--- _id: '14718' abstract: - lang: eng text: 'Binary decision diagrams (BDDs) are one of the fundamental data structures in formal methods and computer science in general. However, the performance of BDD-based algorithms greatly depends on memory latency due to the reliance on large hash tables and thus, by extension, on the speed of random memory access. This hinders the full utilisation of resources available on modern CPUs, since the absolute memory latency has not improved significantly for at least a decade. In this paper, we explore several implementation techniques that improve the performance of BDD manipulation either through enhanced memory locality or by partially eliminating random memory access. On a benchmark suite of 600+ BDDs derived from real-world applications, we demonstrate runtime that is comparable or better than parallelising the same operations on eight CPU cores. ' acknowledgement: "This work was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101034413 and the\r\n“VAMOS” grant ERC-2020-AdG 101020093." article_processing_charge: No author: - first_name: Samuel full_name: Pastva, Samuel id: 07c5ea74-f61c-11ec-a664-aa7c5d957b2b last_name: Pastva orcid: 0000-0003-1993-0331 - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 citation: ama: 'Pastva S, Henzinger TA. Binary decision diagrams on modern hardware. In: Proceedings of the 23rd Conference on Formal Methods in Computer-Aided Design. TU Vienna Academic Press; 2023:122-131. doi:10.34727/2023/isbn.978-3-85448-060-0_20' apa: 'Pastva, S., & Henzinger, T. A. (2023). Binary decision diagrams on modern hardware. In Proceedings of the 23rd Conference on Formal Methods in Computer-Aided Design (pp. 122–131). Ames, IA, United States: TU Vienna Academic Press. https://doi.org/10.34727/2023/isbn.978-3-85448-060-0_20' chicago: Pastva, Samuel, and Thomas A Henzinger. “Binary Decision Diagrams on Modern Hardware.” In Proceedings of the 23rd Conference on Formal Methods in Computer-Aided Design, 122–31. TU Vienna Academic Press, 2023. https://doi.org/10.34727/2023/isbn.978-3-85448-060-0_20. ieee: S. Pastva and T. A. Henzinger, “Binary decision diagrams on modern hardware,” in Proceedings of the 23rd Conference on Formal Methods in Computer-Aided Design, Ames, IA, United States, 2023, pp. 122–131. ista: 'Pastva S, Henzinger TA. 2023. Binary decision diagrams on modern hardware. Proceedings of the 23rd Conference on Formal Methods in Computer-Aided Design. FMCAD: Conference on Formal Methods in Computer-aided design, 122–131.' mla: Pastva, Samuel, and Thomas A. Henzinger. “Binary Decision Diagrams on Modern Hardware.” Proceedings of the 23rd Conference on Formal Methods in Computer-Aided Design, TU Vienna Academic Press, 2023, pp. 122–31, doi:10.34727/2023/isbn.978-3-85448-060-0_20. short: S. Pastva, T.A. Henzinger, in:, Proceedings of the 23rd Conference on Formal Methods in Computer-Aided Design, TU Vienna Academic Press, 2023, pp. 122–131. conference: end_date: 2023-10-27 location: Ames, IA, United States name: 'FMCAD: Conference on Formal Methods in Computer-aided design' start_date: 2023-10-25 date_created: 2023-12-31T23:01:03Z date_published: 2023-10-01T00:00:00Z date_updated: 2024-01-02T08:16:28Z day: '01' ddc: - '000' department: - _id: ToHe doi: 10.34727/2023/isbn.978-3-85448-060-0_20 ec_funded: 1 file: - access_level: open_access checksum: 818d6e13dd508f3a04f0941081022e5d content_type: application/pdf creator: dernst date_created: 2024-01-02T08:14:23Z date_updated: 2024-01-02T08:14:23Z file_id: '14721' file_name: 2023_FMCAD_Pastva.pdf file_size: 524321 relation: main_file success: 1 file_date_updated: 2024-01-02T08:14:23Z has_accepted_license: '1' language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '10' oa: 1 oa_version: Published Version page: 122-131 project: - _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c call_identifier: H2020 grant_number: '101034413' name: 'IST-BRIDGE: International