---
_id: '14085'
abstract:
- lang: eng
text: We show an (1+ϵ)-approximation algorithm for maintaining maximum s-t flow
under m edge insertions in m1/2+o(1)ϵ−1/2 amortized update time for directed,
unweighted graphs. This constitutes the first sublinear dynamic maximum flow algorithm
in general sparse graphs with arbitrarily good approximation guarantee.
acknowledgement: "This project has received funding from the European Research Council
(ERC) under the European Union’s Horizon 2020 research and innovation programme
(Grant agreement No.\r\n101019564 “The Design of Modern Fully Dynamic Data Structures
(MoDynStruct)” and from the\r\nAustrian Science Fund (FWF) project “Static and Dynamic
Hierarchical Graph Decompositions”,\r\nI 5982-N, and project “Fast Algorithms for
a Reactive Network Layer (ReactNet)”, P 33775-N, with additional funding from the
netidee SCIENCE Stiftung, 2020–2024.\r\nThis work was done in part while Gramoz
Goranci was at Institute for Theoretical Studies, ETH Zurich, Switzerland. There,
he was supported by Dr. Max Rössler, the Walter Haefner Foundation and the ETH Zürich
Foundation. We also thank Richard Peng, Thatchaphol Saranurak, Sebastian Forster
and Sushant Sachdeva for helpful discussions, and the anonymous reviewers for their
insightful comments."
alternative_title:
- LIPIcs
article_number: '69'
article_processing_charge: Yes
arxiv: 1
author:
- first_name: Gramoz
full_name: Goranci, Gramoz
last_name: Goranci
- first_name: Monika H
full_name: Henzinger, Monika H
id: 540c9bbd-f2de-11ec-812d-d04a5be85630
last_name: Henzinger
orcid: 0000-0002-5008-6530
citation:
ama: 'Goranci G, Henzinger M. Efficient data structures for incremental exact and
approximate maximum flow. In: 50th International Colloquium on Automata, Languages,
and Programming. Vol 261. Schloss Dagstuhl - Leibniz-Zentrum für Informatik;
2023. doi:10.4230/LIPIcs.ICALP.2023.69'
apa: 'Goranci, G., & Henzinger, M. (2023). Efficient data structures for incremental
exact and approximate maximum flow. In 50th International Colloquium on Automata,
Languages, and Programming (Vol. 261). Paderborn, Germany: Schloss Dagstuhl
- Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.ICALP.2023.69'
chicago: Goranci, Gramoz, and Monika Henzinger. “Efficient Data Structures for Incremental
Exact and Approximate Maximum Flow.” In 50th International Colloquium on Automata,
Languages, and Programming, Vol. 261. Schloss Dagstuhl - Leibniz-Zentrum für
Informatik, 2023. https://doi.org/10.4230/LIPIcs.ICALP.2023.69.
ieee: G. Goranci and M. Henzinger, “Efficient data structures for incremental exact
and approximate maximum flow,” in 50th International Colloquium on Automata,
Languages, and Programming, Paderborn, Germany, 2023, vol. 261.
ista: 'Goranci G, Henzinger M. 2023. Efficient data structures for incremental exact
and approximate maximum flow. 50th International Colloquium on Automata, Languages,
and Programming. ICALP: Automata, Languages and Programming, LIPIcs, vol. 261,
69.'
mla: Goranci, Gramoz, and Monika Henzinger. “Efficient Data Structures for Incremental
Exact and Approximate Maximum Flow.” 50th International Colloquium on Automata,
Languages, and Programming, vol. 261, 69, Schloss Dagstuhl - Leibniz-Zentrum
für Informatik, 2023, doi:10.4230/LIPIcs.ICALP.2023.69.
short: G. Goranci, M. Henzinger, in:, 50th International Colloquium on Automata,
Languages, and Programming, Schloss Dagstuhl - Leibniz-Zentrum für Informatik,
2023.
conference:
end_date: 2023-07-14
location: Paderborn, Germany
name: 'ICALP: Automata, Languages and Programming'
start_date: 2023-07-10
corr_author: '1'
date_created: 2023-08-20T22:01:14Z
date_published: 2023-07-01T00:00:00Z
date_updated: 2025-06-04T07:19:37Z
day: '01'
ddc:
- '000'
department:
- _id: MoHe
doi: 10.4230/LIPIcs.ICALP.2023.69
ec_funded: 1
external_id:
arxiv:
- '2211.09606'
file:
- access_level: open_access
checksum: 074177e815a1656de5d4071c7a3dffa6
content_type: application/pdf
creator: dernst
date_created: 2023-08-21T06:59:05Z
date_updated: 2023-08-21T06:59:05Z
file_id: '14089'
file_name: 2023_LIPIcsICALP_Goranci.pdf
file_size: 875910
relation: main_file
success: 1
file_date_updated: 2023-08-21T06:59:05Z
has_accepted_license: '1'
intvolume: ' 261'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '07'
oa: 1
oa_version: Published Version
project:
- _id: bd9ca328-d553-11ed-ba76-dc4f890cfe62
call_identifier: H2020
grant_number: '101019564'
name: The design and evaluation of modern fully dynamic data structures
- _id: bda196b2-d553-11ed-ba76-8e8ee6c21103
grant_number: I05982
name: Static and Dynamic Hierarchical Graph Decompositions
- _id: bd9e3a2e-d553-11ed-ba76-8aa684ce17fe
grant_number: P33775
name: Fast Algorithms for a Reactive Network Layer
publication: 50th International Colloquium on Automata, Languages, and Programming
publication_identifier:
isbn:
- '9783959772785'
issn:
- 1868-8969
publication_status: published
publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
quality_controlled: '1'
scopus_import: '1'
status: public
title: Efficient data structures for incremental exact and approximate maximum flow
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: 261
year: '2023'
...
---
_id: '14086'
abstract:
- lang: eng
text: "The maximization of submodular functions have found widespread application
in areas such as machine learning, combinatorial optimization, and economics,
where practitioners often wish to enforce various constraints; the matroid constraint
has been investigated extensively due to its algorithmic properties and expressive
power. Though tight approximation algorithms for general matroid constraints exist
in theory, the running times of such algorithms typically scale quadratically,
and are not practical for truly large scale settings. Recent progress has focused
on fast algorithms for important classes of matroids given in explicit form. Currently,
nearly-linear time algorithms only exist for graphic and partition matroids [Alina
Ene and Huy L. Nguyen, 2019]. In this work, we develop algorithms for monotone
submodular maximization constrained by graphic, transversal matroids, or laminar
matroids in time near-linear in the size of their representation. Our algorithms
achieve an optimal approximation of 1-1/e-ε and both generalize and accelerate
the results of Ene and Nguyen [Alina Ene and Huy L. Nguyen, 2019]. In fact, the
running time of our algorithm cannot be improved within the fast continuous greedy
framework of Badanidiyuru and Vondrák [Ashwinkumar Badanidiyuru and Jan Vondrák,
2014].\r\nTo achieve near-linear running time, we make use of dynamic data structures
that maintain bases with approximate maximum cardinality and weight under certain
element updates. These data structures need to support a weight decrease operation
and a novel Freeze operation that allows the algorithm to freeze elements (i.e.
force to be contained) in its basis regardless of future data structure operations.
For the laminar matroid, we present a new dynamic data structure using the top
tree interface of Alstrup, Holm, de Lichtenberg, and Thorup [Stephen Alstrup et
al., 2005] that maintains the maximum weight basis under insertions and deletions
of elements in O(log n) time. This data structure needs to support certain subtree
query and path update operations that are performed every insertion and deletion
that are non-trivial to handle in conjunction. For the transversal matroid the
Freeze operation corresponds to requiring the data structure to keep a certain
set S of vertices matched, a property that we call S-stability. While there is
a large body of work on dynamic matching algorithms, none are S-stable and maintain
an approximate maximum weight matching under vertex updates. We give the first
such algorithm for bipartite graphs with total running time linear (up to log
factors) in the number of edges."
acknowledgement: " Monika Henzinger: This project has received funding from the European
Research Council\r\n(ERC) under the European Union’s Horizon 2020 research and innovation
programme (Grant\r\nagreement No. 101019564 “The Design of Modern Fully Dynamic
Data Structures (MoDynStruct)” and from the Austrian Science Fund (FWF) project
“Static and Dynamic Hierarchical Graph Decompositions”, I 5982-N, and project “Fast
Algorithms for a Reactive Network Layer (ReactNet)”, P 33775-N, with additional
funding from the netidee SCIENCE Stiftung, 2020–2024. Jan Vondrák: Supported by
NSF Award 2127781."
alternative_title:
- LIPIcs
article_number: '74'
article_processing_charge: Yes
arxiv: 1
author:
- first_name: Monika H
full_name: Henzinger, Monika H
id: 540c9bbd-f2de-11ec-812d-d04a5be85630
last_name: Henzinger
orcid: 0000-0002-5008-6530
- first_name: Paul
full_name: Liu, Paul
last_name: Liu
- first_name: Jan
full_name: Vondrák, Jan
last_name: Vondrák
- first_name: Da Wei
full_name: Zheng, Da Wei
last_name: Zheng
citation:
ama: 'Henzinger M, Liu P, Vondrák J, Zheng DW. Faster submodular maximization for
several classes of matroids. In: 50th International Colloquium on Automata,
Languages, and Programming. Vol 261. Schloss Dagstuhl - Leibniz-Zentrum für
Informatik; 2023. doi:10.4230/LIPIcs.ICALP.2023.74'
apa: 'Henzinger, M., Liu, P., Vondrák, J., & Zheng, D. W. (2023). Faster submodular
maximization for several classes of matroids. In 50th International Colloquium
on Automata, Languages, and Programming (Vol. 261). Paderborn, Germany: Schloss
Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.ICALP.2023.74'
chicago: Henzinger, Monika, Paul Liu, Jan Vondrák, and Da Wei Zheng. “Faster Submodular
Maximization for Several Classes of Matroids.” In 50th International Colloquium
on Automata, Languages, and Programming, Vol. 261. Schloss Dagstuhl - Leibniz-Zentrum
für Informatik, 2023. https://doi.org/10.4230/LIPIcs.ICALP.2023.74.
ieee: M. Henzinger, P. Liu, J. Vondrák, and D. W. Zheng, “Faster submodular maximization
for several classes of matroids,” in 50th International Colloquium on Automata,
Languages, and Programming, Paderborn, Germany, 2023, vol. 261.
ista: 'Henzinger M, Liu P, Vondrák J, Zheng DW. 2023. Faster submodular maximization
for several classes of matroids. 50th International Colloquium on Automata, Languages,
and Programming. ICALP: Automata, Languages and Programming, LIPIcs, vol. 261,
74.'
mla: Henzinger, Monika, et al. “Faster Submodular Maximization for Several Classes
of Matroids.” 50th International Colloquium on Automata, Languages, and Programming,
vol. 261, 74, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023, doi:10.4230/LIPIcs.ICALP.2023.74.
short: M. Henzinger, P. Liu, J. Vondrák, D.W. Zheng, in:, 50th International Colloquium
on Automata, Languages, and Programming, Schloss Dagstuhl - Leibniz-Zentrum für
Informatik, 2023.
