---
_id: '14890'
abstract:
- lang: eng
text: We consider a system of N interacting bosons in the mean-field scaling regime
and construct corrections to the Bogoliubov dynamics that approximate the true
N-body dynamics in norm to arbitrary precision. The N-independent corrections
are given in terms of the solutions of the Bogoliubov and Hartree equations and
satisfy a generalized form of Wick's theorem. We determine the n-point correlation
functions of the excitations around the condensate, as well as the reduced densities
of the N-body system, to arbitrary accuracy, given only the knowledge of the two-point
functions of a quasi-free state and the solution of the Hartree equation. In this
way, the complex problem of computing all n-point correlation functions for an
interacting N-body system is essentially reduced to the problem of solving the
Hartree equation and the PDEs for the Bogoliubov two-point functions.
acknowledgement: "We are grateful for the hospitality of Central China Normal University
(CCNU),\r\nwhere parts of this work were done, and thank Phan Th`anh Nam, Simone\r\nRademacher,
Robert Seiringer and Stefan Teufel for helpful discussions. L.B. gratefully acknowledges
the support by the German Research Foundation (DFG) within the Research\r\nTraining
Group 1838 “Spectral Theory and Dynamics of Quantum Systems”, and the funding\r\nfrom
the European Union’s Horizon 2020 research and innovation programme under the Marie\r\nSk
lodowska-Curie Grant Agreement No. 754411."
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Lea
full_name: Bossmann, Lea
id: A2E3BCBE-5FCC-11E9-AA4B-76F3E5697425
last_name: Bossmann
orcid: 0000-0002-6854-1343
- first_name: Sören P
full_name: Petrat, Sören P
id: 40AC02DC-F248-11E8-B48F-1D18A9856A87
last_name: Petrat
orcid: 0000-0002-9166-5889
- first_name: Peter
full_name: Pickl, Peter
last_name: Pickl
- first_name: Avy
full_name: Soffer, Avy
last_name: Soffer
citation:
ama: Bossmann L, Petrat SP, Pickl P, Soffer A. Beyond Bogoliubov dynamics. Pure
and Applied Analysis. 2021;3(4):677-726. doi:10.2140/paa.2021.3.677
apa: Bossmann, L., Petrat, S. P., Pickl, P., & Soffer, A. (2021). Beyond Bogoliubov
dynamics. Pure and Applied Analysis. Mathematical Sciences Publishers.
https://doi.org/10.2140/paa.2021.3.677
chicago: Bossmann, Lea, Sören P Petrat, Peter Pickl, and Avy Soffer. “Beyond Bogoliubov
Dynamics.” Pure and Applied Analysis. Mathematical Sciences Publishers,
2021. https://doi.org/10.2140/paa.2021.3.677.
ieee: L. Bossmann, S. P. Petrat, P. Pickl, and A. Soffer, “Beyond Bogoliubov dynamics,”
Pure and Applied Analysis, vol. 3, no. 4. Mathematical Sciences Publishers,
pp. 677–726, 2021.
ista: Bossmann L, Petrat SP, Pickl P, Soffer A. 2021. Beyond Bogoliubov dynamics.
Pure and Applied Analysis. 3(4), 677–726.
mla: Bossmann, Lea, et al. “Beyond Bogoliubov Dynamics.” Pure and Applied Analysis,
vol. 3, no. 4, Mathematical Sciences Publishers, 2021, pp. 677–726, doi:10.2140/paa.2021.3.677.
short: L. Bossmann, S.P. Petrat, P. Pickl, A. Soffer, Pure and Applied Analysis
3 (2021) 677–726.
date_created: 2024-01-28T23:01:43Z
date_published: 2021-10-01T00:00:00Z
date_updated: 2024-02-05T09:26:31Z
day: '01'
department:
- _id: RoSe
doi: 10.2140/paa.2021.3.677
ec_funded: 1
external_id:
arxiv:
- '1912.11004'
intvolume: ' 3'
issue: '4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1912.11004
month: '10'
oa: 1
oa_version: Preprint
page: 677-726
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: Pure and Applied Analysis
publication_identifier:
eissn:
- 2578-5885
issn:
- 2578-5893
publication_status: published
publisher: Mathematical Sciences Publishers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Beyond Bogoliubov dynamics
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 3
year: '2021'
...
---
_id: '14984'
abstract:
- lang: eng
text: Hybrid zones are narrow geographic regions where different populations, races
or interbreeding species meet and mate, producing mixed ‘hybrid’ offspring. They
are relatively common and can be found in a diverse range of organisms and environments.
The study of hybrid zones has played an important role in our understanding of
the origin of species, with hybrid zones having been described as ‘natural laboratories’.
This is because they allow us to study,in situ, the conditions and evolutionary
forces that enable divergent taxa to remain distinct despite some ongoing gene
exchange between them.
article_processing_charge: No
author:
- first_name: Sean
full_name: Stankowski, Sean
id: 43161670-5719-11EA-8025-FABC3DDC885E
last_name: Stankowski
- first_name: Daria
full_name: Shipilina, Daria
id: 428A94B0-F248-11E8-B48F-1D18A9856A87
last_name: Shipilina
orcid: 0000-0002-1145-9226
- first_name: Anja M
full_name: Westram, Anja M
id: 3C147470-F248-11E8-B48F-1D18A9856A87
last_name: Westram
orcid: 0000-0003-1050-4969
citation:
ama: 'Stankowski S, Shipilina D, Westram AM. Hybrid Zones. In: Encyclopedia of
Life Sciences. Vol 2. eLS. Wiley; 2021. doi:10.1002/9780470015902.a0029355'
apa: Stankowski, S., Shipilina, D., & Westram, A. M. (2021). Hybrid Zones. In
Encyclopedia of Life Sciences (Vol. 2). Wiley. https://doi.org/10.1002/9780470015902.a0029355
chicago: Stankowski, Sean, Daria Shipilina, and Anja M Westram. “Hybrid Zones.”
