---
_id: '12819'
abstract:
- lang: eng
text: 'Reaching a high cavity population with a coherent pump in the strong-coupling
regime of a single-atom laser is impossible due to the photon blockade effect.
In this Letter, we experimentally demonstrate that in a single-atom maser based
on a transmon strongly coupled to two resonators, it is possible to pump over
a dozen photons into the system. The first high-quality resonator plays the role
of a usual lasing cavity, and the second one presents a controlled dissipation
channel, bolstering population inversion, and modifies the energy-level structure
to lift the blockade. As confirmation of the lasing action, we observe conventional
laser features such as a narrowing of the emission linewidth and external signal
amplification. Additionally, we report unique single-atom features: self-quenching
and several lasing thresholds.'
acknowledgement: We thank N.N. Abramov for assistance with the experimental setup.
The sample was fabricated using equipment of MIPT Shared Facilities Center. This
research was supported by Russian Science Foundation, grant no. 21-72-30026.
article_number: L031701
article_processing_charge: No
article_type: letter_note
author:
- first_name: Alesya
full_name: Sokolova, Alesya
id: 2d0a0600-edfb-11eb-afb5-c0f5fa7f4f3a
last_name: Sokolova
orcid: 0000-0002-8308-4144
- first_name: D. A.
full_name: Kalacheva, D. A.
last_name: Kalacheva
- first_name: G. P.
full_name: Fedorov, G. P.
last_name: Fedorov
- first_name: O. V.
full_name: Astafiev, O. V.
last_name: Astafiev
citation:
ama: Sokolova A, Kalacheva DA, Fedorov GP, Astafiev OV. Overcoming photon blockade
in a circuit-QED single-atom maser with engineered metastability and strong coupling.
Physical Review A. 2023;107(3). doi:10.1103/PhysRevA.107.L031701
apa: Sokolova, A., Kalacheva, D. A., Fedorov, G. P., & Astafiev, O. V. (2023).
Overcoming photon blockade in a circuit-QED single-atom maser with engineered
metastability and strong coupling. Physical Review A. American Physical
Society. https://doi.org/10.1103/PhysRevA.107.L031701
chicago: Sokolova, Alesya, D. A. Kalacheva, G. P. Fedorov, and O. V. Astafiev. “Overcoming
Photon Blockade in a Circuit-QED Single-Atom Maser with Engineered Metastability
and Strong Coupling.” Physical Review A. American Physical Society, 2023.
https://doi.org/10.1103/PhysRevA.107.L031701.
ieee: A. Sokolova, D. A. Kalacheva, G. P. Fedorov, and O. V. Astafiev, “Overcoming
photon blockade in a circuit-QED single-atom maser with engineered metastability
and strong coupling,” Physical Review A, vol. 107, no. 3. American Physical
Society, 2023.
ista: Sokolova A, Kalacheva DA, Fedorov GP, Astafiev OV. 2023. Overcoming photon
blockade in a circuit-QED single-atom maser with engineered metastability and
strong coupling. Physical Review A. 107(3), L031701.
mla: Sokolova, Alesya, et al. “Overcoming Photon Blockade in a Circuit-QED Single-Atom
Maser with Engineered Metastability and Strong Coupling.” Physical Review A,
vol. 107, no. 3, L031701, American Physical Society, 2023, doi:10.1103/PhysRevA.107.L031701.
short: A. Sokolova, D.A. Kalacheva, G.P. Fedorov, O.V. Astafiev, Physical Review
A 107 (2023).
date_created: 2023-04-09T22:01:00Z
date_published: 2023-03-22T00:00:00Z
date_updated: 2023-08-01T14:06:05Z
day: '22'
department:
- _id: JoFi
doi: 10.1103/PhysRevA.107.L031701
external_id:
arxiv:
- '2209.05165'
isi:
- '000957799000006'
intvolume: ' 107'
isi: 1
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2209.05165
month: '03'
oa: 1
oa_version: Preprint
publication: Physical Review A
publication_identifier:
eissn:
- 2469-9934
issn:
- 2469-9926
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Overcoming photon blockade in a circuit-QED single-atom maser with engineered
metastability and strong coupling
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 107
year: '2023'
...
