--- _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' ...