---
_id: '14241'
abstract:
- lang: eng
text: We present a technique to optimize the reflectivity of a surface while preserving
its overall shape. The naïve optimization of the mesh vertices using the gradients
of reflectivity simulations results in undesirable distortion. In contrast, our
robust formulation optimizes the surface normal as an independent variable that
bridges the reflectivity term with differential rendering, and the regularization
term with as-rigid-as-possible elastic energy. We further adaptively subdivide
the input mesh to improve the convergence. Consequently, our method can minimize
the retroreflectivity of a wide range of input shapes, resulting in sharply creased
shapes ubiquitous among stealth aircraft and Sci-Fi vehicles. Furthermore, by
changing the reward for the direction of the outgoing light directions, our method
can be applied to other reflectivity design tasks, such as the optimization of
architectural walls to concentrate light in a specific region. We have tested
the proposed method using light-transport simulations and real-world 3D-printed
objects.
acknowledgement: "The authors would like to thank Yuki Koyama and Takeo Igarashi for
early discussions, and Yuta Yaguchi for support in 3D printing. This research is
partially supported by the Israel Science Foundation grant number 1390/19.\r\n"
article_number: '20'
article_processing_charge: No
author:
- first_name: Kenji
full_name: Tojo, Kenji
last_name: Tojo
- first_name: Ariel
full_name: Shamir, Ariel
last_name: Shamir
- first_name: Bernd
full_name: Bickel, Bernd
id: 49876194-F248-11E8-B48F-1D18A9856A87
last_name: Bickel
orcid: 0000-0001-6511-9385
- first_name: Nobuyuki
full_name: Umetani, Nobuyuki
last_name: Umetani
citation:
ama: 'Tojo K, Shamir A, Bickel B, Umetani N. Stealth shaper: Reflectivity optimization
as surface stylization. In: SIGGRAPH 2023 Conference Proceedings. Association
for Computing Machinery; 2023. doi:10.1145/3588432.3591542'
apa: 'Tojo, K., Shamir, A., Bickel, B., & Umetani, N. (2023). Stealth shaper:
Reflectivity optimization as surface stylization. In SIGGRAPH 2023 Conference
Proceedings. Los Angeles, CA, United States: Association for Computing Machinery.
https://doi.org/10.1145/3588432.3591542'
chicago: 'Tojo, Kenji, Ariel Shamir, Bernd Bickel, and Nobuyuki Umetani. “Stealth
Shaper: Reflectivity Optimization as Surface Stylization.” In SIGGRAPH 2023
Conference Proceedings. Association for Computing Machinery, 2023. https://doi.org/10.1145/3588432.3591542.'
ieee: 'K. Tojo, A. Shamir, B. Bickel, and N. Umetani, “Stealth shaper: Reflectivity
optimization as surface stylization,” in SIGGRAPH 2023 Conference Proceedings,
Los Angeles, CA, United States, 2023.'
ista: 'Tojo K, Shamir A, Bickel B, Umetani N. 2023. Stealth shaper: Reflectivity
optimization as surface stylization. SIGGRAPH 2023 Conference Proceedings. SIGGRAPH:
Computer Graphics and Interactive Techniques Conference, 20.'
mla: 'Tojo, Kenji, et al. “Stealth Shaper: Reflectivity Optimization as Surface
Stylization.” SIGGRAPH 2023 Conference Proceedings, 20, Association for
Computing Machinery, 2023, doi:10.1145/3588432.3591542.'
short: K. Tojo, A. Shamir, B. Bickel, N. Umetani, in:, SIGGRAPH 2023 Conference
Proceedings, Association for Computing Machinery, 2023.
conference:
end_date: 2023-08-10
location: Los Angeles, CA, United States
name: 'SIGGRAPH: Computer Graphics and Interactive Techniques Conference'
start_date: 2023-08-06
date_created: 2023-08-27T22:01:17Z
date_published: 2023-07-23T00:00:00Z
date_updated: 2023-09-05T07:22:03Z
day: '23'
department:
- _id: BeBi
doi: 10.1145/3588432.3591542
external_id:
arxiv:
- '2305.05944'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2305.05944
month: '07'
oa: 1
oa_version: Preprint
publication: SIGGRAPH 2023 Conference Proceedings
publication_identifier:
isbn:
- '9798400701597'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Stealth shaper: Reflectivity optimization as surface stylization'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '12562'
abstract:
- lang: eng
text: Presynaptic inputs determine the pattern of activation of postsynaptic neurons
in a neural circuit. Molecular and genetic pathways that regulate the selective
formation of subsets of presynaptic inputs are largely unknown, despite significant
understanding of the general process of synaptogenesis. In this study, we have
begun to identify such factors using the spinal monosynaptic stretch reflex circuit
as a model system. In this neuronal circuit, Ia proprioceptive afferents establish
monosynaptic connections with spinal motor neurons that project to the same muscle
(termed homonymous connections) or muscles with related or synergistic function.
However, monosynaptic connections are not formed with motor neurons innervating
muscles with antagonistic functions. The ETS transcription factor ER81 (also known
as ETV1) is expressed by all proprioceptive afferents, but only a small set of
motor neuron pools in the lumbar spinal cord of the mouse. Here we use conditional
mouse genetic techniques to eliminate Er81 expression selectively from motor neurons.
We find that ablation of Er81 in motor neurons reduces synaptic inputs from proprioceptive
afferents conveying information from homonymous and synergistic muscles, with
no change observed in the connectivity pattern from antagonistic proprioceptive
afferents. In summary, these findings suggest a role for ER81 in defined motor
neuron pools to control the assembly of specific presynaptic inputs and thereby
influence the profile of activation of these motor neurons.
acknowledgement: The authors gratefully thank Dr. Silvia Arber, University of Basel
and Friedrich Miescher Institute for Biomedical Research, for support and in whose
lab the data were collected. For advice on statistical analysis, we thank Michael
Bottomley from the Statistical Consulting Center, College of Science and Mathematics,
Wright State University.
article_processing_charge: No
article_type: original
author:
- first_name: David R.
full_name: Ladle, David R.
last_name: Ladle
- first_name: Simon
full_name: Hippenmeyer, Simon
id: 37B36620-F248-11E8-B48F-1D18A9856A87
last_name: Hippenmeyer
orcid: 0000-0003-2279-1061
citation:
ama: Ladle DR, Hippenmeyer S. Loss of ETV1/ER81 in motor neurons leads to reduced
monosynaptic inputs from proprioceptive sensory neurons. Journal of Neurophysiology.
2023;129(3):501-512. doi:10.1152/jn.00172.2022
apa: Ladle, D. R., & Hippenmeyer, S. (2023). Loss of ETV1/ER81 in motor neurons
leads to reduced monosynaptic inputs from proprioceptive sensory neurons. Journal
of Neurophysiology. American Physiological Society. https://doi.org/10.1152/jn.00172.2022
chicago: Ladle, David R., and Simon Hippenmeyer. “Loss of ETV1/ER81 in Motor Neurons
Leads to Reduced Monosynaptic Inputs from Proprioceptive Sensory Neurons.” Journal
of Neurophysiology. American Physiological Society, 2023. https://doi.org/10.1152/jn.00172.2022.
ieee: D. R. Ladle and S. Hippenmeyer, “Loss of ETV1/ER81 in motor neurons leads
to reduced monosynaptic inputs from proprioceptive sensory neurons,” Journal
of Neurophysiology, vol. 129, no. 3. American Physiological Society, pp. 501–512,
2023.
ista: Ladle DR, Hippenmeyer S. 2023. Loss of ETV1/ER81 in motor neurons leads to
reduced monosynaptic inputs from proprioceptive sensory neurons. Journal of Neurophysiology.
129(3), 501–512.
mla: Ladle, David R., and Simon Hippenmeyer. “Loss of ETV1/ER81 in Motor Neurons
Leads to Reduced Monosynaptic Inputs from Proprioceptive Sensory Neurons.” Journal
of Neurophysiology, vol. 129, no. 3, American Physiological Society, 2023,
pp. 501–12, doi:10.1152/jn.00172.2022.
short: D.R. Ladle, S. Hippenmeyer, Journal of Neurophysiology 129 (2023) 501–512.
date_created: 2023-02-15T14:46:14Z
date_published: 2023-03-01T00:00:00Z
date_updated: 2023-09-05T12:13:34Z
day: '01'
department:
- _id: SiHi
doi: 10.1152/jn.00172.2022
external_id:
isi:
- '000957721600001'
pmid:
- '36695533'
intvolume: ' 129'
isi: 1
issue: '3'
keyword:
- Physiology
- General Neuroscience
language:
- iso: eng
month: '03'
oa_version: None
page: 501-512
pmid: 1
publication: Journal of Neurophysiology
publication_identifier:
eissn:
- 1522-1598
issn:
- 0022-3077
publication_status: published
publisher: American Physiological Society
quality_controlled: '1'
status: public
title: Loss of ETV1/ER81 in motor neurons leads to reduced monosynaptic inputs from
proprioceptive sensory neurons
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 129
year: '2023'
...
---
_id: '13310'
abstract:
- lang: eng
text: Machine-learned systems are in widespread use for making decisions about humans,
and it is important that they are fair, i.e., not biased against individuals based
on sensitive attributes. We present runtime verification of algorithmic fairness
for systems whose models are unknown, but are assumed to have a Markov chain structure.
We introduce a specification language that can model many common algorithmic fairness
properties, such as demographic parity, equal opportunity, and social burden.
We build monitors that observe a long sequence of events as generated by a given
system, and output, after each observation, a quantitative estimate of how fair
or biased the system was on that run until that point in time. The estimate is
proven to be correct modulo a variable error bound and a given confidence level,
where the error bound gets tighter as the observed sequence gets longer. Our monitors
are of two types, and use, respectively, frequentist and Bayesian statistical
inference techniques. While the frequentist monitors compute estimates that are
objectively correct with respect to the ground truth, the Bayesian monitors compute
estimates that are correct subject to a given prior belief about the system’s
model. Using a prototype implementation, we show how we can monitor if a bank
is fair in giving loans to applicants from different social backgrounds, and if
a college is fair in admitting students while maintaining a reasonable financial
burden on the society. Although they exhibit different theoretical complexities
in certain cases, in our experiments, both frequentist and Bayesian monitors took
less than a millisecond to update their verdicts after each observation.
acknowledgement: 'This work is supported by the European Research Council under Grant
No.: ERC-2020-AdG101020093.'
alternative_title:
- LNCS
article_processing_charge: Yes (in subscription journal)
author:
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: Mahyar
full_name: Karimi, Mahyar
id: f1dedef5-2f78-11ee-989a-c4c97bccf506
last_name: Karimi
orcid: 0009-0005-0820-1696
- first_name: Konstantin
full_name: Kueffner, Konstantin
id: 8121a2d0-dc85-11ea-9058-af578f3b4515
last_name: Kueffner
orcid: 0000-0001-8974-2542
- first_name: Kaushik
full_name: Mallik, Kaushik
id: 0834ff3c-6d72-11ec-94e0-b5b0a4fb8598
last_name: Mallik
orcid: 0000-0001-9864-7475
citation:
ama: 'Henzinger TA, Karimi M, Kueffner K, Mallik K. Monitoring algorithmic fairness.
