---
_id: '13146'
abstract:
- lang: eng
text: 'A recent line of work has analyzed the theoretical properties of deep neural
networks via the Neural Tangent Kernel (NTK). In particular, the smallest eigenvalue
of the NTK has been related to the memorization capacity, the global convergence
of gradient descent algorithms and the generalization of deep nets. However, existing
results either provide bounds in the two-layer setting or assume that the spectrum
of the NTK matrices is bounded away from 0 for multi-layer networks. In this paper,
we provide tight bounds on the smallest eigenvalue of NTK matrices for deep ReLU
nets, both in the limiting case of infinite widths and for finite widths. In the
finite-width setting, the network architectures we consider are fairly general:
we require the existence of a wide layer with roughly order of N neurons, N being
the number of data samples; and the scaling of the remaining layer widths is arbitrary
(up to logarithmic factors). To obtain our results, we analyze various quantities
of independent interest: we give lower bounds on the smallest singular value of
hidden feature matrices, and upper bounds on the Lipschitz constant of input-output
feature maps.'
acknowledgement: The authors would like to thank the anonymous reviewers for their
helpful comments. MM was partially supported by the 2019 Lopez-Loreta Prize. QN
and GM acknowledge support from the European Research Council (ERC) under the European
Union’s Horizon 2020 research and innovation programme (grant agreement no 757983).
article_processing_charge: No
arxiv: 1
author:
- first_name: Quynh
full_name: Nguyen, Quynh
last_name: Nguyen
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
- first_name: Guido
full_name: Montufar, Guido
last_name: Montufar
citation:
ama: 'Nguyen Q, Mondelli M, Montufar G. Tight bounds on the smallest Eigenvalue
of the neural tangent kernel for deep ReLU networks. In: Proceedings of the
38th International Conference on Machine Learning. Vol 139. ML Research Press;
2021:8119-8129.'
apa: 'Nguyen, Q., Mondelli, M., & Montufar, G. (2021). Tight bounds on the smallest
Eigenvalue of the neural tangent kernel for deep ReLU networks. In Proceedings
of the 38th International Conference on Machine Learning (Vol. 139, pp. 8119–8129).
Virtual: ML Research Press.'
chicago: Nguyen, Quynh, Marco Mondelli, and Guido Montufar. “Tight Bounds on the
Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks.” In Proceedings
of the 38th International Conference on Machine Learning, 139:8119–29. ML
Research Press, 2021.
ieee: Q. Nguyen, M. Mondelli, and G. Montufar, “Tight bounds on the smallest Eigenvalue
of the neural tangent kernel for deep ReLU networks,” in Proceedings of the
38th International Conference on Machine Learning, Virtual, 2021, vol. 139,
pp. 8119–8129.
ista: Nguyen Q, Mondelli M, Montufar G. 2021. Tight bounds on the smallest Eigenvalue
of the neural tangent kernel for deep ReLU networks. Proceedings of the 38th International
Conference on Machine Learning. International Conference on Machine Learning vol.
139, 8119–8129.
mla: Nguyen, Quynh, et al. “Tight Bounds on the Smallest Eigenvalue of the Neural
Tangent Kernel for Deep ReLU Networks.” Proceedings of the 38th International
Conference on Machine Learning, vol. 139, ML Research Press, 2021, pp. 8119–29.
short: Q. Nguyen, M. Mondelli, G. Montufar, in:, Proceedings of the 38th International
Conference on Machine Learning, ML Research Press, 2021, pp. 8119–8129.
conference:
end_date: 2021-07-24
location: Virtual
name: International Conference on Machine Learning
start_date: 2021-07-18
date_created: 2023-06-18T22:00:48Z
date_published: 2021-07-01T00:00:00Z
date_updated: 2024-09-10T13:03:17Z
day: '01'
ddc:
- '000'
department:
- _id: MaMo
external_id:
arxiv:
- '2012.11654'
file:
- access_level: open_access
checksum: 19489cf5e16a0596b1f92e317d97c9b0
content_type: application/pdf
creator: dernst
date_created: 2023-06-19T10:49:12Z
date_updated: 2023-06-19T10:49:12Z
file_id: '13155'
file_name: 2021_PMLR_Nguyen.pdf
file_size: 591332
relation: main_file
success: 1
file_date_updated: 2023-06-19T10:49:12Z
has_accepted_license: '1'
intvolume: ' 139'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 8119-8129
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: Proceedings of the 38th International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
isbn:
- '9781713845065'
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep
ReLU networks
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: 139
year: '2021'
...
---
_id: '13147'
abstract:
- lang: eng
text: "We investigate fast and communication-efficient algorithms for the classic
problem of minimizing a sum of strongly convex and smooth functions that are distributed
among n\r\n different nodes, which can communicate using a limited number of bits.
Most previous communication-efficient approaches for this problem are limited
to first-order optimization, and therefore have \\emph{linear} dependence on the
condition number in their communication complexity. We show that this dependence
is not inherent: communication-efficient methods can in fact have sublinear dependence
on the condition number. For this, we design and analyze the first communication-efficient
distributed variants of preconditioned gradient descent for Generalized Linear
Models, and for Newton’s method. Our results rely on a new technique for quantizing
both the preconditioner and the descent direction at each step of the algorithms,
while controlling their convergence rate. We also validate our findings experimentally,
showing faster convergence and reduced communication relative to previous methods."
acknowledgement: The authors would like to thank Janne Korhonen, Aurelien Lucchi,
Celestine MendlerDunner and Antonio Orvieto for helpful discussions. FA ¨and DA
were supported during this work by the European Research Council (ERC) under the
European Union’s Horizon 2020 research and innovation programme (grant agreement
No 805223 ScaleML). PD was supported by the European Union’s Horizon 2020 programme
under the Marie Skłodowska-Curie grant agreement No. 754411.
article_processing_charge: No
arxiv: 1
author:
- first_name: Foivos
full_name: Alimisis, Foivos
last_name: Alimisis
- first_name: Peter
full_name: Davies, Peter
id: 11396234-BB50-11E9-B24C-90FCE5697425
last_name: Davies
orcid: 0000-0002-5646-9524
- 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: 'Alimisis F, Davies P, Alistarh D-A. Communication-efficient distributed optimization
with quantized preconditioners. In: Proceedings of the 38th International Conference
on Machine Learning. Vol 139. ML Research Press; 2021:196-206.'
apa: 'Alimisis, F., Davies, P., & Alistarh, D.-A. (2021). Communication-efficient
distributed optimization with quantized preconditioners. In Proceedings of
the 38th International Conference on Machine Learning (Vol. 139, pp. 196–206).
Virtual: ML Research Press.'
chicago: Alimisis, Foivos, Peter Davies, and Dan-Adrian Alistarh. “Communication-Efficient
Distributed Optimization with Quantized Preconditioners.” In Proceedings of
the 38th International Conference on Machine Learning, 139:196–206. ML Research
Press, 2021.
ieee: F. Alimisis, P. Davies, and D.-A. Alistarh, “Communication-efficient distributed
optimization with quantized preconditioners,” in Proceedings of the 38th International
Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 196–206.
ista: Alimisis F, Davies P, Alistarh D-A. 2021. Communication-efficient distributed
optimization with quantized preconditioners. Proceedings of the 38th International
Conference on Machine Learning. International Conference on Machine Learning vol.
139, 196–206.
mla: Alimisis, Foivos, et al. “Communication-Efficient Distributed Optimization
with Quantized Preconditioners.” Proceedings of the 38th International Conference
on Machine Learning, vol. 139, ML Research Press, 2021, pp. 196–206.
short: F. Alimisis, P. Davies, D.-A. Alistarh, in:, Proceedings of the 38th International
Conference on Machine Learning, ML Research Press, 2021, pp. 196–206.
conference:
end_date: 2021-07-24
location: Virtual
name: International Conference on Machine Learning
start_date: 2021-07-18
date_created: 2023-06-18T22:00:48Z
date_published: 2021-07-01T00:00:00Z
date_updated: 2023-06-19T10:44:38Z
day: '01'
ddc:
- '000'
department:
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '2102.07214'
file:
- access_level: open_access
checksum: 7ec0d59bac268b49c76bf2e036dedd7a
content_type: application/pdf
creator: dernst
date_created: 2023-06-19T10:41:05Z
date_updated: 2023-06-19T10:41:05Z
file_id: '13154'
file_name: 2021_PMLR_Alimisis.pdf
file_size: 429087
relation: main_file
success: 1
file_date_updated: 2023-06-19T10:41:05Z
has_accepted_license: '1'
intvolume: ' 139'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 196-206
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: Proceedings of the 38th International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
isbn:
- '9781713845065'
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Communication-efficient distributed optimization with quantized preconditioners
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: 139
year: '2021'
...
---
_id: '14117'
abstract:
- lang: eng
text: 'The two fields of machine learning and graphical causality arose and are
developed separately. However, there is, now, cross-pollination and increasing
interest in both fields to benefit from the advances of the other. In this article,
we review fundamental concepts of causal inference and relate them to crucial
open problems of machine learning, including transfer and generalization, thereby
assaying how causality can contribute to modern machine learning research. This
also applies in the opposite direction: we note that most work in causality starts
from the premise that the causal variables are given. A central problem for AI
and causality is, thus, causal representation learning, that is, the discovery
of high-level causal variables from low-level observations. Finally, we delineate
some implications of causality for machine learning and propose key research areas
at the intersection of both communities.'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Bernhard
full_name: Scholkopf, Bernhard
last_name: Scholkopf
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Stefan
full_name: Bauer, Stefan
last_name: Bauer
- first_name: Nan Rosemary
full_name: Ke, Nan Rosemary
last_name: Ke
- first_name: Nal
full_name: Kalchbrenner, Nal
last_name: Kalchbrenner
- first_name: Anirudh
full_name: Goyal, Anirudh
last_name: Goyal
- first_name: Yoshua
full_name: Bengio, Yoshua
last_name: Bengio
citation:
ama: Scholkopf B, Locatello F, Bauer S, et al. Toward causal representation learning.