In Encyclopedia of Life Sciences, Vol. 2. ELS. Wiley, 2021. https://doi.org/10.1002/9780470015902.a0029355.
ieee: S. Stankowski, D. Shipilina, and A. M. Westram, “Hybrid Zones,” in Encyclopedia
of Life Sciences, vol. 2, Wiley, 2021.
ista: 'Stankowski S, Shipilina D, Westram AM. 2021.Hybrid Zones. In: Encyclopedia
of Life Sciences. vol. 2.'
mla: Stankowski, Sean, et al. “Hybrid Zones.” Encyclopedia of Life Sciences,
vol. 2, Wiley, 2021, doi:10.1002/9780470015902.a0029355.
short: S. Stankowski, D. Shipilina, A.M. Westram, in:, Encyclopedia of Life Sciences,
Wiley, 2021.
date_created: 2024-02-14T12:05:50Z
date_published: 2021-05-28T00:00:00Z
date_updated: 2024-02-19T09:54:18Z
day: '28'
department:
- _id: NiBa
doi: 10.1002/9780470015902.a0029355
intvolume: ' 2'
language:
- iso: eng
month: '05'
oa_version: None
publication: Encyclopedia of Life Sciences
publication_identifier:
eisbn:
- '9780470015902'
isbn:
- '9780470016176'
publication_status: published
publisher: Wiley
quality_controlled: '1'
series_title: eLS
status: public
title: Hybrid Zones
type: book_chapter
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2
year: '2021'
...
---
_id: '14987'
abstract:
- lang: eng
text: "The goal of zero-shot learning is to construct a classifier that can identify
object classes for which no training examples are available. When training data
for some of the object classes is available but not for others, the name generalized
zero-shot learning is commonly used.\r\nIn a wider sense, the phrase zero-shot
is also used to describe other machine learning-based approaches that require
no training data from the problem of interest, such as zero-shot action recognition
or zero-shot machine translation."
article_processing_charge: No
author:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Lampert C. Zero-Shot Learning. In: Ikeuchi K, ed. Computer Vision.
2nd ed. Cham: Springer; 2021:1395-1397. doi:10.1007/978-3-030-63416-2_874'
apa: 'Lampert, C. (2021). Zero-Shot Learning. In K. Ikeuchi (Ed.), Computer Vision
(2nd ed., pp. 1395–1397). Cham: Springer. https://doi.org/10.1007/978-3-030-63416-2_874'
chicago: 'Lampert, Christoph. “Zero-Shot Learning.” In Computer Vision, edited
by Katsushi Ikeuchi, 2nd ed., 1395–97. Cham: Springer, 2021. https://doi.org/10.1007/978-3-030-63416-2_874.'
ieee: 'C. Lampert, “Zero-Shot Learning,” in Computer Vision, 2nd ed., K.
Ikeuchi, Ed. Cham: Springer, 2021, pp. 1395–1397.'
ista: 'Lampert C. 2021.Zero-Shot Learning. In: Computer Vision. , 1395–1397.'
mla: Lampert, Christoph. “Zero-Shot Learning.” Computer Vision, edited by
Katsushi Ikeuchi, 2nd ed., Springer, 2021, pp. 1395–97, doi:10.1007/978-3-030-63416-2_874.
short: C. Lampert, in:, K. Ikeuchi (Ed.), Computer Vision, 2nd ed., Springer, Cham,
2021, pp. 1395–1397.
date_created: 2024-02-14T14:05:32Z
date_published: 2021-10-13T00:00:00Z
date_updated: 2024-02-19T10:59:04Z
day: '13'
department:
- _id: ChLa
doi: 10.1007/978-3-030-63416-2_874
edition: '2'
editor:
- first_name: Katsushi
full_name: Ikeuchi, Katsushi
last_name: Ikeuchi
language:
- iso: eng
month: '10'
oa_version: None
page: 1395-1397
place: Cham
publication: Computer Vision
publication_identifier:
eisbn:
- '9783030634162'
isbn:
- '9783030634155'
publication_status: published
publisher: Springer
quality_controlled: '1'
status: public
title: Zero-Shot Learning
type: book_chapter
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '14988'
abstract:
- lang: eng
text: Raw data generated from the publication - The TPLATE complex mediates membrane
bending during plant clathrin-mediated endocytosis by Johnson et al., 2021 In
PNAS
article_processing_charge: No
author:
- first_name: Alexander J
full_name: Johnson, Alexander J
id: 46A62C3A-F248-11E8-B48F-1D18A9856A87
last_name: Johnson
orcid: 0000-0002-2739-8843
citation:
ama: Johnson AJ. Raw data from Johnson et al, PNAS, 2021. 2021. doi:10.5281/ZENODO.5747100
apa: Johnson, A. J. (2021). Raw data from Johnson et al, PNAS, 2021. Zenodo. https://doi.org/10.5281/ZENODO.5747100
chicago: Johnson, Alexander J. “Raw Data from Johnson et Al, PNAS, 2021.” Zenodo,
2021. https://doi.org/10.5281/ZENODO.5747100.
ieee: A. J. Johnson, “Raw data from Johnson et al, PNAS, 2021.” Zenodo, 2021.
ista: Johnson AJ. 2021. Raw data from Johnson et al, PNAS, 2021, Zenodo, 10.5281/ZENODO.5747100.
mla: Johnson, Alexander J. Raw Data from Johnson et Al, PNAS, 2021. Zenodo,
2021, doi:10.5281/ZENODO.5747100.
short: A.J. Johnson, (2021).
date_created: 2024-02-14T14:13:48Z
date_published: 2021-12-01T00:00:00Z
date_updated: 2024-02-19T11:06:09Z
day: '01'
ddc:
- '580'
department:
- _id: JiFr
doi: 10.5281/ZENODO.5747100
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.5747100
month: '12'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '9887'
relation: used_in_publication
status: public
status: public
title: Raw data from Johnson et al, PNAS, 2021
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: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '15013'
abstract:
- lang: eng
text: We consider random n×n matrices X with independent and centered entries and
a general variance profile. We show that the spectral radius of X converges with
very high probability to the square root of the spectral radius of the variance
matrix of X when n tends to infinity. We also establish the optimal rate of convergence,
that is a new result even for general i.i.d. matrices beyond the explicitly solvable
Gaussian cases. The main ingredient is the proof of the local inhomogeneous circular
law [arXiv:1612.07776] at the spectral edge.