postdoctoral program' - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication: Proceedings of the 23rd Conference on Formal Methods in Computer-Aided Design publication_identifier: isbn: - '9783854480600' publication_status: published publisher: TU Vienna Academic Press quality_controlled: '1' scopus_import: '1' status: public title: Binary decision diagrams on modern hardware 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 year: '2023' ... --- _id: '14830' abstract: - lang: eng text: We study the problem of learning controllers for discrete-time non-linear stochastic dynamical systems with formal reach-avoid guarantees. This work presents the first method for providing formal reach-avoid guarantees, which combine and generalize stability and safety guarantees, with a tolerable probability threshold p in [0,1] over the infinite time horizon. Our method leverages advances in machine learning literature and it represents formal certificates as neural networks. In particular, we learn a certificate in the form of a reach-avoid supermartingale (RASM), a novel notion that we introduce in this work. Our RASMs provide reachability and avoidance guarantees by imposing constraints on what can be viewed as a stochastic extension of level sets of Lyapunov functions for deterministic systems. Our approach solves several important problems -- it can be used to learn a control policy from scratch, to verify a reach-avoid specification for a fixed control policy, or to fine-tune a pre-trained policy if it does not satisfy the reach-avoid specification. We validate our approach on 3 stochastic non-linear reinforcement learning tasks. acknowledgement: This work was supported in part by the ERC-2020-AdG 101020093, ERC CoG 863818 (FoRM-SMArt) and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 665385. article_processing_charge: No author: - first_name: Dorde full_name: Zikelic, Dorde id: 294AA7A6-F248-11E8-B48F-1D18A9856A87 last_name: Zikelic orcid: 0000-0002-4681-1699 - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - 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: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X citation: ama: 'Zikelic D, Lechner M, Henzinger TA, Chatterjee K. Learning control policies for stochastic systems with reach-avoid guarantees. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence. Vol 37. Association for the Advancement of Artificial Intelligence; 2023:11926-11935. doi:10.1609/aaai.v37i10.26407' apa: 'Zikelic, D., Lechner, M., Henzinger, T. A., & Chatterjee, K. (2023). Learning control policies for stochastic systems with reach-avoid guarantees. In Proceedings of the 37th AAAI Conference on Artificial Intelligence (Vol. 37, pp. 11926–11935). Washington, DC, United States: Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v37i10.26407' chicago: Zikelic, Dorde, Mathias Lechner, Thomas A Henzinger, and Krishnendu Chatterjee. “Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees.” In Proceedings of the 37th AAAI Conference on Artificial Intelligence, 37:11926–35. Association for the Advancement of Artificial Intelligence, 2023. https://doi.org/10.1609/aaai.v37i10.26407. ieee: D. Zikelic, M. Lechner, T. A. Henzinger, and K. Chatterjee, “Learning control policies for stochastic systems with reach-avoid guarantees,” in Proceedings of the 37th AAAI Conference on Artificial Intelligence, Washington, DC, United States, 2023, vol. 37, no. 10, pp. 11926–11935. ista: 'Zikelic D, Lechner M, Henzinger TA, Chatterjee K. 2023. Learning control policies for stochastic systems with reach-avoid guarantees. Proceedings of the 37th AAAI Conference on Artificial Intelligence. AAAI: Conference on Artificial Intelligence vol. 37, 11926–11935.' mla: Zikelic, Dorde, et al. “Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees.” Proceedings of the 37th AAAI Conference on Artificial Intelligence, vol. 37, no. 10, Association for the Advancement of Artificial