---
_id: '12536'
abstract:
- lang: eng
text: 'We consider the problem of estimating a rank-1 signal corrupted by structured
rotationally invariant noise, and address the following question: how well do
inference algorithms perform when the noise statistics is unknown and hence Gaussian
noise is assumed? While the matched Bayes-optimal setting with unstructured noise
is well understood, the analysis of this mismatched problem is only at its premises.
In this paper, we make a step towards understanding the effect of the strong source
of mismatch which is the noise statistics. Our main technical contribution is
the rigorous analysis of a Bayes estimator and of an approximate message passing
(AMP) algorithm, both of which incorrectly assume a Gaussian setup. The first
result exploits the theory of spherical integrals and of low-rank matrix perturbations;
the idea behind the second one is to design and analyze an artificial AMP which,
by taking advantage of the flexibility in the denoisers, is able to "correct"
the mismatch. Armed with these sharp asymptotic characterizations, we unveil a
rich and often unexpected phenomenology. For example, despite AMP is in principle
designed to efficiently compute the Bayes estimator, the former is outperformed
by the latter in terms of mean-square error. We show that this performance gap
is due to an incorrect estimation of the signal norm. In fact, when the SNR is
large enough, the overlaps of the AMP and the Bayes estimator coincide, and they
even match those of optimal estimators taking into account the structure of the
noise.'
acknowledgement: "M. Mondelli was partially supported by the 2019 Lopez-Loreta Prize.
The authors acknowledge\r\ndiscussions with A. Krajenbrink, M. Robinson, A. Depope,
N. Macris and F. Pourkamali.\r\n"
alternative_title:
- NeurIPS
article_processing_charge: No
arxiv: 1
author:
- first_name: Jean
full_name: Barbier, Jean
last_name: Barbier
- first_name: TianQi
full_name: Hou, TianQi
last_name: Hou
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
- first_name: Manuel
full_name: Saenz, Manuel
last_name: Saenz
citation:
ama: 'Barbier J, Hou T, Mondelli M, Saenz M. The price of ignorance: How much does
it cost to forget noise structure in low-rank matrix estimation? In: 36th Annual
Conference on Neural Information Processing Systems. Vol 35. ; 2022.'
apa: 'Barbier, J., Hou, T., Mondelli, M., & Saenz, M. (2022). The price of ignorance:
How much does it cost to forget noise structure in low-rank matrix estimation?
In 36th Annual Conference on Neural Information Processing Systems (Vol.
35). New Orleans, LA, United States.'
chicago: 'Barbier, Jean, TianQi Hou, Marco Mondelli, and Manuel Saenz. “The Price
of Ignorance: How Much Does It Cost to Forget Noise Structure in Low-Rank Matrix
Estimation?” In 36th Annual Conference on Neural Information Processing Systems,
Vol. 35, 2022.'
ieee: 'J. Barbier, T. Hou, M. Mondelli, and M. Saenz, “The price of ignorance: How
much does it cost to forget noise structure in low-rank matrix estimation?,” in
36th Annual Conference on Neural Information Processing Systems, New Orleans,
LA, United States, 2022, vol. 35.'
ista: 'Barbier J, Hou T, Mondelli M, Saenz M. 2022. The price of ignorance: How
much does it cost to forget noise structure in low-rank matrix estimation? 36th
Annual Conference on Neural Information Processing Systems. NeurIPS: Neural Information
Processing Systems, NeurIPS, vol. 35.'
mla: 'Barbier, Jean, et al. “The Price of Ignorance: How Much Does It Cost to Forget
Noise Structure in Low-Rank Matrix Estimation?” 36th Annual Conference on Neural
Information Processing Systems, vol. 35, 2022.'
short: J. Barbier, T. Hou, M. Mondelli, M. Saenz, in:, 36th Annual Conference on
Neural Information Processing Systems, 2022.
conference:
end_date: 2022-12-09
location: New Orleans, LA, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2022-11-28
corr_author: '1'
date_created: 2023-02-10T13:45:41Z
date_published: 2022-11-20T00:00:00Z
date_updated: 2024-10-09T21:04:25Z
day: '20'
department:
- _id: MaMo
external_id:
arxiv:
- '2205.10009'
intvolume: ' 35'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2205.10009
month: '11'
oa: 1
oa_version: Preprint
publication: 36th Annual Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713871088'
publication_status: published
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'The price of ignorance: How much does it cost to forget noise structure in
low-rank matrix estimation?'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2022'
...
---
OA_place: repository
OA_type: green
_id: '12537'
abstract:
- lang: eng
text: 'The Neural Tangent Kernel (NTK) has emerged as a powerful tool to provide
memorization, optimization and generalization guarantees in deep neural networks.
A line of work has studied the NTK spectrum for two-layer and deep networks with
at least a layer with Ω(N) neurons, N being the number of training samples. Furthermore,
there is increasing evidence suggesting that deep networks with sub-linear layer
widths are powerful memorizers and optimizers, as long as the number of parameters
exceeds the number of samples. Thus, a natural open question is whether the NTK
is well conditioned in such a challenging sub-linear setup. In this paper, we
answer this question in the affirmative. Our key technical contribution is a lower
bound on the smallest NTK eigenvalue for deep networks with the minimum possible
over-parameterization: the number of parameters is roughly Ω(N) and, hence, the
number of neurons is as little as Ω(N−−√). To showcase the applicability of our
NTK bounds, we provide two results concerning memorization capacity and optimization
guarantees for gradient descent training.'
acknowledgement: "The authors were partially supported by the 2019 Lopez-Loreta prize,
and they would like to thank\r\nQuynh Nguyen, Mahdi Soltanolkotabi and Adel Javanmard
for helpful discussions.\r\n"
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Simone
full_name: Bombari, Simone
id: ca726dda-de17-11ea-bc14-f9da834f63aa
last_name: Bombari
- first_name: Mohammad Hossein
full_name: Amani, Mohammad Hossein
last_name: Amani
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
citation:
ama: 'Bombari S, Amani MH, Mondelli M. Memorization and optimization in deep neural
networks with minimum over-parameterization. In: 36th Conference on Neural
Information Processing Systems. Vol 35. Neural Information Processing Systems
Foundation; 2022:7628-7640.'
apa: 'Bombari, S., Amani, M. H., & Mondelli, M. (2022). Memorization and optimization
in deep neural networks with minimum over-parameterization. In 36th Conference
on Neural Information Processing Systems (Vol. 35, pp. 7628–7640). New Orleans,
LA, United States: Neural Information Processing Systems Foundation.'
chicago: Bombari, Simone, Mohammad Hossein Amani, and Marco Mondelli. “Memorization
and Optimization in Deep Neural Networks with Minimum Over-Parameterization.”
In 36th Conference on Neural Information Processing Systems, 35:7628–40.
Neural Information Processing Systems Foundation, 2022.
ieee: S. Bombari, M. H. Amani, and M. Mondelli, “Memorization and optimization in
deep neural networks with minimum over-parameterization,” in 36th Conference
on Neural Information Processing Systems, New Orleans, LA, United States,
2022, vol. 35, pp. 7628–7640.
ista: 'Bombari S, Amani MH, Mondelli M. 2022. Memorization and optimization in deep
neural networks with minimum over-parameterization. 36th Conference on Neural
Information Processing Systems. NeurIPS: Neural Information Processing Systems,
Advances in Neural Information Processing Systems, vol. 35, 7628–7640.'
mla: Bombari, Simone, et al. “Memorization and Optimization in Deep Neural Networks
with Minimum Over-Parameterization.” 36th Conference on Neural Information
Processing Systems, vol. 35, Neural Information Processing Systems Foundation,
2022, pp. 7628–40.
short: S. Bombari, M.H. Amani, M. Mondelli, in:, 36th Conference on Neural Information
Processing Systems, Neural Information Processing Systems Foundation, 2022, pp.
7628–7640.
conference:
end_date: 2022-12-09
location: New Orleans, LA, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2022-11-28
corr_author: '1'
date_created: 2023-02-10T13:46:37Z
date_published: 2022-07-24T00:00:00Z
date_updated: 2025-05-14T11:28:22Z
day: '24'
department:
- _id: MaMo
external_id:
arxiv:
- '2205.10217'
intvolume: ' 35'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2205.10217'
month: '07'
oa: 1
oa_version: Preprint
page: 7628-7640
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: 36th Conference on Neural Information Processing Systems
publication_identifier:
eissn:
- 1049-5258
isbn:
- '9781713871088'
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
status: public
title: Memorization and optimization in deep neural networks with minimum over-parameterization
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2022'
...
---
_id: '12538'
abstract:
- lang: eng
text: In this paper, we study the compression of a target two-layer neural network
with N nodes into a compressed network with MIEEE Information Theory Workshop.
2022:588-593. doi:10.1109/ITW54588.2022.9965870
apa: 'Amani, M. H., Bombari, S., Mondelli, M., Pukdee, R., & Rini, S. (2022).
Sharp asymptotics on the compression of two-layer neural networks. IEEE Information
Theory Workshop. Mumbai, India: IEEE. https://doi.org/10.1109/ITW54588.2022.9965870'
chicago: Amani, Mohammad Hossein, Simone Bombari, Marco Mondelli, Rattana Pukdee,
and Stefano Rini. “Sharp Asymptotics on the Compression of Two-Layer Neural Networks.”
IEEE Information Theory Workshop. IEEE, 2022. https://doi.org/10.1109/ITW54588.2022.9965870.
ieee: M. H. Amani, S. Bombari, M. Mondelli, R. Pukdee, and S. Rini, “Sharp asymptotics
on the compression of two-layer neural networks,” IEEE Information Theory Workshop.