acknowledgement: Partially supported by ERC Starting Grant RandMat No. 715539 and
the SwissMap grant of Swiss National Science Foundation. Partially supported by
ERC Advanced Grant RanMat No. 338804. Partially supported by the Hausdorff Center
for Mathematics in Bonn.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Johannes
full_name: Alt, Johannes
id: 36D3D8B6-F248-11E8-B48F-1D18A9856A87
last_name: Alt
- first_name: László
full_name: Erdös, László
id: 4DBD5372-F248-11E8-B48F-1D18A9856A87
last_name: Erdös
orcid: 0000-0001-5366-9603
- first_name: Torben H
full_name: Krüger, Torben H
id: 3020C786-F248-11E8-B48F-1D18A9856A87
last_name: Krüger
orcid: 0000-0002-4821-3297
citation:
ama: Alt J, Erdös L, Krüger TH. Spectral radius of random matrices with independent
entries. Probability and Mathematical Physics. 2021;2(2):221-280. doi:10.2140/pmp.2021.2.221
apa: Alt, J., Erdös, L., & Krüger, T. H. (2021). Spectral radius of random matrices
with independent entries. Probability and Mathematical Physics. Mathematical
Sciences Publishers. https://doi.org/10.2140/pmp.2021.2.221
chicago: Alt, Johannes, László Erdös, and Torben H Krüger. “Spectral Radius of Random
Matrices with Independent Entries.” Probability and Mathematical Physics.
Mathematical Sciences Publishers, 2021. https://doi.org/10.2140/pmp.2021.2.221.
ieee: J. Alt, L. Erdös, and T. H. Krüger, “Spectral radius of random matrices with
independent entries,” Probability and Mathematical Physics, vol. 2, no.
2. Mathematical Sciences Publishers, pp. 221–280, 2021.
ista: Alt J, Erdös L, Krüger TH. 2021. Spectral radius of random matrices with independent
entries. Probability and Mathematical Physics. 2(2), 221–280.
mla: Alt, Johannes, et al. “Spectral Radius of Random Matrices with Independent
Entries.” Probability and Mathematical Physics, vol. 2, no. 2, Mathematical
Sciences Publishers, 2021, pp. 221–80, doi:10.2140/pmp.2021.2.221.
short: J. Alt, L. Erdös, T.H. Krüger, Probability and Mathematical Physics 2 (2021)
221–280.
date_created: 2024-02-18T23:01:03Z
date_published: 2021-05-21T00:00:00Z
date_updated: 2024-02-19T08:30:00Z
day: '21'
department:
- _id: LaEr
doi: 10.2140/pmp.2021.2.221
ec_funded: 1
external_id:
arxiv:
- '1907.13631'
intvolume: ' 2'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1907.13631
month: '05'
oa: 1
oa_version: Preprint
page: 221-280
project:
- _id: 258DCDE6-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '338804'
name: Random matrices, universality and disordered quantum systems
publication: Probability and Mathematical Physics
publication_identifier:
eissn:
- 2690-1005
issn:
- 2690-0998
publication_status: published
publisher: Mathematical Sciences Publishers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Spectral radius of random matrices with independent entries
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2
year: '2021'
...
---
_id: '13057'
abstract:
- lang: eng
text: 'This dataset comprises all data shown in the figures of the submitted article
"Geometric superinductance qubits: Controlling phase delocalization across a single
Josephson junction". Additional raw data are available from the corresponding
author on reasonable request.'
article_processing_charge: No
author:
- first_name: Matilda
full_name: Peruzzo, Matilda
id: 3F920B30-F248-11E8-B48F-1D18A9856A87
last_name: Peruzzo
orcid: 0000-0002-3415-4628
- first_name: Farid
full_name: Hassani, Farid
id: 2AED110C-F248-11E8-B48F-1D18A9856A87
last_name: Hassani
orcid: 0000-0001-6937-5773
- first_name: Grisha
full_name: Szep, Grisha
last_name: Szep
- first_name: Andrea
full_name: Trioni, Andrea
id: 42F71B44-F248-11E8-B48F-1D18A9856A87
last_name: Trioni
- first_name: Elena
full_name: Redchenko, Elena
id: 2C21D6E8-F248-11E8-B48F-1D18A9856A87
last_name: Redchenko
- first_name: Martin
full_name: Zemlicka, Martin
id: 2DCF8DE6-F248-11E8-B48F-1D18A9856A87
last_name: Zemlicka
- first_name: Johannes M
full_name: Fink, Johannes M
id: 4B591CBA-F248-11E8-B48F-1D18A9856A87
last_name: Fink
orcid: 0000-0001-8112-028X
citation:
ama: 'Peruzzo M, Hassani F, Szep G, et al. Geometric superinductance qubits: Controlling
phase delocalization across a single Josephson junction. 2021. doi:10.5281/ZENODO.5592103'
apa: 'Peruzzo, M., Hassani, F., Szep, G., Trioni, A., Redchenko, E., Zemlicka, M.,
& Fink, J. M. (2021). Geometric superinductance qubits: Controlling phase
delocalization across a single Josephson junction. Zenodo. https://doi.org/10.5281/ZENODO.5592103'
chicago: 'Peruzzo, Matilda, Farid Hassani, Grisha Szep, Andrea Trioni, Elena Redchenko,
Martin Zemlicka, and Johannes M Fink. “Geometric Superinductance Qubits: Controlling
Phase Delocalization across a Single Josephson Junction.” Zenodo, 2021. https://doi.org/10.5281/ZENODO.5592103.'
ieee: 'M. Peruzzo et al., “Geometric superinductance qubits: Controlling
phase delocalization across a single Josephson junction.” Zenodo, 2021.'
ista: 'Peruzzo M, Hassani F, Szep G, Trioni A, Redchenko E, Zemlicka M, Fink JM.
2021. Geometric superinductance qubits: Controlling phase delocalization across
a single Josephson junction, Zenodo, 10.5281/ZENODO.5592103.'
mla: 'Peruzzo, Matilda, et al. Geometric Superinductance Qubits: Controlling
Phase Delocalization across a Single Josephson Junction. Zenodo, 2021, doi:10.5281/ZENODO.5592103.'
short: M. Peruzzo, F. Hassani, G. Szep, A. Trioni, E. Redchenko, M. Zemlicka, J.M.
