[{"publication_status":"published","title":"Quantitative monitoring of software","month":"02","_id":"10891","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","department":[{"_id":"ToHe"}],"acknowledgement":"The formal framework for quantitative monitoring which is presented in this invited talk was defined jointly with N. Ege Saraç at LICS 2021. This work was supported in part by the Wittgenstein Award Z211-N23 of the Austrian Science Fund.","status":"public","abstract":[{"lang":"eng","text":"We present a formal framework for the online black-box monitoring of software using monitors with quantitative verdict functions. Quantitative verdict functions have several advantages. First, quantitative monitors can be approximate, i.e., the value of the verdict function does not need to correspond exactly to the value of the property under observation. Second, quantitative monitors can be quantified universally, i.e., for every possible observed behavior, the monitor tries to make the best effort to estimate the value of the property under observation. Third, quantitative monitors can watch boolean as well as quantitative properties, such as average response time. Fourth, quantitative monitors can use non-finite-state resources, such as counters. As a consequence, quantitative monitors can be compared according to how many resources they use (e.g., the number of counters) and how precisely they approximate the property under observation. This allows for a rich spectrum of cost-precision trade-offs in monitoring software."}],"intvolume":"     13124","citation":{"ama":"Henzinger TA. Quantitative monitoring of software. In: <i>Software Verification</i>. Vol 13124. LNCS. Springer Nature; 2022:3-6. doi:<a href=\"https://doi.org/10.1007/978-3-030-95561-8_1\">10.1007/978-3-030-95561-8_1</a>","short":"T.A. Henzinger, in:, Software Verification, Springer Nature, 2022, pp. 3–6.","mla":"Henzinger, Thomas A. “Quantitative Monitoring of Software.” <i>Software Verification</i>, vol. 13124, Springer Nature, 2022, pp. 3–6, doi:<a href=\"https://doi.org/10.1007/978-3-030-95561-8_1\">10.1007/978-3-030-95561-8_1</a>.","ieee":"T. A. Henzinger, “Quantitative monitoring of software,” in <i>Software Verification</i>, New Haven, CT, United States, 2022, vol. 13124, pp. 3–6.","chicago":"Henzinger, Thomas A. “Quantitative Monitoring of Software.” In <i>Software Verification</i>, 13124:3–6. LNCS. Springer Nature, 2022. <a href=\"https://doi.org/10.1007/978-3-030-95561-8_1\">https://doi.org/10.1007/978-3-030-95561-8_1</a>.","apa":"Henzinger, T. A. (2022). Quantitative monitoring of software. In <i>Software Verification</i> (Vol. 13124, pp. 3–6). New Haven, CT, United States: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-030-95561-8_1\">https://doi.org/10.1007/978-3-030-95561-8_1</a>","ista":"Henzinger TA. 2022. Quantitative monitoring of software. Software Verification. NSV: Numerical Software VerificationLNCS vol. 13124, 3–6."},"day":"22","language":[{"iso":"eng"}],"type":"conference","doi":"10.1007/978-3-030-95561-8_1","publisher":"Springer Nature","publication":"Software Verification","author":[{"full_name":"Henzinger, Thomas A","first_name":"Thomas A","last_name":"Henzinger","orcid":"0000-0002-2985-7724","id":"40876CD8-F248-11E8-B48F-1D18A9856A87"}],"conference":{"location":"New Haven, CT, United States","end_date":"2021-10-19","name":"NSV: Numerical Software Verification","start_date":"2021-10-18"},"oa_version":"None","external_id":{"isi":["000771713200001"]},"date_published":"2022-02-22T00:00:00Z","isi":1,"volume":13124,"series_title":"LNCS","year":"2022","date_created":"2022-03-20T23:01:40Z","page":"3-6","quality_controlled":"1","project":[{"_id":"25F42A32-B435-11E9-9278-68D0E5697425","grant_number":"Z211","name":"The Wittgenstein Prize","call_identifier":"FWF"}],"date_updated":"2023-08-03T06:11:55Z","publication_identifier":{"eissn":["1611-3349"],"issn":["0302-9743"],"isbn":["9783030955601"]},"scopus_import":"1","article_processing_charge":"No"},{"volume":13241,"file":[{"checksum":"7f6f860b20b8de2a249e9c1b4eee15cf","relation":"main_file","creator":"dernst","file_size":479146,"file_name":"2022_LNCS_Bartocci.pdf","success":1,"access_level":"open_access","date_updated":"2022-05-09T06:52:44Z","file_id":"11357","content_type":"application/pdf","date_created":"2022-05-09T06:52:44Z"}],"isi":1,"date_updated":"2023-08-03T07:03:40Z","project":[{"call_identifier":"H2020","name":"Vigilant Algorithmic Monitoring of Software","grant_number":"101020093","_id":"62781420-2b32-11ec-9570-8d9b63373d4d"}],"quality_controlled":"1","page":"3-22","year":"2022","date_created":"2022-05-08T22:01:44Z","scopus_import":"1","publication_identifier":{"eissn":["1611-3349"],"issn":["0302-9743"],"isbn":["9783030994280"]},"article_processing_charge":"No","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"},"file_date_updated":"2022-05-09T06:52:44Z","type":"conference","language":[{"iso":"eng"}],"publisher":"Springer Nature","doi":"10.1007/978-3-030-99429-7_1","ddc":["000"],"author":[{"full_name":"Bartocci, Ezio","first_name":"Ezio","last_name":"Bartocci"},{"first_name":"Thomas","full_name":"Ferrere, Thomas","last_name":"Ferrere","orcid":"0000-0001-5199-3143","id":"40960E6E-F248-11E8-B48F-1D18A9856A87"},{"id":"40876CD8-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2985-7724","full_name":"Henzinger, Thomas A","first_name":"Thomas A","last_name":"Henzinger"},{"last_name":"Nickovic","full_name":"Nickovic, Dejan","first_name":"Dejan","id":"41BCEE5C-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Da Costa","full_name":"Da Costa, Ana Oliveira","first_name":"Ana Oliveira"}],"publication":"Fundamental Approaches to Software Engineering","date_published":"2022-03-29T00:00:00Z","oa_version":"Published Version","external_id":{"isi":["000782393600001"]},"conference":{"name":"FASE: Fundamental Approaches to Software Engineering","start_date":"2022-04-02","location":"Munich, Germany","end_date":"2022-04-07"},"abstract":[{"text":"Contract-based design is a promising methodology for taming the complexity of developing sophisticated systems. A formal contract distinguishes between assumptions, which are constraints that the designer of a component puts on the environments in which the component can be used safely, and guarantees, which are promises that the designer asks from the team that implements the component. A theory of formal contracts can be formalized as an interface theory, which supports the composition and refinement of both assumptions and guarantees.\r\nAlthough there is a rich landscape of contract-based design methods that address functional and extra-functional properties, we present the first interface theory that is designed for ensuring system-wide security properties. Our framework provides a refinement relation and a composition operation that support both incremental design and independent implementability. We develop our theory for both stateless and stateful interfaces. We illustrate the applicability of our framework with an example inspired from the automotive domain.","lang":"eng"}],"status":"public","ec_funded":1,"intvolume":"     13241","citation":{"chicago":"Bartocci, Ezio, Thomas Ferrere, Thomas A Henzinger, Dejan Nickovic, and Ana Oliveira Da Costa. “Information-Flow Interfaces.” In <i>Fundamental Approaches to Software Engineering</i>, 13241:3–22. Springer Nature, 2022. <a href=\"https://doi.org/10.1007/978-3-030-99429-7_1\">https://doi.org/10.1007/978-3-030-99429-7_1</a>.","ista":"Bartocci E, Ferrere T, Henzinger TA, Nickovic D, Da Costa AO. 2022. Information-flow interfaces. Fundamental Approaches to Software Engineering. FASE: Fundamental Approaches to Software Engineering, LNCS, vol. 13241, 3–22.","apa":"Bartocci, E., Ferrere, T., Henzinger, T. A., Nickovic, D., &#38; Da Costa, A. O. (2022). Information-flow interfaces. In <i>Fundamental Approaches to Software Engineering</i> (Vol. 13241, pp. 3–22). Munich, Germany: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-030-99429-7_1\">https://doi.org/10.1007/978-3-030-99429-7_1</a>","ieee":"E. Bartocci, T. Ferrere, T. A. Henzinger, D. Nickovic, and A. O. Da Costa, “Information-flow interfaces,” in <i>Fundamental Approaches to Software Engineering</i>, Munich, Germany, 2022, vol. 13241, pp. 3–22.","mla":"Bartocci, Ezio, et al. “Information-Flow Interfaces.” <i>Fundamental Approaches to Software Engineering</i>, vol. 13241, Springer Nature, 2022, pp. 3–22, doi:<a href=\"https://doi.org/10.1007/978-3-030-99429-7_1\">10.1007/978-3-030-99429-7_1</a>.","short":"E. Bartocci, T. Ferrere, T.A. Henzinger, D. Nickovic, A.O. Da Costa, in:, Fundamental Approaches to Software Engineering, Springer Nature, 2022, pp. 3–22.","ama":"Bartocci E, Ferrere T, Henzinger TA, Nickovic D, Da Costa AO. Information-flow interfaces. In: <i>Fundamental Approaches to Software Engineering</i>. Vol 13241. Springer Nature; 2022:3-22. doi:<a href=\"https://doi.org/10.1007/978-3-030-99429-7_1\">10.1007/978-3-030-99429-7_1</a>"},"has_accepted_license":"1","day":"29","oa":1,"title":"Information-flow interfaces","publication_status":"published","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","_id":"11355","month":"03","alternative_title":["LNCS"],"acknowledgement":"This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 956123 and was funded in part by the FWF project W1255-N23 and by the ERC-2020-AdG 101020093.","department":[{"_id":"ToHe"}]},{"abstract":[{"lang":"eng","text":"Deep learning has enabled breakthroughs in challenging computing problems and has emerged as the standard problem-solving tool for computer vision and natural language processing tasks.\r\nOne exception to this trend is safety-critical tasks where robustness and resilience requirements contradict the black-box nature of neural networks. \r\nTo deploy deep learning methods for these tasks, it is vital to provide guarantees on neural network agents' safety and robustness criteria. \r\nThis can be achieved by developing formal verification methods to verify the safety and robustness properties of neural networks.