IEEE, pp. 588–593, 2022.
ista: Amani MH, Bombari S, Mondelli M, Pukdee R, Rini S. 2022. Sharp asymptotics
on the compression of two-layer neural networks. IEEE Information Theory Workshop.,
588–593.
mla: Amani, Mohammad Hossein, et al. “Sharp Asymptotics on the Compression of Two-Layer
Neural Networks.” IEEE Information Theory Workshop, IEEE, 2022, pp. 588–93,
doi:10.1109/ITW54588.2022.9965870.
short: M.H. Amani, S. Bombari, M. Mondelli, R. Pukdee, S. Rini, IEEE Information
Theory Workshop (2022) 588–593.
conference:
end_date: 2022-11-09
location: Mumbai, India
name: 'ITW: Information Theory Workshop'
start_date: 2022-11-01
date_created: 2023-02-10T13:47:56Z
date_published: 2022-11-16T00:00:00Z
date_updated: 2025-09-10T09:53:31Z
day: '16'
department:
- _id: MaMo
doi: 10.1109/ITW54588.2022.9965870
external_id:
arxiv:
- '2205.08199'
isi:
- '000904341100099'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2205.08199'
month: '11'
oa: 1
oa_version: Preprint
page: 588-593
publication: IEEE Information Theory Workshop
publication_identifier:
isbn:
- '9781665483414'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Sharp asymptotics on the compression of two-layer neural networks
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2022'
...
---
_id: '12540'
abstract:
- lang: eng
text: We consider the problem of signal estimation in generalized linear models
defined via rotationally invariant design matrices. Since these matrices can have
an arbitrary spectral distribution, this model is well suited for capturing complex
correlation structures which often arise in applications. We propose a novel family
of approximate message passing (AMP) algorithms for signal estimation, and rigorously
characterize their performance in the high-dimensional limit via a state evolution
recursion. Our rotationally invariant AMP has complexity of the same order as
the existing AMP derived under the restrictive assumption of a Gaussian design;
our algorithm also recovers this existing AMP as a special case. Numerical results
showcase a performance close to Vector AMP (which is conjectured to be Bayes-optimal
in some settings), but obtained with a much lower complexity, as the proposed
algorithm does not require a computationally expensive singular value decomposition.
acknowledgement: The authors would like to thank the anonymous reviewers for their
helpful comments. KK and MM were partially supported by the 2019 Lopez-Loreta Prize.
article_number: '22'
article_processing_charge: No
author:
- first_name: Ramji
full_name: Venkataramanan, Ramji
last_name: Venkataramanan
- first_name: Kevin
full_name: Kögler, Kevin
id: 94ec913c-dc85-11ea-9058-e5051ab2428b
last_name: Kögler
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
citation:
ama: 'Venkataramanan R, Kögler K, Mondelli M. Estimation in rotationally invariant
generalized linear models via approximate message passing. In: Proceedings
of the 39th International Conference on Machine Learning. Vol 162. ML Research
Press; 2022.'
apa: 'Venkataramanan, R., Kögler, K., & Mondelli, M. (2022). Estimation in rotationally
invariant generalized linear models via approximate message passing. In Proceedings
of the 39th International Conference on Machine Learning (Vol. 162). Baltimore,
MD, United States: ML Research Press.'
chicago: Venkataramanan, Ramji, Kevin Kögler, and Marco Mondelli. “Estimation in
Rotationally Invariant Generalized Linear Models via Approximate Message Passing.”
In Proceedings of the 39th International Conference on Machine Learning,
Vol. 162. ML Research Press, 2022.
ieee: R. Venkataramanan, K. Kögler, and M. Mondelli, “Estimation in rotationally
invariant generalized linear models via approximate message passing,” in Proceedings
of the 39th International Conference on Machine Learning, Baltimore, MD, United
States, 2022, vol. 162.
ista: 'Venkataramanan R, Kögler K, Mondelli M. 2022. Estimation in rotationally
invariant generalized linear models via approximate message passing. Proceedings
of the 39th International Conference on Machine Learning. ICML: International
Conference on Machine Learning vol. 162, 22.'
mla: Venkataramanan, Ramji, et al. “Estimation in Rotationally Invariant Generalized
Linear Models via Approximate Message Passing.” Proceedings of the 39th International
Conference on Machine Learning, vol. 162, 22, ML Research Press, 2022.
short: R. Venkataramanan, K. Kögler, M. Mondelli, in:, Proceedings of the 39th International
Conference on Machine Learning, ML Research Press, 2022.
conference:
end_date: 2022-07-23
location: Baltimore, MD, United States
name: 'ICML: International Conference on Machine Learning'
start_date: 2022-07-17
corr_author: '1'
date_created: 2023-02-10T13:49:04Z
date_published: 2022-01-01T00:00:00Z
date_updated: 2025-04-15T07:50:16Z
ddc:
- '000'
department:
- _id: MaMo
file:
- access_level: open_access
checksum: 67436eb0a660789514cdf9db79e84683
content_type: application/pdf
creator: dernst
date_created: 2023-02-13T10:53:11Z
date_updated: 2023-02-13T10:53:11Z
file_id: '12547'
file_name: 2022_PMLR_Venkataramanan.pdf
file_size: 2341343
relation: main_file
success: 1
file_date_updated: 2023-02-13T10:53:11Z
has_accepted_license: '1'
intvolume: ' 162'
language:
- iso: eng
oa: 1
oa_version: Published Version
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: Proceedings of the 39th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
status: public
title: Estimation in rotationally invariant generalized linear models via approximate
message passing
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 162
year: '2022'
...
---
_id: '12568'
abstract:
- lang: eng
text: We treat the problem of risk-aware control for stochastic shortest path (SSP)
on Markov decision processes (MDP). Typically, expectation is considered for SSP,
which however is oblivious to the incurred risk. We present an alternative view,
instead optimizing conditional value-at-risk (CVaR), an established risk measure.
We treat both Markov chains as well as MDP and introduce, through novel insights,
two algorithms, based on linear programming and value iteration, respectively.
Both algorithms offer precise and provably correct solutions. Evaluation of our
prototype implementation shows that risk-aware control is feasible on several
moderately sized models.
article_processing_charge: No
arxiv: 1
author:
- first_name: Tobias
full_name: Meggendorfer, Tobias
id: b21b0c15-30a2-11eb-80dc-f13ca25802e1
last_name: Meggendorfer
orcid: 0000-0002-1712-2165
citation:
ama: 'Meggendorfer T. Risk-aware stochastic shortest path. In: Proceedings of
the 36th AAAI Conference on Artificial Intelligence, AAAI 2022. Vol 36. Association
for the Advancement of Artificial Intelligence; 2022:9858-9867. doi:10.1609/aaai.v36i9.21222'
apa: 'Meggendorfer, T. (2022). Risk-aware stochastic shortest path. In Proceedings
of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022 (Vol. 36,
pp. 9858–9867). Virtual: Association for the Advancement of Artificial Intelligence.
https://doi.org/10.1609/aaai.v36i9.21222'
chicago: Meggendorfer, Tobias. “Risk-Aware Stochastic Shortest Path.” In Proceedings
of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, 36:9858–67.
Association for the Advancement of Artificial Intelligence, 2022. https://doi.org/10.1609/aaai.v36i9.21222.
ieee: T. Meggendorfer, “Risk-aware stochastic shortest path,” in Proceedings
of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, Virtual,
2022, vol. 36, no. 9, pp. 9858–9867.
ista: Meggendorfer T. 2022. Risk-aware stochastic shortest path. Proceedings of
the 36th AAAI Conference on Artificial Intelligence, AAAI 2022. Conference on
Artificial Intelligence vol. 36, 9858–9867.
mla: Meggendorfer, Tobias. “Risk-Aware Stochastic Shortest Path.” Proceedings
of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, vol. 36,
no. 9, Association for the Advancement of Artificial Intelligence, 2022, pp. 9858–67,
doi:10.1609/aaai.v36i9.21222.
short: T. Meggendorfer, in:, Proceedings of the 36th AAAI Conference on Artificial
Intelligence, AAAI 2022, Association for the Advancement of Artificial Intelligence,
2022, pp. 9858–9867.
conference:
end_date: 2022-03-01
location: Virtual
name: Conference on Artificial Intelligence
start_date: 2022-02-22
corr_author: '1'
date_created: 2023-02-19T23:00:56Z
date_published: 2022-06-28T00:00:00Z
date_updated: 2024-10-09T21:04:32Z
day: '28'
department:
- _id: KrCh
doi: 10.1609/aaai.v36i9.21222
external_id:
arxiv:
- '2203.01640'
intvolume: ' 36'
issue: '9'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2203.01640'
month: '06'
oa: 1
oa_version: Preprint
page: 9858-9867
publication: Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI
2022
publication_identifier:
eissn:
- 2374-3468
isbn:
- '1577358767'
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: Risk-aware stochastic shortest path
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 36
year: '2022'
...
---
_id: '12670'
abstract:
- lang: eng
text: DNA methylation plays essential homeostatic functions in eukaryotic genomes.
In animals, DNA methylation is also developmentally regulated and, in turn, regulates
development. In the past two decades, huge research effort has endorsed the understanding
that DNA methylation plays a similar role in plant development, especially during
sexual reproduction. The power of whole-genome sequencing and cell isolation techniques,
as well as bioinformatics tools, have enabled recent studies to reveal dynamic
changes in DNA methylation during germline development. Furthermore, the combination
of these technological advances with genetics, developmental biology and cell
biology tools has revealed functional methylation reprogramming events that control
gene and transposon activities in flowering plant germlines. In this review, we
discuss the major advances in our knowledge of DNA methylation dynamics during
male and female germline development in flowering plants.
article_processing_charge: No
article_type: review
author:
- first_name: Shengbo
full_name: He, Shengbo
last_name: He
- first_name: Xiaoqi
full_name: Feng, Xiaoqi
id: e0164712-22ee-11ed-b12a-d80fcdf35958
last_name: Feng
orcid: 0000-0002-4008-1234
citation:
ama: He S, Feng X. DNA methylation dynamics during germline development. Journal
of Integrative Plant Biology. 2022;64(12):2240-2251. doi:10.1111/jipb.13422
apa: He, S., & Feng, X. (2022). DNA methylation dynamics during germline development.