Fink, (2021).
date_created: 2023-05-23T13:42:27Z
date_published: 2021-10-22T00:00:00Z
date_updated: 2023-08-11T10:44:21Z
day: '22'
ddc:
- '530'
department:
- _id: JoFi
doi: 10.5281/ZENODO.5592103
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.5592104
month: '10'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '9928'
relation: used_in_publication
status: public
status: public
title: 'Geometric superinductance qubits: Controlling phase delocalization across
a single Josephson junction'
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: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '13058'
abstract:
- lang: eng
text: The zip file includes source data used in the main text of the manuscript
"Theory of branching morphogenesis by local interactions and global guidance",
as well as a representative Jupyter notebook to reproduce the main figures. A
sample script for the simulations of branching and annihilating random walks is
also included (Sample_script_for_simulations_of_BARWs.ipynb) to generate exemplary
branched networks under external guidance. A detailed description of the simulation
setup is provided in the supplementary information of the manuscipt.
article_processing_charge: No
author:
- first_name: Mehmet C
full_name: Ucar, Mehmet C
id: 50B2A802-6007-11E9-A42B-EB23E6697425
last_name: Ucar
orcid: 0000-0003-0506-4217
citation:
ama: Ucar MC. Source data for the manuscript “Theory of branching morphogenesis
by local interactions and global guidance.” 2021. doi:10.5281/ZENODO.5257160
apa: Ucar, M. C. (2021). Source data for the manuscript “Theory of branching morphogenesis
by local interactions and global guidance.” Zenodo. https://doi.org/10.5281/ZENODO.5257160
chicago: Ucar, Mehmet C. “Source Data for the Manuscript ‘Theory of Branching Morphogenesis
by Local Interactions and Global Guidance.’” Zenodo, 2021. https://doi.org/10.5281/ZENODO.5257160.
ieee: M. C. Ucar, “Source data for the manuscript ‘Theory of branching morphogenesis
by local interactions and global guidance.’” Zenodo, 2021.
ista: Ucar MC. 2021. Source data for the manuscript ‘Theory of branching morphogenesis
by local interactions and global guidance’, Zenodo, 10.5281/ZENODO.5257160.
mla: Ucar, Mehmet C. Source Data for the Manuscript “Theory of Branching Morphogenesis
by Local Interactions and Global Guidance.” Zenodo, 2021, doi:10.5281/ZENODO.5257160.
short: M.C. Ucar, (2021).
date_created: 2023-05-23T13:46:34Z
date_published: 2021-08-25T00:00:00Z
date_updated: 2023-08-14T13:18:46Z
day: '25'
ddc:
- '570'
department:
- _id: EdHa
doi: 10.5281/ZENODO.5257160
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.5257161
month: '08'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '10402'
relation: used_in_publication
status: public
status: public
title: Source data for the manuscript "Theory of branching morphogenesis by local
interactions and global guidance"
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: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '13061'
abstract:
- lang: eng
text: Infections early in life can have enduring effects on an organism’s development
and immunity. In this study, we show that this equally applies to developing “superorganisms”
– incipient social insect colonies. When we exposed newly mated Lasius niger ant
queens to a low pathogen dose, their colonies grew more slowly than controls before
winter, but reached similar sizes afterwards. Independent of exposure, queen hibernation
survival improved when the ratio of pupae to workers was small. Queens that reared
fewer pupae before worker emergence exhibited lower pathogen levels, indicating
that high brood rearing efforts interfere with the ability of the queen’s immune
system to suppress pathogen proliferation. Early-life queen pathogen-exposure
also improved the immunocompetence of her worker offspring, as demonstrated by
challenging the workers to the same pathogen a year later. Transgenerational transfer
of the queen’s pathogen experience to her workforce can hence durably reduce the
disease susceptibility of the whole superorganism.
article_processing_charge: No
author:
- first_name: Barbara E
full_name: Casillas Perez, Barbara E
id: 351ED2AA-F248-11E8-B48F-1D18A9856A87
last_name: Casillas Perez
- first_name: Christopher
full_name: Pull, Christopher
id: 3C7F4840-F248-11E8-B48F-1D18A9856A87
last_name: Pull
orcid: 0000-0003-1122-3982
- first_name: Filip
full_name: Naiser, Filip
last_name: Naiser
- first_name: Elisabeth
full_name: Naderlinger, Elisabeth
last_name: Naderlinger
- first_name: Jiri
full_name: Matas, Jiri
last_name: Matas
- first_name: Sylvia
full_name: Cremer, Sylvia
id: 2F64EC8C-F248-11E8-B48F-1D18A9856A87
last_name: Cremer
orcid: 0000-0002-2193-3868
citation:
ama: Casillas Perez BE, Pull C, Naiser F, Naderlinger E, Matas J, Cremer S. Early
queen infection shapes developmental dynamics and induces long-term disease protection
in incipient ant colonies. 2021. doi:10.5061/DRYAD.7PVMCVDTJ
apa: Casillas Perez, B. E., Pull, C., Naiser, F., Naderlinger, E., Matas, J., &
Cremer, S. (2021). Early queen infection shapes developmental dynamics and induces
long-term disease protection in incipient ant colonies. Dryad. https://doi.org/10.5061/DRYAD.7PVMCVDTJ
chicago: Casillas Perez, Barbara E, Christopher Pull, Filip Naiser, Elisabeth Naderlinger,
Jiri Matas, and Sylvia Cremer. “Early Queen Infection Shapes Developmental Dynamics
and Induces Long-Term Disease Protection in Incipient Ant Colonies.” Dryad, 2021.
https://doi.org/10.5061/DRYAD.7PVMCVDTJ.
ieee: B. E. Casillas Perez, C. Pull, F. Naiser, E. Naderlinger, J. Matas, and S.
Cremer, “Early queen infection shapes developmental dynamics and induces long-term
disease protection in incipient ant colonies.” Dryad, 2021.
ista: Casillas Perez BE, Pull C, Naiser F, Naderlinger E, Matas J, Cremer S. 2021.
Early queen infection shapes developmental dynamics and induces long-term disease
protection in incipient ant colonies, Dryad, 10.5061/DRYAD.7PVMCVDTJ.
mla: Casillas Perez, Barbara E., et al. Early Queen Infection Shapes Developmental
Dynamics and Induces Long-Term Disease Protection in Incipient Ant Colonies.