conference:
end_date: 2023-07-14
location: Paderborn, Germany
name: 'ICALP: Automata, Languages and Programming'
start_date: 2023-07-10
corr_author: '1'
date_created: 2023-08-20T22:01:14Z
date_published: 2023-07-01T00:00:00Z
date_updated: 2025-07-10T11:50:45Z
day: '01'
ddc:
- '000'
department:
- _id: MoHe
doi: 10.4230/LIPIcs.ICALP.2023.74
ec_funded: 1
external_id:
arxiv:
- '2305.00122'
file:
- access_level: open_access
checksum: a5eef225014e003efbfbe4830fdd23cb
content_type: application/pdf
creator: dernst
date_created: 2023-08-21T07:04:36Z
date_updated: 2023-08-21T07:04:36Z
file_id: '14090'
file_name: 2023_LIPIcsICALP_HenzingerM.pdf
file_size: 930943
relation: main_file
success: 1
file_date_updated: 2023-08-21T07:04:36Z
has_accepted_license: '1'
intvolume: ' 261'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
project:
- _id: bd9ca328-d553-11ed-ba76-dc4f890cfe62
call_identifier: H2020
grant_number: '101019564'
name: The design and evaluation of modern fully dynamic data structures
- _id: bda196b2-d553-11ed-ba76-8e8ee6c21103
grant_number: I05982
name: Static and Dynamic Hierarchical Graph Decompositions
- _id: bd9e3a2e-d553-11ed-ba76-8aa684ce17fe
grant_number: P33775
name: Fast Algorithms for a Reactive Network Layer
publication: 50th International Colloquium on Automata, Languages, and Programming
publication_identifier:
isbn:
- '9783959772785'
issn:
- 1868-8969
publication_status: published
publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
quality_controlled: '1'
scopus_import: '1'
status: public
title: Faster submodular maximization for several classes of matroids
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: 261
year: '2023'
...
---
_id: '14087'
abstract:
- lang: eng
text: Polar active matter of self-propelled particles sustain spontaneous flows
through the full-integer topological defects. We study theoretically the incompressible
flow profiles around ±1 defects induced by polar and dipolar active forces. We
show that dipolar forces induce vortical flows around the +1 defect, while the
flow around the −1 defect has an 8-fold rotational symmetry. The vortical flow
changes its chirality near the +1 defect core in the absence of the friction with
a substrate. We show analytically that the flow induced by polar active forces
is vortical near the +1 defect and is 4-fold symmetric near the −1 defect, while
it becomes uniform in the far-field. For a pair of oppositely charged defects,
this polar flow contributes to a mutual interaction force that depends only on
the orientation of the defect pair relative to the background polarization, and
that enhances defect pair annihilation. This is in contradiction with the effect
of dipolar active forces which decay inversely proportional with the defect separation
distance. As such, our analyses reveals a long-ranged mechanism for the pairwise
interaction between topological defects in polar active matter.
acknowledgement: J. Rø and L. A. acknowledge support from the Research Council of
Norway through the Center of Excellence funding scheme, Project No. 262644 (PoreLab).
A. D. acknowledges funding from the Novo Nordisk Foundation (grant No. NNF18SA0035142
and NERD grant No. NNF21OC0068687), Villum Fonden Grant no. 29476, and the European
Union via the ERC-Starting Grant PhysCoMeT. Views and opinions expressed are however
those of the authors only and do not necessarily reflect those of the European Union
or the European Research Council. Neither the European Union nor the granting authority
can be held responsible for them.
article_processing_charge: Yes (in subscription journal)
article_type: original
arxiv: 1
author:
- first_name: Jonas
full_name: Rønning, Jonas
last_name: Rønning
- first_name: Julian B
full_name: Renaud, Julian B
id: 7af6767d-14eb-11ed-b536-a32449ae867c
last_name: Renaud
- first_name: Amin
full_name: Doostmohammadi, Amin
last_name: Doostmohammadi
- first_name: Luiza
full_name: Angheluta, Luiza
last_name: Angheluta
citation:
ama: Rønning J, Renaud JB, Doostmohammadi A, Angheluta L. Spontaneous flows and
dynamics of full-integer topological defects in polar active matter. Soft Matter.
2023;39:7513-7527. doi:10.1039/d3sm00316g
apa: Rønning, J., Renaud, J. B., Doostmohammadi, A., & Angheluta, L. (2023).
Spontaneous flows and dynamics of full-integer topological defects in polar active
matter. Soft Matter. Royal Society of Chemistry. https://doi.org/10.1039/d3sm00316g
chicago: Rønning, Jonas, Julian B Renaud, Amin Doostmohammadi, and Luiza Angheluta.
“Spontaneous Flows and Dynamics of Full-Integer Topological Defects in Polar Active
Matter.” Soft Matter. Royal Society of Chemistry, 2023. https://doi.org/10.1039/d3sm00316g.
ieee: J. Rønning, J. B. Renaud, A. Doostmohammadi, and L. Angheluta, “Spontaneous
flows and dynamics of full-integer topological defects in polar active matter,”
Soft Matter, vol. 39. Royal Society of Chemistry, pp. 7513–7527, 2023.
ista: Rønning J, Renaud JB, Doostmohammadi A, Angheluta L. 2023. Spontaneous flows
and dynamics of full-integer topological defects in polar active matter. Soft
Matter. 39, 7513–7527.
mla: Rønning, Jonas, et al. “Spontaneous Flows and Dynamics of Full-Integer Topological
Defects in Polar Active Matter.” Soft Matter, vol. 39, Royal Society of
Chemistry, 2023, pp. 7513–27, doi:10.1039/d3sm00316g.
short: J. Rønning, J.B. Renaud, A. Doostmohammadi, L. Angheluta, Soft Matter 39
(2023) 7513–7527.
date_created: 2023-08-20T22:01:15Z
date_published: 2023-09-01T00:00:00Z
date_updated: 2025-04-23T13:03:12Z
day: '01'
ddc:
- '540'
department:
- _id: GradSch
doi: 10.1039/d3sm00316g
external_id:
arxiv:
- '2303.07063'
isi:
- '001035766100001'
pmid:
- '37493084'
file:
- access_level: open_access
checksum: b936747170d0b708172b518078c4081a
content_type: application/pdf
creator: dernst
date_created: 2024-01-30T12:48:24Z
date_updated: 2024-01-30T12:48:24Z
file_id: '14908'
file_name: 2023_SoftMatter_Ronning.pdf
file_size: 7660662
relation: main_file
success: 1
file_date_updated: 2024-01-30T12:48:24Z
has_accepted_license: '1'
intvolume: ' 39'
isi: 1
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: 7513-7527
pmid: 1
publication: Soft Matter
publication_identifier:
eissn:
- 1744-6848
issn:
- 1744-683X
publication_status: published
publisher: Royal Society of Chemistry
quality_controlled: '1'
scopus_import: '1'
status: public
title: Spontaneous flows and dynamics of full-integer topological defects in polar
active matter
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: 39
year: '2023'
...
---
_id: '14103'
abstract:
- lang: eng
text: Observations of individual massive stars, super-luminous supernovae, gamma-ray
bursts, and gravitational wave events involving spectacular black hole mergers
indicate that the low-metallicity Universe is fundamentally different from our
own Galaxy. Many transient phenomena will remain enigmatic until we achieve a
firm understanding of the physics and evolution of massive stars at low metallicity
(Z). The Hubble Space Telescope has devoted 500 orbits to observing ∼250 massive
stars at low Z in the ultraviolet (UV) with the COS and STIS spectrographs under
the ULLYSES programme. The complementary X-Shooting ULLYSES (XShootU) project
provides an enhanced legacy value with high-quality optical and near-infrared
spectra obtained with the wide-wavelength coverage X-shooter spectrograph at ESO’s
Very Large Telescope. We present an overview of the XShootU project, showing that
combining ULLYSES UV and XShootU optical spectra is critical for the uniform determination
of stellar parameters such as effective temperature, surface gravity, luminosity,
and abundances, as well as wind properties such as mass-loss rates as a function
of Z. As uncertainties in stellar and wind parameters percolate into many adjacent
areas of astrophysics, the data and modelling of the XShootU project is expected
to be a game changer for our physical understanding of massive stars at low Z.
To be able to confidently interpret James Webb Space Telescope spectra of the
first stellar generations, the individual spectra of low-Z stars need to be understood,
which is exactly where XShootU can deliver.
article_number: A154
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Jorick S.
full_name: Vink, Jorick S.
last_name: Vink
- first_name: A.
full_name: Mehner, A.
last_name: Mehner
- first_name: P. A.
full_name: Crowther, P. A.
last_name: Crowther
- first_name: A.
full_name: Fullerton, A.
last_name: Fullerton
- first_name: M.
full_name: Garcia, M.
last_name: Garcia
- first_name: F.
full_name: Martins, F.
last_name: Martins
- first_name: N.
full_name: Morrell, N.
last_name: Morrell
- first_name: L. M.
full_name: Oskinova, L. M.
last_name: Oskinova
- first_name: N.
full_name: St-Louis, N.
last_name: St-Louis
- first_name: A.
full_name: ud-Doula, A.
last_name: ud-Doula
- first_name: A. A. C.
full_name: Sander, A. A. C.
last_name: Sander
- first_name: H.
full_name: Sana, H.
last_name: Sana
- first_name: J.-C.
full_name: Bouret, J.-C.
last_name: Bouret
- first_name: B.
full_name: Kubátová, B.
last_name: Kubátová
- first_name: P.
full_name: Marchant, P.
last_name: Marchant
- first_name: L. P.
full_name: Martins, L. P.
last_name: Martins
- first_name: A.
full_name: Wofford, A.
last_name: Wofford
- first_name: J. Th.
full_name: van Loon, J. Th.
last_name: van Loon
- first_name: O.
full_name: Grace Telford, O.
last_name: Grace Telford
- first_name: Ylva Louise Linsdotter
full_name: Götberg, Ylva Louise Linsdotter
id: d0648d0c-0f64-11ee-a2e0-dd0faa2e4f7d
last_name: Götberg
orcid: 0000-0002-6960-6911
- first_name: D. M.
full_name: Bowman, D. M.
last_name: Bowman
- first_name: C.
full_name: Erba, C.
last_name: Erba
- first_name: V. M.
full_name: Kalari, V. M.
last_name: Kalari
- first_name: M.
full_name: Abdul-Masih, M.
last_name: Abdul-Masih
- first_name: T.
full_name: Alkousa, T.
last_name: Alkousa
- first_name: F.
full_name: Backs, F.
last_name: Backs
- first_name: C. L.
full_name: Barbosa, C. L.
last_name: Barbosa
- first_name: S. R.
full_name: Berlanas, S. R.
last_name: Berlanas
- first_name: M.
full_name: Bernini-Peron, M.
last_name: Bernini-Peron
- first_name: J. M.
full_name: Bestenlehner, J. M.
last_name: Bestenlehner
- first_name: R.
full_name: Blomme, R.
last_name: Blomme
- first_name: J.
full_name: Bodensteiner, J.
last_name: Bodensteiner
- first_name: S. A.
full_name: Brands, S. A.
last_name: Brands
- first_name: C. J.
full_name: Evans, C. J.
last_name: Evans
- first_name: A.
full_name: David-Uraz, A.
last_name: David-Uraz
- first_name: F. A.
full_name: Driessen, F. A.