Journal of Integrative Plant Biology. Wiley. https://doi.org/10.1111/jipb.13422
chicago: He, Shengbo, and Xiaoqi Feng. “DNA Methylation Dynamics during Germline
Development.” Journal of Integrative Plant Biology. Wiley, 2022. https://doi.org/10.1111/jipb.13422.
ieee: S. He and X. Feng, “DNA methylation dynamics during germline development,”
Journal of Integrative Plant Biology, vol. 64, no. 12. Wiley, pp. 2240–2251,
2022.
ista: He S, Feng X. 2022. DNA methylation dynamics during germline development.
Journal of Integrative Plant Biology. 64(12), 2240–2251.
mla: He, Shengbo, and Xiaoqi Feng. “DNA Methylation Dynamics during Germline Development.”
Journal of Integrative Plant Biology, vol. 64, no. 12, Wiley, 2022, pp.
2240–51, doi:10.1111/jipb.13422.
short: S. He, X. Feng, Journal of Integrative Plant Biology 64 (2022) 2240–2251.
date_created: 2023-02-23T09:15:57Z
date_published: 2022-12-07T00:00:00Z
date_updated: 2024-10-14T12:03:14Z
day: '07'
department:
- _id: XiFe
doi: 10.1111/jipb.13422
extern: '1'
external_id:
pmid:
- '36478632'
intvolume: ' 64'
issue: '12'
keyword:
- Plant Science
- General Biochemistry
- Genetics and Molecular Biology
- Biochemistry
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1111/jipb.13422
month: '12'
oa: 1
oa_version: Published Version
page: 2240-2251
pmid: 1
publication: Journal of Integrative Plant Biology
publication_identifier:
eissn:
- 1744-7909
issn:
- 1672-9072
publication_status: published
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: DNA methylation dynamics during germline development
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 64
year: '2022'
...
---
_id: '12671'
abstract:
- lang: eng
text: Sperm chromatin is typically transformed by protamines into a compact and
transcriptionally inactive state1,2. Sperm cells of flowering plants lack protamines,
yet they have small, transcriptionally active nuclei with chromatin condensed
through an unknown mechanism3,4. Here we show that a histone variant, H2B.8, mediates
sperm chromatin and nuclear condensation in Arabidopsis thaliana. Loss of H2B.8
causes enlarged sperm nuclei with dispersed chromatin, whereas ectopic expression
in somatic cells produces smaller nuclei with aggregated chromatin. This result
demonstrates that H2B.8 is sufficient for chromatin condensation. H2B.8 aggregates
transcriptionally inactive AT-rich chromatin into phase-separated condensates,
which facilitates nuclear compaction without reducing transcription. Reciprocal
crosses show that mutation of h2b.8 reduces male transmission, which suggests
that H2B.8-mediated sperm compaction is important for fertility. Altogether, our
results reveal a new mechanism of nuclear compaction through global aggregation
of unexpressed chromatin. We propose that H2B.8 is an evolutionary innovation
of flowering plants that achieves nuclear condensation compatible with active
transcription.
article_processing_charge: No
article_type: original
author:
- first_name: Toby
full_name: Buttress, Toby
last_name: Buttress
- first_name: Shengbo
full_name: He, Shengbo
last_name: He
- first_name: Liang
full_name: Wang, Liang
last_name: Wang
- first_name: Shaoli
full_name: Zhou, Shaoli
last_name: Zhou
- first_name: Gerhard
full_name: Saalbach, Gerhard
last_name: Saalbach
- first_name: Martin
full_name: Vickers, Martin
last_name: Vickers
- first_name: Guohong
full_name: Li, Guohong
last_name: Li
- first_name: Pilong
full_name: Li, Pilong
last_name: Li
- first_name: Xiaoqi
full_name: Feng, Xiaoqi
id: e0164712-22ee-11ed-b12a-d80fcdf35958
last_name: Feng
orcid: 0000-0002-4008-1234
citation:
ama: Buttress T, He S, Wang L, et al. Histone H2B.8 compacts flowering plant sperm
through chromatin phase separation. Nature. 2022;611(7936):614-622. doi:10.1038/s41586-022-05386-6
apa: Buttress, T., He, S., Wang, L., Zhou, S., Saalbach, G., Vickers, M., … Feng,
X. (2022). Histone H2B.8 compacts flowering plant sperm through chromatin phase
separation. Nature. Springer Nature. https://doi.org/10.1038/s41586-022-05386-6
chicago: Buttress, Toby, Shengbo He, Liang Wang, Shaoli Zhou, Gerhard Saalbach,
Martin Vickers, Guohong Li, Pilong Li, and Xiaoqi Feng. “Histone H2B.8 Compacts
Flowering Plant Sperm through Chromatin Phase Separation.” Nature. Springer
Nature, 2022. https://doi.org/10.1038/s41586-022-05386-6.
ieee: T. Buttress et al., “Histone H2B.8 compacts flowering plant sperm through
chromatin phase separation,” Nature, vol. 611, no. 7936. Springer Nature,
pp. 614–622, 2022.
ista: Buttress T, He S, Wang L, Zhou S, Saalbach G, Vickers M, Li G, Li P, Feng
X. 2022. Histone H2B.8 compacts flowering plant sperm through chromatin phase
separation. Nature. 611(7936), 614–622.
mla: Buttress, Toby, et al. “Histone H2B.8 Compacts Flowering Plant Sperm through
Chromatin Phase Separation.” Nature, vol. 611, no. 7936, Springer Nature,
2022, pp. 614–22, doi:10.1038/s41586-022-05386-6.
short: T. Buttress, S. He, L. Wang, S. Zhou, G. Saalbach, M. Vickers, G. Li, P.
Li, X. Feng, Nature 611 (2022) 614–622.
date_created: 2023-02-23T09:17:05Z
date_published: 2022-11-17T00:00:00Z
date_updated: 2024-10-14T12:03:36Z
day: '17'
department:
- _id: XiFe
doi: 10.1038/s41586-022-05386-6
extern: '1'
external_id:
pmid:
- '36323776'
intvolume: ' 611'
issue: '7936'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1038/s41586-022-05386-6
month: '11'
oa: 1
oa_version: Published Version
page: 614-622
pmid: 1
publication: Nature
publication_identifier:
eissn:
- 1476-4687
issn:
- 0028-0836
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Histone H2B.8 compacts flowering plant sperm through chromatin phase separation
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 611
year: '2022'
...
---
_id: '12677'
abstract:
- lang: eng
text: "In modern sample-driven Prophet Inequality, an adversary chooses a sequence
of n items with values v1,v2,…,vn to be presented to a decision maker (DM). The
process follows in two phases. In the first phase (sampling phase), some items,
possibly selected at random, are revealed to the DM, but she can never accept
them. In the second phase, the DM is presented with the other items in a random
order and online fashion. For each item, she must make an irrevocable decision
to either accept the item and stop the process or reject the item forever and
proceed to the next item. The goal of the DM is to maximize the expected value
as compared to a Prophet (or offline algorithm) that has access to all information.
In this setting, the sampling phase has no cost and is not part of the optimization
process. However, in many scenarios, the samples are obtained as part of the decision-making
process.\r\nWe model this aspect as a two-phase Prophet Inequality where an adversary
chooses a sequence of 2n items with values v1,v2,…,v2n and the items are randomly
ordered. Finally, there are two phases of the Prophet Inequality problem with
the first n-items and the rest of the items, respectively. We show that some basic
algorithms achieve a ratio of at most 0.450. We present an algorithm that achieves
a ratio of at least 0.495. Finally, we show that for every algorithm the ratio
it can achieve is at most 0.502. Hence our algorithm is near-optimal."
acknowledgement: This research was partially supported by the ERC CoG 863818 (ForM-SMArt)
grant.
article_number: '2209.14368'
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: Mona
full_name: Mohammadi, Mona
id: 4363614d-b686-11ed-a7d5-ac9e4a24bc2e
last_name: Mohammadi
- first_name: Raimundo J
full_name: Saona Urmeneta, Raimundo J
id: BD1DF4C4-D767-11E9-B658-BC13E6697425
last_name: Saona Urmeneta
orcid: 0000-0001-5103-038X
citation:
ama: Chatterjee K, Mohammadi M, Saona Urmeneta RJ. Repeated prophet inequality with
near-optimal bounds. arXiv. doi:10.48550/ARXIV.2209.14368
apa: Chatterjee, K., Mohammadi, M., & Saona Urmeneta, R. J. (n.d.). Repeated
prophet inequality with near-optimal bounds. arXiv. https://doi.org/10.48550/ARXIV.2209.14368
chicago: Chatterjee, Krishnendu, Mona Mohammadi, and Raimundo J Saona Urmeneta.