---
_id: '12861'
abstract:
- lang: eng
text: The field of indirect reciprocity investigates how social norms can foster
cooperation when individuals continuously monitor and assess each other’s social
interactions. By adhering to certain social norms, cooperating individuals can
improve their reputation and, in turn, receive benefits from others. Eight social
norms, known as the “leading eight," have been shown to effectively promote the
evolution of cooperation as long as information is public and reliable. These
norms categorize group members as either ’good’ or ’bad’. In this study, we examine
a scenario where individuals instead assign nuanced reputation scores to each
other, and only cooperate with those whose reputation exceeds a certain threshold.
We find both analytically and through simulations that such quantitative assessments
are error-correcting, thus facilitating cooperation in situations where information
is private and unreliable. Moreover, our results identify four specific norms
that are robust to such conditions, and may be relevant for helping to sustain
cooperation in natural populations.
acknowledgement: 'This work was supported by the European Research Council CoG 863818
(ForM-SMArt) (to K.C.) and the European Research Council Starting Grant 850529:
E-DIRECT (to C.H.). L.S. received additional partial support by the Austrian Science
Fund (FWF) under grant Z211-N23 (Wittgenstein Award), and also thanks the support
by the Stochastic Analysis and Application Research Center (SAARC) under National
Research Foundation of Korea grant NRF-2019R1A5A1028324. The authors additionally
thank Stefan Schmid for providing access to his lab infrastructure at the University
of Vienna for the purpose of collecting simulation data.'
article_number: '2086'
article_processing_charge: No
article_type: original
author:
- first_name: Laura
full_name: Schmid, Laura
id: 38B437DE-F248-11E8-B48F-1D18A9856A87
last_name: Schmid
orcid: 0000-0002-6978-7329
- first_name: Farbod
full_name: Ekbatani, Farbod
last_name: Ekbatani
- first_name: Christian
full_name: Hilbe, Christian
id: 2FDF8F3C-F248-11E8-B48F-1D18A9856A87
last_name: Hilbe
orcid: 0000-0001-5116-955X
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
citation:
ama: Schmid L, Ekbatani F, Hilbe C, Chatterjee K. Quantitative assessment can stabilize
indirect reciprocity under imperfect information. Nature Communications.
2023;14. doi:10.1038/s41467-023-37817-x
apa: Schmid, L., Ekbatani, F., Hilbe, C., & Chatterjee, K. (2023). Quantitative
assessment can stabilize indirect reciprocity under imperfect information. Nature
Communications. Springer Nature. https://doi.org/10.1038/s41467-023-37817-x
chicago: Schmid, Laura, Farbod Ekbatani, Christian Hilbe, and Krishnendu Chatterjee.
“Quantitative Assessment Can Stabilize Indirect Reciprocity under Imperfect Information.”
Nature Communications. Springer Nature, 2023. https://doi.org/10.1038/s41467-023-37817-x.
ieee: L. Schmid, F. Ekbatani, C. Hilbe, and K. Chatterjee, “Quantitative assessment
can stabilize indirect reciprocity under imperfect information,” Nature Communications,
vol. 14. Springer Nature, 2023.
ista: Schmid L, Ekbatani F, Hilbe C, Chatterjee K. 2023. Quantitative assessment
can stabilize indirect reciprocity under imperfect information. Nature Communications.
