---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
PlanS_conform: '1'
_id: '21343'
abstract:
- lang: eng
text: "The large sieve is used to estimate the density of quadratic polynomials
Q ∈ Z[x],\r\nsuch that there exists an odd degree polynomial defined over Z which
has resultant ±1 with Q.\r\nGiven a monic polynomial R ∈ Z[x] of odd degree, this
is used to show that for almost all\r\nquadratic polynomials Q ∈ Z[x], there exists
a prime p such that Q and R share a common\r\nroot in Fp. Using recent work of
Landesman, an application to the average size of the odd part\r\nof the class
group of quadratic number fields is also given"
- lang: fre
text: ' Le grand crible est utilisé pour estimer la densité des polynômes quadratiques
Q ∈ Z[x] tels qu’il existe un polynôme de degré impair défini sur Z dont le résultant
avec Q est égal à ±1. Étant donné un polynôme unitaire R ∈ Z[x] de degré impair,
on s’en sert pour montrer que, pour presque tous les polynômes quadratiques Q
∈ Z[x], il existe un nombre premier p tel que Q et R aient une racine commune
dans Fp. En utilisant des travaux récents de Landesman, on obtient également une
application concernant la taille moyenne de la partie impaire du groupe de classe
des corps quadratiques.'
acknowledgement: While working on this paper, the first author was supported by a
FWF grant (DOI 10.55776/P36278).
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Timothy D
full_name: Browning, Timothy D
id: 35827D50-F248-11E8-B48F-1D18A9856A87
last_name: Browning
orcid: 0000-0002-8314-0177
- first_name: Yik Tung
full_name: Chan, Yik Tung
id: c4c0afc8-9262-11ed-9231-d8b0bc743af1
last_name: Chan
orcid: 0000-0001-8467-4106
citation:
ama: Browning TD, Chan S. Solubility of a resultant equation and applications. Journal
de l’Ecole Polytechnique - Mathematiques. 2025;12:1677-1691. doi:10.5802/jep.320
apa: Browning, T. D., & Chan, S. (2025). Solubility of a resultant equation
and applications. Journal de l’Ecole Polytechnique - Mathematiques. Ecole
Polytechnique. https://doi.org/10.5802/jep.320
chicago: Browning, Timothy D, and Stephanie Chan. “Solubility of a Resultant Equation
and Applications.” Journal de l’Ecole Polytechnique - Mathematiques. Ecole
Polytechnique, 2025. https://doi.org/10.5802/jep.320.
ieee: T. D. Browning and S. Chan, “Solubility of a resultant equation and applications,”
Journal de l’Ecole Polytechnique - Mathematiques, vol. 12. Ecole Polytechnique,
pp. 1677–1691, 2025.
ista: Browning TD, Chan S. 2025. Solubility of a resultant equation and applications.
Journal de l’Ecole Polytechnique - Mathematiques. 12, 1677–1691.
mla: Browning, Timothy D., and Stephanie Chan. “Solubility of a Resultant Equation
and Applications.” Journal de l’Ecole Polytechnique - Mathematiques, vol.
12, Ecole Polytechnique, 2025, pp. 1677–91, doi:10.5802/jep.320.
short: T.D. Browning, S. Chan, Journal de l’Ecole Polytechnique - Mathematiques
12 (2025) 1677–1691.
corr_author: '1'
das_tickbox: '0'
date_created: 2026-02-22T23:01:36Z
date_published: 2025-10-21T00:00:00Z
date_updated: 2026-08-12T11:17:50Z
day: '21'
ddc:
- '510'
department:
- _id: TiBr
doi: 10.5802/jep.320
external_id:
arxiv:
- '2411.09264'
file:
- access_level: open_access
checksum: 828577ea48ac6109d3e9dd1aeddd45c4
content_type: application/pdf
creator: dernst
date_created: 2026-02-24T07:56:34Z
date_updated: 2026-02-24T07:56:34Z
file_id: '21356'
file_name: 2025_JEP_Browning.pdf
file_size: 1003689
relation: main_file
success: 1
file_date_updated: 2026-02-24T07:56:34Z
has_accepted_license: '1'
intvolume: ' 12'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: 1677-1691
project:
- _id: bd8a4fdc-d553-11ed-ba76-80a0167441a3
grant_number: P36278
name: Rational curves via function field analytic number theory
publication: Journal de l'Ecole Polytechnique - Mathematiques
publication_identifier:
eissn:
- 2270-518X
issn:
- 2429-7100
publication_status: published
publisher: Ecole Polytechnique
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: Solubility of a resultant equation and applications
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: 12
year: '2025'
...
---
APC_amount: 2792,52 EUR
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '15033'
abstract:
- lang: eng
text: The GNOM (GN) Guanine nucleotide Exchange Factor for ARF small GTPases (ARF-GEF)
is among the best studied trafficking regulators in plants, playing crucial and
unique developmental roles in patterning and polarity. The current models place
GN at the Golgi apparatus (GA), where it mediates secretion/recycling, and at
the plasma membrane (PM) presumably contributing to clathrin-mediated endocytosis
(CME). The mechanistic basis of the developmental function of GN, distinct from
the other ARF-GEFs including its closest homologue GNOM-LIKE1 (GNL1), remains
elusive. Insights from this study largely extend the current notions of GN function.
We show that GN, but not GNL1, localizes to the cell periphery at long-lived structures
distinct from clathrin-coated pits, while CME and secretion proceed normally in
gn knockouts. The functional GN mutant variant GNfewerroots,
absent from the GA, suggests that the cell periphery is the major site of GN action
responsible for its developmental function. Following inhibition by Brefeldin
A, GN, but not GNL1, relocates to the PM likely on exocytic vesicles, suggesting
selective molecular associations en route to the cell periphery. A study of GN-GNL1
chimeric ARF-GEFs indicates that all GN domains contribute to the specific GN
function in a partially redundant manner. Together, this study offers significant
steps toward the elucidation of the mechanism underlying unique cellular and development
functions of GNOM.
acknowledgement: The authors would like to gratefully acknowledge Dr Xixi Zhang for
cloning the GNL1/pDONR221 construct and for useful discussions.H2020 European Research
Council Advanced Grant ETAP742985 to Jiří Friml, Austrian Science Fund I 3630-B25
to Jiří Friml
article_processing_charge: Yes
article_type: original
author:
- first_name: Maciek
full_name: Adamowski, Maciek
id: 45F536D2-F248-11E8-B48F-1D18A9856A87
last_name: Adamowski
orcid: 0000-0001-6463-5257
- first_name: Ivana
full_name: Matijevic, Ivana
id: 83c17ce3-15b2-11ec-abd3-f486545870bd
last_name: Matijevic
- first_name: Jiří
full_name: Friml, Jiří
id: 4159519E-F248-11E8-B48F-1D18A9856A87
last_name: Friml
orcid: 0000-0002-8302-7596
citation:
ama: Adamowski M, Matijevic I, Friml J. Developmental patterning function of GNOM
ARF-GEF mediated from the cell periphery. eLife. 2024;13. doi:10.7554/elife.68993
apa: Adamowski, M., Matijevic, I., & Friml, J. (2024). Developmental patterning
function of GNOM ARF-GEF mediated from the cell periphery. ELife. eLife
Sciences Publications. https://doi.org/10.7554/elife.68993
chicago: Adamowski, Maciek, Ivana Matijevic, and Jiří Friml. “Developmental Patterning
Function of GNOM ARF-GEF Mediated from the Cell Periphery.” ELife. eLife
Sciences Publications, 2024. https://doi.org/10.7554/elife.68993.
ieee: M. Adamowski, I. Matijevic, and J. Friml, “Developmental patterning function
of GNOM ARF-GEF mediated from the cell periphery,” eLife, vol. 13. eLife
Sciences Publications, 2024.
ista: Adamowski M, Matijevic I, Friml J. 2024. Developmental patterning function
of GNOM ARF-GEF mediated from the cell periphery. eLife. 13.
mla: Adamowski, Maciek, et al. “Developmental Patterning Function of GNOM ARF-GEF
Mediated from the Cell Periphery.” ELife, vol. 13, eLife Sciences Publications,
2024, doi:10.7554/elife.68993.
short: M. Adamowski, I. Matijevic, J. Friml, ELife 13 (2024).
corr_author: '1'
date_created: 2024-02-27T07:10:11Z
date_published: 2024-02-21T00:00:00Z
date_updated: 2025-10-15T06:31:47Z
day: '21'
ddc:
- '580'
department:
- _id: JiFr
doi: 10.7554/elife.68993
ec_funded: 1
external_id:
isi:
- '001174278000001'
pmid:
- '38381485'
file:
- access_level: open_access
checksum: b2b2d583b433823af731842f1420113e
content_type: application/pdf
creator: dernst
date_created: 2024-07-22T11:51:50Z
date_updated: 2024-07-22T11:51:50Z
file_id: '17310'
file_name: 2024_eLife_Adamowski.pdf
file_size: 15675744
relation: main_file
success: 1
file_date_updated: 2024-07-22T11:51:50Z
has_accepted_license: '1'
intvolume: ' 13'
isi: 1
keyword:
- General Immunology and Microbiology
- General Biochemistry
- Genetics and Molecular Biology
- General Medicine
- General Neuroscience
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 261099A6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '742985'
name: Tracing Evolution of Auxin Transport and Polarity in Plants
- _id: 26538374-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: I03630
name: Molecular mechanisms of endocytic cargo recognition in plants
- _id: 3AC91DDA-15DF-11EA-824D-93A3E7B544D1
call_identifier: FWF
name: FWF Open Access Fund
publication: eLife
publication_identifier:
issn:
- 2050-084X
publication_status: published
publisher: eLife Sciences Publications
quality_controlled: '1'
scopus_import: '1'
status: public
title: Developmental patterning function of GNOM ARF-GEF mediated from the cell periphery
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 13
year: '2024'
...
---
_id: '15385'
abstract:
- lang: eng
text: "Relevant information about the data can be found in the 'Readme_Data.txt'
file. \r\nA previous version of the publication can be found on BioRxiv: https://www.biorxiv.org/content/10.1101/2022.10.11.511691v4\r\nand
published in Plos Biology (2024)"
acknowledged_ssus:
- _id: PreCl
- _id: M-Shop
- _id: LifeSc
- _id: Bio
acknowledgement: 'We thank Armel Nicolas, Bella Bruszel and Ewelina Dutkiewicz from
the ISTA Mass Spectrometry Service (Lab Services Facilities) for all Proteomics
work, including samples preparation, LC/MS data acquisition, searches and data evaluation.