“Repeated Prophet Inequality with Near-Optimal Bounds.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2209.14368.
ieee: K. Chatterjee, M. Mohammadi, and R. J. Saona Urmeneta, “Repeated prophet inequality
with near-optimal bounds,” arXiv. .
ista: Chatterjee K, Mohammadi M, Saona Urmeneta RJ. Repeated prophet inequality
with near-optimal bounds. arXiv, 2209.14368.
mla: Chatterjee, Krishnendu, et al. “Repeated Prophet Inequality with Near-Optimal
Bounds.” ArXiv, 2209.14368, doi:10.48550/ARXIV.2209.14368.
short: K. Chatterjee, M. Mohammadi, R.J. Saona Urmeneta, ArXiv (n.d.).
corr_author: '1'
date_created: 2023-02-24T12:21:40Z
date_published: 2022-09-28T00:00:00Z
date_updated: 2025-04-14T07:52:48Z
day: '28'
department:
- _id: GradSch
- _id: KrCh
doi: 10.48550/ARXIV.2209.14368
ec_funded: 1
external_id:
arxiv:
- '2209.14368'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2209.14368'
month: '09'
oa: 1
oa_version: Preprint
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: arXiv
publication_status: submitted
status: public
title: Repeated prophet inequality with near-optimal bounds
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '12684'
abstract:
- lang: eng
text: Given a place ω of a global function field K over a finite field, with
associated affine function ring Rω and completion Kω , the aim of this paper
is to give an effective joint equidistribution result for renormalized primitive
lattice points (a,b)∈Rω2 in the plane Kω2 , and for renormalized solutions
to the gcd equation ax+by=1 . The main tools are techniques of Goronik and Nevo
for counting lattice points in well-rounded families of subsets. This gives a
sharper analog in positive characteristic of a result of Nevo and the first author
for the equidistribution of the primitive lattice points in \ZZ2 .
acknowledgement: "The authors warmly thank Amos Nevo for having presented the authors
to each other during\r\na beautiful conference in Goa in February 2016, where the
idea of this paper was born. The\r\nfirst author thanks the IHES for two post-doctoral
years when most of this paper was discussed,\r\nand the Topology team in Orsay for
financial support at the final stage. The first author was\r\nsupported by the EPRSC
EP/P026710/1 grant. Finally, we warmly thank the referee for many\r\nvery helpful
comments that have improved the readability of this paper."
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Tal
full_name: Horesh, Tal
id: C8B7BF48-8D81-11E9-BCA9-F536E6697425
last_name: Horesh
- first_name: Frédéric
full_name: Paulin, Frédéric
last_name: Paulin
citation:
ama: Horesh T, Paulin F. Effective equidistribution of lattice points in positive
characteristic. Journal de Theorie des Nombres de Bordeaux. 2022;34(3):679-703.
doi:10.5802/JTNB.1222
apa: Horesh, T., & Paulin, F. (2022). Effective equidistribution of lattice
points in positive characteristic. Journal de Theorie Des Nombres de Bordeaux.
Université de Bordeaux. https://doi.org/10.5802/JTNB.1222
chicago: Horesh, Tal, and Frédéric Paulin. “Effective Equidistribution of Lattice
Points in Positive Characteristic.” Journal de Theorie Des Nombres de Bordeaux.
Université de Bordeaux, 2022. https://doi.org/10.5802/JTNB.1222.
ieee: T. Horesh and F. Paulin, “Effective equidistribution of lattice points in
positive characteristic,” Journal de Theorie des Nombres de Bordeaux, vol.
34, no. 3. Université de Bordeaux, pp. 679–703, 2022.
ista: Horesh T, Paulin F. 2022. Effective equidistribution of lattice points in
positive characteristic. Journal de Theorie des Nombres de Bordeaux. 34(3), 679–703.
mla: Horesh, Tal, and Frédéric Paulin. “Effective Equidistribution of Lattice Points
in Positive Characteristic.” Journal de Theorie Des Nombres de Bordeaux,
vol. 34, no. 3, Université de Bordeaux, 2022, pp. 679–703, doi:10.5802/JTNB.1222.
short: T. Horesh, F. Paulin, Journal de Theorie Des Nombres de Bordeaux 34 (2022)
679–703.
corr_author: '1'
date_created: 2023-02-26T23:01:02Z
date_published: 2022-01-27T00:00:00Z
date_updated: 2025-05-14T11:23:08Z
day: '27'
ddc:
- '510'
department:
- _id: TiBr
doi: 10.5802/JTNB.1222
external_id:
arxiv:
- '2001.01534'
isi:
- '000926504300003'
file:
- access_level: open_access
checksum: 08f28fded270251f568f610cf5166d69
content_type: application/pdf
creator: dernst
date_created: 2023-02-27T09:10:13Z
date_updated: 2023-02-27T09:10:13Z
file_id: '12689'
file_name: 2023_JourTheorieNombreBordeaux_Horesh.pdf
file_size: 870468
relation: main_file
success: 1
file_date_updated: 2023-02-27T09:10:13Z
has_accepted_license: '1'
intvolume: ' 34'
isi: 1
issue: '3'
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
page: 679-703
publication: Journal de Theorie des Nombres de Bordeaux
publication_identifier:
eissn:
- 2118-8572
issn:
- 1246-7405
publication_status: published
publisher: Université de Bordeaux
quality_controlled: '1'
scopus_import: '1'
status: public
title: Effective equidistribution of lattice points in positive characteristic
tmp:
image: /image/cc_by_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nd/4.0/legalcode
name: Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)
short: CC BY-ND (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 34
year: '2022'
...
---
_id: '12775'
abstract:
- lang: eng
text: "We consider the problem of approximating the reachability probabilities in
Markov decision processes (MDP) with uncountable (continuous) state and action
spaces. While there are algorithms that, for special classes of such MDP, provide
a sequence of approximations converging to the true value in the limit, our aim
is to obtain an algorithm with guarantees on the precision of the approximation.\r\nAs
this problem is undecidable in general, assumptions on the MDP are necessary.
Our main contribution is to identify sufficient assumptions that are as weak as
possible, thus approaching the \"boundary\" of which systems can be correctly
and reliably analyzed. To this end, we also argue why each of our assumptions
is necessary for algorithms based on processing finitely many observations.\r\nWe
present two solution variants. The first one provides converging lower bounds
under weaker assumptions than typical ones from previous works concerned with
guarantees. The second one then utilizes stronger assumptions to additionally
provide converging upper bounds. Altogether, we obtain an anytime algorithm, i.e.
yielding a sequence of approximants with known and iteratively improving precision,
converging to the true value in the limit. Besides, due to the generality of our
assumptions, our algorithms are very general templates, readily allowing for various
heuristics from literature in contrast to, e.g., a specific discretization algorithm.
Our theoretical contribution thus paves the way for future practical improvements
without sacrificing correctness guarantees."
acknowledgement: "Kush Grover: The author has been supported by the DFG research training
group GRK\r\n2428 ConVeY.\r\nMaximilian Weininger: The author has been partially
supported by DFG projects 383882557\r\nStatistical Unbounded Verification (SUV)
and 427755713 Group-By Objectives in Probabilistic\r\nVerification (GOPro)"
alternative_title:
- LIPIcs
article_number: '11'
article_processing_charge: No
arxiv: 1
author:
- first_name: Kush
full_name: Grover, Kush
last_name: Grover
- first_name: Jan
full_name: Kretinsky, Jan
id: 44CEF464-F248-11E8-B48F-1D18A9856A87
last_name: Kretinsky
orcid: 0000-0002-8122-2881
- first_name: Tobias
full_name: Meggendorfer, Tobias
id: b21b0c15-30a2-11eb-80dc-f13ca25802e1
last_name: Meggendorfer
orcid: 0000-0002-1712-2165
- first_name: Maimilian
full_name: Weininger, Maimilian
last_name: Weininger
citation:
ama: 'Grover K, Kretinsky J, Meggendorfer T, Weininger M. Anytime guarantees for
reachability in uncountable Markov decision processes. In: 33rd International
Conference on Concurrency Theory . Vol 243. Schloss Dagstuhl - Leibniz-Zentrum
für Informatik; 2022. doi:10.4230/LIPIcs.CONCUR.2022.11'
apa: 'Grover, K., Kretinsky, J., Meggendorfer, T., & Weininger, M. (2022). Anytime
guarantees for reachability in uncountable Markov decision processes. In 33rd
International Conference on Concurrency Theory (Vol. 243). Warsaw, Poland:
Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.CONCUR.2022.11'
chicago: Grover, Kush, Jan Kretinsky, Tobias Meggendorfer, and Maimilian Weininger.
“Anytime Guarantees for Reachability in Uncountable Markov Decision Processes.”
In 33rd International Conference on Concurrency Theory , Vol. 243. Schloss
Dagstuhl - Leibniz-Zentrum für Informatik, 2022. https://doi.org/10.4230/LIPIcs.CONCUR.2022.11.
ieee: K. Grover, J. Kretinsky, T. Meggendorfer, and M. Weininger, “Anytime guarantees
for reachability in uncountable Markov decision processes,” in 33rd International
Conference on Concurrency Theory , Warsaw, Poland, 2022, vol. 243.
ista: 'Grover K, Kretinsky J, Meggendorfer T, Weininger M. 2022. Anytime guarantees
for reachability in uncountable Markov decision processes. 33rd International
Conference on Concurrency Theory . CONCUR: Conference on Concurrency Theory, LIPIcs,
vol. 243, 11.'
mla: Grover, Kush, et al. “Anytime Guarantees for Reachability in Uncountable Markov
Decision Processes.” 33rd International Conference on Concurrency Theory ,
vol. 243, 11, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022, doi:10.4230/LIPIcs.CONCUR.2022.11.
short: K. Grover, J. Kretinsky, T. Meggendorfer, M. Weininger, in:, 33rd International
Conference on Concurrency Theory , Schloss Dagstuhl - Leibniz-Zentrum für Informatik,
2022.
conference:
end_date: 2022-09-16
location: Warsaw, Poland
name: 'CONCUR: Conference on Concurrency Theory'
start_date: 2022-09-13
corr_author: '1'
date_created: 2023-03-28T08:09:32Z
date_published: 2022-09-15T00:00:00Z
date_updated: 2024-10-09T21:04:52Z
day: '15'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.4230/LIPIcs.CONCUR.2022.11
external_id:
arxiv:
- '2008.04824'
file:
- access_level: open_access
checksum: e282e43d3ae0ba6e067b72f4583e13c0
content_type: application/pdf
creator: dernst
date_created: 2023-09-26T10:43:15Z
date_updated: 2023-09-26T10:43:15Z
file_id: '14372'
file_name: 2022_LIPIcS_Grover.pdf
file_size: 960036
relation: main_file
success: 1
file_date_updated: 2023-09-26T10:43:15Z
has_accepted_license: '1'
intvolume: ' 243'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
publication: '33rd International Conference on Concurrency Theory '
publication_identifier:
issn:
- 1868-8969
publication_status: published
publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
quality_controlled: '1'
scopus_import: '1'
status: public
title: Anytime guarantees for reachability in uncountable Markov decision processes
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: 243
year: '2022'
...
