---
_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.
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