---
_id: '10666'
abstract:
- lang: eng
text: Adversarial training is an effective method to train deep learning models
that are resilient to norm-bounded perturbations, with the cost of nominal performance
drop. While adversarial training appears to enhance the robustness and safety
of a deep model deployed in open-world decision-critical applications, counterintuitively,
it induces undesired behaviors in robot learning settings. In this paper, we show
theoretically and experimentally that neural controllers obtained via adversarial
training are subjected to three types of defects, namely transient, systematic,
and conditional errors. We first generalize adversarial training to a safety-domain
optimization scheme allowing for more generic specifications. We then prove that
such a learning process tends to cause certain error profiles. We support our
theoretical results by a thorough experimental safety analysis in a robot-learning
task. Our results suggest that adversarial training is not yet ready for robot
learning.
acknowledgement: M.L. and T.A.H. are supported in part by the Austrian Science Fund
(FWF) under grant Z211-N23 (Wittgenstein Award). R.H. and D.R. are supported by
Boeing and R.G. by Horizon-2020 ECSEL Project grant no. 783163 (iDev40).
article_processing_charge: No
author:
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Radu
full_name: Grosu, Radu
last_name: Grosu
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
- 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: 'Lechner M, Hasani R, Grosu R, Rus D, Henzinger TA. Adversarial training is
not ready for robot learning. In: 2021 IEEE International Conference on Robotics
and Automation. ICRA. ; 2021:4140-4147. doi:10.1109/ICRA48506.2021.9561036'
apa: Lechner, M., Hasani, R., Grosu, R., Rus, D., & Henzinger, T. A. (2021).
Adversarial training is not ready for robot learning. In 2021 IEEE International
Conference on Robotics and Automation (pp. 4140–4147). Xi’an, China. https://doi.org/10.1109/ICRA48506.2021.9561036
chicago: Lechner, Mathias, Ramin Hasani, Radu Grosu, Daniela Rus, and Thomas A Henzinger.
“Adversarial Training Is Not Ready for Robot Learning.” In 2021 IEEE International
Conference on Robotics and Automation, 4140–47. ICRA, 2021. https://doi.org/10.1109/ICRA48506.2021.9561036.
ieee: M. Lechner, R. Hasani, R. Grosu, D. Rus, and T. A. Henzinger, “Adversarial
training is not ready for robot learning,” in 2021 IEEE International Conference
on Robotics and Automation, Xi’an, China, 2021, pp. 4140–4147.
ista: 'Lechner M, Hasani R, Grosu R, Rus D, Henzinger TA. 2021. Adversarial training
is not ready for robot learning. 2021 IEEE International Conference on Robotics
and Automation. ICRA: International Conference on Robotics and AutomationICRA,
4140–4147.'
mla: Lechner, Mathias, et al. “Adversarial Training Is Not Ready for Robot Learning.”
2021 IEEE International Conference on Robotics and Automation, 2021, pp.
4140–47, doi:10.1109/ICRA48506.2021.9561036.
short: M. Lechner, R. Hasani, R. Grosu, D. Rus, T.A. Henzinger, in:, 2021 IEEE International
Conference on Robotics and Automation, 2021, pp. 4140–4147.
conference:
end_date: 2021-06-05
location: Xi'an, China
name: 'ICRA: International Conference on Robotics and Automation'
start_date: 2021-05-30
date_created: 2022-01-25T15:44:54Z
date_published: 2021-01-01T00:00:00Z
date_updated: 2023-08-17T06:58:38Z
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
doi: 10.1109/ICRA48506.2021.9561036
external_id:
arxiv:
- '2103.08187'
isi:
- '000765738803040'
has_accepted_license: '1'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2103.08187
oa: 1
oa_version: None
page: 4140-4147
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: 2021 IEEE International Conference on Robotics and Automation
publication_identifier:
eisbn:
- 978-1-7281-9077-8
eissn:
- 2577-087X
isbn:
- 978-1-7281-9078-5
issn:
- 1050-4729
publication_status: published
quality_controlled: '1'
related_material:
record:
- id: '11362'
relation: dissertation_contains
status: public
series_title: ICRA
status: public
title: Adversarial training is not ready for robot learning
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND
3.0)
short: CC BY-NC-ND (3.0)
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2021'
...
