Wasserstein Geometry
and Ricci Curvature Bounds for Poisson Spaces.” Journal de l’Ecole Polytechnique
- Mathematiques. Ecole Polytechnique, 2024. https://doi.org/10.5802/jep.270.
ieee: L. Dello Schiavo, R. Herry, and K. Suzuki, “Wasserstein geometry and Ricci
curvature bounds for Poisson spaces,” Journal de l’Ecole Polytechnique - Mathematiques,
vol. 11. Ecole Polytechnique, pp. 957–1010, 2024.
ista: Dello Schiavo L, Herry R, Suzuki K. 2024. Wasserstein geometry and Ricci curvature
bounds for Poisson spaces. Journal de l’Ecole Polytechnique - Mathematiques. 11,
957–1010.
mla: Dello Schiavo, Lorenzo, et al. “Wasserstein Geometry and Ricci Curvature Bounds
for Poisson Spaces.” Journal de l’Ecole Polytechnique - Mathematiques,
vol. 11, Ecole Polytechnique, 2024, pp. 957–1010, doi:10.5802/jep.270.
short: L. Dello Schiavo, R. Herry, K. Suzuki, Journal de l’Ecole Polytechnique -
Mathematiques 11 (2024) 957–1010.
corr_author: '1'
date_created: 2024-09-29T22:01:38Z
date_published: 2024-01-01T00:00:00Z
date_updated: 2025-09-08T09:50:50Z
day: '01'
ddc:
- '510'
department:
- _id: JaMa
doi: 10.5802/jep.270
external_id:
arxiv:
- '2303.00398'
isi:
- '001367254000003'
file:
- access_level: open_access
checksum: 5a51da5fb5f7fcaada378d43444cced8
content_type: application/pdf
creator: dernst
date_created: 2024-10-01T07:31:56Z
date_updated: 2024-10-01T07:31:56Z
file_id: '18164'
file_name: 2024_JourEcolePolytechniqueMath_DelloSchiavo.pdf
file_size: 1250553
relation: main_file
success: 1
file_date_updated: 2024-10-01T07:31:56Z
has_accepted_license: '1'
intvolume: ' 11'
isi: 1
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
page: 957-1010
publication: Journal de l'Ecole Polytechnique - Mathematiques
publication_identifier:
eissn:
- 2270-518X
issn:
- 2429-7100
publication_status: published
publisher: Ecole Polytechnique
quality_controlled: '1'
scopus_import: '1'
status: public
title: Wasserstein geometry and Ricci curvature bounds for Poisson spaces
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: 11
year: '2024'
...
---
_id: '18159'
abstract:
- lang: eng
text: "Markov Decision Processes (MDPs) are a classical model for decision making
in the presence of uncertainty. Often they are viewed as state transformers with
planning objectives defned with respect to paths over MDP states. An increasingly\r\npopular
alternative is to view them as distribution transformers, giving rise to a sequence
of probability distributions over MDP states. For instance, reachability and safety
properties in modeling robot swarms or chemical reaction networks are naturally
defned in terms of probability distributions over states. Verifying such distributional
properties is known to be hard and often beyond the reach of classical state-based
verifcation techniques. In this work, we consider the problems of certifed policy
(i.e. controller) verifcation and synthesis in MDPs under distributional reach-avoidance
specifcations. By certifed we mean that, along with a policy, we also aim to synthesize
a (checkable) certifcate ensuring that the MDP indeed satisfes the property. Thus,
given the target set of distributions and an unsafe set of distributions over
MDP states, our goal is to either synthesize a certifcate for a given policy or
synthesize a policy along with a certifcate, proving that the target distribution
can be reached while avoiding unsafe distributions. To solve this problem, we
introduce the novel notion of distributional reach-avoid certifcates and present
automated procedures for (1) synthesizing a certifcate for a given policy, and
(2) synthesizing a policy together with the certifcate, both providing formal
guarantees on certifcate correctness. Our experimental evaluation demonstrates
the ability of our method to solve several non-trivial examples, including a multi-agent
robot-swarm model, to synthesize certifed policies and to certify existing policies. "
acknowledgement: This work was supported in part by the ERC-2020-CoG 863818 (FoRM-SMArt),
the Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 1 grant,
Google Research Award 2023 and the SBI Foundation Hub for Data and Analytics.
