---
_id: '12251'
abstract:
- lang: eng
text: Amyloid formation is linked to devastating neurodegenerative diseases, motivating
detailed studies of the mechanisms of amyloid formation. For Aβ, the peptide associated
with Alzheimer’s disease, the mechanism and rate of aggregation have been established
for a range of variants and conditions in vitro and
in bodily fluids. A key outstanding question is how the relative stabilities of
monomers, fibrils and intermediates affect each step in the fibril formation process.
By monitoring the kinetics of aggregation of Aβ42, in the presence of urea or
guanidinium hydrochloride (GuHCl), we here determine the rates of the underlying
microscopic steps and establish the importance of changes in relative stability
induced by the presence of denaturant for each individual step. Denaturants shift
the equilibrium towards the unfolded state of each species. We find that a non-ionic
denaturant, urea, reduces the overall aggregation rate, and that the effect on
nucleation is stronger than the effect on elongation. Urea reduces the rate of
secondary nucleation by decreasing the coverage of fibril surfaces and the rate
of nucleus formation. It also reduces the rate of primary nucleation, increasing
its reaction order. The ionic denaturant, GuHCl, accelerates the aggregation at
low denaturant concentrations and decelerates the aggregation at high denaturant
concentrations. Below approximately 0.25 M GuHCl, the screening of repulsive electrostatic
interactions between peptides by the charged denaturant dominates, leading to
an increased aggregation rate. At higher GuHCl concentrations, the electrostatic
repulsion is completely screened, and the denaturing effect dominates. The results
illustrate how the differential effects of denaturants on stability of monomer,
oligomer and fibril translate to differential effects on microscopic steps, with
the rate of nucleation being most strongly reduced.
acknowledgement: This work was supported by grants from the Swedish Research Council
(grant no. 2015-00143) and the European Research Council (grant no. 340890).
article_number: '943355'
article_processing_charge: No
article_type: original
author:
- first_name: Tanja
full_name: Weiffert, Tanja
last_name: Weiffert
- first_name: Georg
full_name: Meisl, Georg
last_name: Meisl
- first_name: Samo
full_name: Curk, Samo
last_name: Curk
- first_name: Risto
full_name: Cukalevski, Risto
last_name: Cukalevski
- first_name: Anđela
full_name: Šarić, Anđela
id: bf63d406-f056-11eb-b41d-f263a6566d8b
last_name: Šarić
orcid: 0000-0002-7854-2139
- first_name: Tuomas P. J.
full_name: Knowles, Tuomas P. J.
last_name: Knowles
- first_name: Sara
full_name: Linse, Sara
last_name: Linse
citation:
ama: Weiffert T, Meisl G, Curk S, et al. Influence of denaturants on amyloid β42
aggregation kinetics. Frontiers in Neuroscience. 2022;16. doi:10.3389/fnins.2022.943355
apa: Weiffert, T., Meisl, G., Curk, S., Cukalevski, R., Šarić, A., Knowles, T. P.
J., & Linse, S. (2022). Influence of denaturants on amyloid β42 aggregation
kinetics. Frontiers in Neuroscience. Frontiers Media. https://doi.org/10.3389/fnins.2022.943355
chicago: Weiffert, Tanja, Georg Meisl, Samo Curk, Risto Cukalevski, Anđela Šarić,
Tuomas P. J. Knowles, and Sara Linse. “Influence of Denaturants on Amyloid Β42
Aggregation Kinetics.” Frontiers in Neuroscience. Frontiers Media, 2022.
https://doi.org/10.3389/fnins.2022.943355.
ieee: T. Weiffert et al., “Influence of denaturants on amyloid β42 aggregation
kinetics,” Frontiers in Neuroscience, vol. 16. Frontiers Media, 2022.
ista: Weiffert T, Meisl G, Curk S, Cukalevski R, Šarić A, Knowles TPJ, Linse S.
2022. Influence of denaturants on amyloid β42 aggregation kinetics. Frontiers
in Neuroscience. 16, 943355.
mla: Weiffert, Tanja, et al. “Influence of Denaturants on Amyloid Β42 Aggregation
Kinetics.” Frontiers in Neuroscience, vol. 16, 943355, Frontiers Media,
2022, doi:10.3389/fnins.2022.943355.
short: T. Weiffert, G. Meisl, S. Curk, R. Cukalevski, A. Šarić, T.P.J. Knowles,
S. Linse, Frontiers in Neuroscience 16 (2022).
date_created: 2023-01-16T09:56:43Z
date_published: 2022-09-20T00:00:00Z
date_updated: 2023-08-04T09:48:56Z
day: '20'
ddc:
- '570'
department:
- _id: AnSa
doi: 10.3389/fnins.2022.943355
external_id:
isi:
- '000866287100001'
file:
- access_level: open_access
checksum: e67d16113ffb4fb4fa38a183d169f210
content_type: application/pdf
creator: dernst
date_created: 2023-01-30T09:15:13Z
date_updated: 2023-01-30T09:15:13Z
file_id: '12442'
file_name: 2022_FrontiersNeuroscience_Weiffert2.pdf
file_size: 19798610
relation: main_file
success: 1
file_date_updated: 2023-01-30T09:15:13Z
has_accepted_license: '1'
intvolume: ' 16'
isi: 1
keyword:
- General Neuroscience
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '09'
oa: 1
oa_version: Published Version
publication: Frontiers in Neuroscience
publication_identifier:
issn:
- 1662-453X
publication_status: published
publisher: Frontiers Media
quality_controlled: '1'
scopus_import: '1'
status: public
title: Influence of denaturants on amyloid β42 aggregation kinetics
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 16
year: '2022'
...
---
_id: '12252'
abstract:
- lang: eng
text: The COVID−19 pandemic not only resulted in a global crisis, but also accelerated
vaccine development and antibody discovery. Herein we report a synthetic humanized
VHH library development pipeline for nanomolar-range affinity VHH binders to SARS-CoV-2
variants of concern (VoC) receptor binding domains (RBD) isolation. Trinucleotide-based
randomization of CDRs by Kunkel mutagenesis with the subsequent rolling-cycle
amplification resulted in more than 1011 diverse phage display
library in a manageable for a single person number of electroporation reactions.
We identified a number of nanomolar-range affinity VHH binders to SARS-CoV-2 variants
of concern (VoC) receptor binding domains (RBD) by screening a novel synthetic
humanized antibody library. In order to explore the most robust and fast method
for affinity improvement, we performed affinity maturation by CDR1 and CDR2 shuffling
and avidity engineering by multivalent trimeric VHH fusion protein construction.
As a result, H7-Fc and G12x3-Fc binders were developed with the affinities in
nM and pM range respectively. Importantly, these affinities are weakly influenced
by most of SARS-CoV-2 VoC mutations and they retain moderate binding to BA.4\5.
The plaque reduction neutralization test (PRNT) resulted in IC50 = 100 ng\ml and
9.6 ng\ml for H7-Fc and G12x3-Fc antibodies, respectively, for the emerging Omicron
BA.1 variant. Therefore, these VHH could expand the present landscape of SARS-CoV-2
neutralization binders with the therapeutic potential for present and future SARS-CoV-2
variants.
acknowledgement: The authors declare that this study received funding from Immunofusion.
The funder was not involved in the study design, collection, analysis, interpretation
of data, the writing of this article or the decision to submit it for publication.
article_number: '965446'
article_processing_charge: No
article_type: original
author:
- first_name: Dmitri
full_name: Dormeshkin, Dmitri
last_name: Dormeshkin
- first_name: Michail
full_name: Shapira, Michail
last_name: Shapira
- first_name: Simon
full_name: Dubovik, Simon
last_name: Dubovik
- first_name: Anton
full_name: Kavaleuski, Anton
id: 4968f7ad-eb97-11eb-a6c2-8ed382e8912c
last_name: Kavaleuski
orcid: 0000-0003-2091-526X
- first_name: Mikalai
full_name: Katsin, Mikalai
last_name: Katsin
- first_name: Alexandr
full_name: Migas, Alexandr
last_name: Migas
- first_name: Alexander
full_name: Meleshko, Alexander
last_name: Meleshko
- first_name: Sergei
full_name: Semyonov, Sergei
last_name: Semyonov
citation:
ama: Dormeshkin D, Shapira M, Dubovik S, et al. Isolation of an escape-resistant
SARS-CoV-2 neutralizing nanobody from a novel synthetic nanobody library. Frontiers
in Immunology. 2022;13. doi:10.3389/fimmu.2022.965446
apa: Dormeshkin, D., Shapira, M., Dubovik, S., Kavaleuski, A., Katsin, M., Migas,
A., … Semyonov, S. (2022). Isolation of an escape-resistant SARS-CoV-2 neutralizing
nanobody from a novel synthetic nanobody library. Frontiers in Immunology.
Frontiers Media. https://doi.org/10.3389/fimmu.2022.965446
chicago: Dormeshkin, Dmitri, Michail Shapira, Simon Dubovik, Anton Kavaleuski, Mikalai
Katsin, Alexandr Migas, Alexander Meleshko, and Sergei Semyonov. “Isolation of
an Escape-Resistant SARS-CoV-2 Neutralizing Nanobody from a Novel Synthetic Nanobody
Library.” Frontiers in Immunology. Frontiers Media, 2022. https://doi.org/10.3389/fimmu.2022.965446.
ieee: D. Dormeshkin et al., “Isolation of an escape-resistant SARS-CoV-2
neutralizing nanobody from a novel synthetic nanobody library,” Frontiers in
Immunology, vol. 13. Frontiers Media, 2022.
ista: Dormeshkin D, Shapira M, Dubovik S, Kavaleuski A, Katsin M, Migas A, Meleshko
A, Semyonov S. 2022. Isolation of an escape-resistant SARS-CoV-2 neutralizing
nanobody from a novel synthetic nanobody library. Frontiers in Immunology. 13,
965446.
mla: Dormeshkin, Dmitri, et al. “Isolation of an Escape-Resistant SARS-CoV-2 Neutralizing
Nanobody from a Novel Synthetic Nanobody Library.” Frontiers in Immunology,
vol. 13, 965446, Frontiers Media, 2022, doi:10.3389/fimmu.2022.965446.
short: D. Dormeshkin, M. Shapira, S. Dubovik, A. Kavaleuski, M. Katsin, A. Migas,
A. Meleshko, S. Semyonov, Frontiers in Immunology 13 (2022).
date_created: 2023-01-16T09:56:57Z
date_published: 2022-09-16T00:00:00Z
date_updated: 2025-06-11T13:42:26Z
day: '16'
ddc:
- '570'
department:
- _id: LeSa
doi: 10.3389/fimmu.2022.965446
external_id:
isi:
- '000862479100001'
pmid:
- '36189235'
file:
- access_level: open_access
checksum: f8f5d8110710033d0532e7e08bf9dad4
content_type: application/pdf
creator: dernst
date_created: 2023-01-30T09:22:26Z
date_updated: 2023-01-30T09:22:26Z
file_id: '12443'
file_name: 2022_FrontiersImmunology_Dormeshkin.pdf
file_size: 5695892
relation: main_file
success: 1
file_date_updated: 2023-01-30T09:22:26Z
has_accepted_license: '1'
intvolume: ' 13'
isi: 1
keyword:
- Immunology
- Immunology and Allergy
- COVID-19
- SARS-CoV-2
- synthetic library
- RBD
- neutralization nanobody
- VHH
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
pmid: 1
publication: Frontiers in Immunology
publication_identifier:
issn:
- 1664-3224
publication_status: published
publisher: Frontiers Media
quality_controlled: '1'
scopus_import: '1'
status: public
title: Isolation of an escape-resistant SARS-CoV-2 neutralizing nanobody from a novel
synthetic nanobody library
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: 13
year: '2022'
...