\r\n\r\nOur goal is to design, develop and assess safety verification methods for neural networks to improve their reliability and trustworthiness in real-world applications.\r\nThis thesis establishes techniques for the verification of compressed and adversarially trained models as well as the design of novel neural networks for verifiably safe decision-making.\r\n\r\nFirst, we establish the problem of verifying quantized neural networks. Quantization is a technique that trades numerical precision for the computational efficiency of running a neural network and is widely adopted in industry.\r\nWe show that neglecting the reduced precision when verifying a neural network can lead to wrong conclusions about the robustness and safety of the network, highlighting that novel techniques for quantized network verification are necessary. We introduce several bit-exact verification methods explicitly designed for quantized neural networks and experimentally confirm on realistic networks that the network's robustness and other formal properties are affected by the quantization.\r\n\r\nFurthermore, we perform a case study providing evidence that adversarial training, a standard technique for making neural networks more robust, has detrimental effects on the network's performance. This robustness-accuracy tradeoff has been studied before regarding the accuracy obtained on classification datasets where each data point is independent of all other data points. On the other hand, we investigate the tradeoff empirically in robot learning settings where a both, a high accuracy and a high robustness, are desirable.\r\nOur results suggest that the negative side-effects of adversarial training outweigh its robustness benefits in practice.\r\n\r\nFinally, we consider the problem of verifying safety when running a Bayesian neural network policy in a feedback loop with systems over the infinite time horizon. Bayesian neural networks are probabilistic models for learning uncertainties in the data and are therefore often used on robotic and healthcare applications where data is inherently stochastic.\r\nWe introduce a method for recalibrating Bayesian neural networks so that they yield probability distributions over safe decisions only.\r\nOur method learns a safety certificate that guarantees safety over the infinite time horizon to determine which decisions are safe in every possible state of the system.\r\nWe demonstrate the effectiveness of our approach on a series of reinforcement learning benchmarks."}],"status":"public","ec_funded":1,"degree_awarded":"PhD","citation":{"ama":"Lechner M. Learning verifiable representations. 2022. doi:<a href=\"https://doi.org/10.15479/at:ista:11362\">10.15479/at:ista:11362</a>","short":"M. Lechner, Learning Verifiable Representations, Institute of Science and Technology Austria, 2022.","ieee":"M. Lechner, “Learning verifiable representations,” Institute of Science and Technology Austria, 2022.","mla":"Lechner, Mathias. <i>Learning Verifiable Representations</i>. Institute of Science and Technology Austria, 2022, doi:<a href=\"https://doi.org/10.15479/at:ista:11362\">10.15479/at:ista:11362</a>.","apa":"Lechner, M. (2022). <i>Learning verifiable representations</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/at:ista:11362\">https://doi.org/10.15479/at:ista:11362</a>","ista":"Lechner M. 2022. Learning verifiable representations. Institute of Science and Technology Austria.","chicago":"Lechner, Mathias. “Learning Verifiable Representations.” Institute of Science and Technology Austria, 2022. <a href=\"https://doi.org/10.15479/at:ista:11362\">https://doi.org/10.15479/at:ista:11362</a>."},"supervisor":[{"last_name":"Henzinger","first_name":"Thomas A","full_name":"Henzinger, Thomas A","orcid":"0000-0002-2985-7724","id":"40876CD8-F248-11E8-B48F-1D18A9856A87"}],"keyword":["neural networks","verification","machine learning"],"has_accepted_license":"1","day":"12","oa":1,"title":"Learning verifiable representations","related_material":{"record":[{"id":"11366","relation":"part_of_dissertation","status":"public"},{"id":"7808","relation":"part_of_dissertation","status":"public"},{"status":"public","relation":"part_of_dissertation","id":"10666"},{"relation":"part_of_dissertation","status":"public","id":"10665"},{"id":"10667","relation":"part_of_dissertation","status":"public"}]},"publication_status":"published","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","_id":"11362","month":"05","alternative_title":["ISTA Thesis"],"department":[{"_id":"GradSch"},{"_id":"ToHe"}],"file":[{"access_level":"closed","date_updated":"2022-05-13T12:49:00Z","file_id":"11378","content_type":"application/zip","date_created":"2022-05-13T12:33:26Z","file_name":"src.zip","creator":"mlechner","file_size":13210143,"checksum":"8eefa9c7c10ca7e1a2ccdd731962a645","relation":"source_file"},{"relation":"main_file","checksum":"1b9e1e5a9a83ed9d89dad2f5133dc026","file_size":2732536,"creator":"mlechner","file_name":"thesis_main-a2.pdf","file_id":"11382","content_type":"application/pdf","date_created":"2022-05-16T08:02:28Z","date_updated":"2022-05-17T15:19:39Z","access_level":"open_access"}],"date_updated":"2025-07-14T09:10:11Z","project":[{"call_identifier":"FWF","grant_number":"Z211","name":"The Wittgenstein Prize","_id":"25F42A32-B435-11E9-9278-68D0E5697425"},{"call_identifier":"H2020","name":"Vigilant Algorithmic Monitoring of Software","grant_number":"101020093","_id":"62781420-2b32-11ec-9570-8d9b63373d4d"}],"page":"124","year":"2022","date_created":"2022-05-12T07:14:01Z","publication_identifier":{"isbn":["978-3-99078-017-6"]},"article_processing_charge":"No","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nd/4.0/legalcode","name":"Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)","image":"/image/cc_by_nd.png","short":"CC BY-ND (4.0)"},"file_date_updated":"2022-05-17T15:19:39Z","type":"dissertation","language":[{"iso":"eng"}],"publisher":"Institute of Science and Technology Austria","doi":"10.15479/at:ista:11362","ddc":["004"],"author":[{"id":"3DC22916-F248-11E8-B48F-1D18A9856A87","full_name":"Lechner, Mathias","first_name":"Mathias","last_name":"Lechner"}],"date_published":"2022-05-12T00:00:00Z","license":"https://creativecommons.org/licenses/by-nd/4.0/","oa_version":"Published Version"},{"abstract":[{"lang":"eng","text":"Adversarial training (i.e., training on adversarially perturbed input data) is a well-studied method for making neural networks robust to potential adversarial attacks during inference. However, the improved robustness does not\r\ncome for free but rather is accompanied by a decrease in overall model accuracy and performance. Recent work has shown that, in practical robot learning applications, the effects of adversarial training do not pose a fair trade-off\r\nbut inflict a net loss when measured in holistic robot performance. This work revisits the robustness-accuracy trade-off in robot learning by systematically analyzing if recent advances in robust training methods and theory in\r\nconjunction with adversarial robot learning can make adversarial training suitable for real-world robot applications. We evaluate a wide variety of robot learning tasks ranging from autonomous driving in a high-fidelity environment\r\namenable to sim-to-real deployment, to mobile robot gesture recognition. Our results demonstrate that, while these techniques make incremental improvements on the trade-off on a relative scale, the negative side-effects caused by\r\nadversarial training still outweigh the improvements by an order of magnitude. We conclude that more substantial advances in robust learning methods are necessary before they can benefit robot learning tasks in practice."}],"status":"public","article_number":"2204.07373","project":[{"grant_number":"101020093","name":"Vigilant Algorithmic Monitoring of Software","_id":"62781420-2b32-11ec-9570-8d9b63373d4d","call_identifier":"H2020"}],"date_updated":"2023-08-01T13:36:50Z","ec_funded":1,"date_created":"2022-05-12T13:20:17Z","year":"2022","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2204.07373"}],"citation":{"ista":"Lechner M, Amini A, Rus D, Henzinger TA. Revisiting the adversarial robustness-accuracy tradeoff in robot learning. arXiv, 2204.07373.","chicago":"Lechner, Mathias, Alexander Amini, Daniela Rus, and Thomas A Henzinger. “Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2204.07373\">https://doi.org/10.48550/arXiv.2204.07373</a>.","apa":"Lechner, M., Amini, A., Rus, D., &#38; Henzinger, T. A. (n.d.). Revisiting the adversarial robustness-accuracy tradeoff in robot learning. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2204.07373\">https://doi.org/10.48550/arXiv.2204.07373</a>","ieee":"M. Lechner, A. Amini, D. Rus, and T. A. Henzinger, “Revisiting the adversarial robustness-accuracy tradeoff in robot learning,” <i>arXiv</i>. .","mla":"Lechner, Mathias, et al. “Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning.” <i>ArXiv</i>, 2204.07373, doi:<a href=\"https://doi.org/10.48550/arXiv.2204.07373\">10.48550/arXiv.2204.07373</a>.","short":"M. Lechner, A. Amini, D. Rus, T.A. Henzinger, ArXiv (n.d.).","ama":"Lechner M, Amini A, Rus D, Henzinger TA. Revisiting the adversarial robustness-accuracy tradeoff in robot learning. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2204.07373\">10.48550/arXiv.2204.07373</a>"},"article_processing_charge":"No","day":"15","type":"preprint","language":[{"iso":"eng"}],"publication_status":"submitted","arxiv":1,"related_material":{"record":[{"relation":"dissertation_contains","status":"public","id":"11362"},{"relation":"later_version","status":"public","id":"12704"}]},"title":"Revisiting the adversarial robustness-accuracy tradeoff in robot learning","oa":1,"month":"04","_id":"11366","doi":"10.48550/arXiv.2204.07373","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"id":"3DC22916-F248-11E8-B48F-1D18A9856A87","last_name":"Lechner","first_name":"Mathias","full_name":"Lechner, Mathias"},{"full_name":"Amini, Alexander","first_name":"Alexander","last_name":"Amini"},{"last_name":"Rus","full_name":"Rus, Daniela","first_name":"Daniela"},{"orcid":"0000-0002-2985-7724","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","last_name":"Henzinger","first_name":"Thomas A","full_name":"Henzinger, Thomas A"}],"publication":"arXiv","department":[{"_id":"ToHe"}],"acknowledgement":"This work was supported in parts by the ERC-2020-AdG 101020093, National Science Foundation (NSF), and JP\r\nMorgan Graduate Fellowships. We thank Christoph Lampert for inspiring this work.