Dryad, 2021, doi:10.5061/DRYAD.7PVMCVDTJ.
short: B.E. Casillas Perez, C. Pull, F. Naiser, E. Naderlinger, J. Matas, S. Cremer,
(2021).
date_created: 2023-05-23T16:14:35Z
date_published: 2021-10-29T00:00:00Z
date_updated: 2023-08-14T11:45:28Z
day: '29'
ddc:
- '570'
department:
- _id: SyCr
doi: 10.5061/DRYAD.7PVMCVDTJ
ec_funded: 1
license: https://creativecommons.org/publicdomain/zero/1.0/
main_file_link:
- open_access: '1'
url: https://doi.org/10.5061/dryad.7pvmcvdtj
month: '10'
oa: 1
oa_version: Published Version
project:
- _id: 2649B4DE-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '771402'
name: Epidemics in ant societies on a chip
publisher: Dryad
related_material:
record:
- id: '10284'
relation: used_in_publication
status: public
status: public
title: Early queen infection shapes developmental dynamics and induces long-term disease
protection in incipient ant colonies
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: '2021'
...
---
_id: '13062'
abstract:
- lang: eng
text: 'This paper analyzes the conditions for local adaptation in a metapopulation
with infinitely many islands under a model of hard selection, where population
size depends on local fitness. Each island belongs to one of two distinct ecological
niches or habitats. Fitness is influenced by an additive trait which is under
habitat-dependent directional selection. Our analysis is based on the diffusion
approximation and accounts for both genetic drift and demographic stochasticity.
By neglecting linkage disequilibria, it yields the joint distribution of allele
frequencies and population size on each island. We find that under hard selection,
the conditions for local adaptation in a rare habitat are more restrictive for
more polygenic traits: even moderate migration load per locus at very many loci
is sufficient for population sizes to decline. This further reduces the efficacy
of selection at individual loci due to increased drift and because smaller populations
are more prone to swamping due to migration, causing a positive feedback between
increasing maladaptation and declining population sizes. Our analysis also highlights
the importance of demographic stochasticity, which exacerbates the decline in
numbers of maladapted populations, leading to population collapse in the rare
habitat at significantly lower migration than predicted by deterministic arguments.'
article_processing_charge: No
author:
- first_name: Eniko
full_name: Szep, Eniko
id: 485BB5A4-F248-11E8-B48F-1D18A9856A87
last_name: Szep
- first_name: Himani
full_name: Sachdeva, Himani
id: 42377A0A-F248-11E8-B48F-1D18A9856A87
last_name: Sachdeva
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
citation:
ama: 'Szep E, Sachdeva H, Barton NH. Supplementary code for: Polygenic local adaptation
in metapopulations: A stochastic eco-evolutionary model. 2021. doi:10.5061/DRYAD.8GTHT76P1'
apa: 'Szep, E., Sachdeva, H., & Barton, N. H. (2021). Supplementary code for:
Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model.
Dryad. https://doi.org/10.5061/DRYAD.8GTHT76P1'
chicago: 'Szep, Eniko, Himani Sachdeva, and Nicholas H Barton. “Supplementary Code
for: Polygenic Local Adaptation in Metapopulations: A Stochastic Eco-Evolutionary
Model.” Dryad, 2021. https://doi.org/10.5061/DRYAD.8GTHT76P1.'
ieee: 'E. Szep, H. Sachdeva, and N. H. Barton, “Supplementary code for: Polygenic
local adaptation in metapopulations: A stochastic eco-evolutionary model.” Dryad,
2021.'
ista: 'Szep E, Sachdeva H, Barton NH. 2021. Supplementary code for: Polygenic local
adaptation in metapopulations: A stochastic eco-evolutionary model, Dryad, 10.5061/DRYAD.8GTHT76P1.'
mla: 'Szep, Eniko, et al. Supplementary Code for: Polygenic Local Adaptation
in Metapopulations: A Stochastic Eco-Evolutionary Model. Dryad, 2021, doi:10.5061/DRYAD.8GTHT76P1.'
short: E. Szep, H. Sachdeva, N.H. Barton, (2021).
date_created: 2023-05-23T16:17:02Z
date_published: 2021-03-02T00:00:00Z
date_updated: 2023-09-05T15:44:05Z
day: '02'
ddc:
- '570'
department:
- _id: NiBa
doi: 10.5061/DRYAD.8GTHT76P1
main_file_link:
- open_access: '1'
url: https://doi.org/10.5061/dryad.8gtht76p1
month: '03'
oa: 1
oa_version: Published Version
publisher: Dryad
related_material:
record:
- id: '9252'
relation: used_in_publication
status: public
status: public
title: 'Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic
eco-evolutionary model'
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: '2021'
...
---
_id: '13063'
abstract:
- lang: eng
text: We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability
estimation, an alternative to marker discovery, and accurate genomic prediction,
taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability
parameters in the UK Biobank. We find that only $\leq$ 10\% of the genetic variation
captured for height, body mass index, cardiovascular disease, and type 2 diabetes
is attributable to proximal regulatory regions within 10kb upstream of genes,
while 12-25% is attributed to coding regions, 32-44% to introns, and 22-28% to
distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each
chromosome are associated with each trait, with over 3,100 independent exonic
and intronic regions and over 5,400 independent regulatory regions having >95%
probability of contributing >0.001% to the genetic variance of these four traits.
Our open-source software (GMRM) provides a scalable alternative to current approaches
for biobank data.
article_processing_charge: No
author:
- 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: Robinson MR. Probabilistic inference of the genetic architecture of functional
enrichment of complex traits. 2021. doi:10.5061/dryad.sqv9s4n51
apa: Robinson, M. R. (2021). Probabilistic inference of the genetic architecture
of functional enrichment of complex traits. Dryad. https://doi.org/10.5061/dryad.sqv9s4n51
chicago: Robinson, Matthew Richard. “Probabilistic Inference of the Genetic Architecture
of Functional Enrichment of Complex Traits.” Dryad, 2021. https://doi.org/10.5061/dryad.sqv9s4n51.
ieee: M. R. Robinson, “Probabilistic inference of the genetic architecture of functional
enrichment of complex traits.” Dryad, 2021.
ista: Robinson MR. 2021. Probabilistic inference of the genetic architecture of
functional enrichment of complex traits, Dryad, 10.5061/dryad.sqv9s4n51.
mla: Robinson, Matthew Richard. Probabilistic Inference of the Genetic Architecture
of Functional Enrichment of Complex Traits. Dryad, 2021, doi:10.5061/dryad.sqv9s4n51.
short: M.R. Robinson, (2021).
date_created: 2023-05-23T16:20:16Z
date_published: 2021-11-04T00:00:00Z
date_updated: 2023-09-26T10:36:15Z
day: '04'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.5061/dryad.sqv9s4n51
main_file_link:
- open_access: '1'
url: https://doi.org/10.5061/dryad.sqv9s4n51
month: '11'
oa: 1
oa_version: Published Version
publisher: Dryad
related_material:
link:
- relation: software
url: https://github.com/medical-genomics-group/gmrm
record:
- id: '8429'
relation: used_in_publication
status: public
status: public
title: Probabilistic inference of the genetic architecture of functional enrichment
of complex traits
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: '2021'
...