Proceedings of the IEEE. 2021;109(5):612-634. doi:10.1109/jproc.2021.3058954
apa: Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal,
A., & Bengio, Y. (2021). Toward causal representation learning. Proceedings
of the IEEE. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/jproc.2021.3058954
chicago: Scholkopf, Bernhard, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke,
Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. “Toward Causal Representation
Learning.” Proceedings of the IEEE. Institute of Electrical and Electronics
Engineers, 2021. https://doi.org/10.1109/jproc.2021.3058954.
ieee: B. Scholkopf et al., “Toward causal representation learning,” Proceedings
of the IEEE, vol. 109, no. 5. Institute of Electrical and Electronics Engineers,
pp. 612–634, 2021.
ista: Scholkopf B, Locatello F, Bauer S, Ke NR, Kalchbrenner N, Goyal A, Bengio
Y. 2021. Toward causal representation learning. Proceedings of the IEEE. 109(5),
612–634.
mla: Scholkopf, Bernhard, et al. “Toward Causal Representation Learning.” Proceedings
of the IEEE, vol. 109, no. 5, Institute of Electrical and Electronics Engineers,
2021, pp. 612–34, doi:10.1109/jproc.2021.3058954.
short: B. Scholkopf, F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal,
Y. Bengio, Proceedings of the IEEE 109 (2021) 612–634.
date_created: 2023-08-21T12:19:30Z
date_published: 2021-05-01T00:00:00Z
date_updated: 2023-09-11T11:43:35Z
day: '01'
department:
- _id: FrLo
doi: 10.1109/jproc.2021.3058954
extern: '1'
external_id:
arxiv:
- '2102.11107'
intvolume: ' 109'
issue: '5'
keyword:
- Electrical and Electronic Engineering
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1109/JPROC.2021.3058954
month: '05'
oa: 1
oa_version: Published Version
page: 612-634
publication: Proceedings of the IEEE
publication_identifier:
eissn:
- 1558-2256
issn:
- 0018-9219
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Toward causal representation learning
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 109
year: '2021'
...
---
_id: '14176'
abstract:
- lang: eng
text: "Intensive care units (ICU) are increasingly looking towards machine learning
for methods to provide online monitoring of critically ill patients. In machine
learning, online monitoring is often formulated as a supervised learning problem.
Recently, contrastive learning approaches have demonstrated promising improvements
over competitive supervised benchmarks. These methods rely on well-understood
data augmentation techniques developed for image data which do not apply to online
monitoring. In this work, we overcome this limitation by\r\nsupplementing time-series
data augmentation techniques with a novel contrastive\r\nlearning objective which
we call neighborhood contrastive learning (NCL). Our objective explicitly groups
together contiguous time segments from each patient while maintaining state-specific
information. Our experiments demonstrate a marked improvement over existing work
applying contrastive methods to medical time-series."
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Hugo
full_name: Yèche, Hugo
last_name: Yèche
- first_name: Gideon
full_name: Dresdner, Gideon
last_name: Dresdner
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Matthias
full_name: Hüser, Matthias
last_name: Hüser
- first_name: Gunnar
full_name: Rätsch, Gunnar
last_name: Rätsch
citation:
ama: 'Yèche H, Dresdner G, Locatello F, Hüser M, Rätsch G. Neighborhood contrastive
learning applied to online patient monitoring. In: Proceedings of 38th International
Conference on Machine Learning. Vol 139. ML Research Press; 2021:11964-11974.'
apa: 'Yèche, H., Dresdner, G., Locatello, F., Hüser, M., & Rätsch, G. (2021).
Neighborhood contrastive learning applied to online patient monitoring. In Proceedings
of 38th International Conference on Machine Learning (Vol. 139, pp. 11964–11974).
Virtual: ML Research Press.'
chicago: Yèche, Hugo, Gideon Dresdner, Francesco Locatello, Matthias Hüser, and
Gunnar Rätsch. “Neighborhood Contrastive Learning Applied to Online Patient Monitoring.”
In Proceedings of 38th International Conference on Machine Learning, 139:11964–74.
ML Research Press, 2021.
ieee: H. Yèche, G. Dresdner, F. Locatello, M. Hüser, and G. Rätsch, “Neighborhood
contrastive learning applied to online patient monitoring,” in Proceedings
of 38th International Conference on Machine Learning, Virtual, 2021, vol.
139, pp. 11964–11974.
ista: Yèche H, Dresdner G, Locatello F, Hüser M, Rätsch G. 2021. Neighborhood contrastive
learning applied to online patient monitoring. Proceedings of 38th International
Conference on Machine Learning. International Conference on Machine Learning,
PMLR, vol. 139, 11964–11974.
mla: Yèche, Hugo, et al. “Neighborhood Contrastive Learning Applied to Online Patient
Monitoring.” Proceedings of 38th International Conference on Machine Learning,
vol. 139, ML Research Press, 2021, pp. 11964–74.
short: H. Yèche, G. Dresdner, F. Locatello, M. Hüser, G. Rätsch, in:, Proceedings
of 38th International Conference on Machine Learning, ML Research Press, 2021,
pp. 11964–11974.
conference:
end_date: 2021-07-24
location: Virtual
name: International Conference on Machine Learning
start_date: 2021-07-18
date_created: 2023-08-22T14:03:04Z
date_published: 2021-08-01T00:00:00Z
date_updated: 2023-09-11T10:16:55Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2106.05142'
intvolume: ' 139'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2106.05142
month: '08'
oa: 1
oa_version: Preprint
page: 11964-11974
publication: Proceedings of 38th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Neighborhood contrastive learning applied to online patient monitoring
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 139
year: '2021'
...
---
_id: '14177'
abstract:
- lang: eng
text: "The focus of disentanglement approaches has been on identifying independent
factors of variation in data. However, the causal variables underlying real-world
observations are often not statistically independent. In this work, we bridge
the gap to real-world scenarios by analyzing the behavior of the most prominent
disentanglement approaches on correlated data in a large-scale empirical study
(including 4260 models). We show and quantify that systematically induced correlations
in the dataset are being learned and reflected in the latent representations,
which has implications for downstream applications of disentanglement such as
fairness. We also demonstrate how to resolve these latent correlations, either
using weak supervision during\r\ntraining or by post-hoc correcting a pre-trained
model with a small number of labels."
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Frederik
full_name: Träuble, Frederik
last_name: Träuble
- first_name: Elliot
full_name: Creager, Elliot
last_name: Creager
- first_name: Niki
full_name: Kilbertus, Niki
last_name: Kilbertus
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Andrea
full_name: Dittadi, Andrea
last_name: Dittadi
- first_name: Anirudh
full_name: Goyal, Anirudh
last_name: Goyal
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Stefan
full_name: Bauer, Stefan
last_name: Bauer
citation:
ama: 'Träuble F, Creager E, Kilbertus N, et al. On disentangled representations
learned from correlated data. In: Proceedings of the 38th International Conference
on Machine Learning. Vol 139. ML Research Press; 2021:10401-10412.'
apa: 'Träuble, F., Creager, E., Kilbertus, N., Locatello, F., Dittadi, A., Goyal,
A., … Bauer, S. (2021). On disentangled representations learned from correlated
data. In Proceedings of the 38th International Conference on Machine Learning
(Vol. 139, pp. 10401–10412). Virtual: ML Research Press.'
chicago: Träuble, Frederik, Elliot Creager, Niki Kilbertus, Francesco Locatello,
Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, and Stefan Bauer. “On Disentangled
Representations Learned from Correlated Data.” In Proceedings of the 38th International
Conference on Machine Learning, 139:10401–12. ML Research Press, 2021.
ieee: F. Träuble et al., “On disentangled representations learned from correlated
data,” in Proceedings of the 38th International Conference on Machine Learning,
Virtual, 2021, vol. 139, pp. 10401–10412.
ista: 'Träuble F, Creager E, Kilbertus N, Locatello F, Dittadi A, Goyal A, Schölkopf
B, Bauer S. 2021. On disentangled representations learned from correlated data.
Proceedings of the 38th International Conference on Machine Learning. ICML: International
Conference on Machine Learning, PMLR, vol. 139, 10401–10412.'
mla: Träuble, Frederik, et al. “On Disentangled Representations Learned from Correlated
Data.” Proceedings of the 38th International Conference on Machine Learning,
vol. 139, ML Research Press, 2021, pp. 10401–12.
short: F. Träuble, E. Creager, N. Kilbertus, F. Locatello, A. Dittadi, A. Goyal,
B. Schölkopf, S. Bauer, in:, Proceedings of the 38th International Conference
on Machine Learning, ML Research Press, 2021, pp. 10401–10412.
conference:
end_date: 2021-07-24
location: Virtual
name: 'ICML: International Conference on Machine Learning'
start_date: 2021-07-18
date_created: 2023-08-22T14:03:47Z
date_published: 2021-08-01T00:00:00Z
date_updated: 2023-09-11T10:18:48Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2006.07886'
intvolume: ' 139'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2006.07886
month: '08'
oa: 1
oa_version: Published Version
page: 10401-10412
publication: Proceedings of the 38th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: On disentangled representations learned from correlated data
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 139
year: '2021'
...