Intelligence, 2023, pp. 11926–35, doi:10.1609/aaai.v37i10.26407. short: D. Zikelic, M. Lechner, T.A. Henzinger, K. Chatterjee, in:, Proceedings of the 37th AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence, 2023, pp. 11926–11935. conference: end_date: 2023-02-14 location: Washington, DC, United States name: 'AAAI: Conference on Artificial Intelligence' start_date: 2023-02-07 date_created: 2024-01-18T07:44:31Z date_published: 2023-06-26T00:00:00Z date_updated: 2024-01-22T14:08:29Z day: '26' department: - _id: ToHe - _id: KrCh doi: 10.1609/aaai.v37i10.26407 ec_funded: 1 external_id: arxiv: - '2210.05308' intvolume: ' 37' issue: '10' keyword: - General Medicine language: - iso: eng month: '06' oa_version: Preprint page: 11926-11935 project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication: Proceedings of the 37th AAAI Conference on Artificial Intelligence publication_identifier: eissn: - 2374-3468 issn: - 2159-5399 publication_status: published publisher: Association for the Advancement of Artificial Intelligence quality_controlled: '1' related_material: record: - id: '14600' relation: earlier_version status: public status: public title: Learning control policies for stochastic systems with reach-avoid guarantees type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 37 year: '2023' ... --- _id: '13234' abstract: - lang: eng text: Neural-network classifiers achieve high accuracy when predicting the class of an input that they were trained to identify. Maintaining this accuracy in dynamic environments, where inputs frequently fall outside the fixed set of initially known classes, remains a challenge. We consider the problem of monitoring the classification decisions of neural networks in the presence of novel classes. For this purpose, we generalize our recently proposed abstraction-based monitor from binary output to real-valued quantitative output. This quantitative output enables new applications, two of which we investigate in the paper. As our first application, we introduce an algorithmic framework for active monitoring of a neural network, which allows us to learn new classes dynamically and yet maintain high monitoring performance. As our second application, we present an offline procedure to retrain the neural network to improve the monitor’s detection performance without deteriorating the network’s classification accuracy. Our experimental evaluation demonstrates both the benefits of our active monitoring framework in dynamic scenarios and the effectiveness of the retraining procedure. acknowledgement: This work was supported in part by the ERC-2020-AdG 101020093, by DIREC - Digital Research Centre Denmark, and by the Villum Investigator Grant S4OS. article_processing_charge: Yes (in subscription journal) article_type: original author: - first_name: Konstantin full_name: Kueffner, Konstantin id: 8121a2d0-dc85-11ea-9058-af578f3b4515 last_name: Kueffner orcid: 0000-0001-8974-2542 - first_name: Anna full_name: Lukina, Anna id: CBA4D1A8-0FE8-11E9-BDE6-07BFE5697425 last_name: Lukina - first_name: Christian full_name: Schilling, Christian id: 3A2F4DCE-F248-11E8-B48F-1D18A9856A87 last_name: Schilling orcid: 0000-0003-3658-1065 - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 citation: ama: 'Kueffner K, Lukina A, Schilling C, Henzinger TA. Into the unknown: Active monitoring of neural networks (extended version). International Journal on Software Tools for Technology Transfer. 