---
_id: '12253'
abstract:
- lang: eng
text: The sculpting of germ layers during gastrulation relies on the coordinated
migration of progenitor cells, yet the cues controlling these long-range directed
movements remain largely unknown. While directional migration often relies on
a chemokine gradient generated from a localized source, we find that zebrafish
ventrolateral mesoderm is guided by a self-generated gradient of the initially
uniformly expressed and secreted protein Toddler/ELABELA/Apela. We show that the
Apelin receptor, which is specifically expressed in mesodermal cells, has a dual
role during gastrulation, acting as a scavenger receptor to generate a Toddler
gradient, and as a chemokine receptor to sense this guidance cue. Thus, we uncover
a single receptor–based self-generated gradient as the enigmatic guidance cue
that can robustly steer the directional migration of mesoderm through the complex
and continuously changing environment of the gastrulating embryo.
acknowledgement: 'We thank K. Aumayer and the team of the biooptics facility at the
Vienna Biocenter, particularly P. Pasierbek and T. Müller, for support with microscopy;
K. Panser, C. Pribitzer, and the animal facility personnel for taking care of zebrafish;
M. Binner and A. Bandura for help with genotyping; M. Codina Tobias for help with
establishing the conditions for the Toddler overexpression compensation experiment;
T. Lubiana Alves for sharing the code for scRNA-Seq analyses; the Heisenberg laboratory,
particularly D. Pinheiro, for joint laboratory meetings, discussions on the project,
and providing the tg(gsc:CAAX-GFP) fish line; the Raz laboratory for providing the
Lifeact-GFP plasmid; A. Andersen, A. Schier, C.-P. Heisenberg, and E. Tanaka for
comments on the manuscript; and the entire Pauli laboratory, particularly K. Gert
and V. Deneke, for valuable discussions and feedback on the manuscript. Funding:
Work in A.P.’s laboratory has been supported by the IMP, which receives institutional
funding from Boehringer Ingelheim and the Austrian Research Promotion Agency (Headquarter
grant FFG-852936), as well as the FWF START program (Y 1031-B28 to A.P.), the Human
Frontier Science Program (HFSP) Career Development Award (CDA00066/2015 to A.P.)
and Young Investigator Grant (RGY0079/2020 to A.P.), the SFB RNA-Deco (project number
F 80 to A.P.), a Whitman Center Fellowship from the Marine Biological Laboratory
(to A.P.), and EMBO-YIP funds (to A.P.). This work was supported by the European
Union (European Research Council Starting Grant 851288 to E.H.). For the purpose
of Open Access, the authors have applied a CC BY public copyright license to any
Author Accepted Manuscript (AAM) version arising from this submission.'
article_number: eadd2488
article_processing_charge: No
article_type: original
author:
- first_name: Jessica
full_name: Stock, Jessica
last_name: Stock
- first_name: Tomas
full_name: Kazmar, Tomas
last_name: Kazmar
- first_name: Friederike
full_name: Schlumm, Friederike
last_name: Schlumm
- first_name: Edouard B
full_name: Hannezo, Edouard B
id: 3A9DB764-F248-11E8-B48F-1D18A9856A87
last_name: Hannezo
orcid: 0000-0001-6005-1561
- first_name: Andrea
full_name: Pauli, Andrea
last_name: Pauli
citation:
ama: Stock J, Kazmar T, Schlumm F, Hannezo EB, Pauli A. A self-generated Toddler
gradient guides mesodermal cell migration. Science Advances. 2022;8(37).
doi:10.1126/sciadv.add2488
apa: Stock, J., Kazmar, T., Schlumm, F., Hannezo, E. B., & Pauli, A. (2022).
A self-generated Toddler gradient guides mesodermal cell migration. Science
Advances. American Association for the Advancement of Science. https://doi.org/10.1126/sciadv.add2488
chicago: Stock, Jessica, Tomas Kazmar, Friederike Schlumm, Edouard B Hannezo, and
Andrea Pauli. “A Self-Generated Toddler Gradient Guides Mesodermal Cell Migration.”
Science Advances. American Association for the Advancement of Science,
2022. https://doi.org/10.1126/sciadv.add2488.
ieee: J. Stock, T. Kazmar, F. Schlumm, E. B. Hannezo, and A. Pauli, “A self-generated
Toddler gradient guides mesodermal cell migration,” Science Advances, vol.
8, no. 37. American Association for the Advancement of Science, 2022.
ista: Stock J, Kazmar T, Schlumm F, Hannezo EB, Pauli A. 2022. A self-generated
Toddler gradient guides mesodermal cell migration. Science Advances. 8(37), eadd2488.
mla: Stock, Jessica, et al. “A Self-Generated Toddler Gradient Guides Mesodermal
Cell Migration.” Science Advances, vol. 8, no. 37, eadd2488, American Association
for the Advancement of Science, 2022, doi:10.1126/sciadv.add2488.
short: J. Stock, T. Kazmar, F. Schlumm, E.B. Hannezo, A. Pauli, Science Advances
8 (2022).
date_created: 2023-01-16T09:57:10Z
date_published: 2022-09-14T00:00:00Z
date_updated: 2025-04-14T07:52:27Z
day: '14'
ddc:
- '570'
department:
- _id: EdHa
doi: 10.1126/sciadv.add2488
ec_funded: 1
external_id:
isi:
- '000888875000009'
pmid:
- '36103529'
file:
- access_level: open_access
checksum: f59cdb824e5d4221045def81f46f6c65
content_type: application/pdf
creator: dernst
date_created: 2023-01-30T09:27:49Z
date_updated: 2023-01-30T09:27:49Z
file_id: '12444'
file_name: 2022_ScienceAdvances_Stock.pdf
file_size: 1636732
relation: main_file
success: 1
file_date_updated: 2023-01-30T09:27:49Z
has_accepted_license: '1'
intvolume: ' 8'
isi: 1
issue: '37'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 05943252-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '851288'
name: Design Principles of Branching Morphogenesis
publication: Science Advances
publication_identifier:
issn:
- 2375-2548
publication_status: published
publisher: American Association for the Advancement of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: A self-generated Toddler gradient guides mesodermal cell migration
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 8
year: '2022'
...
---
_id: '12259'
abstract:
- lang: eng
text: 'Theoretical foundations of chaos have been predominantly laid out for finite-dimensional
dynamical systems, such as the three-body problem in classical mechanics and the
Lorenz model in dissipative systems. In contrast, many real-world chaotic phenomena,
e.g., weather, arise in systems with many (formally infinite) degrees of freedom,
which limits direct quantitative analysis of such systems using chaos theory.
In the present work, we demonstrate that the hydrodynamic pilot-wave systems offer
a bridge between low- and high-dimensional chaotic phenomena by allowing for a
systematic study of how the former connects to the latter. Specifically, we present
experimental results, which show the formation of low-dimensional chaotic attractors
upon destabilization of regular dynamics and a final transition to high-dimensional
chaos via the merging of distinct chaotic regions through a crisis bifurcation.
Moreover, we show that the post-crisis dynamics of the system can be rationalized
as consecutive scatterings from the nonattracting chaotic sets with lifetimes
following exponential distributions. '
acknowledgement: 'This work was partially funded by the Institute of Science and Technology
Austria Interdisciplinary Project Committee Grant “Pilot-Wave Hydrodynamics: Chaos
and Quantum Analogies.”'
article_number: '093138'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: George H
full_name: Choueiri, George H
id: 448BD5BC-F248-11E8-B48F-1D18A9856A87
last_name: Choueiri
- first_name: Balachandra
full_name: Suri, Balachandra
id: 47A5E706-F248-11E8-B48F-1D18A9856A87
last_name: Suri
- first_name: Jack
full_name: Merrin, Jack
id: 4515C308-F248-11E8-B48F-1D18A9856A87
last_name: Merrin
orcid: 0000-0001-5145-4609
- first_name: Maksym
full_name: Serbyn, Maksym
id: 47809E7E-F248-11E8-B48F-1D18A9856A87
last_name: Serbyn
orcid: 0000-0002-2399-5827
- first_name: Björn
full_name: Hof, Björn
id: 3A374330-F248-11E8-B48F-1D18A9856A87
last_name: Hof
orcid: 0000-0003-2057-2754
- first_name: Nazmi B
full_name: Budanur, Nazmi B
id: 3EA1010E-F248-11E8-B48F-1D18A9856A87
last_name: Budanur
orcid: 0000-0003-0423-5010
citation:
ama: 'Choueiri GH, Suri B, Merrin J, Serbyn M, Hof B, Budanur NB. Crises and chaotic
scattering in hydrodynamic pilot-wave experiments. Chaos: An Interdisciplinary
Journal of Nonlinear Science. 2022;32(9). doi:10.1063/5.0102904'
apa: 'Choueiri, G. H., Suri, B., Merrin, J., Serbyn, M., Hof, B., & Budanur,
N. B. (2022). Crises and chaotic scattering in hydrodynamic pilot-wave experiments.
Chaos: An Interdisciplinary Journal of Nonlinear Science. AIP Publishing.
https://doi.org/10.1063/5.0102904'
chicago: 'Choueiri, George H, Balachandra Suri, Jack Merrin, Maksym Serbyn, Björn
Hof, and Nazmi B Budanur. “Crises and Chaotic Scattering in Hydrodynamic Pilot-Wave
Experiments.” Chaos: An Interdisciplinary Journal of Nonlinear Science.
AIP Publishing, 2022. https://doi.org/10.1063/5.0102904.'
ieee: 'G. H. Choueiri, B. Suri, J. Merrin, M. Serbyn, B. Hof, and N. B. Budanur,
“Crises and chaotic scattering in hydrodynamic pilot-wave experiments,” Chaos:
An Interdisciplinary Journal of Nonlinear Science, vol. 32, no. 9. AIP Publishing,
2022.'
ista: 'Choueiri GH, Suri B, Merrin J, Serbyn M, Hof B, Budanur NB. 2022. Crises
and chaotic scattering in hydrodynamic pilot-wave experiments. Chaos: An Interdisciplinary
Journal of Nonlinear Science. 32(9), 093138.'
mla: 'Choueiri, George H., et al. “Crises and Chaotic Scattering in Hydrodynamic
Pilot-Wave Experiments.” Chaos: An Interdisciplinary Journal of Nonlinear Science,
vol. 32, no. 9, 093138, AIP Publishing, 2022, doi:10.1063/5.0102904.'
short: 'G.H. Choueiri, B. Suri, J. Merrin, M. Serbyn, B. Hof, N.B. Budanur, Chaos:
An Interdisciplinary Journal of Nonlinear Science 32 (2022).'
date_created: 2023-01-16T09:58:16Z
date_published: 2022-09-26T00:00:00Z
date_updated: 2025-06-11T13:41:34Z
day: '26'
ddc:
- '530'
department:
- _id: MaSe
- _id: BjHo
- _id: NanoFab
doi: 10.1063/5.0102904
external_id:
arxiv:
- '2206.01531'
isi:
- '000861009600005'
pmid:
- '36182399'
file:
- access_level: open_access
checksum: 17881eff8b21969359a2dd64620120ba
content_type: application/pdf
creator: dernst
date_created: 2023-01-30T09:41:12Z
date_updated: 2023-01-30T09:41:12Z
file_id: '12445'
file_name: 2022_Chaos_Choueiri.pdf
file_size: 3209644
relation: main_file
success: 1
file_date_updated: 2023-01-30T09:41:12Z
has_accepted_license: '1'
intvolume: ' 32'
isi: 1
issue: '9'
keyword:
- Applied Mathematics
- General Physics and Astronomy
- Mathematical Physics
- Statistical and Nonlinear Physics
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
pmid: 1
publication: 'Chaos: An Interdisciplinary Journal of Nonlinear Science'
publication_identifier:
eissn:
- 1089-7682
issn:
- 1054-1500
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
scopus_import: '1'
status: public
title: Crises and chaotic scattering in hydrodynamic pilot-wave experiments
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: 32
year: '2022'
...