---
_id: '12776'
abstract:
- lang: eng
text: An improved asymptotic formula is established for the number of rational points
of bounded height on the split smooth del Pezzo surface of degree 5. The proof
uses the five conic bundle structures on the surface.
acknowledgement: This work was begun while the author was participating in the programme
on "Diophantine equations" at the Hausdorff Research Institute for Mathematics in
Bonn in 2009. The hospitality and financial support of the institute is gratefully
acknowledged. The idea of using conic bundles to study the split del Pezzo surface
of degree 5 was explained to the author by Professor Salberger. The author is very
grateful to him for his input into this project and also to Shuntaro Yamagishi for
many useful comments on an earlier version of this manuscript. While working on
this paper the author was supported by FWF grant P32428-N35.
article_processing_charge: No
article_type: original
author:
- first_name: Timothy D
full_name: Browning, Timothy D
id: 35827D50-F248-11E8-B48F-1D18A9856A87
last_name: Browning
orcid: 0000-0002-8314-0177
citation:
ama: Browning TD. Revisiting the Manin–Peyre conjecture for the split del Pezzo
surface of degree 5. New York Journal of Mathematics. 2022;28:1193-1229.
apa: Browning, T. D. (2022). Revisiting the Manin–Peyre conjecture for the split
del Pezzo surface of degree 5. New York Journal of Mathematics. State University
of New York.
chicago: Browning, Timothy D. “Revisiting the Manin–Peyre Conjecture for the Split
Del Pezzo Surface of Degree 5.” New York Journal of Mathematics. State
University of New York, 2022.
ieee: T. D. Browning, “Revisiting the Manin–Peyre conjecture for the split del Pezzo
surface of degree 5,” New York Journal of Mathematics, vol. 28. State University
of New York, pp. 1193–1229, 2022.
ista: Browning TD. 2022. Revisiting the Manin–Peyre conjecture for the split del
Pezzo surface of degree 5. New York Journal of Mathematics. 28, 1193–1229.
mla: Browning, Timothy D. “Revisiting the Manin–Peyre Conjecture for the Split Del
Pezzo Surface of Degree 5.” New York Journal of Mathematics, vol. 28, State
University of New York, 2022, pp. 1193–229.
short: T.D. Browning, New York Journal of Mathematics 28 (2022) 1193–1229.
corr_author: '1'
date_created: 2023-03-28T09:21:09Z
date_published: 2022-08-24T00:00:00Z
date_updated: 2025-04-15T07:39:01Z
day: '24'
ddc:
- '510'
department:
- _id: TiBr
file:
- access_level: open_access
checksum: c01e8291794a1bdb7416aa103cb68ef8
content_type: application/pdf
creator: dernst
date_created: 2023-03-30T07:09:35Z
date_updated: 2023-03-30T07:09:35Z
file_id: '12778'
file_name: 2022_NYJM_Browning.pdf
file_size: 897267
relation: main_file
success: 1
file_date_updated: 2023-03-30T07:09:35Z
has_accepted_license: '1'
intvolume: ' 28'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
page: 1193 - 1229
project:
- _id: 26AEDAB2-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P32428
name: New frontiers of the Manin conjecture
publication: New York Journal of Mathematics
publication_identifier:
issn:
- 1076-9803
publication_status: published
publisher: State University of New York
quality_controlled: '1'
status: public
title: Revisiting the Manin–Peyre conjecture for the split del Pezzo surface of degree
5
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 28
year: '2022'
...
---
_id: '12793'
abstract:
- lang: eng
text: "Let F be a global function field with constant field Fq. Let G be a reductive
group over Fq. We establish a variant of Arthur's truncated kernel for G and for
its Lie algebra which generalizes Arthur's original construction. We establish
a coarse geometric expansion for our variant truncation.\r\nAs applications, we
consider some existence and uniqueness problems of some cuspidal automorphic representations
for the functions field of the projective line P1Fq with two points of ramifications."
acknowledgement: 'I’d like to thank Prof. Chaudouard for introducing me to this area.
I’d like to thank Prof. Harris for asking me the question that makes Section 10
possible. I’m grateful for the support of Prof. Hausel and IST Austria. The author
was funded by an ISTplus fellowship: This project has received funding from the
European Union’s Horizon 2020 research and innovation programme under the Marie
Skłodowska-Curie Grant Agreement No. 754411.'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Hongjie
full_name: Yu, Hongjie
id: 3D7DD9BE-F248-11E8-B48F-1D18A9856A87
last_name: Yu
orcid: 0000-0001-5128-7126
citation:
ama: Yu H. A coarse geometric expansion of a variant of Arthur’s truncated traces
and some applications. Pacific Journal of Mathematics. 2022;321(1):193-237.
doi:10.2140/pjm.2022.321.193
apa: Yu, H. (2022). A coarse geometric expansion of a variant of Arthur’s truncated
traces and some applications. Pacific Journal of Mathematics. Mathematical
Sciences Publishers. https://doi.org/10.2140/pjm.2022.321.193
chicago: Yu, Hongjie. “ A Coarse Geometric Expansion of a Variant of Arthur’s Truncated
Traces and Some Applications.” Pacific Journal of Mathematics. Mathematical
Sciences Publishers, 2022. https://doi.org/10.2140/pjm.2022.321.193.
ieee: H. Yu, “ A coarse geometric expansion of a variant of Arthur’s truncated traces
and some applications,” Pacific Journal of Mathematics, vol. 321, no. 1.
Mathematical Sciences Publishers, pp. 193–237, 2022.
ista: Yu H. 2022. A coarse geometric expansion of a variant of Arthur’s truncated
traces and some applications. Pacific Journal of Mathematics. 321(1), 193–237.
mla: Yu, Hongjie. “ A Coarse Geometric Expansion of a Variant of Arthur’s Truncated
Traces and Some Applications.” Pacific Journal of Mathematics, vol. 321,
no. 1, Mathematical Sciences Publishers, 2022, pp. 193–237, doi:10.2140/pjm.2022.321.193.
short: H. Yu, Pacific Journal of Mathematics 321 (2022) 193–237.
corr_author: '1'
date_created: 2023-04-02T22:01:11Z
date_published: 2022-08-29T00:00:00Z
date_updated: 2025-04-14T07:44:01Z
day: '29'
department:
- _id: TaHa
doi: 10.2140/pjm.2022.321.193
ec_funded: 1
external_id:
arxiv:
- '2109.10245'
isi:
- '000954466300006'
intvolume: ' 321'
isi: 1
issue: '1'
keyword:
- Arthur–Selberg trace formula
- cuspidal automorphic representations
- global function fields
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2109.10245
month: '08'
oa: 1
oa_version: Preprint
page: 193-237
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: Pacific Journal of Mathematics
publication_identifier:
eissn:
- 1945-5844
issn:
- 0030-8730
publication_status: published
publisher: Mathematical Sciences Publishers
quality_controlled: '1'
scopus_import: '1'
status: public
title: ' A coarse geometric expansion of a variant of Arthur''s truncated traces and
some applications'
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 321
year: '2022'
...
---
_id: '12860'
abstract:
- lang: eng
text: 'Memorization of the relation between entities in a dataset can lead to privacy
issues when using a trained model for question answering. We introduce Relational
Memorization (RM) to understand, quantify and control this phenomenon. While bounding
general memorization can have detrimental effects on the performance of a trained
model, bounding RM does not prevent effective learning. The difference is most
pronounced when the data distribution is long-tailed, with many queries having
only few training examples: Impeding general memorization prevents effective learning,
while impeding only relational memorization still allows learning general properties
of the underlying concepts. We formalize the notion of Relational Privacy (RP)
and, inspired by Differential Privacy (DP), we provide a possible definition of
Differential Relational Privacy (DrP). These notions can be used to describe and
compute bounds on the amount of RM in a trained model. We illustrate Relational
Privacy concepts in experiments with large-scale models for Question Answering.'
article_number: '2203.16701'
article_processing_charge: No
arxiv: 1
author:
- first_name: Simone
full_name: Bombari, Simone
id: ca726dda-de17-11ea-bc14-f9da834f63aa
last_name: Bombari
- first_name: Alessandro
full_name: Achille, Alessandro
last_name: Achille
- first_name: Zijian
full_name: Wang, Zijian
last_name: Wang
- first_name: Yu-Xiang
full_name: Wang, Yu-Xiang
last_name: Wang
- first_name: Yusheng
full_name: Xie, Yusheng
last_name: Xie
- first_name: Kunwar Yashraj
full_name: Singh, Kunwar Yashraj
last_name: Singh
- first_name: Srikar
full_name: Appalaraju, Srikar
last_name: Appalaraju
- first_name: Vijay
full_name: Mahadevan, Vijay
last_name: Mahadevan
- first_name: Stefano
full_name: Soatto, Stefano
last_name: Soatto
citation:
ama: Bombari S, Achille A, Wang Z, et al. Towards differential relational privacy
and its use in question answering. arXiv. doi:10.48550/arXiv.2203.16701
apa: Bombari, S., Achille, A., Wang, Z., Wang, Y.-X., Xie, Y., Singh, K. Y., … Soatto,
S. (n.d.). Towards differential relational privacy and its use in question answering.
arXiv. https://doi.org/10.48550/arXiv.2203.16701
chicago: Bombari, Simone, Alessandro Achille, Zijian Wang, Yu-Xiang Wang, Yusheng
Xie, Kunwar Yashraj Singh, Srikar Appalaraju, Vijay Mahadevan, and Stefano Soatto.