2023;25:575-592. doi:10.1007/s10009-023-00711-4' apa: 'Kueffner, K., Lukina, A., Schilling, C., & Henzinger, T. A. (2023). Into the unknown: Active monitoring of neural networks (extended version). International Journal on Software Tools for Technology Transfer. Springer Nature. https://doi.org/10.1007/s10009-023-00711-4' chicago: 'Kueffner, Konstantin, Anna Lukina, Christian Schilling, and Thomas A Henzinger. “Into the Unknown: Active Monitoring of Neural Networks (Extended Version).” International Journal on Software Tools for Technology Transfer. Springer Nature, 2023. https://doi.org/10.1007/s10009-023-00711-4.' ieee: 'K. Kueffner, A. Lukina, C. Schilling, and T. A. Henzinger, “Into the unknown: Active monitoring of neural networks (extended version),” International Journal on Software Tools for Technology Transfer, vol. 25. Springer Nature, pp. 575–592, 2023.' ista: 'Kueffner K, Lukina A, Schilling C, Henzinger TA. 2023. Into the unknown: Active monitoring of neural networks (extended version). International Journal on Software Tools for Technology Transfer. 25, 575–592.' mla: 'Kueffner, Konstantin, et al. “Into the Unknown: Active Monitoring of Neural Networks (Extended Version).” International Journal on Software Tools for Technology Transfer, vol. 25, Springer Nature, 2023, pp. 575–92, doi:10.1007/s10009-023-00711-4.' short: K. Kueffner, A. Lukina, C. Schilling, T.A. Henzinger, International Journal on Software Tools for Technology Transfer 25 (2023) 575–592. date_created: 2023-07-16T22:01:11Z date_published: 2023-08-01T00:00:00Z date_updated: 2024-01-30T12:06:57Z day: '01' ddc: - '000' department: - _id: ToHe doi: 10.1007/s10009-023-00711-4 ec_funded: 1 external_id: arxiv: - '2009.06429' isi: - '001020160000001' file: - access_level: open_access checksum: 3c4b347f39412a76872f9a6f30101f94 content_type: application/pdf creator: dernst date_created: 2024-01-30T12:06:07Z date_updated: 2024-01-30T12:06:07Z file_id: '14903' file_name: 2023_JourSoftwareTools_Kueffner.pdf file_size: 13387667 relation: main_file success: 1 file_date_updated: 2024-01-30T12:06:07Z has_accepted_license: '1' intvolume: ' 25' isi: 1 language: - iso: eng month: '08' oa: 1 oa_version: Published Version page: 575-592 project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication: International Journal on Software Tools for Technology Transfer publication_identifier: eissn: - 1433-2787 issn: - 1433-2779 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: record: - id: '10206' relation: shorter_version status: public scopus_import: '1' status: public title: 'Into the unknown: Active monitoring of neural networks (extended version)' 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: 25 year: '2023' ... --- _id: '14920' abstract: - lang: eng text: "We consider fixpoint algorithms for two-player games on graphs with $\\omega$-regular winning conditions, where the environment is constrained by a strong transition fairness assumption. Strong transition fairness is a widely occurring special case of strong fairness, which requires that any execution is strongly fair with respect to a specified set of live edges: whenever the\r\nsource vertex of a live edge is visited infinitely often along a play, the edge itself is traversed infinitely often along the play as well. We show that, surprisingly, strong transition fairness retains the algorithmic characteristics of the fixpoint algorithms for $\\omega$-regular games -- the new algorithms have the same alternation depth as the classical algorithms but invoke a new type of predecessor operator. For Rabin games with $k$ pairs, the complexity of the new algorithm is $O(n^{k+2}k!)$ symbolic steps, which is independent of the number of live edges in the strong transition fairness assumption. Further, we show that GR(1) specifications with strong transition fairness assumptions can be solved with a 3-nested fixpoint algorithm, same as the usual algorithm. In contrast, strong fairness necessarily requires increasing the alternation depth depending on the number of fairness assumptions. We get symbolic algorithms for (generalized) Rabin, parity and GR(1) objectives under strong transition fairness assumptions as well as a direct symbolic algorithm for qualitative winning in stochastic\r\n$\\omega$-regular games that runs in $O(n^{k+2}k!)