last_name: Driessen
- first_name: K.
full_name: Dsilva, K.
last_name: Dsilva
- first_name: S.
full_name: Geen, S.
last_name: Geen
- first_name: V. M. A.
full_name: Gómez-González, V. M. A.
last_name: Gómez-González
- first_name: L.
full_name: Grassitelli, L.
last_name: Grassitelli
- first_name: W.-R.
full_name: Hamann, W.-R.
last_name: Hamann
- first_name: C.
full_name: Hawcroft, C.
last_name: Hawcroft
- first_name: A.
full_name: Herrero, A.
last_name: Herrero
- first_name: E. R.
full_name: Higgins, E. R.
last_name: Higgins
- first_name: D.
full_name: John Hillier, D.
last_name: John Hillier
- first_name: R.
full_name: Ignace, R.
last_name: Ignace
- first_name: A. G.
full_name: Istrate, A. G.
last_name: Istrate
- first_name: L.
full_name: Kaper, L.
last_name: Kaper
- first_name: N. D.
full_name: Kee, N. D.
last_name: Kee
- first_name: C.
full_name: Kehrig, C.
last_name: Kehrig
- first_name: Z.
full_name: Keszthelyi, Z.
last_name: Keszthelyi
- first_name: J.
full_name: Klencki, J.
last_name: Klencki
- first_name: A.
full_name: de Koter, A.
last_name: de Koter
- first_name: R.
full_name: Kuiper, R.
last_name: Kuiper
- first_name: E.
full_name: Laplace, E.
last_name: Laplace
- first_name: C. J. K.
full_name: Larkin, C. J. K.
last_name: Larkin
- first_name: R. R.
full_name: Lefever, R. R.
last_name: Lefever
- first_name: C.
full_name: Leitherer, C.
last_name: Leitherer
- first_name: D. J.
full_name: Lennon, D. J.
last_name: Lennon
- first_name: L.
full_name: Mahy, L.
last_name: Mahy
- first_name: J.
full_name: Maíz Apellániz, J.
last_name: Maíz Apellániz
- first_name: G.
full_name: Maravelias, G.
last_name: Maravelias
- first_name: W.
full_name: Marcolino, W.
last_name: Marcolino
- first_name: A. F.
full_name: McLeod, A. F.
last_name: McLeod
- first_name: S. E.
full_name: de Mink, S. E.
last_name: de Mink
- first_name: F.
full_name: Najarro, F.
last_name: Najarro
- first_name: M. S.
full_name: Oey, M. S.
last_name: Oey
- first_name: T. N.
full_name: Parsons, T. N.
last_name: Parsons
- first_name: D.
full_name: Pauli, D.
last_name: Pauli
- first_name: M. G.
full_name: Pedersen, M. G.
last_name: Pedersen
- first_name: R. K.
full_name: Prinja, R. K.
last_name: Prinja
- first_name: V.
full_name: Ramachandran, V.
last_name: Ramachandran
- first_name: M. C.
full_name: Ramírez-Tannus, M. C.
last_name: Ramírez-Tannus
- first_name: G. N.
full_name: Sabhahit, G. N.
last_name: Sabhahit
- first_name: A.
full_name: Schootemeijer, A.
last_name: Schootemeijer
- first_name: S.
full_name: Reyero Serantes, S.
last_name: Reyero Serantes
- first_name: T.
full_name: Shenar, T.
last_name: Shenar
- first_name: G. S.
full_name: Stringfellow, G. S.
last_name: Stringfellow
- first_name: N.
full_name: Sudnik, N.
last_name: Sudnik
- first_name: F.
full_name: Tramper, F.
last_name: Tramper
- first_name: L.
full_name: Wang, L.
last_name: Wang
citation:
ama: 'Vink JS, Mehner A, Crowther PA, et al. X-shooting ULLYSES: Massive stars at
low metallicity. I. Project description. Astronomy & Astrophysics.
2023;675. doi:10.1051/0004-6361/202245650'
apa: 'Vink, J. S., Mehner, A., Crowther, P. A., Fullerton, A., Garcia, M., Martins,
F., … Wang, L. (2023). X-shooting ULLYSES: Massive stars at low metallicity. I.
Project description. Astronomy & Astrophysics. EDP Sciences. https://doi.org/10.1051/0004-6361/202245650'
chicago: 'Vink, Jorick S., A. Mehner, P. A. Crowther, A. Fullerton, M. Garcia, F.
Martins, N. Morrell, et al. “X-Shooting ULLYSES: Massive Stars at Low Metallicity.
I. Project Description.” Astronomy & Astrophysics. EDP Sciences, 2023.
https://doi.org/10.1051/0004-6361/202245650.'
ieee: 'J. S. Vink et al., “X-shooting ULLYSES: Massive stars at low metallicity.
I. Project description,” Astronomy & Astrophysics, vol. 675. EDP Sciences,
2023.'
ista: 'Vink JS, Mehner A, Crowther PA, Fullerton A, Garcia M, Martins F, Morrell
N, Oskinova LM, St-Louis N, ud-Doula A, Sander AAC, Sana H, Bouret J-C, Kubátová
B, Marchant P, Martins LP, Wofford A, van Loon JT, Grace Telford O, Götberg YLL,
Bowman DM, Erba C, Kalari VM, Abdul-Masih M, Alkousa T, Backs F, Barbosa CL, Berlanas
SR, Bernini-Peron M, Bestenlehner JM, Blomme R, Bodensteiner J, Brands SA, Evans
CJ, David-Uraz A, Driessen FA, Dsilva K, Geen S, Gómez-González VMA, Grassitelli
L, Hamann W-R, Hawcroft C, Herrero A, Higgins ER, John Hillier D, Ignace R, Istrate
AG, Kaper L, Kee ND, Kehrig C, Keszthelyi Z, Klencki J, de Koter A, Kuiper R,
Laplace E, Larkin CJK, Lefever RR, Leitherer C, Lennon DJ, Mahy L, Maíz Apellániz
J, Maravelias G, Marcolino W, McLeod AF, de Mink SE, Najarro F, Oey MS, Parsons
TN, Pauli D, Pedersen MG, Prinja RK, Ramachandran V, Ramírez-Tannus MC, Sabhahit
GN, Schootemeijer A, Reyero Serantes S, Shenar T, Stringfellow GS, Sudnik N, Tramper
F, Wang L. 2023. X-shooting ULLYSES: Massive stars at low metallicity. I. Project
description. Astronomy & Astrophysics. 675, A154.'
mla: 'Vink, Jorick S., et al. “X-Shooting ULLYSES: Massive Stars at Low Metallicity.
I. Project Description.” Astronomy & Astrophysics, vol. 675, A154,
EDP Sciences, 2023, doi:10.1051/0004-6361/202245650.'
short: J.S. Vink, A. Mehner, P.A. Crowther, A. Fullerton, M. Garcia, F. Martins,
N. Morrell, L.M. Oskinova, N. St-Louis, A. ud-Doula, A.A.C. Sander, H. Sana, J.-C.
Bouret, B. Kubátová, P. Marchant, L.P. Martins, A. Wofford, J.T. van Loon, O.
Grace Telford, Y.L.L. Götberg, D.M. Bowman, C. Erba, V.M. Kalari, M. Abdul-Masih,
T. Alkousa, F. Backs, C.L. Barbosa, S.R. Berlanas, M. Bernini-Peron, J.M. Bestenlehner,
R. Blomme, J. Bodensteiner, S.A. Brands, C.J. Evans, A. David-Uraz, F.A. Driessen,
K. Dsilva, S. Geen, V.M.A. Gómez-González, L. Grassitelli, W.-R. Hamann, C. Hawcroft,
A. Herrero, E.R. Higgins, D. John Hillier, R. Ignace, A.G. Istrate, L. Kaper,
N.D. Kee, C. Kehrig, Z. Keszthelyi, J. Klencki, A. de Koter, R. Kuiper, E. Laplace,
C.J.K. Larkin, R.R. Lefever, C. Leitherer, D.J. Lennon, L. Mahy, J. Maíz Apellániz,
G. Maravelias, W. Marcolino, A.F. McLeod, S.E. de Mink, F. Najarro, M.S. Oey,
T.N. Parsons, D. Pauli, M.G. Pedersen, R.K. Prinja, V. Ramachandran, M.C. Ramírez-Tannus,
G.N. Sabhahit, A. Schootemeijer, S. Reyero Serantes, T. Shenar, G.S. Stringfellow,
N. Sudnik, F. Tramper, L. Wang, Astronomy & Astrophysics 675 (2023).
date_created: 2023-08-21T10:12:35Z
date_published: 2023-07-01T00:00:00Z
date_updated: 2023-08-22T11:01:07Z
day: '01'
doi: 10.1051/0004-6361/202245650
extern: '1'
external_id:
arxiv:
- '2305.06376'
intvolume: ' 675'
keyword:
- Space and Planetary Science
- Astronomy and Astrophysics
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1051/0004-6361/202245650
month: '07'
oa: 1
oa_version: Published Version
publication: Astronomy & Astrophysics
publication_identifier:
eissn:
- 1432-0746
issn:
- 0004-6361
publication_status: published
publisher: EDP Sciences
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'X-shooting ULLYSES: Massive stars at low metallicity. I. Project description'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 675
year: '2023'
...
---
_id: '14104'
abstract:
- lang: eng
text: 'Thorne–Żytkow objects (TŻO) are potential end products of the merger of a
neutron star with a non-degenerate star. In this work, we have computed the first
grid of evolutionary models of TŻOs with the MESA stellar evolution code. With
these models, we predict several observational properties of TŻOs, including their
surface temperatures and luminosities, pulsation periods, and nucleosynthetic
products. We expand the range of possible TŻO solutions to cover 3.45≲log(Teff/K)≲3.65
and 4.85≲log(L/L⊙)≲5.5. Due to the much higher densities our TŻOs reach compared
to previous models, if TŻOs form we expect them to be stable over a larger mass
range than previously predicted, without exhibiting a gap in their mass distribution.