We thank Prof. Peter Jonas for his suggestion on the involvement of potassium channels
and members of the Neuroethology group for their comments on the manuscript. Katalin
Szigeti and Julie Murmann for experimental help. This research was supported by
the Scientific Service Units of ISTA through resources provided by the Lab Support
Facility, the Imaging and Optics Facility, the Machine Shop Unit and the Preclinical
Facility, especially Freyja Langer and Michael Schunn. '
article_processing_charge: No
author:
- first_name: Laura
full_name: Burnett, Laura
id: 3B717F68-F248-11E8-B48F-1D18A9856A87
last_name: Burnett
orcid: 0000-0002-8937-410X
- first_name: Peter
full_name: Koppensteiner, Peter
id: 3B8B25A8-F248-11E8-B48F-1D18A9856A87
last_name: Koppensteiner
orcid: 0000-0002-3509-1948
- first_name: Olga
full_name: Symonova, Olga
id: 3C0C7BC6-F248-11E8-B48F-1D18A9856A87
last_name: Symonova
orcid: 0000-0003-2012-9947
- first_name: Tomas
full_name: Masson, Tomas
id: 93ac43e8-8599-11eb-9b86-f6efb0a4c207
last_name: Masson
orcid: 0000-0002-2634-6283
- first_name: Tomas A
full_name: Vega Zuniga, Tomas A
id: 2E7C4E78-F248-11E8-B48F-1D18A9856A87
last_name: Vega Zuniga
- first_name: Ximena
full_name: Contreras, Ximena
id: 475990FE-F248-11E8-B48F-1D18A9856A87
last_name: Contreras
- first_name: Thomas
full_name: Rülicke, Thomas
last_name: Rülicke
- first_name: Ryuichi
full_name: Shigemoto, Ryuichi
id: 499F3ABC-F248-11E8-B48F-1D18A9856A87
last_name: Shigemoto
orcid: 0000-0001-8761-9444
- first_name: Gaia
full_name: Novarino, Gaia
id: 3E57A680-F248-11E8-B48F-1D18A9856A87
last_name: Novarino
orcid: 0000-0002-7673-7178
- first_name: Maximilian A
full_name: Jösch, Maximilian A
id: 2BD278E6-F248-11E8-B48F-1D18A9856A87
last_name: Jösch
orcid: 0000-0002-3937-1330
citation:
ama: Burnett L, Koppensteiner P, Symonova O, et al. Shared behavioural impairments
in visual perception and place avoidance across different autism models are driven
by periaqueductal grey hypoexcitability in Setd5 haploinsufficient mice. 2024.
doi:10.15479/AT:ISTA:15385
apa: Burnett, L., Koppensteiner, P., Symonova, O., Masson, T., Vega Zuniga, T. A.,
Contreras, X., … Jösch, M. A. (2024). Shared behavioural impairments in visual
perception and place avoidance across different autism models are driven by periaqueductal
grey hypoexcitability in Setd5 haploinsufficient mice. Institute of Science and
Technology Austria. https://doi.org/10.15479/AT:ISTA:15385
chicago: Burnett, Laura, Peter Koppensteiner, Olga Symonova, Tomas Masson, Tomas
A Vega Zuniga, Ximena Contreras, Thomas Rülicke, Ryuichi Shigemoto, Gaia Novarino,
and Maximilian A Jösch. “Shared Behavioural Impairments in Visual Perception and
Place Avoidance across Different Autism Models Are Driven by Periaqueductal Grey
Hypoexcitability in Setd5 Haploinsufficient Mice.” Institute of Science and Technology
Austria, 2024. https://doi.org/10.15479/AT:ISTA:15385.
ieee: L. Burnett et al., “Shared behavioural impairments in visual perception
and place avoidance across different autism models are driven by periaqueductal
grey hypoexcitability in Setd5 haploinsufficient mice.” Institute of Science and
Technology Austria, 2024.
ista: Burnett L, Koppensteiner P, Symonova O, Masson T, Vega Zuniga TA, Contreras
X, Rülicke T, Shigemoto R, Novarino G, Jösch MA. 2024. Shared behavioural impairments
in visual perception and place avoidance across different autism models are driven
by periaqueductal grey hypoexcitability in Setd5 haploinsufficient mice, Institute
of Science and Technology Austria, 10.15479/AT:ISTA:15385.
mla: Burnett, Laura, et al. Shared Behavioural Impairments in Visual Perception
and Place Avoidance across Different Autism Models Are Driven by Periaqueductal
Grey Hypoexcitability in Setd5 Haploinsufficient Mice. Institute of Science
and Technology Austria, 2024, doi:10.15479/AT:ISTA:15385.
short: L. Burnett, P. Koppensteiner, O. Symonova, T. Masson, T.A. Vega Zuniga, X.
Contreras, T. Rülicke, R. Shigemoto, G. Novarino, M.A. Jösch, (2024).
corr_author: '1'
date_created: 2024-05-13T15:04:04Z
date_published: 2024-05-15T00:00:00Z
date_updated: 2025-09-08T07:57:11Z
day: '15'
ddc:
- '570'
department:
- _id: MaJö
- _id: PreCl
- _id: SiHi
- _id: RySh
- _id: GaNo
doi: 10.15479/AT:ISTA:15385
file:
- access_level: open_access
checksum: 9205eb0876f0f08552dbad80d6884b4b
content_type: application/zip
creator: mjoesch
date_created: 2024-05-15T06:09:17Z
date_updated: 2024-05-15T06:09:17Z
file_id: '15396'
file_name: PatchClamp.zip
file_size: '1149617663'
relation: main_file
success: 1
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content_type: application/zip
creator: mjoesch
date_created: 2024-05-15T06:09:12Z
date_updated: 2024-05-15T06:09:12Z
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file_name: SiliconProbe.zip
file_size: '564903112'
relation: main_file
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content_type: application/zip
creator: mjoesch
date_created: 2024-05-15T06:09:14Z
date_updated: 2024-05-15T06:09:14Z
file_id: '15398'
file_name: WesternBlot.zip
file_size: '11685703'
relation: main_file
success: 1
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checksum: beeeeaa43770090f3b291209ed6b0623
content_type: application/zip
creator: mjoesch
date_created: 2024-05-15T06:09:38Z
date_updated: 2024-05-15T06:09:38Z
file_id: '15399'
file_name: Behaviour.zip
file_size: '1335626779'
relation: main_file
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content_type: text/plain
creator: mjoesch
date_created: 2024-05-16T09:08:20Z
date_updated: 2024-05-16T09:08:20Z
file_id: '15400'
file_name: Readme_Data.txt
file_size: 18841
relation: main_file
success: 1
file_date_updated: 2024-05-16T09:08:20Z
has_accepted_license: '1'
keyword:
- ASD
- periaqueductal gray
- perception
- behavior
- potassium channels
month: '05'
oa: 1
oa_version: Published Version
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '17142'
relation: used_in_publication
status: public
status: public
title: Shared behavioural impairments in visual perception and place avoidance across
different autism models are driven by periaqueductal grey hypoexcitability in Setd5
haploinsufficient mice
tmp:
image: /images/cc_by_nc.png
legal_code_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode
name: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
short: CC BY-NC (4.0)
type: research_data
user_id: 68b8ca59-c5b3-11ee-8790-cd641c68093d
year: '2024'
...
---
OA_place: repository
_id: '18498'
abstract:
- lang: eng
text: 'Scripts and data used in the research study Predicting rapid adaptation in
time from adaptation in space: a 30-year field experiment in marine snails. https://doi.org/10.1101/2023.09.27.559715'
article_processing_charge: No
author:
- first_name: Diego Fernando
full_name: Garcia Castillo, Diego Fernando
id: ae681a14-dc74-11ea-a0a7-c6ef18161701
last_name: Garcia Castillo
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
- first_name: Rui
full_name: Faria, Rui
last_name: Faria
- first_name: Jenny
full_name: Larsson, Jenny
last_name: Larsson
- first_name: Sean
full_name: Stankowski, Sean
id: 43161670-5719-11EA-8025-FABC3DDC885E
last_name: Stankowski
- first_name: Roger
full_name: Butlin, Roger
last_name: Butlin
- first_name: Kerstin
full_name: Johannesson, Kerstin
last_name: Johannesson
- 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: 'Garcia Castillo DF, Barton NH, Faria R, et al. Data and code for: Predicting
rapid adaptation in time from adaptation in space: a 30-year field experiment
in marine snails. 2024. doi:10.5281/ZENODO.12159343'
apa: 'Garcia Castillo, D. F., Barton, N. H., Faria, R., Larsson, J., Stankowski,
S., Butlin, R., … Westram, A. M. (2024). Data and code for: Predicting rapid adaptation
in time from adaptation in space: a 30-year field experiment in marine snails.
Zenodo. https://doi.org/10.5281/ZENODO.12159343'
chicago: 'Garcia Castillo, Diego Fernando, Nicholas H Barton, Rui Faria, Jenny Larsson,
Sean Stankowski, Roger Butlin, Kerstin Johannesson, and Anja M Westram. “Data
and Code for: Predicting Rapid Adaptation in Time from Adaptation in Space: A
30-Year Field Experiment in Marine Snails.” Zenodo, 2024. https://doi.org/10.5281/ZENODO.12159343.'
ieee: 'D. F. Garcia Castillo et al., “Data and code for: Predicting rapid
adaptation in time from adaptation in space: a 30-year field experiment in marine
snails.” Zenodo, 2024.'
ista: 'Garcia Castillo DF, Barton NH, Faria R, Larsson J, Stankowski S, Butlin R,
Johannesson K, Westram AM. 2024. Data and code for: Predicting rapid adaptation
in time from adaptation in space: a 30-year field experiment in marine snails,
Zenodo, 10.5281/ZENODO.12159343.'
mla: 'Garcia Castillo, Diego Fernando, et al. Data and Code for: Predicting Rapid
Adaptation in Time from Adaptation in Space: A 30-Year Field Experiment in Marine
Snails. Zenodo, 2024, doi:10.5281/ZENODO.12159343.'
short: D.F. Garcia Castillo, N.H. Barton, R. Faria, J. Larsson, S. Stankowski, R.
Butlin, K. Johannesson, A.M. Westram, (2024).
corr_author: '1'
date_created: 2024-11-04T09:33:17Z
date_published: 2024-06-19T00:00:00Z
date_updated: 2026-04-16T12:20:37Z
day: '19'
ddc:
- '570'
department:
- _id: NiBa
doi: 10.5281/ZENODO.12159343
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.12159344
month: '06'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '20991'
relation: used_in_publication
status: public
- id: '18491'
relation: used_in_publication
status: public
status: public
title: 'Data and code for: Predicting rapid adaptation in time from adaptation in
space: a 30-year field experiment in marine snails'
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: '2024'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '18856'
abstract:
- lang: eng
text: This research is aimed to solve the tweet/user geolocation prediction task
and provide a flexible methodology for the geo-tagging of textual big data. The
suggested approach implements neural networks for natural language processing
(NLP) to estimate the location as coordinate pairs (longitude, latitude) and two-dimensional
Gaussian Mixture Models (GMMs). The scope of proposed models has been finetuned
on a Twitter dataset using pretrained Bidirectional Encoder Representations from
Transformers (BERT) as base models. Performance metrics show a median error of
fewer than 30 km on a worldwide-level, and fewer than 15 km on the US-level datasets
for the models trained and evaluated on text features of tweets' content and metadata
context. Our source code and data are available at https://github.com/K4TEL/geo-twitter.git.
acknowledgement: The authors acknowledge the Institute of Science and Technology (ISTA)
for their material support and for granting access to the Twitter database archive,
which was essential for the research.
article_processing_charge: Yes
article_type: original
author:
- first_name: Kateryna
full_name: Lutsai, Kateryna
last_name: Lutsai
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: Lutsai K, Lampert C. Predicting the geolocation of tweets using transformer
models on customized data. Journal of Spatial Information Science. 2024;(29):69-99.
doi:10.5311/JOSIS.2024.29.295
apa: Lutsai, K., & Lampert, C. (2024). Predicting the geolocation of tweets
using transformer models on customized data. Journal of Spatial Information
Science. University of Maine. https://doi.org/10.5311/JOSIS.2024.29.295
chicago: Lutsai, Kateryna, and Christoph Lampert. “Predicting the Geolocation of
Tweets Using Transformer Models on Customized Data.” Journal of Spatial Information
Science. University of Maine, 2024. https://doi.org/10.5311/JOSIS.2024.29.295.
ieee: K. Lutsai and C. Lampert, “Predicting the geolocation of tweets using transformer
models on customized data,” Journal of Spatial Information Science, no.