---
_id: '10206'
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. The typical approach is to detect inputs from
novel classes and retrain the classifier on an augmented dataset. However, not
only the classifier but also the detection mechanism needs to adapt in order to
distinguish between newly learned and yet unknown input classes. To address this
challenge, we introduce an algorithmic framework for active monitoring of a neural
network. A monitor wrapped in our framework operates in parallel with the neural
network and interacts with a human user via a series of interpretable labeling
queries for incremental adaptation. In addition, we propose an adaptive quantitative
monitor to improve precision. An experimental evaluation on a diverse set of benchmarks
with varying numbers of classes confirms the benefits of our active monitoring
framework in dynamic scenarios.
acknowledgement: We thank Christoph Lampert and Alex Greengold for fruitful discussions.
This research was supported in part by the Simons Institute for the Theory of Computing,
the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award), and the
European Union’s Horizon 2020 research and innovation programme under the Marie
Skłodowska-Curie grant agreement No. 754411.
alternative_title:
- LNCS
article_processing_charge: No
author:
- 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: 'Lukina A, Schilling C, Henzinger TA. Into the unknown: active monitoring of neural
networks. In: 21st International Conference on Runtime Verification. Vol
12974. Cham: Springer Nature; 2021:42-61. doi:10.1007/978-3-030-88494-9_3'
apa: 'Lukina, A., Schilling, C., & Henzinger, T. A. (2021). Into the unknown:
active monitoring of neural networks. In 21st International Conference on Runtime
Verification (Vol. 12974, pp. 42–61). Cham: Springer Nature. https://doi.org/10.1007/978-3-030-88494-9_3'
chicago: 'Lukina, Anna, Christian Schilling, and Thomas A Henzinger. “Into the Unknown:
Active Monitoring of Neural Networks.” In 21st International Conference on
Runtime Verification, 12974:42–61. Cham: Springer Nature, 2021. https://doi.org/10.1007/978-3-030-88494-9_3.'
ieee: 'A. Lukina, C. Schilling, and T. A. Henzinger, “Into the unknown: active monitoring
of neural networks,” in 21st International Conference on Runtime Verification,
Virtual, 2021, vol. 12974, pp. 42–61.'
ista: 'Lukina A, Schilling C, Henzinger TA. 2021. Into the unknown: active monitoring
of neural networks. 21st International Conference on Runtime Verification. RV:
Runtime Verification, LNCS, vol. 12974, 42–61.'
mla: 'Lukina, Anna, et al. “Into the Unknown: Active Monitoring of Neural Networks.”
21st International Conference on Runtime Verification, vol. 12974, Springer
Nature, 2021, pp. 42–61, doi:10.1007/978-3-030-88494-9_3.'
short: A. Lukina, C. Schilling, T.A. Henzinger, in:, 21st International Conference
on Runtime Verification, Springer Nature, Cham, 2021, pp. 42–61.
conference:
end_date: 2021-10-14
location: Virtual
name: 'RV: Runtime Verification'
start_date: 2021-10-11
date_created: 2021-10-31T23:01:31Z
date_published: 2021-10-06T00:00:00Z
date_updated: 2024-01-30T12:06:56Z
day: '06'
department:
- _id: ToHe
doi: 10.1007/978-3-030-88494-9_3
ec_funded: 1
external_id:
arxiv:
- '2009.06429'
isi:
- '000719383800003'
isi: 1
keyword:
- monitoring
- neural networks
- novelty detection
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2009.06429
month: '10'
oa: 1
oa_version: Preprint
page: 42-61
place: Cham
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: 21st International Conference on Runtime Verification
publication_identifier:
eisbn:
- 978-3-030-88494-9
eissn:
- 1611-3349
isbn:
- 9-783-0308-8493-2
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '13234'
relation: extended_version
status: public
scopus_import: '1'
status: public
title: 'Into the unknown: active monitoring of neural networks'
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: '12974 '
year: '2021'
...