---
_id: '14093'
abstract:
- lang: eng
text: ' We propose a stochastic conditional gradient method (CGM) for minimizing
convex finite-sum objectives formed as a sum of smooth and non-smooth terms. Existing
CGM variants for this template either suffer from slow convergence rates, or require
carefully increasing the batch size over the course of the algorithm’s execution,
which leads to computing full gradients. In contrast, the proposed method, equipped
with a stochastic average gradient (SAG) estimator, requires only one sample per
iteration. Nevertheless, it guarantees fast convergence rates on par with more
sophisticated variance reduction techniques. In applications we put special emphasis
on problems with a large number of separable constraints. Such problems are prevalent
among semidefinite programming (SDP) formulations arising in machine learning
and theoretical computer science. We provide numerical experiments on matrix completion,
unsupervised clustering, and sparsest-cut SDPs. '
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Gideon
full_name: Dresdner, Gideon
last_name: Dresdner
- first_name: Maria-Luiza
full_name: Vladarean, Maria-Luiza
last_name: Vladarean
- first_name: Gunnar
full_name: Rätsch, Gunnar
last_name: Rätsch
- 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
- first_name: Alp
full_name: Yurtsever, Alp
last_name: Yurtsever
citation:
ama: 'Dresdner G, Vladarean M-L, Rätsch G, Locatello F, Cevher V, Yurtsever A. Faster
one-sample stochastic conditional gradient method for composite convex minimization.
In: Proceedings of the 25th International Conference on Artificial Intelligence
and Statistics. Vol 151. ML Research Press; 2022:8439-8457.'
apa: 'Dresdner, G., Vladarean, M.-L., Rätsch, G., Locatello, F., Cevher, V., &
Yurtsever, A. (2022). Faster one-sample stochastic conditional gradient method
for composite convex minimization. In Proceedings of the 25th International
Conference on Artificial Intelligence and Statistics (Vol. 151, pp. 8439–8457).
Virtual: ML Research Press.'
chicago: Dresdner, Gideon, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello,
Volkan Cevher, and Alp Yurtsever. “ Faster One-Sample Stochastic Conditional Gradient
Method for Composite Convex Minimization.” In Proceedings of the 25th International
Conference on Artificial Intelligence and Statistics, 151:8439–57. ML Research
Press, 2022.
ieee: G. Dresdner, M.-L. Vladarean, G. Rätsch, F. Locatello, V. Cevher, and A. Yurtsever,
“ Faster one-sample stochastic conditional gradient method for composite convex
minimization,” in Proceedings of the 25th International Conference on Artificial
Intelligence and Statistics, Virtual, 2022, vol. 151, pp. 8439–8457.
ista: 'Dresdner G, Vladarean M-L, Rätsch G, Locatello F, Cevher V, Yurtsever A.
2022. Faster one-sample stochastic conditional gradient method for composite
convex minimization. Proceedings of the 25th International Conference on Artificial
Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence and
Statistics, PMLR, vol. 151, 8439–8457.'
mla: Dresdner, Gideon, et al. “ Faster One-Sample Stochastic Conditional Gradient
Method for Composite Convex Minimization.” Proceedings of the 25th International
Conference on Artificial Intelligence and Statistics, vol. 151, ML Research
Press, 2022, pp. 8439–57.
short: G. Dresdner, M.-L. Vladarean, G. Rätsch, F. Locatello, V. Cevher, A. Yurtsever,
in:, Proceedings of the 25th International Conference on Artificial Intelligence
and Statistics, ML Research Press, 2022, pp. 8439–8457.
conference:
end_date: 2022-03-30
location: Virtual
name: 'AISTATS: Conference on Artificial Intelligence and Statistics'
start_date: 2022-03-28
date_created: 2023-08-21T09:27:43Z
date_published: 2022-04-01T00:00:00Z
date_updated: 2023-09-06T10:28:17Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2202.13212'
intvolume: ' 151'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2202.13212
month: '04'
oa: 1
oa_version: Preprint
page: 8439-8457
publication: Proceedings of the 25th International Conference on Artificial Intelligence
and Statistics
publication_identifier:
issn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: ' Faster one-sample stochastic conditional gradient method for composite convex
minimization'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 151
year: '2022'
...
---
_id: '14106'
abstract:
- lang: eng
text: "We show that deep networks trained to satisfy demographic parity often do
so\r\nthrough a form of race or gender awareness, and that the more we force a
network\r\nto be fair, the more accurately we can recover race or gender from
the internal state\r\nof the network. Based on this observation, we investigate
an alternative fairness\r\napproach: we add a second classification head to the
network to explicitly predict\r\nthe protected attribute (such as race or gender)
alongside the original task. After\r\ntraining the two-headed network, we enforce
demographic parity by merging the\r\ntwo heads, creating a network with the same
architecture as the original network.\r\nWe establish a close relationship between
existing approaches and our approach\r\nby showing (1) that the decisions of a
fair classifier are well-approximated by our\r\napproach, and (2) that an unfair
and optimally accurate classifier can be recovered\r\nfrom a fair classifier and
our second head predicting the protected attribute. We use\r\nour explicit formulation
to argue that the existing fairness approaches, just as ours,\r\ndemonstrate disparate
treatment and that they are likely to be unlawful in a wide\r\nrange of scenarios
under US law."
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Michael
full_name: Lohaus, Michael
last_name: Lohaus
- first_name: Matthäus
full_name: Kleindessner, Matthäus
last_name: Kleindessner
- first_name: Krishnaram
full_name: Kenthapadi, Krishnaram
last_name: Kenthapadi
- 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: 'Lohaus M, Kleindessner M, Kenthapadi K, Locatello F, Russell C. Are two heads
the same as one? Identifying disparate treatment in fair neural networks. In:
36th Conference on Neural Information Processing Systems. Vol 35. Neural
Information Processing Systems Foundation; 2022:16548-16562.'
apa: 'Lohaus, M., Kleindessner, M., Kenthapadi, K., Locatello, F., & Russell,
C. (2022). Are two heads the same as one? Identifying disparate treatment in fair
neural networks. In 36th Conference on Neural Information Processing Systems
(Vol. 35, pp. 16548–16562). New Orleans, LA, United States: Neural Information
Processing Systems Foundation.'
chicago: Lohaus, Michael, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco
Locatello, and Chris Russell. “Are Two Heads the Same as One? Identifying Disparate
Treatment in Fair Neural Networks.” In 36th Conference on Neural Information
Processing Systems, 35:16548–62. Neural Information Processing Systems Foundation,
2022.
ieee: M. Lohaus, M. Kleindessner, K. Kenthapadi, F. Locatello, and C. Russell, “Are
two heads the same as one? Identifying disparate treatment in fair neural networks,”
in 36th Conference on Neural Information Processing Systems, New Orleans,
LA, United States, 2022, vol. 35, pp. 16548–16562.
ista: 'Lohaus M, Kleindessner M, Kenthapadi K, Locatello F, Russell C. 2022. Are
two heads the same as one? Identifying disparate treatment in fair neural networks.
36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information
Processing Systems, Advances in Neural Information Processing Systems, vol. 35,
16548–16562.'
mla: Lohaus, Michael, et al. “Are Two Heads the Same as One? Identifying Disparate
Treatment in Fair Neural Networks.” 36th Conference on Neural Information Processing
Systems, vol. 35, Neural Information Processing Systems Foundation, 2022,
pp. 16548–62.
short: M. Lohaus, M. Kleindessner, K. Kenthapadi, F. Locatello, C. Russell, in:,
36th Conference on Neural Information Processing Systems, Neural Information Processing
Systems Foundation, 2022, pp. 16548–16562.
conference:
end_date: 2022-12-09
location: New Orleans, LA, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2022-11-28
date_created: 2023-08-21T12:12:42Z
date_published: 2022-12-15T00:00:00Z
date_updated: 2024-10-14T12:27:01Z
day: '15'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2204.04440'
intvolume: ' 35'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2204.04440
month: '12'
oa: 1
oa_version: Preprint
page: 16548-16562
publication: 36th Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713871088'
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: Are two heads the same as one? Identifying disparate treatment in fair neural
networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2022'
...
---
_id: '14107'
abstract:
- lang: eng
text: "Amodal perception requires inferring the full shape of an object that is
partially occluded. This task is particularly challenging on two levels: (1) it
requires more information than what is contained in the instant retina or imaging
sensor, (2) it is difficult to obtain enough well-annotated amodal labels for
supervision. To this end, this paper develops a new framework of\r\nSelf-supervised
amodal Video object segmentation (SaVos). Our method efficiently leverages the
visual information of video temporal sequences to infer the amodal mask of objects.
The key intuition is that the occluded part of an object can be explained away
if that part is visible in other frames, possibly deformed as long as the deformation
can be reasonably learned.\r\nAccordingly, we derive a novel self-supervised learning
paradigm that efficiently utilizes the visible object parts as the supervision
to guide the training on videos. In addition to learning type prior to complete
masks for known types, SaVos also learns the spatiotemporal prior, which is also
useful for the amodal task and could generalize to unseen types. The proposed\r\nframework
achieves the state-of-the-art performance on the synthetic amodal segmentation
benchmark FISHBOWL and the real world benchmark KINS-Video-Car. Further, it lends
itself well to being transferred to novel distributions using test-time adaptation,
outperforming existing models even after the transfer to a new distribution."
article_processing_charge: No
arxiv: 1
author:
- first_name: Jian
full_name: Yao, Jian
last_name: Yao
- first_name: Yuxin
full_name: Hong, Yuxin
last_name: Hong
- first_name: Chiyu
full_name: Wang, Chiyu
last_name: Wang
- first_name: Tianjun
full_name: Xiao, Tianjun
last_name: Xiao
- first_name: Tong
full_name: He, Tong
last_name: He
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: David
full_name: Wipf, David
last_name: Wipf
- first_name: Yanwei
full_name: Fu, Yanwei
last_name: Fu
- first_name: Zheng
full_name: Zhang, Zheng
last_name: Zhang
citation:
ama: 'Yao J, Hong Y, Wang C, et al. Self-supervised amodal video object segmentation.