29. University of Maine, pp. 69–99, 2024.
ista: Lutsai K, Lampert C. 2024. Predicting the geolocation of tweets using transformer
models on customized data. Journal of Spatial Information Science. (29), 69–99.
mla: Lutsai, Kateryna, and Christoph Lampert. “Predicting the Geolocation of Tweets
Using Transformer Models on Customized Data.” Journal of Spatial Information
Science, no. 29, University of Maine, 2024, pp. 69–99, doi:10.5311/JOSIS.2024.29.295.
short: K. Lutsai, C. Lampert, Journal of Spatial Information Science (2024) 69–99.
corr_author: '1'
date_created: 2025-01-19T23:01:53Z
date_published: 2024-12-26T00:00:00Z
date_updated: 2025-06-05T13:47:12Z
day: '26'
ddc:
- '500'
department:
- _id: ChLa
doi: 10.5311/JOSIS.2024.29.295
file:
- access_level: open_access
checksum: b82413f00398ffb5168e8e747571a98d
content_type: application/pdf
creator: dernst
date_created: 2025-01-20T08:41:10Z
date_updated: 2025-01-20T08:41:10Z
file_id: '18857'
file_name: 2024_JourSpatialInfoScience_Lutsai.pdf
file_size: 7250655
relation: main_file
success: 1
file_date_updated: 2025-01-20T08:41:10Z
has_accepted_license: '1'
issue: '29'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: 69-99
publication: Journal of Spatial Information Science
publication_identifier:
eissn:
- 1948-660X
publication_status: published
publisher: University of Maine
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/K4TEL/geo-twitter.git
scopus_import: '1'
status: public
title: Predicting the geolocation of tweets using transformer models on customized
data
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/3.0/legalcode
name: Creative Commons Attribution 3.0 Unported (CC BY 3.0)
short: CC BY (3.0)
type: journal_article
user_id: 68b8ca59-c5b3-11ee-8790-cd641c68093d
year: '2024'
...
---
OA_place: publisher
OA_type: hybrid
_id: '18934'
abstract:
- lang: eng
text: The assembly of biomolecular condensate in eukaryotic cells and the accumulation
of amyloid deposits in neurons are processes involving the nucleation and growth
(NAG) of new protein phases. To therapeutically target protein phase separation,
drug candidates are tested in in vitro assays that monitor the increase in the
mass or size of the new phase. Limited mechanistic insight is, however, provided
if empirical or untestable kinetic models are fitted to these progress curves.
Here we present the web server NAGPKin that quantifies NAG rates using mass-based
or size-based progress curves as the input data. A report is generated containing
the fitted NAG parameters and elucidating the phase separation mechanisms at play.
The NAG parameters can be used to predict particle size distributions of, for
example, protein droplets formed by liquid-liquid phase separation (LLPS) or amyloid
fibrils formed by protein aggregation. Because minimal intervention is required
from the user, NAGPKin is a good platform for standardized reporting of LLPS and
protein self-assembly data. NAGPKin is useful for drug discovery as well as for
fundamental studies on protein phase separation. NAGPKin is freely available (no
login required) at https://nagpkin.i3s.up.pt .
acknowledgement: We thank Professor José Paulo Leal, Department of Computer Science
− Faculdade de Ciências da Universidade do Porto, for his invaluable help during
the Implementation of NAGPKin. This work is part of a project that has received
funding from the European Union’s Horizon 2020 research and innovation programme
under grant agreement no. 952334 (PhasAGE). This research was funded by the Portuguese
Foundation for Science and Technology (FCT) in the framework of project PTDC/QUI-COL/2444/2021.
article_number: mr1
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Zsuzsa
full_name: Sárkány, Zsuzsa
last_name: Sárkány
- first_name: Francisco
full_name: Figueiredo, Francisco
id: 8125cbe2-9661-11ed-a754-afe96018f37d
last_name: Figueiredo
- first_name: Sandra
full_name: Macedo-Ribeiro, Sandra
last_name: Macedo-Ribeiro
- first_name: Pedro M.
full_name: Martins, Pedro M.
last_name: Martins
citation:
ama: 'Sárkány Z, Figueiredo F, Macedo-Ribeiro S, Martins PM. NAGPKin: Nucleation-and-growth
parameters from the kinetics of protein phase separation. Molecular Biology
of the Cell. 2024;35(3). doi:10.1091/mbc.e23-07-0289'
apa: 'Sárkány, Z., Figueiredo, F., Macedo-Ribeiro, S., & Martins, P. M. (2024).
NAGPKin: Nucleation-and-growth parameters from the kinetics of protein phase separation.
Molecular Biology of the Cell. American Society for Cell Biology. https://doi.org/10.1091/mbc.e23-07-0289'
chicago: 'Sárkány, Zsuzsa, Francisco Figueiredo, Sandra Macedo-Ribeiro, and Pedro
M. Martins. “NAGPKin: Nucleation-and-Growth Parameters from the Kinetics of Protein
Phase Separation.” Molecular Biology of the Cell. American Society for
Cell Biology, 2024. https://doi.org/10.1091/mbc.e23-07-0289.'
ieee: 'Z. Sárkány, F. Figueiredo, S. Macedo-Ribeiro, and P. M. Martins, “NAGPKin:
Nucleation-and-growth parameters from the kinetics of protein phase separation,”
Molecular Biology of the Cell, vol. 35, no. 3. American Society for Cell
Biology, 2024.'
ista: 'Sárkány Z, Figueiredo F, Macedo-Ribeiro S, Martins PM. 2024. NAGPKin: Nucleation-and-growth
parameters from the kinetics of protein phase separation. Molecular Biology of
the Cell. 35(3), mr1.'
mla: 'Sárkány, Zsuzsa, et al. “NAGPKin: Nucleation-and-Growth Parameters from the
Kinetics of Protein Phase Separation.” Molecular Biology of the Cell, vol.
35, no. 3, mr1, American Society for Cell Biology, 2024, doi:10.1091/mbc.e23-07-0289.'
short: Z. Sárkány, F. Figueiredo, S. Macedo-Ribeiro, P.M. Martins, Molecular Biology
of the Cell 35 (2024).
date_created: 2025-01-29T07:58:40Z
date_published: 2024-03-01T00:00:00Z
date_updated: 2025-01-29T08:16:20Z
day: '01'
ddc:
- '570'
department:
- _id: FlSc
doi: 10.1091/mbc.e23-07-0289
external_id:
pmid:
- '38117593'
file:
- access_level: open_access
checksum: d7deb6390f294da69321cfbe352ed611
content_type: application/pdf
creator: dernst
date_created: 2025-01-29T08:12:11Z
date_updated: 2025-01-29T08:12:11Z
file_id: '18935'
file_name: 2024_MolecularBioCell_Sarkany.pdf
file_size: 1699180
relation: main_file
success: 1
file_date_updated: 2025-01-29T08:12:11Z
has_accepted_license: '1'
intvolume: ' 35'
issue: '3'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
pmid: 1
publication: Molecular Biology of the Cell
publication_identifier:
eissn:
- 1939-4586
issn:
- 1059-1524
publication_status: published
publisher: American Society for Cell Biology
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'NAGPKin: Nucleation-and-growth parameters from the kinetics of protein phase
separation'
tmp:
image: /images/cc_by_nc_sa.png
legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC
BY-NC-SA 4.0)
short: CC BY-NC-SA (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '18956'
abstract:
- lang: eng
text: 'Group Activity Recognition (GAR) aims to detect the activity performed by
multiple actors in a scene. Prior works model the spatio-temporal features based
on the RGB, optical flow or keypoint data types. On the contrary, our hypothesis
is that by only using the RGB data without temporality, the performance can be
maintained with a negligible loss in accuracy. To that end, we propose a novel
GAR technique for volleyball videos, DECOMPL, which consists of two complementary
branches. In the visual branch, it extracts the features using attention pooling.
In the coordinate branch, it considers the configuration of the players and extracts
the spatial information from the box coordinates. Moreover, we analyzed the Volleyball
dataset that the recent literature is mostly based on, and systematically reannotated
it to emphasize the group concept. Experimental results demonstrated the effectiveness
of the proposed model DECOMPL, which delivered the best/second best GAR performance
with the reannotations/original annotations among the comparable state-of-the-art
methods. Code and new annotations are available at GitHub: https://github.com/berkerdemirel/decompl'
article_processing_charge: No
arxiv: 1
author:
- first_name: Berker
full_name: Demirel, Berker
id: 8b4bc47f-3200-11ee-973b-8f0e7be21a9f
last_name: Demirel
- first_name: Huseyin
full_name: Ozkan, Huseyin
last_name: Ozkan
citation:
ama: 'Demirel B, Ozkan H. Decompl: Decompositional learning with attention pooling
for group activity recognition from a single volleyball image. In: 2024 IEEE
International Conference on Image Processing. IEEE; 2024:977-983. doi:10.1109/icip51287.2024.10647499'
apa: 'Demirel, B., & Ozkan, H. (2024). Decompl: Decompositional learning with
attention pooling for group activity recognition from a single volleyball image.
In 2024 IEEE International Conference on Image Processing (pp. 977–983).
Abu Dhabi, United Arab Emirates: IEEE. https://doi.org/10.1109/icip51287.2024.10647499'
chicago: 'Demirel, Berker, and Huseyin Ozkan. “Decompl: Decompositional Learning
with Attention Pooling for Group Activity Recognition from a Single Volleyball
Image.” In 2024 IEEE International Conference on Image Processing, 977–83.
IEEE, 2024. https://doi.org/10.1109/icip51287.2024.10647499.'
ieee: 'B. Demirel and H. Ozkan, “Decompl: Decompositional learning with attention
pooling for group activity recognition from a single volleyball image,” in 2024
IEEE International Conference on Image Processing, Abu Dhabi, United Arab
Emirates, 2024, pp. 977–983.'
ista: 'Demirel B, Ozkan H. 2024. Decompl: Decompositional learning with attention
pooling for group activity recognition from a single volleyball image. 2024 IEEE
International Conference on Image Processing. ICIP: International Conference on
Image Processing, 977–983.'
mla: 'Demirel, Berker, and Huseyin Ozkan. “Decompl: Decompositional Learning with
Attention Pooling for Group Activity Recognition from a Single Volleyball Image.”
