--- _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 relation: main_file success: 1 file_date_updated: 2023-05-02T07:39:04Z has_accepted_license: '1' intvolume: ' 39' isi: 1 issue: '4' language: - iso: eng month: '04' oa: 1 oa_version: Published Version pmid: 1 project: - _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c call_identifier: H2020 grant_number: '101034413' name: 'IST-BRIDGE: International postdoctoral program' publication: Bioinformatics publication_identifier: eissn: - 1367-4811 publication_status: published publisher: Oxford Academic quality_controlled: '1' related_material: link: - relation: software url: https://doi.org/10.5281/zenodo.7688740 scopus_import: '1' status: public title: 'Boolean network sketches: A unifying framework for logical model inference' 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: 39 year: '2023' ...