article_processing_charge: No
arxiv: 1
author:
- first_name: S
full_name: Akshay, S
last_name: Akshay
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Tobias
full_name: Meggendorfer, Tobias
id: b21b0c15-30a2-11eb-80dc-f13ca25802e1
last_name: Meggendorfer
orcid: 0000-0002-1712-2165
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
citation:
ama: 'Akshay S, Chatterjee K, Meggendorfer T, Zikelic D. Certified policy verification
and synthesis for MDPs under distributional reach-avoidance properties. In: Proceedings
of the Thirty-Third International Joint Conference on Artificial Intelligence.
International Joint Conferences on Artificial Intelligence; 2024:3-12. doi:10.24963/ijcai.2024/1'
apa: 'Akshay, S., Chatterjee, K., Meggendorfer, T., & Zikelic, D. (2024). Certified
policy verification and synthesis for MDPs under distributional reach-avoidance
properties. In Proceedings of the Thirty-Third International Joint Conference
on Artificial Intelligence (pp. 3–12). Jeju, Korea: International Joint Conferences
on Artificial Intelligence. https://doi.org/10.24963/ijcai.2024/1'
chicago: Akshay, S, Krishnendu Chatterjee, Tobias Meggendorfer, and Dorde Zikelic.
“Certified Policy Verification and Synthesis for MDPs under Distributional Reach-Avoidance
Properties.” In Proceedings of the Thirty-Third International Joint Conference
on Artificial Intelligence, 3–12. International Joint Conferences on Artificial
Intelligence, 2024. https://doi.org/10.24963/ijcai.2024/1.
ieee: S. Akshay, K. Chatterjee, T. Meggendorfer, and D. Zikelic, “Certified policy
verification and synthesis for MDPs under distributional reach-avoidance properties,”
in Proceedings of the Thirty-Third International Joint Conference on Artificial
Intelligence, Jeju, Korea, 2024, pp. 3–12.
ista: 'Akshay S, Chatterjee K, Meggendorfer T, Zikelic D. 2024. Certified policy
verification and synthesis for MDPs under distributional reach-avoidance properties.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence.
IJCAI: International Joint Conference on Artificial Intelligence, 3–12.'
mla: Akshay, S., et al. “Certified Policy Verification and Synthesis for MDPs under
Distributional Reach-Avoidance Properties.” Proceedings of the Thirty-Third
International Joint Conference on Artificial Intelligence, International Joint
Conferences on Artificial Intelligence, 2024, pp. 3–12, doi:10.24963/ijcai.2024/1.
short: S. Akshay, K. Chatterjee, T. Meggendorfer, D. Zikelic, in:, Proceedings of
the Thirty-Third International Joint Conference on Artificial Intelligence, International
Joint Conferences on Artificial Intelligence, 2024, pp. 3–12.