2024 IEEE International Conference on Image Processing, IEEE, 2024, pp.
977–83, doi:10.1109/icip51287.2024.10647499.'
short: B. Demirel, H. Ozkan, in:, 2024 IEEE International Conference on Image Processing,
IEEE, 2024, pp. 977–983.
conference:
end_date: 2024-10-30
location: Abu Dhabi, United Arab Emirates
name: 'ICIP: International Conference on Image Processing'
start_date: 2024-10-27
corr_author: '1'
date_created: 2025-01-29T12:22:24Z
date_published: 2024-11-01T00:00:00Z
date_updated: 2025-09-09T12:13:12Z
day: '01'
department:
- _id: FrLo
doi: 10.1109/icip51287.2024.10647499
external_id:
arxiv:
- '2303.06439'
isi:
- '001442947000143'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2303.06439
month: '11'
oa: 1
oa_version: Preprint
page: 977-983
publication: 2024 IEEE International Conference on Image Processing
publication_identifier:
eisbn:
- '9798350349399'
eissn:
- 2381-8549
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/berkerdemirel/decompl
status: public
title: 'Decompl: Decompositional learning with attention pooling for group activity
recognition from a single volleyball image'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '18964'
abstract:
- lang: eng
text: Object-centric learning (OCL) extracts the representation of objects with
slots, offering an exceptional blend of flexibility and interpretability for abstracting
low-level perceptual features. A widely adopted method within OCL is slot attention,
which utilizes attention mechanisms to iteratively refine slot representations.
However, a major draw-back of most object-centric models, including slot attention,
is their reliance on predefining the number of slots. This not only necessitates
prior knowledge of the dataset but also overlooks the inherent variability in
the number of objects present in each instance. To overcome this fundamental limitation,
we present a novel complexity-aware object auto-encoder framework. Within this
framework, we introduce an adaptive slot attention (AdaSlot) mecha-nism that dynamically
determines the optimal number of slots based on the content of the data. This
is achieved by proposing a discrete slot sampling module that is responsible for
selecting an appropriate number of slots from a candidate list. Furthermore, we
introduce a masked slot decoder that suppresses unselected slots during the decoding
process. Our framework, tested extensively on object discovery tasks with various
datasets, shows performance matching or exceeding top fixed-slot models. Moreover,
our analysis substantiates that our method exhibits the capability to dynamically
adapt the slot number according to each instance's complexity, offering the potential
for further exploration in slot attention research. Project will be available
at https://kfan21.github.io/AdaSlot/
acknowledgement: Yanwei Fu is the corresponding authour. Yanwei Fu is with School
of Data Science, Fudan University, Shanghai Key Lab of Intelligent Information Processing,
Fudan University, and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence,
Zhejiang Normal University, Jinhua, China.
article_processing_charge: No
arxiv: 1
author:
- first_name: Ke
full_name: Fan, Ke
last_name: Fan
- first_name: Zechen
full_name: Bai, Zechen
last_name: Bai
- first_name: Tianjun
full_name: Xiao, Tianjun
last_name: Xiao
- first_name: Tong
full_name: He, Tong
last_name: He
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Yanwei
full_name: Fu, Yanwei
last_name: Fu
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Zheng
full_name: Zhang, Zheng
last_name: Zhang
citation:
ama: 'Fan K, Bai Z, Xiao T, et al. Adaptive slot attention: Object discovery with
dynamic slot number. In: 2024 IEEE/CVF Conference on Computer Vision and Pattern
Recognition. IEEE; 2024. doi:10.1109/cvpr52733.2024.02176'
apa: 'Fan, K., Bai, Z., Xiao, T., He, T., Horn, M., Fu, Y., … Zhang, Z. (2024).
Adaptive slot attention: Object discovery with dynamic slot number. In 2024
IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA,
United States: IEEE. https://doi.org/10.1109/cvpr52733.2024.02176'
chicago: 'Fan, Ke, Zechen Bai, Tianjun Xiao, Tong He, Max Horn, Yanwei Fu, Francesco
Locatello, and Zheng Zhang. “Adaptive Slot Attention: Object Discovery with Dynamic
Slot Number.” In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
IEEE, 2024. https://doi.org/10.1109/cvpr52733.2024.02176.'
ieee: 'K. Fan et al., “Adaptive slot attention: Object discovery with dynamic
slot number,” in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition,
Seattle, WA, United States, 2024.'
ista: 'Fan K, Bai Z, Xiao T, He T, Horn M, Fu Y, Locatello F, Zhang Z. 2024. Adaptive
slot attention: Object discovery with dynamic slot number. 2024 IEEE/CVF Conference
on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision
and Pattern Recognition.'
mla: 'Fan, Ke, et al. “Adaptive Slot Attention: Object Discovery with Dynamic Slot
Number.” 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition,
IEEE, 2024, doi:10.1109/cvpr52733.2024.02176.'
short: K. Fan, Z. Bai, T. Xiao, T. He, M. Horn, Y. Fu, F. Locatello, Z. Zhang, in:,
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2024.
conference:
end_date: 2024-06-22
location: Seattle, WA, United States
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2024-06-16
date_created: 2025-01-29T14:27:39Z
date_published: 2024-06-15T00:00:00Z
date_updated: 2025-09-09T12:15:17Z
day: '15'
department:
- _id: FrLo
doi: 10.1109/cvpr52733.2024.02176
external_id:
arxiv:
- '2406.09196'
isi:
- '001342515506043'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2406.09196
month: '06'
oa: 1
oa_version: Preprint
publication: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
eisbn:
- '9798350353006'
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://kfan21.github.io/AdaSlot/
status: public
title: 'Adaptive slot attention: Object discovery with dynamic slot number'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '18971'
abstract:
- lang: eng
text: 'Models prone to spurious correlations in training data often produce brittle
predictions and introduce unintended biases. Addressing this challenge typically
involves methods relying on prior knowledge and group annotation to remove spurious
correlations, which may not be readily available in many applications. In this
paper, we establish a novel connection between unsupervised object-centric learning
and mitigation of spurious correlations. Instead of directly inferring subgroups
with varying correlations with labels, our approach focuses on discovering concepts:
discrete ideas that are shared across input samples. Leveraging existing object-centric
representation learning, we introduce CoBalT: a concept balancing technique that
effectively mitigates spurious correlations without requiring human labeling of
subgroups. Evaluation across the benchmark datasets for sub-population shifts
demonstrate superior or competitive performance compared state-of-the-art baselines,
without the need for group annotation. Code is available at https://github.com/rarefin/CoBalT'
acknowledgement: "We acknowledge the support of the Canada CIFAR AI Chair Program
and IVADO. We thank Mila and Compute Canada for providing computational resources.\r\n"
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Rifat
full_name: Arefin, Rifat
last_name: Arefin
- first_name: Yan
full_name: Zhang, Yan
last_name: Zhang
- first_name: Aristide
full_name: Baratin, Aristide
last_name: Baratin
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Irina
full_name: Rish, Irina
last_name: Rish
- first_name: Dianbo
full_name: Liu, Dianbo
last_name: Liu
- first_name: Kenji
full_name: Kawaguchi, Kenji
last_name: Kawaguchi
citation:
ama: 'Arefin R, Zhang Y, Baratin A, et al. Unsupervised concept discovery mitigates
spurious correlations. In: Proceedings of the 41st International Conference
on Machine Learning. Vol 235. ML Research Press; 2024:1672-1688.'
apa: 'Arefin, R., Zhang, Y., Baratin, A., Locatello, F., Rish, I., Liu, D., &
Kawaguchi, K. (2024). Unsupervised concept discovery mitigates spurious correlations.
In Proceedings of the 41st International Conference on Machine Learning
(Vol. 235, pp. 1672–1688). Vienna, Austria: ML Research Press.'
chicago: Arefin, Rifat, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina
Rish, Dianbo Liu, and Kenji Kawaguchi. “Unsupervised Concept Discovery Mitigates
Spurious Correlations.” In Proceedings of the 41st International Conference
on Machine Learning, 235:1672–88. ML Research Press, 2024.
ieee: R. Arefin et al., “Unsupervised concept discovery mitigates spurious
correlations,” in Proceedings of the 41st International Conference on Machine
Learning, Vienna, Austria, 2024, vol. 235, pp. 1672–1688.
ista: 'Arefin R, Zhang Y, Baratin A, Locatello F, Rish I, Liu D, Kawaguchi K. 2024.
Unsupervised concept discovery mitigates spurious correlations. Proceedings of
the 41st International Conference on Machine Learning. ICML: International Conference
on Machine Learning, PMLR, vol. 235, 1672–1688.'
mla: Arefin, Rifat, et al. “Unsupervised Concept Discovery Mitigates Spurious Correlations.”
Proceedings of the 41st International Conference on Machine Learning, vol.
235, ML Research Press, 2024, pp. 1672–88.
short: R. Arefin, Y. Zhang, A. Baratin, F. Locatello, I. Rish, D. Liu, K. Kawaguchi,
in:, Proceedings of the 41st International Conference on Machine Learning, ML
Research Press, 2024, pp. 1672–1688.
conference:
end_date: 2024-07-27
location: Vienna, Austria
name: 'ICML: International Conference on Machine Learning'
start_date: 2024-07-21
date_created: 2025-01-30T07:21:57Z
date_published: 2024-07-30T00:00:00Z
date_updated: 2025-01-30T07:23:10Z
day: '30'
department:
- _id: FrLo
external_id:
arxiv:
- '2402.13368'
intvolume: ' 235'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2402.13368
month: '07'
oa: 1
oa_version: Preprint
page: 1672-1688
publication: Proceedings of the 41st International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/rarefin/CoBalT
scopus_import: '1'
status: public
title: Unsupervised concept discovery mitigates spurious correlations
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 235
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '18996'
abstract:
- lang: eng
text: 'We consider the linear causal representation learning setting where we observe
a linear mixing of d unknown latent factors, which follow a linear structural
causal model. Recent work has shown that it is possible to recover the latent
factors as well as the underlying structural causal model over them, up to permutation
and scaling, provided that we have at least d environments, each of which corresponds
to perfect interventions on a single latent node (factor). After this powerful
result, a key open problem faced by the community has been to relax these conditions:
allow for coarser than perfect single-node interventions, and allow for fewer
than d of them, since the number of latent factors d could be very large. In this
work, we consider precisely such a setting, where we allow a smaller than d number
of environments, and also allow for very coarse interventions that can very coarsely
\textit{change the entire causal graph over the latent factors}. On the flip side,
we relax what we wish to extract to simply the \textit{list of nodes that have
shifted between one or more environments}. We provide a surprising identifiability
result that it is indeed possible, under some very mild standard assumptions,
to identify the set of shifted nodes. Our identifiability proof moreover is a
constructive one: we explicitly provide necessary and sufficient conditions for
a node to be a shifted node, and show that we can check these conditions given
observed data. Our algorithm lends itself very naturally to the sample setting
where instead of just interventional distributions, we are provided datasets of
samples from each of these distributions. We corroborate our results on both synthetic
experiments as well as an interesting psychometric dataset. The code can be found
at https://github.com/TianyuCodings/iLCS.'