In: Computer Aided Verification. Vol 13965. Springer Nature; 2023:358–382.
doi:10.1007/978-3-031-37703-7_17'
apa: 'Henzinger, T. A., Karimi, M., Kueffner, K., & Mallik, K. (2023). Monitoring
algorithmic fairness. In Computer Aided Verification (Vol. 13965, pp. 358–382).
Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-37703-7_17'
chicago: Henzinger, Thomas A, Mahyar Karimi, Konstantin Kueffner, and Kaushik Mallik.
“Monitoring Algorithmic Fairness.” In Computer Aided Verification, 13965:358–382.
Springer Nature, 2023. https://doi.org/10.1007/978-3-031-37703-7_17.
ieee: T. A. Henzinger, M. Karimi, K. Kueffner, and K. Mallik, “Monitoring algorithmic
fairness,” in Computer Aided Verification, Paris, France, 2023, vol. 13965,
pp. 358–382.
ista: 'Henzinger TA, Karimi M, Kueffner K, Mallik K. 2023. Monitoring algorithmic
fairness. Computer Aided Verification. CAV: Computer Aided Verification, LNCS,
vol. 13965, 358–382.'
mla: Henzinger, Thomas A., et al. “Monitoring Algorithmic Fairness.” Computer
Aided Verification, vol. 13965, Springer Nature, 2023, pp. 358–382, doi:10.1007/978-3-031-37703-7_17.
short: T.A. Henzinger, M. Karimi, K. Kueffner, K. Mallik, in:, Computer Aided Verification,
Springer Nature, 2023, pp. 358–382.
conference:
end_date: 2023-07-22
location: Paris, France
name: 'CAV: Computer Aided Verification'
start_date: 2023-07-17
date_created: 2023-07-25T18:32:40Z
date_published: 2023-07-18T00:00:00Z
date_updated: 2023-09-05T15:14:00Z
day: '18'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
doi: 10.1007/978-3-031-37703-7_17
ec_funded: 1
external_id:
arxiv:
- '2305.15979'
file:
- access_level: open_access
checksum: ccaf94bf7d658ba012c016e11869b54c
content_type: application/pdf
creator: dernst
date_created: 2023-07-31T08:11:20Z
date_updated: 2023-07-31T08:11:20Z
file_id: '13327'
file_name: 2023_LNCS_CAV_HenzingerT.pdf
file_size: 647760
relation: main_file
success: 1
file_date_updated: 2023-07-31T08:11:20Z
has_accepted_license: '1'
intvolume: ' 13965'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '07'
oa: 1
oa_version: Published Version
page: 358–382
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: Computer Aided Verification
publication_identifier:
eisbn:
- '9783031377037'
eissn:
- 1611-3349
isbn:
- '9783031377020'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
status: public
title: Monitoring algorithmic fairness
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 13965
year: '2023'
...
---
_id: '12205'
abstract:
- lang: eng
text: "Background: This study seeks to evaluate the impact of breast cancer (BRCA)
gene status on tumor dissemination pattern, surgical outcome and survival in a
multicenter cohort of paired primary ovarian cancer (pOC) and recurrent ovarian
cancer (rOC).\r\n\r\nPatients and Methods: Medical records and follow-up data
from 190 patients were gathered retrospectively. All patients had surgery at pOC
and at least one further rOC surgery at four European high-volume centers. Patients
were divided into one cohort with confirmed mutation for BRCA1 and/or BRCA2 (BRCAmut)
and a second cohort with BRCA wild type or unknown (BRCAwt). Patterns of tumor
presentation, surgical outcome and survival data were analyzed between the two
groups.\r\n\r\nResults: Patients with BRCAmut disease were on average 4 years
younger and had significantly more tumor involvement upon diagnosis. Patients
with BRCAmut disease showed higher debulking rates at all stages. Multivariate
analysis showed that only patient age had significant predictive value for complete
tumor resection in pOC. At rOC, however, only BRCAmut status significantly correlated
with optimal debulking. Patients with BRCAmut disease showed significantly prolonged
overall survival (OS) by 24.3 months. Progression-free survival (PFS) was prolonged
in the BRCAmut group at all stages as well, reaching statistical significance
during recurrence.\r\n\r\nConclusions: Patients with BRCAmut disease showed a
more aggressive course of disease with earlier onset and more extensive tumor
dissemination at pOC. However, surgical outcome and OS were significantly better
in patients with BRCAmut disease compared with patients with BRCAwt disease. We
therefore propose to consider BRCAmut status in regard to patient selection for
cytoreductive surgery, especially in rOC."
acknowledgement: "E.I.B. is a Feodor Lynen fellow of the Humboldt Foundation and a
participant of the Charité Clinical Scientist Program funded by the Charité Universitätsmedizin
Berlin and the Berlin Institute of Health. This work was supported by European Commission’s
Seventh Framework Programme under grant agreement no. 279113 (OCTIPS; www.octips.eu).\r\nOpen
Access funding enabled and organized by Projekt DEAL."
article_processing_charge: No
article_type: original
author:
- first_name: Jacek
full_name: Glajzer, Jacek
last_name: Glajzer
- first_name: Dan Cacsire
full_name: Castillo-Tong, Dan Cacsire
last_name: Castillo-Tong
- first_name: Rolf
full_name: Richter, Rolf
last_name: Richter
- first_name: Ignace
full_name: Vergote, Ignace
last_name: Vergote
- first_name: Hagen
full_name: Kulbe, Hagen
last_name: Kulbe
- first_name: Adriaan
full_name: Vanderstichele, Adriaan
last_name: Vanderstichele
- first_name: Ilary
full_name: Ruscito, Ilary
last_name: Ruscito
- first_name: Fabian
full_name: Trillsch, Fabian
last_name: Trillsch
- first_name: Alexander
full_name: Mustea, Alexander
last_name: Mustea
- first_name: Caroline
full_name: Kreuzinger, Caroline
id: 382077BA-F248-11E8-B48F-1D18A9856A87
last_name: Kreuzinger
- first_name: Charlie
full_name: Gourley, Charlie
last_name: Gourley
- first_name: Hani
full_name: Gabra, Hani
last_name: Gabra
- first_name: Eliane T.
full_name: Taube, Eliane T.
last_name: Taube
- first_name: Oliver
full_name: Dorigo, Oliver
last_name: Dorigo
- first_name: David
full_name: Horst, David
last_name: Horst
- first_name: Carlotta
full_name: Keunecke, Carlotta
last_name: Keunecke
- first_name: Joanna
full_name: Baum, Joanna
last_name: Baum
- first_name: Timothy
full_name: Angelotti, Timothy
last_name: Angelotti
- first_name: Jalid
full_name: Sehouli, Jalid
last_name: Sehouli
- first_name: Elena Ioana
full_name: Braicu, Elena Ioana
last_name: Braicu
citation:
ama: 'Glajzer J, Castillo-Tong DC, Richter R, et al. Impact of BRCA mutation status
on tumor dissemination pattern, surgical outcome and patient survival in primary
and recurrent high-grade serous ovarian cancer: A multicenter retrospective study
by the Ovarian Cancer Therapy-Innovative Models Prolong Survival (OCTIPS) consortium.
Annals of Surgical Oncology. 2023;30:35-45. doi:10.1245/s10434-022-12459-3'
apa: 'Glajzer, J., Castillo-Tong, D. C., Richter, R., Vergote, I., Kulbe, H., Vanderstichele,
A., … Braicu, E. I. (2023). Impact of BRCA mutation status on tumor dissemination
pattern, surgical outcome and patient survival in primary and recurrent high-grade
serous ovarian cancer: A multicenter retrospective study by the Ovarian Cancer
Therapy-Innovative Models Prolong Survival (OCTIPS) consortium. Annals of Surgical
Oncology. Springer Nature. https://doi.org/10.1245/s10434-022-12459-3'
chicago: 'Glajzer, Jacek, Dan Cacsire Castillo-Tong, Rolf Richter, Ignace Vergote,
Hagen Kulbe, Adriaan Vanderstichele, Ilary Ruscito, et al. “Impact of BRCA Mutation
Status on Tumor Dissemination Pattern, Surgical Outcome and Patient Survival in
Primary and Recurrent High-Grade Serous Ovarian Cancer: A Multicenter Retrospective
Study by the Ovarian Cancer Therapy-Innovative Models Prolong Survival (OCTIPS) Consortium.”
Annals of Surgical Oncology. Springer Nature, 2023. https://doi.org/10.1245/s10434-022-12459-3.'
ieee: 'J. Glajzer et al., “Impact of BRCA mutation status on tumor dissemination
pattern, surgical outcome and patient survival in primary and recurrent high-grade
serous ovarian cancer: A multicenter retrospective study by the Ovarian Cancer
Therapy-Innovative Models Prolong Survival (OCTIPS) consortium,” Annals of
Surgical Oncology, vol. 30. Springer Nature, pp. 35–45, 2023.'
ista: 'Glajzer J, Castillo-Tong DC, Richter R, Vergote I, Kulbe H, Vanderstichele
A, Ruscito I, Trillsch F, Mustea A, Kreuzinger C, Gourley C, Gabra H, Taube ET,
Dorigo O, Horst D, Keunecke C, Baum J, Angelotti T, Sehouli J, Braicu EI. 2023.
Impact of BRCA mutation status on tumor dissemination pattern, surgical outcome
and patient survival in primary and recurrent high-grade serous ovarian cancer:
A multicenter retrospective study by the Ovarian Cancer Therapy-Innovative Models
Prolong Survival (OCTIPS) consortium. Annals of Surgical Oncology. 30, 35–45.'
mla: 'Glajzer, Jacek, et al. “Impact of BRCA Mutation Status on Tumor Dissemination
Pattern, Surgical Outcome and Patient Survival in Primary and Recurrent High-Grade
Serous Ovarian Cancer: A Multicenter Retrospective Study by the Ovarian Cancer
Therapy-Innovative Models Prolong Survival (OCTIPS) Consortium.” Annals of
Surgical Oncology, vol. 30, Springer Nature, 2023, pp. 35–45, doi:10.1245/s10434-022-12459-3.'
short: J. Glajzer, D.C. Castillo-Tong, R. Richter, I. Vergote, H. Kulbe, A. Vanderstichele,
I. Ruscito, F. Trillsch, A. Mustea, C. Kreuzinger, C. Gourley, H. Gabra, E.T.
Taube, O. Dorigo, D. Horst, C. Keunecke, J. Baum, T. Angelotti, J. Sehouli, E.I.