“Towards Differential Relational Privacy and Its Use in Question Answering.” ArXiv,
n.d. https://doi.org/10.48550/arXiv.2203.16701.
ieee: S. Bombari et al., “Towards differential relational privacy and its
use in question answering,” arXiv. .
ista: Bombari S, Achille A, Wang Z, Wang Y-X, Xie Y, Singh KY, Appalaraju S, Mahadevan
V, Soatto S. Towards differential relational privacy and its use in question answering.
arXiv, 2203.16701.
mla: Bombari, Simone, et al. “Towards Differential Relational Privacy and Its Use
in Question Answering.” ArXiv, 2203.16701, doi:10.48550/arXiv.2203.16701.
short: S. Bombari, A. Achille, Z. Wang, Y.-X. Wang, Y. Xie, K.Y. Singh, S. Appalaraju,
V. Mahadevan, S. Soatto, ArXiv (n.d.).
date_created: 2023-04-23T16:11:48Z
date_published: 2022-03-30T00:00:00Z
date_updated: 2023-04-25T07:34:49Z
day: '30'
department:
- _id: GradSch
- _id: MaMo
doi: 10.48550/arXiv.2203.16701
external_id:
arxiv:
- '2203.16701'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2203.16701
month: '03'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Towards differential relational privacy and its use in question answering
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '12894'
acknowledgement: "The abstracts in this booklet are licenced under a CC BY 4.0 licence
(https://creativecommons.org/licenses/by/4.0/legalcode), except Markus Wallerberger’s
contribution at page 21, licenced under a CC BY-SA 4.0 licence (https://creativecommons.org/licenses/by-sa/4.0/legalcode).\r\n"
article_processing_charge: No
author:
- first_name: Alois
full_name: Schlögl, Alois
id: 45BF87EE-F248-11E8-B48F-1D18A9856A87
last_name: Schlögl
orcid: 0000-0002-5621-8100
- first_name: Andrei
full_name: Hornoiu, Andrei
id: 77129392-B450-11EA-8745-D4653DDC885E
last_name: Hornoiu
- first_name: Stefano
full_name: Elefante, Stefano
id: 490F40CE-F248-11E8-B48F-1D18A9856A87
last_name: Elefante
- first_name: Stephan
full_name: Stadlbauer, Stephan
id: 4D0BC184-F248-11E8-B48F-1D18A9856A87
last_name: Stadlbauer
citation:
ama: 'Schlögl A, Hornoiu A, Elefante S, Stadlbauer S. Where is the sweet spot? A
procurement story of general purpose compute nodes. In: ASHPC22 - Austrian-Slovenian
HPC Meeting 2022. EuroCC Austria c/o Universität Wien; 2022:7. doi:10.25365/phaidra.337'
apa: 'Schlögl, A., Hornoiu, A., Elefante, S., & Stadlbauer, S. (2022). Where
is the sweet spot? A procurement story of general purpose compute nodes. In ASHPC22
- Austrian-Slovenian HPC Meeting 2022 (p. 7). Grundlsee, Austria: EuroCC Austria
c/o Universität Wien. https://doi.org/10.25365/phaidra.337'
chicago: Schlögl, Alois, Andrei Hornoiu, Stefano Elefante, and Stephan Stadlbauer.
“Where Is the Sweet Spot? A Procurement Story of General Purpose Compute Nodes.”
In ASHPC22 - Austrian-Slovenian HPC Meeting 2022, 7. EuroCC Austria c/o
Universität Wien, 2022. https://doi.org/10.25365/phaidra.337.
ieee: A. Schlögl, A. Hornoiu, S. Elefante, and S. Stadlbauer, “Where is the sweet
spot? A procurement story of general purpose compute nodes,” in ASHPC22 - Austrian-Slovenian
HPC Meeting 2022, Grundlsee, Austria, 2022, p. 7.
ista: 'Schlögl A, Hornoiu A, Elefante S, Stadlbauer S. 2022. Where is the sweet
spot? A procurement story of general purpose compute nodes. ASHPC22 - Austrian-Slovenian
HPC Meeting 2022. ASHPC: Austrian-Slovenian HPC Meeting, 7.'
mla: Schlögl, Alois, et al. “Where Is the Sweet Spot? A Procurement Story of General
Purpose Compute Nodes.” ASHPC22 - Austrian-Slovenian HPC Meeting 2022,
EuroCC Austria c/o Universität Wien, 2022, p. 7, doi:10.25365/phaidra.337.
short: A. Schlögl, A. Hornoiu, S. Elefante, S. Stadlbauer, in:, ASHPC22 - Austrian-Slovenian
HPC Meeting 2022, EuroCC Austria c/o Universität Wien, 2022, p. 7.
conference:
end_date: 2022-06-02
location: Grundlsee, Austria
name: 'ASHPC: Austrian-Slovenian HPC Meeting'
start_date: 2022-05-31
corr_author: '1'
date_created: 2023-05-05T09:13:42Z
date_published: 2022-06-02T00:00:00Z
date_updated: 2024-10-09T21:05:24Z
day: '02'
ddc:
- '000'
department:
- _id: ScienComp
doi: 10.25365/phaidra.337
file:
- access_level: open_access
checksum: e3f8c240b85422ce2190e7b203cc2563
content_type: application/pdf
creator: schloegl
date_created: 2023-05-05T09:06:00Z
date_updated: 2023-05-05T09:06:00Z
file_id: '12895'
file_name: BOOKLET_ASHPC22.pdf
file_size: 7180531
relation: main_file
success: 1
file_date_updated: 2023-05-05T09:06:00Z
has_accepted_license: '1'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
page: '7'
publication: ASHPC22 - Austrian-Slovenian HPC Meeting 2022
publication_identifier:
isbn:
- 978-3-200-08499-5
publication_status: published
publisher: EuroCC Austria c/o Universität Wien
status: public
title: Where is the sweet spot? A procurement story of general purpose compute nodes
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_abstract
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '13064'
abstract:
- lang: eng
text: Genetically informed, deep-phenotyped biobanks are an important research resource
and it is imperative that the most powerful, versatile, and efficient analysis
approaches are used. Here, we apply our recently developed Bayesian grouped mixture
of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest
genomic prediction accuracy reported to date across 21 heritable traits. When
compared to other approaches, GMRM accuracy was greater than annotation prediction
models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%),
respectively, and was 18% (SE 3%) greater than a baseline BayesR model without
single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage
disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy
R 2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h SNP
2 . We then extend our GMRM prediction model to provide mixed-linear model association
(MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which
increased the independent loci detected to 16,162 in unrelated UK Biobank individuals,
compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase,
respectively. The average χ2 value of the leading markers increased by 15.24 (SE
0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR
model across the traits. Thus, we show that modeling genetic associations accounting
for MAF and LD differences among SNP markers, and incorporating prior knowledge
of genomic function, is important for both genomic prediction and discovery in
large-scale individual-level studies.
article_processing_charge: No
author:
- first_name: Etienne
full_name: Orliac, Etienne
last_name: Orliac
- first_name: Daniel
full_name: Trejo Banos, Daniel
last_name: Trejo Banos
- first_name: Sven
full_name: Ojavee, Sven
last_name: Ojavee
- first_name: Kristi
full_name: Läll, Kristi
last_name: Läll
- first_name: Reedik
full_name: Mägi, Reedik
last_name: Mägi
- first_name: Peter
full_name: Visscher, Peter
last_name: Visscher
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
citation:
ama: Orliac E, Trejo Banos D, Ojavee S, et al. Improving genome-wide association
discovery and genomic prediction accuracy in biobank data. 2022. doi:10.5061/DRYAD.GTHT76HMZ
apa: Orliac, E., Trejo Banos, D., Ojavee, S., Läll, K., Mägi, R., Visscher, P.,
& Robinson, M. R. (2022). Improving genome-wide association discovery and
genomic prediction accuracy in biobank data. Dryad. https://doi.org/10.5061/DRYAD.GTHT76HMZ
chicago: Orliac, Etienne, Daniel Trejo Banos, Sven Ojavee, Kristi Läll, Reedik Mägi,
Peter Visscher, and Matthew Richard Robinson. “Improving Genome-Wide Association
Discovery and Genomic Prediction Accuracy in Biobank Data.” Dryad, 2022. https://doi.org/10.5061/DRYAD.GTHT76HMZ.
ieee: E. Orliac et al., “Improving genome-wide association discovery and
genomic prediction accuracy in biobank data.” Dryad, 2022.
ista: Orliac E, Trejo Banos D, Ojavee S, Läll K, Mägi R, Visscher P, Robinson MR.
2022. Improving genome-wide association discovery and genomic prediction accuracy
in biobank data, Dryad, 10.5061/DRYAD.GTHT76HMZ.
mla: Orliac, Etienne, et al. Improving Genome-Wide Association Discovery and
Genomic Prediction Accuracy in Biobank Data. Dryad, 2022, doi:10.5061/DRYAD.GTHT76HMZ.
short: E. Orliac, D. Trejo Banos, S. Ojavee, K. Läll, R. Mägi, P. Visscher, M.R.