In: 36th Conference on Neural Information Processing Systems. ; 2022. doi:10.48550/arXiv.2210.12733'
apa: Yao, J., Hong, Y., Wang, C., Xiao, T., He, T., Locatello, F., … Zhang, Z. (2022).
Self-supervised amodal video object segmentation. In 36th Conference on Neural
Information Processing Systems. New Orleans, LA, United States. https://doi.org/10.48550/arXiv.2210.12733
chicago: Yao, Jian, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello,
David Wipf, Yanwei Fu, and Zheng Zhang. “Self-Supervised Amodal Video Object Segmentation.”
In 36th Conference on Neural Information Processing Systems, 2022. https://doi.org/10.48550/arXiv.2210.12733.
ieee: J. Yao et al., “Self-supervised amodal video object segmentation,”
in 36th Conference on Neural Information Processing Systems, New Orleans,
LA, United States, 2022.
ista: 'Yao J, Hong Y, Wang C, Xiao T, He T, Locatello F, Wipf D, Fu Y, Zhang Z.
2022. Self-supervised amodal video object segmentation. 36th Conference on Neural
Information Processing Systems. NeurIPS: Neural Information Processing Systems.'
mla: Yao, Jian, et al. “Self-Supervised Amodal Video Object Segmentation.” 36th
Conference on Neural Information Processing Systems, 2022, doi:10.48550/arXiv.2210.12733.
short: J. Yao, Y. Hong, C. Wang, T. Xiao, T. He, F. Locatello, D. Wipf, Y. Fu, Z.
Zhang, in:, 36th Conference on Neural Information Processing Systems, 2022.
conference:
end_date: 2022-12-01
location: New Orleans, LA, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2022-11-28
date_created: 2023-08-21T12:13:25Z
date_published: 2022-10-23T00:00:00Z
date_updated: 2023-09-11T09:34:17Z
day: '23'
department:
- _id: FrLo
doi: 10.48550/arXiv.2210.12733
extern: '1'
external_id:
arxiv:
- '2210.12733'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2210.12733
month: '10'
oa: 1
oa_version: Preprint
publication: 36th Conference on Neural Information Processing Systems
publication_status: published
status: public
title: Self-supervised amodal video object segmentation
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '14114'
abstract:
- lang: eng
text: Algorithmic fairness is frequently motivated in terms of a trade-off in which
overall performance is decreased so as to improve performance on disadvantaged
groups where the algorithm would otherwise be less accurate. Contrary to this,
we find that applying existing fairness approaches to computer vision improve
fairness by degrading the performance of classifiers across all groups (with increased
degradation on the best performing groups). Extending the bias-variance decomposition
for classification to fairness, we theoretically explain why the majority of fairness
methods designed for low capacity models should not be used in settings involving
high-capacity models, a scenario common to computer vision. We corroborate this
analysis with extensive experimental support that shows that many of the fairness
heuristics used in computer vision also degrade performance on the most disadvantaged
groups. Building on these insights, we propose an adaptive augmentation strategy
that, uniquely, of all methods tested, improves performance for the disadvantaged
groups.
article_processing_charge: No
arxiv: 1
author:
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Michael
full_name: Lohaus, Michael
last_name: Lohaus
- first_name: Guha
full_name: Balakrishnan, Guha
last_name: Balakrishnan
- first_name: Matthaus
full_name: Kleindessner, Matthaus
last_name: Kleindessner
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Bernhard
full_name: Scholkopf, Bernhard
last_name: Scholkopf
- first_name: Chris
full_name: Russell, Chris
last_name: Russell
citation:
ama: 'Zietlow D, Lohaus M, Balakrishnan G, et al. Leveling down in computer vision:
Pareto inefficiencies in fair deep classifiers. In: 2022 IEEE/CVF Conference
on Computer Vision and Pattern Recognition. Institute of Electrical and Electronics
Engineers; 2022:10400-10411. doi:10.1109/cvpr52688.2022.01016'
apa: 'Zietlow, D., Lohaus, M., Balakrishnan, G., Kleindessner, M., Locatello, F.,
Scholkopf, B., & Russell, C. (2022). Leveling down in computer vision: Pareto
inefficiencies in fair deep classifiers. In 2022 IEEE/CVF Conference on Computer
Vision and Pattern Recognition (pp. 10400–10411). New Orleans, LA, United
States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01016'
chicago: 'Zietlow, Dominik, Michael Lohaus, Guha Balakrishnan, Matthaus Kleindessner,
Francesco Locatello, Bernhard Scholkopf, and Chris Russell. “Leveling down in
Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.” In 2022 IEEE/CVF
Conference on Computer Vision and Pattern Recognition, 10400–411. Institute
of Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/cvpr52688.2022.01016.'
ieee: 'D. Zietlow et al., “Leveling down in computer vision: Pareto inefficiencies
in fair deep classifiers,” in 2022 IEEE/CVF Conference on Computer Vision and
Pattern Recognition, New Orleans, LA, United States, 2022, pp. 10400–10411.'
ista: 'Zietlow D, Lohaus M, Balakrishnan G, Kleindessner M, Locatello F, Scholkopf
B, Russell C. 2022. Leveling down in computer vision: Pareto inefficiencies in
fair deep classifiers. 2022 IEEE/CVF Conference on Computer Vision and Pattern
Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 10400–10411.'
mla: 'Zietlow, Dominik, et al. “Leveling down in Computer Vision: Pareto Inefficiencies
in Fair Deep Classifiers.” 2022 IEEE/CVF Conference on Computer Vision and
Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022,
pp. 10400–11, doi:10.1109/cvpr52688.2022.01016.'
short: D. Zietlow, M. Lohaus, G. Balakrishnan, M. Kleindessner, F. Locatello, B.
Scholkopf, C. Russell, in:, 2022 IEEE/CVF Conference on Computer Vision and Pattern
Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–10411.
conference:
end_date: 2022-06-24
location: New Orleans, LA, United States
name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
start_date: 2022-06-18
date_created: 2023-08-21T12:18:00Z
date_published: 2022-07-01T00:00:00Z
date_updated: 2023-09-11T09:19:14Z
day: '01'
department:
- _id: FrLo
doi: 10.1109/cvpr52688.2022.01016
extern: '1'
external_id:
arxiv:
- '2203.04913'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2203.04913
month: '07'
oa: 1
oa_version: Preprint
page: 10400-10411
publication: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
eissn:
- 2575-7075
isbn:
- '9781665469470'
issn:
- 1063-6919
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '14168'
abstract:
- lang: eng
text: "Recent work has seen the development of general purpose neural architectures\r\nthat
can be trained to perform tasks across diverse data modalities. General\r\npurpose
models typically make few assumptions about the underlying\r\ndata-structure and
are known to perform well in the large-data regime. At the\r\nsame time, there
has been growing interest in modular neural architectures that\r\nrepresent the
data using sparsely interacting modules. These models can be more\r\nrobust out-of-distribution,
computationally efficient, and capable of\r\nsample-efficient adaptation to new
data. However, they tend to make\r\ndomain-specific assumptions about the data,
and present challenges in how\r\nmodule behavior (i.e., parameterization) and
connectivity (i.e., their layout)\r\ncan be jointly learned. In this work, we
introduce a general purpose, yet\r\nmodular neural architecture called Neural
Attentive Circuits (NACs) that\r\njointly learns the parameterization and a sparse
connectivity of neural modules\r\nwithout using domain knowledge. NACs are best
understood as the combination of\r\ntwo systems that are jointly trained end-to-end:
one that determines the module\r\nconfiguration and the other that executes it
on an input. We demonstrate\r\nqualitatively that NACs learn diverse and meaningful
module configurations on\r\nthe NLVR2 dataset without additional supervision.
Quantitatively, we show that\r\nby incorporating modularity in this way, NACs
improve upon a strong non-modular\r\nbaseline in terms of low-shot adaptation
on CIFAR and CUBs dataset by about\r\n10%, and OOD robustness on Tiny ImageNet-R
by about 2.5%. Further, we find that\r\nNACs can achieve an 8x speedup at inference
time while losing less than 3%\r\nperformance. Finally, we find NACs to yield
competitive results on diverse data\r\nmodalities spanning point-cloud classification,
symbolic processing and\r\ntext-classification from ASCII bytes, thereby confirming
its general purpose\r\nnature."
alternative_title:
- ' Advances in Neural Information Processing Systems'
article_processing_charge: No
arxiv: 1
author:
- first_name: Nasim
full_name: Rahaman, Nasim
last_name: Rahaman
- first_name: Martin
full_name: Weiss, Martin
last_name: Weiss
- 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: Pal, Chris
last_name: Pal
- first_name: Yoshua
full_name: Bengio, Yoshua
last_name: Bengio
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Li Erran
full_name: Li, Li Erran
last_name: Li
- first_name: Nicolas
full_name: Ballas, Nicolas
last_name: Ballas
citation:
ama: 'Rahaman N, Weiss M, Locatello F, et al. Neural attentive circuits. In: 36th
Conference on Neural Information Processing Systems. Vol 35. ; 2022.'
apa: Rahaman, N., Weiss, M., Locatello, F., Pal, C., Bengio, Y., Schölkopf, B.,
… Ballas, N. (2022). Neural attentive circuits. In 36th Conference on Neural
Information Processing Systems (Vol. 35). New Orleans, United States.
chicago: Rahaman, Nasim, Martin Weiss, Francesco Locatello, Chris Pal, Yoshua Bengio,
Bernhard Schölkopf, Li Erran Li, and Nicolas Ballas. “Neural Attentive Circuits.”
In 36th Conference on Neural Information Processing Systems, Vol. 35, 2022.
ieee: N. Rahaman et al., “Neural attentive circuits,” in 36th Conference
on Neural Information Processing Systems, New Orleans, United States, 2022,
vol. 35.
ista: 'Rahaman N, Weiss M, Locatello F, Pal C, Bengio Y, Schölkopf B, Li LE, Ballas
N. 2022. Neural attentive circuits. 36th Conference on Neural Information Processing
Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information
Processing Systems, vol. 35.'
mla: Rahaman, Nasim, et al. “Neural Attentive Circuits.” 36th Conference on Neural
Information Processing Systems, vol. 35, 2022.
short: N. Rahaman, M. Weiss, F. Locatello, C. Pal, Y. Bengio, B. Schölkopf, L.E.
Li, N. Ballas, in:, 36th Conference on Neural Information Processing Systems,
2022.
conference:
end_date: 2022-12-01
location: New Orleans, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2022-11-29
date_created: 2023-08-22T13:57:27Z
date_published: 2022-10-14T00:00:00Z
date_updated: 2023-09-11T09:29:09Z
day: '14'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2210.08031'
intvolume: ' 35'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2210.08031
month: '10'
oa: 1
oa_version: Preprint
publication: 36th Conference on Neural Information Processing Systems
publication_status: published
status: public
title: Neural attentive circuits
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2022'
...