Braicu, Annals of Surgical Oncology 30 (2023) 35–45.
date_created: 2023-01-16T09:44:36Z
date_published: 2023-01-01T00:00:00Z
date_updated: 2023-09-05T15:18:37Z
day: '01'
ddc:
- '610'
department:
- _id: JoDa
doi: 10.1245/s10434-022-12459-3
external_id:
isi:
- '000852125500006'
file:
- access_level: open_access
checksum: 36a1200e1011f4b2155a8041d0308f34
content_type: application/pdf
creator: dernst
date_created: 2023-02-02T13:01:20Z
date_updated: 2023-02-02T13:01:20Z
file_id: '12490'
file_name: 2023_AnnalsSurgicalOncology_Glajzer.pdf
file_size: 365865
relation: main_file
success: 1
file_date_updated: 2023-02-02T13:01:20Z
has_accepted_license: '1'
intvolume: ' 30'
isi: 1
keyword:
- Oncology
- Surgery
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
page: 35-45
publication: Annals of Surgical Oncology
publication_identifier:
eissn:
- 1534-4681
issn:
- 1068-9265
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '12115'
relation: other
status: public
scopus_import: '1'
status: public
title: 'Impact of BRCA mutation status on tumor dissemination pattern, surgical outcome
and patient survival in primary and recurrent high-grade serous ovarian cancer:
A multicenter retrospective study by the Ovarian Cancer Therapy-Innovative Models
Prolong Survival (OCTIPS) consortium'
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: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 30
year: '2023'
...
---
_id: '12115'
acknowledgement: This work was supported by European Commission’s Seventh Framework
Programme under Grant Agreement No. 279113 (OCTIPS; www.octips.eu).
article_processing_charge: No
article_type: original
author:
- first_name: Jacek
full_name: Glajzer, Jacek
last_name: Glajzer
- first_name: Dan Cacsire
full_name: Castillo-Tong, Dan Cacsire
last_name: Castillo-Tong
- first_name: Rolf
full_name: Richter, Rolf
last_name: Richter
- first_name: Ignace
full_name: Vergote, Ignace
last_name: Vergote
- first_name: Hagen
full_name: Kulbe, Hagen
last_name: Kulbe
- first_name: Adriaan
full_name: Vanderstichele, Adriaan
last_name: Vanderstichele
- first_name: Ilary
full_name: Ruscito, Ilary
last_name: Ruscito
- first_name: Fabian
full_name: Trillsch, Fabian
last_name: Trillsch
- first_name: Alexander
full_name: Mustea, Alexander
last_name: Mustea
- first_name: Caroline
full_name: Kreuzinger, Caroline
id: 382077BA-F248-11E8-B48F-1D18A9856A87
last_name: Kreuzinger
- first_name: Charlie
full_name: Gourley, Charlie
last_name: Gourley
- first_name: Hani
full_name: Gabra, Hani
last_name: Gabra
- first_name: Eliane T.
full_name: Taube, Eliane T.
last_name: Taube
- first_name: Oliver
full_name: Dorigo, Oliver
last_name: Dorigo
- first_name: David
full_name: Horst, David
last_name: Horst
- first_name: Carlotta
full_name: Keunecke, Carlotta
last_name: Keunecke
- first_name: Joanna
full_name: Baum, Joanna
last_name: Baum
- first_name: Timothy
full_name: Angelotti, Timothy
last_name: Angelotti
- first_name: Jalid
full_name: Sehouli, Jalid
last_name: Sehouli
- first_name: Elena Ioana
full_name: Braicu, Elena Ioana
last_name: Braicu
citation:
ama: 'Glajzer J, Castillo-Tong DC, Richter R, et al. ASO Visual Abstract: Impact
of BRCA mutation status on tumor dissemination pattern, surgical outcome, and
patient survival in primary and recurrent high-grade serous ovarian cancer (HGSOC).
A multicenter, retrospective study of the ovarian cancer therapy—innovative models
prolong survival (OCTIPS) consortium. Annals of Surgical Oncology. 2023;30:46-47.
doi:10.1245/s10434-022-12681-z'
apa: 'Glajzer, J., Castillo-Tong, D. C., Richter, R., Vergote, I., Kulbe, H., Vanderstichele,
A., … Braicu, E. I. (2023). ASO Visual Abstract: Impact of BRCA mutation status
on tumor dissemination pattern, surgical outcome, and patient survival in primary
and recurrent high-grade serous ovarian cancer (HGSOC). A multicenter, retrospective
study of the ovarian cancer therapy—innovative models prolong survival (OCTIPS)
consortium. Annals of Surgical Oncology. Springer Nature. https://doi.org/10.1245/s10434-022-12681-z'
chicago: 'Glajzer, Jacek, Dan Cacsire Castillo-Tong, Rolf Richter, Ignace Vergote,
Hagen Kulbe, Adriaan Vanderstichele, Ilary Ruscito, et al. “ASO Visual Abstract:
Impact of BRCA Mutation Status on Tumor Dissemination Pattern, Surgical Outcome,
and Patient Survival in Primary and Recurrent High-Grade Serous Ovarian Cancer
(HGSOC). A Multicenter, Retrospective Study of the Ovarian Cancer Therapy—Innovative
Models Prolong Survival (OCTIPS) Consortium.” Annals of Surgical Oncology.
Springer Nature, 2023. https://doi.org/10.1245/s10434-022-12681-z.'
ieee: 'J. Glajzer et al., “ASO Visual Abstract: Impact of BRCA mutation status
on tumor dissemination pattern, surgical outcome, and patient survival in primary
and recurrent high-grade serous ovarian cancer (HGSOC). A multicenter, retrospective
study of the ovarian cancer therapy—innovative models prolong survival (OCTIPS)
consortium,” Annals of Surgical Oncology, vol. 30. Springer Nature, pp.
46–47, 2023.'
ista: 'Glajzer J, Castillo-Tong DC, Richter R, Vergote I, Kulbe H, Vanderstichele
A, Ruscito I, Trillsch F, Mustea A, Kreuzinger C, Gourley C, Gabra H, Taube ET,
Dorigo O, Horst D, Keunecke C, Baum J, Angelotti T, Sehouli J, Braicu EI. 2023.
ASO Visual Abstract: Impact of BRCA mutation status on tumor dissemination pattern,
surgical outcome, and patient survival in primary and recurrent high-grade serous
ovarian cancer (HGSOC). A multicenter, retrospective study of the ovarian cancer
therapy—innovative models prolong survival (OCTIPS) consortium. Annals of Surgical
Oncology. 30, 46–47.'
mla: 'Glajzer, Jacek, et al. “ASO Visual Abstract: Impact of BRCA Mutation Status
on Tumor Dissemination Pattern, Surgical Outcome, and Patient Survival in Primary
and Recurrent High-Grade Serous Ovarian Cancer (HGSOC). A Multicenter, Retrospective
Study of the Ovarian Cancer Therapy—Innovative Models Prolong Survival (OCTIPS)
Consortium.” Annals of Surgical Oncology, vol. 30, Springer Nature, 2023,
pp. 46–47, doi:10.1245/s10434-022-12681-z.'
short: J. Glajzer, D.C. Castillo-Tong, R. Richter, I. Vergote, H. Kulbe, A. Vanderstichele,
I. Ruscito, F. Trillsch, A. Mustea, C. Kreuzinger, C. Gourley, H. Gabra, E.T.
Taube, O. Dorigo, D. Horst, C. Keunecke, J. Baum, T. Angelotti, J. Sehouli, E.I.
Braicu, Annals of Surgical Oncology 30 (2023) 46–47.
date_created: 2023-01-12T11:56:22Z
date_published: 2023-01-01T00:00:00Z
date_updated: 2023-09-05T15:18:36Z
day: '01'
department:
- _id: JoDa
doi: 10.1245/s10434-022-12681-z
external_id:
isi:
- '000879151800001'
intvolume: ' 30'
isi: 1
keyword:
- Oncology
- Surgery
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1245/s10434-022-12681-z
month: '01'
oa: 1
oa_version: Published Version
page: 46-47
publication: Annals of Surgical Oncology
publication_identifier:
eissn:
- 1534-4681
issn:
- 1068-9265
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '12205'
relation: other
status: public
scopus_import: '1'
status: public
title: 'ASO Visual Abstract: Impact of BRCA mutation status on tumor dissemination
pattern, surgical outcome, and patient survival in primary and recurrent high-grade
serous ovarian cancer (HGSOC). A multicenter, retrospective study of the ovarian
cancer therapy—innovative models prolong survival (OCTIPS) consortium'
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 30
year: '2023'
...
---
_id: '14253'
abstract:
- lang: eng
text: Junctions between the endoplasmic reticulum (ER) and the plasma membrane (PM)
are specialized membrane contacts ubiquitous in eukaryotic cells. Concentration
of intracellular signaling machinery near ER-PM junctions allows these domains
to serve critical roles in lipid and Ca2+ signaling and homeostasis. Subcellular
compartmentalization of protein kinase A (PKA) signaling also regulates essential
cellular functions, however, no specific association between PKA and ER-PM junctional
domains is known. Here, we show that in brain neurons type I PKA is directed to
Kv2.1 channel-dependent ER-PM junctional domains via SPHKAP, a type I PKA-specific
anchoring protein. SPHKAP association with type I PKA regulatory subunit RI and
ER-resident VAP proteins results in the concentration of type I PKA between stacked
ER cisternae associated with ER-PM junctions. This ER-associated PKA signalosome
enables reciprocal regulation between PKA and Ca2+ signaling machinery to support
Ca2+ influx and excitation-transcription coupling. These data reveal that neuronal
ER-PM junctions support a receptor-independent form of PKA signaling driven by
membrane depolarization and intracellular Ca2+, allowing conversion of information
encoded in electrical signals into biochemical changes universally recognized
throughout the cell.
acknowledgement: We thank Kayla Templeton and Peter Turcanu for technical assistance,
Michelle Salemi for assistance with LC-MS data acquisition and analysis, Dr. Belvin
Gong for advice on monoclonal antibody generation, Drs. Maria Casas Prat and Eamonn
Dickson for assistance with super-resolution TIRF microscopy, Dr. Oscar Cerda for
assistance with the design of TAT-FFAT peptides, Dr. Fernando Santana for helpful
discussions, and Dr. Jodi Nunnari for a careful reading of our manuscript. We also
thank Dr. Alan Howe, Dr. Sohum Mehta, and Dr. Jin Zhang for providing plasmids used
in this study. This project was funded by NIH Grants R01NS114210 and R21NS101648
(J.S.T.), and F32NS108519 (N.C.V.).
article_number: '5231'
article_processing_charge: Yes
article_type: original
author:
- first_name: Nicholas C.
full_name: Vierra, Nicholas C.
last_name: Vierra
- first_name: Luisa
full_name: Ribeiro-Silva, Luisa
last_name: Ribeiro-Silva
- first_name: Michael
full_name: Kirmiz, Michael
last_name: Kirmiz
- first_name: Deborah
full_name: Van Der List, Deborah
last_name: Van Der List
- first_name: Pradeep
full_name: Bhandari, Pradeep
id: 45EDD1BC-F248-11E8-B48F-1D18A9856A87
last_name: Bhandari
orcid: 0000-0003-0863-4481
- first_name: Olivia A.
full_name: Mack, Olivia A.
last_name: Mack
- first_name: James
full_name: Carroll, James
last_name: Carroll
- first_name: Elodie
full_name: Le Monnier, Elodie
id: 3B59276A-F248-11E8-B48F-1D18A9856A87
last_name: Le Monnier
- first_name: Sue A.
full_name: Aicher, Sue A.
last_name: Aicher
- first_name: Ryuichi
full_name: Shigemoto, Ryuichi
id: 499F3ABC-F248-11E8-B48F-1D18A9856A87
last_name: Shigemoto
orcid: 0000-0001-8761-9444
- first_name: James S.
full_name: Trimmer, James S.
last_name: Trimmer
citation:
ama: Vierra NC, Ribeiro-Silva L, Kirmiz M, et al. Neuronal ER-plasma membrane junctions
couple excitation to Ca2+-activated PKA signaling. Nature Communications.