\r\n","date_published":"2022-04-15T00:00:00Z","oa_version":"Preprint","external_id":{"arxiv":["2204.07373"]}},{"ec_funded":1,"abstract":[{"lang":"eng","text":"Quantitative monitoring can be universal and approximate: For every finite sequence of observations, the specification provides a value and the monitor outputs a best-effort approximation of it. The quality of the approximation may depend on the resources that are available to the monitor. By taking to the limit the sequences of specification values and monitor outputs, we obtain precision-resource trade-offs also for limit monitoring. This paper provides a formal framework for studying such trade-offs using an abstract interpretation for monitors: For each natural number n, the aggregate semantics of a monitor at time n is an equivalence relation over all sequences of at most n observations so that two equivalent sequences are indistinguishable to the monitor and thus mapped to the same output. This abstract interpretation of quantitative monitors allows us to measure the number of equivalence classes (or “resource use”) that is necessary for a certain precision up to a certain time, or at any time. Our framework offers several insights. For example, we identify a family of specifications for which any resource-optimal exact limit monitor is independent of any error permitted over finite traces. Moreover, we present a specification for which any resource-optimal approximate limit monitor does not minimize its resource use at any time. "}],"status":"public","has_accepted_license":"1","day":"23","intvolume":"     13498","citation":{"short":"T.A. Henzinger, N.A. Mazzocchi, N.E. Sarac, in:, 22nd International Conference on Runtime Verification, Springer Nature, 2022, pp. 200–220.","ama":"Henzinger TA, Mazzocchi NA, Sarac NE. Abstract monitors for quantitative specifications. In: <i>22nd International Conference on Runtime Verification</i>. Vol 13498. Springer Nature; 2022:200-220. doi:<a href=\"https://doi.org/10.1007/978-3-031-17196-3_11\">10.1007/978-3-031-17196-3_11</a>","apa":"Henzinger, T. A., Mazzocchi, N. A., &#38; Sarac, N. E. (2022). Abstract monitors for quantitative specifications. In <i>22nd International Conference on Runtime Verification</i> (Vol. 13498, pp. 200–220). Tbilisi, Georgia: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-031-17196-3_11\">https://doi.org/10.1007/978-3-031-17196-3_11</a>","ista":"Henzinger TA, Mazzocchi NA, Sarac NE. 2022. Abstract monitors for quantitative specifications. 22nd International Conference on Runtime Verification. RV: Runtime Verification, LNCS, vol. 13498, 200–220.","chicago":"Henzinger, Thomas A, Nicolas Adrien Mazzocchi, and Naci E Sarac. “Abstract Monitors for Quantitative Specifications.” In <i>22nd International Conference on Runtime Verification</i>, 13498:200–220. Springer Nature, 2022. <a href=\"https://doi.org/10.1007/978-3-031-17196-3_11\">https://doi.org/10.1007/978-3-031-17196-3_11</a>.","ieee":"T. A. Henzinger, N. A. Mazzocchi, and N. E. Sarac, “Abstract monitors for quantitative specifications,” in <i>22nd International Conference on Runtime Verification</i>, Tbilisi, Georgia, 2022, vol. 13498, pp. 200–220.","mla":"Henzinger, Thomas A., et al. “Abstract Monitors for Quantitative Specifications.” <i>22nd International Conference on Runtime Verification</i>, vol. 13498, Springer Nature, 2022, pp. 200–20, doi:<a href=\"https://doi.org/10.1007/978-3-031-17196-3_11\">10.1007/978-3-031-17196-3_11</a>."},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","_id":"11775","month":"09","oa":1,"title":"Abstract monitors for quantitative specifications","publication_status":"published","department":[{"_id":"GradSch"},{"_id":"ToHe"}],"acknowledgement":"We thank the anonymous reviewers for their helpful comments. This work was supported in part by the ERC-2020-AdG 101020093.","alternative_title":["LNCS"],"date_updated":"2023-08-03T13:38:46Z","project":[{"call_identifier":"H2020","_id":"62781420-2b32-11ec-9570-8d9b63373d4d","grant_number":"101020093","name":"Vigilant Algorithmic Monitoring of Software"}],"page":"200-220","quality_controlled":"1","year":"2022","date_created":"2022-08-08T17:09:09Z","volume":13498,"file":[{"checksum":"05c7dcfbb9053a98f46441fb2eccb213","relation":"main_file","creator":"dernst","file_size":477110,"file_name":"2022_LNCS_RV_Henzinger.pdf","success":1,"date_updated":"2023-01-20T07:34:50Z","access_level":"open_access","file_id":"12317","date_created":"2023-01-20T07:34:50Z","content_type":"application/pdf"}],"isi":1,"article_processing_charge":"Yes","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"},"scopus_import":"1","publication_identifier":{"issn":["0302-9743"]},"publisher":"Springer Nature","doi":"10.1007/978-3-031-17196-3_11","file_date_updated":"2023-01-20T07:34:50Z","type":"conference","language":[{"iso":"eng"}],"date_published":"2022-09-23T00:00:00Z","oa_version":"Published Version","conference":{"end_date":"2022-09-30","location":"Tbilisi, Georgia","start_date":"2022-09-28","name":"RV: Runtime Verification"},"external_id":{"isi":["000866539700011"]},"ddc":["000"],"author":[{"orcid":"0000-0002-2985-7724","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","last_name":"Henzinger","full_name":"Henzinger, Thomas A","first_name":"Thomas A"},{"last_name":"Mazzocchi","full_name":"Mazzocchi, Nicolas Adrien","first_name":"Nicolas Adrien","id":"b26baa86-3308-11ec-87b0-8990f34baa85"},{"id":"8C6B42F8-C8E6-11E9-A03A-F2DCE5697425","first_name":"Naci E","full_name":"Sarac, Naci E","last_name":"Sarac"}],"publication":"22nd International Conference on Runtime Verification"},{"article_processing_charge":"No","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2103.04909","open_access":"1"}],"scopus_import":"1","publication_identifier":{"isbn":["9781728196817"],"issn":["1050-4729"]},"project":[{"name":"Vigilant Algorithmic Monitoring of Software","grant_number":"101020093","_id":"62781420-2b32-11ec-9570-8d9b63373d4d","call_identifier":"H2020"},{"call_identifier":"FWF","name":"The Wittgenstein Prize","grant_number":"Z211","_id":"25F42A32-B435-11E9-9278-68D0E5697425"}],"date_updated":"2022-09-05T08:46:12Z","date_created":"2022-09-04T22:02:02Z","year":"2022","page":"7513-7520","quality_controlled":"1","date_published":"2022-07-12T00:00:00Z","external_id":{"arxiv":["2103.04909"]},"oa_version":"Preprint","conference":{"start_date":"2022-05-23","name":"ICRA: International Conference on Robotics and Automation","end_date":"2022-05-27","location":"Philadelphia, PA, United States"},"author":[{"full_name":"Brunnbauer, Axel","first_name":"Axel","last_name":"Brunnbauer"},{"last_name":"Berducci","full_name":"Berducci, Luigi","first_name":"Luigi"},{"last_name":"Brandstatter","first_name":"Andreas","full_name":"Brandstatter, Andreas"},{"last_name":"Lechner","full_name":"Lechner, Mathias","first_name":"Mathias","id":"3DC22916-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Ramin","full_name":"Hasani, Ramin","last_name":"Hasani"},{"last_name":"Rus","full_name":"Rus, Daniela","first_name":"Daniela"},{"last_name":"Grosu","first_name":"Radu","full_name":"Grosu, Radu"}],"publication":"2022 International Conference on Robotics and Automation","publisher":"IEEE","doi":"10.1109/ICRA46639.2022.9811650","type":"conference","language":[{"iso":"eng"}],"arxiv":1,"day":"12","citation":{"ista":"Brunnbauer A, Berducci L, Brandstatter A, Lechner M, Hasani R, Rus D, Grosu R. 2022. Latent imagination facilitates zero-shot transfer in autonomous racing. 2022 International Conference on Robotics and Automation. ICRA: International Conference on Robotics and Automation, 7513–7520.","apa":"Brunnbauer, A., Berducci, L., Brandstatter, A., Lechner, M., Hasani, R., Rus, D., &#38; Grosu, R. (2022). Latent imagination facilitates zero-shot transfer in autonomous racing. In <i>2022 International Conference on Robotics and Automation</i> (pp. 7513–7520). Philadelphia, PA, United States: IEEE. <a href=\"https://doi.org/10.1109/ICRA46639.2022.9811650\">https://doi.org/10.1109/ICRA46639.2022.9811650</a>","chicago":"Brunnbauer, Axel, Luigi Berducci, Andreas Brandstatter, Mathias Lechner, Ramin Hasani, Daniela Rus, and Radu Grosu. “Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing.” In <i>2022 International Conference on Robotics and Automation</i>, 7513–20. IEEE, 2022. <a href=\"https://doi.org/10.1109/ICRA46639.2022.9811650\">https://doi.org/10.1109/ICRA46639.2022.9811650</a>.","ieee":"A. Brunnbauer <i>et al.</i>, “Latent imagination facilitates zero-shot transfer in autonomous racing,” in <i>2022 International Conference on Robotics and Automation</i>, Philadelphia, PA, United States, 2022, pp. 7513–7520.","mla":"Brunnbauer, Axel, et al. “Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing.” <i>2022 International Conference on Robotics and Automation</i>, IEEE, 2022, pp. 7513–20, doi:<a href=\"https://doi.org/10.1109/ICRA46639.2022.9811650\">10.1109/ICRA46639.2022.9811650</a>.","short":"A. Brunnbauer, L. Berducci, A. Brandstatter, M. Lechner, R. Hasani, D. Rus, R. Grosu, in:, 2022 International Conference on Robotics and Automation, IEEE, 2022, pp. 7513–7520.","ama":"Brunnbauer A, Berducci L, Brandstatter A, et al. Latent imagination facilitates zero-shot transfer in autonomous racing. In: <i>2022 International Conference on Robotics and Automation</i>. IEEE; 2022:7513-7520. doi:<a href=\"https://doi.org/10.1109/ICRA46639.2022.9811650\">10.1109/ICRA46639.2022.9811650</a>"},"ec_funded":1,"abstract":[{"lang":"eng","text":"World models learn behaviors in a latent imagination space to enhance the sample-efficiency of deep reinforcement learning (RL) algorithms. While learning world models for high-dimensional observations (e.g., pixel inputs) has become practicable on standard RL benchmarks and some games, their effectiveness in real-world robotics applications has not been explored. In this paper, we investigate how such agents generalize to real-world autonomous vehicle control tasks, where advanced model-free deep RL algorithms fail. In particular, we set up a series of time-lap tasks for an F1TENTH racing robot, equipped with a high-dimensional LiDAR sensor, on a set of test tracks with a gradual increase in their complexity. In this continuous-control setting, we show that model-based agents capable of learning in imagination substantially outperform model-free agents with respect to performance, sample efficiency, successful task completion, and generalization. Moreover, we show that the generalization ability of model-based agents strongly depends on the choice of their observation model. We provide extensive empirical evidence for the effectiveness of world models provided with long enough memory horizons in sim2real tasks."