---
_id: '10673'
abstract:
- lang: eng
text: We propose a neural information processing system obtained by re-purposing
the function of a biological neural circuit model to govern simulated and real-world
control tasks. Inspired by the structure of the nervous system of the soil-worm,
C. elegans, we introduce ordinary neural circuits (ONCs), defined as the model
of biological neural circuits reparameterized for the control of alternative tasks.
We first demonstrate that ONCs realize networks with higher maximum flow compared
to arbitrary wired networks. We then learn instances of ONCs to control a series
of robotic tasks, including the autonomous parking of a real-world rover robot.
For reconfiguration of the purpose of the neural circuit, we adopt a search-based
optimization algorithm. Ordinary neural circuits perform on par and, in some cases,
significantly surpass the performance of contemporary deep learning models. ONC
networks are compact, 77% sparser than their counterpart neural controllers, and
their neural dynamics are fully interpretable at the cell-level.
acknowledgement: "RH and RG are partially supported by Horizon-2020 ECSEL Project
grant No. 783163 (iDev40), Productive 4.0, and ATBMBFW CPS-IoT Ecosystem. ML was
supported in part by the Austrian Science Fund (FWF) under grant Z211-N23\r\n(Wittgenstein
Award). AA is supported by the National Science Foundation (NSF) Graduate Research
Fellowship\r\nProgram. RH and DR are partially supported by The Boeing Company and
JP Morgan Chase. This research work is\r\npartially drawn from the PhD dissertation
of RH.\r\n"
alternative_title:
- PMLR
article_processing_charge: No
author:
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Alexander
full_name: Amini, Alexander
last_name: Amini
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
- first_name: Radu
full_name: Grosu, Radu
last_name: Grosu
citation:
ama: 'Hasani R, Lechner M, Amini A, Rus D, Grosu R. A natural lottery ticket winner:
Reinforcement learning with ordinary neural circuits. In: Proceedings of the
37th International Conference on Machine Learning. PMLR. ; 2020:4082-4093.'
apa: 'Hasani, R., Lechner, M., Amini, A., Rus, D., & Grosu, R. (2020). A natural
lottery ticket winner: Reinforcement learning with ordinary neural circuits. In
Proceedings of the 37th International Conference on Machine Learning (pp.
4082–4093). Virtual.'
chicago: 'Hasani, Ramin, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu
Grosu. “A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary
Neural Circuits.” In Proceedings of the 37th International Conference on Machine
Learning, 4082–93. PMLR, 2020.'
ieee: 'R. Hasani, M. Lechner, A. Amini, D. Rus, and R. Grosu, “A natural lottery
ticket winner: Reinforcement learning with ordinary neural circuits,” in Proceedings
of the 37th International Conference on Machine Learning, Virtual, 2020, pp.
4082–4093.'
ista: 'Hasani R, Lechner M, Amini A, Rus D, Grosu R. 2020. A natural lottery ticket
winner: Reinforcement learning with ordinary neural circuits. Proceedings of the
37th International Conference on Machine Learning. ML: Machine LearningPMLR, PMLR,
, 4082–4093.'
mla: 'Hasani, Ramin, et al. “A Natural Lottery Ticket Winner: Reinforcement Learning
with Ordinary Neural Circuits.” Proceedings of the 37th International Conference
on Machine Learning, 2020, pp. 4082–93.'
short: R. Hasani, M. Lechner, A. Amini, D. Rus, R. Grosu, in:, Proceedings of the
37th International Conference on Machine Learning, 2020, pp. 4082–4093.