conference:
end_date: 2024-08-09
location: Jeju, Korea
name: 'IJCAI: International Joint Conference on Artificial Intelligence'
start_date: 2024-08-03
corr_author: '1'
date_created: 2024-09-29T22:01:38Z
date_published: 2024-09-01T00:00:00Z
date_updated: 2025-04-14T07:52:46Z
day: '01'
department:
- _id: KrCh
doi: 10.24963/ijcai.2024/1
ec_funded: 1
external_id:
arxiv:
- '2405.04015'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2405.04015
month: '09'
oa: 1
oa_version: Preprint
page: 3-12
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: Proceedings of the Thirty-Third International Joint Conference on Artificial
Intelligence
publication_identifier:
isbn:
- '9781956792041'
issn:
- 1045-0823
publication_status: published
publisher: International Joint Conferences on Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: Certified policy verification and synthesis for MDPs under distributional reach-avoidance
properties
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '18160'
abstract:
- lang: eng
text: 'Markov decision processes (MDPs) provide a standard framework for sequential
decision making under uncertainty. However, MDPs do not take uncertainty in transition
probabilities into account. Robust Markov decision processes (RMDPs) address this
shortcoming of MDPs by assigning to each transition an uncertainty set rather
than a single probability value. In this work, we consider polytopic RMDPs in
which all uncertainty sets are polytopes and study the problem of solving long-run
average reward polytopic RMDPs. We present a novel perspective on this problem
and show that it can be reduced to solving long-run average reward turn-based
stochastic games with finite state and action spaces. This reduction allows us
to derive several important consequences that were hitherto not known to hold
for polytopic RMDPs. First, we derive new computational complexity bounds for
solving long-run average reward polytopic RMDPs, showing for the first time that
the threshold decision problem for them is in NP∩CONP and that they admit a randomized
algorithm with sub-exponential expected runtime. Second, we present Robust Polytopic
Policy Iteration (RPPI), a novel policy iteration algorithm for solving long-run
average reward polytopic RMDPs. Our experimental evaluation shows that RPPI is
much more efficient in solving long-run average reward polytopic RMDPs compared
to state-of-the-art methods based on value iteration. '
acknowledgement: "This work was supported in part by the ERC-2020-CoG 863818 (FoRM-SMArt)
and the Czech Science Foundation\r\ngrant no. GA23-06963S."
article_processing_charge: No
arxiv: 1
author:
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Ehsan
full_name: Kafshdar Goharshadi, Ehsan
id: 103b4fa0-896a-11ed-bdf8-87b697bef40d
last_name: Kafshdar Goharshadi
orcid: 0000-0002-8595-0587
- first_name: Mehrdad
full_name: Karrabi, Mehrdad
id: 67638922-f394-11eb-9cf6-f20423e08757
last_name: Karrabi
- first_name: Petr
full_name: Novotný, Petr
id: 3CC3B868-F248-11E8-B48F-1D18A9856A87
last_name: Novotný
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
citation:
ama: 'Chatterjee K, Goharshady E, Karrabi M, Novotný P, Zikelic D. Solving long-run
average reward robust MDPs via stochastic games. In: 33rd International Joint
Conference on Artificial Intelligence. International Joint Conferences on
Artificial Intelligence; 2024:6707-6715. doi:10.24963/ijcai.2024/741'
apa: 'Chatterjee, K., Goharshady, E., Karrabi, M., Novotný, P., & Zikelic, D.
(2024). Solving long-run average reward robust MDPs via stochastic games. In 33rd
International Joint Conference on Artificial Intelligence (pp. 6707–6715).
Jeju, South Korea: International Joint Conferences on Artificial Intelligence.
https://doi.org/10.24963/ijcai.2024/741'
chicago: Chatterjee, Krishnendu, Ehsan Goharshady, Mehrdad Karrabi, Petr Novotný,
and Dorde Zikelic. “Solving Long-Run Average Reward Robust MDPs via Stochastic
Games.” In 33rd International Joint Conference on Artificial Intelligence,
6707–15. International Joint Conferences on Artificial Intelligence, 2024. https://doi.org/10.24963/ijcai.2024/741.
ieee: K. Chatterjee, E. Goharshady, M. Karrabi, P. Novotný, and D. Zikelic, “Solving
long-run average reward robust MDPs via stochastic games,” in 33rd International
Joint Conference on Artificial Intelligence, Jeju, South Korea, 2024, pp.
6707–6715.
ista: 'Chatterjee K, Goharshady E, Karrabi M, Novotný P, Zikelic D. 2024. Solving
long-run average reward robust MDPs via stochastic games. 33rd International Joint
Conference on Artificial Intelligence. IJCAI: International Joint Conference on
Artificial Intelligence, 6707–6715.'
mla: Chatterjee, Krishnendu, et al. “Solving Long-Run Average Reward Robust MDPs
via Stochastic Games.” 33rd International Joint Conference on Artificial Intelligence,
International Joint Conferences on Artificial Intelligence, 2024, pp. 6707–15,
doi:10.24963/ijcai.2024/741.
short: K. Chatterjee, E. Goharshady, M. Karrabi, P. Novotný, D. Zikelic, in:, 33rd
International Joint Conference on Artificial Intelligence, International Joint
Conferences on Artificial Intelligence, 2024, pp. 6707–6715.