}],"status":"public","department":[{"_id":"ToHe"}],"acknowledgement":"L.B. was supported by the Doctoral College Resilient Embedded Systems. M.L. was supported in part by the ERC2020-AdG 101020093 and the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award). R.H. and D.R. were supported by The Boeing Company and the Office of Naval Research (ONR) Grant N00014-18-1-2830. R.G. was partially supported by the Horizon-2020 ECSEL Project grant No. 783163 (iDev40) and A.B. by FFG Project ADEX.","month":"07","_id":"12010","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","oa":1,"title":"Latent imagination facilitates zero-shot transfer in autonomous racing"},{"issue":"11","publication":"Nature Machine Intelligence","ddc":["000"],"author":[{"first_name":"Ramin","full_name":"Hasani, Ramin","last_name":"Hasani"},{"full_name":"Lechner, Mathias","first_name":"Mathias","last_name":"Lechner","id":"3DC22916-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Amini","full_name":"Amini, Alexander","first_name":"Alexander"},{"full_name":"Liebenwein, Lucas","first_name":"Lucas","last_name":"Liebenwein"},{"last_name":"Ray","first_name":"Aaron","full_name":"Ray, Aaron"},{"last_name":"Tschaikowski","full_name":"Tschaikowski, Max","first_name":"Max"},{"full_name":"Teschl, Gerald","first_name":"Gerald","last_name":"Teschl"},{"last_name":"Rus","full_name":"Rus, Daniela","first_name":"Daniela"}],"oa_version":"Published Version","external_id":{"isi":["000884215600003"],"arxiv":["2106.13898"]},"date_published":"2022-11-15T00:00:00Z","arxiv":1,"language":[{"iso":"eng"}],"file_date_updated":"2023-01-24T09:49:44Z","type":"journal_article","doi":"10.1038/s42256-022-00556-7","publisher":"Springer Nature","publication_identifier":{"issn":["2522-5839"]},"scopus_import":"1","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"},"article_processing_charge":"No","volume":4,"isi":1,"file":[{"file_name":"2022_NatureMachineIntelligence_Hasani.pdf","access_level":"open_access","date_updated":"2023-01-24T09:49:44Z","success":1,"content_type":"application/pdf","file_id":"12355","date_created":"2023-01-24T09:49:44Z","checksum":"b4789122ce04bfb4ac042390f59aaa8b","relation":"main_file","creator":"dernst","file_size":3259553}],"quality_controlled":"1","page":"992-1003","year":"2022","date_created":"2023-01-12T12:07:21Z","date_updated":"2023-08-04T09:00:10Z","project":[{"_id":"25F42A32-B435-11E9-9278-68D0E5697425","grant_number":"Z211","name":"The Wittgenstein Prize","call_identifier":"FWF"}],"acknowledgement":"This research was supported in part by the AI2050 program at Schmidt Futures (grant G-22-63172), the Boeing Company, and the United States Air Force Research Laboratory and the United States Air Force Artificial Intelligence Accelerator and was accomplished under cooperative agreement number FA8750-19-2-1000. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the United States Air Force or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes, notwithstanding any copyright notation herein. This work was further supported by The Boeing Company and Office of Naval Research grant N00014-18-1-2830. M.T. is supported by the Poul Due Jensen Foundation, grant 883901. M.L. was supported in part by the Austrian Science Fund under grant Z211-N23 (Wittgenstein Award). A.A. was supported by the National Science Foundation Graduate Research Fellowship Program. We thank T.-H. Wang, P. Kao, M. Chahine, W. Xiao, X. Li, L. Yin and Y. Ben for useful suggestions and for testing of CfC models to confirm the results across other domains.","department":[{"_id":"ToHe"}],"oa":1,"title":"Closed-form continuous-time neural networks","related_material":{"link":[{"url":"https://doi.org/10.1038/s42256-022-00597-y","relation":"erratum"}]},"publication_status":"published","article_type":"original","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","month":"11","_id":"12147","keyword":["Artificial Intelligence","Computer Networks and Communications","Computer Vision and Pattern Recognition","Human-Computer Interaction","Software"],"citation":{"short":"R. Hasani, M. Lechner, A. Amini, L. Liebenwein, A. Ray, M. Tschaikowski, G. Teschl, D. Rus, Nature Machine Intelligence 4 (2022) 992–1003.","ama":"Hasani R, Lechner M, Amini A, et al. Closed-form continuous-time neural networks. <i>Nature Machine Intelligence</i>. 2022;4(11):992-1003. doi:<a href=\"https://doi.org/10.1038/s42256-022-00556-7\">10.1038/s42256-022-00556-7</a>","ista":"Hasani R, Lechner M, Amini A, Liebenwein L, Ray A, Tschaikowski M, Teschl G, Rus D. 2022. Closed-form continuous-time neural networks. Nature Machine Intelligence. 4(11), 992–1003.","chicago":"Hasani, Ramin, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Aaron Ray, Max Tschaikowski, Gerald Teschl, and Daniela Rus. “Closed-Form Continuous-Time Neural Networks.” <i>Nature Machine Intelligence</i>. Springer Nature, 2022. <a href=\"https://doi.org/10.1038/s42256-022-00556-7\">https://doi.org/10.1038/s42256-022-00556-7</a>.","apa":"Hasani, R., Lechner, M., Amini, A., Liebenwein, L., Ray, A., Tschaikowski, M., … Rus, D. (2022). Closed-form continuous-time neural networks. <i>Nature Machine Intelligence</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s42256-022-00556-7\">https://doi.org/10.1038/s42256-022-00556-7</a>","mla":"Hasani, Ramin, et al. “Closed-Form Continuous-Time Neural Networks.” <i>Nature Machine Intelligence</i>, vol. 4, no. 11, Springer Nature, 2022, pp. 992–1003, doi:<a href=\"https://doi.org/10.1038/s42256-022-00556-7\">10.1038/s42256-022-00556-7</a>.","ieee":"R. Hasani <i>et al.</i>, “Closed-form continuous-time neural networks,” <i>Nature Machine Intelligence</i>, vol. 4, no. 11. Springer Nature, pp. 992–1003, 2022."},"intvolume":"         4","day":"15","has_accepted_license":"1","status":"public","abstract":[{"text":"Continuous-time neural networks are a class of machine learning systems that can tackle representation learning on spatiotemporal decision-making tasks. These models are typically represented by continuous differential equations. However, their expressive power when they are deployed on computers is bottlenecked by numerical differential equation solvers. This limitation has notably slowed down the scaling and understanding of numerous natural physical phenomena such as the dynamics of nervous systems. Ideally, we would circumvent this bottleneck by solving the given dynamical system in closed form. This is known to be intractable in general. Here, we show that it is possible to closely approximate the interaction between neurons and synapses—the building blocks of natural and artificial neural networks—constructed by liquid time-constant networks efficiently in closed form. To this end, we compute a tightly bounded approximation of the solution of an integral appearing in liquid time-constant dynamics that has had no known closed-form solution so far. This closed-form solution impacts the design of continuous-time and continuous-depth neural models. For instance, since time appears explicitly in closed form, the formulation relaxes the need for complex numerical solvers. Consequently, we obtain models that are between one and five orders of magnitude faster in training and inference compared with differential equation-based counterparts. More importantly, in contrast to ordinary differential equation-based continuous networks, closed-form networks can scale remarkably well compared with other deep learning instances. Lastly, as these models are derived from liquid networks, they show good performance in time-series modelling compared with advanced recurrent neural network models.","lang":"eng"}]},{"date_published":"2021-04-01T00:00:00Z","oa_version":"Submitted Version","external_id":{"isi":["000640531100038"]},"author":[{"last_name":"Sarac","first_name":"Naci E","full_name":"Sarac, Naci E","id":"8C6B42F8-C8E6-11E9-A03A-F2DCE5697425"},{"last_name":"Altun","full_name":"Altun, Ömer Faruk","first_name":"Ömer Faruk"},{"first_name":"Kamil Tolga","full_name":"Atam, Kamil Tolga","last_name":"Atam"},{"last_name":"Karahoda","first_name":"Sertac","full_name":"Karahoda, Sertac"},{"last_name":"Kaya","first_name":"Kamer","full_name":"Kaya, Kamer"},{"last_name":"Yenigün","first_name":"Hüsnü","full_name":"Yenigün, Hüsnü"}],"ddc":["000"],"issue":"4","publication":"Expert Systems with Applications","publisher":"Elsevier","doi":"10.1016/j.eswa.2020.114203","type":"journal_article","file_date_updated":"2020-12-02T13:33:51Z","language":[{"iso":"eng"}],"article_processing_charge":"No","scopus_import":"1","publication_identifier":{"issn":["09574174"]},"project":[{"call_identifier":"FWF","name":"The Wittgenstein Prize","grant_number":"Z211","_id":"25F42A32-B435-11E9-9278-68D0E5697425"}],"date_updated":"2023-08-04T11:19:00Z","date_created":"2020-12-02T13:34:25Z","year":"2021","quality_controlled":"1","file":[{"file_name":"synchroPaperRevised.pdf","date_created":"2020-12-02T13:33:51Z","content_type":"application/pdf","file_id":"8913","date_updated":"2020-12-02T13:33:51Z","access_level":"open_access","relation":"main_file","checksum":"600c2f81bc898a725bcfa7cf26ff4fed","file_size":634967,"creator":"esarac"}],"isi":1,"volume":167,"acknowledgement":"This work was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) [grant number 114E569]. This research was supported in part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award). We would like to thank the authors of (Roman & Szykula, 2015) for providing their heuristics implementations, which we used to compare our SynchroP implementation as given in Table 11.","department":[{"_id":"ToHe"}],"month":"04","_id":"8912","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","article_type":"original","publication_status":"published","title":"Boosting expensive synchronizing heuristics","oa":1,"has_accepted_license":"1","day":"01","intvolume":"       167","citation":{"short":"N.E. Sarac, Ö.F. Altun, K.T. Atam, S. Karahoda, K. Kaya, H. Yenigün, Expert Systems with Applications 167 (2021).","ama":"Sarac NE, Altun ÖF, Atam KT, Karahoda S, Kaya K, Yenigün H. Boosting expensive synchronizing heuristics. <i>Expert Systems with Applications</i>. 