Using the GYRE stellar pulsation code we show that TŻOs should have fundamental
pulsation periods of 1000–2000 d, and period ratios of ≈0.2–0.3. Models computed
with a large 399 isotope fully coupled nuclear network show a nucleosynthetic
signal that is different to previously predicted. We propose a new nucleosynthetic
signal to determine a star’s status as a TŻO: the isotopologues 44TiO2 and 44TiO,
which will have a shift in their spectral features as compared to stable titanium-containing
molecules. We find that in the local Universe (∼SMC metallicities and above) TŻOs
show little heavy metal enrichment, potentially explaining the difficulty in finding
TŻOs to-date.'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: R
full_name: Farmer, R
last_name: Farmer
- first_name: M
full_name: Renzo, M
last_name: Renzo
- first_name: Ylva Louise Linsdotter
full_name: Götberg, Ylva Louise Linsdotter
id: d0648d0c-0f64-11ee-a2e0-dd0faa2e4f7d
last_name: Götberg
orcid: 0000-0002-6960-6911
- first_name: E
full_name: Bellinger, E
last_name: Bellinger
- first_name: S
full_name: Justham, S
last_name: Justham
- first_name: S E
full_name: de Mink, S E
last_name: de Mink
citation:
ama: Farmer R, Renzo M, Götberg YLL, Bellinger E, Justham S, de Mink SE. Observational
predictions for Thorne–Żytkow objects. Monthly Notices of the Royal Astronomical
Society. 2023;524(2):1692-1709. doi:10.1093/mnras/stad1977
apa: Farmer, R., Renzo, M., Götberg, Y. L. L., Bellinger, E., Justham, S., &
de Mink, S. E. (2023). Observational predictions for Thorne–Żytkow objects. Monthly
Notices of the Royal Astronomical Society. Oxford University Press. https://doi.org/10.1093/mnras/stad1977
chicago: Farmer, R, M Renzo, Ylva Louise Linsdotter Götberg, E Bellinger, S Justham,
and S E de Mink. “Observational Predictions for Thorne–Żytkow Objects.” Monthly
Notices of the Royal Astronomical Society. Oxford University Press, 2023.
https://doi.org/10.1093/mnras/stad1977.
ieee: R. Farmer, M. Renzo, Y. L. L. Götberg, E. Bellinger, S. Justham, and S. E.
de Mink, “Observational predictions for Thorne–Żytkow objects,” Monthly Notices
of the Royal Astronomical Society, vol. 524, no. 2. Oxford University Press,
pp. 1692–1709, 2023.
ista: Farmer R, Renzo M, Götberg YLL, Bellinger E, Justham S, de Mink SE. 2023.
Observational predictions for Thorne–Żytkow objects. Monthly Notices of the Royal
Astronomical Society. 524(2), 1692–1709.
mla: Farmer, R., et al. “Observational Predictions for Thorne–Żytkow Objects.” Monthly
Notices of the Royal Astronomical Society, vol. 524, no. 2, Oxford University
Press, 2023, pp. 1692–709, doi:10.1093/mnras/stad1977.
short: R. Farmer, M. Renzo, Y.L.L. Götberg, E. Bellinger, S. Justham, S.E. de Mink,
Monthly Notices of the Royal Astronomical Society 524 (2023) 1692–1709.
date_created: 2023-08-21T10:13:56Z
date_published: 2023-09-01T00:00:00Z
date_updated: 2023-08-21T12:12:48Z
day: '01'
doi: 10.1093/mnras/stad1977
extern: '1'
external_id:
arxiv:
- '2305.07337'
intvolume: ' 524'
issue: '2'
keyword:
- Space and Planetary Science
- Astronomy and Astrophysics
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2305.07337
month: '09'
oa: 1
oa_version: Preprint
page: 1692-1709
publication: Monthly Notices of the Royal Astronomical Society
publication_identifier:
eissn:
- 1365-2966
issn:
- 0035-8711
publication_status: published
publisher: Oxford University Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Observational predictions for Thorne–Żytkow objects
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 524
year: '2023'
...
---
_id: '14105'
abstract:
- lang: eng
text: "Despite their recent success, deep neural networks continue to perform poorly
when they encounter distribution shifts at test time. Many recently proposed approaches
try to counter this by aligning the model to the new distribution prior to inference.
With no labels available this requires unsupervised objectives to adapt the model
on the observed test data. In this paper, we propose Test-Time SelfTraining (TeST):
a technique that takes as input a model trained on some source data and a novel
data distribution at test time, and learns invariant and robust representations
using a student-teacher framework. We find that models adapted using TeST significantly
improve over baseline testtime adaptation algorithms. TeST achieves competitive
performance to modern domain adaptation algorithms [4, 43], while having access
to 5-10x less data at time of adaption. We thoroughly evaluate a variety of baselines
on two tasks:\r\nobject detection and image segmentation and find that models
adapted with TeST. We find that TeST sets the new stateof-the art for test-time
domain adaptation algorithms. "
article_processing_charge: No
arxiv: 1
author:
- first_name: Samarth
full_name: Sinha, Samarth
last_name: Sinha
- first_name: Peter
full_name: Gehler, Peter
last_name: Gehler
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Bernt
full_name: Schiele, Bernt
last_name: Schiele
citation:
ama: 'Sinha S, Gehler P, Locatello F, Schiele B. TeST: Test-time Self-Training under
distribution shift. In: 2023 IEEE/CVF Winter Conference on Applications of
Computer Vision. Institute of Electrical and Electronics Engineers; 2023.
doi:10.1109/wacv56688.2023.00278'
apa: 'Sinha, S., Gehler, P., Locatello, F., & Schiele, B. (2023). TeST: Test-time
Self-Training under distribution shift. In 2023 IEEE/CVF Winter Conference
on Applications of Computer Vision. Waikoloa, HI, United States: Institute
of Electrical and Electronics Engineers. https://doi.org/10.1109/wacv56688.2023.00278'
chicago: 'Sinha, Samarth, Peter Gehler, Francesco Locatello, and Bernt Schiele.
“TeST: Test-Time Self-Training under Distribution Shift.” In 2023 IEEE/CVF
Winter Conference on Applications of Computer Vision. Institute of Electrical
and Electronics Engineers, 2023. https://doi.org/10.1109/wacv56688.2023.00278.'
ieee: 'S. Sinha, P. Gehler, F. Locatello, and B. Schiele, “TeST: Test-time Self-Training
under distribution shift,” in 2023 IEEE/CVF Winter Conference on Applications
of Computer Vision, Waikoloa, HI, United States, 2023.'
ista: 'Sinha S, Gehler P, Locatello F, Schiele B. 2023. TeST: Test-time Self-Training
under distribution shift. 2023 IEEE/CVF Winter Conference on Applications of Computer
Vision. WACV: Winter Conference on Applications of Computer Vision.'
mla: 'Sinha, Samarth, et al. “TeST: Test-Time Self-Training under Distribution Shift.”
2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Institute
of Electrical and Electronics Engineers, 2023, doi:10.1109/wacv56688.2023.00278.'
short: S. Sinha, P. Gehler, F. Locatello, B. Schiele, in:, 2023 IEEE/CVF Winter
Conference on Applications of Computer Vision, Institute of Electrical and Electronics
Engineers, 2023.
conference:
end_date: 2023-01-07
location: Waikoloa, HI, United States
name: 'WACV: Winter Conference on Applications of Computer Vision'
start_date: 2023-01-02
date_created: 2023-08-21T12:11:38Z
date_published: 2023-02-06T00:00:00Z
date_updated: 2023-09-06T10:26:56Z
day: '06'
department:
- _id: FrLo
doi: 10.1109/wacv56688.2023.00278
extern: '1'
external_id:
arxiv:
- '2209.11459'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2209.11459
month: '02'
oa: 1
oa_version: Preprint
publication: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision
publication_identifier:
eissn:
- 2642-9381
isbn:
- '9781665493475'
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'TeST: Test-time Self-Training under distribution shift'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14192'
abstract:
- lang: eng
text: For the Fröhlich model of the large polaron, we prove that the ground state
energy as a function of the total momentum has a unique global minimum at momentum
zero. This implies the non-existence of a ground state of the translation invariant
Fröhlich Hamiltonian and thus excludes the possibility of a localization transition
at finite coupling.
acknowledgement: D.M. and K.M. thank Robert Seiringer for helpful discussions. Open
access funding provided by Institute of Science and Technology (IST Austria). Financial
support from the Agence Nationale de la Recherche (ANR) through the projects ANR-17-CE40-0016,
ANR-17-CE40-0007-01, ANR-17-EURE-0002 (J.L.) and from the European Union’s Horizon
2020 research and innovation programme under the Maria Skłodowska-Curie grant agreement
No. 665386 (K.M.) is gratefully acknowledged.
article_number: '17'
article_processing_charge: Yes (via OA deal)
article_type: original
arxiv: 1
author:
- first_name: Jonas
full_name: Lampart, Jonas
last_name: Lampart
- first_name: David Johannes
full_name: Mitrouskas, David Johannes
id: cbddacee-2b11-11eb-a02e-a2e14d04e52d
last_name: Mitrouskas
- first_name: Krzysztof
full_name: Mysliwy, Krzysztof
id: 316457FC-F248-11E8-B48F-1D18A9856A87
last_name: Mysliwy
citation:
ama: Lampart J, Mitrouskas DJ, Mysliwy K. On the global minimum of the energy–momentum
relation for the polaron. Mathematical Physics, Analysis and Geometry.
2023;26(3). doi:10.1007/s11040-023-09460-x
apa: Lampart, J., Mitrouskas, D. J., & Mysliwy, K. (2023). On the global minimum
of the energy–momentum relation for the polaron. Mathematical Physics, Analysis
and Geometry. Springer Nature. https://doi.org/10.1007/s11040-023-09460-x
chicago: Lampart, Jonas, David Johannes Mitrouskas, and Krzysztof Mysliwy. “On the
Global Minimum of the Energy–Momentum Relation for the Polaron.” Mathematical
Physics, Analysis and Geometry. Springer Nature, 2023. https://doi.org/10.1007/s11040-023-09460-x.
ieee: J. Lampart, D. J. Mitrouskas, and K. Mysliwy, “On the global minimum of the
energy–momentum relation for the polaron,” Mathematical Physics, Analysis and
Geometry, vol. 26, no. 3. Springer Nature, 2023.
ista: Lampart J, Mitrouskas DJ, Mysliwy K. 2023. On the global minimum of the energy–momentum
relation for the polaron. Mathematical Physics, Analysis and Geometry. 26(3),
17.
mla: Lampart, Jonas, et al. “On the Global Minimum of the Energy–Momentum Relation
for the Polaron.” Mathematical Physics, Analysis and Geometry, vol. 26,
no. 3, 17, Springer Nature, 2023, doi:10.1007/s11040-023-09460-x.
short: J. Lampart, D.J. Mitrouskas, K. Mysliwy, Mathematical Physics, Analysis and
Geometry 26 (2023).
corr_author: '1'
date_created: 2023-08-22T14:09:47Z
date_published: 2023-07-26T00:00:00Z
date_updated: 2024-10-09T21:06:41Z
day: '26'
ddc:
- '510'
department:
- _id: RoSe
doi: 10.1007/s11040-023-09460-x
external_id:
arxiv:
- '2206.14708'
isi:
- '001032992600001'
file:
- access_level: open_access
checksum: f0941cc66cb3ed06a12ca4b7e356cfd6
content_type: application/pdf
creator: dernst
date_created: 2023-08-23T10:59:15Z
date_updated: 2023-08-23T10:59:15Z
file_id: '14225'
file_name: 2023_MathPhysics_Lampart.pdf
file_size: 317026
relation: main_file
success: 1
file_date_updated: 2023-08-23T10:59:15Z
has_accepted_license: '1'
intvolume: ' 26'
isi: 1
issue: '3'
keyword:
- Geometry and Topology
- Mathematical Physics
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
publication: Mathematical Physics, Analysis and Geometry
publication_identifier:
eissn:
- 1572-9656
issn:
- 1385-0172
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: On the global minimum of the energy–momentum relation for the polaron
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: 26
year: '2023'
...