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Tianyu
full_name: Chen, Tianyu
last_name: Chen
- first_name: Kevin
full_name: Bello, Kevin
last_name: Bello
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Bryon
full_name: Aragam, Bryon
last_name: Aragam
- first_name: Pradeep Kumar
full_name: Ravikumar, Pradeep Kumar
last_name: Ravikumar
citation:
ama: 'Chen T, Bello K, Locatello F, Aragam B, Ravikumar PK. Identifying general
mechanism shifts in linear causal representations. In: 38th Conference on Neural
Information Processing Systems. Vol 37. Neural Information Processing Systems
Foundation; 2024.'
apa: 'Chen, T., Bello, K., Locatello, F., Aragam, B., & Ravikumar, P. K. (2024).
Identifying general mechanism shifts in linear causal representations. In 38th
Conference on Neural Information Processing Systems (Vol. 37). Vancouver,
Canada: Neural Information Processing Systems Foundation.'
chicago: Chen, Tianyu, Kevin Bello, Francesco Locatello, Bryon Aragam, and Pradeep
Kumar Ravikumar. “Identifying General Mechanism Shifts in Linear Causal Representations.”
In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural
Information Processing Systems Foundation, 2024.
ieee: T. Chen, K. Bello, F. Locatello, B. Aragam, and P. K. Ravikumar, “Identifying
general mechanism shifts in linear causal representations,” in 38th Conference
on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.
ista: 'Chen T, Bello K, Locatello F, Aragam B, Ravikumar PK. 2024. Identifying general
mechanism shifts in linear causal representations. 38th Conference on Neural Information
Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in
Neural Information Processing Systems, vol. 37.'
mla: Chen, Tianyu, et al. “Identifying General Mechanism Shifts in Linear Causal
Representations.” 38th Conference on Neural Information Processing Systems,
vol. 37, Neural Information Processing Systems Foundation, 2024.
short: T. Chen, K. Bello, F. Locatello, B. Aragam, P.K. Ravikumar, in:, 38th Conference
on Neural Information Processing Systems, Neural Information Processing Systems
Foundation, 2024.
conference:
end_date: 2024-12-16
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2024-12-16
date_created: 2025-02-04T13:09:34Z
date_published: 2024-09-25T00:00:00Z
date_updated: 2025-07-07T13:23:49Z
day: '25'
ddc:
- '000'
department:
- _id: FrLo
external_id:
arxiv:
- '2410.24059'
file:
- access_level: open_access
checksum: 75c3091e70bd2916cd94afbf40a0c425
content_type: application/pdf
creator: dernst
date_created: 2025-02-04T13:09:08Z
date_updated: 2025-02-04T13:09:08Z
file_id: '18997'
file_name: 2024_NeurIPS_Chen.pdf
file_size: 5659119
relation: main_file
success: 1
file_date_updated: 2025-02-04T13:09:08Z
has_accepted_license: '1'
intvolume: ' 37'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
publication: 38th Conference on Neural Information Processing Systems
publication_identifier:
eissn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: Identifying general mechanism shifts in linear causal representations
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: 37
year: '2024'
...
---
OA_place: publisher
OA_type: gold
_id: '19005'
abstract:
- lang: eng
text: "Causal representation learning promises to extend causal models to hidden
causal\r\nvariables from raw entangled measurements. However, most progress has
focused\r\non proving identifiability results in different settings, and we are
not aware of any\r\nsuccessful real-world application. At the same time, the field
of dynamical systems\r\nbenefited from deep learning and scaled to countless applications
but does not allow\r\nparameter identification. In this paper, we draw a clear
connection between the two\r\nand their key assumptions, allowing us to apply
identifiable methods developed\r\nin causal representation learning to dynamical
systems. At the same time, we can\r\nleverage scalable differentiable solvers
developed for differential equations to build\r\nmodels that are both identifiable
and practical. Overall, we learn explicitly controllable models that isolate the
trajectory-specific parameters for further downstream\r\ntasks such as out-of-distribution
classification or treatment effect estimation. We\r\nexperiment with a wind simulator
with partially known factors of variation. We\r\nalso apply the resulting model
to real-world climate data and successfully answer\r\ndownstream causal questions
in line with existing literature on climate change.\r\nCode is available at https://github.com/CausalLearningAI/crl-dynamical-systems."
acknowledgement: "We thank Niklas Boers for recommending the SpeedyWeather simulator
and Valentino Maiorca\r\nfor guidance on Fourier transformation for SST data. We
are also grateful to Shimeng Huang and Riccardo Cadei for their feedback on the
treatment effect estimation experiment and to Jiale Chen and Adeel Pervez for their
assistance with the solver implementation. Finally, we appreciate the anonymous
reviewers for their insightful suggestions, which helped improve the manuscript. "
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Dingling
full_name: Yao, Dingling
id: d3e02e50-48a8-11ee-8f62-c108061797fa
last_name: Yao
- first_name: Caroline J
full_name: Muller, Caroline J
id: f978ccb0-3f7f-11eb-b193-b0e2bd13182b
last_name: Muller
orcid: 0000-0001-5836-5350
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Yao D, Muller CJ, Locatello F. Marrying causal representation learning with
dynamical systems for science. In: 38th Conference on Neural Information Processing
Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.'
apa: 'Yao, D., Muller, C. J., & Locatello, F. (2024). Marrying causal representation
learning with dynamical systems for science. In 38th Conference on Neural Information
Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing
Systems Foundation.'
chicago: Yao, Dingling, Caroline J Muller, and Francesco Locatello. “Marrying Causal
Representation Learning with Dynamical Systems for Science.” In 38th Conference
on Neural Information Processing Systems, Vol. 37. Neural Information Processing
Systems Foundation, 2024.
ieee: D. Yao, C. J. Muller, and F. Locatello, “Marrying causal representation learning
with dynamical systems for science,” in 38th Conference on Neural Information
Processing Systems, Vancouver, Canada, 2024, vol. 37.
ista: 'Yao D, Muller CJ, Locatello F. 2024. Marrying causal representation learning
with dynamical systems for science. 38th Conference on Neural Information Processing
Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information
Processing Systems, vol. 37.'
mla: Yao, Dingling, et al. “Marrying Causal Representation Learning with Dynamical
Systems for Science.” 38th Conference on Neural Information Processing Systems,
vol. 37, Neural Information Processing Systems Foundation, 2024.
short: D. Yao, C.J. Muller, F. Locatello, in:, 38th Conference on Neural Information
Processing Systems, Neural Information Processing Systems Foundation, 2024.
conference:
end_date: 2024-12-16
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2024-12-16
corr_author: '1'
date_created: 2025-02-05T07:49:00Z
date_published: 2024-12-01T00:00:00Z
date_updated: 2025-07-10T11:51:32Z
day: '01'
ddc:
- '000'
- '550'
department:
- _id: CaMu
- _id: FrLo
external_id:
arxiv:
- '2405.13888'
file:
- access_level: open_access
checksum: fe8832367e7143876f178244385d859e
content_type: application/pdf
creator: dernst
date_created: 2025-02-05T07:44:58Z
date_updated: 2025-02-05T07:44:58Z
file_id: '19006'
file_name: 2024_NeurIPS_Yao.pdf
file_size: 2595855
relation: main_file
success: 1
file_date_updated: 2025-02-05T07:44:58Z
has_accepted_license: '1'
intvolume: ' 37'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
publication: 38th Conference on Neural Information Processing Systems
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/CausalLearningAI/crl-dynamical-systems
scopus_import: '1'
status: public
title: Marrying causal representation learning with dynamical systems for science
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: 37
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '19486'
abstract:
- lang: eng
text: Consider the family of elliptic curves En:y2=x3+n2, where n varies over positive
cubefree integers. There is a rational 3-isogeny ϕ from En to E^n:y2=x3−27n2 and
a dual isogeny ϕ^:E^n→En. We show that for almost all n, the rank of Selϕ(En)
is 0, and the rank of Selϕ^(E^n) is determined by the number of prime factors
of n that are congruent to 2mod3 and the congruence class of nmod9.
acknowledgement: The author would like to thank Peter Koymans and Carlo Pagano for
helpful discussions.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Yik Tung
full_name: Chan, Yik Tung
id: c4c0afc8-9262-11ed-9231-d8b0bc743af1
last_name: Chan
orcid: 0000-0001-8467-4106
citation:
ama: Chan S. The 3-isogeny selmer groups of the elliptic curves y2=x3+n2. International
Mathematics Research Notices. 2024;2024(9):7571-7593. doi:10.1093/imrn/rnad266
apa: Chan, S. (2024). The 3-isogeny selmer groups of the elliptic curves y2=x3+n2.
International Mathematics Research Notices. Oxford University Press. https://doi.org/10.1093/imrn/rnad266
chicago: Chan, Stephanie. “The 3-Isogeny Selmer Groups of the Elliptic Curves Y2=x3+n2.”
International Mathematics Research Notices. Oxford University Press, 2024.
https://doi.org/10.1093/imrn/rnad266.
ieee: S. Chan, “The 3-isogeny selmer groups of the elliptic curves y2=x3+n2,” International
Mathematics Research Notices, vol. 2024, no. 9. Oxford University Press, pp.
7571–7593, 2024.
ista: Chan S. 2024. The 3-isogeny selmer groups of the elliptic curves y2=x3+n2.
International Mathematics Research Notices. 2024(9), 7571–7593.
mla: Chan, Stephanie. “The 3-Isogeny Selmer Groups of the Elliptic Curves Y2=x3+n2.”
International Mathematics Research Notices, vol. 2024, no. 9, Oxford University
Press, 2024, pp. 7571–93, doi:10.1093/imrn/rnad266.
short: S. Chan, International Mathematics Research Notices 2024 (2024) 7571–7593.
date_created: 2025-04-05T10:50:33Z
date_published: 2024-05-01T00:00:00Z
date_updated: 2025-07-10T11:51:44Z
day: '01'
doi: 10.1093/imrn/rnad266
extern: '1'
external_id:
arxiv:
- '2211.06062'
intvolume: ' 2024'
issue: '9'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2211.06062
month: '05'
oa: 1
oa_version: Preprint
page: 7571-7593
publication: International Mathematics Research Notices
publication_identifier:
eissn:
- 1687-0247
issn:
- 1073-7928
publication_status: published
publisher: Oxford University Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: The 3-isogeny selmer groups of the elliptic curves y2=x3+n2
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2024
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '19510'
abstract:
- lang: eng
text: "We propose a new variant of the Adam optimizer [Kingma and Ba, 2014] called\r\nMICROADAM
that specifically minimizes memory overheads, while maintaining\r\ntheoretical
convergence guarantees. We achieve this by compressing the gradient\r\ninformation
before it is fed into the optimizer state, thereby reducing its memory\r\nfootprint
significantly. We control the resulting compression error via a novel\r\ninstance
of the classical error feedback mechanism from distributed optimization [Seide
et al., 2014, Alistarh et al., 2018, Karimireddy et al., 2019] in which\r\nthe
error correction information is itself compressed to allow for practical memory\r\ngains.