conference:
end_date: 2024-08-09
location: Jeju, South Korea
name: 'IJCAI: International Joint Conference on Artificial Intelligence'
start_date: 2024-08-03
corr_author: '1'
date_created: 2024-09-29T22:01:39Z
date_published: 2024-09-01T00:00:00Z
date_updated: 2025-04-14T07:52:46Z
day: '01'
department:
- _id: KrCh
doi: 10.24963/ijcai.2024/741
ec_funded: 1
external_id:
arxiv:
- '2312.13912'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2312.13912
month: '09'
oa: 1
oa_version: Preprint
page: 6707-6715
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: 33rd International Joint Conference on Artificial Intelligence
publication_identifier:
isbn:
- '9781956792041'
issn:
- 1045-0823
publication_status: published
publisher: International Joint Conferences on Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: Solving long-run average reward robust MDPs via stochastic games
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
OA_type: closed access
_id: '18167'
abstract:
- lang: eng
text: 'Holdase chaperones are essential in the mitochondrial membrane-protein biogenesis
as they stabilize preproteins and keep them in an import-competent state as they
travel through the aqueous cytosol and intermembrane space. The small TIM chaperones
of the mitochondrial intermembrane space function within a fine balance of client
promiscuity and high affinity binding, while being also able to release their
client proteins without significant energy barrier to the downstream insertases/translocases.
The tendency of the preproteins to aggregate and the dynamic nature of the preprotein—chaperone
complexes makes the preparation of these complexes challenging. Here we present
two optimized methods for complex formation of highly hydrophobic precursor proteins
and chaperones: a pull-down approach and an in-vitro translation strategy. In
the former, attaching the client protein to an affinity resin keeps the individual
client protein copies apart from each other and decreases the client self-aggregation
probability, thereby favouring complex formation. In the latter approach, a purified
chaperone, added to the cell-free protein synthesis, captures the nascent precursor
protein. The choice of method will depend on the desired client-chaperone complex
amount, or the need for specific labeling scheme.'
article_processing_charge: No
author:
- first_name: Undina
full_name: Guillerm, Undina
id: bb74f472-ae54-11eb-9835-bc9c22fb1183
last_name: Guillerm
- first_name: Iva
full_name: Sučec, Iva
last_name: Sučec
- first_name: Paul
full_name: Schanda, Paul
id: 7B541462-FAF6-11E9-A490-E8DFE5697425
last_name: Schanda
orcid: 0000-0002-9350-7606
citation:
ama: 'Guillerm U, Sučec I, Schanda P. Generation of TIM chaperone substrate complexes.
In: Methods in Enzymology. Vol 707. Elsevier; 2024:391-422. doi:10.1016/bs.mie.2024.07.051'
apa: Guillerm, U., Sučec, I., & Schanda, P. (2024). Generation of TIM chaperone
substrate complexes. In Methods in Enzymology (Vol. 707, pp. 391–422).
Elsevier. https://doi.org/10.1016/bs.mie.2024.07.051
chicago: Guillerm, Undina, Iva Sučec, and Paul Schanda. “Generation of TIM Chaperone
Substrate Complexes.” In Methods in Enzymology, 707:391–422. Elsevier,
2024. https://doi.org/10.1016/bs.mie.2024.07.051.
ieee: U. Guillerm, I. Sučec, and P. Schanda, “Generation of TIM chaperone substrate
complexes,” in Methods in Enzymology, vol. 707, Elsevier, 2024, pp. 391–422.
ista: 'Guillerm U, Sučec I, Schanda P. 2024.Generation of TIM chaperone substrate
complexes. In: Methods in Enzymology. vol. 707, 391–422.'
mla: Guillerm, Undina, et al. “Generation of TIM Chaperone Substrate Complexes.”