2023;14. doi:10.1038/s41467-023-40930-6
apa: Vierra, N. C., Ribeiro-Silva, L., Kirmiz, M., Van Der List, D., Bhandari, P.,
Mack, O. A., … Trimmer, J. S. (2023). Neuronal ER-plasma membrane junctions couple
excitation to Ca2+-activated PKA signaling. Nature Communications. Springer
Nature. https://doi.org/10.1038/s41467-023-40930-6
chicago: Vierra, Nicholas C., Luisa Ribeiro-Silva, Michael Kirmiz, Deborah Van Der
List, Pradeep Bhandari, Olivia A. Mack, James Carroll, et al. “Neuronal ER-Plasma
Membrane Junctions Couple Excitation to Ca2+-Activated PKA Signaling.” Nature
Communications. Springer Nature, 2023. https://doi.org/10.1038/s41467-023-40930-6.
ieee: N. C. Vierra et al., “Neuronal ER-plasma membrane junctions couple
excitation to Ca2+-activated PKA signaling,” Nature Communications, vol.
14. Springer Nature, 2023.
ista: Vierra NC, Ribeiro-Silva L, Kirmiz M, Van Der List D, Bhandari P, Mack OA,
Carroll J, Le Monnier E, Aicher SA, Shigemoto R, Trimmer JS. 2023. Neuronal ER-plasma
membrane junctions couple excitation to Ca2+-activated PKA signaling. Nature Communications.
14, 5231.
mla: Vierra, Nicholas C., et al. “Neuronal ER-Plasma Membrane Junctions Couple Excitation
to Ca2+-Activated PKA Signaling.” Nature Communications, vol. 14, 5231,
Springer Nature, 2023, doi:10.1038/s41467-023-40930-6.
short: N.C. Vierra, L. Ribeiro-Silva, M. Kirmiz, D. Van Der List, P. Bhandari, O.A.
Mack, J. Carroll, E. Le Monnier, S.A. Aicher, R. Shigemoto, J.S. Trimmer, Nature
Communications 14 (2023).
date_created: 2023-09-03T22:01:14Z
date_published: 2023-08-26T00:00:00Z
date_updated: 2023-09-06T06:53:32Z
day: '26'
ddc:
- '570'
department:
- _id: RySh
doi: 10.1038/s41467-023-40930-6
external_id:
pmid:
- '37633939'
file:
- access_level: open_access
checksum: 6ab8aab4e957f626a09a1c73db3388fb
content_type: application/pdf
creator: dernst
date_created: 2023-09-06T06:50:07Z
date_updated: 2023-09-06T06:50:07Z
file_id: '14270'
file_name: 2023_NatureComm_Vierra.pdf
file_size: 9412549
relation: main_file
success: 1
file_date_updated: 2023-09-06T06:50:07Z
has_accepted_license: '1'
intvolume: ' 14'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
pmid: 1
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Neuronal ER-plasma membrane junctions couple excitation to Ca2+-activated PKA
signaling
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: 14
year: '2023'
...
---
_id: '14259'
abstract:
- lang: eng
text: "We provide a learning-based technique for guessing a winning strategy in
a parity game originating from an LTL synthesis problem. A cheaply obtained guess
can be useful in several applications. Not only can the guessed strategy be applied
as best-effort in cases where the game’s huge size prohibits rigorous approaches,
but it can also increase the scalability of rigorous LTL synthesis in several
ways. Firstly, checking whether a guessed strategy is winning is easier than constructing
one. Secondly, even if the guess is wrong in some places, it can be fixed by strategy
iteration faster than constructing one from scratch. Thirdly, the guess can be
used in on-the-fly approaches to prioritize exploration in the most fruitful directions.\r\nIn
contrast to previous works, we (i) reflect the highly structured logical information
in game’s states, the so-called semantic labelling, coming from the recent LTL-to-automata
translations, and (ii) learn to reflect it properly by learning from previously
solved games, bringing the solving process closer to human-like reasoning."
acknowledgement: This research was funded in part by the German Research Foundation
(DFG) project 427755713 Group-By Objectives in Probabilistic Verification (GOPro).
alternative_title:
- LNCS
article_processing_charge: Yes (in subscription journal)
author:
- first_name: Jan
full_name: Kretinsky, Jan
id: 44CEF464-F248-11E8-B48F-1D18A9856A87
last_name: Kretinsky
orcid: 0000-0002-8122-2881
- first_name: Tobias
full_name: Meggendorfer, Tobias
id: b21b0c15-30a2-11eb-80dc-f13ca25802e1
last_name: Meggendorfer
orcid: 0000-0002-1712-2165
- first_name: Maximilian
full_name: Prokop, Maximilian
last_name: Prokop
- first_name: Sabine
full_name: Rieder, Sabine
last_name: Rieder
citation:
ama: 'Kretinsky J, Meggendorfer T, Prokop M, Rieder S. Guessing winning policies
in LTL synthesis by semantic learning. In: 35th International Conference on
Computer Aided Verification . Vol 13964. Springer Nature; 2023:390-414. doi:10.1007/978-3-031-37706-8_20'
apa: 'Kretinsky, J., Meggendorfer, T., Prokop, M., & Rieder, S. (2023). Guessing
winning policies in LTL synthesis by semantic learning. In 35th International
Conference on Computer Aided Verification (Vol. 13964, pp. 390–414). Paris,
France: Springer Nature. https://doi.org/10.1007/978-3-031-37706-8_20'
chicago: Kretinsky, Jan, Tobias Meggendorfer, Maximilian Prokop, and Sabine Rieder.
“Guessing Winning Policies in LTL Synthesis by Semantic Learning.” In 35th
International Conference on Computer Aided Verification , 13964:390–414. Springer
Nature, 2023. https://doi.org/10.1007/978-3-031-37706-8_20.
ieee: J. Kretinsky, T. Meggendorfer, M. Prokop, and S. Rieder, “Guessing winning
policies in LTL synthesis by semantic learning,” in 35th International Conference
on Computer Aided Verification , Paris, France, 2023, vol. 13964, pp. 390–414.
ista: 'Kretinsky J, Meggendorfer T, Prokop M, Rieder S. 2023. Guessing winning policies
in LTL synthesis by semantic learning. 35th International Conference on Computer
Aided Verification . CAV: Computer Aided Verification, LNCS, vol. 13964, 390–414.'
mla: Kretinsky, Jan, et al. “Guessing Winning Policies in LTL Synthesis by Semantic
Learning.” 35th International Conference on Computer Aided Verification ,
vol. 13964, Springer Nature, 2023, pp. 390–414, doi:10.1007/978-3-031-37706-8_20.
short: J. Kretinsky, T. Meggendorfer, M. Prokop, S. Rieder, in:, 35th International
Conference on Computer Aided Verification , Springer Nature, 2023, pp. 390–414.
conference:
end_date: 2023-07-22
location: Paris, France
name: 'CAV: Computer Aided Verification'
start_date: 2023-07-17
date_created: 2023-09-03T22:01:16Z
date_published: 2023-07-17T00:00:00Z
date_updated: 2023-09-06T08:27:33Z
day: '17'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.1007/978-3-031-37706-8_20
file:
- access_level: open_access
checksum: ed66278b61bb869e1baba3d9b9081271
content_type: application/pdf
creator: dernst
date_created: 2023-09-06T08:25:50Z
date_updated: 2023-09-06T08:25:50Z
file_id: '14276'
file_name: 2023_LNCS_CAV_Kretinsky.pdf
file_size: 428354
relation: main_file
success: 1
file_date_updated: 2023-09-06T08:25:50Z
has_accepted_license: '1'
intvolume: ' 13964'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 390-414
publication: '35th International Conference on Computer Aided Verification '
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783031377051'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Guessing winning policies in LTL synthesis by semantic learning
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13964
year: '2023'
...
---
_id: '14105'
abstract:
- lang: eng
text: "Despite their recent success, deep neural networks continue to perform poorly
when they encounter distribution shifts at test time. Many recently proposed approaches
try to counter this by aligning the model to the new distribution prior to inference.
With no labels available this requires unsupervised objectives to adapt the model
on the observed test data. In this paper, we propose Test-Time SelfTraining (TeST):
a technique that takes as input a model trained on some source data and a novel
data distribution at test time, and learns invariant and robust representations
using a student-teacher framework. We find that models adapted using TeST significantly
improve over baseline testtime adaptation algorithms. TeST achieves competitive
performance to modern domain adaptation algorithms [4, 43], while having access
to 5-10x less data at time of adaption. We thoroughly evaluate a variety of baselines
on two tasks:\r\nobject detection and image segmentation and find that models
adapted with TeST. We find that TeST sets the new stateof-the art for test-time
domain adaptation algorithms. "
article_processing_charge: No
author:
- first_name: Samarth
full_name: Sinha, Samarth
last_name: Sinha
- first_name: Peter
full_name: Gehler, Peter
last_name: Gehler
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Bernt
full_name: Schiele, Bernt
last_name: Schiele
citation:
ama: 'Sinha S, Gehler P, Locatello F, Schiele B. TeST: Test-time Self-Training under
distribution shift. In: 2023 IEEE/CVF Winter Conference on Applications of
Computer Vision. Institute of Electrical and Electronics Engineers; 2023.
doi:10.1109/wacv56688.2023.00278'
apa: 'Sinha, S., Gehler, P., Locatello, F., & Schiele, B. (2023). TeST: Test-time
Self-Training under distribution shift. In 2023 IEEE/CVF Winter Conference
on Applications of Computer Vision. Waikoloa, HI, United States: Institute
of Electrical and Electronics Engineers. https://doi.org/10.1109/wacv56688.2023.00278'
chicago: 'Sinha, Samarth, Peter Gehler, Francesco Locatello, and Bernt Schiele.
“TeST: Test-Time Self-Training under Distribution Shift.” In 2023 IEEE/CVF
Winter Conference on Applications of Computer Vision. Institute of Electrical
and Electronics Engineers, 2023. https://doi.org/10.1109/wacv56688.2023.00278.'
ieee: 'S. Sinha, P. Gehler, F. Locatello, and B. Schiele, “TeST: Test-time Self-Training
under distribution shift,” in 2023 IEEE/CVF Winter Conference on Applications
of Computer Vision, Waikoloa, HI, United States, 2023.'
ista: 'Sinha S, Gehler P, Locatello F, Schiele B. 2023. TeST: Test-time Self-Training
under distribution shift. 2023 IEEE/CVF Winter Conference on Applications of Computer
Vision. WACV: Winter Conference on Applications of Computer Vision.'
mla: 'Sinha, Samarth, et al. “TeST: Test-Time Self-Training under Distribution Shift.”