---
_id: '13068'
abstract:
- lang: eng
text: Source data and source code for the graphs in "Spatiotemporal dynamics of
self-organized branching pancreatic cancer-derived organoids".
article_processing_charge: No
author:
- first_name: Samuel
full_name: Randriamanantsoa, Samuel
last_name: Randriamanantsoa
- first_name: Aristeidis
full_name: Papargyriou, Aristeidis
last_name: Papargyriou
- first_name: Carlo
full_name: Maurer, Carlo
last_name: Maurer
- first_name: Katja
full_name: Peschke, Katja
last_name: Peschke
- first_name: Maximilian
full_name: Schuster, Maximilian
last_name: Schuster
- first_name: Giulia
full_name: Zecchin, Giulia
last_name: Zecchin
- first_name: Katja
full_name: Steiger, Katja
last_name: Steiger
- first_name: Rupert
full_name: Öllinger, Rupert
last_name: Öllinger
- first_name: Dieter
full_name: Saur, Dieter
last_name: Saur
- first_name: Christina
full_name: Scheel, Christina
last_name: Scheel
- first_name: Roland
full_name: Rad, Roland
last_name: Rad
- first_name: Edouard B
full_name: Hannezo, Edouard B
id: 3A9DB764-F248-11E8-B48F-1D18A9856A87
last_name: Hannezo
orcid: 0000-0001-6005-1561
- first_name: Maximilian
full_name: Reichert, Maximilian
last_name: Reichert
- first_name: Andreas R.
full_name: Bausch, Andreas R.
last_name: Bausch
citation:
ama: Randriamanantsoa S, Papargyriou A, Maurer C, et al. Spatiotemporal dynamics
of self-organized branching in pancreas-derived organoids. 2021. doi:10.5281/ZENODO.5148117
apa: Randriamanantsoa, S., Papargyriou, A., Maurer, C., Peschke, K., Schuster, M.,
Zecchin, G., … Bausch, A. R. (2021). Spatiotemporal dynamics of self-organized
branching in pancreas-derived organoids. Zenodo. https://doi.org/10.5281/ZENODO.5148117
chicago: Randriamanantsoa, Samuel, Aristeidis Papargyriou, Carlo Maurer, Katja Peschke,
Maximilian Schuster, Giulia Zecchin, Katja Steiger, et al. “Spatiotemporal Dynamics
of Self-Organized Branching in Pancreas-Derived Organoids.” Zenodo, 2021. https://doi.org/10.5281/ZENODO.5148117.
ieee: S. Randriamanantsoa et al., “Spatiotemporal dynamics of self-organized
branching in pancreas-derived organoids.” Zenodo, 2021.
ista: Randriamanantsoa S, Papargyriou A, Maurer C, Peschke K, Schuster M, Zecchin
G, Steiger K, Öllinger R, Saur D, Scheel C, Rad R, Hannezo EB, Reichert M, Bausch
AR. 2021. Spatiotemporal dynamics of self-organized branching in pancreas-derived
organoids, Zenodo, 10.5281/ZENODO.5148117.
mla: Randriamanantsoa, Samuel, et al. Spatiotemporal Dynamics of Self-Organized
Branching in Pancreas-Derived Organoids. Zenodo, 2021, doi:10.5281/ZENODO.5148117.
short: S. Randriamanantsoa, A. Papargyriou, C. Maurer, K. Peschke, M. Schuster,
G. Zecchin, K. Steiger, R. Öllinger, D. Saur, C. Scheel, R. Rad, E.B. Hannezo,
M. Reichert, A.R. Bausch, (2021).
date_created: 2023-05-23T16:39:24Z
date_published: 2021-07-30T00:00:00Z
date_updated: 2023-08-04T09:25:23Z
day: '30'
ddc:
- '570'
department:
- _id: EdHa
doi: 10.5281/ZENODO.5148117
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.6577226
month: '07'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '12217'
relation: used_in_publication
status: public
status: public
title: Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids
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: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '13069'
abstract:
- lang: eng
text: To survive elevated temperatures, ectotherms adjust the fluidity of membranes
by fine-tuning lipid desaturation levels in a process previously described to
be cell-autonomous. We have discovered that, in Caenorhabditis elegans, neuronal
Heat shock Factor 1 (HSF-1), the conserved master regulator of the heat shock
response (HSR)- causes extensive fat remodelling in peripheral tissues. These
changes include a decrease in fat desaturase and acid lipase expression in the
intestine, and a global shift in the saturation levels of plasma membrane’s phospholipids.
The observed remodelling of plasma membrane is in line with ectothermic adaptive
responses and gives worms a cumulative advantage to warm temperatures. We have
determined that at least six TAX-2/TAX-4 cGMP gated channel expressing sensory
neurons and TGF-β/BMP are required for signalling across tissues to modulate fat
desaturation. We also find neuronal hsf-1 is not only sufficient but also partially
necessary to control the fat remodelling response and for survival at warm temperatures.