Methods in Enzymology, vol. 707, Elsevier, 2024, pp. 391–422, doi:10.1016/bs.mie.2024.07.051.
short: U. Guillerm, I. Sučec, P. Schanda, in:, Methods in Enzymology, Elsevier,
2024, pp. 391–422.
corr_author: '1'
date_created: 2024-10-01T10:58:27Z
date_published: 2024-09-13T00:00:00Z
date_updated: 2025-10-22T06:40:54Z
day: '13'
department:
- _id: PaSc
doi: 10.1016/bs.mie.2024.07.051
external_id:
pmid:
- '39488384'
intvolume: ' 707'
language:
- iso: eng
month: '09'
oa_version: None
page: 391-422
pmid: 1
publication: Methods in Enzymology
publication_identifier:
issn:
- 0076-6879
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Generation of TIM chaperone substrate complexes
type: book_chapter
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 707
year: '2024'
...
---
_id: '18168'
abstract:
- lang: eng
text: 'Despite the considerable interest in the recombinant production of synthetic
spider silk fibers that possess mechanical properties similar to those of native
spider silks, such as the cost-effectiveness, tunability, and scalability realization,
is still lacking. To address this long-standing challenge, we have constructed
an artificial spider silk gene using Golden Gate assembly for the recombinant
bacterial production of dragline-mimicking silk, incorporating all the essential
components: the N-terminal domain, a 33-residue-long major-ampullate-spidroin-inspired
segment repeated 16 times, and the C-terminal domain (N16C). This designed silk-like
protein was successfully expressed in Escherichia coli, purified, and cast into
films from formic acid. We produced uniformly 13C–15N-labeled N16C films and employed
solid-state magic-angle spinning nuclear magnetic resonance (NMR) for characterization.
Thus, we could demonstrate that our bioengineered silk-like protein self-assembles
into a film where, when hydrated, the solvent-exposed layer of the rigid, β-nanocrystalline
polyalanine core undergoes a transition to an α-helical structure, gaining mobility
to the extent that it fully dissolves in water and transforms into a highly dynamic
random coil. This hydration-induced behavior induces chain dynamics in the glycine-rich
amorphous soft segments on the microsecond time scale, contributing to the elasticity
of the solid material. Our findings not only reveal the presence of structurally
and dynamically distinct segments within the film’s superstructure but also highlight
the complexity of the self-organization responsible for the exceptional mechanical
properties observed in proteins that mimic dragline silk.'
acknowledgement: We thank Dr. Pavel Kielkowski for performing the MS/MS measurement
and providing feedback on the manuscript. We are grateful to Rodrigo Ledesma Amaro
for introducing the Golden Gate Assembly technique in our lab. We acknowledge the
support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)─SFB
1309-325871075, the Center for NanoScience (CeNS), the Fonds der Chemischen Industrie,
and Universitätsgesellschaft München.
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Dongqing
full_name: Wu, Dongqing
last_name: Wu
- first_name: Anamaria
full_name: Koscic, Anamaria
last_name: Koscic
- first_name: Sonja
full_name: Schneider, Sonja
last_name: Schneider
- first_name: Romeo C. A.
full_name: Dubini, Romeo C. A.
last_name: Dubini
- first_name: Diana C.
full_name: Rodriguez Camargo, Diana C.
last_name: Rodriguez Camargo
- first_name: Sabine
full_name: Schneider, Sabine
last_name: Schneider
- first_name: Petra
full_name: Rovo, Petra
id: c316e53f-b965-11eb-b128-bb26acc59c00
last_name: Rovo
orcid: 0000-0001-8729-7326
citation:
ama: Wu D, Koscic A, Schneider S, et al. Unveiling the dynamic self-assembly of
a recombinant dragline-silk-mimicking protein. Biomacromolecules. 2024;25(3):1759-1774.
doi:10.1021/acs.biomac.3c01239
apa: Wu, D., Koscic, A., Schneider, S., Dubini, R. C. A., Rodriguez Camargo, D.
C., Schneider, S., & Rovo, P. (2024). Unveiling the dynamic self-assembly
of a recombinant dragline-silk-mimicking protein. Biomacromolecules. American
Chemical Society. https://doi.org/10.1021/acs.biomac.3c01239
chicago: Wu, Dongqing, Anamaria Koscic, Sonja Schneider, Romeo C. A. Dubini, Diana
C. Rodriguez Camargo, Sabine Schneider, and Petra Rovo. “Unveiling the Dynamic
Self-Assembly of a Recombinant Dragline-Silk-Mimicking Protein.” Biomacromolecules.