14, 2086.
mla: Schmid, Laura, et al. “Quantitative Assessment Can Stabilize Indirect Reciprocity
under Imperfect Information.” Nature Communications, vol. 14, 2086, Springer
Nature, 2023, doi:10.1038/s41467-023-37817-x.
short: L. Schmid, F. Ekbatani, C. Hilbe, K. Chatterjee, Nature Communications 14
(2023).
date_created: 2023-04-23T22:01:03Z
date_published: 2023-04-12T00:00:00Z
date_updated: 2023-08-01T14:15:57Z
day: '12'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.1038/s41467-023-37817-x
ec_funded: 1
external_id:
isi:
- '001003644100020'
pmid:
- '37045828'
file:
- access_level: open_access
checksum: a4b3b7b36fbef068cabf4fb99501fef6
content_type: application/pdf
creator: dernst
date_created: 2023-04-25T09:13:53Z
date_updated: 2023-04-25T09:13:53Z
file_id: '12868'
file_name: 2023_NatureComm_Schmid.pdf
file_size: 1786475
relation: main_file
success: 1
file_date_updated: 2023-04-25T09:13:53Z
has_accepted_license: '1'
intvolume: ' 14'
isi: 1
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantitative assessment can stabilize indirect reciprocity under imperfect
information
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 14
year: '2023'
...
---
_id: '12862'
abstract:
- lang: eng
text: Despite the considerable progress of in vivo neural recording techniques,
inferring the biophysical mechanisms underlying large scale coordination of brain
activity from neural data remains challenging. One obstacle is the difficulty
to link high dimensional functional connectivity measures to mechanistic models
of network activity. We address this issue by investigating spike-field coupling
(SFC) measurements, which quantify the synchronization between, on the one hand,
the action potentials produced by neurons, and on the other hand mesoscopic “field”
signals, reflecting subthreshold activities at possibly multiple recording sites.
As the number of recording sites gets large, the amount of pairwise SFC measurements
becomes overwhelmingly challenging to interpret. We develop Generalized Phase
Locking Analysis (GPLA) as an interpretable dimensionality reduction of this multivariate
SFC. GPLA describes the dominant coupling between field activity and neural ensembles
across space and frequencies. We show that GPLA features are biophysically interpretable
when used in conjunction with appropriate network models, such that we can identify
the influence of underlying circuit properties on these features. We demonstrate
the statistical benefits and interpretability of this approach in various computational
models and Utah array recordings. The results suggest that GPLA, used jointly
with biophysical modeling, can help uncover the contribution of recurrent microcircuits
to the spatio-temporal dynamics observed in multi-channel experimental recordings.
acknowledgement: "We thank Britni Crocker for help with preprocessing of the data
and spike sorting; Joachim Werner and Michael Schnabel for their excellent IT support;
Andreas Tolias for help with the initial implantation’s of the Utah arrays.\r\nAll
authors were supported by the Max Planck Society. M.B. was supported by the German\r\nFederal
Ministry of Education and Research (BMBF) through the funding scheme received by\r\nthe
Tübingen AI Center, FKZ: 01IS18039B. N.K.L. and V.K. acknowledge the support from
the\r\nShanghai Municipal Science and Technology Major Project (Grant No. 2019SHZDZX02).
The funders had no role in study design, data collection and analysis, decision
to publish, or preparation of the manuscript. "
article_number: e1010983
article_processing_charge: No
article_type: original
author:
- first_name: Shervin
full_name: Safavi, Shervin
last_name: Safavi
- first_name: Theofanis I.
full_name: Panagiotaropoulos, Theofanis I.
last_name: Panagiotaropoulos
- first_name: Vishal
full_name: Kapoor, Vishal
last_name: Kapoor
- first_name: Juan F
full_name: Ramirez Villegas, Juan F
id: 44B06F76-F248-11E8-B48F-1D18A9856A87
last_name: Ramirez Villegas
- first_name: Nikos K.
full_name: Logothetis, Nikos K.
last_name: Logothetis
- first_name: Michel
full_name: Besserve, Michel
last_name: Besserve
citation:
ama: Safavi S, Panagiotaropoulos TI, Kapoor V, Ramirez Villegas JF, Logothetis NK,
Besserve M. Uncovering the organization of neural circuits with Generalized Phase
Locking Analysis. PLoS Computational Biology. 2023;19(4). doi:10.1371/journal.pcbi.1010983
apa: Safavi, S., Panagiotaropoulos, T. I., Kapoor, V., Ramirez Villegas, J. F.,
Logothetis, N. K., & Besserve, M. (2023). Uncovering the organization of neural
circuits with Generalized Phase Locking Analysis. PLoS Computational Biology.