---
_id: '14170'
abstract:
- lang: eng
text: "The idea behind object-centric representation learning is that natural scenes
can better be modeled as compositions of objects and their relations as opposed
to distributed representations. This inductive bias can be injected into neural
networks to potentially improve systematic generalization and performance of downstream
tasks in scenes with multiple objects. In this paper, we train state-of-the-art
unsupervised models on five common multi-object datasets and evaluate segmentation
metrics and downstream object property prediction. In addition, we study generalization
and robustness by investigating the settings where either a single object is out
of distribution -- e.g., having an unseen color, texture, or shape -- or global
properties of the scene are altered -- e.g., by occlusions, cropping, or increasing
the number of objects. From our experimental study, we find object-centric representations
to be useful for\r\ndownstream tasks and generally robust to most distribution
shifts affecting objects. However, when the distribution shift affects the input
in a less structured manner, robustness in terms of segmentation and downstream
task performance may vary significantly across models and distribution shifts. "
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Andrea
full_name: Dittadi, Andrea
last_name: Dittadi
- first_name: Samuele
full_name: Papa, Samuele
last_name: Papa
- first_name: Michele De
full_name: Vita, Michele De
last_name: Vita
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Ole
full_name: Winther, Ole
last_name: Winther
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Dittadi A, Papa S, Vita MD, Schölkopf B, Winther O, Locatello F. Generalization
and robustness implications in object-centric learning. In: Proceedings of
the 39th International Conference on Machine Learning. Vol 2022. ML Research
Press; :5221-5285.'
apa: 'Dittadi, A., Papa, S., Vita, M. D., Schölkopf, B., Winther, O., & Locatello,
F. (n.d.). Generalization and robustness implications in object-centric learning.
In Proceedings of the 39th International Conference on Machine Learning
(Vol. 2022, pp. 5221–5285). Baltimore, MD, United States: ML Research Press.'
chicago: Dittadi, Andrea, Samuele Papa, Michele De Vita, Bernhard Schölkopf, Ole
Winther, and Francesco Locatello. “Generalization and Robustness Implications
in Object-Centric Learning.” In Proceedings of the 39th International Conference
on Machine Learning, 2022:5221–85. ML Research Press, n.d.
ieee: A. Dittadi, S. Papa, M. D. Vita, B. Schölkopf, O. Winther, and F. Locatello,
“Generalization and robustness implications in object-centric learning,” in Proceedings
of the 39th International Conference on Machine Learning, Baltimore, MD, United
States, vol. 2022, pp. 5221–5285.
ista: Dittadi A, Papa S, Vita MD, Schölkopf B, Winther O, Locatello F. Generalization
and robustness implications in object-centric learning. Proceedings of the 39th
International Conference on Machine Learning. International Conference on Machine
Learning, PMLR, vol. 2022, 5221–5285.
mla: Dittadi, Andrea, et al. “Generalization and Robustness Implications in Object-Centric
Learning.” Proceedings of the 39th International Conference on Machine Learning,
vol. 2022, ML Research Press, pp. 5221–85.
short: A. Dittadi, S. Papa, M.D. Vita, B. Schölkopf, O. Winther, F. Locatello, in:,
Proceedings of the 39th International Conference on Machine Learning, ML Research
Press, n.d., pp. 5221–5285.
conference:
end_date: 2022-07-23
location: Baltimore, MD, United States
name: International Conference on Machine Learning
start_date: 2022-07-17
date_created: 2023-08-22T13:59:55Z
date_published: 2022-07-22T00:00:00Z
date_updated: 2023-09-11T10:08:14Z
day: '22'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2107.00637'
intvolume: ' 2022'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2107.00637
month: '07'
oa: 1
oa_version: Preprint
page: 5221-5285
publication: Proceedings of the 39th International Conference on Machine Learning
publication_status: submitted
publisher: ML Research Press
quality_controlled: '1'
status: public
title: Generalization and robustness implications in object-centric learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2022
year: '2022'
...
---
_id: '14171'
abstract:
- lang: eng
text: "This paper demonstrates how to recover causal graphs from the score of the\r\ndata
distribution in non-linear additive (Gaussian) noise models. Using score\r\nmatching
algorithms as a building block, we show how to design a new generation\r\nof scalable
causal discovery methods. To showcase our approach, we also propose\r\na new efficient
method for approximating the score's Jacobian, enabling to\r\nrecover the causal
graph. Empirically, we find that the new algorithm, called\r\nSCORE, is competitive
with state-of-the-art causal discovery methods while\r\nbeing significantly faster."
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Paul
full_name: Rolland, Paul
last_name: Rolland
- first_name: Volkan
full_name: Cevher, Volkan
last_name: Cevher
- first_name: Matthäus
full_name: Kleindessner, Matthäus
last_name: Kleindessner
- first_name: Chris
full_name: Russel, Chris
last_name: Russel
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Dominik
full_name: Janzing, Dominik
last_name: Janzing
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Rolland P, Cevher V, Kleindessner M, et al. Score matching enables causal
discovery of nonlinear additive noise models. In: Proceedings of the 39th
International Conference on Machine Learning. Vol 162. ML Research Press;
2022:18741-18753.'
apa: 'Rolland, P., Cevher, V., Kleindessner, M., Russel, C., Schölkopf, B., Janzing,
D., & Locatello, F. (2022). Score matching enables causal discovery of nonlinear
additive noise models. In Proceedings of the 39th International Conference
on Machine Learning (Vol. 162, pp. 18741–18753). Baltimore, MD, United States:
ML Research Press.'
chicago: Rolland, Paul, Volkan Cevher, Matthäus Kleindessner, Chris Russel, Bernhard
Schölkopf, Dominik Janzing, and Francesco Locatello. “Score Matching Enables Causal
Discovery of Nonlinear Additive Noise Models.” In Proceedings of the 39th
International Conference on Machine Learning, 162:18741–53. ML Research Press,
2022.
ieee: P. Rolland et al., “Score matching enables causal discovery of nonlinear
additive noise models,” in Proceedings of the 39th International Conference
on Machine Learning, Baltimore, MD, United States, 2022, vol. 162, pp. 18741–18753.
ista: Rolland P, Cevher V, Kleindessner M, Russel C, Schölkopf B, Janzing D, Locatello
F. 2022. Score matching enables causal discovery of nonlinear additive noise
models. Proceedings of the 39th International Conference on Machine Learning.
International Conference on Machine Learning, PMLR, vol. 162, 18741–18753.
mla: Rolland, Paul, et al. “Score Matching Enables Causal Discovery of Nonlinear
Additive Noise Models.” Proceedings of the 39th International Conference on
Machine Learning, vol. 162, ML Research Press, 2022, pp. 18741–53.
short: P. Rolland, V. Cevher, M. Kleindessner, C. Russel, B. Schölkopf, D. Janzing,
F. Locatello, in:, Proceedings of the 39th International Conference on Machine
Learning, ML Research Press, 2022, pp. 18741–18753.
conference:
end_date: 2022-07-23
location: Baltimore, MD, United States
name: International Conference on Machine Learning
start_date: 2022-07-17
date_created: 2023-08-22T14:00:18Z
date_published: 2022-07-22T00:00:00Z
date_updated: 2023-09-11T10:14:20Z
day: '22'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2203.04413'
intvolume: ' 162'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2203.04413
month: '07'
oa: 1
oa_version: Preprint
page: 18741-18753
publication: Proceedings of the 39th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
status: public
title: Score matching enables causal discovery of nonlinear additive noise models
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 162
year: '2022'
...
---
_id: '14172'
abstract:
- lang: eng
text: "An important component for generalization in machine learning is to uncover
underlying latent factors of variation as well as the mechanism through which
each factor acts in the world. In this paper, we test whether 17 unsupervised,
weakly supervised, and fully supervised representation learning approaches correctly
infer the generative factors of variation in simple datasets (dSprites, Shapes3D,
MPI3D) from controlled environments, and on our contributed CelebGlow dataset.
In contrast to prior robustness work that introduces novel factors of variation
during test time, such as blur or other (un)structured noise, we here recompose,
interpolate, or extrapolate only existing factors of variation from the training
data set (e.g., small and medium-sized objects during training and large objects
during testing). Models\r\nthat learn the correct mechanism should be able to
generalize to this benchmark. In total, we train and test 2000+ models and observe
that all of them struggle to learn the underlying mechanism regardless of supervision
signal and architectural bias. Moreover, the generalization capabilities of all
tested models drop significantly as we move from artificial datasets towards\r\nmore
realistic real-world datasets. Despite their inability to identify the correct
mechanism, the models are quite modular as their ability to infer other in-distribution
factors remains fairly stable, providing only a single factoris out-of-distribution.
These results point to an important yet understudied problem of learning mechanistic
models of observations that can facilitate\r\ngeneralization."
article_processing_charge: No
arxiv: 1
author:
- first_name: Lukas
full_name: Schott, Lukas
last_name: Schott
- first_name: Julius von
full_name: Kügelgen, Julius von
last_name: Kügelgen
- first_name: Frederik
full_name: Träuble, Frederik
last_name: Träuble
- first_name: Peter
full_name: Gehler, Peter
last_name: Gehler
- first_name: Chris
full_name: Russell, Chris
last_name: Russell
- first_name: Matthias
full_name: Bethge, Matthias
last_name: Bethge
- 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
- first_name: Wieland
full_name: Brendel, Wieland
last_name: Brendel
citation:
ama: 'Schott L, Kügelgen J von, Träuble F, et al. Visual representation learning
does not generalize strongly within the same domain. In: 10th International
Conference on Learning Representations. ; 2022.'
apa: Schott, L., Kügelgen, J. von, Träuble, F., Gehler, P., Russell, C., Bethge,
M., … Brendel, W. (2022). Visual representation learning does not generalize strongly
within the same domain. In 10th International Conference on Learning Representations.
Virtual.
chicago: Schott, Lukas, Julius von Kügelgen, Frederik Träuble, Peter Gehler, Chris
Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, and Wieland
Brendel. “Visual Representation Learning Does Not Generalize Strongly within the
Same Domain.” In 10th International Conference on Learning Representations,
2022.
ieee: L. Schott et al., “Visual representation learning does not generalize
strongly within the same domain,” in 10th International Conference on Learning
Representations, Virtual, 2022.
ista: 'Schott L, Kügelgen J von, Träuble F, Gehler P, Russell C, Bethge M, Schölkopf
B, Locatello F, Brendel W. 2022. Visual representation learning does not generalize
strongly within the same domain. 10th International Conference on Learning Representations.
ICLR: International Conference on Learning Representations.'
mla: Schott, Lukas, et al. “Visual Representation Learning Does Not Generalize Strongly
within the Same Domain.” 10th International Conference on Learning Representations,
2022.
short: L. Schott, J. von Kügelgen, F. Träuble, P. Gehler, C. Russell, M. Bethge,
B. Schölkopf, F. Locatello, W. Brendel, in:, 10th International Conference on
Learning Representations, 2022.
conference:
end_date: 2022-04-29
location: Virtual
name: 'ICLR: International Conference on Learning Representations'
start_date: 2022-04-25
date_created: 2023-08-22T14:00:50Z
date_published: 2022-04-25T00:00:00Z
date_updated: 2023-09-11T09:40:52Z
day: '25'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2107.08221'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2107.08221
month: '04'
oa: 1
oa_version: Preprint
publication: 10th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: Visual representation learning does not generalize strongly within the same
domain
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '14173'
abstract:
- lang: eng
text: "Since out-of-distribution generalization is a generally ill-posed problem,
various proxy targets (e.g., calibration, adversarial robustness, algorithmic
corruptions, invariance across shifts) were studied across different research
programs resulting in different recommendations. While sharing the same aspirational
goal, these approaches have never been tested under the same\r\nexperimental conditions
on real data. In this paper, we take a unified view of previous work, highlighting
message discrepancies that we address empirically, and providing recommendations
on how to measure the robustness of a model and how to improve it. To this end,
we collect 172 publicly available dataset pairs for training and out-of-distribution
evaluation of accuracy, calibration error, adversarial attacks, environment invariance,
and synthetic corruptions. We fine-tune over 31k networks, from nine different
architectures in the many- and\r\nfew-shot setting. Our findings confirm that
in- and out-of-distribution accuracies tend to increase jointly, but show that
their relation is largely dataset-dependent, and in general more nuanced and more
complex than posited by previous, smaller scale studies."