2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Institute
of Electrical and Electronics Engineers, 2023, doi:10.1109/wacv56688.2023.00278.'
short: S. Sinha, P. Gehler, F. Locatello, B. Schiele, in:, 2023 IEEE/CVF Winter
Conference on Applications of Computer Vision, Institute of Electrical and Electronics
Engineers, 2023.
conference:
end_date: 2023-01-07
location: Waikoloa, HI, United States
name: 'WACV: Winter Conference on Applications of Computer Vision'
start_date: 2023-01-02
date_created: 2023-08-21T12:11:38Z
date_published: 2023-02-06T00:00:00Z
date_updated: 2023-09-06T10:26:56Z
day: '06'
department:
- _id: FrLo
doi: 10.1109/wacv56688.2023.00278
extern: '1'
external_id:
arxiv:
- '2209.11459'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2209.11459
month: '02'
oa: 1
oa_version: Preprint
publication: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision
publication_identifier:
eissn:
- 2642-9381
isbn:
- '9781665493475'
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'TeST: Test-time Self-Training under distribution shift'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14256'
abstract:
- lang: eng
text: "Context. Space asteroseismology is revolutionizing our knowledge of the internal
structure and dynamics of stars. A breakthrough is ongoing with the recent discoveries
of signatures of strong magnetic fields in the core of red giant stars. The key
signature for such a detection is the asymmetry these fields induce in the frequency
splittings of observed dipolar mixed gravito-acoustic modes.\r\nAims. We investigate
the ability of the observed asymmetries of the frequency splittings of dipolar
mixed modes to constrain the geometrical properties of deep magnetic fields.\r\nMethods.
We used the powerful analytical Racah-Wigner algebra used in quantum mechanics
to characterize the geometrical couplings of dipolar mixed oscillation modes with
various realistically plausible topologies of fossil magnetic fields. We also
computed the induced perturbation of their frequencies.\r\nResults. First, in
the case of an oblique magnetic dipole, we provide the exact analytical expression
of the asymmetry as a function of the angle between the rotation and magnetic
axes. Its value provides a direct measure of this angle. Second, considering a
combination of axisymmetric dipolar and quadrupolar fields, we show how the asymmetry
is blind to the unraveling of the relative strength and sign of each component.
Finally, in the case of a given multipole, we show that a negative asymmetry is
a signature of non-axisymmetric topologies.\r\nConclusions. Asymmetries of dipolar
mixed modes provide a key bit of information on the geometrical topology of deep
fossil magnetic fields, but this is insufficient on its own. Asteroseismic constraints
should therefore be combined with spectropolarimetric observations and numerical
simulations, which aim to predict the more probable stable large-scale geometries."
acknowledgement: The authors are grateful to the referee for her/his detailed and
constructive report, which has allowed us to improve our article. S. M. acknowledges
support from the CNES GOLF-SOHO and PLATO grants at CEA/DAp and PNPS (CNRS/INSU).
We thank R. A. Garcia for fruitful discussions and suggestions.
article_number: L9
article_processing_charge: Yes (in subscription journal)
article_type: letter_note
author:
- first_name: S.
full_name: Mathis, S.
last_name: Mathis
- first_name: Lisa Annabelle
full_name: Bugnet, Lisa Annabelle
id: d9edb345-f866-11ec-9b37-d119b5234501
last_name: Bugnet
orcid: 0000-0003-0142-4000
citation:
ama: 'Mathis S, Bugnet LA. Asymmetries of frequency splittings of dipolar mixed
modes: A window on the topology of deep magnetic fields. Astronomy and Astrophysics.
2023;676. doi:10.1051/0004-6361/202346832'
apa: 'Mathis, S., & Bugnet, L. A. (2023). Asymmetries of frequency splittings
of dipolar mixed modes: A window on the topology of deep magnetic fields. Astronomy
and Astrophysics. EDP Sciences. https://doi.org/10.1051/0004-6361/202346832'
chicago: 'Mathis, S., and Lisa Annabelle Bugnet. “Asymmetries of Frequency Splittings
of Dipolar Mixed Modes: A Window on the Topology of Deep Magnetic Fields.” Astronomy
and Astrophysics. EDP Sciences, 2023. https://doi.org/10.1051/0004-6361/202346832.'
ieee: 'S. Mathis and L. A. Bugnet, “Asymmetries of frequency splittings of dipolar
mixed modes: A window on the topology of deep magnetic fields,” Astronomy and
Astrophysics, vol. 676. EDP Sciences, 2023.'
ista: 'Mathis S, Bugnet LA. 2023. Asymmetries of frequency splittings of dipolar
mixed modes: A window on the topology of deep magnetic fields. Astronomy and Astrophysics.
676, L9.'
mla: 'Mathis, S., and Lisa Annabelle Bugnet. “Asymmetries of Frequency Splittings
of Dipolar Mixed Modes: A Window on the Topology of Deep Magnetic Fields.” Astronomy
and Astrophysics, vol. 676, L9, EDP Sciences, 2023, doi:10.1051/0004-6361/202346832.'
short: S. Mathis, L.A. Bugnet, Astronomy and Astrophysics 676 (2023).
date_created: 2023-09-03T22:01:15Z
date_published: 2023-08-01T00:00:00Z
date_updated: 2023-09-06T11:05:58Z
day: '01'
ddc:
- '520'
department:
- _id: LiBu
doi: 10.1051/0004-6361/202346832
external_id:
arxiv:
- '2306.11587'
isi:
- '001046037700007'
file:
- access_level: open_access
checksum: 7b30d26fb2b7bcb5b5be1414950615f9
content_type: application/pdf
creator: dernst
date_created: 2023-09-06T07:13:19Z
date_updated: 2023-09-06T07:13:19Z
file_id: '14271'
file_name: 2023_AstronomyAstrophysics_Mathis.pdf
file_size: 458120
relation: main_file
success: 1
file_date_updated: 2023-09-06T07:13:19Z
has_accepted_license: '1'
intvolume: ' 676'
isi: 1
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
publication: Astronomy and Astrophysics
publication_identifier:
eissn:
- 1432-0746
issn:
- 0004-6361
publication_status: published
publisher: EDP Sciences
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Asymmetries of frequency splittings of dipolar mixed modes: A window on the
topology of deep magnetic fields'
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: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 676
year: '2023'
...
---
_id: '14261'
abstract:
- lang: eng
text: In this work, a generalized, adapted Numerov implementation capable of determining
band structures of periodic quantum systems is outlined. Based on the input potential,
the presented approach numerically solves the Schrödinger equation in position
space at each momentum space point. Thus, in addition to the band structure, the
method inherently provides information about the state functions and probability
densities in position space at each momentum space point considered. The generalized,
adapted Numerov framework provided reliable estimates for a variety of increasingly
complex test suites in one, two, and three dimensions. The accuracy of the proposed
methodology was benchmarked against results obtained for the analytically solvable
Kronig-Penney model. Furthermore, the presented numerical solver was applied to
a model potential representing a 2D optical lattice being a challenging application
relevant, for example, in the field of quantum computing.
acknowledgement: Financial supports for this work via a PhD scholarship for J. Gamper
issued by the Leopold-Franzens-University of Innsbruck (Vicerector Prof. Dr Ulrike
Tanzer) are gratefully acknowledged. The computational results presented have been
achieved (in part) using the HPC infrastructure of the University of Innsbruck.
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Jakob
full_name: Gamper, Jakob
last_name: Gamper
- first_name: Florian
full_name: Kluibenschedl, Florian
id: 7499e70e-eb2c-11ec-b98b-f925648bc9d9
last_name: Kluibenschedl
- first_name: Alexander K.H.
full_name: Weiss, Alexander K.H.
last_name: Weiss
- first_name: Thomas S.
full_name: Hofer, Thomas S.
last_name: Hofer
citation:
ama: Gamper J, Kluibenschedl F, Weiss AKH, Hofer TS. Accessing position space wave
functions in band structure calculations of periodic systems - a generalized,
adapted numerov implementation for one-, two-, and three-dimensional quantum problems.
Journal of Physical Chemistry Letters. 2023;14(33):7395-7403. doi:10.1021/acs.jpclett.3c01707
apa: Gamper, J., Kluibenschedl, F., Weiss, A. K. H., & Hofer, T. S. (2023).
Accessing position space wave functions in band structure calculations of periodic
systems - a generalized, adapted numerov implementation for one-, two-, and three-dimensional
quantum problems. Journal of Physical Chemistry Letters. American Chemical
Society. https://doi.org/10.1021/acs.jpclett.3c01707
chicago: Gamper, Jakob, Florian Kluibenschedl, Alexander K.H. Weiss, and Thomas
S. Hofer. “Accessing Position Space Wave Functions in Band Structure Calculations
of Periodic Systems - a Generalized, Adapted Numerov Implementation for One-,
Two-, and Three-Dimensional Quantum Problems.” Journal of Physical Chemistry
Letters. American Chemical Society, 2023. https://doi.org/10.1021/acs.jpclett.3c01707.
ieee: J. Gamper, F. Kluibenschedl, A. K. H. Weiss, and T. S. Hofer, “Accessing position
space wave functions in band structure calculations of periodic systems - a generalized,
adapted numerov implementation for one-, two-, and three-dimensional quantum problems,”
Journal of Physical Chemistry Letters, vol. 14, no. 33. American Chemical
Society, pp. 7395–7403, 2023.
ista: Gamper J, Kluibenschedl F, Weiss AKH, Hofer TS. 2023. Accessing position space
wave functions in band structure calculations of periodic systems - a generalized,
adapted numerov implementation for one-, two-, and three-dimensional quantum problems.
Journal of Physical Chemistry Letters. 14(33), 7395–7403.
mla: Gamper, Jakob, et al. “Accessing Position Space Wave Functions in Band Structure
Calculations of Periodic Systems - a Generalized, Adapted Numerov Implementation
for One-, Two-, and Three-Dimensional Quantum Problems.” Journal of Physical
Chemistry Letters, vol. 14, no. 33, American Chemical Society, 2023, pp. 7395–403,
doi:10.1021/acs.jpclett.3c01707.
short: J. Gamper, F. Kluibenschedl, A.K.H. Weiss, T.S. Hofer, Journal of Physical
Chemistry Letters 14 (2023) 7395–7403.
date_created: 2023-09-03T22:01:16Z
date_published: 2023-08-11T00:00:00Z
date_updated: 2023-09-06T11:04:31Z
day: '11'
ddc:
- '530'
- '540'
department:
- _id: GradSch
doi: 10.1021/acs.jpclett.3c01707
external_id:
isi:
- '001048165800001'
pmid:
- '37566743'
file:
- access_level: open_access
checksum: 637454e2b3a357498d8d622d241c4bf6
content_type: application/pdf
creator: dernst
date_created: 2023-09-06T07:32:39Z
date_updated: 2023-09-06T07:32:39Z
file_id: '14272'
file_name: 2023_JourPhysChemistry_Gamper.pdf
file_size: 4986859
relation: main_file
success: 1
file_date_updated: 2023-09-06T07:32:39Z
has_accepted_license: '1'
intvolume: ' 14'
isi: 1
issue: '33'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
page: 7395-7403
pmid: 1
publication: Journal of Physical Chemistry Letters
publication_identifier:
eissn:
- 1948-7185
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Accessing position space wave functions in band structure calculations of periodic
systems - a generalized, adapted numerov implementation for one-, two-, and three-dimensional
quantum problems
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: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 14
year: '2023'
...