Public Library of Science. https://doi.org/10.1371/journal.pcbi.1010983
chicago: Safavi, Shervin, Theofanis I. Panagiotaropoulos, Vishal Kapoor, Juan F
Ramirez Villegas, Nikos K. Logothetis, and Michel Besserve. “Uncovering the Organization
of Neural Circuits with Generalized Phase Locking Analysis.” PLoS Computational
Biology. Public Library of Science, 2023. https://doi.org/10.1371/journal.pcbi.1010983.
ieee: S. Safavi, T. I. Panagiotaropoulos, V. Kapoor, J. F. Ramirez Villegas, N.
K. Logothetis, and M. Besserve, “Uncovering the organization of neural circuits
with Generalized Phase Locking Analysis,” PLoS Computational Biology, vol.
19, no. 4. Public Library of Science, 2023.
ista: Safavi S, Panagiotaropoulos TI, Kapoor V, Ramirez Villegas JF, Logothetis
NK, Besserve M. 2023. Uncovering the organization of neural circuits with Generalized
Phase Locking Analysis. PLoS Computational Biology. 19(4), e1010983.
mla: Safavi, Shervin, et al. “Uncovering the Organization of Neural Circuits with
Generalized Phase Locking Analysis.” PLoS Computational Biology, vol. 19,
no. 4, e1010983, Public Library of Science, 2023, doi:10.1371/journal.pcbi.1010983.
short: S. Safavi, T.I. Panagiotaropoulos, V. Kapoor, J.F. Ramirez Villegas, N.K.
Logothetis, M. Besserve, PLoS Computational Biology 19 (2023).
date_created: 2023-04-23T22:01:03Z
date_published: 2023-04-01T00:00:00Z
date_updated: 2023-08-01T14:15:16Z
day: '01'
ddc:
- '570'
department:
- _id: JoCs
doi: 10.1371/journal.pcbi.1010983
external_id:
isi:
- '000962668700002'
file:
- access_level: open_access
checksum: edeb9d09f3e41ba7c0251308b9e372e7
content_type: application/pdf
creator: dernst
date_created: 2023-04-25T08:59:18Z
date_updated: 2023-04-25T08:59:18Z
file_id: '12867'
file_name: 2023_PLoSCompBio_Safavi.pdf
file_size: 4737671
relation: main_file
success: 1
file_date_updated: 2023-04-25T08:59:18Z
has_accepted_license: '1'
intvolume: ' 19'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
publication: PLoS Computational Biology
publication_identifier:
eissn:
- 1553-7358
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/shervinsafavi/gpla.git
scopus_import: '1'
status: public
title: Uncovering the organization of neural circuits with Generalized Phase Locking
Analysis
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 19
year: '2023'
...
---
_id: '12879'
abstract:
- lang: eng
text: Machine learning (ML) has been widely applied to chemical property prediction,
most prominently for the energies and forces in molecules and materials. The strong
interest in predicting energies in particular has led to a ‘local energy’-based
paradigm for modern atomistic ML models, which ensures size-extensivity and a
linear scaling of computational cost with system size. However, many electronic
properties (such as excitation energies or ionization energies) do not necessarily
scale linearly with system size and may even be spatially localized. Using size-extensive
models in these cases can lead to large errors. In this work, we explore different
strategies for learning intensive and localized properties, using HOMO energies
in organic molecules as a representative test case. In particular, we analyze
the pooling functions that atomistic neural networks use to predict molecular
properties, and suggest an orbital weighted average (OWA) approach that enables
the accurate prediction of orbital energies and locations.
acknowledgement: KC acknowledges funding from the China Scholarship Council. KC is
grateful for the TUM graduate school finance support to visit Bingqing Cheng's group
in IST for two months. We also thankfully acknowledge computational resources provided
by the MPCDF Supercomputing Centre.