conference:
end_date: 2020-07-18
location: Virtual
name: 'ML: Machine Learning'
start_date: 2020-07-12
date_created: 2022-01-25T15:50:34Z
date_published: 2020-01-01T00:00:00Z
date_updated: 2022-01-26T11:14:27Z
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
file:
- access_level: open_access
checksum: c9a4a29161777fc1a89ef451c040e3b1
content_type: application/pdf
creator: cchlebak
date_created: 2022-01-26T11:08:51Z
date_updated: 2022-01-26T11:08:51Z
file_id: '10691'
file_name: 2020_PMLR_Hasani.pdf
file_size: 2329798
relation: main_file
success: 1
file_date_updated: 2022-01-26T11:08:51Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://proceedings.mlr.press/v119/hasani20a.html
oa: 1
oa_version: Published Version
page: 4082-4093
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: Proceedings of the 37th International Conference on Machine Learning
publication_identifier:
issn:
- 2640-3498
publication_status: published
quality_controlled: '1'
scopus_import: '1'
series_title: PMLR
status: public
title: 'A natural lottery ticket winner: Reinforcement learning with ordinary neural
circuits'
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND
3.0)
short: CC BY-NC-ND (3.0)
type: conference
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2020'
...
---
_id: '7348'
abstract:
- lang: eng
text: 'The monitoring of event frequencies can be used to recognize behavioral anomalies,
to identify trends, and to deduce or discard hypotheses about the underlying system.
For example, the performance of a web server may be monitored based on the ratio
of the total count of requests from the least and most active clients. Exact frequency
monitoring, however, can be prohibitively expensive; in the above example it would
require as many counters as there are clients. In this paper, we propose the efficient
probabilistic monitoring of common frequency properties, including the mode (i.e.,
the most common event) and the median of an event sequence. We define a logic
to express composite frequency properties as a combination of atomic frequency
properties. Our main contribution is an algorithm that, under suitable probabilistic
assumptions, can be used to monitor these important frequency properties with
four counters, independent of the number of different events. Our algorithm samples
longer and longer subwords of an infinite event sequence. We prove the almost-sure
convergence of our algorithm by generalizing ergodic theory from increasing-length
prefixes to increasing-length subwords of an infinite sequence. A similar algorithm
could be used to learn a connected Markov chain of a given structure from observing
its outputs, to arbitrary precision, for a given confidence. '
alternative_title:
- LIPIcs
article_number: '20'
article_processing_charge: No
author:
- first_name: Thomas
full_name: Ferrere, Thomas
id: 40960E6E-F248-11E8-B48F-1D18A9856A87
last_name: Ferrere
orcid: 0000-0001-5199-3143
- 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: Bernhard
full_name: Kragl, Bernhard
id: 320FC952-F248-11E8-B48F-1D18A9856A87
last_name: Kragl
orcid: 0000-0001-7745-9117
citation:
ama: 'Ferrere T, Henzinger TA, Kragl B. Monitoring event frequencies. In: 28th
EACSL Annual Conference on Computer Science Logic. Vol 152. Schloss Dagstuhl
- Leibniz-Zentrum für Informatik; 2020. doi:10.4230/LIPIcs.CSL.2020.20'
apa: 'Ferrere, T., Henzinger, T. A., & Kragl, B. (2020). Monitoring event frequencies.
In 28th EACSL Annual Conference on Computer Science Logic (Vol. 152). Barcelona,
Spain: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.CSL.2020.20'
chicago: Ferrere, Thomas, Thomas A Henzinger, and Bernhard Kragl. “Monitoring Event
Frequencies.” In 28th EACSL Annual Conference on Computer Science Logic,
Vol. 152. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020. https://doi.org/10.4230/LIPIcs.CSL.2020.20.
ieee: T. Ferrere, T. A. Henzinger, and B. Kragl, “Monitoring event frequencies,”
in 28th EACSL Annual Conference on Computer Science Logic, Barcelona, Spain,
2020, vol. 152.