American Chemical Society, 2024. https://doi.org/10.1021/acs.biomac.3c01239.
ieee: D. Wu et al., “Unveiling the dynamic self-assembly of a recombinant
dragline-silk-mimicking protein,” Biomacromolecules, vol. 25, no. 3. American
Chemical Society, pp. 1759–1774, 2024.
ista: Wu D, Koscic A, Schneider S, Dubini RCA, Rodriguez Camargo DC, Schneider S,
Rovo P. 2024. Unveiling the dynamic self-assembly of a recombinant dragline-silk-mimicking
protein. Biomacromolecules. 25(3), 1759–1774.
mla: Wu, Dongqing, et al. “Unveiling the Dynamic Self-Assembly of a Recombinant
Dragline-Silk-Mimicking Protein.” Biomacromolecules, vol. 25, no. 3, American
Chemical Society, 2024, pp. 1759–74, doi:10.1021/acs.biomac.3c01239.
short: D. Wu, A. Koscic, S. Schneider, R.C.A. Dubini, D.C. Rodriguez Camargo, S.
Schneider, P. Rovo, Biomacromolecules 25 (2024) 1759–1774.
corr_author: '1'
date_created: 2024-10-02T10:09:53Z
date_published: 2024-03-11T00:00:00Z
date_updated: 2025-09-08T09:52:18Z
day: '11'
ddc:
- '540'
department:
- _id: NMR
doi: 10.1021/acs.biomac.3c01239
external_id:
isi:
- '001166501000001'
pmid:
- '38343096'
file:
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checksum: 9552b6d52f1e8a350764849a535fc13e
content_type: application/pdf
creator: dernst
date_created: 2024-10-07T08:33:35Z
date_updated: 2024-10-07T08:33:35Z
file_id: '18180'
file_name: 2024_BioMacromolecules_Wu.pdf
file_size: 6597227
relation: main_file
success: 1
file_date_updated: 2024-10-07T08:33:35Z
has_accepted_license: '1'
intvolume: ' 25'
isi: 1
issue: '3'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: 1759-1774
pmid: 1
publication: Biomacromolecules
publication_identifier:
eissn:
- 1526-4602
issn:
- 1525-7797
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Unveiling the dynamic self-assembly of a recombinant dragline-silk-mimicking
protein
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: 25
year: '2024'
...
---
OA_type: closed access
_id: '18171'
abstract:
- lang: eng
text: Defense against pathogens and parasites requires substantial investment of
energy and resources on part of the host. This makes the host immune function
dependent on availability and accessibility of resources. A resource deprived
host is therefore expected to be more susceptible to infections, although empirical
results do not always align with this prediction. Limiting host access to resources
can additionally impact within-host pathogen numbers, either directly by altering
the amount of resources available to the pathogens for proliferation or indirectly
by altering the efficiency of the host immune system. We tested for the effects
of host starvation (complete deprivation of resources) on susceptibility to bacterial
pathogens, and within-host pathogen proliferation, in Drosophila melanogaster
females. Our results show that starvation increases post-infection mortality of
the host, but in a pathogen-specific manner. This increase in mortality is always
accompanied by increased within-host pathogen proliferation. We therefore propose
that starvation compromises host resistance to bacterial infections in Drosophila
melanogaster females thereby increasing susceptibility to infections.
acknowledgement: "The authors thank Tejashwini Hegde for logistical support during
execution of the experiments reported here. The authors thank Prof. P. Cornelis
(Vrije Universiteit Brussel, Belgium) for providing the Pseudomonas entomophila
isolate, Dr. Elio Sucena and Tania Paulo (Instituto Gulbenkian Ciencia, Portugal)
for providing the Erwinia c. carotovora, and Prof. Brian Lazzaro (Cornell University,
USA) for providing the Enterococcus faecalis and Providencia rettgeri isolates,
and Dr. Karan Singh (IISER Mohali, India) for isolating the Staphylococcus succinus
isolate used in the experiments. The authors also thank Dr. Manas Geeta Arun for
maintenance of the LH fly populations used in this study.\r\nThe study was funded
by intra-mural funding from IISER Mohali, India, to NGP. AB was supported by Senior
Research Fellowship for PhD students from CSIR, Govt. of India. AS was supported
by Senior Research Fellowship for PhD students from UGC, Govt. of India. SS and
TM were supported by KVPY fellowships for undergraduate studies from DST, Govt.