This is the first study to show that a thermostat-based mechanism can cell non-autonomously
coordinate membrane saturation and composition across tissues in a multicellular
animal.
article_processing_charge: No
author:
- first_name: Laetitia
full_name: Chauve, Laetitia
last_name: Chauve
- first_name: Francesca
full_name: Hodge, Francesca
last_name: Hodge
- first_name: Sharlene
full_name: Murdoch, Sharlene
last_name: Murdoch
- first_name: Fatemah
full_name: Masoudzadeh, Fatemah
last_name: Masoudzadeh
- first_name: Harry-Jack
full_name: Mann, Harry-Jack
last_name: Mann
- first_name: Andrea
full_name: Lopez-Clavijo, Andrea
last_name: Lopez-Clavijo
- first_name: Hanneke
full_name: Okkenhaug, Hanneke
last_name: Okkenhaug
- first_name: Greg
full_name: West, Greg
last_name: West
- first_name: Bebiana C.
full_name: Sousa, Bebiana C.
last_name: Sousa
- first_name: Anne
full_name: Segonds-Pichon, Anne
last_name: Segonds-Pichon
- first_name: Cheryl
full_name: Li, Cheryl
last_name: Li
- first_name: Steven
full_name: Wingett, Steven
last_name: Wingett
- first_name: Hermine
full_name: Kienberger, Hermine
last_name: Kienberger
- first_name: Karin
full_name: Kleigrewe, Karin
last_name: Kleigrewe
- first_name: Mario
full_name: de Bono, Mario
id: 4E3FF80E-F248-11E8-B48F-1D18A9856A87
last_name: de Bono
orcid: 0000-0001-8347-0443
- first_name: Michael
full_name: Wakelam, Michael
last_name: Wakelam
- first_name: Olivia
full_name: Casanueva, Olivia
last_name: Casanueva
citation:
ama: Chauve L, Hodge F, Murdoch S, et al. Neuronal HSF-1 coordinates the propagation
of fat desaturation across tissues to enable adaptation to high temperatures in
C. elegans. 2021. doi:10.5281/ZENODO.5519410
apa: Chauve, L., Hodge, F., Murdoch, S., Masoudzadeh, F., Mann, H.-J., Lopez-Clavijo,
A., … Casanueva, O. (2021). Neuronal HSF-1 coordinates the propagation of fat
desaturation across tissues to enable adaptation to high temperatures in C. elegans.
Zenodo. https://doi.org/10.5281/ZENODO.5519410
chicago: Chauve, Laetitia, Francesca Hodge, Sharlene Murdoch, Fatemah Masoudzadeh,
Harry-Jack Mann, Andrea Lopez-Clavijo, Hanneke Okkenhaug, et al. “Neuronal HSF-1
Coordinates the Propagation of Fat Desaturation across Tissues to Enable Adaptation
to High Temperatures in C. Elegans.” Zenodo, 2021. https://doi.org/10.5281/ZENODO.5519410.
ieee: L. Chauve et al., “Neuronal HSF-1 coordinates the propagation of fat
desaturation across tissues to enable adaptation to high temperatures in C. elegans.”
Zenodo, 2021.
ista: Chauve L, Hodge F, Murdoch S, Masoudzadeh F, Mann H-J, Lopez-Clavijo A, Okkenhaug
H, West G, Sousa BC, Segonds-Pichon A, Li C, Wingett S, Kienberger H, Kleigrewe
K, de Bono M, Wakelam M, Casanueva O. 2021. Neuronal HSF-1 coordinates the propagation
of fat desaturation across tissues to enable adaptation to high temperatures in
C. elegans, Zenodo, 10.5281/ZENODO.5519410.
mla: Chauve, Laetitia, et al. Neuronal HSF-1 Coordinates the Propagation of Fat
Desaturation across Tissues to Enable Adaptation to High Temperatures in C. Elegans.
Zenodo, 2021, doi:10.5281/ZENODO.5519410.
short: L. Chauve, F. Hodge, S. Murdoch, F. Masoudzadeh, H.-J. Mann, A. Lopez-Clavijo,
H. Okkenhaug, G. West, B.C. Sousa, A. Segonds-Pichon, C. Li, S. Wingett, H. Kienberger,
K. Kleigrewe, M. de Bono, M. Wakelam, O. Casanueva, (2021).
date_created: 2023-05-23T16:40:56Z
date_published: 2021-12-25T00:00:00Z
date_updated: 2023-08-14T11:53:26Z
day: '25'
ddc:
- '570'
department:
- _id: MaDe
doi: 10.5281/ZENODO.5519410
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.5547464
month: '12'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '10322'
relation: used_in_publication
status: public
status: public
title: Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues
to enable adaptation to high temperatures in C. elegans
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: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '13072'
abstract:
- lang: eng
text: CpGs and corresponding mean weights for DNAm-based prediction of cognitive
abilities (6 traits)
article_processing_charge: No
author:
- first_name: Daniel L
full_name: McCartney, Daniel L
last_name: McCartney
- first_name: Robert F
full_name: Hillary, Robert F
last_name: Hillary
- first_name: Eleanor LS
full_name: Conole, Eleanor LS
last_name: Conole
- first_name: Daniel
full_name: Trejo Banos, Daniel
last_name: Trejo Banos
- first_name: Danni A
full_name: Gadd, Danni A
last_name: Gadd
- first_name: Rosie M
full_name: Walker, Rosie M
last_name: Walker
- first_name: Cliff
full_name: Nangle, Cliff
last_name: Nangle
- first_name: Robin
full_name: Flaig, Robin
last_name: Flaig
- first_name: Archie
full_name: Campbell, Archie
last_name: Campbell
- first_name: Alison D
full_name: Murray, Alison D
last_name: Murray
- first_name: Susana
full_name: Munoz Maniega, Susana
last_name: Munoz Maniega
- first_name: Maria
full_name: del C Valdes-Hernandez, Maria
last_name: del C Valdes-Hernandez
- first_name: Mathew A
full_name: Harris, Mathew A
last_name: Harris
- first_name: Mark E
full_name: Bastin, Mark E
last_name: Bastin
- first_name: Joanna M
full_name: Wardlaw, Joanna M
last_name: Wardlaw
- first_name: Sarah E
full_name: Harris, Sarah E
last_name: Harris
- first_name: David J
full_name: Porteous, David J
last_name: Porteous
- first_name: Elliot M
full_name: Tucker-Drob, Elliot M
last_name: Tucker-Drob
- first_name: Andrew M
full_name: McIntosh, Andrew M
last_name: McIntosh
- first_name: Kathryn L
full_name: Evans, Kathryn L
last_name: Evans
- first_name: Ian J
full_name: Deary, Ian J
last_name: Deary
- first_name: Simon R
full_name: Cox, Simon R
last_name: Cox
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
- first_name: Riccardo E
full_name: Marioni, Riccardo E
last_name: Marioni
citation:
ama: McCartney DL, Hillary RF, Conole EL, et al. Blood-based epigenome-wide analyses
of cognitive abilities. 2021. doi:10.5281/ZENODO.5794028
apa: McCartney, D. L., Hillary, R. F., Conole, E. L., Trejo Banos, D., Gadd, D.