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Florian
full_name: Wenzel, Florian
last_name: Wenzel
- first_name: Andrea
full_name: Dittadi, Andrea
last_name: Dittadi
- first_name: Peter Vincent
full_name: Gehler, Peter Vincent
last_name: Gehler
- first_name: Carl-Johann Simon-Gabriel
full_name: Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel
last_name: Carl-Johann Simon-Gabriel
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: David
full_name: Kernert, David
last_name: Kernert
- first_name: Chris
full_name: Russell, Chris
last_name: Russell
- first_name: Thomas
full_name: Brox, Thomas
last_name: Brox
- first_name: Bernt
full_name: Schiele, Bernt
last_name: Schiele
- 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: 'Wenzel F, Dittadi A, Gehler PV, et al. Assaying out-of-distribution generalization
in transfer learning. In: 36th Conference on Neural Information Processing
Systems. Vol 35. Neural Information Processing Systems Foundation; 2022:7181-7198.'
apa: 'Wenzel, F., Dittadi, A., Gehler, P. V., Carl-Johann Simon-Gabriel, C.-J. S.-G.,
Horn, M., Zietlow, D., … Locatello, F. (2022). Assaying out-of-distribution generalization
in transfer learning. In 36th Conference on Neural Information Processing Systems
(Vol. 35, pp. 7181–7198). New Orleans, LA, United States: Neural Information Processing
Systems Foundation.'
chicago: Wenzel, Florian, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel
Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, et al. “Assaying
Out-of-Distribution Generalization in Transfer Learning.” In 36th Conference
on Neural Information Processing Systems, 35:7181–98. Neural Information Processing
Systems Foundation, 2022.
ieee: F. Wenzel et al., “Assaying out-of-distribution generalization in transfer
learning,” in 36th Conference on Neural Information Processing Systems,
New Orleans, LA, United States, 2022, vol. 35, pp. 7181–7198.
ista: 'Wenzel F, Dittadi A, Gehler PV, Carl-Johann Simon-Gabriel C-JS-G, Horn M,
Zietlow D, Kernert D, Russell C, Brox T, Schiele B, Schölkopf B, Locatello F.
2022. Assaying out-of-distribution generalization in transfer learning. 36th Conference
on Neural Information Processing Systems. NeurIPS: Neural Information Processing
Systems, Advances in Neural Information Processing Systems, vol. 35, 7181–7198.'
mla: Wenzel, Florian, et al. “Assaying Out-of-Distribution Generalization in Transfer
Learning.” 36th Conference on Neural Information Processing Systems, vol.
35, Neural Information Processing Systems Foundation, 2022, pp. 7181–98.
short: F. Wenzel, A. Dittadi, P.V. Gehler, C.-J.S.-G. Carl-Johann Simon-Gabriel,
M. Horn, D. Zietlow, D. Kernert, C. Russell, T. Brox, B. Schiele, B. Schölkopf,
F. Locatello, in:, 36th Conference on Neural Information Processing Systems, Neural
Information Processing Systems Foundation, 2022, pp. 7181–7198.
conference:
end_date: 2022-12-09
location: New Orleans, LA, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2022-11-28
date_created: 2023-08-22T14:01:13Z
date_published: 2022-12-15T00:00:00Z
date_updated: 2023-09-06T10:34:43Z
day: '15'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2207.09239'
intvolume: ' 35'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2207.09239
month: '12'
oa: 1
oa_version: Preprint
page: 7181-7198
publication: 36th Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713871088'
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: Assaying out-of-distribution generalization in transfer learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2022'
...
---
_id: '14174'
abstract:
- lang: eng
text: "Building sample-efficient agents that generalize out-of-distribution (OOD)
in real-world settings remains a fundamental unsolved problem on the path towards
achieving higher-level cognition. One particularly promising approach is to begin
with low-dimensional, pretrained representations of our world, which should facilitate
efficient downstream learning and generalization. By training 240 representations
and over 10,000 reinforcement learning (RL) policies on a simulated robotic setup,
we evaluate to what extent different properties of\r\npretrained VAE-based representations
affect the OOD generalization of downstream agents. We observe that many agents
are surprisingly robust to realistic distribution shifts, including the challenging
sim-to-real case. In addition, we find that the generalization performance of
a simple downstream proxy task reliably predicts the generalization performance
of our RL agents\r\nunder a wide range of OOD settings. Such proxy tasks can thus
be used to select pretrained representations that will lead to agents that generalize."
article_processing_charge: No
arxiv: 1
author:
- first_name: Andrea
full_name: Dittadi, Andrea
last_name: Dittadi
- first_name: Frederik
full_name: Träuble, Frederik
last_name: Träuble
- first_name: Manuel
full_name: Wüthrich, Manuel
last_name: Wüthrich
- first_name: Felix
full_name: Widmaier, Felix
last_name: Widmaier
- first_name: Peter
full_name: Gehler, Peter
last_name: Gehler
- first_name: Ole
full_name: Winther, Ole
last_name: Winther
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Olivier
full_name: Bachem, Olivier
last_name: Bachem
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Stefan
full_name: Bauer, Stefan
last_name: Bauer
citation:
ama: 'Dittadi A, Träuble F, Wüthrich M, et al. The role of pretrained representations
for the OOD generalization of reinforcement learning agents. In: 10th International
Conference on Learning Representations. ; 2022.'
apa: Dittadi, A., Träuble, F., Wüthrich, M., Widmaier, F., Gehler, P., Winther,
O., … Bauer, S. (2022). The role of pretrained representations for the OOD generalization
of reinforcement learning agents. In 10th International Conference on Learning
Representations. Virtual.
chicago: Dittadi, Andrea, Frederik Träuble, Manuel Wüthrich, Felix Widmaier, Peter
Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf,
and Stefan Bauer. “The Role of Pretrained Representations for the OOD Generalization
of Reinforcement Learning Agents.” In 10th International Conference on Learning
Representations, 2022.
ieee: A. Dittadi et al., “The role of pretrained representations for the
OOD generalization of reinforcement learning agents,” in 10th International
Conference on Learning Representations, Virtual, 2022.
ista: 'Dittadi A, Träuble F, Wüthrich M, Widmaier F, Gehler P, Winther O, Locatello
F, Bachem O, Schölkopf B, Bauer S. 2022. The role of pretrained representations
for the OOD generalization of reinforcement learning agents. 10th International
Conference on Learning Representations. ICLR: International Conference on Learning
Representations.'
mla: Dittadi, Andrea, et al. “The Role of Pretrained Representations for the OOD
Generalization of Reinforcement Learning Agents.” 10th International Conference
on Learning Representations, 2022.
short: A. Dittadi, F. Träuble, M. Wüthrich, F. Widmaier, P. Gehler, O. Winther,
F. Locatello, O. Bachem, B. Schölkopf, S. Bauer, in:, 10th International Conference
on Learning Representations, 2022.
conference:
end_date: 2022-04-29
location: Virtual
name: 'ICLR: International Conference on Learning Representations'
start_date: 2022-04-25
date_created: 2023-08-22T14:02:13Z
date_published: 2022-04-25T00:00:00Z
date_updated: 2023-09-11T09:48:36Z
day: '25'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2107.05686'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2107.05686'
month: '04'
oa: 1
oa_version: Preprint
publication: 10th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: The role of pretrained representations for the OOD generalization of reinforcement
learning agents
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '14175'
abstract:
- lang: eng
text: "Predicting the future trajectory of a moving agent can be easy when the past
trajectory continues smoothly but is challenging when complex interactions with
other agents are involved. Recent deep learning approaches for trajectory prediction
show promising performance and partially attribute this to successful reasoning
about agent-agent interactions. However, it remains unclear which features such
black-box models actually learn to use for making predictions. This paper proposes
a procedure that quantifies the contributions\r\nof different cues to model performance
based on a variant of Shapley values. Applying this procedure to state-of-the-art
trajectory prediction methods on standard benchmark datasets shows that they are,
in fact, unable to reason about interactions. Instead, the past trajectory of
the target is the only feature used for predicting its future. For a task with
richer social\r\ninteraction patterns, on the other hand, the tested models do
pick up such interactions to a certain extent, as quantified by our feature attribution
method. We discuss the limits of the proposed method and its links to causality."
article_processing_charge: No
arxiv: 1
author:
- first_name: Osama
full_name: Makansi, Osama
last_name: Makansi
- 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: Dominik
full_name: Janzing, Dominik
last_name: Janzing
- first_name: Thomas
full_name: Brox, Thomas
last_name: Brox
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
citation:
ama: 'Makansi O, Kügelgen J von, Locatello F, et al. You mostly walk alone: Analyzing
feature attribution in trajectory prediction. In: 10th International Conference
on Learning Representations. ; 2022.'
apa: 'Makansi, O., Kügelgen, J. von, Locatello, F., Gehler, P., Janzing, D., Brox,
T., & Schölkopf, B. (2022). You mostly walk alone: Analyzing feature attribution
in trajectory prediction. In 10th International Conference on Learning Representations.
Virtual.'
chicago: 'Makansi, Osama, Julius von Kügelgen, Francesco Locatello, Peter Gehler,
Dominik Janzing, Thomas Brox, and Bernhard Schölkopf. “You Mostly Walk Alone:
Analyzing Feature Attribution in Trajectory Prediction.” In 10th International
Conference on Learning Representations, 2022.'
ieee: 'O. Makansi et al., “You mostly walk alone: Analyzing feature attribution
in trajectory prediction,” in 10th International Conference on Learning Representations,
Virtual, 2022.'
ista: 'Makansi O, Kügelgen J von, Locatello F, Gehler P, Janzing D, Brox T, Schölkopf
B. 2022. You mostly walk alone: Analyzing feature attribution in trajectory prediction.
10th International Conference on Learning Representations. ICLR: International
Conference on Learning Representations.'
mla: 'Makansi, Osama, et al. “You Mostly Walk Alone: Analyzing Feature Attribution
in Trajectory Prediction.” 10th International Conference on Learning Representations,
2022.'
short: O. Makansi, J. von Kügelgen, F. Locatello, P. Gehler, D. Janzing, T. Brox,
B. Schölkopf, in:, 10th International Conference on Learning Representations,
2022.