---
_id: '14208'
abstract:
- lang: eng
text: This paper focuses on over-parameterized deep neural networks (DNNs) with
ReLU activation functions and proves that when the data distribution is well-separated,
DNNs can achieve Bayes-optimal test error for classification while obtaining (nearly)
zero-training error under the lazy training regime. For this purpose, we unify
three interrelated concepts of overparameterization, benign overfitting, and the
Lipschitz constant of DNNs. Our results indicate that interpolating with smoother
functions leads to better generalization. Furthermore, we investigate the special
case where interpolating smooth ground-truth functions is performed by DNNs under
the Neural Tangent Kernel (NTK) regime for generalization. Our result demonstrates
that the generalization error converges to a constant order that only depends
on label noise and initialization noise, which theoretically verifies benign overfitting.
Our analysis provides a tight lower bound on the normalized margin under non-smooth
activation functions, as well as the minimum eigenvalue of NTK under high-dimensional
settings, which has its own interest in learning theory.
alternative_title:
- PMLR
article_processing_charge: No
author:
- first_name: Zhenyu
full_name: Zhu, Zhenyu
last_name: Zhu
- first_name: Fanghui
full_name: Liu, Fanghui
last_name: Liu
- first_name: Grigorios G
full_name: Chrysos, Grigorios G
last_name: Chrysos
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Volkan
full_name: Cevher, Volkan
last_name: Cevher
citation:
ama: 'Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. Benign overfitting in deep
neural networks under lazy training. In: Proceedings of the 40th International
Conference on Machine Learning. Vol 202. ML Research Press; 2023:43105-43128.'
apa: 'Zhu, Z., Liu, F., Chrysos, G. G., Locatello, F., & Cevher, V. (2023).
Benign overfitting in deep neural networks under lazy training. In Proceedings
of the 40th International Conference on Machine Learning (Vol. 202, pp. 43105–43128).
Honolulu, Hawaii, United States: ML Research Press.'
chicago: Zhu, Zhenyu, Fanghui Liu, Grigorios G Chrysos, Francesco Locatello, and
Volkan Cevher. “Benign Overfitting in Deep Neural Networks under Lazy Training.”
In Proceedings of the 40th International Conference on Machine Learning,
202:43105–28. ML Research Press, 2023.
ieee: Z. Zhu, F. Liu, G. G. Chrysos, F. Locatello, and V. Cevher, “Benign overfitting
in deep neural networks under lazy training,” in Proceedings of the 40th International
Conference on Machine Learning, Honolulu, Hawaii, United States, 2023, vol.
202, pp. 43105–43128.
ista: Zhu Z, Liu F, Chrysos GG, Locatello F, Cevher V. 2023. Benign overfitting
in deep neural networks under lazy training. Proceedings of the 40th International
Conference on Machine Learning. International Conference on Machine Learning,
PMLR, vol. 202, 43105–43128.
mla: Zhu, Zhenyu, et al. “Benign Overfitting in Deep Neural Networks under Lazy
Training.” Proceedings of the 40th International Conference on Machine Learning,
vol. 202, ML Research Press, 2023, pp. 43105–28.
short: Z. Zhu, F. Liu, G.G. Chrysos, F. Locatello, V. Cevher, in:, Proceedings of
the 40th International Conference on Machine Learning, ML Research Press, 2023,
pp. 43105–43128.
conference:
end_date: 2023-07-29
location: Honolulu, Hawaii, United States
name: International Conference on Machine Learning
start_date: 2023-07-23
date_created: 2023-08-22T14:18:18Z
date_published: 2023-05-30T00:00:00Z
date_updated: 2023-09-13T08:46:46Z
day: '30'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2305.19377'
intvolume: ' 202'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2305.19377
month: '05'
oa: 1
oa_version: Preprint
page: 43105-43128
publication: Proceedings of the 40th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
status: public
title: Benign overfitting in deep neural networks under lazy training
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 202
year: '2023'
...
---
_id: '14209'
abstract:
- lang: eng
text: Diffusion models excel at generating photorealistic images from text-queries.
Naturally, many approaches have been proposed to use these generative abilities
to augment training datasets for downstream tasks, such as classification. However,
diffusion models are themselves trained on large noisily supervised, but nonetheless,
annotated datasets. It is an open question whether the generalization capabilities
of diffusion models beyond using the additional data of the pre-training process
for augmentation lead to improved downstream performance. We perform a systematic
evaluation of existing methods to generate images from diffusion models and study
new extensions to assess their benefit for data augmentation. While we find that
personalizing diffusion models towards the target data outperforms simpler prompting
strategies, we also show that using the training data of the diffusion model alone,
via a simple nearest neighbor retrieval procedure, leads to even stronger downstream
performance. Overall, our study probes the limitations of diffusion models for
data augmentation but also highlights its potential in generating new training
data to improve performance on simple downstream vision tasks.
article_number: '2304.10253'
article_processing_charge: No
author:
- first_name: Max F.
full_name: Burg, Max F.
last_name: Burg
- first_name: Florian
full_name: Wenzel, Florian
last_name: Wenzel
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Osama
full_name: Makansi, Osama
last_name: Makansi
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Chris
full_name: Russell, Chris
last_name: Russell
citation:
ama: Burg MF, Wenzel F, Zietlow D, et al. A data augmentation perspective on diffusion
models and retrieval. arXiv. doi:10.48550/arXiv.2304.10253
apa: Burg, M. F., Wenzel, F., Zietlow, D., Horn, M., Makansi, O., Locatello, F.,
& Russell, C. (n.d.). A data augmentation perspective on diffusion models
and retrieval. arXiv. https://doi.org/10.48550/arXiv.2304.10253
chicago: Burg, Max F., Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi,
Francesco Locatello, and Chris Russell. “A Data Augmentation Perspective on Diffusion
Models and Retrieval.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2304.10253.
ieee: M. F. Burg et al., “A data augmentation perspective on diffusion models
and retrieval,” arXiv. .
ista: Burg MF, Wenzel F, Zietlow D, Horn M, Makansi O, Locatello F, Russell C. A
data augmentation perspective on diffusion models and retrieval. arXiv, 2304.10253.
mla: Burg, Max F., et al. “A Data Augmentation Perspective on Diffusion Models and
Retrieval.” ArXiv, 2304.10253, doi:10.48550/arXiv.2304.10253.
short: M.F. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell,
ArXiv (n.d.).
date_created: 2023-08-22T14:18:43Z
date_published: 2023-04-20T00:00:00Z
date_updated: 2023-09-13T08:51:56Z
day: '20'
department:
- _id: FrLo
doi: 10.48550/arXiv.2304.10253
extern: '1'
external_id:
arxiv:
- '2304.10253'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2304.10253
month: '04'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: A data augmentation perspective on diffusion models and retrieval
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14211'
abstract:
- lang: eng
text: 'Causal discovery methods are intrinsically constrained by the set of assumptions
needed to ensure structure identifiability. Moreover additional restrictions are
often imposed in order to simplify the inference task: this is the case for the
Gaussian noise assumption on additive non-linear models, which is common to many
causal discovery approaches. In this paper we show the shortcomings of inference
under this hypothesis, analyzing the risk of edge inversion under violation of
Gaussianity of the noise terms. Then, we propose a novel method for inferring
the topological ordering of the variables in the causal graph, from data generated
according to an additive non-linear model with a generic noise distribution. This
leads to NoGAM (Not only Gaussian Additive noise Models), a causal discovery algorithm
with a minimal set of assumptions and state of the art performance, experimentally
benchmarked on synthetic data.'
article_processing_charge: No
author:
- first_name: Francesco
full_name: Montagna, Francesco
last_name: Montagna
- first_name: Nicoletta
full_name: Noceti, Nicoletta
last_name: Noceti
- first_name: Lorenzo
full_name: Rosasco, Lorenzo
last_name: Rosasco
- first_name: Kun
full_name: Zhang, Kun
last_name: Zhang
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Causal discovery with
score matching on additive models with arbitrary noise. In: 2nd Conference
on Causal Learning and Reasoning. ; 2023.'
apa: Montagna, F., Noceti, N., Rosasco, L., Zhang, K., & Locatello, F. (2023).
Causal discovery with score matching on additive models with arbitrary noise.
In 2nd Conference on Causal Learning and Reasoning. Tübingen, Germany.
chicago: Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and
Francesco Locatello. “Causal Discovery with Score Matching on Additive Models
with Arbitrary Noise.” In 2nd Conference on Causal Learning and Reasoning,
2023.
ieee: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Causal discovery
with score matching on additive models with arbitrary noise,” in 2nd Conference
on Causal Learning and Reasoning, Tübingen, Germany, 2023.
ista: 'Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. 2023. Causal discovery
with score matching on additive models with arbitrary noise. 2nd Conference on
Causal Learning and Reasoning. CLeaR: Conference on Causal Learning and Reasoning.'
mla: Montagna, Francesco, et al. “Causal Discovery with Score Matching on Additive
Models with Arbitrary Noise.” 2nd Conference on Causal Learning and Reasoning,
2023.
short: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference
on Causal Learning and Reasoning, 2023.
conference:
end_date: 2023-04-14
location: Tübingen, Germany
name: 'CLeaR: Conference on Causal Learning and Reasoning'
start_date: 2023-04-11
date_created: 2023-08-22T14:19:21Z
date_published: 2023-04-01T00:00:00Z
date_updated: 2023-09-13T09:00:31Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2304.03265'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2304.03265
month: '04'
oa: 1
oa_version: Preprint
publication: 2nd Conference on Causal Learning and Reasoning
publication_status: published
quality_controlled: '1'
scopus_import: '1'
status: public
title: Causal discovery with score matching on additive models with arbitrary noise
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14212'
abstract:
- lang: eng
text: This paper demonstrates how to discover the whole causal graph from the second
derivative of the log-likelihood in observational non-linear additive Gaussian
noise models. Leveraging scalable machine learning approaches to approximate the
score function ∇logp(X), we extend the work of Rolland et al. (2022) that only
recovers the topological order from the score and requires an expensive pruning
step removing spurious edges among those admitted by the ordering. Our analysis
leads to DAS (acronym for Discovery At Scale), a practical algorithm that reduces
the complexity of the pruning by a factor proportional to the graph size. In practice,
DAS achieves competitive accuracy with current state-of-the-art while being over
an order of magnitude faster. Overall, our approach enables principled and scalable
causal discovery, significantly lowering the compute bar.
article_processing_charge: No
author:
- first_name: Francesco
full_name: Montagna, Francesco
last_name: Montagna
- first_name: Nicoletta
full_name: Noceti, Nicoletta
last_name: Noceti
- first_name: Lorenzo
full_name: Rosasco, Lorenzo
last_name: Rosasco
- first_name: Kun
full_name: Zhang, Kun
last_name: Zhang
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. Scalable causal discovery
with score matching. In: 2nd Conference on Causal Learning and Reasoning.
; 2023.'
apa: Montagna, F., Noceti, N., Rosasco, L., Zhang, K., & Locatello, F. (2023).