of India."
article_number: '108209'
article_processing_charge: No
article_type: original
author:
- first_name: Aabeer
full_name: Basu, Aabeer
last_name: Basu
- first_name: Aparajita
full_name: Singh, Aparajita
last_name: Singh
- first_name: Suhaas
full_name: Sehgal, Suhaas
last_name: Sehgal
- first_name: Tanvi
full_name: Madaan, Tanvi
id: 419917ac-5355-11ee-ae5a-c952babcaad9
last_name: Madaan
- first_name: Nagaraj Guru
full_name: Prasad, Nagaraj Guru
last_name: Prasad
citation:
ama: Basu A, Singh A, Sehgal S, Madaan T, Prasad NG. Starvation increases susceptibility
to bacterial infection and promotes systemic pathogen proliferation in Drosophila
melanogaster females. Journal of Invertebrate Pathology. 2024;207(11).
doi:10.1016/j.jip.2024.108209
apa: Basu, A., Singh, A., Sehgal, S., Madaan, T., & Prasad, N. G. (2024). Starvation
increases susceptibility to bacterial infection and promotes systemic pathogen
proliferation in Drosophila melanogaster females. Journal of Invertebrate Pathology.
Elsevier. https://doi.org/10.1016/j.jip.2024.108209
chicago: Basu, Aabeer, Aparajita Singh, Suhaas Sehgal, Tanvi Madaan, and Nagaraj
Guru Prasad. “Starvation Increases Susceptibility to Bacterial Infection and Promotes
Systemic Pathogen Proliferation in Drosophila Melanogaster Females.” Journal
of Invertebrate Pathology. Elsevier, 2024. https://doi.org/10.1016/j.jip.2024.108209.
ieee: A. Basu, A. Singh, S. Sehgal, T. Madaan, and N. G. Prasad, “Starvation increases
susceptibility to bacterial infection and promotes systemic pathogen proliferation
in Drosophila melanogaster females,” Journal of Invertebrate Pathology,
vol. 207, no. 11. Elsevier, 2024.
ista: Basu A, Singh A, Sehgal S, Madaan T, Prasad NG. 2024. Starvation increases
susceptibility to bacterial infection and promotes systemic pathogen proliferation
in Drosophila melanogaster females. Journal of Invertebrate Pathology. 207(11),
108209.
mla: Basu, Aabeer, et al. “Starvation Increases Susceptibility to Bacterial Infection
and Promotes Systemic Pathogen Proliferation in Drosophila Melanogaster Females.”
Journal of Invertebrate Pathology, vol. 207, no. 11, 108209, Elsevier,
2024, doi:10.1016/j.jip.2024.108209.
short: A. Basu, A. Singh, S. Sehgal, T. Madaan, N.G. Prasad, Journal of Invertebrate
Pathology 207 (2024).
corr_author: '1'
date_created: 2024-10-06T22:01:11Z
date_published: 2024-11-01T00:00:00Z
date_updated: 2025-09-08T09:53:50Z
day: '01'
department:
- _id: SyCr
doi: 10.1016/j.jip.2024.108209
external_id:
isi:
- '001327816900001'
pmid:
- '39322010'
intvolume: ' 207'
isi: 1
issue: '11'
language:
- iso: eng
month: '11'
oa_version: None
pmid: 1
publication: Journal of Invertebrate Pathology
publication_identifier:
eissn:
- 1096-0805
issn:
- 0022-2011
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Starvation increases susceptibility to bacterial infection and promotes systemic
pathogen proliferation in Drosophila melanogaster females
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 207
year: '2024'
...