Robinson, (2022).
corr_author: '1'
date_created: 2023-05-23T16:28:13Z
date_published: 2022-09-02T00:00:00Z
date_updated: 2025-06-12T06:22:36Z
day: '02'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.5061/DRYAD.GTHT76HMZ
main_file_link:
- open_access: '1'
url: https://doi.org/10.5061/dryad.gtht76hmz
month: '09'
oa: 1
oa_version: Published Version
publisher: Dryad
related_material:
record:
- id: '11733'
relation: used_in_publication
status: public
status: public
title: Improving genome-wide association discovery and genomic prediction accuracy
in biobank data
tmp:
image: /images/cc_0.png
legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode
name: Creative Commons Public Domain Dedication (CC0 1.0)
short: CC0 (1.0)
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '13066'
abstract:
- lang: eng
text: Chromosomal inversions have been shown to play a major role in local adaptation
by suppressing recombination between alternative arrangements and maintaining
beneficial allele combinations. However, so far, their importance relative to
the remaining genome remains largely unknown. Understanding the genetic architecture
of adaptation requires better estimates of how loci of different effect sizes
contribute to phenotypic variation. Here, we used three Swedish islands where
the marine snail Littorina saxatilis has repeatedly evolved into two distinct
ecotypes along a habitat transition. We estimated the contribution of inversion
polymorphisms to phenotypic divergence while controlling for polygenic effects
in the remaining genome using a quantitative genetics framework. We confirmed
the importance of inversions but showed that contributions of loci outside inversions
are of similar magnitude, with variable proportions dependent on the trait and
the population. Some inversions showed consistent effects across all sites, whereas
others exhibited site-specific effects, indicating that the genomic basis for
replicated phenotypic divergence is only partly shared. The contributions of sexual
dimorphism as well as environmental factors to phenotypic variation were significant
but minor compared to inversions and polygenic background. Overall, this integrated
approach provides insight into the multiple mechanisms contributing to parallel
phenotypic divergence.
article_processing_charge: No
author:
- first_name: Eva
full_name: Koch, Eva
last_name: Koch
- first_name: Mark
full_name: Ravinet, Mark
last_name: Ravinet
- first_name: Anja M
full_name: Westram, Anja M
id: 3C147470-F248-11E8-B48F-1D18A9856A87
last_name: Westram
orcid: 0000-0003-1050-4969
- first_name: Kerstin
full_name: Jonannesson, Kerstin
last_name: Jonannesson
- first_name: Roger
full_name: Butlin, Roger
last_name: Butlin
citation:
ama: 'Koch E, Ravinet M, Westram AM, Jonannesson K, Butlin R. Data from: Genetic
architecture of repeated phenotypic divergence in Littorina saxatilis ecotype
evolution. 2022. doi:10.5061/DRYAD.M905QFV4B'
apa: 'Koch, E., Ravinet, M., Westram, A. M., Jonannesson, K., & Butlin, R. (2022).
Data from: Genetic architecture of repeated phenotypic divergence in Littorina
saxatilis ecotype evolution. Dryad. https://doi.org/10.5061/DRYAD.M905QFV4B'
chicago: 'Koch, Eva, Mark Ravinet, Anja M Westram, Kerstin Jonannesson, and Roger
Butlin. “Data from: Genetic Architecture of Repeated Phenotypic Divergence in
Littorina Saxatilis Ecotype Evolution.” Dryad, 2022. https://doi.org/10.5061/DRYAD.M905QFV4B.'
ieee: 'E. Koch, M. Ravinet, A. M. Westram, K. Jonannesson, and R. Butlin, “Data
from: Genetic architecture of repeated phenotypic divergence in Littorina saxatilis
ecotype evolution.” Dryad, 2022.'
ista: 'Koch E, Ravinet M, Westram AM, Jonannesson K, Butlin R. 2022. Data from:
Genetic architecture of repeated phenotypic divergence in Littorina saxatilis
ecotype evolution, Dryad, 10.5061/DRYAD.M905QFV4B.'
mla: 'Koch, Eva, et al. Data from: Genetic Architecture of Repeated Phenotypic
Divergence in Littorina Saxatilis Ecotype Evolution. Dryad, 2022, doi:10.5061/DRYAD.M905QFV4B.'
short: E. Koch, M. Ravinet, A.M. Westram, K. Jonannesson, R. Butlin, (2022).
date_created: 2023-05-23T16:33:12Z
date_published: 2022-07-28T00:00:00Z
date_updated: 2023-08-04T09:42:10Z
day: '28'
ddc:
- '570'
department:
- _id: NiBa
doi: 10.5061/DRYAD.M905QFV4B
main_file_link:
- open_access: '1'
url: https://doi.org/10.5061/dryad.m905qfv4b
month: '07'
oa: 1
oa_version: Published Version
publisher: Dryad
related_material:
record:
- id: '12247'
relation: used_in_publication
status: public
status: public
title: 'Data from: Genetic architecture of repeated phenotypic divergence in Littorina
saxatilis ecotype evolution'
tmp:
image: /images/cc_0.png
legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode
name: Creative Commons Public Domain Dedication (CC0 1.0)
short: CC0 (1.0)
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '13076'
abstract:
- lang: eng
text: "The source code for replicating experiments presented in the paper.\r\n\r\nThe
implementation of the designed priority schedulers can be found in Galois-2.2.1/include/Galois/WorkList/:\r\nStealingMultiQueue.h
is the StealingMultiQueue.\r\nMQOptimized/ contains MQ Optimized variants.\r\n\r\nWe
provide images that contain all the dependencies and datasets. Images can be pulled
from npostnikova/mq-based-schedulers repository, or downloaded from Zenodo. See
readme for more detail."
article_processing_charge: No
author:
- first_name: Anastasiia
full_name: Postnikova, Anastasiia
last_name: Postnikova
- first_name: Nikita
full_name: Koval, Nikita
id: 2F4DB10C-F248-11E8-B48F-1D18A9856A87
last_name: Koval
- first_name: Giorgi
full_name: Nadiradze, Giorgi
id: 3279A00C-F248-11E8-B48F-1D18A9856A87
last_name: Nadiradze
orcid: 0000-0001-5634-0731
- 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: Postnikova A, Koval N, Nadiradze G, Alistarh D-A. Multi-queues can be state-of-the-art
priority schedulers. 2022. doi:10.5281/ZENODO.5733408
apa: Postnikova, A., Koval, N., Nadiradze, G., & Alistarh, D.-A. (2022). Multi-queues
can be state-of-the-art priority schedulers. Zenodo. https://doi.org/10.5281/ZENODO.5733408
chicago: Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian
Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.” Zenodo,
2022. https://doi.org/10.5281/ZENODO.5733408.
ieee: A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can
be state-of-the-art priority schedulers.” Zenodo, 2022.
ista: Postnikova A, Koval N, Nadiradze G, Alistarh D-A. 2022. Multi-queues can be
state-of-the-art priority schedulers, Zenodo, 10.5281/ZENODO.5733408.
mla: Postnikova, Anastasiia, et al. Multi-Queues Can Be State-of-the-Art Priority
Schedulers. Zenodo, 2022, doi:10.5281/ZENODO.5733408.
short: A. Postnikova, N. Koval, G. Nadiradze, D.-A. Alistarh, (2022).
corr_author: '1'
date_created: 2023-05-23T17:05:40Z
date_published: 2022-01-03T00:00:00Z
date_updated: 2025-04-14T13:51:59Z
day: '03'
ddc:
- '510'
department:
- _id: DaAl
doi: 10.5281/ZENODO.5733408
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.5813846
month: '01'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
link:
- relation: software
url: https://github.com/npostnikova/mq-based-schedulers/tree/v1.1
record:
- id: '11180'
relation: used_in_publication
status: public
status: public
title: Multi-queues can be state-of-the-art priority schedulers
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '13239'
abstract:
- lang: eng
text: Brains are thought to engage in predictive learning - learning to predict
upcoming stimuli - to construct an internal model of their environment. This is
especially notable for spatial navigation, as first described by Tolman’s latent
learning tasks. However, predictive learning has also been observed in sensory
cortex, in settings unrelated to spatial navigation. Apart from normative frameworks
such as active inference or efficient coding, what could be the utility of learning
to predict the patterns of occurrence of correlated stimuli? Here we show that
prediction, and thereby the construction of an internal model of sequential stimuli,
can bootstrap the learning process of a working memory task in a recurrent neural
network. We implemented predictive learning alongside working memory match-tasks,
and networks emerged to solve the prediction task first by encoding information
across time to predict upcoming stimuli, and then eavesdropped on this solution
to solve the matching task. Eavesdropping was most beneficial when neural resources
were limited. Hence, predictive learning acts as a general neural mechanism to
learn to store sensory information that can later be essential for working memory
tasks.
acknowledgement: "The authors would like to thank members of the Vogels lab and Manohar
lab, as well as Adam Packer, Andrew Saxe, Stefano Sarao Mannelli and Jacob Bakermans
for fruitful discussions and comments on earlier versions of the manuscript.\r\nTLvdP
was supported by funding from the Biotechnology and Biological Sciences Research
Council (BBSRC) [grant number BB/M011224/1]. TPV was supported by an ERC Consolidator
Grant (SYNAPSEEK). SGM was funded by a MRC Clinician Scientist Fellowship MR/P00878X
and Leverhulme Grant RPG-2018-310."
article_processing_charge: No
author:
- first_name: Thijs L.
full_name: Van Der Plas, Thijs L.
last_name: Van Der Plas
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
- first_name: Sanjay G.
full_name: Manohar, Sanjay G.
last_name: Manohar
citation:
ama: 'Van Der Plas TL, Vogels TP, Manohar SG. Predictive learning enables neural
networks to learn complex working memory tasks. In: Proceedings of Machine
Learning Research. Vol 199. ML Research Press; 2022:518-531.'
apa: Van Der Plas, T. L., Vogels, T. P., & Manohar, S. G. (2022). Predictive
learning enables neural networks to learn complex working memory tasks. In Proceedings
of Machine Learning Research (Vol. 199, pp. 518–531). ML Research Press.
chicago: Van Der Plas, Thijs L., Tim P Vogels, and Sanjay G. Manohar. “Predictive
Learning Enables Neural Networks to Learn Complex Working Memory Tasks.” In Proceedings
of Machine Learning Research, 199:518–31. ML Research Press, 2022.
ieee: T. L. Van Der Plas, T. P. Vogels, and S. G. Manohar, “Predictive learning
enables neural networks to learn complex working memory tasks,” in Proceedings
of Machine Learning Research, 2022, vol. 199, pp. 518–531.
ista: Van Der Plas TL, Vogels TP, Manohar SG. 2022. Predictive learning enables
neural networks to learn complex working memory tasks. Proceedings of Machine
Learning Research. vol. 199, 518–531.
mla: Van Der Plas, Thijs L., et al. “Predictive Learning Enables Neural Networks
to Learn Complex Working Memory Tasks.” Proceedings of Machine Learning Research,
vol. 199, ML Research Press, 2022, pp. 518–31.
short: T.L. Van Der Plas, T.P. Vogels, S.G. Manohar, in:, Proceedings of Machine
Learning Research, ML Research Press, 2022, pp. 518–531.
date_created: 2023-07-16T22:01:12Z
date_published: 2022-12-01T00:00:00Z
date_updated: 2025-04-14T07:54:31Z
day: '01'
ddc:
- '000'
department:
- _id: TiVo
ec_funded: 1
file:
- access_level: open_access
checksum: 7530a93ef42e10b4db1e5e4b69796e93
content_type: application/pdf
creator: dernst
date_created: 2023-07-18T06:32:38Z
date_updated: 2023-07-18T06:32:38Z
file_id: '13243'
file_name: 2022_PMLR_vanderPlas.pdf
file_size: 585135
relation: main_file
success: 1
file_date_updated: 2023-07-18T06:32:38Z
has_accepted_license: '1'
intvolume: ' 199'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: 518-531
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
publication: Proceedings of Machine Learning Research
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Predictive learning enables neural networks to learn complex working memory
tasks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 199
year: '2022'
...