Scalable causal discovery with score matching. In 2nd Conference on Causal
Learning and Reasoning. Tübingen, Germany.
chicago: Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and
Francesco Locatello. “Scalable Causal Discovery with Score Matching.” In 2nd
Conference on Causal Learning and Reasoning, 2023.
ieee: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Scalable
causal discovery with score matching,” in 2nd Conference on Causal Learning
and Reasoning, Tübingen, Germany, 2023.
ista: 'Montagna F, Noceti N, Rosasco L, Zhang K, Locatello F. 2023. Scalable causal
discovery with score matching. 2nd Conference on Causal Learning and Reasoning.
CLeaR: Conference on Causal Learning and Reasoning.'
mla: Montagna, Francesco, et al. “Scalable Causal Discovery with Score Matching.”
2nd Conference on Causal Learning and Reasoning, 2023.
short: F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference
on Causal Learning and Reasoning, 2023.
conference:
end_date: 2023-04-14
location: Tübingen, Germany
name: 'CLeaR: Conference on Causal Learning and Reasoning'
start_date: 2023-04-11
date_created: 2023-08-22T14:19:40Z
date_published: 2023-04-01T00:00:00Z
date_updated: 2023-09-13T09:03:24Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2304.03382'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2304.03382
month: '04'
oa: 1
oa_version: Preprint
publication: 2nd Conference on Causal Learning and Reasoning
publication_status: published
quality_controlled: '1'
scopus_import: '1'
status: public
title: Scalable causal discovery with score matching
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14214'
abstract:
- lang: eng
text: 'Recent years have seen a surge of interest in learning high-level causal
representations from low-level image pairs under interventions. Yet, existing
efforts are largely limited to simple synthetic settings that are far away from
real-world problems. In this paper, we present Causal Triplet, a causal representation
learning benchmark featuring not only visually more complex scenes, but also two
crucial desiderata commonly overlooked in previous works: (i) an actionable counterfactual
setting, where only certain object-level variables allow for counterfactual observations
whereas others do not; (ii) an interventional downstream task with an emphasis
on out-of-distribution robustness from the independent causal mechanisms principle.
Through extensive experiments, we find that models built with the knowledge of
disentangled or object-centric representations significantly outperform their
distributed counterparts. However, recent causal representation learning methods
still struggle to identify such latent structures, indicating substantial challenges
and opportunities for future work.'
article_processing_charge: No
author:
- first_name: Yuejiang
full_name: Liu, Yuejiang
last_name: Liu
- first_name: Alexandre
full_name: Alahi, Alexandre
last_name: Alahi
- first_name: Chris
full_name: Russell, Chris
last_name: Russell
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Liu Y, Alahi A, Russell C, et al. Causal triplet: An open challenge for intervention-centric
causal representation learning. In: 2nd Conference on Causal Learning and Reasoning.
; 2023.'
apa: 'Liu, Y., Alahi, A., Russell, C., Horn, M., Zietlow, D., Schölkopf, B., &
Locatello, F. (2023). Causal triplet: An open challenge for intervention-centric
causal representation learning. In 2nd Conference on Causal Learning and Reasoning.
Tübingen, Germany.'
chicago: 'Liu, Yuejiang, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow,
Bernhard Schölkopf, and Francesco Locatello. “Causal Triplet: An Open Challenge
for Intervention-Centric Causal Representation Learning.” In 2nd Conference
on Causal Learning and Reasoning, 2023.'
ieee: 'Y. Liu et al., “Causal triplet: An open challenge for intervention-centric
causal representation learning,” in 2nd Conference on Causal Learning and Reasoning,
Tübingen, Germany, 2023.'
ista: 'Liu Y, Alahi A, Russell C, Horn M, Zietlow D, Schölkopf B, Locatello F. 2023.
Causal triplet: An open challenge for intervention-centric causal representation
learning. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on
Causal Learning and Reasoning.'
mla: 'Liu, Yuejiang, et al. “Causal Triplet: An Open Challenge for Intervention-Centric
Causal Representation Learning.” 2nd Conference on Causal Learning and Reasoning,
2023.'
short: Y. Liu, A. Alahi, C. Russell, M. Horn, D. Zietlow, B. Schölkopf, F. Locatello,
in:, 2nd Conference on Causal Learning and Reasoning, 2023.
conference:
end_date: 2023-04-14
location: Tübingen, Germany
name: 'CLeaR: Conference on Causal Learning and Reasoning'
start_date: 2023-04-11
date_created: 2023-08-22T14:20:18Z
date_published: 2023-04-12T00:00:00Z
date_updated: 2023-09-13T09:23:08Z
day: '12'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2301.05169'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2301.05169
month: '04'
oa: 1
oa_version: Preprint
publication: 2nd Conference on Causal Learning and Reasoning
publication_status: published
quality_controlled: '1'
status: public
title: 'Causal triplet: An open challenge for intervention-centric causal representation
learning'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14217'
abstract:
- lang: eng
text: 'Neural networks embed the geometric structure of a data manifold lying in
a high-dimensional space into latent representations. Ideally, the distribution
of the data points in the latent space should depend only on the task, the data,
the loss, and other architecture-specific constraints. However, factors such as
the random weights initialization, training hyperparameters, or other sources
of randomness in the training phase may induce incoherent latent spaces that hinder
any form of reuse. Nevertheless, we empirically observe that, under the same data
and modeling choices, the angles between the encodings within distinct latent
spaces do not change. In this work, we propose the latent similarity between each
sample and a fixed set of anchors as an alternative data representation, demonstrating
that it can enforce the desired invariances without any additional training. We
show how neural architectures can leverage these relative representations to guarantee,
in practice, invariance to latent isometries and rescalings, effectively enabling
latent space communication: from zero-shot model stitching to latent space comparison
between diverse settings. We extensively validate the generalization capability
of our approach on different datasets, spanning various modalities (images, text,
graphs), tasks (e.g., classification, reconstruction) and architectures (e.g.,
CNNs, GCNs, transformers).'
article_processing_charge: No
author:
- first_name: Luca
full_name: Moschella, Luca
last_name: Moschella
- first_name: Valentino
full_name: Maiorca, Valentino
last_name: Maiorca
- first_name: Marco
full_name: Fumero, Marco
last_name: Fumero
- first_name: Antonio
full_name: Norelli, Antonio
last_name: Norelli
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Emanuele
full_name: Rodolà, Emanuele
last_name: Rodolà
citation:
ama: 'Moschella L, Maiorca V, Fumero M, Norelli A, Locatello F, Rodolà E. Relative
representations enable zero-shot latent space communication. In: The 11th International
Conference on Learning Representations. ; 2023.'
apa: Moschella, L., Maiorca, V., Fumero, M., Norelli, A., Locatello, F., & Rodolà,
E. (2023). Relative representations enable zero-shot latent space communication.
In The 11th International Conference on Learning Representations. Kigali,
Rwanda.
chicago: Moschella, Luca, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco
Locatello, and Emanuele Rodolà. “Relative Representations Enable Zero-Shot Latent
Space Communication.” In The 11th International Conference on Learning Representations,
2023.
ieee: L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, and E. Rodolà,
“Relative representations enable zero-shot latent space communication,” in The
11th International Conference on Learning Representations, Kigali, Rwanda,
2023.
ista: Moschella L, Maiorca V, Fumero M, Norelli A, Locatello F, Rodolà E. 2023.
Relative representations enable zero-shot latent space communication. The 11th
International Conference on Learning Representations. International Conference
on Machine Learning Representations.
mla: Moschella, Luca, et al. “Relative Representations Enable Zero-Shot Latent Space
Communication.” The 11th International Conference on Learning Representations,
2023.
short: L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, E. Rodolà,
in:, The 11th International Conference on Learning Representations, 2023.
conference:
end_date: 2023-05-05
location: Kigali, Rwanda
name: International Conference on Machine Learning Representations
start_date: 2023-05-01
date_created: 2023-08-22T14:22:20Z
date_published: 2023-05-01T00:00:00Z
date_updated: 2023-09-13T09:44:26Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2209.15430'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2209.15430
month: '05'
oa: 1
oa_version: Preprint
publication: The 11th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: Relative representations enable zero-shot latent space communication
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14222'
abstract:
- lang: eng
text: Learning generative object models from unlabelled videos is a long standing
problem and required for causal scene modeling. We decompose this problem into
three easier subtasks, and provide candidate solutions for each of them. Inspired
by the Common Fate Principle of Gestalt Psychology, we first extract (noisy) masks
of moving objects via unsupervised motion segmentation. Second, generative models
are trained on the masks of the background and the moving objects, respectively.
Third, background and foreground models are combined in a conditional "dead leaves"
scene model to sample novel scene configurations where occlusions and depth layering
arise naturally. To evaluate the individual stages, we introduce the Fishbowl
dataset positioned between complex real-world scenes and common object-centric
benchmarks of simplistic objects. We show that our approach allows learning generative
models that generalize beyond the occlusions present in the input videos, and
represent scenes in a modular fashion that allows sampling plausible scenes outside
the training distribution by permitting, for instance, object numbers or densities
not observed in the training set.
article_number: '2110.06562'
article_processing_charge: No
author:
- first_name: Matthias
full_name: Tangemann, Matthias
last_name: Tangemann
- first_name: Steffen
full_name: Schneider, Steffen
last_name: Schneider
- first_name: Julius von
full_name: Kügelgen, Julius von
last_name: Kügelgen
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Peter
full_name: Gehler, Peter
last_name: Gehler
- first_name: Thomas
full_name: Brox, Thomas
last_name: Brox
- first_name: Matthias
full_name: Kümmerer, Matthias
last_name: Kümmerer
- first_name: Matthias
full_name: Bethge, Matthias
last_name: Bethge
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
citation:
ama: 'Tangemann M, Schneider S, Kügelgen J von, et al. Unsupervised object learning
via common fate. In: 2nd Conference on Causal Learning and Reasoning. ;
2023.'
apa: Tangemann, M., Schneider, S., Kügelgen, J. von, Locatello, F., Gehler, P.,
Brox, T., … Schölkopf, B. (2023). Unsupervised object learning via common fate.
In 2nd Conference on Causal Learning and Reasoning. Tübingen, Germany.
chicago: Tangemann, Matthias, Steffen Schneider, Julius von Kügelgen, Francesco
Locatello, Peter Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, and
Bernhard Schölkopf. “Unsupervised Object Learning via Common Fate.” In 2nd
Conference on Causal Learning and Reasoning, 2023.
ieee: M. Tangemann et al., “Unsupervised object learning via common fate,”
in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany,
2023.
ista: 'Tangemann M, Schneider S, Kügelgen J von, Locatello F, Gehler P, Brox T,
Kümmerer M, Bethge M, Schölkopf B. 2023. Unsupervised object learning via common
fate. 2nd Conference on Causal Learning and Reasoning. CLeaR: Conference on Causal
Learning and Reasoning, 2110.06562.'
mla: Tangemann, Matthias, et al. “Unsupervised Object Learning via Common Fate.”
2nd Conference on Causal Learning and Reasoning, 2110.06562, 2023.
short: M. Tangemann, S. Schneider, J. von Kügelgen, F. Locatello, P. Gehler, T.