ista: 'Ferrere T, Henzinger TA, Kragl B. 2020. Monitoring event frequencies. 28th
EACSL Annual Conference on Computer Science Logic. CSL: Computer Science Logic,
LIPIcs, vol. 152, 20.'
mla: Ferrere, Thomas, et al. “Monitoring Event Frequencies.” 28th EACSL Annual
Conference on Computer Science Logic, vol. 152, 20, Schloss Dagstuhl - Leibniz-Zentrum
für Informatik, 2020, doi:10.4230/LIPIcs.CSL.2020.20.
short: T. Ferrere, T.A. Henzinger, B. Kragl, in:, 28th EACSL Annual Conference on
Computer Science Logic, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.
conference:
end_date: 2020-01-16
location: Barcelona, Spain
name: 'CSL: Computer Science Logic'
start_date: 2020-01-13
date_created: 2020-01-21T11:22:21Z
date_published: 2020-01-15T00:00:00Z
date_updated: 2021-01-12T08:13:12Z
day: '15'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.4230/LIPIcs.CSL.2020.20
external_id:
arxiv:
- '1910.06097'
file:
- access_level: open_access
checksum: b9a691d658d075c6369d3304d17fb818
content_type: application/pdf
creator: bkragl
date_created: 2020-01-21T11:21:04Z
date_updated: 2020-07-14T12:47:56Z
file_id: '7349'
file_name: main.pdf
file_size: 617206
relation: main_file
file_date_updated: 2020-07-14T12:47:56Z
has_accepted_license: '1'
intvolume: ' 152'
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
project:
- _id: 25F2ACDE-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S11402-N23
name: Rigorous Systems Engineering
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: 28th EACSL Annual Conference on Computer Science Logic
publication_identifier:
isbn:
- '9783959771320'
issn:
- 1868-8969
publication_status: published
publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
quality_controlled: '1'
scopus_import: 1
status: public
title: Monitoring event frequencies
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: 152
year: '2020'
...
---
_id: '8572'
abstract:
- lang: eng
text: 'We present the results of the ARCH 2020 friendly competition for formal verification
of continuous and hybrid systems with linear continuous dynamics. In its fourth
edition, eight tools have been applied to solve eight different benchmark problems
in the category for linear continuous dynamics (in alphabetical order): CORA,
C2E2, HyDRA, Hylaa, Hylaa-Continuous, JuliaReach, SpaceEx, and XSpeed. This report
is a snapshot of the current landscape of tools and the types of benchmarks they
are particularly suited for. Due to the diversity of problems, we are not ranking
tools, yet the presented results provide one of the most complete assessments
of tools for the safety verification of continuous and hybrid systems with linear
continuous dynamics up to this date.'
acknowledgement: "The authors gratefully acknowledge financial support by the European
Commission project\r\njustITSELF under grant number 817629, by the Austrian Science
Fund (FWF) under grant\r\nZ211-N23 (Wittgenstein Award), by the European Union’s
Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie
grant agreement No. 754411, and by the\r\nScience and Engineering Research Board
(SERB) project with file number IMP/2018/000523.\r\nThis material is based upon
work supported by the Air Force Office of Scientific Research under\r\naward number
FA9550-19-1-0288. Any opinions, finding, and conclusions or recommendations\r\nexpressed
in this material are those of the author(s) and do not necessarily reflect the views
of\r\nthe United States Air Force."