article_processing_charge: No
article_type: original
author:
- first_name: Ke
full_name: Chen, Ke
id: c636c5ca-e8b8-11ed-b2d4-cc2c37613a8d
last_name: Chen
- first_name: Christian
full_name: Kunkel, Christian
last_name: Kunkel
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Karsten
full_name: Reuter, Karsten
last_name: Reuter
- first_name: Johannes T.
full_name: Margraf, Johannes T.
last_name: Margraf
citation:
ama: Chen K, Kunkel C, Cheng B, Reuter K, Margraf JT. Physics-inspired machine learning
of localized intensive properties. Chemical Science. 2023. doi:10.1039/d3sc00841j
apa: Chen, K., Kunkel, C., Cheng, B., Reuter, K., & Margraf, J. T. (2023). Physics-inspired
machine learning of localized intensive properties. Chemical Science. Royal
Society of Chemistry. https://doi.org/10.1039/d3sc00841j
chicago: Chen, Ke, Christian Kunkel, Bingqing Cheng, Karsten Reuter, and Johannes
T. Margraf. “Physics-Inspired Machine Learning of Localized Intensive Properties.”
Chemical Science. Royal Society of Chemistry, 2023. https://doi.org/10.1039/d3sc00841j.
ieee: K. Chen, C. Kunkel, B. Cheng, K. Reuter, and J. T. Margraf, “Physics-inspired
machine learning of localized intensive properties,” Chemical Science.
Royal Society of Chemistry, 2023.
ista: Chen K, Kunkel C, Cheng B, Reuter K, Margraf JT. 2023. Physics-inspired machine
learning of localized intensive properties. Chemical Science.
mla: Chen, Ke, et al. “Physics-Inspired Machine Learning of Localized Intensive
Properties.” Chemical Science, Royal Society of Chemistry, 2023, doi:10.1039/d3sc00841j.
short: K. Chen, C. Kunkel, B. Cheng, K. Reuter, J.T. Margraf, Chemical Science (2023).
date_created: 2023-04-30T22:01:06Z
date_published: 2023-04-10T00:00:00Z
date_updated: 2023-08-01T14:18:10Z
day: '10'
ddc:
- '000'
- '540'
department:
- _id: BiCh
doi: 10.1039/d3sc00841j
external_id:
isi:
- '000971508100001'
file:
- access_level: open_access
checksum: 5eeec69a51e192dcd94b955d84423836
content_type: application/pdf
creator: dernst
date_created: 2023-05-02T07:17:05Z
date_updated: 2023-05-02T07:17:05Z
file_id: '12883'
file_name: 2023_ChemialScience_Chen.pdf
file_size: 1515446
relation: main_file
success: 1
file_date_updated: 2023-05-02T07:17:05Z
has_accepted_license: '1'
isi: 1
language:
- iso: eng
license: https://creativecommons.org/licenses/by/3.0/
month: '04'
oa: 1
oa_version: Published Version
publication: Chemical Science
publication_identifier:
eissn:
- 2041-6539
issn:
- 2041-6520
publication_status: published
publisher: Royal Society of Chemistry
quality_controlled: '1'
scopus_import: '1'
status: public
title: Physics-inspired machine learning of localized intensive properties
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/3.0/legalcode
name: Creative Commons Attribution 3.0 Unported (CC BY 3.0)
short: CC BY (3.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2023'
...