---
_id: '14207'
abstract:
- lang: eng
text: The binding problem in human cognition, concerning how the brain represents
and connects objects within a fixed network of neural connections, remains a subject
of intense debate. Most machine learning efforts addressing this issue in an unsupervised
setting have focused on slot-based methods, which may be limiting due to their
discrete nature and difficulty to express uncertainty. Recently, the Complex AutoEncoder
was proposed as an alternative that learns continuous and distributed object-centric
representations. However, it is only applicable to simple toy data. In this paper,
we present Rotating Features, a generalization of complex-valued features to higher
dimensions, and a new evaluation procedure for extracting objects from distributed
representations. Additionally, we show the applicability of our approach to pre-trained
features. Together, these advancements enable us to scale distributed object-centric
representations from simple toy to real-world data. We believe this work advances
a new paradigm for addressing the binding problem in machine learning and has
the potential to inspire further innovation in the field.
article_number: '2306.00600'
article_processing_charge: No
arxiv: 1
author:
- first_name: Sindy
full_name: Löwe, Sindy
last_name: Löwe
- first_name: Phillip
full_name: Lippe, Phillip
last_name: Lippe
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Max
full_name: Welling, Max
last_name: Welling
citation:
ama: Löwe S, Lippe P, Locatello F, Welling M. Rotating features for object discovery.
arXiv. doi:10.48550/arXiv.2306.00600
apa: Löwe, S., Lippe, P., Locatello, F., & Welling, M. (n.d.). Rotating features
for object discovery. arXiv. https://doi.org/10.48550/arXiv.2306.00600
chicago: Löwe, Sindy, Phillip Lippe, Francesco Locatello, and Max Welling. “Rotating
Features for Object Discovery.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2306.00600.
ieee: S. Löwe, P. Lippe, F. Locatello, and M. Welling, “Rotating features for object
discovery,” arXiv. .
ista: Löwe S, Lippe P, Locatello F, Welling M. Rotating features for object discovery.
arXiv, 2306.00600.
mla: Löwe, Sindy, et al. “Rotating Features for Object Discovery.” ArXiv,
2306.00600, doi:10.48550/arXiv.2306.00600.
short: S. Löwe, P. Lippe, F. Locatello, M. Welling, ArXiv (n.d.).
corr_author: '1'
date_created: 2023-08-22T14:18:00Z
date_published: 2023-06-01T00:00:00Z
date_updated: 2024-10-09T21:06:53Z
day: '01'
department:
- _id: FrLo
doi: 10.48550/arXiv.2306.00600
external_id:
arxiv:
- '2306.00600'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2306.00600
month: '06'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Rotating features for object discovery
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14208'
abstract:
- lang: eng
text: This paper focuses on over-parameterized deep neural networks (DNNs) with
ReLU activation functions and proves that when the data distribution is well-separated,
DNNs can achieve Bayes-optimal test error for classification while obtaining (nearly)
zero-training error under the lazy training regime. For this purpose, we unify
three interrelated concepts of overparameterization, benign overfitting, and the
Lipschitz constant of DNNs. Our results indicate that interpolating with smoother
functions leads to better generalization. Furthermore, we investigate the special
case where interpolating smooth ground-truth functions is performed by DNNs under
the Neural Tangent Kernel (NTK) regime for generalization. Our result demonstrates
that the generalization error converges to a constant order that only depends
on label noise and initialization noise, which theoretically verifies benign overfitting.
Our analysis provides a tight lower bound on the normalized margin under non-smooth
activation functions, as well as the minimum eigenvalue of NTK under high-dimensional
settings, which has its own interest in learning theory.
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Zhenyu
full_name: Zhu, Zhenyu
last_name: Zhu
- first_name: Fanghui
full_name: Liu, Fanghui
last_name: Liu
- first_name: Grigorios G
full_name: Chrysos, Grigorios G
last_name: Chrysos
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Volkan
full_name: Cevher, Volkan
last_name: Cevher
citation:
ama: 'Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. Benign overfitting in deep
neural networks under lazy training. In: Proceedings of the 40th International
Conference on Machine Learning. Vol 202. ML Research Press; 2023:43105-43128.'
apa: 'Zhu, Z., Liu, F., Chrysos, G. G., Locatello, F., & Cevher, V. (2023).
Benign overfitting in deep neural networks under lazy training. In Proceedings
of the 40th International Conference on Machine Learning (Vol. 202, pp. 43105–43128).
Honolulu, Hawaii, United States: ML Research Press.'
chicago: Zhu, Zhenyu, Fanghui Liu, Grigorios G Chrysos, Francesco Locatello, and
Volkan Cevher. “Benign Overfitting in Deep Neural Networks under Lazy Training.”
In Proceedings of the 40th International Conference on Machine Learning,
202:43105–28. ML Research Press, 2023.
ieee: Z. Zhu, F. Liu, G. G. Chrysos, F. Locatello, and V. Cevher, “Benign overfitting
in deep neural networks under lazy training,” in Proceedings of the 40th International
Conference on Machine Learning, Honolulu, Hawaii, United States, 2023, vol.
202, pp. 43105–43128.
ista: Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. 2023. Benign overfitting
in deep neural networks under lazy training. Proceedings of the 40th International
Conference on Machine Learning. International Conference on Machine Learning,
PMLR, vol. 202, 43105–43128.
mla: Zhu, Zhenyu, et al. “Benign Overfitting in Deep Neural Networks under Lazy
Training.” Proceedings of the 40th International Conference on Machine Learning,
vol. 202, ML Research Press, 2023, pp. 43105–28.
short: Z. Zhu, F. Liu, G.G. Chrysos, F. Locatello, V. Cevher, in:, Proceedings of
the 40th International Conference on Machine Learning, ML Research Press, 2023,
pp. 43105–43128.
conference:
end_date: 2023-07-29
location: Honolulu, Hawaii, United States
name: International Conference on Machine Learning
start_date: 2023-07-23
date_created: 2023-08-22T14:18:18Z
date_published: 2023-05-30T00:00:00Z
date_updated: 2023-09-13T08:46:46Z
day: '30'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2305.19377'
intvolume: ' 202'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2305.19377
month: '05'
oa: 1
oa_version: Preprint
page: 43105-43128
publication: Proceedings of the 40th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
status: public
title: Benign overfitting in deep neural networks under lazy training
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 202
year: '2023'
...
---
_id: '14209'
abstract:
- lang: eng
text: Diffusion models excel at generating photorealistic images from text-queries.
Naturally, many approaches have been proposed to use these generative abilities
to augment training datasets for downstream tasks, such as classification. However,
diffusion models are themselves trained on large noisily supervised, but nonetheless,
annotated datasets. It is an open question whether the generalization capabilities
of diffusion models beyond using the additional data of the pre-training process
for augmentation lead to improved downstream performance. We perform a systematic
evaluation of existing methods to generate images from diffusion models and study
new extensions to assess their benefit for data augmentation. While we find that
personalizing diffusion models towards the target data outperforms simpler prompting
strategies, we also show that using the training data of the diffusion model alone,
via a simple nearest neighbor retrieval procedure, leads to even stronger downstream
performance. Overall, our study probes the limitations of diffusion models for
data augmentation but also highlights its potential in generating new training
data to improve performance on simple downstream vision tasks.
article_number: '2304.10253'
article_processing_charge: No
arxiv: 1
author:
- first_name: Max F.
full_name: Burg, Max F.
last_name: Burg
- first_name: Florian
full_name: Wenzel, Florian
last_name: Wenzel
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Osama
full_name: Makansi, Osama
last_name: Makansi
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Chris
full_name: Russell, Chris
last_name: Russell
citation:
ama: Burg MF, Wenzel F, Zietlow D, et al. A data augmentation perspective on diffusion
models and retrieval. arXiv. doi:10.48550/arXiv.2304.10253
apa: Burg, M. F., Wenzel, F., Zietlow, D., Horn, M., Makansi, O., Locatello, F.,
& Russell, C. (n.d.). A data augmentation perspective on diffusion models
and retrieval. arXiv. https://doi.org/10.48550/arXiv.2304.10253
chicago: Burg, Max F., Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi,
Francesco Locatello, and Chris Russell. “A Data Augmentation Perspective on Diffusion
Models and Retrieval.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2304.10253.
ieee: M. F. Burg et al., “A data augmentation perspective on diffusion models
and retrieval,” arXiv. .
ista: Burg MF, Wenzel F, Zietlow D, Horn M, Makansi O, Locatello F, Russell C. A
data augmentation perspective on diffusion models and retrieval. arXiv, 2304.10253.
mla: Burg, Max F., et al. “A Data Augmentation Perspective on Diffusion Models and
Retrieval.” ArXiv, 2304.10253, doi:10.48550/arXiv.2304.10253.
short: M.F. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell,
ArXiv (n.d.).
date_created: 2023-08-22T14:18:43Z
date_published: 2023-04-20T00:00:00Z
date_updated: 2023-09-13T08:51:56Z
day: '20'
department:
- _id: FrLo
doi: 10.48550/arXiv.2304.10253
extern: '1'
external_id:
arxiv:
- '2304.10253'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2304.10253
month: '04'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: A data augmentation perspective on diffusion models and retrieval
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14210'
abstract:
- lang: eng
text: Recovering the latent factors of variation of high dimensional data has so
far focused on simple synthetic settings. Mostly building on unsupervised and
weakly-supervised objectives, prior work missed out on the positive implications
for representation learning on real world data. In this work, we propose to leverage
knowledge extracted from a diversified set of supervised tasks to learn a common
disentangled representation. Assuming each supervised task only depends on an
unknown subset of the factors of variation, we disentangle the feature space of
a supervised multi-task model, with features activating sparsely across different
tasks and information being shared as appropriate. Importantly, we never directly
observe the factors of variations but establish that access to multiple tasks
is sufficient for identifiability under sufficiency and minimality assumptions.
We validate our approach on six real world distribution shift benchmarks, and
different data modalities (images, text), demonstrating how disentangled representations
can be transferred to real settings.
article_number: '2304.07939'
article_processing_charge: No
arxiv: 1
author:
- first_name: Marco
full_name: Fumero, Marco
last_name: Fumero
- first_name: Florian
full_name: Wenzel, Florian
last_name: Wenzel
- first_name: Luca
full_name: Zancato, Luca
last_name: Zancato
- first_name: Alessandro
full_name: Achille, Alessandro
last_name: Achille
- first_name: Emanuele
full_name: Rodolà, Emanuele
last_name: Rodolà
- first_name: Stefano
full_name: Soatto, Stefano
last_name: Soatto
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: Fumero M, Wenzel F, Zancato L, et al. Leveraging sparse and shared feature
activations for disentangled representation learning. arXiv. doi:10.48550/arXiv.2304.07939
apa: Fumero, M., Wenzel, F., Zancato, L., Achille, A., Rodolà, E., Soatto, S., …
Locatello, F. (n.d.). Leveraging sparse and shared feature activations for disentangled
representation learning. arXiv. https://doi.org/10.48550/arXiv.2304.07939
chicago: Fumero, Marco, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele
Rodolà, Stefano Soatto, Bernhard Schölkopf, and Francesco Locatello. “Leveraging
Sparse and Shared Feature Activations for Disentangled Representation Learning.”