2021;167(4). doi:<a href=\"https://doi.org/10.1016/j.eswa.2020.114203\">10.1016/j.eswa.2020.114203</a>","apa":"Sarac, N. E., Altun, Ö. F., Atam, K. T., Karahoda, S., Kaya, K., &#38; Yenigün, H. (2021). Boosting expensive synchronizing heuristics. <i>Expert Systems with Applications</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.eswa.2020.114203\">https://doi.org/10.1016/j.eswa.2020.114203</a>","chicago":"Sarac, Naci E, Ömer Faruk Altun, Kamil Tolga Atam, Sertac Karahoda, Kamer Kaya, and Hüsnü Yenigün. “Boosting Expensive Synchronizing Heuristics.” <i>Expert Systems with Applications</i>. Elsevier, 2021. <a href=\"https://doi.org/10.1016/j.eswa.2020.114203\">https://doi.org/10.1016/j.eswa.2020.114203</a>.","ista":"Sarac NE, Altun ÖF, Atam KT, Karahoda S, Kaya K, Yenigün H. 2021. Boosting expensive synchronizing heuristics. Expert Systems with Applications. 167(4), 114203.","mla":"Sarac, Naci E., et al. “Boosting Expensive Synchronizing Heuristics.” <i>Expert Systems with Applications</i>, vol. 167, no. 4, 114203, Elsevier, 2021, doi:<a href=\"https://doi.org/10.1016/j.eswa.2020.114203\">10.1016/j.eswa.2020.114203</a>.","ieee":"N. E. Sarac, Ö. F. Altun, K. T. Atam, S. Karahoda, K. Kaya, and H. Yenigün, “Boosting expensive synchronizing heuristics,” <i>Expert Systems with Applications</i>, vol. 167, no. 4. Elsevier, 2021."},"abstract":[{"text":"For automata, synchronization, the problem of bringing an automaton to a particular state regardless of its initial state, is important. It has several applications in practice and is related to a fifty-year-old conjecture on the length of the shortest synchronizing word. Although using shorter words increases the effectiveness in practice, finding a shortest one (which is not necessarily unique) is NP-hard. For this reason, there exist various heuristics in the literature. However, high-quality heuristics such as SynchroP producing relatively shorter sequences are very expensive and can take hours when the automaton has tens of thousands of states. The SynchroP heuristic has been frequently used as a benchmark to evaluate the performance of the new heuristics. In this work, we first improve the runtime of SynchroP and its variants by using algorithmic techniques. We then focus on adapting SynchroP for many-core architectures,\r\nand overall, we obtain more than 1000× speedup on GPUs compared to naive sequential implementation that has been frequently used as a benchmark to evaluate new heuristics in the literature. We also propose two SynchroP variants and evaluate their performance.","lang":"eng"}],"status":"public","article_number":"114203"},{"title":"Synthesis of hybrid automata with affine dynamics from time-series data","oa":1,"publication_status":"published","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","_id":"9200","month":"05","acknowledgement":"This research was supported in part by 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.","department":[{"_id":"ToHe"}],"status":"public","abstract":[{"text":"Formal design of embedded and cyber-physical systems relies on mathematical modeling. In this paper, we consider the model class of hybrid automata whose dynamics are defined by affine differential equations. Given a set of time-series data, we present an algorithmic approach to synthesize a hybrid automaton exhibiting behavior that is close to the data, up to a specified precision, and changes in synchrony with the data. A fundamental problem in our synthesis algorithm is to check membership of a time series in a hybrid automaton. Our solution integrates reachability and optimization techniques for affine dynamical systems to obtain both a sufficient and a necessary condition for membership, combined in a refinement framework. The algorithm processes one time series at a time and hence can be interrupted, provide an intermediate result, and be resumed. We report experimental results demonstrating the applicability of our synthesis approach.","lang":"eng"}],"ec_funded":1,"keyword":["hybrid automaton","membership","system identification"],"citation":{"ieee":"M. Garcia Soto, T. A. Henzinger, and C. Schilling, “Synthesis of hybrid automata with affine dynamics from time-series data,” in <i>HSCC ’21: Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control</i>, Nashville, TN, United States, 2021, p. 2102.12734.","mla":"Garcia Soto, Miriam, et al. “Synthesis of Hybrid Automata with Affine Dynamics from Time-Series Data.” <i>HSCC ’21: Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control</i>, Association for Computing Machinery, 2021, p. 2102.12734, doi:<a href=\"https://doi.org/10.1145/3447928.3456704\">10.1145/3447928.3456704</a>.","apa":"Garcia Soto, M., Henzinger, T. A., &#38; Schilling, C. (2021). Synthesis of hybrid automata with affine dynamics from time-series data. In <i>HSCC ’21: Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control</i> (p. 2102.12734). Nashville, TN, United States: Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3447928.3456704\">https://doi.org/10.1145/3447928.3456704</a>","chicago":"Garcia Soto, Miriam, Thomas A Henzinger, and Christian Schilling. “Synthesis of Hybrid Automata with Affine Dynamics from Time-Series Data.” In <i>HSCC ’21: Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control</i>, 2102.12734. Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3447928.3456704\">https://doi.org/10.1145/3447928.3456704</a>.","ista":"Garcia Soto M, Henzinger TA, Schilling C. 2021. Synthesis of hybrid automata with affine dynamics from time-series data. HSCC ’21: Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control. HSCC: International Conference on Hybrid Systems Computation and Control, 2102.12734.","ama":"Garcia Soto M, Henzinger TA, Schilling C. Synthesis of hybrid automata with affine dynamics from time-series data. In: <i>HSCC ’21: Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control</i>. Association for Computing Machinery; 2021:2102.12734. doi:<a href=\"https://doi.org/10.1145/3447928.3456704\">10.1145/3447928.3456704</a>","short":"M. Garcia Soto, T.A. Henzinger, C. Schilling, in:, HSCC ’21: Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control, Association for Computing Machinery, 2021, p. 2102.12734."},"day":"01","has_accepted_license":"1","language":[{"iso":"eng"}],"arxiv":1,"file_date_updated":"2021-05-25T13:53:22Z","type":"conference","doi":"10.1145/3447928.3456704","publisher":"Association for Computing Machinery","publication":"HSCC '21: Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control","ddc":["000"],"author":[{"id":"4B3207F6-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-2936-5719","last_name":"Garcia Soto","first_name":"Miriam","full_name":"Garcia Soto, Miriam"},{"id":"40876CD8-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2985-7724","full_name":"Henzinger, Thomas A","first_name":"Thomas A","last_name":"Henzinger"},{"id":"3A2F4DCE-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-3658-1065","first_name":"Christian","full_name":"Schilling, Christian","last_name":"Schilling"}],"oa_version":"Published Version","conference":{"start_date":"2021-05-19","name":"HSCC: International Conference on Hybrid Systems Computation and Control","end_date":"2021-05-21","location":"Nashville, TN, United States"},"external_id":{"isi":["000932821700028"],"arxiv":["2102.12734"]},"date_published":"2021-05-01T00:00:00Z","isi":1,"file":[{"file_id":"9424","content_type":"application/pdf","date_created":"2021-05-25T13:53:22Z","success":1,"access_level":"open_access","date_updated":"2021-05-25T13:53:22Z","file_name":"2021_HSCC_Soto.pdf","file_size":1474786,"creator":"kschuh","relation":"main_file","checksum":"4c1202c1abf71384c3ee6fea88c2f80e"}],"quality_controlled":"1","page":"2102.12734","year":"2021","date_created":"2021-02-26T16:30:39Z","date_updated":"2023-08-07T13:49:33Z","project":[{"_id":"25F42A32-B435-11E9-9278-68D0E5697425","grant_number":"Z211","name":"The Wittgenstein Prize","call_identifier":"FWF"},{"call_identifier":"H2020","name":"ISTplus - Postdoctoral Fellowships","grant_number":"754411","_id":"260C2330-B435-11E9-9278-68D0E5697425"}],"publication_identifier":{"isbn":["9781450383394"]},"scopus_import":"1","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"},"article_processing_charge":"No"},{"isi":1,"volume":119,"date_created":"2021-03-14T23:01:32Z","year":"2021","page":"133-144","quality_controlled":"1","date_updated":"2023-08-07T14:08:34Z","publication_identifier":{"issn":["0022-0000"],"eissn":["1090-2724"]},"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.1905.03835","open_access":"1"}],"scopus_import":"1","article_processing_charge":"No","arxiv":1,"language":[{"iso":"eng"}],"type":"journal_article","doi":"10.1016/j.jcss.2021.02.008","publisher":"Elsevier","issue":"8","publication":"Journal of Computer and System Sciences","author":[{"orcid":"0000-0001-5588-8287","id":"463C8BC2-F248-11E8-B48F-1D18A9856A87","full_name":"Avni, Guy","first_name":"Guy","last_name":"Avni"},{"id":"40876CD8-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2985-7724","first_name":"Thomas A","full_name":"Henzinger, Thomas A","last_name":"Henzinger"},{"last_name":"Žikelić","full_name":"Žikelić, Đorđe","first_name":"Đorđe"}],"external_id":{"arxiv":["1905.03835"],"isi":["000634149800009"]},"oa_version":"Preprint","date_published":"2021-03-03T00:00:00Z","status":"public","abstract":[{"lang":"eng","text":"A graph game proceeds as follows: two players move a token through a graph to produce a finite or infinite path, which determines the payoff of the game. We study bidding games in which in each turn, an auction determines which player moves the token. Bidding games were largely studied in combination with two variants of first-price auctions called “Richman” and “poorman” bidding. We study taxman bidding, which span the spectrum between the two. The game is parameterized by a constant : portion τ of the winning bid is paid to the other player, and portion  to the bank. While finite-duration (reachability) taxman games have been studied before, we present, for the first time, results on infinite-duration taxman games: we unify, generalize, and simplify previous equivalences between bidding games and a class of stochastic games called random-turn games."