---
_id: '13240'
abstract:
- lang: eng
text: Ustilago maydis is a biotrophic phytopathogenic fungus that causes corn smut
disease. As a well-established model system, U. maydis is genetically fully accessible
with large omics datasets available and subject to various biological questions
ranging from DNA-repair, RNA-transport, and protein secretion to disease biology.
For many genetic approaches, tight control of transgene regulation is important.
Here we established an optimised version of the Tetracycline-ON (TetON) system
for U. maydis. We demonstrate the Tetracycline concentration-dependent expression
of fluorescent protein transgenes and the system’s suitability for the induced
expression of the toxic protein BCL2 Associated X-1 (Bax1). The Golden Gate compatible
vector system contains a native minimal promoter from the mating factor a-1 encoding
gene, mfa with ten copies of the tet-regulated operator (tetO) and a codon optimised
Tet-repressor (tetR*) which is translationally fused to the native transcriptional
corepressor Mql1 (UMAG_05501). The metabolism-independent transcriptional regulator
system is functional both, in liquid culture as well as on solid media in the
presence of the inducer and can become a useful tool for toxin-antitoxin studies,
identification of antifungal proteins, and to study functions of toxic gene products
in Ustilago maydis.
acknowledgement: "The research leading to these results received funding from the
European Research Council under the European Union’s Seventh Framework Programme
ERC-2013-STG (grant agreement: 335691), the Austrian Science Fund (I 3033-B22),
the Austrian Academy of Sciences, and the Deutsche Forschungsgemeinschaft (DFG,
German Research Foundation) under Germany's Excellence Strategy EXC-2070-390732324
(PhenoRob) and DFG grant (DJ 64/5-1).\r\nWe would like to thank the GMI/IMBA/IMP
core facilities for their excellent technical support. We would like to acknowledge
Dr. Sinéad A. O’Sullivan from DZNE, University of Bonn for providing anti-GFP antibodies.
The authors are thankful to the Excellence University of Bonn for providing infrastructure
and instrumentation facilities at the INRES-Plant Pathology department."
article_number: '1029114'
article_processing_charge: Yes
article_type: original
author:
- first_name: Kishor D.
full_name: Ingole, Kishor D.
last_name: Ingole
- first_name: Nithya
full_name: Nagarajan, Nithya
last_name: Nagarajan
- first_name: Simon
full_name: Uhse, Simon
last_name: Uhse
- first_name: Caterina
full_name: Giannini, Caterina
id: e3fdddd5-f6e0-11ea-865d-ca99ee6367f4
last_name: Giannini
- first_name: Armin
full_name: Djamei, Armin
last_name: Djamei
citation:
ama: Ingole KD, Nagarajan N, Uhse S, Giannini C, Djamei A. Tetracycline-controlled
(TetON) gene expression system for the smut fungus Ustilago maydis. Frontiers
in Fungal Biology. 2022;3. doi:10.3389/ffunb.2022.1029114
apa: Ingole, K. D., Nagarajan, N., Uhse, S., Giannini, C., & Djamei, A. (2022).
Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago
maydis. Frontiers in Fungal Biology. Frontiers Media. https://doi.org/10.3389/ffunb.2022.1029114
chicago: Ingole, Kishor D., Nithya Nagarajan, Simon Uhse, Caterina Giannini, and
Armin Djamei. “Tetracycline-Controlled (TetON) Gene Expression System for the
Smut Fungus Ustilago Maydis.” Frontiers in Fungal Biology. Frontiers Media,
2022. https://doi.org/10.3389/ffunb.2022.1029114.
ieee: K. D. Ingole, N. Nagarajan, S. Uhse, C. Giannini, and A. Djamei, “Tetracycline-controlled
(TetON) gene expression system for the smut fungus Ustilago maydis,” Frontiers
in Fungal Biology, vol. 3. Frontiers Media, 2022.
ista: Ingole KD, Nagarajan N, Uhse S, Giannini C, Djamei A. 2022. Tetracycline-controlled
(TetON) gene expression system for the smut fungus Ustilago maydis. Frontiers
in Fungal Biology. 3, 1029114.
mla: Ingole, Kishor D., et al. “Tetracycline-Controlled (TetON) Gene Expression
System for the Smut Fungus Ustilago Maydis.” Frontiers in Fungal Biology,
vol. 3, 1029114, Frontiers Media, 2022, doi:10.3389/ffunb.2022.1029114.
short: K.D. Ingole, N. Nagarajan, S. Uhse, C. Giannini, A. Djamei, Frontiers in
Fungal Biology 3 (2022).
date_created: 2023-07-16T22:01:12Z
date_published: 2022-10-19T00:00:00Z
date_updated: 2024-03-06T14:01:57Z
day: '19'
ddc:
- '579'
department:
- _id: JiFr
doi: 10.3389/ffunb.2022.1029114
file:
- access_level: open_access
checksum: 2254e0119c0749d6f7237084fefcece6
content_type: application/pdf
creator: dernst
date_created: 2023-07-17T11:46:34Z
date_updated: 2023-07-17T11:46:34Z
file_id: '13242'
file_name: 2023_FrontiersFungalBio_Ingole.pdf
file_size: 27966699
relation: main_file
success: 1
file_date_updated: 2023-07-17T11:46:34Z
has_accepted_license: '1'
intvolume: ' 3'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
publication: Frontiers in Fungal Biology
publication_identifier:
eissn:
- 2673-6128
publication_status: published
publisher: Frontiers Media
quality_controlled: '1'
scopus_import: '1'
status: public
title: Tetracycline-controlled (TetON) gene expression system for the smut fungus
Ustilago maydis
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: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 3
year: '2022'
...
---
_id: '13241'
abstract:
- lang: eng
text: Addressing fairness concerns about machine learning models is a crucial step
towards their long-term adoption in real-world automated systems. Many approaches
for training fair models from data have been developed and an implicit assumption
about such algorithms is that they are able to recover a fair model, despite potential
historical biases in the data. In this work we show a number of impossibility
results that indicate that there is no learning algorithm that can recover a fair
model when a proportion of the dataset is subject to arbitrary manipulations.
Specifically, we prove that there are situations in which an adversary can force
any learner to return a biased classifier, with or without degrading accuracy,
and that the strength of this bias increases for learning problems with underrepresented
protected groups in the data. Our results emphasize on the importance of studying
further data corruption models of various strength and of establishing stricter
data collection practices for fairness-aware learning.
acknowledgement: "This paper is a shortened, workshop version of Konstantinov and
Lampert (2021),\r\nhttps://arxiv.org/abs/2102.06004. For further results, including
an analysis of algorithms achieving the lower bounds from this paper, we refer to
the full version."
article_processing_charge: No
arxiv: 1
author:
- first_name: Nikola H
full_name: Konstantinov, Nikola H
id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87
last_name: Konstantinov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Konstantinov NH, Lampert C. On the impossibility of fairness-aware learning
from corrupted data. In: Proceedings of Machine Learning Research. Vol
171. ML Research Press; 2022:59-83.'
apa: Konstantinov, N. H., & Lampert, C. (2022). On the impossibility of fairness-aware
learning from corrupted data. In Proceedings of Machine Learning Research
(Vol. 171, pp. 59–83). ML Research Press.
chicago: Konstantinov, Nikola H, and Christoph Lampert. “On the Impossibility of
Fairness-Aware Learning from Corrupted Data.” In Proceedings of Machine Learning
Research, 171:59–83. ML Research Press, 2022.
ieee: N. H. Konstantinov and C. Lampert, “On the impossibility of fairness-aware
learning from corrupted data,” in Proceedings of Machine Learning Research,
2022, vol. 171, pp. 59–83.
ista: Konstantinov NH, Lampert C. 2022. On the impossibility of fairness-aware learning
from corrupted data. Proceedings of Machine Learning Research. vol. 171, 59–83.
mla: Konstantinov, Nikola H., and Christoph Lampert. “On the Impossibility of Fairness-Aware
Learning from Corrupted Data.” Proceedings of Machine Learning Research,
vol. 171, ML Research Press, 2022, pp. 59–83.
short: N.H. Konstantinov, C. Lampert, in:, Proceedings of Machine Learning Research,
ML Research Press, 2022, pp. 59–83.
corr_author: '1'
date_created: 2023-07-16T22:01:13Z
date_published: 2022-12-01T00:00:00Z
date_updated: 2024-10-09T21:05:54Z
day: '01'
department:
- _id: ChLa
external_id:
arxiv:
- '2102.06004'
intvolume: ' 171'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2102.06004
month: '12'
oa: 1
oa_version: Preprint
page: 59-83
publication: Proceedings of Machine Learning Research
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
record:
- id: '10802'
relation: extended_version
status: public
scopus_import: '1'
status: public
title: On the impossibility of fairness-aware learning from corrupted data
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 171
year: '2022'
...