conference:
end_date: 2022-04-29
location: Virtual
name: 'ICLR: International Conference on Learning Representations'
start_date: 2022-04-25
date_created: 2023-08-22T14:02:34Z
date_published: 2022-04-25T00:00:00Z
date_updated: 2023-09-11T09:52:20Z
day: '25'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2110.05304'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2110.05304
month: '04'
oa: 1
oa_version: Preprint
publication: 10th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: 'You mostly walk alone: Analyzing feature attribution in trajectory prediction'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '14215'
abstract:
- lang: eng
text: Geospatial Information Systems are used by researchers and Humanitarian Assistance
and Disaster Response (HADR) practitioners to support a wide variety of important
applications. However, collaboration between these actors is difficult due to
the heterogeneous nature of geospatial data modalities (e.g., multi-spectral images
of various resolutions, timeseries, weather data) and diversity of tasks (e.g.,
regression of human activity indicators or detecting forest fires). In this work,
we present a roadmap towards the construction of a general-purpose neural architecture
(GPNA) with a geospatial inductive bias, pre-trained on large amounts of unlabelled
earth observation data in a self-supervised manner. We envision how such a model
may facilitate cooperation between members of the community. We show preliminary
results on the first step of the roadmap, where we instantiate an architecture
that can process a wide variety of geospatial data modalities and demonstrate
that it can achieve competitive performance with domain-specific architectures
on tasks relating to the U.N.'s Sustainable Development Goals.
article_processing_charge: No
arxiv: 1
author:
- first_name: Nasim
full_name: Rahaman, Nasim
last_name: Rahaman
- first_name: Martin
full_name: Weiss, Martin
last_name: Weiss
- first_name: Frederik
full_name: Träuble, Frederik
last_name: Träuble
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Alexandre
full_name: Lacoste, Alexandre
last_name: Lacoste
- first_name: Yoshua
full_name: Bengio, Yoshua
last_name: Bengio
- first_name: Chris
full_name: Pal, Chris
last_name: Pal
- first_name: Li Erran
full_name: Li, Li Erran
last_name: Li
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
citation:
ama: 'Rahaman N, Weiss M, Träuble F, et al. A general purpose neural architecture
for geospatial systems. In: 36th Conference on Neural Information Processing
Systems.'
apa: Rahaman, N., Weiss, M., Träuble, F., Locatello, F., Lacoste, A., Bengio, Y.,
… Schölkopf, B. (n.d.). A general purpose neural architecture for geospatial systems.
In 36th Conference on Neural Information Processing Systems. New Orleans,
LA, United States.
chicago: Rahaman, Nasim, Martin Weiss, Frederik Träuble, Francesco Locatello, Alexandre
Lacoste, Yoshua Bengio, Chris Pal, Li Erran Li, and Bernhard Schölkopf. “A General
Purpose Neural Architecture for Geospatial Systems.” In 36th Conference on
Neural Information Processing Systems, n.d.
ieee: N. Rahaman et al., “A general purpose neural architecture for geospatial
systems,” in 36th Conference on Neural Information Processing Systems,
New Orleans, LA, United States.
ista: 'Rahaman N, Weiss M, Träuble F, Locatello F, Lacoste A, Bengio Y, Pal C, Li
LE, Schölkopf B. A general purpose neural architecture for geospatial systems.
36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information
Processing Systems.'
mla: Rahaman, Nasim, et al. “A General Purpose Neural Architecture for Geospatial
Systems.” 36th Conference on Neural Information Processing Systems.
short: N. Rahaman, M. Weiss, F. Träuble, F. Locatello, A. Lacoste, Y. Bengio, C.
Pal, L.E. Li, B. Schölkopf, in:, 36th Conference on Neural Information Processing
Systems, n.d.
conference:
end_date: 2022-12-09
location: New Orleans, LA, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2022-11-28
date_created: 2023-08-22T14:21:47Z
date_published: 2022-11-04T00:00:00Z
date_updated: 2023-09-13T09:35:59Z
day: '04'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2211.02348'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2211.02348
month: '11'
oa: 1
oa_version: Preprint
publication: 36th Conference on Neural Information Processing Systems
publication_status: submitted
quality_controlled: '1'
status: public
title: A general purpose neural architecture for geospatial systems
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '14220'
abstract:
- lang: eng
text: Although reinforcement learning has seen remarkable progress over the last
years, solving robust dexterous object-manipulation tasks in multi-object settings
remains a challenge. In this paper, we focus on models that can learn manipulation
tasks in fixed multi-object settings and extrapolate this skill zero-shot without
any drop in performance when the number of objects changes. We consider the generic
task of bringing a specific cube out of a set to a goal position. We find that
previous approaches, which primarily leverage attention and graph neural network-based
architectures, do not generalize their skills when the number of input objects
changes while scaling as K2. We propose an alternative plug-and-play module based
on relational inductive biases to overcome these limitations. Besides exceeding
performances in their training environment, we show that our approach, which scales
linearly in K, allows agents to extrapolate and generalize zero-shot to any new
object number.
article_number: '2201.13388'
article_processing_charge: No
arxiv: 1
author:
- first_name: Davide
full_name: Mambelli, Davide
last_name: Mambelli
- first_name: Frederik
full_name: Träuble, Frederik
last_name: Träuble
- first_name: Stefan
full_name: Bauer, Stefan
last_name: Bauer
- 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: Mambelli D, Träuble F, Bauer S, Schölkopf B, Locatello F. Compositional multi-object
reinforcement learning with linear relation networks. arXiv. doi:10.48550/arXiv.2201.13388
apa: Mambelli, D., Träuble, F., Bauer, S., Schölkopf, B., & Locatello, F. (n.d.).
Compositional multi-object reinforcement learning with linear relation networks.
arXiv. https://doi.org/10.48550/arXiv.2201.13388
chicago: Mambelli, Davide, Frederik Träuble, Stefan Bauer, Bernhard Schölkopf, and
Francesco Locatello. “Compositional Multi-Object Reinforcement Learning with Linear
Relation Networks.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2201.13388.
ieee: D. Mambelli, F. Träuble, S. Bauer, B. Schölkopf, and F. Locatello, “Compositional
multi-object reinforcement learning with linear relation networks,” arXiv.
.
ista: Mambelli D, Träuble F, Bauer S, Schölkopf B, Locatello F. Compositional multi-object
reinforcement learning with linear relation networks. arXiv, 2201.13388.
mla: Mambelli, Davide, et al. “Compositional Multi-Object Reinforcement Learning
with Linear Relation Networks.” ArXiv, 2201.13388, doi:10.48550/arXiv.2201.13388.
short: D. Mambelli, F. Träuble, S. Bauer, B. Schölkopf, F. Locatello, ArXiv (n.d.).
date_created: 2023-08-22T14:23:16Z
date_published: 2022-01-31T00:00:00Z
date_updated: 2024-10-14T12:27:39Z
day: '31'
department:
- _id: FrLo
doi: 10.48550/arXiv.2201.13388
extern: '1'
external_id:
arxiv:
- '2201.13388'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2201.13388
month: '01'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Compositional multi-object reinforcement learning with linear relation networks
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '14355'
abstract:
- lang: eng
text: 'Purpose: The mediator (MED) multisubunit-complex modulates the activity of
the transcriptional machinery, and genetic defects in different MED subunits (17,
20, 27) have been implicated in neurologic diseases. In this study, we identified
a recurrent homozygous variant in MED11 (c.325C>T; p.Arg109Ter) in 7 affected
individuals from 5 unrelated families. Methods: To investigate the genetic cause
of the disease, exome or genome sequencing were performed in 5 unrelated families
identified via different research networks and Matchmaker Exchange. Deep clinical
and brain imaging evaluations were performed by clinical pediatric neurologists
and neuroradiologists. The functional effect of the candidate variant on both
MED11 RNA and protein was assessed using reverse transcriptase polymerase chain
reaction and western blotting using fibroblast cell lines derived from 1 affected
individual and controls and through computational approaches. Knockouts in zebrafish
were generated using clustered regularly interspaced short palindromic repeats/Cas9.
Results: The disease was characterized by microcephaly, profound neurodevelopmental
impairment, exaggerated startle response, myoclonic seizures, progressive widespread
neurodegeneration, and premature death. Functional studies on patient-derived
fibroblasts did not show a loss of protein function but rather disruption of the
C-terminal of MED11, likely impairing binding to other MED subunits. A zebrafish
knockout model recapitulates key clinical phenotypes. Conclusion: Loss of the
C-terminal of MED subunit 11 may affect its binding efficiency to other MED subunits,
thus implicating the MED-complex stability in brain development and neurodegeneration.
(C) 2022 The Authors. Published by Elsevier Inc. on behalf of American College
of Medical Genetics and Genomics.'
article_processing_charge: No
article_type: original
author:
- first_name: Elisa
full_name: Cali, Elisa
last_name: Cali
- first_name: Sheng-Jia
full_name: Lin, Sheng-Jia
last_name: Lin
- first_name: Clarissa
full_name: Rocca, Clarissa
last_name: Rocca
- first_name: Yavuz
full_name: Sahin, Yavuz
last_name: Sahin
- first_name: Aisha
full_name: Al Shamsi, Aisha
last_name: Al Shamsi
- first_name: Salima
full_name: El Chehadeh, Salima
last_name: El Chehadeh
- first_name: Myriam
full_name: Chaabouni, Myriam
last_name: Chaabouni
- first_name: Kshitij
full_name: Mankad, Kshitij
last_name: Mankad
- first_name: Evangelia
full_name: Galanaki, Evangelia
last_name: Galanaki
- first_name: Stephanie
full_name: Efthymiou, Stephanie
last_name: Efthymiou
- first_name: Sniya
full_name: Sudhakar, Sniya
last_name: Sudhakar
- first_name: Alkyoni
full_name: Athanasiou-Fragkouli, Alkyoni
last_name: Athanasiou-Fragkouli
- first_name: Tamer
full_name: Celik, Tamer
last_name: Celik
- first_name: Nejat
full_name: Narli, Nejat
last_name: Narli
- first_name: Sebastiano
full_name: Bianca, Sebastiano
last_name: Bianca
- first_name: David
full_name: Murphy, David
last_name: Murphy
- first_name: Francisco Martins De Carvalho
full_name: Moreira, Francisco Martins De Carvalho
last_name: Moreira
- first_name: Andrea
full_name: Accogli, Andrea
last_name: Accogli
- first_name: Cassidy
full_name: Petree, Cassidy
last_name: Petree
- first_name: Kevin
full_name: Huang, Kevin
id: 3b3d2888-1ff6-11ee-9fa6-8f209ca91fe3
last_name: Huang
orcid: 0000-0002-2512-7812
- first_name: Kamel
full_name: Monastiri, Kamel
last_name: Monastiri
- first_name: Masoud
full_name: Edizadeh, Masoud
last_name: Edizadeh
- first_name: Rosaria
full_name: Nardello, Rosaria
last_name: Nardello
- first_name: Marzia
full_name: Ognibene, Marzia
last_name: Ognibene
- first_name: Patrizia
full_name: De Marco, Patrizia
last_name: De Marco
- first_name: Martino
full_name: Ruggieri, Martino
last_name: Ruggieri
- first_name: Federico
full_name: Zara, Federico
last_name: Zara
- first_name: Pasquale
full_name: Striano, Pasquale
last_name: Striano
- first_name: Yavuz
full_name: Sahin, Yavuz
last_name: Sahin
- first_name: Lihadh
full_name: Al-Gazali, Lihadh
last_name: Al-Gazali
- first_name: Marie Therese Abi
full_name: Warde, Marie Therese Abi
last_name: Warde
- first_name: Benedicte
full_name: Gerard, Benedicte
last_name: Gerard
- first_name: Giovanni
full_name: Zifarelli, Giovanni
last_name: Zifarelli
- first_name: Christian
full_name: Beetz, Christian
last_name: Beetz
- first_name: Sara
full_name: Fortuna, Sara
last_name: Fortuna
- first_name: Miguel
full_name: Soler, Miguel
last_name: Soler
- first_name: Enza Maria
full_name: Valente, Enza Maria
last_name: Valente
- first_name: Gaurav
full_name: Varshney, Gaurav
last_name: Varshney
- first_name: Reza
full_name: Maroofian, Reza
last_name: Maroofian
- first_name: Vincenzo
full_name: Salpietro, Vincenzo
last_name: Salpietro
- first_name: Henry
full_name: Houlden, Henry
last_name: Houlden
- first_name: SYNaPS Study
full_name: Grp, SYNaPS Study
last_name: Grp
citation:
ama: Cali E, Lin S-J, Rocca C, et al. A homozygous MED11 C-terminal variant causes
a lethal neurodegenerative disease. Genetics in Medicine. 2022;24(10):2194-2203.