}],"intvolume":"       119","citation":{"mla":"Avni, Guy, et al. “Bidding Mechanisms in Graph Games.” <i>Journal of Computer and System Sciences</i>, vol. 119, no. 8, Elsevier, 2021, pp. 133–44, doi:<a href=\"https://doi.org/10.1016/j.jcss.2021.02.008\">10.1016/j.jcss.2021.02.008</a>.","ieee":"G. Avni, T. A. Henzinger, and Đ. Žikelić, “Bidding mechanisms in graph games,” <i>Journal of Computer and System Sciences</i>, vol. 119, no. 8. Elsevier, pp. 133–144, 2021.","ista":"Avni G, Henzinger TA, Žikelić Đ. 2021. Bidding mechanisms in graph games. Journal of Computer and System Sciences. 119(8), 133–144.","apa":"Avni, G., Henzinger, T. A., &#38; Žikelić, Đ. (2021). Bidding mechanisms in graph games. <i>Journal of Computer and System Sciences</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.jcss.2021.02.008\">https://doi.org/10.1016/j.jcss.2021.02.008</a>","chicago":"Avni, Guy, Thomas A Henzinger, and Đorđe Žikelić. “Bidding Mechanisms in Graph Games.” <i>Journal of Computer and System Sciences</i>. Elsevier, 2021. <a href=\"https://doi.org/10.1016/j.jcss.2021.02.008\">https://doi.org/10.1016/j.jcss.2021.02.008</a>.","ama":"Avni G, Henzinger TA, Žikelić Đ. Bidding mechanisms in graph games. <i>Journal of Computer and System Sciences</i>. 2021;119(8):133-144. doi:<a href=\"https://doi.org/10.1016/j.jcss.2021.02.008\">10.1016/j.jcss.2021.02.008</a>","short":"G. Avni, T.A. Henzinger, Đ. Žikelić, Journal of Computer and System Sciences 119 (2021) 133–144."},"day":"03","publication_status":"published","title":"Bidding mechanisms in graph games","related_material":{"record":[{"id":"6884","relation":"earlier_version","status":"public"}]},"oa":1,"article_type":"original","_id":"9239","month":"03","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","department":[{"_id":"ToHe"}]},{"article_number":"2103.11389","status":"public","abstract":[{"lang":"eng","text":"We comment on two formal proofs of Fermat's sum of two squares theorem, written using the Mathematical Components libraries of the Coq proof assistant. The first one follows Zagier's celebrated one-sentence proof; the second follows David Christopher's recent new proof relying on partition-theoretic arguments. Both formal proofs rely on a general property of involutions of finite sets, of independent interest. The proof technique consists for the most part of automating recurrent tasks (such as case distinctions and computations on natural numbers) via ad hoc tactics."}],"year":"2021","date_created":"2021-03-23T05:38:48Z","ec_funded":1,"date_updated":"2023-05-03T10:26:45Z","project":[{"call_identifier":"H2020","name":"ISTplus - Postdoctoral Fellowships","grant_number":"754411","_id":"260C2330-B435-11E9-9278-68D0E5697425"}],"citation":{"ama":"Dubach G, Mühlböck F. Formal verification of Zagier’s one-sentence proof. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2103.11389\">10.48550/arXiv.2103.11389</a>","short":"G. Dubach, F. Mühlböck, ArXiv (n.d.).","ieee":"G. Dubach and F. Mühlböck, “Formal verification of Zagier’s one-sentence proof,” <i>arXiv</i>. .","mla":"Dubach, Guillaume, and Fabian Mühlböck. “Formal Verification of Zagier’s One-Sentence Proof.” <i>ArXiv</i>, 2103.11389, doi:<a href=\"https://doi.org/10.48550/arXiv.2103.11389\">10.48550/arXiv.2103.11389</a>.","ista":"Dubach G, Mühlböck F. Formal verification of Zagier’s one-sentence proof. arXiv, 2103.11389.","chicago":"Dubach, Guillaume, and Fabian Mühlböck. “Formal Verification of Zagier’s One-Sentence Proof.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2103.11389\">https://doi.org/10.48550/arXiv.2103.11389</a>.","apa":"Dubach, G., &#38; Mühlböck, F. (n.d.). Formal verification of Zagier’s one-sentence proof. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2103.11389\">https://doi.org/10.48550/arXiv.2103.11389</a>"},"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2103.11389"}],"day":"21","article_processing_charge":"No","title":"Formal verification of Zagier's one-sentence proof","related_material":{"record":[{"id":"9946","status":"public","relation":"other"}]},"oa":1,"arxiv":1,"publication_status":"submitted","language":[{"iso":"eng"}],"type":"preprint","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"9281","doi":"10.48550/arXiv.2103.11389","month":"03","publication":"arXiv","author":[{"first_name":"Guillaume","full_name":"Dubach, Guillaume","last_name":"Dubach","id":"D5C6A458-10C4-11EA-ABF4-A4B43DDC885E","orcid":"0000-0001-6892-8137"},{"first_name":"Fabian","full_name":"Mühlböck, Fabian","last_name":"Mühlböck","orcid":"0000-0003-1548-0177","id":"6395C5F6-89DF-11E9-9C97-6BDFE5697425"}],"oa_version":"Preprint","external_id":{"arxiv":["2103.11389"]},"date_published":"2021-03-21T00:00:00Z","department":[{"_id":"LaEr"},{"_id":"ToHe"}]},{"publication_status":"published","oa":1,"title":"Quantitative and approximate monitoring","_id":"9356","month":"06","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","acknowledgement":"We thank the anonymous reviewers for their helpful comments. This research was supported in part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award).","department":[{"_id":"GradSch"},{"_id":"ToHe"}],"status":"public","article_number":"9470547","abstract":[{"lang":"eng","text":"In runtime verification, a monitor watches a trace of a system and, if possible, decides after observing each finite prefix whether or not the unknown infinite trace satisfies a given specification. We generalize the theory of runtime verification to monitors that attempt to estimate numerical values of quantitative trace properties (instead of attempting to conclude boolean values of trace specifications), such as maximal or average response time along a trace. Quantitative monitors are approximate: with every finite prefix, they can improve their estimate of the infinite trace's unknown property value. Consequently, quantitative monitors can be compared with regard to a precision-cost trade-off: better approximations of the property value require more monitor resources, such as states (in the case of finite-state monitors) or registers, and additional resources yield better approximations. We introduce a formal framework for quantitative and approximate monitoring, show how it conservatively generalizes the classical boolean setting for monitoring, and give several precision-cost trade-offs for monitors. For example, we prove that there are quantitative properties for which every additional register improves monitoring precision."}],"citation":{"short":"T.A. Henzinger, N.E. Sarac, in:, Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science, Institute of Electrical and Electronics Engineers, 2021.","ama":"Henzinger TA, Sarac NE. Quantitative and approximate monitoring. In: <i>Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science</i>. Institute of Electrical and Electronics Engineers; 2021. doi:<a href=\"https://doi.org/10.1109/LICS52264.2021.9470547\">10.1109/LICS52264.2021.9470547</a>","apa":"Henzinger, T. A., &#38; Sarac, N. E. (2021). Quantitative and approximate monitoring. In <i>Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science</i>. Online: Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/LICS52264.2021.9470547\">https://doi.org/10.1109/LICS52264.2021.9470547</a>","chicago":"Henzinger, Thomas A, and Naci E Sarac. “Quantitative and Approximate Monitoring.” In <i>Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science</i>. Institute of Electrical and Electronics Engineers, 2021. <a href=\"https://doi.org/10.1109/LICS52264.2021.9470547\">https://doi.org/10.1109/LICS52264.2021.9470547</a>.","ista":"Henzinger TA, Sarac NE. 2021. Quantitative and approximate monitoring. Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science. LICS: Symposium on Logic in Computer Science, 9470547.","ieee":"T. A. Henzinger and N. E. Sarac, “Quantitative and approximate monitoring,” in <i>Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science</i>, Online, 2021.","mla":"Henzinger, Thomas A., and Naci E. Sarac. “Quantitative and Approximate Monitoring.” <i>Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science</i>, 9470547, Institute of Electrical and Electronics Engineers, 2021, doi:<a href=\"https://doi.org/10.1109/LICS52264.2021.9470547\">10.1109/LICS52264.2021.9470547</a>."