---
_id: '14178'
abstract:
- lang: eng
text: Learning meaningful representations that disentangle the underlying structure
of the data generating process is considered to be of key importance in machine
learning. While disentangled representations were found to be useful for diverse
tasks such as abstract reasoning and fair classification, their scalability and
real-world impact remain questionable. We introduce a new high-resolution dataset
with 1M simulated images and over 1,800 annotated real-world images of the same
setup. In contrast to previous work, this new dataset exhibits correlations, a
complex underlying structure, and allows to evaluate transfer to unseen simulated
and real-world settings where the encoder i) remains in distribution or ii) is
out of distribution. We propose new architectures in order to scale disentangled
representation learning to realistic high-resolution settings and conduct a large-scale
empirical study of disentangled representations on this dataset. We observe that
disentanglement is a good predictor for out-of-distribution (OOD) task performance.
article_processing_charge: No
arxiv: 1
author:
- first_name: Andrea
full_name: Dittadi, Andrea
last_name: Dittadi
- first_name: Frederik
full_name: Träuble, Frederik
last_name: Träuble
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Manuel
full_name: Wüthrich, Manuel
last_name: Wüthrich
- first_name: Vaibhav
full_name: Agrawal, Vaibhav
last_name: Agrawal
- first_name: Ole
full_name: Winther, Ole
last_name: Winther
- first_name: Stefan
full_name: Bauer, Stefan
last_name: Bauer
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
citation:
ama: 'Dittadi A, Träuble F, Locatello F, et al. On the transfer of disentangled
representations in realistic settings. In: The Ninth International Conference
on Learning Representations. ; 2021.'
apa: Dittadi, A., Träuble, F., Locatello, F., Wüthrich, M., Agrawal, V., Winther,
O., … Schölkopf, B. (2021). On the transfer of disentangled representations in
realistic settings. In The Ninth International Conference on Learning Representations.
Virtual.
chicago: Dittadi, Andrea, Frederik Träuble, Francesco Locatello, Manuel Wüthrich,
Vaibhav Agrawal, Ole Winther, Stefan Bauer, and Bernhard Schölkopf. “On the Transfer
of Disentangled Representations in Realistic Settings.” In The Ninth International
Conference on Learning Representations, 2021.
ieee: A. Dittadi et al., “On the transfer of disentangled representations
in realistic settings,” in The Ninth International Conference on Learning Representations,
Virtual, 2021.
ista: 'Dittadi A, Träuble F, Locatello F, Wüthrich M, Agrawal V, Winther O, Bauer
S, Schölkopf B. 2021. On the transfer of disentangled representations in realistic
settings. The Ninth International Conference on Learning Representations. ICLR:
International Conference on Learning Representations.'
mla: Dittadi, Andrea, et al. “On the Transfer of Disentangled Representations in
Realistic Settings.” The Ninth International Conference on Learning Representations,
2021.
short: A. Dittadi, F. Träuble, F. Locatello, M. Wüthrich, V. Agrawal, O. Winther,
S. Bauer, B. Schölkopf, in:, The Ninth International Conference on Learning Representations,
2021.
conference:
end_date: 2021-05-07
location: Virtual
name: 'ICLR: International Conference on Learning Representations'
start_date: 2021-05-03
date_created: 2023-08-22T14:04:16Z
date_published: 2021-05-04T00:00:00Z
date_updated: 2023-09-11T10:55:30Z
day: '04'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2010.14407'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2010.14407
month: '05'
oa: 1
oa_version: Preprint
publication: The Ninth International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: On the transfer of disentangled representations in realistic settings
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '14179'
abstract:
- lang: eng
text: Self-supervised representation learning has shown remarkable success in a
number of domains. A common practice is to perform data augmentation via hand-crafted
transformations intended to leave the semantics of the data invariant. We seek
to understand the empirical success of this approach from a theoretical perspective.
We formulate the augmentation process as a latent variable model by postulating
a partition of the latent representation into a content component, which is assumed
invariant to augmentation, and a style component, which is allowed to change.
Unlike prior work on disentanglement and independent component analysis, we allow
for both nontrivial statistical and causal dependencies in the latent space. We
study the identifiability of the latent representation based on pairs of views
of the observations and prove sufficient conditions that allow us to identify
the invariant content partition up to an invertible mapping in both generative
and discriminative settings. We find numerical simulations with dependent latent
variables are consistent with our theory. Lastly, we introduce Causal3DIdent,
a dataset of high-dimensional, visually complex images with rich causal dependencies,
which we use to study the effect of data augmentations performed in practice.
article_processing_charge: No
arxiv: 1
author:
- first_name: Julius von
full_name: Kügelgen, Julius von
last_name: Kügelgen
- first_name: Yash
full_name: Sharma, Yash
last_name: Sharma
- first_name: Luigi
full_name: Gresele, Luigi
last_name: Gresele
- first_name: Wieland
full_name: Brendel, Wieland
last_name: Brendel
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Michel
full_name: Besserve, Michel
last_name: Besserve
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Kügelgen J von, Sharma Y, Gresele L, et al. Self-supervised learning with
data augmentations provably isolates content from style. In: Advances in Neural
Information Processing Systems. Vol 34. ; 2021:16451-16467.'
apa: Kügelgen, J. von, Sharma, Y., Gresele, L., Brendel, W., Schölkopf, B., Besserve,
M., & Locatello, F. (2021). Self-supervised learning with data augmentations
provably isolates content from style. In Advances in Neural Information Processing
Systems (Vol. 34, pp. 16451–16467). Virtual.
chicago: Kügelgen, Julius von, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard
Schölkopf, Michel Besserve, and Francesco Locatello. “Self-Supervised Learning
with Data Augmentations Provably Isolates Content from Style.” In Advances
in Neural Information Processing Systems, 34:16451–67, 2021.
ieee: J. von Kügelgen et al., “Self-supervised learning with data augmentations
provably isolates content from style,” in Advances in Neural Information Processing
Systems, Virtual, 2021, vol. 34, pp. 16451–16467.
ista: 'Kügelgen J von, Sharma Y, Gresele L, Brendel W, Schölkopf B, Besserve M,
Locatello F. 2021. Self-supervised learning with data augmentations provably isolates
content from style. Advances in Neural Information Processing Systems. NeurIPS:
Neural Information Processing Systems vol. 34, 16451–16467.'
mla: Kügelgen, Julius von, et al. “Self-Supervised Learning with Data Augmentations
Provably Isolates Content from Style.” Advances in Neural Information Processing
Systems, vol. 34, 2021, pp. 16451–67.
short: J. von Kügelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve,
F. Locatello, in:, Advances in Neural Information Processing Systems, 2021, pp.
16451–16467.
conference:
end_date: 2021-12-10
location: Virtual
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-07
date_created: 2023-08-22T14:04:36Z
date_published: 2021-06-08T00:00:00Z
date_updated: 2023-09-11T10:33:19Z
day: '08'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2106.04619'
intvolume: ' 34'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2106.04619
month: '06'
oa: 1
oa_version: Preprint
page: 16451-16467
publication: Advances in Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713845393'
publication_status: published
quality_controlled: '1'
status: public
title: Self-supervised learning with data augmentations provably isolates content
from style
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 34
year: '2021'
...
---
_id: '14180'
abstract:
- lang: eng
text: 'Modern neural network architectures can leverage large amounts of data to
generalize well within the training distribution. However, they are less capable
of systematic generalization to data drawn from unseen but related distributions,
a feat that is hypothesized to require compositional reasoning and reuse of knowledge.
In this work, we present Neural Interpreters, an architecture that factorizes
inference in a self-attention network as a system of modules, which we call \emph{functions}.
Inputs to the model are routed through a sequence of functions in a way that is
end-to-end learned. The proposed architecture can flexibly compose computation
along width and depth, and lends itself well to capacity extension after training.
To demonstrate the versatility of Neural Interpreters, we evaluate it in two distinct
settings: image classification and visual abstract reasoning on Raven Progressive
Matrices. In the former, we show that Neural Interpreters perform on par with
the vision transformer using fewer parameters, while being transferrable to a
new task in a sample efficient manner. In the latter, we find that Neural Interpreters
are competitive with respect to the state-of-the-art in terms of systematic generalization. '
article_processing_charge: No
arxiv: 1
author:
- first_name: Nasim
full_name: Rahaman, Nasim
last_name: Rahaman
- first_name: Muhammad Waleed
full_name: Gondal, Muhammad Waleed
last_name: Gondal
- first_name: Shruti
full_name: Joshi, Shruti
last_name: Joshi
- first_name: Peter
full_name: Gehler, Peter
last_name: Gehler
- first_name: Yoshua
full_name: Bengio, Yoshua
last_name: Bengio
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
citation:
ama: 'Rahaman N, Gondal MW, Joshi S, et al. Dynamic inference with neural interpreters.
In: Advances in Neural Information Processing Systems. Vol 34. ; 2021:10985-10998.'
apa: Rahaman, N., Gondal, M. W., Joshi, S., Gehler, P., Bengio, Y., Locatello, F.,
& Schölkopf, B. (2021). Dynamic inference with neural interpreters. In Advances
in Neural Information Processing Systems (Vol. 34, pp. 10985–10998). Virtual.
chicago: Rahaman, Nasim, Muhammad Waleed Gondal, Shruti Joshi, Peter Gehler, Yoshua
Bengio, Francesco Locatello, and Bernhard Schölkopf. “Dynamic Inference with Neural
Interpreters.” In Advances in Neural Information Processing Systems, 34:10985–98,
2021.
ieee: N. Rahaman et al., “Dynamic inference with neural interpreters,” in
Advances in Neural Information Processing Systems, Virtual, 2021, vol.
34, pp. 10985–10998.
ista: 'Rahaman N, Gondal MW, Joshi S, Gehler P, Bengio Y, Locatello F, Schölkopf
B. 2021. Dynamic inference with neural interpreters. Advances in Neural Information
Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 10985–10998.'
mla: Rahaman, Nasim, et al. “Dynamic Inference with Neural Interpreters.” Advances
in Neural Information Processing Systems, vol. 34, 2021, pp. 10985–98.
short: N. Rahaman, M.W. Gondal, S. Joshi, P. Gehler, Y. Bengio, F. Locatello, B.
Schölkopf, in:, Advances in Neural Information Processing Systems, 2021, pp. 10985–10998.
conference:
end_date: 2021-12-10
location: Virtual
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-07
date_created: 2023-08-22T14:04:55Z
date_published: 2021-10-12T00:00:00Z
date_updated: 2023-09-11T11:33:46Z
day: '12'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2110.06399'
intvolume: ' 34'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2110.06399
month: '10'
oa: 1
oa_version: Preprint
page: 10985-10998
publication: Advances in Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713845393'
publication_status: published
quality_controlled: '1'
status: public
title: Dynamic inference with neural interpreters
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 34
year: '2021'
...