We prove that the resulting approach maintains theoretical convergence\r\nguarantees
competitive to those of AMSGrad, while providing good practical performance. Specifically,
we show that MICROADAM can be implemented efficiently\r\non GPUs: on both million-scale
(BERT) and billion-scale (LLaMA) models, MICROADAM provides practical convergence
competitive to that of the uncompressed\r\nAdam baseline, with lower memory usage
and similar running time. Our code is\r\navailable at https://github.com/IST-DASLab/MicroAdam."
acknowledged_ssus:
- _id: CampIT
acknowledgement: The authors thank Razvan Pascanu, Mahdi Nikdan and Soroush Tabesh
for their valuable feedback, the IT department from Institute of Science and Technology
Austria for the hardware support and Weights and Biases for the infrastructure to
track all our experiments. Mher Safaryan has received funding from the European
Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie
grant agreement No 101034413.
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Ionut-Vlad
full_name: Modoranu, Ionut-Vlad
id: 449f7a18-f128-11eb-9611-9b430c0c6333
last_name: Modoranu
- first_name: Mher
full_name: Safaryan, Mher
id: dd546b39-0804-11ed-9c55-ef075c39778d
last_name: Safaryan
- first_name: Grigory
full_name: Malinovsky, Grigory
last_name: Malinovsky
- first_name: Eldar
full_name: Kurtic, Eldar
id: 47beb3a5-07b5-11eb-9b87-b108ec578218
last_name: Kurtic
- first_name: Thomas
full_name: Robert, Thomas
id: de632733-1457-11f0-ae22-b5914b8c1c41
last_name: Robert
- first_name: Peter
full_name: Richtárik, Peter
last_name: Richtárik
- 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: 'Modoranu I-V, Safaryan M, Malinovsky G, et al. MICROADAM: Accurate adaptive
optimization with low space overhead and provable convergence. In: 38th Conference
on Neural Information Processing Systems. Vol 37. Neural Information Processing
Systems Foundation; 2024.'
apa: 'Modoranu, I.-V., Safaryan, M., Malinovsky, G., Kurtic, E., Robert, T., Richtárik,
P., & Alistarh, D.-A. (2024). MICROADAM: Accurate adaptive optimization with
low space overhead and provable convergence. In 38th Conference on Neural Information
Processing Systems (Vol. 37). Neural Information Processing Systems Foundation.'
chicago: 'Modoranu, Ionut-Vlad, Mher Safaryan, Grigory Malinovsky, Eldar Kurtic,
Thomas Robert, Peter Richtárik, and Dan-Adrian Alistarh. “MICROADAM: Accurate
Adaptive Optimization with Low Space Overhead and Provable Convergence.” In 38th
Conference on Neural Information Processing Systems, Vol. 37. Neural Information
Processing Systems Foundation, 2024.'
ieee: 'I.-V. Modoranu et al., “MICROADAM: Accurate adaptive optimization
with low space overhead and provable convergence,” in 38th Conference on Neural
Information Processing Systems, 2024, vol. 37.'
ista: 'Modoranu I-V, Safaryan M, Malinovsky G, Kurtic E, Robert T, Richtárik P,
Alistarh D-A. 2024. MICROADAM: Accurate adaptive optimization with low space overhead
and provable convergence. 38th Conference on Neural Information Processing Systems.
, Advances in Neural Information Processing Systems, vol. 37.'
mla: 'Modoranu, Ionut-Vlad, et al. “MICROADAM: Accurate Adaptive Optimization with
Low Space Overhead and Provable Convergence.” 38th Conference on Neural Information
Processing Systems, vol. 37, Neural Information Processing Systems Foundation,
2024.'
short: I.-V. Modoranu, M. Safaryan, G. Malinovsky, E. Kurtic, T. Robert, P. Richtárik,
D.-A. Alistarh, in:, 38th Conference on Neural Information Processing Systems,
Neural Information Processing Systems Foundation, 2024.
corr_author: '1'
date_created: 2025-04-06T22:01:32Z
date_published: 2024-12-20T00:00:00Z
date_updated: 2025-05-14T11:32:52Z
day: '20'
department:
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '2405.15593'
intvolume: ' 37'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2405.15593
month: '12'
oa: 1
oa_version: Preprint
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: 38th Conference on Neural Information Processing Systems
publication_identifier:
issn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/IST-DASLab/MicroAdam
scopus_import: '1'
status: public
title: 'MICROADAM: Accurate adaptive optimization with low space overhead and provable
convergence'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2024'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
PlanS_conform: '1'
_id: '20060'
abstract:
- lang: spa
text: "Un fenómeno a menudo asociado con el autismo es un modo atípico de función
ejecutiva, cuyas manifestaciones incluyen dificultad para iniciar tareas. En algunos
casos, esto va acompañado de sentimientos de inercia y sensaciones que pueden
describirse como inquietud y parálisis simultáneas. En consecuencia, la dificultad
para iniciar las tareas puede dar lugar a la procrastinación, ya sea simplemente
posponiendo el trabajo en la tarea objetivo o realizando otras tareas no relacionadas
antes de dedicarse a la tarea objetivo. Curiosamente, sin embargo, también está
documentado que, una vez iniciada una tarea, los autistas pueden centrarse en
ella intensamente y durante periodos prolongados de tiempo, especialmente cuando
les resulta interesante.
\r\nEste trabajo utiliza el procesamiento predictivo
y la inferencia activa para modelar la relación entre la función ejecutiva, la
procrastinación y la hiperfocalización en el autismo. Este modelo integra las
causas conocidas y propuestas de los déficits en la función ejecutiva y el papel
que desempeña el interés en la regulación de la atención y la motivación. El modelo
propone que la procrastinación es el resultado de procesos diferenciales de minimización
de errores de predicción, como la ponderación de estímulos sensoriales. Se discuten
los vínculos con modelos propuestos previamente, como la coherencia central débil
(CCC), y la teoría de los priores altos e inflexibles de los errores de predicción
en el autismo (HIPPEA)."
- lang: eng
text: A phenomenon often associated with autism is an atypical mode of executive function,
manifestations of which include difficulty in initiating tasks. In some cases, this
is accompanied by feelings of inertia and sensations that can be described as simultaneous
restlessness and paralysis. Consequently, difficulty in getting started on tasks can result in
procrastination, either by simply postponing working on the target task or by performing
other unrelated tasks before engaging in the target task. Interestingly, however, it is also
documented that once a task has been started, autistic persons may focus on it intensely
and for prolonged periods of time, especially when it is interesting to them.This paper uses predictive processing and active inference to model therelationship
between executive function, procrastination, and hyperfocus in autism. This model
integrates the known and proposed causes of deficits in executive function and the role
played by interest in attention regulation and motivation. The model proposes that
procrastination is the outcome of differential prediction-error minimizing processes, such
as weighting of sensory stimuli. Links to previously proposed models such as weak
central coherence (WCC), and the theory of high, inflexible priors of prediction errors in autism
(HIPPEA) are discussed
acknowledgement: Sidney Carls-Diamante was supported by the Federal Ministry of Education
and Research (BMBF) and the Baden-Württemberg Ministry of Science as part of the
Excellence Strategy of the German Federal and State Governments. Alice Laciny was
supported by the 2021 UNESCO L’Oréal grant for women in science, awarded for the
project “Neurodiversity and anthropomorphism in social insect research”.
article_processing_charge: Yes
article_type: original
author:
- first_name: Sidney
full_name: Carls-Diamante, Sidney
last_name: Carls-Diamante
- first_name: Alice
full_name: Laciny, Alice
id: accace3f-3f77-11eb-b1df-f1221b04cb95
last_name: Laciny
orcid: 0000-0002-5485-1391
citation:
ama: 'Carls-Diamante S, Laciny A. Stuck in uncertainty: A predictive processing/
active inference account of procrastination-like behaviour in autism. Lógoi
Revista de Filosofía. 2024;(45):88-114. doi:10.62876/lr.vi45.6481'
apa: 'Carls-Diamante, S., & Laciny, A. (2024). Stuck in uncertainty: A predictive
processing/ active inference account of procrastination-like behaviour in autism.
Lógoi. Revista de Filosofía. Universidad Católica Andrés Bello. https://doi.org/10.62876/lr.vi45.6481'
chicago: 'Carls-Diamante, Sidney, and Alice Laciny. “Stuck in Uncertainty: A Predictive
Processing/ Active Inference Account of Procrastination-like Behaviour in Autism.”
Lógoi. Revista de Filosofía. Universidad Católica Andrés Bello, 2024. https://doi.org/10.62876/lr.vi45.6481.'
ieee: 'S. Carls-Diamante and A. Laciny, “Stuck in uncertainty: A predictive processing/
active inference account of procrastination-like behaviour in autism,” Lógoi.
Revista de Filosofía, no. 45. Universidad Católica Andrés Bello, pp. 88–114,
2024.'
ista: 'Carls-Diamante S, Laciny A. 2024. Stuck in uncertainty: A predictive processing/
active inference account of procrastination-like behaviour in autism. Lógoi. Revista
de Filosofía. (45), 88–114.'
mla: 'Carls-Diamante, Sidney, and Alice Laciny. “Stuck in Uncertainty: A Predictive
Processing/ Active Inference Account of Procrastination-like Behaviour in Autism.”
Lógoi. Revista de Filosofía, no. 45, Universidad Católica Andrés Bello,
2024, pp. 88–114, doi:10.62876/lr.vi45.6481.'
short: S. Carls-Diamante, A. Laciny, Lógoi. Revista de Filosofía (2024) 88–114.
corr_author: '1'
date_created: 2025-07-21T10:35:39Z
date_published: 2024-03-19T00:00:00Z
date_updated: 2025-09-09T08:51:00Z
day: '19'
ddc:
- '570'
doi: 10.62876/lr.vi45.6481
file:
- access_level: open_access
checksum: 04c950f26ad68455c3a303c87ccf2349
content_type: application/pdf
creator: dernst
date_created: 2025-09-09T08:48:53Z
date_updated: 2025-09-09T08:48:53Z
file_id: '20317'
file_name: 2025_Logoi_CarlsDiamante.pdf
file_size: 354124
relation: main_file
success: 1
file_date_updated: 2025-09-09T08:48:53Z
has_accepted_license: '1'
issue: '45'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: 88-114
publication: Lógoi. Revista de Filosofía
publication_identifier:
eissn:
- 1316-693X
issn:
- 2790-5144
publication_status: published
publisher: Universidad Católica Andrés Bello
quality_controlled: '1'
status: public
title: 'Stuck in uncertainty: A predictive processing/ active inference account of
procrastination-like behaviour in autism'
tmp:
image: /images/cc_by_nc_sa.png
legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC
BY-NC-SA 4.0)
short: CC BY-NC-SA (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
OA_type: closed access
_id: '20527'
abstract:
- lang: eng
text: "Arising from C. Yang et al. Nature Chemistry https://doi.org/10.1038/s41557-023-01212-2
(2023)\r\n\r\nIn this work Yang et al.1 claim that an enantioselective Michael
addition reaction with a barrier of 16 kcal mol−1 occurs at the single-molecule
level in frozen solvent by measuring fluctuations in current flowing across graphene-based
molecular devices. The article, however, contains major scientific errors that
undermine their conclusions. We highlight issues with the fabrication of the devices,
a lack of characterization, discrepancies between theory and experiment, unreliable
inelastic electron tunnelling spectra (IETS) and a perceived misinterpretation
of noise as evidence of reaction."
article_processing_charge: No
article_type: letter_note
author:
- first_name: Latha
full_name: Venkataraman, Latha
id: 9ebb78a5-cc0d-11ee-8322-fae086a32caf
last_name: Venkataraman
orcid: 0000-0002-6957-6089
- first_name: Jan
full_name: van Ruitenbeek, Jan
last_name: van Ruitenbeek
citation:
ama: Venkataraman L, van Ruitenbeek J. Questioning claims of monitoring the Michael
addition reaction at the single-molecule level. Nature Chemistry. 2024;16(11):1767-1769.
doi:10.1038/s41557-024-01631-9
apa: Venkataraman, L., & van Ruitenbeek, J. (2024). Questioning claims of monitoring
the Michael addition reaction at the single-molecule level. Nature Chemistry.