doi:10.1016/j.gim.2022.07.013
apa: Cali, E., Lin, S.-J., Rocca, C., Sahin, Y., Al Shamsi, A., El Chehadeh, S.,
… Grp, Syn. S. (2022). A homozygous MED11 C-terminal variant causes a lethal neurodegenerative
disease. Genetics in Medicine. Elsevier. https://doi.org/10.1016/j.gim.2022.07.013
chicago: Cali, Elisa, Sheng-Jia Lin, Clarissa Rocca, Yavuz Sahin, Aisha Al Shamsi,
Salima El Chehadeh, Myriam Chaabouni, et al. “A Homozygous MED11 C-Terminal Variant
Causes a Lethal Neurodegenerative Disease.” Genetics in Medicine. Elsevier,
2022. https://doi.org/10.1016/j.gim.2022.07.013.
ieee: E. Cali et al., “A homozygous MED11 C-terminal variant causes a lethal
neurodegenerative disease,” Genetics in Medicine, vol. 24, no. 10. Elsevier,
pp. 2194–2203, 2022.
ista: Cali E, Lin S-J, Rocca C, Sahin Y, Al Shamsi A, El Chehadeh S, Chaabouni M,
Mankad K, Galanaki E, Efthymiou S, Sudhakar S, Athanasiou-Fragkouli A, Celik T,
Narli N, Bianca S, Murphy D, Moreira FMDC, Accogli A, Petree C, Huang K, Monastiri
K, Edizadeh M, Nardello R, Ognibene M, De Marco P, Ruggieri M, Zara F, Striano
P, Sahin Y, Al-Gazali L, Warde MTA, Gerard B, Zifarelli G, Beetz C, Fortuna S,
Soler M, Valente EM, Varshney G, Maroofian R, Salpietro V, Houlden H, Grp SynS.
2022. A homozygous MED11 C-terminal variant causes a lethal neurodegenerative
disease. Genetics in Medicine. 24(10), 2194–2203.
mla: Cali, Elisa, et al. “A Homozygous MED11 C-Terminal Variant Causes a Lethal
Neurodegenerative Disease.” Genetics in Medicine, vol. 24, no. 10, Elsevier,
2022, pp. 2194–203, doi:10.1016/j.gim.2022.07.013.
short: E. Cali, S.-J. Lin, C. Rocca, Y. Sahin, A. Al Shamsi, S. El Chehadeh, M.
Chaabouni, K. Mankad, E. Galanaki, S. Efthymiou, S. Sudhakar, A. Athanasiou-Fragkouli,
T. Celik, N. Narli, S. Bianca, D. Murphy, F.M.D.C. Moreira, A. Accogli, C. Petree,
K. Huang, K. Monastiri, M. Edizadeh, R. Nardello, M. Ognibene, P. De Marco, M.
Ruggieri, F. Zara, P. Striano, Y. Sahin, L. Al-Gazali, M.T.A. Warde, B. Gerard,
G. Zifarelli, C. Beetz, S. Fortuna, M. Soler, E.M. Valente, G. Varshney, R. Maroofian,
V. Salpietro, H. Houlden, Syn.S. Grp, Genetics in Medicine 24 (2022) 2194–2203.
date_created: 2023-09-20T20:57:18Z
date_published: 2022-10-01T00:00:00Z
date_updated: 2023-09-25T08:57:07Z
day: '01'
ddc:
- '570'
department:
- _id: GradSch
doi: 10.1016/j.gim.2022.07.013
extern: '1'
file:
- access_level: open_access
checksum: 8117175a89129eb5022d81ffe7625f9f
content_type: application/pdf
creator: dernst
date_created: 2023-09-25T08:56:06Z
date_updated: 2023-09-25T08:56:06Z
file_id: '14371'
file_name: 2022_GeneticsMedicine_Calin.pdf
file_size: 1434037
relation: main_file
success: 1
file_date_updated: 2023-09-25T08:56:06Z
has_accepted_license: '1'
intvolume: ' 24'
issue: '10'
keyword:
- Human mediator complex
- MED11
- MEDopathies
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: 2194-2203
publication: Genetics in Medicine
publication_identifier:
issn:
- 1098-3600
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: A homozygous MED11 C-terminal variant causes a lethal neurodegenerative disease
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: 24
year: '2022'
...
---
_id: '14381'
abstract:
- lang: eng
text: Expander graphs (sparse but highly connected graphs) have, since their inception,
been the source of deep links between Mathematics and Computer Science as well
as applications to other areas. In recent years, a fascinating theory of high-dimensional
expanders has begun to emerge, which is still in a formative stage but has nonetheless
already lead to a number of striking results. Unlike for graphs, in higher dimensions
there is a rich array of non-equivalent notions of expansion (coboundary expansion,
cosystolic expansion, topological expansion, spectral expansion, etc.), with differents
strengths and applications. In this talk, we will survey this landscape of high-dimensional
expansion, with a focus on two main results. First, we will present Gromov’s Topological
Overlap Theorem, which asserts that coboundary expansion (a quantitative version
of vanishing mod 2 cohomology) implies topological expansion (roughly, the property
that for every map from a simplicial complex to a manifold of the same dimension,
the images of a positive fraction of the simplices have a point in common). Second,
we will outline a construction of bounded degree 2-dimensional topological expanders,
due to Kaufman, Kazhdan, and Lubotzky.
article_processing_charge: No
article_type: original
author:
- first_name: Uli
full_name: Wagner, Uli
id: 36690CA2-F248-11E8-B48F-1D18A9856A87
last_name: Wagner
orcid: 0000-0002-1494-0568
citation:
ama: Wagner U. High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky,
and others). Bulletin de la Societe Mathematique de France. 2022;438:281-294.
doi:10.24033/ast.1188
apa: Wagner, U. (2022). High-dimensional expanders (after Gromov, Kaufman, Kazhdan,
Lubotzky, and others). Bulletin de La Societe Mathematique de France. Societe
Mathematique de France. https://doi.org/10.24033/ast.1188
chicago: Wagner, Uli. “High-Dimensional Expanders (after Gromov, Kaufman, Kazhdan,
Lubotzky, and Others).” Bulletin de La Societe Mathematique de France.
Societe Mathematique de France, 2022. https://doi.org/10.24033/ast.1188.
ieee: U. Wagner, “High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky,
and others),” Bulletin de la Societe Mathematique de France, vol. 438.
Societe Mathematique de France, pp. 281–294, 2022.
ista: Wagner U. 2022. High-dimensional expanders (after Gromov, Kaufman, Kazhdan,
Lubotzky, and others). Bulletin de la Societe Mathematique de France. 438, 281–294.
mla: Wagner, Uli. “High-Dimensional Expanders (after Gromov, Kaufman, Kazhdan, Lubotzky,
and Others).” Bulletin de La Societe Mathematique de France, vol. 438,
Societe Mathematique de France, 2022, pp. 281–94, doi:10.24033/ast.1188.
short: U. Wagner, Bulletin de La Societe Mathematique de France 438 (2022) 281–294.
corr_author: '1'
date_created: 2023-10-01T22:01:14Z
date_published: 2022-01-01T00:00:00Z
date_updated: 2025-09-10T09:55:10Z
day: '01'
department:
- _id: UlWa
doi: 10.24033/ast.1188
external_id:
isi:
- '000958364400007'
intvolume: ' 438'
isi: 1
language:
- iso: eng
month: '01'
oa_version: None
page: 281-294
publication: Bulletin de la Societe Mathematique de France
publication_identifier:
eissn:
- 2102-622X
issn:
- 0037-9484
publication_status: published
publisher: Societe Mathematique de France
quality_controlled: '1'
scopus_import: '1'
status: public
title: High-dimensional expanders (after Gromov, Kaufman, Kazhdan, Lubotzky, and others)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 438
year: '2022'
...
---
_id: '14437'
abstract:
- lang: eng
text: Future LEDs could be based on lead halide perovskites. A breakthrough in preparing
device-compatible solids composed of nanoscale perovskite crystals overcomes a
long-standing hurdle in making blue perovskite LEDs.
article_processing_charge: No
article_type: letter_note
author:
- first_name: Hendrik
full_name: Utzat, Hendrik
last_name: Utzat
- first_name: Maria
full_name: Ibáñez, Maria
id: 43C61214-F248-11E8-B48F-1D18A9856A87
last_name: Ibáñez
orcid: 0000-0001-5013-2843
citation:
ama: Utzat H, Ibáñez M. Molecular engineering enables bright blue LEDs. Nature.
2022;612(7941):638-639. doi:10.1038/d41586-022-04447-0
apa: Utzat, H., & Ibáñez, M. (2022). Molecular engineering enables bright blue
LEDs. Nature. Springer Nature. https://doi.org/10.1038/d41586-022-04447-0
chicago: Utzat, Hendrik, and Maria Ibáñez. “Molecular Engineering Enables Bright
Blue LEDs.” Nature. Springer Nature, 2022. https://doi.org/10.1038/d41586-022-04447-0.
ieee: H. Utzat and M. Ibáñez, “Molecular engineering enables bright blue LEDs,”
Nature, vol. 612, no. 7941. Springer Nature, pp. 638–639, 2022.
ista: Utzat H, Ibáñez M. 2022. Molecular engineering enables bright blue LEDs. Nature.
612(7941), 638–639.
mla: Utzat, Hendrik, and Maria Ibáñez. “Molecular Engineering Enables Bright Blue
LEDs.” Nature, vol. 612, no. 7941, Springer Nature, 2022, pp. 638–39, doi:10.1038/d41586-022-04447-0.
short: H. Utzat, M. Ibáñez, Nature 612 (2022) 638–639.
corr_author: '1'
date_created: 2023-10-17T11:14:43Z
date_published: 2022-12-21T00:00:00Z
date_updated: 2025-09-10T09:55:51Z
day: '21'
department:
- _id: MaIb
doi: 10.1038/d41586-022-04447-0
external_id:
isi:
- '000934065100010'
pmid:
- '36543947'
intvolume: ' 612'
isi: 1
issue: '7941'
keyword:
- Multidisciplinary
language:
- iso: eng
month: '12'
oa_version: None
page: 638-639
pmid: 1
publication: Nature
publication_identifier:
eissn:
- 1476-4687
issn:
- 0028-0836
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Molecular engineering enables bright blue LEDs
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 612
year: '2022'
...