A., Walker, R. M., … Marioni, R. E. (2021). Blood-based epigenome-wide analyses
of cognitive abilities. Zenodo. https://doi.org/10.5281/ZENODO.5794028
chicago: McCartney, Daniel L, Robert F Hillary, Eleanor LS Conole, Daniel Trejo
Banos, Danni A Gadd, Rosie M Walker, Cliff Nangle, et al. “Blood-Based Epigenome-Wide
Analyses of Cognitive Abilities.” Zenodo, 2021. https://doi.org/10.5281/ZENODO.5794028.
ieee: D. L. McCartney et al., “Blood-based epigenome-wide analyses of cognitive
abilities.” Zenodo, 2021.
ista: McCartney DL, Hillary RF, Conole EL, Trejo Banos D, Gadd DA, Walker RM, Nangle
C, Flaig R, Campbell A, Murray AD, Munoz Maniega S, del C Valdes-Hernandez M,
Harris MA, Bastin ME, Wardlaw JM, Harris SE, Porteous DJ, Tucker-Drob EM, McIntosh
AM, Evans KL, Deary IJ, Cox SR, Robinson MR, Marioni RE. 2021. Blood-based epigenome-wide
analyses of cognitive abilities, Zenodo, 10.5281/ZENODO.5794028.
mla: McCartney, Daniel L., et al. Blood-Based Epigenome-Wide Analyses of Cognitive
Abilities. Zenodo, 2021, doi:10.5281/ZENODO.5794028.
short: D.L. McCartney, R.F. Hillary, E.L. Conole, D. Trejo Banos, D.A. Gadd, R.M.
Walker, C. Nangle, R. Flaig, A. Campbell, A.D. Murray, S. Munoz Maniega, M. del
C Valdes-Hernandez, M.A. Harris, M.E. Bastin, J.M. Wardlaw, S.E. Harris, D.J.
Porteous, E.M. Tucker-Drob, A.M. McIntosh, K.L. Evans, I.J. Deary, S.R. Cox, M.R.
Robinson, R.E. Marioni, (2021).
date_created: 2023-05-23T16:46:20Z
date_published: 2021-12-20T00:00:00Z
date_updated: 2023-08-02T14:05:12Z
day: '20'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.5281/ZENODO.5794028
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.5794029
month: '12'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '10702'
relation: used_in_publication
status: public
status: public
title: Blood-based epigenome-wide analyses of cognitive abilities
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: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '13080'
abstract:
- lang: eng
text: "Data for the manuscript 'Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor
Nanowire' ([2006.01275] Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor
Nanowire (arxiv.org))\r\n\r\nWe upload a pdf with extended data sets, and the
raw data for these extended datasets as well."
article_processing_charge: No
author:
- first_name: Denise
full_name: Puglia, Denise
id: 4D495994-AE37-11E9-AC72-31CAE5697425
last_name: Puglia
- first_name: Esteban
full_name: Martinez, Esteban
last_name: Martinez
- first_name: Gerbold
full_name: Menard, Gerbold
last_name: Menard
- first_name: Andreas
full_name: Pöschl, Andreas
last_name: Pöschl
- first_name: Sergei
full_name: Gronin, Sergei
last_name: Gronin
- first_name: Geoffrey
full_name: Gardner, Geoffrey
last_name: Gardner
- first_name: Ray
full_name: Kallaher, Ray
last_name: Kallaher
- first_name: Michael
full_name: Manfra, Michael
last_name: Manfra
- first_name: Charles
full_name: Marcus, Charles
last_name: Marcus
- first_name: Andrew P
full_name: Higginbotham, Andrew P
id: 4AD6785A-F248-11E8-B48F-1D18A9856A87
last_name: Higginbotham
orcid: 0000-0003-2607-2363
- first_name: Lucas
full_name: Casparis, Lucas
last_name: Casparis
citation:
ama: Puglia D, Martinez E, Menard G, et al. Data for ’Closing of the Induced Gap
in a Hybrid Superconductor-Semiconductor Nanowire. 2021. doi:10.5281/ZENODO.4592435
apa: Puglia, D., Martinez, E., Menard, G., Pöschl, A., Gronin, S., Gardner, G.,
… Casparis, L. (2021). Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor
Nanowire. Zenodo. https://doi.org/10.5281/ZENODO.4592435
chicago: Puglia, Denise, Esteban Martinez, Gerbold Menard, Andreas Pöschl, Sergei
Gronin, Geoffrey Gardner, Ray Kallaher, et al. “Data for ’Closing of the Induced
Gap in a Hybrid Superconductor-Semiconductor Nanowire.” Zenodo, 2021. https://doi.org/10.5281/ZENODO.4592435.
ieee: D. Puglia et al., “Data for ’Closing of the Induced Gap in a Hybrid
Superconductor-Semiconductor Nanowire.” Zenodo, 2021.
ista: Puglia D, Martinez E, Menard G, Pöschl A, Gronin S, Gardner G, Kallaher R,
Manfra M, Marcus C, Higginbotham AP, Casparis L. 2021. Data for ’Closing of the
Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire, Zenodo, 10.5281/ZENODO.4592435.
mla: Puglia, Denise, et al. Data for ’Closing of the Induced Gap in a Hybrid
Superconductor-Semiconductor Nanowire. Zenodo, 2021, doi:10.5281/ZENODO.4592435.
short: D. Puglia, E. Martinez, G. Menard, A. Pöschl, S. Gronin, G. Gardner, R. Kallaher,
M. Manfra, C. Marcus, A.P. Higginbotham, L. Casparis, (2021).
date_created: 2023-05-23T17:11:28Z
date_published: 2021-03-09T00:00:00Z
date_updated: 2023-08-08T14:08:07Z
day: '09'
ddc:
- '530'
department:
- _id: AnHi
doi: 10.5281/ZENODO.4592435
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.4592460
month: '03'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
link:
- relation: software
url: https://github.com/caslu85/Induced-Gap-Closing-Shared/tree/1.1.3
record:
- id: '9570'
relation: used_in_publication
status: public
status: public
title: Data for 'Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor
Nanowire
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_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'
...