---
_id: '12876'
abstract:
- lang: eng
text: "Motivation: The problem of model inference is of fundamental importance to
systems biology. Logical models (e.g. Boolean networks; BNs) represent a computationally
attractive approach capable of handling large biological networks. The models
are typically inferred from experimental data. However, even with a substantial
amount of experimental data supported by some prior knowledge, existing inference
methods often focus on a small sample of admissible candidate models only.\r\n\r\nResults:
We propose Boolean network sketches as a new formal instrument for the inference
of Boolean networks. A sketch integrates (typically partial) knowledge about the
network’s topology and the update logic (obtained through, e.g. a biological knowledge
base or a literature search), as well as further assumptions about the properties
of the network’s transitions (e.g. the form of its attractor landscape), and additional
restrictions on the model dynamics given by the measured experimental data. Our
new BNs inference algorithm starts with an ‘initial’ sketch, which is extended
by adding restrictions representing experimental data to a ‘data-informed’ sketch
and subsequently computes all BNs consistent with the data-informed sketch. Our
algorithm is based on a symbolic representation and coloured model-checking. Our
approach is unique in its ability to cover a broad spectrum of knowledge and efficiently
produce a compact representation of all inferred BNs. We evaluate the method on
a non-trivial collection of real-world and simulated data."
acknowledgement: This work was partially supported by GACR [grant No. GA22-10845S];
and Grant Agency of Masaryk University [grant No. MUNI/G/1771/2020]. This work was
partially supported by European Union’s Horizon 2020 research and innovation programme
under the Marie Skłodowska-Curie [Grant Agreement No. 101034413 to S.P.].
article_number: btad158
article_processing_charge: No
article_type: original
author:
- first_name: Nikola
full_name: Beneš, Nikola
last_name: Beneš
- first_name: Luboš
full_name: Brim, Luboš
last_name: Brim
- first_name: Ondřej
full_name: Huvar, Ondřej
last_name: Huvar
- first_name: Samuel
full_name: Pastva, Samuel
id: 07c5ea74-f61c-11ec-a664-aa7c5d957b2b
last_name: Pastva
- first_name: David
full_name: Šafránek, David
last_name: Šafránek
citation:
ama: 'Beneš N, Brim L, Huvar O, Pastva S, Šafránek D. Boolean network sketches:
A unifying framework for logical model inference. Bioinformatics. 2023;39(4).
doi:10.1093/bioinformatics/btad158'
apa: 'Beneš, N., Brim, L., Huvar, O., Pastva, S., & Šafránek, D. (2023). Boolean
network sketches: A unifying framework for logical model inference. Bioinformatics.
Oxford Academic. https://doi.org/10.1093/bioinformatics/btad158'
chicago: 'Beneš, Nikola, Luboš Brim, Ondřej Huvar, Samuel Pastva, and David Šafránek.
“Boolean Network Sketches: A Unifying Framework for Logical Model Inference.”
Bioinformatics. Oxford Academic, 2023. https://doi.org/10.1093/bioinformatics/btad158.'
ieee: 'N. Beneš, L. Brim, O. Huvar, S. Pastva, and D. Šafránek, “Boolean network
sketches: A unifying framework for logical model inference,” Bioinformatics,
vol. 39, no. 4. Oxford Academic, 2023.'
ista: 'Beneš N, Brim L, Huvar O, Pastva S, Šafránek D. 2023. Boolean network sketches:
A unifying framework for logical model inference. Bioinformatics. 39(4), btad158.'
mla: 'Beneš, Nikola, et al. “Boolean Network Sketches: A Unifying Framework for
Logical Model Inference.” Bioinformatics, vol. 39, no. 4, btad158, Oxford
Academic, 2023, doi:10.1093/bioinformatics/btad158.'
short: N. Beneš, L. Brim, O. Huvar, S. Pastva, D. Šafránek, Bioinformatics 39 (2023).
date_created: 2023-04-30T22:01:05Z
date_published: 2023-04-03T00:00:00Z
date_updated: 2023-08-01T14:27:28Z
day: '03'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1093/bioinformatics/btad158
ec_funded: 1
external_id:
isi:
- '000976610800001'
pmid:
- '37004199'
file:
- access_level: open_access
checksum: 2cb90ddf781baefddf47eac4b54e2a03
content_type: application/pdf
creator: dernst
date_created: 2023-05-02T07:39:04Z
date_updated: 2023-05-02T07:39:04Z
file_id: '12886'
file_name: 2023_Bioinformatics_Benes.pdf
file_size: 478740
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title: 'Boolean network sketches: A unifying framework for logical model inference'
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