---
_id: '14181'
abstract:
- lang: eng
text: Variational Inference makes a trade-off between the capacity of the variational
family and the tractability of finding an approximate posterior distribution.
Instead, Boosting Variational Inference allows practitioners to obtain increasingly
good posterior approximations by spending more compute. The main obstacle to widespread
adoption of Boosting Variational Inference is the amount of resources necessary
to improve over a strong Variational Inference baseline. In our work, we trace
this limitation back to the global curvature of the KL-divergence. We characterize
how the global curvature impacts time and memory consumption, address the problem
with the notion of local curvature, and provide a novel approximate backtracking
algorithm for estimating local curvature. We give new theoretical convergence
rates for our algorithms and provide experimental validation on synthetic and
real-world datasets.
article_processing_charge: No
arxiv: 1
author:
- first_name: Gideon
full_name: Dresdner, Gideon
last_name: Dresdner
- first_name: Saurav
full_name: Shekhar, Saurav
last_name: Shekhar
- first_name: Fabian
full_name: Pedregosa, Fabian
last_name: Pedregosa
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Gunnar
full_name: Rätsch, Gunnar
last_name: Rätsch
citation:
ama: 'Dresdner G, Shekhar S, Pedregosa F, Locatello F, Rätsch G. Boosting variational
inference with locally adaptive step-sizes. In: Proceedings of the Thirtieth
International Joint Conference on Artificial Intelligence. International Joint
Conferences on Artificial Intelligence; 2021:2337-2343. doi:10.24963/ijcai.2021/322'
apa: 'Dresdner, G., Shekhar, S., Pedregosa, F., Locatello, F., & Rätsch, G.
(2021). Boosting variational inference with locally adaptive step-sizes. In Proceedings
of the Thirtieth International Joint Conference on Artificial Intelligence
(pp. 2337–2343). Montreal, Canada: International Joint Conferences on Artificial
Intelligence. https://doi.org/10.24963/ijcai.2021/322'
chicago: Dresdner, Gideon, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello,
and Gunnar Rätsch. “Boosting Variational Inference with Locally Adaptive Step-Sizes.”
In Proceedings of the Thirtieth International Joint Conference on Artificial
Intelligence, 2337–43. International Joint Conferences on Artificial Intelligence,
2021. https://doi.org/10.24963/ijcai.2021/322.
ieee: G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, and G. Rätsch, “Boosting
variational inference with locally adaptive step-sizes,” in Proceedings of
the Thirtieth International Joint Conference on Artificial Intelligence, Montreal,
Canada, 2021, pp. 2337–2343.
ista: 'Dresdner G, Shekhar S, Pedregosa F, Locatello F, Rätsch G. 2021. Boosting
variational inference with locally adaptive step-sizes. Proceedings of the Thirtieth
International Joint Conference on Artificial Intelligence. IJCAI: International
Joint Conference on Artificial Intelligence, 2337–2343.'
mla: Dresdner, Gideon, et al. “Boosting Variational Inference with Locally Adaptive
Step-Sizes.” Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, International Joint Conferences on Artificial Intelligence,
2021, pp. 2337–43, doi:10.24963/ijcai.2021/322.
short: G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, G. Rätsch, in:, Proceedings
of the Thirtieth International Joint Conference on Artificial Intelligence, International
Joint Conferences on Artificial Intelligence, 2021, pp. 2337–2343.
conference:
end_date: 2021-08-27
location: Montreal, Canada
name: 'IJCAI: International Joint Conference on Artificial Intelligence'
start_date: 2021-08-19
date_created: 2023-08-22T14:05:14Z
date_published: 2021-05-19T00:00:00Z
date_updated: 2023-09-11T11:14:30Z
day: '19'
department:
- _id: FrLo
doi: 10.24963/ijcai.2021/322
extern: '1'
external_id:
arxiv:
- '2105.09240'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2105.09240
month: '05'
oa: 1
oa_version: Published Version
page: 2337-2343
publication: Proceedings of the Thirtieth International Joint Conference on Artificial
Intelligence
publication_identifier:
eisbn:
- '9780999241196'
publication_status: published
publisher: International Joint Conferences on Artificial Intelligence
quality_controlled: '1'
status: public
title: Boosting variational inference with locally adaptive step-sizes
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '14182'
abstract:
- lang: eng
text: "When machine learning systems meet real world applications, accuracy is only\r\none
of several requirements. In this paper, we assay a complementary\r\nperspective
originating from the increasing availability of pre-trained and\r\nregularly improving
state-of-the-art models. While new improved models develop\r\nat a fast pace,
downstream tasks vary more slowly or stay constant. Assume that\r\nwe have a large
unlabelled data set for which we want to maintain accurate\r\npredictions. Whenever
a new and presumably better ML models becomes available,\r\nwe encounter two problems:
(i) given a limited budget, which data points should\r\nbe re-evaluated using
the new model?; and (ii) if the new predictions differ\r\nfrom the current ones,
should we update? Problem (i) is about compute cost,\r\nwhich matters for very
large data sets and models. Problem (ii) is about\r\nmaintaining consistency of
the predictions, which can be highly relevant for\r\ndownstream applications;
our demand is to avoid negative flips, i.e., changing\r\ncorrect to incorrect
predictions. In this paper, we formalize the Prediction\r\nUpdate Problem and
present an efficient probabilistic approach as answer to the\r\nabove questions.
In extensive experiments on standard classification benchmark\r\ndata sets, we
show that our method outperforms alternative strategies along key\r\nmetrics for
backward-compatible prediction updates."
article_processing_charge: No
arxiv: 1
author:
- first_name: Frederik
full_name: Träuble, Frederik
last_name: Träuble
- first_name: Julius von
full_name: Kügelgen, Julius von
last_name: Kügelgen
- first_name: Matthäus
full_name: Kleindessner, Matthäus
last_name: Kleindessner
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Peter
full_name: Gehler, Peter
last_name: Gehler
citation:
ama: 'Träuble F, Kügelgen J von, Kleindessner M, Locatello F, Schölkopf B, Gehler
P. Backward-compatible prediction updates: A probabilistic approach. In: 35th
Conference on Neural Information Processing Systems. Vol 34. ; 2021:116-128.'
apa: 'Träuble, F., Kügelgen, J. von, Kleindessner, M., Locatello, F., Schölkopf,
B., & Gehler, P. (2021). Backward-compatible prediction updates: A probabilistic
approach. In 35th Conference on Neural Information Processing Systems (Vol.
34, pp. 116–128). Virtual.'
chicago: 'Träuble, Frederik, Julius von Kügelgen, Matthäus Kleindessner, Francesco
Locatello, Bernhard Schölkopf, and Peter Gehler. “Backward-Compatible Prediction
Updates: A Probabilistic Approach.” In 35th Conference on Neural Information
Processing Systems, 34:116–28, 2021.'
ieee: 'F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf,
and P. Gehler, “Backward-compatible prediction updates: A probabilistic approach,”
in 35th Conference on Neural Information Processing Systems, Virtual, 2021,
vol. 34, pp. 116–128.'
ista: 'Träuble F, Kügelgen J von, Kleindessner M, Locatello F, Schölkopf B, Gehler
P. 2021. Backward-compatible prediction updates: A probabilistic approach. 35th
Conference on Neural Information Processing Systems. NeurIPS: Neural Information
Processing Systems vol. 34, 116–128.'
mla: 'Träuble, Frederik, et al. “Backward-Compatible Prediction Updates: A Probabilistic
Approach.” 35th Conference on Neural Information Processing Systems, vol.
34, 2021, pp. 116–28.'
short: F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf,
P. Gehler, in:, 35th Conference on Neural Information Processing Systems, 2021,
pp. 116–128.
conference:
end_date: 2021-12-10
location: Virtual
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-07
date_created: 2023-08-22T14:05:41Z
date_published: 2021-07-02T00:00:00Z
date_updated: 2023-09-11T11:31:59Z
day: '02'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2107.01057'
intvolume: ' 34'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2107.01057
month: '07'
oa: 1
oa_version: Preprint
page: 116-128
publication: 35th Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713845393'
publication_status: published
quality_controlled: '1'
status: public
title: 'Backward-compatible prediction updates: A probabilistic approach'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 34
year: '2021'
...
---
_id: '14221'
abstract:
- lang: eng
text: 'The world is structured in countless ways. It may be prudent to enforce corresponding
structural properties to a learning algorithm''s solution, such as incorporating
prior beliefs, natural constraints, or causal structures. Doing so may translate
to faster, more accurate, and more flexible models, which may directly relate
to real-world impact. In this dissertation, we consider two different research
areas that concern structuring a learning algorithm''s solution: when the structure
is known and when it has to be discovered.'
article_number: '2111.13693'
article_processing_charge: No
arxiv: 1
author:
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: Locatello F. Enforcing and discovering structure in machine learning. arXiv.
doi:10.48550/arXiv.2111.13693
apa: Locatello, F. (n.d.). Enforcing and discovering structure in machine learning.
arXiv. https://doi.org/10.48550/arXiv.2111.13693
chicago: Locatello, Francesco. “Enforcing and Discovering Structure in Machine Learning.”
ArXiv, n.d. https://doi.org/10.48550/arXiv.2111.13693.
ieee: F. Locatello, “Enforcing and discovering structure in machine learning,” arXiv.
.
ista: Locatello F. Enforcing and discovering structure in machine learning. arXiv,
2111.13693.
mla: Locatello, Francesco. “Enforcing and Discovering Structure in Machine Learning.”