},"day":"29","has_accepted_license":"1","language":[{"iso":"eng"}],"arxiv":1,"type":"conference","file_date_updated":"2021-06-16T08:23:54Z","doi":"10.1109/LICS52264.2021.9470547","publisher":"Institute of Electrical and Electronics Engineers","publication":"Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science","author":[{"id":"40876CD8-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2985-7724","last_name":"Henzinger","first_name":"Thomas A","full_name":"Henzinger, Thomas A"},{"last_name":"Sarac","first_name":"Naci E","full_name":"Sarac, Naci E","id":"8C6B42F8-C8E6-11E9-A03A-F2DCE5697425"}],"ddc":["000"],"external_id":{"isi":["000947350400021"],"arxiv":["2105.08353"]},"conference":{"location":"Online","end_date":"2021-07-02","name":"LICS: Symposium on Logic in Computer Science","start_date":"2021-06-29"},"oa_version":"Published Version","date_published":"2021-06-29T00:00:00Z","file":[{"relation":"main_file","checksum":"6e4cba3f72775f479c5b1b75d1a4a0c4","file_size":641990,"creator":"esarac","file_name":"qam.pdf","content_type":"application/pdf","file_id":"9557","date_created":"2021-06-16T08:23:54Z","success":1,"access_level":"open_access","date_updated":"2021-06-16T08:23:54Z"}],"isi":1,"year":"2021","date_created":"2021-04-30T17:30:47Z","quality_controlled":"1","project":[{"grant_number":"Z211","name":"The Wittgenstein Prize","_id":"25F42A32-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"}],"date_updated":"2023-08-08T13:52:56Z","scopus_import":"1","article_processing_charge":"No"},{"title":"Differential monitoring","oa":1,"related_material":{"record":[{"id":"9946","status":"public","relation":"extended_version"}]},"publication_status":"published","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","month":"10","_id":"10108","place":"Cham","alternative_title":["LNCS"],"acknowledgement":"The authors would like to thank Borzoo Bonakdarpour, Derek Dreyer, Adrian Francalanza, Owolabi Legunsen, Mae Milano, Manuel Rigger, Cesar Sanchez, and the members of the IST Verification Seminar for their helpful comments and insights on various stages of this work, as well as the reviewers of RV’21 for their helpful suggestions on the actual paper.","department":[{"_id":"ToHe"}],"status":"public","abstract":[{"lang":"eng","text":"We argue that the time is ripe to investigate differential monitoring, in which the specification of a program's behavior is implicitly given by a second program implementing the same informal specification. Similar ideas have been proposed before, and are currently implemented in restricted form for testing and specialized run-time analyses, aspects of which we combine. We discuss the challenges of implementing differential monitoring as a general-purpose, black-box run-time monitoring framework, and present promising results of a preliminary implementation, showing low monitoring overheads for diverse programs."}],"keyword":["run-time verification","software engineering","implicit specification"],"citation":{"short":"F. Mühlböck, T.A. Henzinger, in:, International Conference on Runtime Verification, Springer Nature, Cham, 2021, pp. 231–243.","ama":"Mühlböck F, Henzinger TA. Differential monitoring. In: <i>International Conference on Runtime Verification</i>. Vol 12974. Cham: Springer Nature; 2021:231-243. doi:<a href=\"https://doi.org/10.1007/978-3-030-88494-9_12\">10.1007/978-3-030-88494-9_12</a>","ista":"Mühlböck F, Henzinger TA. 2021. Differential monitoring. International Conference on Runtime Verification. RV: Runtime Verification, LNCS, vol. 12974, 231–243.","chicago":"Mühlböck, Fabian, and Thomas A Henzinger. “Differential Monitoring.” In <i>International Conference on Runtime Verification</i>, 12974:231–43. Cham: Springer Nature, 2021. <a href=\"https://doi.org/10.1007/978-3-030-88494-9_12\">https://doi.org/10.1007/978-3-030-88494-9_12</a>.","apa":"Mühlböck, F., &#38; Henzinger, T. A. (2021). Differential monitoring. In <i>International Conference on Runtime Verification</i> (Vol. 12974, pp. 231–243). Cham: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-030-88494-9_12\">https://doi.org/10.1007/978-3-030-88494-9_12</a>","mla":"Mühlböck, Fabian, and Thomas A. Henzinger. “Differential Monitoring.” <i>International Conference on Runtime Verification</i>, vol. 12974, Springer Nature, 2021, pp. 231–43, doi:<a href=\"https://doi.org/10.1007/978-3-030-88494-9_12\">10.1007/978-3-030-88494-9_12</a>.","ieee":"F. Mühlböck and T. A. Henzinger, “Differential monitoring,” in <i>International Conference on Runtime Verification</i>, Virtual, 2021, vol. 12974, pp. 231–243."},"intvolume":"     12974","day":"06","has_accepted_license":"1","language":[{"iso":"eng"}],"file_date_updated":"2021-10-07T23:32:18Z","type":"conference","doi":"10.1007/978-3-030-88494-9_12","publisher":"Springer Nature","publication":"International Conference on Runtime Verification","ddc":["005"],"author":[{"id":"6395C5F6-89DF-11E9-9C97-6BDFE5697425","orcid":"0000-0003-1548-0177","last_name":"Mühlböck","first_name":"Fabian","full_name":"Mühlböck, Fabian"},{"last_name":"Henzinger","full_name":"Henzinger, Thomas A","first_name":"Thomas A","orcid":"0000-0002-2985-7724","id":"40876CD8-F248-11E8-B48F-1D18A9856A87"}],"external_id":{"isi":["000719383800012"]},"oa_version":"Preprint","conference":{"end_date":"2021-10-14","location":"Virtual","start_date":"2021-10-11","name":"RV: Runtime Verification"},"date_published":"2021-10-06T00:00:00Z","volume":12974,"isi":1,"file":[{"relation":"main_file","checksum":"554c7fdb259eda703a8b6328a6dad55a","file_size":350632,"creator":"fmuehlbo","file_name":"differentialmonitoring-cameraready-openaccess.pdf","file_id":"10109","date_created":"2021-10-07T23:32:18Z","content_type":"application/pdf","success":1,"access_level":"open_access","date_updated":"2021-10-07T23:32:18Z"}],"page":"231-243","quality_controlled":"1","date_created":"2021-10-07T23:30:10Z","year":"2021","date_updated":"2023-08-14T07:20:30Z","project":[{"call_identifier":"FWF","_id":"25F42A32-B435-11E9-9278-68D0E5697425","name":"The Wittgenstein Prize","grant_number":"Z211"}],"publication_identifier":{"eisbn":["978-3-030-88494-9"],"isbn":["978-3-030-88493-2"],"issn":["0302-9743"],"eissn":["1611-3349"]},"scopus_import":"1","article_processing_charge":"No"},{"quality_controlled":"1","year":"2021","date_created":"2021-10-19T12:48:44Z","date_updated":"2021-11-12T11:30:07Z","project":[{"call_identifier":"FWF","grant_number":"Z211","name":"The Wittgenstein Prize","_id":"25F42A32-B435-11E9-9278-68D0E5697425"}],"volume":5,"file":[{"file_name":"monnom-oopsla21.pdf","access_level":"open_access","success":1,"date_updated":"2021-10-19T12:52:23Z","file_id":"10154","content_type":"application/pdf","date_created":"2021-10-19T12:52:23Z","checksum":"71011efd2da771cafdec7f0d9693f8c1","relation":"main_file","creator":"fmuehlbo","file_size":770269}],"article_processing_charge":"No","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nd/4.0/legalcode","name":"Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)","image":"/image/cc_by_nd.png","short":"CC BY-ND (4.0)"},"publication_identifier":{"eissn":["2475-1421"]},"doi":"10.1145/3485504","publisher":"Association for Computing Machinery","language":[{"iso":"eng"}],"file_date_updated":"2021-10-19T12:52:23Z","type":"journal_article","oa_version":"Published Version","conference":{"start_date":"2021-10-17","name":"OOPSLA: Object-Oriented Programming, Systems, Languages, and Applications","end_date":"2021-10-23","location":"Chicago, IL, United States"},"date_published":"2021-10-15T00:00:00Z","publication":"Proceedings of the ACM on Programming Languages","ddc":["005"],"author":[{"last_name":"Mühlböck","full_name":"Mühlböck, Fabian","first_name":"Fabian","orcid":"0000-0003-1548-0177","id":"6395C5F6-89DF-11E9-9C97-6BDFE5697425"},{"full_name":"Tate, Ross","first_name":"Ross","last_name":"Tate"}],"article_number":"127","status":"public","abstract":[{"lang":"eng","text":"Gradual typing is a principled means for mixing typed and untyped code. But typed and untyped code often exhibit different programming patterns. There is already substantial research investigating gradually giving types to code exhibiting typical untyped patterns, and some research investigating gradually removing types from code exhibiting typical typed patterns. This paper investigates how to extend these established gradual-typing concepts to give formal guarantees not only about how to change types as code evolves but also about how to change such programming patterns as well.\r\n\r\nIn particular, we explore mixing untyped \"structural\" code with typed \"nominal\" code in an object-oriented language. But whereas previous work only allowed \"nominal\" objects to be treated as \"structural\" objects, we also allow \"structural\" objects to dynamically acquire certain nominal types, namely interfaces. We present a calculus that supports such \"cross-paradigm\" code migration and interoperation in a manner satisfying both the static and dynamic gradual guarantees, and demonstrate that the calculus can be implemented efficiently."}],"day":"15","has_accepted_license":"1","keyword":["gradual typing","gradual guarantee","nominal","structural","call tags"],"intvolume":"         5","citation":{"short":"F. Mühlböck, R. Tate, Proceedings of the ACM on Programming Languages 5 (2021).","ama":"Mühlböck F, Tate R. Transitioning from structural to nominal code with efficient gradual typing. <i>Proceedings of the ACM on Programming Languages</i>. 2021;5. doi:<a href=\"https://doi.org/10.1145/3485504\">10.1145/3485504</a>","apa":"Mühlböck, F., &#38; Tate, R. (2021). Transitioning from structural to nominal code with efficient gradual typing. <i>Proceedings of the ACM on Programming Languages</i>. Chicago, IL, United States: Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3485504\">https://doi.org/10.1145/3485504</a>","ista":"Mühlböck F, Tate R. 2021. Transitioning from structural to nominal code with efficient gradual typing. Proceedings of the ACM on Programming Languages. 5, 127.","chicago":"Mühlböck, Fabian, and Ross Tate. “Transitioning from Structural to Nominal Code with Efficient Gradual Typing.” <i>Proceedings of the ACM on Programming Languages</i>. Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3485504\">https://doi.org/10.1145/3485504</a>.","mla":"Mühlböck, Fabian, and Ross Tate. “Transitioning from Structural to Nominal Code with Efficient Gradual Typing.” <i>Proceedings of the ACM on Programming Languages</i>, vol. 5, 127, Association for Computing Machinery, 2021, doi:<a href=\"https://doi.org/10.1145/3485504\">10.1145/3485504</a>.","ieee":"F. Mühlböck and R. Tate, “Transitioning from structural to nominal code with efficient gradual typing,” <i>Proceedings of the ACM on Programming Languages</i>, vol. 5. Association for Computing Machinery, 2021."