ArXiv, n.d. https://doi.org/10.48550/arXiv.2304.07939.
ieee: M. Fumero et al., “Leveraging sparse and shared feature activations
for disentangled representation learning,” arXiv. .
ista: Fumero M, Wenzel F, Zancato L, Achille A, Rodolà E, Soatto S, Schölkopf B,
Locatello F. Leveraging sparse and shared feature activations for disentangled
representation learning. arXiv, 2304.07939.
mla: Fumero, Marco, et al. “Leveraging Sparse and Shared Feature Activations for
Disentangled Representation Learning.” ArXiv, 2304.07939, doi:10.48550/arXiv.2304.07939.
short: M. Fumero, F. Wenzel, L. Zancato, A. Achille, E. Rodolà, S. Soatto, B. Schölkopf,
F. Locatello, ArXiv (n.d.).
corr_author: '1'
date_created: 2023-08-22T14:19:03Z
date_published: 2023-04-17T00:00:00Z
date_updated: 2024-10-09T21:06:54Z
day: '17'
department:
- _id: FrLo
doi: 10.48550/arXiv.2304.07939
external_id:
arxiv:
- '2304.07939'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2304.07939
month: '04'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Leveraging sparse and shared feature activations for disentangled representation
learning
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14211'
abstract:
- lang: eng
text: 'Causal discovery methods are intrinsically constrained by the set of assumptions
needed to ensure structure identifiability. Moreover additional restrictions are
often imposed in order to simplify the inference task: this is the case for the
Gaussian noise assumption on additive non-linear models, which is common to many
causal discovery approaches. In this paper we show the shortcomings of inference
under this hypothesis, analyzing the risk of edge inversion under violation of
Gaussianity of the noise terms. Then, we propose a novel method for inferring
the topological ordering of the variables in the causal graph, from data generated
according to an additive non-linear model with a generic noise distribution. This
leads to NoGAM (Not only Gaussian Additive noise Models), a causal discovery algorithm
with a minimal set of assumptions and state of the art performance, experimentally
benchmarked on synthetic data.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Francesco
full_name: Montagna, Francesco
last_name: Montagna
- first_name: Nicoletta
full_name: Noceti, Nicoletta
last_name: Noceti
- first_name: Lorenzo
full_name: Rosasco, Lorenzo
last_name: Rosasco
- first_name: Kun
full_name: Zhang, Kun
last_name: Zhang
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Causal discovery with
score matching on additive models with arbitrary noise. In: 2nd Conference
on Causal Learning and Reasoning. ; 2023.'
apa: Montagna, F., Noceti, N., Rosasco, L., Zhang, K., & Locatello, F. (2023).
Causal discovery with score matching on additive models with arbitrary noise.
In 2nd Conference on Causal Learning and Reasoning. Tübingen, Germany.
chicago: Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and
Francesco Locatello. “Causal Discovery with Score Matching on Additive Models
with Arbitrary Noise.” In 2nd Conference on Causal Learning and Reasoning,
2023.
ieee: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Causal discovery
with score matching on additive models with arbitrary noise,” in 2nd Conference
on Causal Learning and Reasoning, Tübingen, Germany, 2023.
ista: 'Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. 2023. Causal discovery
with score matching on additive models with arbitrary noise. 2nd Conference on
Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning.'
mla: Montagna, Francesco, et al. “Causal Discovery with Score Matching on Additive
Models with Arbitrary Noise.” 2nd Conference on Causal Learning and Reasoning,
2023.
short: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference
on Causal Learning and Reasoning, 2023.
conference:
end_date: 2023-04-14
location: Tübingen, Germany
name: 'CLeaR: Conference on Causal Learning and Reasoning'
start_date: 2023-04-11
date_created: 2023-08-22T14:19:21Z
date_published: 2023-04-01T00:00:00Z
date_updated: 2024-10-14T12:30:04Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2304.03265'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2304.03265
month: '04'
oa: 1
oa_version: Preprint
publication: 2nd Conference on Causal Learning and Reasoning
publication_status: published
quality_controlled: '1'
scopus_import: '1'
status: public
title: Causal discovery with score matching on additive models with arbitrary noise
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14212'
abstract:
- lang: eng
text: This paper demonstrates how to discover the whole causal graph from the second
derivative of the log-likelihood in observational non-linear additive Gaussian
noise models. Leveraging scalable machine learning approaches to approximate the
score function ∇logp(X), we extend the work of Rolland et al. (2022) that only
recovers the topological order from the score and requires an expensive pruning
step removing spurious edges among those admitted by the ordering. Our analysis
leads to DAS (acronym for Discovery At Scale), a practical algorithm that reduces
the complexity of the pruning by a factor proportional to the graph size. In practice,
DAS achieves competitive accuracy with current state-of-the-art while being over
an order of magnitude faster. Overall, our approach enables principled and scalable
causal discovery, significantly lowering the compute bar.
article_processing_charge: No
arxiv: 1
author:
- first_name: Francesco
full_name: Montagna, Francesco
last_name: Montagna
- first_name: Nicoletta
full_name: Noceti, Nicoletta
last_name: Noceti
- first_name: Lorenzo
full_name: Rosasco, Lorenzo
last_name: Rosasco
- first_name: Kun
full_name: Zhang, Kun
last_name: Zhang
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Scalable causal discovery
with score matching. In: 2nd Conference on Causal Learning and Reasoning.
; 2023.'
apa: Montagna, F., Noceti, N., Rosasco, L., Zhang, K., & Locatello, F. (2023).
Scalable causal discovery with score matching. In 2nd Conference on Causal
Learning and Reasoning. Tübingen, Germany.
chicago: Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and
Francesco Locatello. “Scalable Causal Discovery with Score Matching.” In 2nd
Conference on Causal Learning and Reasoning, 2023.
ieee: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Scalable
causal discovery with score matching,” in 2nd Conference on Causal Learning
and Reasoning, Tübingen, Germany, 2023.
ista: 'Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. 2023. Scalable causal
discovery with score matching. 2nd Conference on Causal Learning and Reasoning.
CLeaR: Conference on Causal Learning and Reasoning.'
mla: Montagna, Francesco, et al. “Scalable Causal Discovery with Score Matching.”
2nd Conference on Causal Learning and Reasoning, 2023.
short: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference
on Causal Learning and Reasoning, 2023.
conference:
end_date: 2023-04-14
location: Tübingen, Germany
name: 'CLeaR: Conference on Causal Learning and Reasoning'
start_date: 2023-04-11
date_created: 2023-08-22T14:19:40Z
date_published: 2023-04-01T00:00:00Z
date_updated: 2024-10-14T12:30:15Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2304.03382'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2304.03382
month: '04'
oa: 1
oa_version: Preprint
publication: 2nd Conference on Causal Learning and Reasoning
publication_status: published
quality_controlled: '1'
scopus_import: '1'
status: public
title: Scalable causal discovery with score matching
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14214'
abstract:
- lang: eng
text: 'Recent years have seen a surge of interest in learning high-level causal
representations from low-level image pairs under interventions. Yet, existing
efforts are largely limited to simple synthetic settings that are far away from
real-world problems. In this paper, we present Causal Triplet, a causal representation
learning benchmark featuring not only visually more complex scenes, but also two
crucial desiderata commonly overlooked in previous works: (i) an actionable counterfactual
setting, where only certain object-level variables allow for counterfactual observations
whereas others do not; (ii) an interventional downstream task with an emphasis
on out-of-distribution robustness from the independent causal mechanisms principle.
Through extensive experiments, we find that models built with the knowledge of
disentangled or object-centric representations significantly outperform their
distributed counterparts. However, recent causal representation learning methods
still struggle to identify such latent structures, indicating substantial challenges
and opportunities for future work.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Yuejiang
full_name: Liu, Yuejiang
last_name: Liu
- first_name: Alexandre
full_name: Alahi, Alexandre
last_name: Alahi
- first_name: Chris
full_name: Russell, Chris
last_name: Russell
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Liu Y, Alahi A, Russell C, et al. Causal triplet: An open challenge for intervention-centric
causal representation learning. In: 2nd Conference on Causal Learning and Reasoning.
; 2023.'
apa: 'Liu, Y., Alahi, A., Russell, C., Horn, M., Zietlow, D., Schölkopf, B., &
Locatello, F. (2023). Causal triplet: An open challenge for intervention-centric
causal representation learning. In 2nd Conference on Causal Learning and Reasoning.
Tübingen, Germany.'
chicago: 'Liu, Yuejiang, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow,
Bernhard Schölkopf, and Francesco Locatello. “Causal Triplet: An Open Challenge
for Intervention-Centric Causal Representation Learning.” In 2nd Conference
on Causal Learning and Reasoning, 2023.'
ieee: 'Y. Liu et al., “Causal triplet: An open challenge for intervention-centric
causal representation learning,” in 2nd Conference on Causal Learning and Reasoning,
Tübingen, Germany, 2023.'
ista: 'Liu Y, Alahi A, Russell C, Horn M, Zietlow D, Schölkopf B, Locatello F. 2023.
Causal triplet: An open challenge for intervention-centric causal representation
learning. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on
Causal Learning and Reasoning.'
mla: 'Liu, Yuejiang, et al. “Causal Triplet: An Open Challenge for Intervention-Centric
Causal Representation Learning.” 2nd Conference on Causal Learning and Reasoning,
2023.'
short: Y. Liu, A. Alahi, C. Russell, M. Horn, D. Zietlow, B. Schölkopf, F. Locatello,
in:, 2nd Conference on Causal Learning and Reasoning, 2023.
conference:
end_date: 2023-04-14
location: Tübingen, Germany
name: 'CLeaR: Conference on Causal Learning and Reasoning'
start_date: 2023-04-11
date_created: 2023-08-22T14:20:18Z
date_published: 2023-04-12T00:00:00Z
date_updated: 2024-10-14T12:30:42Z
day: '12'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2301.05169'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2301.05169
month: '04'
oa: 1
oa_version: Preprint
publication: 2nd Conference on Causal Learning and Reasoning
publication_status: published
quality_controlled: '1'
status: public
title: 'Causal triplet: An open challenge for intervention-centric causal representation
learning'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
OA_type: green
_id: '14216'
abstract:
- lang: eng
text: CLIP proved that aligning visual and language spaces is key to solving many
vision tasks without explicit training, but required to train image and text encoders
from scratch on a huge dataset. LiT improved this by only training the text encoder
and using a pre-trained vision network. In this paper, we show that a common space
can be created without any training at all, using single-domain encoders (trained
with or without supervision) and a much smaller amount of image-text pairs. Furthermore,
our model has unique properties. Most notably, deploying a new version with updated
training samples can be done in a matter of seconds. Additionally, the representations
in the common space are easily interpretable as every dimension corresponds to
the similarity of the input to a unique entry in the multimodal dataset. Experiments
on standard zero-shot visual benchmarks demonstrate the typical transfer ability
of image-text models. Overall, our method represents a simple yet surprisingly
strong baseline for foundation multi-modal models, raising important questions
on their data efficiency and on the role of retrieval in machine learning.
acknowledgement: "AN, MF, and FL partially worked on ASIF when they were at Amazon
Web Services in Tübingen,\r\nGermany. This paper is financially supported by the
PRIN 2020 project no.2020TA3K9N (LEGO.AI), PNRR MUR project PE0000013-FAIR, and
ERC Grant no.802554 (SPECGEO)."