ArXiv, 2111.13693, doi:10.48550/arXiv.2111.13693.
short: F. Locatello, ArXiv (n.d.).
date_created: 2023-08-22T14:23:35Z
date_published: 2021-11-26T00:00:00Z
date_updated: 2023-09-12T07:04:44Z
day: '26'
department:
- _id: FrLo
doi: 10.48550/arXiv.2111.13693
extern: '1'
external_id:
arxiv:
- '2111.13693'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2111.13693
month: '11'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Enforcing and discovering structure in machine learning
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '14278'
abstract:
- lang: eng
text: 'The Birkhoff conjecture says that the boundary of a strictly convex integrable
billiard table is necessarily an ellipse. In this article, we consider a stronger
notion of integrability, namely, integrability close to the boundary, and prove
a local version of this conjecture: a small perturbation of almost every ellipse
that preserves integrability near the boundary, is itself an ellipse. We apply
this result to study local spectral rigidity of ellipses using the connection
between the wave trace of the Laplacian and the dynamics near the boundary and
establish rigidity for almost all of them.'
article_number: '2111.12171'
article_processing_charge: No
arxiv: 1
author:
- first_name: Illya
full_name: Koval, Illya
id: 2eed1f3b-896a-11ed-bdf8-93c7c4bf159e
last_name: Koval
citation:
ama: Koval I. Local strong Birkhoff conjecture and local spectral rigidity of almost
every ellipse. arXiv. doi:10.48550/ARXIV.2111.12171
apa: Koval, I. (n.d.). Local strong Birkhoff conjecture and local spectral rigidity
of almost every ellipse. arXiv. https://doi.org/10.48550/ARXIV.2111.12171
chicago: Koval, Illya. “Local Strong Birkhoff Conjecture and Local Spectral Rigidity
of Almost Every Ellipse.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2111.12171.
ieee: I. Koval, “Local strong Birkhoff conjecture and local spectral rigidity of
almost every ellipse,” arXiv. .
ista: Koval I. Local strong Birkhoff conjecture and local spectral rigidity of almost
every ellipse. arXiv, 2111.12171.
mla: Koval, Illya. “Local Strong Birkhoff Conjecture and Local Spectral Rigidity
of Almost Every Ellipse.” ArXiv, 2111.12171, doi:10.48550/ARXIV.2111.12171.
short: I. Koval, ArXiv (n.d.).
date_created: 2023-09-06T08:35:43Z
date_published: 2021-11-23T00:00:00Z
date_updated: 2023-09-15T06:44:00Z
day: '23'
department:
- _id: GradSch
doi: 10.48550/ARXIV.2111.12171
external_id:
arxiv:
- '2111.12171'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2111.12171
month: '11'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Local strong Birkhoff conjecture and local spectral rigidity of almost every
ellipse
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '9002'
abstract:
- lang: eng
text: ' We prove that, for the binary erasure channel (BEC), the polar-coding paradigm
gives rise to codes that not only approach the Shannon limit but do so under the
best possible scaling of their block length as a function of the gap to capacity.
This result exhibits the first known family of binary codes that attain both optimal
scaling and quasi-linear complexity of encoding and decoding. Our proof is based
on the construction and analysis of binary polar codes with large kernels. When
communicating reliably at rates within ε>0 of capacity, the code length n often
scales as O(1/εμ), where the constant μ is called the scaling exponent. It is
known that the optimal scaling exponent is μ=2, and it is achieved by random linear
codes. The scaling exponent of conventional polar codes (based on the 2×2 kernel)
on the BEC is μ=3.63. This falls far short of the optimal scaling guaranteed by
random codes. Our main contribution is a rigorous proof of the following result:
for the BEC, there exist ℓ×ℓ binary kernels, such that polar codes constructed
from these kernels achieve scaling exponent μ(ℓ) that tends to the optimal value
of 2 as ℓ grows. We furthermore characterize precisely how large ℓ needs to be
as a function of the gap between μ(ℓ) and 2. The resulting binary codes maintain
the recursive structure of conventional polar codes, and thereby achieve construction
complexity O(n) and encoding/decoding complexity O(nlogn).'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Arman
full_name: Fazeli, Arman
last_name: Fazeli
- first_name: Hamed
full_name: Hassani, Hamed
last_name: Hassani
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
- first_name: Alexander
full_name: Vardy, Alexander
last_name: Vardy
citation:
ama: 'Fazeli A, Hassani H, Mondelli M, Vardy A. Binary linear codes with optimal
scaling: Polar codes with large kernels. IEEE Transactions on Information Theory.
2021;67(9):5693-5710. doi:10.1109/TIT.2020.3038806'
apa: 'Fazeli, A., Hassani, H., Mondelli, M., & Vardy, A. (2021). Binary linear
codes with optimal scaling: Polar codes with large kernels. IEEE Transactions
on Information Theory. IEEE. https://doi.org/10.1109/TIT.2020.3038806'
chicago: 'Fazeli, Arman, Hamed Hassani, Marco Mondelli, and Alexander Vardy. “Binary
Linear Codes with Optimal Scaling: Polar Codes with Large Kernels.” IEEE Transactions
on Information Theory. IEEE, 2021. https://doi.org/10.1109/TIT.2020.3038806.'
ieee: 'A. Fazeli, H. Hassani, M. Mondelli, and A. Vardy, “Binary linear codes with
optimal scaling: Polar codes with large kernels,” IEEE Transactions on Information
Theory, vol. 67, no. 9. IEEE, pp. 5693–5710, 2021.'
ista: 'Fazeli A, Hassani H, Mondelli M, Vardy A. 2021. Binary linear codes with
optimal scaling: Polar codes with large kernels. IEEE Transactions on Information
Theory. 67(9), 5693–5710.'
mla: 'Fazeli, Arman, et al. “Binary Linear Codes with Optimal Scaling: Polar Codes
with Large Kernels.” IEEE Transactions on Information Theory, vol. 67,
no. 9, IEEE, 2021, pp. 5693–710, doi:10.1109/TIT.2020.3038806.'
short: A. Fazeli, H. Hassani, M. Mondelli, A. Vardy, IEEE Transactions on Information
Theory 67 (2021) 5693–5710.
date_created: 2021-01-10T23:01:18Z
date_published: 2021-09-01T00:00:00Z
date_updated: 2024-03-07T12:18:50Z
day: '01'
department:
- _id: MaMo
doi: 10.1109/TIT.2020.3038806
external_id:
arxiv:
- '1711.01339'
intvolume: ' 67'
issue: '9'
language:
- iso: eng
month: '09'
oa_version: Preprint
page: 5693-5710
publication: IEEE Transactions on Information Theory
publication_identifier:
eissn:
- 1557-9654
issn:
- 0018-9448
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
record:
- id: '6665'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: 'Binary linear codes with optimal scaling: Polar codes with large kernels'
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 67
year: '2021'
...
---
_id: '9005'
abstract:
- lang: eng
text: Studies on the experimental realization of two-dimensional anyons in terms
of quasiparticles have been restricted, so far, to only anyons on the plane. It
is known, however, that the geometry and topology of space can have significant
effects on quantum statistics for particles moving on it. Here, we have undertaken
the first step toward realizing the emerging fractional statistics for particles
restricted to move on the sphere instead of on the plane. We show that such a
model arises naturally in the context of quantum impurity problems. In particular,
we demonstrate a setup in which the lowest-energy spectrum of two linear bosonic
or fermionic molecules immersed in a quantum many-particle environment can coincide
with the anyonic spectrum on the sphere. This paves the way toward the experimental
realization of anyons on the sphere using molecular impurities. Furthermore, since
a change in the alignment of the molecules corresponds to the exchange of the
particles on the sphere, such a realization reveals a novel type of exclusion
principle for molecular impurities, which could also be of use as a powerful technique
to measure the statistics parameter. Finally, our approach opens up a simple numerical
route to investigate the spectra of many anyons on the sphere. Accordingly, we
present the spectrum of two anyons on the sphere in the presence of a Dirac monopole
field.
acknowledgement: "We are grateful to A. Ghazaryan for valuable discussions and also
thank the anonymous referees for comments. D.L. acknowledges financial support from
the G¨oran Gustafsson Foundation (grant no. 1804) and LMU Munich. M.L. gratefully
acknowledges financial support\r\nby the European Research Council (ERC) under the
European Union’s Horizon 2020 research and innovation programme (grant agreements
No 801770)."
article_number: '015301'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Morris
full_name: Brooks, Morris
id: B7ECF9FC-AA38-11E9-AC9A-0930E6697425
last_name: Brooks
orcid: 0000-0002-6249-0928
- first_name: Mikhail
full_name: Lemeshko, Mikhail
id: 37CB05FA-F248-11E8-B48F-1D18A9856A87
last_name: Lemeshko
orcid: 0000-0002-6990-7802
- first_name: D.
full_name: Lundholm, D.
last_name: Lundholm
- first_name: Enderalp
full_name: Yakaboylu, Enderalp
id: 38CB71F6-F248-11E8-B48F-1D18A9856A87
last_name: Yakaboylu
orcid: 0000-0001-5973-0874
citation:
ama: Brooks M, Lemeshko M, Lundholm D, Yakaboylu E. Molecular impurities as a realization
of anyons on the two-sphere. Physical Review Letters. 2021;126(1). doi:10.1103/PhysRevLett.126.015301
apa: Brooks, M., Lemeshko, M., Lundholm, D., & Yakaboylu, E. (2021). Molecular
impurities as a realization of anyons on the two-sphere. Physical Review Letters.
American Physical Society. https://doi.org/10.1103/PhysRevLett.126.015301
chicago: Brooks, Morris, Mikhail Lemeshko, D. Lundholm, and Enderalp Yakaboylu.