$ symbolic steps, improving the state of the art. Finally, we have implemented a BDD-based synthesis engine based on our algorithm. We show on a set of synthetic and real benchmarks that our algorithm is scalable, parallelizable, and outperforms previous algorithms by orders of magnitude." acknowledgement: A previous version of this paper has appeared in TACAS 2022. Authors ordered alphabetically. T. Banerjee was interning with MPI-SWS when this research was conducted. R. Majumdar and A.-K. Schmuck are partially supported by DFG project 389792660 TRR 248–CPEC. A.-K. Schmuck is additionally funded through DFG project (SCHM 3541/1-1). K. Mallik is supported by the ERC project ERC-2020-AdG 101020093. article_number: '4' article_processing_charge: Yes article_type: original author: - first_name: Tamajit full_name: Banerjee, Tamajit last_name: Banerjee - first_name: Rupak full_name: Majumdar, Rupak last_name: Majumdar - first_name: Kaushik full_name: Mallik, Kaushik id: 0834ff3c-6d72-11ec-94e0-b5b0a4fb8598 last_name: Mallik orcid: 0000-0001-9864-7475 - first_name: Anne-Kathrin full_name: Schmuck, Anne-Kathrin last_name: Schmuck - first_name: Sadegh full_name: Soudjani, Sadegh last_name: Soudjani citation: ama: Banerjee T, Majumdar R, Mallik K, Schmuck A-K, Soudjani S. Fast symbolic algorithms for mega-regular games under strong transition fairness. TheoretiCS. 2023;2. doi:10.46298/theoretics.23.4 apa: Banerjee, T., Majumdar, R., Mallik, K., Schmuck, A.-K., & Soudjani, S. (2023). Fast symbolic algorithms for mega-regular games under strong transition fairness. TheoretiCS. EPI Sciences. https://doi.org/10.46298/theoretics.23.4 chicago: Banerjee, Tamajit, Rupak Majumdar, Kaushik Mallik, Anne-Kathrin Schmuck, and Sadegh Soudjani. “Fast Symbolic Algorithms for Mega-Regular Games under Strong Transition Fairness.” TheoretiCS. EPI Sciences, 2023. https://doi.org/10.46298/theoretics.23.4. ieee: T. Banerjee, R. Majumdar, K. Mallik, A.-K. Schmuck, and S. Soudjani, “Fast symbolic algorithms for mega-regular games under strong transition fairness,” TheoretiCS, vol. 2. EPI Sciences, 2023. ista: Banerjee T, Majumdar R, Mallik K, Schmuck A-K, Soudjani S. 2023. Fast symbolic algorithms for mega-regular games under strong transition fairness. TheoretiCS. 2, 4. mla: Banerjee, Tamajit, et al. “Fast Symbolic Algorithms for Mega-Regular Games under Strong Transition Fairness.” TheoretiCS, vol. 2, 4, EPI Sciences, 2023, doi:10.46298/theoretics.23.4. short: T. Banerjee, R. Majumdar, K. Mallik, A.-K. Schmuck, S. Soudjani, TheoretiCS 2 (2023). date_created: 2024-01-31T13:40:49Z date_published: 2023-02-24T00:00:00Z date_updated: 2024-02-05T10:21:51Z day: '24' ddc: - '000' department: - _id: ToHe doi: 10.46298/theoretics.23.4 ec_funded: 1 external_id: arxiv: - '2202.07480' file: - access_level: open_access checksum: 2972d531122a6f15727b396110fb3f5c content_type: application/pdf creator: dernst date_created: 2024-02-05T10:19:35Z date_updated: 2024-02-05T10:19:35Z file_id: '14940' file_name: 2023_TheoretiCS_Banerjee.pdf file_size: 917076 relation: main_file success: 1 file_date_updated: 2024-02-05T10:19:35Z has_accepted_license: '1' intvolume: ' 2' language: - iso: eng month: '02' oa: 1 oa_version: Published Version project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication: TheoretiCS publication_identifier: issn: - 2751-4838 publication_status: published publisher: EPI Sciences quality_controlled: '1' status: public title: Fast symbolic algorithms for mega-regular games under strong transition 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: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 2 year: '2023' ... --- _id: '14411' abstract: - lang: eng text: "Partially specified Boolean networks (PSBNs) represent a promising framework for the qualitative modelling of biological systems in which the logic of interactions is not completely known. Phenotype control aims to stabilise the network in states exhibiting specific traits.