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Antonio
full_name: Norelli, Antonio
last_name: Norelli
- first_name: Marco
full_name: Fumero, Marco
last_name: Fumero
- first_name: Valentino
full_name: Maiorca, Valentino
last_name: Maiorca
- first_name: Luca
full_name: Moschella, Luca
last_name: Moschella
- first_name: Emanuele
full_name: Rodolà, Emanuele
last_name: Rodolà
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Norelli A, Fumero M, Maiorca V, Moschella L, Rodolà E, Locatello F. ASIF:
Coupled data turns unimodal models to multimodal without training. In: 37th
Conference on Neural Information Processing Systems. Vol 36. Neural Information
Processing Systems Foundation; 2023:15303-15319.'
apa: 'Norelli, A., Fumero, M., Maiorca, V., Moschella, L., Rodolà, E., & Locatello,
F. (2023). ASIF: Coupled data turns unimodal models to multimodal without training.
In 37th Conference on Neural Information Processing Systems (Vol. 36, pp.
15303–15319). New Orleans, LA, United States: Neural Information Processing Systems
Foundation.'
chicago: 'Norelli, Antonio, Marco Fumero, Valentino Maiorca, Luca Moschella, Emanuele
Rodolà, and Francesco Locatello. “ASIF: Coupled Data Turns Unimodal Models to
Multimodal without Training.” In 37th Conference on Neural Information Processing
Systems, 36:15303–19. Neural Information Processing Systems Foundation, 2023.'
ieee: 'A. Norelli, M. Fumero, V. Maiorca, L. Moschella, E. Rodolà, and F. Locatello,
“ASIF: Coupled data turns unimodal models to multimodal without training,” in
37th Conference on Neural Information Processing Systems, New Orleans,
LA, United States, 2023, vol. 36, pp. 15303–15319.'
ista: 'Norelli A, Fumero M, Maiorca V, Moschella L, Rodolà E, Locatello F. 2023.
ASIF: Coupled data turns unimodal models to multimodal without training. 37th
Conference on Neural Information Processing Systems. NeurIPS: Neural Information
Processing Systems, Advances in Neural Information Processing Systems, vol. 36,
15303–15319.'
mla: 'Norelli, Antonio, et al. “ASIF: Coupled Data Turns Unimodal Models to Multimodal
without Training.” 37th Conference on Neural Information Processing Systems,
vol. 36, Neural Information Processing Systems Foundation, 2023, pp. 15303–19.'
short: A. Norelli, M. Fumero, V. Maiorca, L. Moschella, E. Rodolà, F. Locatello,
in:, 37th Conference on Neural Information Processing Systems, Neural Information
Processing Systems Foundation, 2023, pp. 15303–15319.
conference:
end_date: 2023-12-14
location: New Orleans, LA, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2023-12-12
corr_author: '1'
date_created: 2023-08-22T14:22:04Z
date_published: 2023-10-04T00:00:00Z
date_updated: 2025-05-14T11:28:52Z
day: '04'
ddc:
- '000'
department:
- _id: FrLo
external_id:
arxiv:
- '2210.01738'
file:
- access_level: open_access
checksum: e51c90300b92d7135050da5c9e3a8015
content_type: application/pdf
creator: dernst
date_created: 2025-02-04T12:16:13Z
date_updated: 2025-02-04T12:16:13Z
file_id: '18994'
file_name: 2023_NeurIPS_Fumero.pdf
file_size: 12648978
relation: main_file
success: 1
file_date_updated: 2025-02-04T12:16:13Z
has_accepted_license: '1'
intvolume: ' 36'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Preprint
page: 15303-15319
publication: 37th Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713899921'
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/noranta4/ASIF
status: public
title: 'ASIF: Coupled data turns unimodal models to multimodal without training'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 36
year: '2023'
...
---
_id: '14217'
abstract:
- lang: eng
text: 'Neural networks embed the geometric structure of a data manifold lying in
a high-dimensional space into latent representations. Ideally, the distribution
of the data points in the latent space should depend only on the task, the data,
the loss, and other architecture-specific constraints. However, factors such as
the random weights initialization, training hyperparameters, or other sources
of randomness in the training phase may induce incoherent latent spaces that hinder
any form of reuse. Nevertheless, we empirically observe that, under the same data
and modeling choices, the angles between the encodings within distinct latent
spaces do not change. In this work, we propose the latent similarity between each
sample and a fixed set of anchors as an alternative data representation, demonstrating
that it can enforce the desired invariances without any additional training. We
show how neural architectures can leverage these relative representations to guarantee,
in practice, invariance to latent isometries and rescalings, effectively enabling
latent space communication: from zero-shot model stitching to latent space comparison
between diverse settings. We extensively validate the generalization capability
of our approach on different datasets, spanning various modalities (images, text,
graphs), tasks (e.g., classification, reconstruction) and architectures (e.g.,
CNNs, GCNs, transformers).'
article_processing_charge: No
arxiv: 1
author:
- first_name: Luca
full_name: Moschella, Luca
last_name: Moschella
- first_name: Valentino
full_name: Maiorca, Valentino
last_name: Maiorca
- first_name: Marco
full_name: Fumero, Marco
last_name: Fumero
- first_name: Antonio
full_name: Norelli, Antonio
last_name: Norelli
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Emanuele
full_name: Rodolà, Emanuele
last_name: Rodolà
citation:
ama: 'Moschella L, Maiorca V, Fumero M, Norelli A, Locatello F, Rodolà E. Relative
representations enable zero-shot latent space communication. In: The 11th International
Conference on Learning Representations. ; 2023.'
apa: Moschella, L., Maiorca, V., Fumero, M., Norelli, A., Locatello, F., & Rodolà,
E. (2023). Relative representations enable zero-shot latent space communication.
In The 11th International Conference on Learning Representations. Kigali,
Rwanda.
chicago: Moschella, Luca, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco
Locatello, and Emanuele Rodolà. “Relative Representations Enable Zero-Shot Latent
Space Communication.” In The 11th International Conference on Learning Representations,
2023.
ieee: L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, and E. Rodolà,
“Relative representations enable zero-shot latent space communication,” in The
11th International Conference on Learning Representations, Kigali, Rwanda,
2023.
ista: Moschella L, Maiorca V, Fumero M, Norelli A, Locatello F, Rodolà E. 2023.
Relative representations enable zero-shot latent space communication. The 11th
International Conference on Learning Representations. International Conference
on Machine Learning Representations.
mla: Moschella, Luca, et al. “Relative Representations Enable Zero-Shot Latent Space
Communication.” The 11th International Conference on Learning Representations,
2023.
short: L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, E. Rodolà,
in:, The 11th International Conference on Learning Representations, 2023.
conference:
end_date: 2023-05-05
location: Kigali, Rwanda
name: International Conference on Machine Learning Representations
start_date: 2023-05-01
date_created: 2023-08-22T14:22:20Z
date_published: 2023-05-01T00:00:00Z
date_updated: 2023-09-13T09:44:26Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2209.15430'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2209.15430
month: '05'
oa: 1
oa_version: Preprint
publication: The 11th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: Relative representations enable zero-shot latent space communication
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14218'
abstract:
- lang: eng
text: Humans naturally decompose their environment into entities at the appropriate
level of abstraction to act in the world. Allowing machine learning algorithms
to derive this decomposition in an unsupervised way has become an important line
of research. However, current methods are restricted to simulated data or require
additional information in the form of motion or depth in order to successfully
discover objects. In this work, we overcome this limitation by showing that reconstructing
features from models trained in a self-supervised manner is a sufficient training
signal for object-centric representations to arise in a fully unsupervised way.
Our approach, DINOSAUR, significantly out-performs existing image-based object-centric
learning models on simulated data and is the first unsupervised object-centric
model that scales to real-world datasets such as COCO and PASCAL VOC. DINOSAUR
is conceptually simple and shows competitive performance compared to more involved
pipelines from the computer vision literature.
article_processing_charge: No
arxiv: 1
author:
- first_name: Maximilian
full_name: Seitzer, Maximilian
last_name: Seitzer
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Andrii
full_name: Zadaianchuk, Andrii
last_name: Zadaianchuk
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Tianjun
full_name: Xiao, Tianjun
last_name: Xiao
- first_name: Carl-Johann Simon-Gabriel
full_name: Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel
last_name: Carl-Johann Simon-Gabriel
- first_name: Tong
full_name: He, Tong
last_name: He
- first_name: Zheng
full_name: Zhang, Zheng
last_name: Zhang
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Thomas
full_name: Brox, Thomas
last_name: Brox
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Seitzer M, Horn M, Zadaianchuk A, et al. Bridging the gap to real-world object-centric
learning. In: The 11th International Conference on Learning Representations.
; 2023.'
apa: Seitzer, M., Horn, M., Zadaianchuk, A., Zietlow, D., Xiao, T., Carl-Johann
Simon-Gabriel, C.-J. S.-G., … Locatello, F. (2023). Bridging the gap to real-world
object-centric learning. In The 11th International Conference on Learning Representations.
Kigali, Rwanda.
chicago: Seitzer, Maximilian, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun
Xiao, Carl-Johann Simon-Gabriel Carl-Johann Simon-Gabriel, Tong He, et al. “Bridging
the Gap to Real-World Object-Centric Learning.” In The 11th International Conference
on Learning Representations, 2023.
ieee: M. Seitzer et al., “Bridging the gap to real-world object-centric learning,”
in The 11th International Conference on Learning Representations, Kigali,
Rwanda, 2023.
ista: 'Seitzer M, Horn M, Zadaianchuk A, Zietlow D, Xiao T, Carl-Johann Simon-Gabriel
C-JS-G, He T, Zhang Z, Schölkopf B, Brox T, Locatello F. 2023. Bridging the gap
to real-world object-centric learning. The 11th International Conference on Learning
Representations. ICLR: International Conference on Learning Representations.'
mla: Seitzer, Maximilian, et al. “Bridging the Gap to Real-World Object-Centric
Learning.” The 11th International Conference on Learning Representations,
2023.
short: M. Seitzer, M. Horn, A. Zadaianchuk, D. Zietlow, T. Xiao, C.-J.S.-G. Carl-Johann
Simon-Gabriel, T. He, Z. Zhang, B. Schölkopf, T. Brox, F. Locatello, in:, The
11th International Conference on Learning Representations, 2023.
conference:
end_date: 2023-05-05
location: Kigali, Rwanda
name: 'ICLR: International Conference on Learning Representations'
start_date: 2023-05-01
date_created: 2023-08-22T14:22:41Z
date_published: 2023-05-10T00:00:00Z
date_updated: 2024-10-14T12:30:54Z
day: '10'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2209.14860'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2209.14860
month: '05'
oa: 1
oa_version: Preprint
publication: The 11th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: Bridging the gap to real-world object-centric learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14219'
abstract:
- lang: eng
text: "In this paper, we show that recent advances in self-supervised feature\r\nlearning
enable unsupervised object discovery and semantic segmentation with a\r\nperformance
that matches the state of the field on supervised semantic\r\nsegmentation 10
years ago. We propose a methodology based on unsupervised\r\nsaliency masks and
self-supervised feature clustering to kickstart object\r\ndiscovery followed by
training a semantic segmentation network on pseudo-labels\r\nto bootstrap the
system on images with multiple objects. We present results on\r\nPASCAL VOC that
go far beyond the current state of the art (50.0 mIoU), and we\r\nreport for the
first time results on MS COCO for the whole set of 81 classes:\r\nour method discovers
34 categories with more than $20\\%$ IoU, while obtaining\r\nan average IoU of
19.6 for all 81 categories."
article_processing_charge: No
arxiv: 1
author:
- first_name: Andrii
full_name: Zadaianchuk, Andrii
last_name: Zadaianchuk
- first_name: Matthaeus
full_name: Kleindessner, Matthaeus
last_name: Kleindessner
- first_name: Yi
full_name: Zhu, Yi
last_name: Zhu
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Thomas
full_name: Brox, Thomas
last_name: Brox
citation:
ama: 'Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. Unsupervised semantic
segmentation with self-supervised object-centric representations. In: The 11th
International Conference on Learning Representations. ; 2023.'
apa: Zadaianchuk, A., Kleindessner, M., Zhu, Y., Locatello, F., & Brox, T. (2023).