“Molecular Impurities as a Realization of Anyons on the Two-Sphere.” Physical
Review Letters. American Physical Society, 2021. https://doi.org/10.1103/PhysRevLett.126.015301.
ieee: M. Brooks, M. Lemeshko, D. Lundholm, and E. Yakaboylu, “Molecular impurities
as a realization of anyons on the two-sphere,” Physical Review Letters,
vol. 126, no. 1. American Physical Society, 2021.
ista: Brooks M, Lemeshko M, Lundholm D, Yakaboylu E. 2021. Molecular impurities
as a realization of anyons on the two-sphere. Physical Review Letters. 126(1),
015301.
mla: Brooks, Morris, et al. “Molecular Impurities as a Realization of Anyons on
the Two-Sphere.” Physical Review Letters, vol. 126, no. 1, 015301, American
Physical Society, 2021, doi:10.1103/PhysRevLett.126.015301.
short: M. Brooks, M. Lemeshko, D. Lundholm, E. Yakaboylu, Physical Review Letters
126 (2021).
date_created: 2021-01-17T23:01:10Z
date_published: 2021-01-08T00:00:00Z
date_updated: 2023-08-07T13:32:10Z
day: '08'
department:
- _id: MiLe
- _id: RoSe
doi: 10.1103/PhysRevLett.126.015301
ec_funded: 1
external_id:
arxiv:
- '2009.05948'
isi:
- '000606325000003'
intvolume: ' 126'
isi: 1
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2009.05948
month: '01'
oa: 1
oa_version: Preprint
project:
- _id: 2688CF98-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '801770'
name: 'Angulon: physics and applications of a new quasiparticle'
publication: Physical Review Letters
publication_identifier:
eissn:
- '10797114'
issn:
- '00319007'
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
related_material:
link:
- description: News on IST Homepage
relation: press_release
url: https://ist.ac.at/en/news/dancing-molecules-and-two-dimensional-particles/
record:
- id: '12390'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Molecular impurities as a realization of anyons on the two-sphere
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 126
year: '2021'
...
---
_id: '9006'
abstract:
- lang: eng
text: Cytoplasm is a gel-like crowded environment composed of various macromolecules,
organelles, cytoskeletal networks, and cytosol. The structure of the cytoplasm
is highly organized and heterogeneous due to the crowding of its constituents
and their effective compartmentalization. In such an environment, the diffusive
dynamics of the molecules are restricted, an effect that is further amplified
by clustering and anchoring of molecules. Despite the crowded nature of the cytoplasm
at the microscopic scale, large-scale reorganization of the cytoplasm is essential
for important cellular functions, such as cell division and polarization. How
such mesoscale reorganization of the cytoplasm is achieved, especially for large
cells such as oocytes or syncytial tissues that can span hundreds of micrometers
in size, is only beginning to be understood. In this review, we will discuss recent
advances in elucidating the molecular, cellular, and biophysical mechanisms by
which the cytoskeleton drives cytoplasmic reorganization across different scales,
structures, and species.
acknowledgement: We would like to thank Justine Renno for illustrations and Edouard
Hannezo and members of the Heisenberg group for their comments on previous versions
of the manuscript.
article_processing_charge: No
article_type: original
author:
- first_name: Shayan
full_name: Shamipour, Shayan
id: 40B34FE2-F248-11E8-B48F-1D18A9856A87
last_name: Shamipour
- first_name: Silvia
full_name: Caballero Mancebo, Silvia
id: 2F1E1758-F248-11E8-B48F-1D18A9856A87
last_name: Caballero Mancebo
orcid: 0000-0002-5223-3346
- first_name: Carl-Philipp J
full_name: Heisenberg, Carl-Philipp J
id: 39427864-F248-11E8-B48F-1D18A9856A87
last_name: Heisenberg
orcid: 0000-0002-0912-4566
citation:
ama: Shamipour S, Caballero Mancebo S, Heisenberg C-PJ. Cytoplasm’s got moves. Developmental
Cell. 2021;56(2):P213-226. doi:10.1016/j.devcel.2020.12.002
apa: Shamipour, S., Caballero Mancebo, S., & Heisenberg, C.-P. J. (2021). Cytoplasm’s
got moves. Developmental Cell. Elsevier. https://doi.org/10.1016/j.devcel.2020.12.002
chicago: Shamipour, Shayan, Silvia Caballero Mancebo, and Carl-Philipp J Heisenberg.
“Cytoplasm’s Got Moves.” Developmental Cell. Elsevier, 2021. https://doi.org/10.1016/j.devcel.2020.12.002.
ieee: S. Shamipour, S. Caballero Mancebo, and C.-P. J. Heisenberg, “Cytoplasm’s
got moves,” Developmental Cell, vol. 56, no. 2. Elsevier, pp. P213-226,
2021.
ista: Shamipour S, Caballero Mancebo S, Heisenberg C-PJ. 2021. Cytoplasm’s got moves.
Developmental Cell. 56(2), P213-226.
mla: Shamipour, Shayan, et al. “Cytoplasm’s Got Moves.” Developmental Cell,
vol. 56, no. 2, Elsevier, 2021, pp. P213-226, doi:10.1016/j.devcel.2020.12.002.
short: S. Shamipour, S. Caballero Mancebo, C.-P.J. Heisenberg, Developmental Cell
56 (2021) P213-226.
date_created: 2021-01-17T23:01:10Z
date_published: 2021-01-25T00:00:00Z
date_updated: 2024-03-25T23:30:10Z
day: '25'
department:
- _id: CaHe
doi: 10.1016/j.devcel.2020.12.002
external_id:
isi:
- '000613273900009'
pmid:
- '33321104'
intvolume: ' 56'
isi: 1
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1016/j.devcel.2020.12.002
month: '01'
oa: 1
oa_version: Published Version
page: P213-226
pmid: 1
publication: Developmental Cell
publication_identifier:
eissn:
- '18781551'
issn:
- '15345807'
publication_status: published
publisher: Elsevier
quality_controlled: '1'
related_material:
record:
- id: '9623'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Cytoplasm's got moves
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 56
year: '2021'
...
---
_id: '9009'
abstract:
- lang: eng
text: Recent advancements in live cell imaging technologies have identified the
phenomenon of intracellular propagation of late apoptotic events, such as cytochrome
c release and caspase activation. The mechanism, prevalence, and speed of apoptosis
propagation remain unclear. Additionally, no studies have demonstrated propagation
of the pro-apoptotic protein, BAX. To evaluate the role of BAX in intracellular
apoptotic propagation, we used high speed live-cell imaging to visualize fluorescently
tagged-BAX recruitment to mitochondria in four immortalized cell lines. We show
that propagation of mitochondrial BAX recruitment occurs in parallel to cytochrome
c and SMAC/Diablo release and is affected by cellular morphology, such that cells
with processes are more likely to exhibit propagation. The initiation of propagation
events is most prevalent in the distal tips of processes, while the rate of propagation
is influenced by the 2-dimensional width of the process. Propagation was rarely
observed in the cell soma, which exhibited near synchronous recruitment of BAX.
Propagation velocity is not affected by mitochondrial volume in segments of processes,
but is negatively affected by mitochondrial density. There was no evidence of
a propagating wave of increased levels of intracellular calcium ions. Alternatively,
we did observe a uniform increase in superoxide build-up in cellular mitochondria,
which was released as a propagating wave simultaneously with the propagating recruitment
of BAX to the mitochondrial outer membrane.
acknowledgement: This work was supported by National Institute of Health grants R01
EY030123, P30 EY016665, and T32 GM081061, an unrestricted research grant from Research
to Prevent Blindness, Inc., and the Frederick A. Davis Endowment from the Department
of Ophthalmology and Visual Sciences at the University of Wisconsin-Madison.
article_processing_charge: No
article_type: original
author:
- first_name: Joshua A.
full_name: Grosser, Joshua A.
last_name: Grosser
- first_name: Margaret E
full_name: Maes, Margaret E
id: 3838F452-F248-11E8-B48F-1D18A9856A87
last_name: Maes
orcid: 0000-0001-9642-1085
- first_name: Robert W.
full_name: Nickells, Robert W.
last_name: Nickells
citation:
ama: Grosser JA, Maes ME, Nickells RW. Characteristics of intracellular propagation
of mitochondrial BAX recruitment during apoptosis. Apoptosis. 2021;26(2):132-145.
doi:10.1007/s10495-020-01654-w
apa: Grosser, J. A., Maes, M. E., & Nickells, R. W. (2021). Characteristics
of intracellular propagation of mitochondrial BAX recruitment during apoptosis.
Apoptosis. Springer Nature. https://doi.org/10.1007/s10495-020-01654-w
chicago: Grosser, Joshua A., Margaret E Maes, and Robert W. Nickells. “Characteristics
of Intracellular Propagation of Mitochondrial BAX Recruitment during Apoptosis.”
Apoptosis. Springer Nature, 2021. https://doi.org/10.1007/s10495-020-01654-w.
ieee: J. A. Grosser, M. E. Maes, and R. W. Nickells, “Characteristics of intracellular
propagation of mitochondrial BAX recruitment during apoptosis,” Apoptosis,
vol. 26, no. 2. Springer Nature, pp. 132–145, 2021.
ista: Grosser JA, Maes ME, Nickells RW. 2021. Characteristics of intracellular propagation
of mitochondrial BAX recruitment during apoptosis. Apoptosis. 26(2), 132–145.
mla: Grosser, Joshua A., et al. “Characteristics of Intracellular Propagation of
Mitochondrial BAX Recruitment during Apoptosis.” Apoptosis, vol. 26, no.
2, Springer Nature, 2021, pp. 132–45, doi:10.1007/s10495-020-01654-w.
short: J.A. Grosser, M.E. Maes, R.W. Nickells, Apoptosis 26 (2021) 132–145.
date_created: 2021-01-17T23:01:11Z
date_published: 2021-02-01T00:00:00Z
date_updated: 2023-08-07T13:32:40Z
day: '01'
department:
- _id: SaSi
doi: 10.1007/s10495-020-01654-w
external_id:
isi:
- '000606722600001'
pmid:
- '33426618'
intvolume: ' 26'
isi: 1
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8082518/
month: '02'
oa: 1
oa_version: Submitted Version
page: 132-145
pmid: 1
publication: Apoptosis
publication_identifier:
eissn:
- 1573-675X
issn:
- 1360-8185
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Characteristics of intracellular propagation of mitochondrial BAX recruitment
during apoptosis
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 26
year: '2021'
...
---
_id: '9010'
abstract:
- lang: eng
text: Availability of the essential macronutrient nitrogen in soil plays a critical
role in plant growth, development, and impacts agricultural productivity. Plants
have evolved different strategies for sensing and responding to heterogeneous
nitrogen distribution. Modulation of root system architecture, including primary
root growth and branching, is among the most essential plant adaptions to ensure
adequate nitrogen acquisition. However, the immediate molecular pathways coordinating
the adjustment of root growth in response to distinct nitrogen sources, such as
nitrate or ammonium, are poorly understood. Here, we show that growth as manifested
by cell division and elongation is synchronized by coordinated auxin flux between
two adjacent outer tissue layers of the root. This coordination is achieved by
nitrate‐dependent dephosphorylation of the PIN2 auxin efflux carrier at a previously
uncharacterized phosphorylation site, leading to subsequent PIN2 lateralization
and thereby regulating auxin flow between adjacent tissues. A dynamic computer
model based on our experimental data successfully recapitulates experimental observations.