\r\nIn this paper, we define the phenotype control problem in the context of asynchronous PSBNs and propose a novel semi-symbolic algorithm for solving this problem with permanent variable perturbations." acknowledgement: This work was supported by the Czech Foundation grant No. GA22-10845S, Grant Agency of Masaryk University grant No. MUNI/G/1771/2020, and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101034413. alternative_title: - LNBI article_processing_charge: No author: - first_name: Nikola full_name: Beneš, Nikola last_name: Beneš - first_name: Luboš full_name: Brim, Luboš last_name: Brim - first_name: Samuel full_name: Pastva, Samuel id: 07c5ea74-f61c-11ec-a664-aa7c5d957b2b last_name: Pastva orcid: 0000-0003-1993-0331 - first_name: David full_name: Šafránek, David last_name: Šafránek - first_name: Eva full_name: Šmijáková, Eva last_name: Šmijáková citation: ama: 'Beneš N, Brim L, Pastva S, Šafránek D, Šmijáková E. Phenotype control of partially specified boolean networks. In: 21st International Conference on Computational Methods in Systems Biology. Vol 14137. Springer Nature; 2023:18-35. doi:10.1007/978-3-031-42697-1_2' apa: 'Beneš, N., Brim, L., Pastva, S., Šafránek, D., & Šmijáková, E. (2023). Phenotype control of partially specified boolean networks. In 21st International Conference on Computational Methods in Systems Biology (Vol. 14137, pp. 18–35). Luxembourg City, Luxembourg: Springer Nature. https://doi.org/10.1007/978-3-031-42697-1_2' chicago: Beneš, Nikola, Luboš Brim, Samuel Pastva, David Šafránek, and Eva Šmijáková. “Phenotype Control of Partially Specified Boolean Networks.” In 21st International Conference on Computational Methods in Systems Biology, 14137:18–35. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-42697-1_2. ieee: N. Beneš, L. Brim, S. Pastva, D. Šafránek, and E. Šmijáková, “Phenotype control of partially specified boolean networks,” in 21st International Conference on Computational Methods in Systems Biology, Luxembourg City, Luxembourg, 2023, vol. 14137, pp. 18–35. ista: 'Beneš N, Brim L, Pastva S, Šafránek D, Šmijáková E. 2023. Phenotype control of partially specified boolean networks. 21st International Conference on Computational Methods in Systems Biology. CMSB: Computational Methods in Systems Biology, LNBI, vol. 14137, 18–35.' mla: Beneš, Nikola, et al. “Phenotype Control of Partially Specified Boolean Networks.” 21st International Conference on Computational Methods in Systems Biology, vol. 14137, Springer Nature, 2023, pp. 18–35, doi:10.1007/978-3-031-42697-1_2. short: N. Beneš, L. Brim, S. Pastva, D. Šafránek, E. Šmijáková, in:, 21st International Conference on Computational Methods in Systems Biology, Springer Nature, 2023, pp. 18–35. conference: end_date: 2023-09-15 location: Luxembourg City, Luxembourg name: 'CMSB: Computational Methods in Systems Biology' start_date: 2023-09-13 date_created: 2023-10-08T22:01:18Z date_published: 2023-09-09T00:00:00Z date_updated: 2024-02-20T09:02:04Z day: '09' ddc: - '000' department: - _id: ToHe doi: 10.1007/978-3-031-42697-1_2 ec_funded: 1 file: - access_level: open_access checksum: 6f71bdaedb770b52380222fd9f4d7937 content_type: application/pdf creator: spastva date_created: 2024-02-16T08:26:32Z date_updated: 2024-02-16T08:26:32Z file_id: '14997' file_name: cmsb2023.pdf file_size: 691582 relation: main_file success: 1 file_date_updated: 2024-02-16T08:26:32Z has_accepted_license: '1' intvolume: ' 14137' language: - iso: eng month: '09' oa: 1 oa_version: Submitted Version page: 18-35 project: - _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c call_identifier: H2020 grant_number: '101034413' name: 'IST-BRIDGE: International postdoctoral program' publication: 21st International Conference on Computational Methods in Systems Biology publication_identifier: eissn: - 1611-3349 isbn: - '9783031426964' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Phenotype control of partially specified boolean networks 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: 14137 year: '2023' ...