Unsupervised semantic segmentation with self-supervised object-centric representations.
In The 11th International Conference on Learning Representations. Kigali,
Rwanda.
chicago: Zadaianchuk, Andrii, Matthaeus Kleindessner, Yi Zhu, Francesco Locatello,
and Thomas Brox. “Unsupervised Semantic Segmentation with Self-Supervised Object-Centric
Representations.” In The 11th International Conference on Learning Representations,
2023.
ieee: A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, and T. Brox, “Unsupervised
semantic segmentation with self-supervised object-centric representations,” in
The 11th International Conference on Learning Representations, Kigali,
Rwanda, 2023.
ista: 'Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. 2023. Unsupervised
semantic segmentation with self-supervised object-centric representations. The
11th International Conference on Learning Representations. ICLR: International
Conference on Learning Representations.'
mla: Zadaianchuk, Andrii, et al. “Unsupervised Semantic Segmentation with Self-Supervised
Object-Centric Representations.” The 11th International Conference on Learning
Representations, 2023.
short: A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, T. Brox, in:, The
11th International Conference on Learning Representations, 2023.
conference:
end_date: 2023-05-05
location: Kigali, Rwanda
name: 'ICLR: International Conference on Learning Representations'
start_date: 2023-05-01
date_created: 2023-08-22T14:22:58Z
date_published: 2023-05-01T00:00:00Z
date_updated: 2023-09-13T11:25:43Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2207.05027'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2207.05027
month: '05'
oa: 1
oa_version: Preprint
publication: The 11th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: Unsupervised semantic segmentation with self-supervised object-centric representations
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14222'
abstract:
- lang: eng
text: Learning generative object models from unlabelled videos is a long standing
problem and required for causal scene modeling. We decompose this problem into
three easier subtasks, and provide candidate solutions for each of them. Inspired
by the Common Fate Principle of Gestalt Psychology, we first extract (noisy) masks
of moving objects via unsupervised motion segmentation. Second, generative models
are trained on the masks of the background and the moving objects, respectively.
Third, background and foreground models are combined in a conditional "dead leaves"
scene model to sample novel scene configurations where occlusions and depth layering
arise naturally. To evaluate the individual stages, we introduce the Fishbowl
dataset positioned between complex real-world scenes and common object-centric
benchmarks of simplistic objects. We show that our approach allows learning generative
models that generalize beyond the occlusions present in the input videos, and
represent scenes in a modular fashion that allows sampling plausible scenes outside
the training distribution by permitting, for instance, object numbers or densities
not observed in the training set.
article_number: '2110.06562'
article_processing_charge: No
arxiv: 1
author:
- first_name: Matthias
full_name: Tangemann, Matthias
last_name: Tangemann
- first_name: Steffen
full_name: Schneider, Steffen
last_name: Schneider
- first_name: Julius von
full_name: Kügelgen, Julius von
last_name: Kügelgen
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Peter
full_name: Gehler, Peter
last_name: Gehler
- first_name: Thomas
full_name: Brox, Thomas
last_name: Brox
- first_name: Matthias
full_name: Kümmerer, Matthias
last_name: Kümmerer
- first_name: Matthias
full_name: Bethge, Matthias
last_name: Bethge
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
citation:
ama: 'Tangemann M, Schneider S, Kügelgen J von, et al. Unsupervised object learning
via common fate. In: 2nd Conference on Causal Learning and Reasoning. ;
2023.'
apa: Tangemann, M., Schneider, S., Kügelgen, J. von, Locatello, F., Gehler, P.,
Brox, T., … Schölkopf, B. (2023). Unsupervised object learning via common fate.
In 2nd Conference on Causal Learning and Reasoning. Tübingen, Germany.
chicago: Tangemann, Matthias, Steffen Schneider, Julius von Kügelgen, Francesco
Locatello, Peter Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, and
Bernhard Schölkopf. “Unsupervised Object Learning via Common Fate.” In 2nd
Conference on Causal Learning and Reasoning, 2023.
ieee: M. Tangemann et al., “Unsupervised object learning via common fate,”
in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany,
2023.
ista: 'Tangemann M, Schneider S, Kügelgen J von, Locatello F, Gehler P, Brox T,
Kümmerer M, Bethge M, Schölkopf B. 2023. Unsupervised object learning via common
fate. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal
Learning and Reasoning, 2110.06562.'
mla: Tangemann, Matthias, et al. “Unsupervised Object Learning via Common Fate.”
2nd Conference on Causal Learning and Reasoning, 2110.06562, 2023.
short: M. Tangemann, S. Schneider, J. von Kügelgen, F. Locatello, P. Gehler, T.
Brox, M. Kümmerer, M. Bethge, B. Schölkopf, in:, 2nd Conference on Causal Learning
and Reasoning, 2023.
conference:
end_date: 2023-04-14
location: Tübingen, Germany
name: 'CLeaR: Conference on Causal Learning and Reasoning'
start_date: 2023-04-11
date_created: 2023-08-22T14:23:54Z
date_published: 2023-04-15T00:00:00Z
date_updated: 2023-09-13T11:31:14Z
day: '15'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2110.06562'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2110.06562
month: '04'
oa: 1
oa_version: Preprint
publication: 2nd Conference on Causal Learning and Reasoning
publication_status: published
quality_controlled: '1'
status: public
title: Unsupervised object learning via common fate
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14238'
abstract:
- lang: eng
text: We demonstrate that a sodium dimer, Na2(13Σ+u), residing on the surface of
a helium nanodroplet, can be set into rotation by a nonresonant 1.0 ps infrared
laser pulse. The time-dependent degree of alignment measured, exhibits a periodic,
gradually decreasing structure that deviates qualitatively from that expected
for gas-phase dimers. Comparison to alignment dynamics calculated from the time-dependent
rotational Schrödinger equation shows that the deviation is due to the alignment
dependent interaction between the dimer and the droplet surface. This interaction
confines the dimer to the tangential plane of the droplet surface at the point
where it resides and is the reason that the observed alignment dynamics is also
well described by a 2D quantum rotor model.
acknowledgement: H. S. acknowledges support from The Villum Foundation through a Villum
Investigator Grant No. 25886. M. L. acknowledges support by the European Research
Council (ERC) Starting Grant No. 801770 (ANGULON). F. J. and R. E. Z. acknowledge
support from the Centre for Scientific Computing, Aarhus and the JKU scientific
computing administration, Linz, respectively.
article_number: '053201'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Lorenz
full_name: Kranabetter, Lorenz
last_name: Kranabetter
- first_name: Henrik H.
full_name: Kristensen, Henrik H.
last_name: Kristensen
- first_name: Areg
full_name: Ghazaryan, Areg
id: 4AF46FD6-F248-11E8-B48F-1D18A9856A87
last_name: Ghazaryan
orcid: 0000-0001-9666-3543
- first_name: Constant A.
full_name: Schouder, Constant A.
last_name: Schouder
- first_name: Adam S.
full_name: Chatterley, Adam S.
last_name: Chatterley
- first_name: Paul
full_name: Janssen, Paul
last_name: Janssen
- first_name: Frank
full_name: Jensen, Frank
last_name: Jensen
- first_name: Robert E.
full_name: Zillich, Robert E.
last_name: Zillich
- first_name: Mikhail
full_name: Lemeshko, Mikhail
id: 37CB05FA-F248-11E8-B48F-1D18A9856A87
last_name: Lemeshko
orcid: 0000-0002-6990-7802
- first_name: Henrik
full_name: Stapelfeldt, Henrik
last_name: Stapelfeldt
citation:
ama: Kranabetter L, Kristensen HH, Ghazaryan A, et al. Nonadiabatic laser-induced
alignment dynamics of molecules on a surface. Physical Review Letters.
2023;131(5). doi:10.1103/PhysRevLett.131.053201
apa: Kranabetter, L., Kristensen, H. H., Ghazaryan, A., Schouder, C. A., Chatterley,
A. S., Janssen, P., … Stapelfeldt, H. (2023). Nonadiabatic laser-induced alignment
dynamics of molecules on a surface. Physical Review Letters. American Physical
Society. https://doi.org/10.1103/PhysRevLett.131.053201
chicago: Kranabetter, Lorenz, Henrik H. Kristensen, Areg Ghazaryan, Constant A.
Schouder, Adam S. Chatterley, Paul Janssen, Frank Jensen, Robert E. Zillich, Mikhail
Lemeshko, and Henrik Stapelfeldt. “Nonadiabatic Laser-Induced Alignment Dynamics
of Molecules on a Surface.” Physical Review Letters. American Physical
Society, 2023. https://doi.org/10.1103/PhysRevLett.131.053201.
ieee: L. Kranabetter et al., “Nonadiabatic laser-induced alignment dynamics
of molecules on a surface,” Physical Review Letters, vol. 131, no. 5. American
Physical Society, 2023.
ista: Kranabetter L, Kristensen HH, Ghazaryan A, Schouder CA, Chatterley AS, Janssen
P, Jensen F, Zillich RE, Lemeshko M, Stapelfeldt H. 2023. Nonadiabatic laser-induced
alignment dynamics of molecules on a surface. Physical Review Letters. 131(5),
053201.
mla: Kranabetter, Lorenz, et al. “Nonadiabatic Laser-Induced Alignment Dynamics
of Molecules on a Surface.” Physical Review Letters, vol. 131, no. 5, 053201,
American Physical Society, 2023, doi:10.1103/PhysRevLett.131.053201.
short: L. Kranabetter, H.H. Kristensen, A. Ghazaryan, C.A. Schouder, A.S. Chatterley,
P. Janssen, F. Jensen, R.E. Zillich, M. Lemeshko, H. Stapelfeldt, Physical Review
Letters 131 (2023).
date_created: 2023-08-27T22:01:16Z
date_published: 2023-08-04T00:00:00Z
date_updated: 2025-04-14T07:48:54Z
day: '04'
department:
- _id: MiLe
doi: 10.1103/PhysRevLett.131.053201
ec_funded: 1
external_id:
arxiv:
- '2308.15247'
isi:
- '001101784100001'
pmid:
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intvolume: ' 131'
isi: 1
issue: '5'
language:
- iso: eng
main_file_link:
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url: https://doi.org/10.48550/arXiv.2308.15247
month: '08'
oa: 1
oa_version: Preprint
pmid: 1
project:
- _id: 2688CF98-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '801770'
name: 'Angulon: physics and applications of a new quasiparticle'
publication: Physical Review Letters
publication_identifier:
eissn:
- 1079-7114
issn:
- 0031-9007
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Nonadiabatic laser-induced alignment dynamics of molecules on a surface
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 131
year: '2023'
...