article_processing_charge: No
author:
- first_name: Matthias
full_name: Althoff, Matthias
last_name: Althoff
- first_name: Stanley
full_name: Bak, Stanley
last_name: Bak
- first_name: Zongnan
full_name: Bao, Zongnan
last_name: Bao
- first_name: Marcelo
full_name: Forets, Marcelo
last_name: Forets
- first_name: Goran
full_name: Frehse, Goran
last_name: Frehse
- first_name: Daniel
full_name: Freire, Daniel
last_name: Freire
- first_name: Niklas
full_name: Kochdumper, Niklas
last_name: Kochdumper
- first_name: Yangge
full_name: Li, Yangge
last_name: Li
- first_name: Sayan
full_name: Mitra, Sayan
last_name: Mitra
- first_name: Rajarshi
full_name: Ray, Rajarshi
last_name: Ray
- first_name: Christian
full_name: Schilling, Christian
id: 3A2F4DCE-F248-11E8-B48F-1D18A9856A87
last_name: Schilling
orcid: 0000-0003-3658-1065
- first_name: Stefan
full_name: Schupp, Stefan
last_name: Schupp
- first_name: Mark
full_name: Wetzlinger, Mark
last_name: Wetzlinger
citation:
ama: 'Althoff M, Bak S, Bao Z, et al. ARCH-COMP20 Category Report: Continuous and
hybrid systems with linear dynamics. In: EPiC Series in Computing. Vol
74. EasyChair; 2020:16-48. doi:10.29007/7dt2'
apa: 'Althoff, M., Bak, S., Bao, Z., Forets, M., Frehse, G., Freire, D., … Wetzlinger,
M. (2020). ARCH-COMP20 Category Report: Continuous and hybrid systems with linear
dynamics. In EPiC Series in Computing (Vol. 74, pp. 16–48). EasyChair.
https://doi.org/10.29007/7dt2'
chicago: 'Althoff, Matthias, Stanley Bak, Zongnan Bao, Marcelo Forets, Goran Frehse,
Daniel Freire, Niklas Kochdumper, et al. “ARCH-COMP20 Category Report: Continuous
and Hybrid Systems with Linear Dynamics.” In EPiC Series in Computing,
74:16–48. EasyChair, 2020. https://doi.org/10.29007/7dt2.'
ieee: 'M. Althoff et al., “ARCH-COMP20 Category Report: Continuous and hybrid
systems with linear dynamics,” in EPiC Series in Computing, 2020, vol.
74, pp. 16–48.'
ista: 'Althoff M, Bak S, Bao Z, Forets M, Frehse G, Freire D, Kochdumper N, Li Y,
Mitra S, Ray R, Schilling C, Schupp S, Wetzlinger M. 2020. ARCH-COMP20 Category
Report: Continuous and hybrid systems with linear dynamics. EPiC Series in Computing.
ARCH: International Workshop on Applied Verification on Continuous and Hybrid
Systems vol. 74, 16–48.'
mla: 'Althoff, Matthias, et al. “ARCH-COMP20 Category Report: Continuous and Hybrid
Systems with Linear Dynamics.” EPiC Series in Computing, vol. 74, EasyChair,
2020, pp. 16–48, doi:10.29007/7dt2.'
short: M. Althoff, S. Bak, Z. Bao, M. Forets, G. Frehse, D. Freire, N. Kochdumper,
Y. Li, S. Mitra, R. Ray, C. Schilling, S. Schupp, M. Wetzlinger, in:, EPiC Series
in Computing, EasyChair, 2020, pp. 16–48.
conference:
end_date: 2020-07-12
name: 'ARCH: International Workshop on Applied Verification on Continuous and Hybrid
Systems'
start_date: 2020-07-12
date_created: 2020-09-26T14:49:43Z
date_published: 2020-09-25T00:00:00Z
date_updated: 2021-01-12T08:20:06Z
day: '25'
department:
- _id: ToHe
doi: 10.29007/7dt2
ec_funded: 1
intvolume: ' 74'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://easychair.org/publications/download/DRpS
month: '09'
oa: 1
oa_version: Published Version
page: 16-48
project:
- _id: 25C5A090-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z00312
name: The Wittgenstein Prize
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: EPiC Series in Computing
publication_status: published
publisher: EasyChair
quality_controlled: '1'
status: public
title: 'ARCH-COMP20 Category Report: Continuous and hybrid systems with linear dynamics'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 74
year: '2020'
...