Our study provides mechanistic insights broadening our understanding of root growth
mechanisms in dynamic environments.
acknowledged_ssus:
- _id: Bio
acknowledgement: 'We acknowledge Gergely Molnar for critical reading of the manuscript,
Alexander Johnson for language editing and Yulija Salanenka for technical assistance.
Work in the Benkova laboratory was supported by the Austrian Science Fund (FWF01_I1774S)
to KO, RA and EB. Work in the Benkova laboratory was supported by the Austrian Science
Fund (FWF01_I1774S) to KO, RA and EB and by the DOC Fellowship Programme of the
AustrianAcademy of Sciences (25008) to C.A. Work in the Wabnik laboratory was supported
by the Programa de Atraccion de Talento 2017 (Comunidad deMadrid, 2017-T1/BIO-5654
to K.W.), Severo Ochoa Programme for Centres of Excellence in R&D from the Agencia
Estatal de Investigacion of Spain (grantSEV-2016-0672 (2017-2021) to K.W. via the
CBGP) and Programa Estatal de Generacion del Conocimiento y Fortalecimiento Científico
y Tecnologico del Sistema de I+D+I 2019 (PGC2018-093387-A-I00) from MICIU (to K.W.).
M.M.was supported by a postdoctoral contract associated to SEV-2016-0672.We acknowledge
the Bioimaging Facility in IST-Austria and the Advanced Microscopy Facility of the
Vienna Bio Center Core Facilities, member of the Vienna Bio Center Austria, for
use of the OMX v43D SIM microscope. AJ was supported by the Austrian Science Fund
(FWF): I03630 to J.F'
article_number: e106862
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Krisztina
full_name: Ötvös, Krisztina
id: 29B901B0-F248-11E8-B48F-1D18A9856A87
last_name: Ötvös
orcid: 0000-0002-5503-4983
- first_name: Marco
full_name: Marconi, Marco
last_name: Marconi
- first_name: Andrea
full_name: Vega, Andrea
last_name: Vega
- first_name: Jose
full_name: O’Brien, Jose
last_name: O’Brien
- first_name: Alexander J
full_name: Johnson, Alexander J
id: 46A62C3A-F248-11E8-B48F-1D18A9856A87
last_name: Johnson
orcid: 0000-0002-2739-8843
- first_name: Rashed
full_name: Abualia, Rashed
id: 4827E134-F248-11E8-B48F-1D18A9856A87
last_name: Abualia
orcid: 0000-0002-9357-9415
- first_name: Livio
full_name: Antonielli, Livio
last_name: Antonielli
- first_name: Juan C
full_name: Montesinos López, Juan C
id: 310A8E3E-F248-11E8-B48F-1D18A9856A87
last_name: Montesinos López
orcid: 0000-0001-9179-6099
- first_name: Yuzhou
full_name: Zhang, Yuzhou
id: 3B6137F2-F248-11E8-B48F-1D18A9856A87
last_name: Zhang
orcid: 0000-0003-2627-6956
- first_name: Shutang
full_name: Tan, Shutang
id: 2DE75584-F248-11E8-B48F-1D18A9856A87
last_name: Tan
orcid: 0000-0002-0471-8285
- first_name: Candela
full_name: Cuesta, Candela
id: 33A3C818-F248-11E8-B48F-1D18A9856A87
last_name: Cuesta
orcid: 0000-0003-1923-2410
- first_name: Christina
full_name: Artner, Christina
id: 45DF286A-F248-11E8-B48F-1D18A9856A87
last_name: Artner
- first_name: Eleonore
full_name: Bouguyon, Eleonore
last_name: Bouguyon
- first_name: Alain
full_name: Gojon, Alain
last_name: Gojon
- first_name: Jiří
full_name: Friml, Jiří
id: 4159519E-F248-11E8-B48F-1D18A9856A87
last_name: Friml
orcid: 0000-0002-8302-7596
- first_name: Rodrigo A.
full_name: Gutiérrez, Rodrigo A.
last_name: Gutiérrez
- first_name: Krzysztof T
full_name: Wabnik, Krzysztof T
id: 4DE369A4-F248-11E8-B48F-1D18A9856A87
last_name: Wabnik
orcid: 0000-0001-7263-0560
- first_name: Eva
full_name: Benková, Eva
id: 38F4F166-F248-11E8-B48F-1D18A9856A87
last_name: Benková
orcid: 0000-0002-8510-9739
citation:
ama: Ötvös K, Marconi M, Vega A, et al. Modulation of plant root growth by nitrogen
source-defined regulation of polar auxin transport. EMBO Journal. 2021;40(3).
doi:10.15252/embj.2020106862
apa: Ötvös, K., Marconi, M., Vega, A., O’Brien, J., Johnson, A. J., Abualia, R.,
… Benková, E. (2021). Modulation of plant root growth by nitrogen source-defined
regulation of polar auxin transport. EMBO Journal. Embo Press. https://doi.org/10.15252/embj.2020106862
chicago: Ötvös, Krisztina, Marco Marconi, Andrea Vega, Jose O’Brien, Alexander J
Johnson, Rashed Abualia, Livio Antonielli, et al. “Modulation of Plant Root Growth
by Nitrogen Source-Defined Regulation of Polar Auxin Transport.” EMBO Journal.
Embo Press, 2021. https://doi.org/10.15252/embj.2020106862.
ieee: K. Ötvös et al., “Modulation of plant root growth by nitrogen source-defined
regulation of polar auxin transport,” EMBO Journal, vol. 40, no. 3. Embo
Press, 2021.
ista: Ötvös K, Marconi M, Vega A, O’Brien J, Johnson AJ, Abualia R, Antonielli L,
Montesinos López JC, Zhang Y, Tan S, Cuesta C, Artner C, Bouguyon E, Gojon A,
Friml J, Gutiérrez RA, Wabnik KT, Benková E. 2021. Modulation of plant root growth
by nitrogen source-defined regulation of polar auxin transport. EMBO Journal.
40(3), e106862.
mla: Ötvös, Krisztina, et al. “Modulation of Plant Root Growth by Nitrogen Source-Defined
Regulation of Polar Auxin Transport.” EMBO Journal, vol. 40, no. 3, e106862,
Embo Press, 2021, doi:10.15252/embj.2020106862.
short: K. Ötvös, M. Marconi, A. Vega, J. O’Brien, A.J. Johnson, R. Abualia, L. Antonielli,
J.C. Montesinos López, Y. Zhang, S. Tan, C. Cuesta, C. Artner, E. Bouguyon, A.
Gojon, J. Friml, R.A. Gutiérrez, K.T. Wabnik, E. Benková, EMBO Journal 40 (2021).
date_created: 2021-01-17T23:01:12Z
date_published: 2021-02-01T00:00:00Z
date_updated: 2024-03-25T23:30:22Z
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call_identifier: FWF
grant_number: I 1774-B16
name: Hormone cross-talk drives nutrient dependent plant development
- _id: 2685A872-B435-11E9-9278-68D0E5697425
name: Hormonal regulation of plant adaptive responses to environmental signals
- _id: 26538374-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: I03630
name: Molecular mechanisms of endocytic cargo recognition in plants
publication: EMBO Journal
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eissn:
- '14602075'
issn:
- '02614189'
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publisher: Embo Press
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related_material:
link:
- description: News on IST Homepage
relation: press_release
url: https://ist.ac.at/en/news/a-plants-way-to-its-favorite-food/
record:
- id: '10303'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Modulation of plant root growth by nitrogen source-defined regulation of polar
auxin transport
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name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
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type: journal_article
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volume: 40
year: '2021'
...
---
_id: '9020'
abstract:
- lang: eng
text: 'We study dynamics and thermodynamics of ion transport in narrow, water-filled
channels, considered as effective 1D Coulomb systems. The long range nature of
the inter-ion interactions comes about due to the dielectric constants mismatch
between the water and the surrounding medium, confining the electric filed to
stay mostly within the water-filled channel. Statistical mechanics of such Coulomb
systems is dominated by entropic effects which may be accurately accounted for
by mapping onto an effective quantum mechanics. In presence of multivalent ions
the corresponding quantum mechanics appears to be non-Hermitian. In this review
we discuss a framework for semiclassical calculations for the effective non-Hermitian
Hamiltonians. Non-Hermiticity elevates WKB action integrals from the real line
to closed cycles on a complex Riemann surfaces where direct calculations are not
attainable. We circumvent this issue by applying tools from algebraic topology,
such as the Picard-Fuchs equation. We discuss how its solutions relate to the
thermodynamics and correlation functions of multivalent solutions within narrow,
water-filled channels. '
acknowledgement: "A.K. was supported by NSF grants DMR-2037654. T.G. acknowledges
funding from the Institute of Science and Technology (IST) Austria, and from the
European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie
Grant Agreement No. 754411.\r\nWe are indebted to Boris Shklovskii for introducing
us to the problem, and Alexander Gorsky and Peter Koroteev for introducing us to
the Picard-Fuchs methods. A very special thanks goes to Michael Janas for several
years of excellent collaboration on these topics. TG thanks Michael Kreshchuk for
introduction to the exact WKB method and great collaboration on related projects.
Figure 3 and Figure 4 are reproduced from Reference [25] with friendly permission
by the Russian Academy of Sciences. Figure 2, Figure 4, Figure 5, Figure 6, and
Figure 8 are reproduced from Reference [26] with friendly permission by IOP Publishing."
article_number: e23010125
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Tobias
full_name: Gulden, Tobias
id: 1083E038-9F73-11E9-A4B5-532AE6697425
last_name: Gulden
orcid: 0000-0001-6814-7541
- first_name: Alex
full_name: Kamenev, Alex
last_name: Kamenev
citation:
ama: Gulden T, Kamenev A. Dynamics of ion channels via non-hermitian quantum mechanics.