Springer Nature. https://doi.org/10.1038/s41557-024-01631-9
chicago: Venkataraman, Latha, and Jan van Ruitenbeek. “Questioning Claims of Monitoring
the Michael Addition Reaction at the Single-Molecule Level.” Nature Chemistry.
Springer Nature, 2024. https://doi.org/10.1038/s41557-024-01631-9.
ieee: L. Venkataraman and J. van Ruitenbeek, “Questioning claims of monitoring the
Michael addition reaction at the single-molecule level,” Nature Chemistry,
vol. 16, no. 11. Springer Nature, pp. 1767–1769, 2024.
ista: Venkataraman L, van Ruitenbeek J. 2024. Questioning claims of monitoring the
Michael addition reaction at the single-molecule level. Nature Chemistry. 16(11),
1767–1769.
mla: Venkataraman, Latha, and Jan van Ruitenbeek. “Questioning Claims of Monitoring
the Michael Addition Reaction at the Single-Molecule Level.” Nature Chemistry,
vol. 16, no. 11, Springer Nature, 2024, pp. 1767–69, doi:10.1038/s41557-024-01631-9.
short: L. Venkataraman, J. van Ruitenbeek, Nature Chemistry 16 (2024) 1767–1769.
date_created: 2025-10-23T12:16:57Z
date_published: 2024-11-01T00:00:00Z
date_updated: 2025-10-23T12:58:52Z
day: '01'
doi: 10.1038/s41557-024-01631-9
extern: '1'
external_id:
pmid:
- '39313629'
intvolume: ' 16'
issue: '11'
language:
- iso: eng
month: '11'
oa_version: None
page: 1767-1769
pmid: 1
publication: Nature Chemistry
publication_identifier:
eissn:
- 1755-4349
issn:
- 1755-4330
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Questioning claims of monitoring the Michael addition reaction at the single-molecule
level
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 16
year: '2024'
...
---
OA_type: closed access
_id: '20615'
abstract:
- lang: eng
text: "Spin/Pin-structures on vector bundles have long featured prominently in differential
geometry, in particular providing part of the foundation for the original proof
of the renowned Atiyah–Singer Index Theory. More recently, they have underpinned
the symplectic topology foundations of the so-called real sector of the mirror
symmetry of string theory.\r\n\r\nThis semi-expository three-part monograph provides
an accessible introduction to Spin- and Pin-structures in general, demonstrates
their role in the orientability considerations in symplectic topology, and presents
their applications in enumerative geometry.\r\n\r\nPart I contains a systematic
treatment of Spin/Pin-structures from different topological perspectives and may
be suitable for an advanced undergraduate reading seminar. This leads to Part
II, which systematically studies orientability problems for the determinants of
real Cauchy–Riemann operators on vector bundles. Part III introduces enumerative
geometry of curves in complex projective varieties and in symplectic manifolds,
demonstrating some applications of the first two parts in the process. Two appendices
review the Čech cohomology perspective on fiber bundles and Lie group covering
spaces."
article_processing_charge: No
author:
- first_name: Xujia
full_name: Chen, Xujia
id: 968ad14a-fd86-11ee-a420-ea29715511a3
last_name: Chen
- first_name: Aleksey
full_name: Zinger, Aleksey
last_name: Zinger
citation:
ama: Chen X, Zinger A. Spin/Pin-Structures and Real Enumerative Geometry.
World Scientific Publishing; 2024. doi:10.1142/13476
apa: Chen, X., & Zinger, A. (2024). Spin/Pin-structures and real enumerative
geometry. World Scientific Publishing. https://doi.org/10.1142/13476
chicago: Chen, Xujia, and Aleksey Zinger. Spin/Pin-Structures and Real Enumerative
Geometry. World Scientific Publishing, 2024. https://doi.org/10.1142/13476.
ieee: X. Chen and A. Zinger, Spin/Pin-structures and real enumerative geometry.
World Scientific Publishing, 2024.
ista: Chen X, Zinger A. 2024. Spin/Pin-structures and real enumerative geometry,
World Scientific Publishing,p.
mla: Chen, Xujia, and Aleksey Zinger. Spin/Pin-Structures and Real Enumerative
Geometry. World Scientific Publishing, 2024, doi:10.1142/13476.
short: X. Chen, A. Zinger, Spin/Pin-Structures and Real Enumerative Geometry, World
Scientific Publishing, 2024.
date_created: 2025-11-10T08:40:10Z
date_published: 2024-01-01T00:00:00Z
date_updated: 2025-11-10T15:28:49Z
day: '01'
doi: 10.1142/13476
extern: '1'
language:
- iso: eng
month: '01'
oa_version: None
publication_identifier:
eisbn:
- '9789811278556'
isbn:
- '9789811278532'
publication_status: published
publisher: World Scientific Publishing
quality_controlled: '1'
scopus_import: '1'
status: public
title: Spin/Pin-structures and real enumerative geometry
type: book
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
_id: '17456'
abstract:
- lang: eng
text: "Data-parallel distributed training of deep neural networks (DNN) has gained
very widespread adoption, but can still experience communication bottlenecks.
To address this issue, entire families of compression mechanisms have been developed,
including quantization, sparsification, and low-rank approximation, some of which
are seeing significant practical adoption. Despite this progress, almost all known
compression schemes apply compression uniformly across DNN layers, although layers
are heterogeneous in terms of parameter count and their impact on model accuracy.In
this work, we provide a general framework for adapting the degree of compression
across the model's layers dynamically during training, improving the overall compression,
while leading to substantial speedups, without sacrificing accuracy. Our framework,
called L-GreCo, is based on an adaptive algorithm, which automatically picks the
optimal compression parameters for model layers guaranteeing the best compression
ratio while satisfying an error constraint. Extensive experiments over image classification
and language modeling tasks shows that L-GreCo is effective across all existing
families of compression methods, and achieves up to 2.5\r\n×\r\n training speedup
and up to 5\r\n×\r\n compression improvement over efficient implementations of
existing approaches, while recovering full accuracy. Moreover, L-GreCo is complementary
to existing adaptive algorithms, improving their compression ratio by 50\\% and
practical throughput by 66\\%. An anonymized implementation is available at https://github.com/LGrCo/L-GreCo."
article_processing_charge: No
arxiv: 1
author:
- first_name: Ilia
full_name: Markov, Ilia
id: D0CF4148-C985-11E9-8066-0BDEE5697425
last_name: Markov
- first_name: Kaveh
full_name: Alimohammadi, Kaveh
last_name: Alimohammadi
- first_name: Elias
full_name: Frantar, Elias
id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f
last_name: Frantar
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Markov I, Alimohammadi K, Frantar E, Alistarh D-A. L-GreCo: Layerwise-adaptive
gradient compression for efficient data-parallel deep learning. In: Gibbons P,
Pekhimenko G, De Sa C, eds. Proceedings of Machine Learning and Systems .
Vol 6. Association for Computing Machinery; 2024.'
apa: 'Markov, I., Alimohammadi, K., Frantar, E., & Alistarh, D.-A. (2024). L-GreCo:
Layerwise-adaptive gradient compression for efficient data-parallel deep learning.
In P. Gibbons, G. Pekhimenko, & C. De Sa (Eds.), Proceedings of Machine
Learning and Systems (Vol. 6). Athens, Greece: Association for Computing
Machinery.'
chicago: 'Markov, Ilia, Kaveh Alimohammadi, Elias Frantar, and Dan-Adrian Alistarh.
“L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient Data-Parallel
Deep Learning.” In Proceedings of Machine Learning and Systems , edited
by P. Gibbons, G. Pekhimenko, and C. De Sa, Vol. 6. Association for Computing
Machinery, 2024.'
ieee: 'I. Markov, K. Alimohammadi, E. Frantar, and D.-A. Alistarh, “L-GreCo: Layerwise-adaptive
gradient compression for efficient data-parallel deep learning,” in Proceedings
of Machine Learning and Systems , Athens, Greece, 2024, vol. 6.'
ista: 'Markov I, Alimohammadi K, Frantar E, Alistarh D-A. 2024. L-GreCo: Layerwise-adaptive
gradient compression for efficient data-parallel deep learning. Proceedings of
Machine Learning and Systems . MLSys: Machine Learning and Systems vol. 6.'
mla: 'Markov, Ilia, et al. “L-GreCo: Layerwise-Adaptive Gradient Compression for
Efficient Data-Parallel Deep Learning.” Proceedings of Machine Learning and
Systems , edited by P. Gibbons et al., vol. 6, Association for Computing Machinery,
2024.'
short: I. Markov, K. Alimohammadi, E. Frantar, D.-A. Alistarh, in:, P. Gibbons,
G. Pekhimenko, C. De Sa (Eds.), Proceedings of Machine Learning and Systems ,
Association for Computing Machinery, 2024.
conference:
end_date: 2024-04-22
location: Athens, Greece
name: 'MLSys: Machine Learning and Systems'
start_date: 2024-04-22
corr_author: '1'
date_created: 2024-08-22T08:29:25Z
date_published: 2024-04-01T00:00:00Z
date_updated: 2026-06-18T17:55:24Z
day: '01'
ddc:
- '000'
department:
- _id: DaAl
editor:
- first_name: P.
full_name: Gibbons, P.
last_name: Gibbons
- first_name: G.
full_name: Pekhimenko, G.
last_name: Pekhimenko
- first_name: C.
full_name: De Sa, C.
last_name: De Sa
external_id:
arxiv:
- '2210.17357'
intvolume: ' 6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.mlsys.org/paper_files/paper/2024/hash/9069a8976ff06f6443e7f4172990a580-Abstract-Conference.html
month: '04'
oa: 1
oa_version: Published Version
publication: 'Proceedings of Machine Learning and Systems '
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
related_material:
record:
- id: '17490'
relation: dissertation_contains
status: public
status: public
title: 'L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel
deep learning'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 6
year: '2024'
...