Brox, M. Kümmerer, M. Bethge, B. Schölkopf, in:, 2nd Conference on Causal Learning
and Reasoning, 2023.
conference:
end_date: 2023-04-14
location: Tübingen, Germany
name: 'CLeaR: Conference on Causal Learning and Reasoning'
start_date: 2023-04-11
date_created: 2023-08-22T14:23:54Z
date_published: 2023-04-15T00:00:00Z
date_updated: 2023-09-13T11:31:14Z
day: '15'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2110.06562'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2110.06562
month: '04'
oa: 1
oa_version: Preprint
publication: 2nd Conference on Causal Learning and Reasoning
publication_status: published
quality_controlled: '1'
status: public
title: Unsupervised object learning via common fate
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14218'
abstract:
- lang: eng
text: Humans naturally decompose their environment into entities at the appropriate
level of abstraction to act in the world. Allowing machine learning algorithms
to derive this decomposition in an unsupervised way has become an important line
of research. However, current methods are restricted to simulated data or require
additional information in the form of motion or depth in order to successfully
discover objects. In this work, we overcome this limitation by showing that reconstructing
features from models trained in a self-supervised manner is a sufficient training
signal for object-centric representations to arise in a fully unsupervised way.
Our approach, DINOSAUR, significantly out-performs existing image-based object-centric
learning models on simulated data and is the first unsupervised object-centric
model that scales to real-world datasets such as COCO and PASCAL VOC. DINOSAUR
is conceptually simple and shows competitive performance compared to more involved
pipelines from the computer vision literature.
article_processing_charge: No
author:
- first_name: Maximilian
full_name: Seitzer, Maximilian
last_name: Seitzer
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Andrii
full_name: Zadaianchuk, Andrii
last_name: Zadaianchuk
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Tianjun
full_name: Xiao, Tianjun
last_name: Xiao
- first_name: Carl-Johann Simon-Gabriel
full_name: Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel
last_name: Carl-Johann Simon-Gabriel
- first_name: Tong
full_name: He, Tong
last_name: He
- first_name: Zheng
full_name: Zhang, Zheng
last_name: Zhang
- first_name: Bernhard
full_name: Schölkopf, Bernhard
last_name: Schölkopf
- first_name: Thomas
full_name: Brox, Thomas
last_name: Brox
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Seitzer M, Horn M, Zadaianchuk A, et al. Bridging the gap to real-world object-centric
learning. In: The 11th International Conference on Learning Representations.
; 2023.'
apa: Seitzer, M., Horn, M., Zadaianchuk, A., Zietlow, D., Xiao, T., Carl-Johann
Simon-Gabriel, C.-J. S.-G., … Locatello, F. (2023). Bridging the gap to real-world
object-centric learning. In The 11th International Conference on Learning Representations.
Kigali, Rwanda.
chicago: Seitzer, Maximilian, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun
Xiao, Carl-Johann Simon-Gabriel Carl-Johann Simon-Gabriel, Tong He, et al. “Bridging
the Gap to Real-World Object-Centric Learning.” In The 11th International Conference
on Learning Representations, 2023.
ieee: M. Seitzer et al., “Bridging the gap to real-world object-centric learning,”
in The 11th International Conference on Learning Representations, Kigali,
Rwanda, 2023.
ista: 'Seitzer M, Horn M, Zadaianchuk A, Zietlow D, Xiao T, Carl-Johann Simon-Gabriel
C-JS-G, He T, Zhang Z, Schölkopf B, Brox T, Locatello F. 2023. Bridging the gap
to real-world object-centric learning. The 11th International Conference on Learning
Representations. ICLR: International Conference on Learning Representations.'
mla: Seitzer, Maximilian, et al. “Bridging the Gap to Real-World Object-Centric
Learning.” The 11th International Conference on Learning Representations,
2023.
short: M. Seitzer, M. Horn, A. Zadaianchuk, D. Zietlow, T. Xiao, C.-J.S.-G. Carl-Johann
Simon-Gabriel, T. He, Z. Zhang, B. Schölkopf, T. Brox, F. Locatello, in:, The
11th International Conference on Learning Representations, 2023.
conference:
end_date: 2023-05-05
location: Kigali, Rwanda
name: 'ICLR: International Conference on Learning Representations'
start_date: 2023-05-01
date_created: 2023-08-22T14:22:41Z
date_published: 2023-05-10T00:00:00Z
date_updated: 2023-09-13T11:37:03Z
day: '10'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2209.14860'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2209.14860
month: '05'
oa: 1
oa_version: Preprint
publication: The 11th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: Bridging the gap to real-world object-centric learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14219'
abstract:
- lang: eng
text: "In this paper, we show that recent advances in self-supervised feature\r\nlearning
enable unsupervised object discovery and semantic segmentation with a\r\nperformance
that matches the state of the field on supervised semantic\r\nsegmentation 10
years ago. We propose a methodology based on unsupervised\r\nsaliency masks and
self-supervised feature clustering to kickstart object\r\ndiscovery followed by
training a semantic segmentation network on pseudo-labels\r\nto bootstrap the
system on images with multiple objects. We present results on\r\nPASCAL VOC that
go far beyond the current state of the art (50.0 mIoU), and we\r\nreport for the
first time results on MS COCO for the whole set of 81 classes:\r\nour method discovers
34 categories with more than $20\\%$ IoU, while obtaining\r\nan average IoU of
19.6 for all 81 categories."
article_processing_charge: No
author:
- first_name: Andrii
full_name: Zadaianchuk, Andrii
last_name: Zadaianchuk
- first_name: Matthaeus
full_name: Kleindessner, Matthaeus
last_name: Kleindessner
- first_name: Yi
full_name: Zhu, Yi
last_name: Zhu
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Thomas
full_name: Brox, Thomas
last_name: Brox
citation:
ama: 'Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. Unsupervised semantic
segmentation with self-supervised object-centric representations. In: The 11th
International Conference on Learning Representations. ; 2023.'
apa: Zadaianchuk, A., Kleindessner, M., Zhu, Y., Locatello, F., & Brox, T. (2023).
Unsupervised semantic segmentation with self-supervised object-centric representations.
In The 11th International Conference on Learning Representations. Kigali,
Rwanda.
chicago: Zadaianchuk, Andrii, Matthaeus Kleindessner, Yi Zhu, Francesco Locatello,
and Thomas Brox. “Unsupervised Semantic Segmentation with Self-Supervised Object-Centric
Representations.” In The 11th International Conference on Learning Representations,
2023.
ieee: A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, and T. Brox, “Unsupervised
semantic segmentation with self-supervised object-centric representations,” in
The 11th International Conference on Learning Representations, Kigali,
Rwanda, 2023.
ista: 'Zadaianchuk A, Kleindessner M, Zhu Y, Locatello F, Brox T. 2023. Unsupervised
semantic segmentation with self-supervised object-centric representations. The
11th International Conference on Learning Representations. ICLR: International
Conference on Learning Representations.'
mla: Zadaianchuk, Andrii, et al. “Unsupervised Semantic Segmentation with Self-Supervised
Object-Centric Representations.” The 11th International Conference on Learning
Representations, 2023.
short: A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, T. Brox, in:, The
11th International Conference on Learning Representations, 2023.
conference:
end_date: 2023-05-05
location: Kigali, Rwanda
name: 'ICLR: International Conference on Learning Representations'
start_date: 2023-05-01
date_created: 2023-08-22T14:22:58Z
date_published: 2023-05-01T00:00:00Z
date_updated: 2023-09-13T11:25:43Z
day: '01'
department:
- _id: FrLo
extern: '1'
external_id:
arxiv:
- '2207.05027'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2207.05027
month: '05'
oa: 1
oa_version: Preprint
publication: The 11th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: Unsupervised semantic segmentation with self-supervised object-centric representations
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14333'
abstract:
- lang: eng
text: "As causal ground truth is incredibly rare, causal discovery algorithms are\r\ncommonly
only evaluated on simulated data. This is concerning, given that\r\nsimulations
reflect common preconceptions about generating processes regarding\r\nnoise distributions,
model classes, and more. In this work, we propose a novel\r\nmethod for falsifying
the output of a causal discovery algorithm in the absence\r\nof ground truth.
Our key insight is that while statistical learning seeks\r\nstability across subsets
of data points, causal learning should seek stability\r\nacross subsets of variables.
Motivated by this insight, our method relies on a\r\nnotion of compatibility between
causal graphs learned on different subsets of\r\nvariables. We prove that detecting
incompatibilities can falsify wrongly\r\ninferred causal relations due to violation
of assumptions or errors from finite\r\nsample effects. Although passing such
compatibility tests is only a necessary\r\ncriterion for good performance, we
argue that it provides strong evidence for\r\nthe causal models whenever compatibility
entails strong implications for the\r\njoint distribution. We also demonstrate
experimentally that detection of\r\nincompatibilities can aid in causal model
selection."
article_number: '2307.09552'
article_processing_charge: No
author:
- first_name: Philipp M.
full_name: Faller, Philipp M.
last_name: Faller
- first_name: Leena Chennuru
full_name: Vankadara, Leena Chennuru
last_name: Vankadara
- first_name: Atalanti A.
full_name: Mastakouri, Atalanti A.
last_name: Mastakouri
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Dominik
full_name: Janzing, Dominik
last_name: Janzing
citation:
ama: 'Faller PM, Vankadara LC, Mastakouri AA, Locatello F, Janzing D. Self-compatibility:
Evaluating causal discovery without ground truth. arXiv. doi:10.48550/arXiv.2307.09552'
apa: 'Faller, P. M., Vankadara, L. C., Mastakouri, A. A., Locatello, F., & Janzing,
D. (n.d.). Self-compatibility: Evaluating causal discovery without ground truth.
arXiv. https://doi.org/10.48550/arXiv.2307.09552'
chicago: 'Faller, Philipp M., Leena Chennuru Vankadara, Atalanti A. Mastakouri,
Francesco Locatello, and Dominik Janzing. “Self-Compatibility: Evaluating Causal
Discovery without Ground Truth.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2307.09552.'
ieee: 'P. M. Faller, L. C. Vankadara, A. A. Mastakouri, F. Locatello, and D. Janzing,
“Self-compatibility: Evaluating causal discovery without ground truth,” arXiv.
.'
ista: 'Faller PM, Vankadara LC, Mastakouri AA, Locatello F, Janzing D. Self-compatibility:
Evaluating causal discovery without ground truth. arXiv, 2307.09552.'
mla: 'Faller, Philipp M., et al. “Self-Compatibility: Evaluating Causal Discovery
without Ground Truth.” ArXiv, 2307.09552, doi:10.48550/arXiv.2307.09552.'
short: P.M. Faller, L.C. Vankadara, A.A. Mastakouri, F. Locatello, D. Janzing, ArXiv
(n.d.).
date_created: 2023-09-13T12:44:59Z
date_published: 2023-07-18T00:00:00Z
date_updated: 2023-09-13T12:47:53Z
day: '18'
department:
- _id: FrLo
doi: 10.48550/arXiv.2307.09552
extern: '1'
external_id:
arxiv:
- '2307.09552'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2307.09552
month: '07'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: 'Self-compatibility: Evaluating causal discovery without ground truth'
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...