Entropy. 2021;23(1). doi:10.3390/e23010125
apa: Gulden, T., & Kamenev, A. (2021). Dynamics of ion channels via non-hermitian
quantum mechanics. Entropy. MDPI. https://doi.org/10.3390/e23010125
chicago: Gulden, Tobias, and Alex Kamenev. “Dynamics of Ion Channels via Non-Hermitian
Quantum Mechanics.” Entropy. MDPI, 2021. https://doi.org/10.3390/e23010125.
ieee: T. Gulden and A. Kamenev, “Dynamics of ion channels via non-hermitian quantum
mechanics,” Entropy, vol. 23, no. 1. MDPI, 2021.
ista: Gulden T, Kamenev A. 2021. Dynamics of ion channels via non-hermitian quantum
mechanics. Entropy. 23(1), e23010125.
mla: Gulden, Tobias, and Alex Kamenev. “Dynamics of Ion Channels via Non-Hermitian
Quantum Mechanics.” Entropy, vol. 23, no. 1, e23010125, MDPI, 2021, doi:10.3390/e23010125.
short: T. Gulden, A. Kamenev, Entropy 23 (2021).
date_created: 2021-01-19T11:12:06Z
date_published: 2021-01-19T00:00:00Z
date_updated: 2023-08-07T13:34:18Z
day: '19'
ddc:
- '530'
department:
- _id: MaSe
doi: 10.3390/e23010125
ec_funded: 1
external_id:
arxiv:
- '2012.01390'
isi:
- '000610122000001'
file:
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checksum: 6cd0e706156827c45c740534bd32c179
content_type: application/pdf
creator: tgulden
date_created: 2021-01-19T11:11:14Z
date_updated: 2021-01-19T11:11:14Z
file_id: '9021'
file_name: Final published paper.pdf
file_size: 981285
relation: main_file
file_date_updated: 2021-01-19T11:11:14Z
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intvolume: ' 23'
isi: 1
issue: '1'
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: Entropy
publication_identifier:
eissn:
- 1099-4300
publication_status: published
publisher: MDPI
quality_controlled: '1'
status: public
title: Dynamics of ion channels via non-hermitian quantum mechanics
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: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 23
year: '2021'
...
---
_id: '9022'
abstract:
- lang: eng
text: "In the first part of the thesis we consider Hermitian random matrices. Firstly,
we consider sample covariance matrices XX∗ with X having independent identically
distributed (i.i.d.) centred entries. We prove a Central Limit Theorem for differences
of linear statistics of XX∗ and its minor after removing the first column of X.
Secondly, we consider Wigner-type matrices and prove that the eigenvalue statistics
near cusp singularities of the limiting density of states are universal and that
they form a Pearcey process. Since the limiting eigenvalue distribution admits
only square root (edge) and cubic root (cusp) singularities, this concludes the
third and last remaining case of the Wigner-Dyson-Mehta universality conjecture.
The main technical ingredients are an optimal local law at the cusp, and the proof
of the fast relaxation to equilibrium of the Dyson Brownian motion in the cusp
regime.\r\nIn the second part we consider non-Hermitian matrices X with centred
i.i.d. entries. We normalise the entries of X to have variance N −1. It is well
known that the empirical eigenvalue density converges to the uniform distribution
on the unit disk (circular law). In the first project, we prove universality of
the local eigenvalue statistics close to the edge of the spectrum. This is the
non-Hermitian analogue of the TracyWidom universality at the Hermitian edge. Technically
we analyse the evolution of the spectral distribution of X along the Ornstein-Uhlenbeck
flow for very long time\r\n(up to t = +∞). In the second project, we consider
linear statistics of eigenvalues for macroscopic test functions f in the Sobolev
space H2+ϵ and prove their convergence to the projection of the Gaussian Free
Field on the unit disk. We prove this result for non-Hermitian matrices with real
or complex entries. The main technical ingredients are: (i) local law for products
of two resolvents at different spectral parameters, (ii) analysis of correlated
Dyson Brownian motions.\r\nIn the third and final part we discuss the mathematically
rigorous application of supersymmetric techniques (SUSY ) to give a lower tail
estimate of the lowest singular value of X − z, with z ∈ C. More precisely, we
use superbosonisation formula to give an integral representation of the resolvent
of (X − z)(X − z)∗ which reduces to two and three contour integrals in the complex
and real case, respectively. The rigorous analysis of these integrals is quite
challenging since simple saddle point analysis cannot be applied (the main contribution
comes from a non-trivial manifold). Our result\r\nimproves classical smoothing
inequalities in the regime |z| ≈ 1; this result is essential to prove edge universality
for i.i.d. non-Hermitian matrices."
acknowledgement: I gratefully acknowledge the financial support from the European
Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie
Grant Agreement No. 665385 and my advisor’s ERC Advanced Grant No. 338804.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Giorgio
full_name: Cipolloni, Giorgio
id: 42198EFA-F248-11E8-B48F-1D18A9856A87
last_name: Cipolloni
orcid: 0000-0002-4901-7992
citation:
ama: Cipolloni G. Fluctuations in the spectrum of random matrices. 2021. doi:10.15479/AT:ISTA:9022
apa: Cipolloni, G. (2021). Fluctuations in the spectrum of random matrices.
Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:9022
chicago: Cipolloni, Giorgio. “Fluctuations in the Spectrum of Random Matrices.”
Institute of Science and Technology Austria, 2021. https://doi.org/10.15479/AT:ISTA:9022.
ieee: G. Cipolloni, “Fluctuations in the spectrum of random matrices,” Institute
of Science and Technology Austria, 2021.
ista: Cipolloni G. 2021. Fluctuations in the spectrum of random matrices. Institute
of Science and Technology Austria.
mla: Cipolloni, Giorgio. Fluctuations in the Spectrum of Random Matrices.
Institute of Science and Technology Austria, 2021, doi:10.15479/AT:ISTA:9022.
short: G. Cipolloni, Fluctuations in the Spectrum of Random Matrices, Institute
of Science and Technology Austria, 2021.
date_created: 2021-01-21T18:16:54Z
date_published: 2021-01-25T00:00:00Z
date_updated: 2023-09-07T13:29:32Z
day: '25'
ddc:
- '510'
degree_awarded: PhD
department:
- _id: GradSch
- _id: LaEr
doi: 10.15479/AT:ISTA:9022
ec_funded: 1
file:
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date_created: 2021-01-25T14:19:03Z
date_updated: 2021-01-25T14:19:03Z
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date_created: 2021-01-25T14:19:10Z
date_updated: 2021-01-25T14:19:10Z
file_id: '9044'
file_name: Thesis_files.zip
file_size: 12775206
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file_date_updated: 2021-01-25T14:19:10Z
has_accepted_license: '1'
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
page: '380'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 258DCDE6-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '338804'
name: Random matrices, universality and disordered quantum systems
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
status: public
supervisor:
- first_name: László
full_name: Erdös, László
id: 4DBD5372-F248-11E8-B48F-1D18A9856A87
last_name: Erdös
orcid: 0000-0001-5366-9603
title: Fluctuations in the spectrum of random matrices
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2021'
...
---
_id: '9036'
abstract:
- lang: eng
text: In this short note, we prove that the square root of the quantum Jensen-Shannon
divergence is a true metric on the cone of positive matrices, and hence in particular
on the quantum state space.
acknowledgement: D. Virosztek was supported by the European Union's Horizon 2020 research
and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 846294,
and partially supported by the Hungarian National Research, Development and Innovation
Office (NKFIH) via grants no. K124152, and no. KH129601.
article_number: '107595'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Daniel
full_name: Virosztek, Daniel
id: 48DB45DA-F248-11E8-B48F-1D18A9856A87
last_name: Virosztek
orcid: 0000-0003-1109-5511
citation:
ama: Virosztek D. The metric property of the quantum Jensen-Shannon divergence.
Advances in Mathematics. 2021;380(3). doi:10.1016/j.aim.2021.107595
apa: Virosztek, D. (2021). The metric property of the quantum Jensen-Shannon divergence.
Advances in Mathematics. Elsevier. https://doi.org/10.1016/j.aim.2021.107595
chicago: Virosztek, Daniel. “The Metric Property of the Quantum Jensen-Shannon Divergence.”
Advances in Mathematics. Elsevier, 2021. https://doi.org/10.1016/j.aim.2021.107595.
ieee: D. Virosztek, “The metric property of the quantum Jensen-Shannon divergence,”
Advances in Mathematics, vol. 380, no. 3. Elsevier, 2021.
ista: Virosztek D. 2021. The metric property of the quantum Jensen-Shannon divergence.
Advances in Mathematics. 380(3), 107595.
mla: Virosztek, Daniel. “The Metric Property of the Quantum Jensen-Shannon Divergence.”
Advances in Mathematics, vol. 380, no. 3, 107595, Elsevier, 2021, doi:10.1016/j.aim.2021.107595.
short: D. Virosztek, Advances in Mathematics 380 (2021).
date_created: 2021-01-22T17:55:17Z
date_published: 2021-03-26T00:00:00Z
date_updated: 2023-08-07T13:34:48Z
day: '26'
department:
- _id: LaEr
doi: 10.1016/j.aim.2021.107595
ec_funded: 1
external_id:
arxiv:
- '1910.10447'
isi:
- '000619676100035'
intvolume: ' 380'
isi: 1
issue: '3'
keyword:
- General Mathematics
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1910.10447
month: '03'
oa: 1
oa_version: Preprint
project:
- _id: 26A455A6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '846294'
name: Geometric study of Wasserstein spaces and free probability
publication: Advances in Mathematics
publication_identifier:
issn:
- 0001-8708
publication_status: published
publisher: Elsevier
quality_controlled: '1'
status: public
title: The metric property of the quantum Jensen-Shannon divergence
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 380
year: '2021'
...