---
OA_place: publisher
OA_type: diamond
_id: '18114'
abstract:
- lang: eng
text: This paper presents Mechanistic Neural Networks, a neural network design for
machine learning applications in the sciences. It incorporates a new Mechanistic
Block in standard architectures to explicitly learn governing differential equations
as representations, revealing the underlying dynamics of data and enhancing interpretability
and efficiency in data modeling. Central to our approach is a novel Relaxed Linear
Programming Solver (NeuRLP) inspired by a technique that reduces solving linear
ODEs to solving linear programs. This integrates well with neural networks and
surpasses the limitations of traditional ODE solvers enabling scalable GPU parallel
processing. Overall, Mechanistic Neural Networks demonstrate their versatility
for scientific machine learning applications, adeptly managing tasks from equation
discovery to dynamic systems modeling. We prove their comprehensive capabilities
in analyzing and interpreting complex scientific data across various applications,
showing significant performance against specialized state-of-the-art methods.
Source code is available at https://github.com/alpz/mech-nn.
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Adeel A
full_name: Pervez, Adeel A
id: fca6d90c-d47f-11ee-bc87-93ff51604981
last_name: Pervez
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Efstratios
full_name: Gavves, Efstratios
last_name: Gavves
citation:
ama: 'Pervez AA, Locatello F, Gavves E. Mechanistic neural networks for scientific
machine learning. In: Proceedings of the 41st International Conference on Machine
Learning. Vol 235. ML Research Press; 2024:40484-40501.'
apa: 'Pervez, A. A., Locatello, F., & Gavves, E. (2024). Mechanistic neural
networks for scientific machine learning. In Proceedings of the 41st International
Conference on Machine Learning (Vol. 235, pp. 40484–40501). Vienna, Austria:
ML Research Press.'
chicago: Pervez, Adeel A, Francesco Locatello, and Efstratios Gavves. “Mechanistic
Neural Networks for Scientific Machine Learning.” In Proceedings of the 41st
International Conference on Machine Learning, 235:40484–501. ML Research Press,
2024.
ieee: A. A. Pervez, F. Locatello, and E. Gavves, “Mechanistic neural networks for
scientific machine learning,” in Proceedings of the 41st International Conference
on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 40484–40501.
ista: 'Pervez AA, Locatello F, Gavves E. 2024. Mechanistic neural networks for scientific
machine learning. Proceedings of the 41st International Conference on Machine
Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235,
40484–40501.'
mla: Pervez, Adeel A., et al. “Mechanistic Neural Networks for Scientific Machine
Learning.” Proceedings of the 41st International Conference on Machine Learning,
vol. 235, ML Research Press, 2024, pp. 40484–501.
short: A.A. Pervez, F. Locatello, E. Gavves, in:, Proceedings of the 41st International
Conference on Machine Learning, ML Research Press, 2024, pp. 40484–40501.
conference:
end_date: 2024-07-27
location: Vienna, Austria
name: 'ICML: International Conference on Machine Learning'
start_date: 2024-07-21
date_created: 2024-09-22T22:01:43Z
date_published: 2024-09-01T00:00:00Z
date_updated: 2026-06-18T17:59:46Z
day: '01'
ddc:
- '000'
department:
- _id: FrLo
external_id:
arxiv:
- '2402.13077'
intvolume: ' 235'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2402.13077
month: '09'
oa: 1
oa_version: Published Version
page: 40484-40501
publication: Proceedings of the 41st International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/alpz/mech-nn
scopus_import: '1'
status: public
title: Mechanistic neural networks for scientific machine learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 235
year: '2024'
...
---
_id: '18117'
abstract:
- lang: eng
text: "We investigate parameter-efficient fine-tuning (PEFT) methods that can provide
good accuracy under limited computational and memory budgets in the context of
large language models (LLMs). We present a new PEFT method called Robust Adaptation
(RoSA) inspired by robust principal component analysis that jointly trains low-rank\r\n
and highly-sparse components on top of a set of fixed pretrained weights to efficiently
approximate the performance of a full-fine-tuning (FFT) solution. Across a series
of challenging generative tasks such as grade-school math and SQL query generation,
which require fine-tuning for good performance, we show that RoSA outperforms
LoRA, pure sparse fine-tuning, and alternative hybrid methods at the same parameter
budget, and can even recover the performance of FFT on some tasks. We provide
system support for RoSA to complement the training algorithm, specifically in
the form of sparse GPU kernels which enable memory- and computationally-efficient
training, and show that it is also compatible with low-precision base weights,
resulting in the first joint representation combining quantization, low-rank and
sparse approximations. Our code is available at https://github.com/IST-DASLab/RoSA."
acknowledgement: The authors would like to thank Eldar Kurtic for experimental support
and useful suggestions throughout the project
article_processing_charge: No
arxiv: 1
author:
- first_name: Mahdi
full_name: Nikdan, Mahdi
id: 66374281-f394-11eb-9cf6-869147deecc0
last_name: Nikdan
- first_name: Soroush
full_name: Tabesh, Soroush
id: 06000900-6068-11ef-8d61-c2472ef2e752
last_name: Tabesh
orcid: 0009-0003-4119-6281
- first_name: Elvir
full_name: Crncevic, Elvir
id: 41888001-440d-11ef-8299-d0e838b8185e
last_name: Crncevic
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Nikdan M, Tabesh S, Crncevic E, Alistarh D-A. RoSA: Accurate parameter-efficient
fine-tuning via robust adaptation. In: Proceedings of the 41st International
Conference on Machine Learning. Vol 235. ML Research Press; 2024:38187-38206.'
apa: 'Nikdan, M., Tabesh, S., Crncevic, E., & Alistarh, D.-A. (2024). RoSA:
Accurate parameter-efficient fine-tuning via robust adaptation. In Proceedings
of the 41st International Conference on Machine Learning (Vol. 235, pp. 38187–38206).
Vienna, Austria: ML Research Press.'
chicago: 'Nikdan, Mahdi, Soroush Tabesh, Elvir Crncevic, and Dan-Adrian Alistarh.
“RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.” In Proceedings
of the 41st International Conference on Machine Learning, 235:38187–206. ML
Research Press, 2024.'
ieee: 'M. Nikdan, S. Tabesh, E. Crncevic, and D.-A. Alistarh, “RoSA: Accurate parameter-efficient
fine-tuning via robust adaptation,” in Proceedings of the 41st International
Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 38187–38206.'
ista: 'Nikdan M, Tabesh S, Crncevic E, Alistarh D-A. 2024. RoSA: Accurate parameter-efficient
fine-tuning via robust adaptation. Proceedings of the 41st International Conference
on Machine Learning. ICML: International Conference on Machine Learning vol. 235,
38187–38206.'
mla: 'Nikdan, Mahdi, et al. “RoSA: Accurate Parameter-Efficient Fine-Tuning via
Robust Adaptation.” Proceedings of the 41st International Conference on Machine
Learning, vol. 235, ML Research Press, 2024, pp. 38187–206.'
short: M. Nikdan, S. Tabesh, E. Crncevic, D.-A. Alistarh, in:, Proceedings of the
41st International Conference on Machine Learning, ML Research Press, 2024, pp.
38187–38206.
conference:
end_date: 2024-07-27
location: Vienna, Austria
name: 'ICML: International Conference on Machine Learning'
start_date: 2024-07-21
corr_author: '1'
date_created: 2024-09-22T22:01:44Z
date_published: 2024-09-01T00:00:00Z
date_updated: 2024-10-01T08:22:01Z
day: '01'
department:
- _id: DaAl
- _id: GradSch
external_id:
arxiv:
- '2401.04679'
intvolume: ' 235'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2401.04679
month: '09'
oa: 1
oa_version: Preprint
page: 38187-38206
publication: Proceedings of the 41st International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/IST-DASLab/RoSA
scopus_import: '1'
status: public
title: 'RoSA: Accurate parameter-efficient fine-tuning via robust adaptation'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 235
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '22197'
abstract:
- lang: eng
text: "We study the distribution of consecutive sums of two squares\r\nin arithmetic
progressions. If {En}n∈N is the sequence of\r\nsums of two squares in increasing
order, we show that for\r\nany modulus q and any congruence classes a1, a2, a3
mod q\r\nwhich are admissible in the sense that there are solutions\r\nto x2 +
y2 ≡ ai mod q, there exist infinitely many n with\r\nEn+i−1 ≡ ai mod q, for i
=1, 2, 3. We also show that for\r\nany r1, r2 ≥ 1, there exist infinitely many
n with En+i−1 ≡\r\na1 mod q for 1 ≤ i ≤ r1 and En+i−1 ≡ a2 mod q for\r\nr1 +1
≤ i ≤ r1 + r2"
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Noam
full_name: Kimmel, Noam
last_name: Kimmel
- first_name: Vivian Zieve
full_name: Kuperberg, Vivian Zieve
id: c3bac823-112d-11f0-a3f5-c264f852e697
last_name: Kuperberg
citation:
ama: Kimmel N, Kuperberg VZ. Consecutive runs of sums of two squares. Journal
of Number Theory. 2024;264:135-147. doi:10.1016/j.jnt.2024.05.003
apa: Kimmel, N., & Kuperberg, V. Z. (2024). Consecutive runs of sums of two
squares. Journal of Number Theory. Elsevier. https://doi.org/10.1016/j.jnt.2024.05.003
chicago: Kimmel, Noam, and Vivian Zieve Kuperberg. “Consecutive Runs of Sums of
Two Squares.” Journal of Number Theory. Elsevier, 2024. https://doi.org/10.1016/j.jnt.2024.05.003.
ieee: N. Kimmel and V. Z. Kuperberg, “Consecutive runs of sums of two squares,”
Journal of Number Theory, vol. 264. Elsevier, pp. 135–147, 2024.
ista: Kimmel N, Kuperberg VZ. 2024. Consecutive runs of sums of two squares. Journal
of Number Theory. 264, 135–147.
mla: Kimmel, Noam, and Vivian Zieve Kuperberg. “Consecutive Runs of Sums of Two
Squares.” Journal of Number Theory, vol. 264, Elsevier, 2024, pp. 135–47,
doi:10.1016/j.jnt.2024.05.003.
short: N. Kimmel, V.Z. Kuperberg, Journal of Number Theory 264 (2024) 135–147.
date_created: 2026-06-29T12:58:28Z
date_published: 2024-11-01T00:00:00Z
date_updated: 2026-07-14T11:10:45Z
day: '01'
doi: 10.1016/j.jnt.2024.05.003
extern: '1'
external_id:
arxiv:
- '2306.12855'
intvolume: ' 264'
language:
- iso: eng
main_file_link:
- url: https://doi.org/10.48550/arXiv.2306.12855
month: '11'
oa_version: Preprint
page: 135-147
publication: Journal of Number Theory
publication_identifier:
issn:
- 0022-314X
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Consecutive runs of sums of two squares
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 264
year: '2024'
...