},"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","month":"10","_id":"10153","title":"Transitioning from structural to nominal code with efficient gradual typing","oa":1,"publication_status":"published","article_type":"original","acknowledgement":"We thank the reviewers for their valuable suggestions towards improving the paper. We also \r\nthank Mae Milano and Adrian Sampson, as well as the members of the Programming Languages Discussion Group at Cornell University and of the Programming Research Laboratory at Northeastern University, for their helpful feedback on preliminary findings of this work.\r\n\r\nThis material is based upon work supported in part by the National Science Foundation (NSF) through grant CCF-1350182 and the Austrian Science Fund (FWF) through grant Z211-N23 (Wittgenstein~Award).\r\nAny opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF or the FWF.","department":[{"_id":"ToHe"}]},{"day":"06","citation":{"short":"A. Lukina, C. Schilling, T.A. Henzinger, in:, 21st International Conference on Runtime Verification, Springer Nature, Cham, 2021, pp. 42–61.","ama":"Lukina A, Schilling C, Henzinger TA. Into the unknown: active monitoring of neural networks. In: <i>21st International Conference on Runtime Verification</i>. Vol 12974. Cham: Springer Nature; 2021:42-61. doi:<a href=\"https://doi.org/10.1007/978-3-030-88494-9_3\">10.1007/978-3-030-88494-9_3</a>","chicago":"Lukina, Anna, Christian Schilling, and Thomas A Henzinger. “Into the Unknown: Active Monitoring of Neural Networks.” In <i>21st International Conference on Runtime Verification</i>, 12974:42–61. Cham: Springer Nature, 2021. <a href=\"https://doi.org/10.1007/978-3-030-88494-9_3\">https://doi.org/10.1007/978-3-030-88494-9_3</a>.","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.","apa":"Lukina, A., Schilling, C., &#38; Henzinger, T. A. (2021). Into the unknown: active monitoring of neural networks. In <i>21st International Conference on Runtime Verification</i> (Vol. 12974, pp. 42–61). Cham: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-030-88494-9_3\">https://doi.org/10.1007/978-3-030-88494-9_3</a>","mla":"Lukina, Anna, et al. “Into the Unknown: Active Monitoring of Neural Networks.” <i>21st International Conference on Runtime Verification</i>, vol. 12974, Springer Nature, 2021, pp. 42–61, doi:<a href=\"https://doi.org/10.1007/978-3-030-88494-9_3\">10.1007/978-3-030-88494-9_3</a>.","ieee":"A. Lukina, C. Schilling, and T. A. Henzinger, “Into the unknown: active monitoring of neural networks,” in <i>21st International Conference on Runtime Verification</i>, Virtual, 2021, vol. 12974, pp. 42–61."},"keyword":["monitoring","neural networks","novelty detection"],"ec_funded":1,"abstract":[{"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.","lang":"eng"}],"status":"public","department":[{"_id":"ToHe"}],"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"],"place":"Cham","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","month":"10","_id":"10206","oa":1,"related_material":{"record":[{"relation":"extended_version","status":"public","id":"13234"}]},"title":"Into the unknown: active monitoring of neural networks","publication_status":"published","article_processing_charge":"No","scopus_import":"1","main_file_link":[{"url":"https://arxiv.org/abs/2009.06429","open_access":"1"}],"publication_identifier":{"eissn":["1611-3349"],"issn":["0302-9743"],"isbn":["9-783-0308-8493-2"],"eisbn":["978-3-030-88494-9"]},"date_updated":"2024-01-30T12:06:56Z","project":[{"call_identifier":"H2020","_id":"260C2330-B435-11E9-9278-68D0E5697425","name":"ISTplus - Postdoctoral Fellowships","grant_number":"754411"},{"name":"The Wittgenstein Prize","grant_number":"Z211","_id":"25F42A32-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"}],"page":"42-61","quality_controlled":"1","year":"2021","date_created":"2021-10-31T23:01:31Z","volume":"12974 ","isi":1,"date_published":"2021-10-06T00:00:00Z","conference":{"name":"RV: Runtime Verification","start_date":"2021-10-11","location":"Virtual","end_date":"2021-10-14"},"oa_version":"Preprint","external_id":{"isi":["000719383800003"],"arxiv":["2009.06429"]},"author":[{"id":"CBA4D1A8-0FE8-11E9-BDE6-07BFE5697425","last_name":"Lukina","first_name":"Anna","full_name":"Lukina, Anna"},{"id":"3A2F4DCE-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-3658-1065","full_name":"Schilling, Christian","first_name":"Christian","last_name":"Schilling"},{"last_name":"Henzinger","full_name":"Henzinger, Thomas A","first_name":"Thomas A","orcid":"0000-0002-2985-7724","id":"40876CD8-F248-11E8-B48F-1D18A9856A87"}],"publication":"21st International Conference on Runtime Verification","publisher":"Springer Nature","doi":"10.1007/978-3-030-88494-9_3","type":"conference","language":[{"iso":"eng"}],"arxiv":1},{"article_type":"original","publication_status":"published","title":"Interactive analysis of CNN robustness","oa":1,"_id":"10404","month":"11","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","department":[{"_id":"ToHe"}],"acknowledgement":"We thank Robert Geirhos and Roland Zimmermann for their participation in the case study and valuable feedback, Chris Olah and Nick Cammarata for valuable discussions in the early phase of the project, as well as the Distill Slack workspace as a platform for discussions. M.L. is supported in part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award). J.B. is supported by the German Federal Ministry of Education and Research\r\n(BMBF) through the Competence Center for Machine Learning (TUE.AI, FKZ 01IS18039A) and the International Max Planck Research School for Intelligent Systems (IMPRS-IS). R.H. is partially supported by Boeing and Horizon-2020 ECSEL (grant 783163, iDev40).\r\n","abstract":[{"text":"While convolutional neural networks (CNNs) have found wide adoption as state-of-the-art models for image-related tasks, their predictions are often highly sensitive to small input perturbations, which the human vision is robust against. This paper presents Perturber, a web-based application that allows users to instantaneously explore how CNN activations and predictions evolve when a 3D input scene is interactively perturbed. Perturber offers a large variety of scene modifications, such as camera controls, lighting and shading effects, background modifications, object morphing, as well as adversarial attacks, to facilitate the discovery of potential vulnerabilities. Fine-tuned model versions can be directly compared for qualitative evaluation of their robustness. Case studies with machine learning experts have shown that Perturber helps users to quickly generate hypotheses about model vulnerabilities and to qualitatively compare model behavior. Using quantitative analyses, we could replicate users’ insights with other CNN architectures and input images, yielding new insights about the vulnerability of adversarially trained models.","lang":"eng"}],"status":"public","citation":{"short":"S. Sietzen, M. Lechner, J. Borowski, R. Hasani, M. Waldner, Computer Graphics Forum 40 (2021) 253–264.","ama":"Sietzen S, Lechner M, Borowski J, Hasani R, Waldner M. Interactive analysis of CNN robustness. <i>Computer Graphics Forum</i>. 2021;40(7):253-264. doi:<a href=\"https://doi.org/10.1111/cgf.14418\">10.1111/cgf.14418</a>","chicago":"Sietzen, Stefan, Mathias Lechner, Judy Borowski, Ramin Hasani, and Manuela Waldner. “Interactive Analysis of CNN Robustness.” <i>Computer Graphics Forum</i>. Wiley, 2021. <a href=\"https://doi.org/10.1111/cgf.14418\">https://doi.org/10.1111/cgf.14418</a>.","apa":"Sietzen, S., Lechner, M., Borowski, J., Hasani, R., &#38; Waldner, M. (2021). Interactive analysis of CNN robustness. <i>Computer Graphics Forum</i>. Wiley. <a href=\"https://doi.org/10.1111/cgf.14418\">https://doi.org/10.1111/cgf.14418</a>","ista":"Sietzen S, Lechner M, Borowski J, Hasani R, Waldner M. 2021. Interactive analysis of CNN robustness. Computer Graphics Forum. 40(7), 253–264.","ieee":"S. Sietzen, M. Lechner, J. Borowski, R. Hasani, and M. Waldner, “Interactive analysis of CNN robustness,” <i>Computer Graphics Forum</i>, vol. 40, no. 7. Wiley, pp. 253–264, 2021.","mla":"Sietzen, Stefan, et al. “Interactive Analysis of CNN Robustness.” <i>Computer Graphics Forum</i>, vol. 40, no. 7, Wiley, 2021, pp. 253–64, doi:<a href=\"https://doi.org/10.1111/cgf.14418\">10.1111/cgf.14418</a>."},"intvolume":"        40","day":"27","type":"journal_article","arxiv":1,"language":[{"iso":"eng"}],"publisher":"Wiley","doi":"10.1111/cgf.14418","author":[{"full_name":"Sietzen, Stefan","first_name":"Stefan","last_name":"Sietzen"},{"id":"3DC22916-F248-11E8-B48F-1D18A9856A87","last_name":"Lechner","full_name":"Lechner, Mathias","first_name":"Mathias"},{"first_name":"Judy","full_name":"Borowski, Judy","last_name":"Borowski"},{"first_name":"Ramin","full_name":"Hasani, Ramin","last_name":"Hasani"},{"last_name":"Waldner","full_name":"Waldner, Manuela","first_name":"Manuela"}],"issue":"7","publication":"Computer Graphics Forum","date_published":"2021-11-27T00:00:00Z","oa_version":"Preprint","external_id":{"isi":["000722952000024"],"arxiv":["2110.07667"]},"isi":1,"volume":40,"project":[{"call_identifier":"FWF","_id":"25F42A32-B435-11E9-9278-68D0E5697425","name":"The Wittgenstein Prize","grant_number":"Z211"}],"date_updated":"2023-08-14T13:11:42Z","year":"2021","date_created":"2021-12-05T23:01:40Z","quality_controlled":"1","page":"253-264","main_file_link":[{"url":"https://arxiv.org/abs/2110.07667","open_access":"1"}],"scopus_import":"1","publication_identifier":{"eissn":["1467-8659"],"issn":["0167-7055"]},"article_processing_charge":"No"},{"has_accepted_license":"1","day":"28","citation":{"mla":"Henzinger, Thomas A., et al. “Scalable Verification of Quantized Neural Networks.” <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, vol. 35, no. 5A, AAAI Press, 2021, pp. 3787–95.","ieee":"T. A. Henzinger, M. Lechner, and D. Zikelic, “Scalable verification of quantized neural networks,” in <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, Virtual, 2021, vol. 35, no. 5A, pp. 3787–3795.","ista":"Henzinger TA, Lechner M, Zikelic D. 2021. Scalable verification of quantized neural networks. Proceedings of the AAAI Conference on Artificial Intelligence. AAAI: Association for the Advancement of Artificial Intelligence, Technical Tracks, vol. 35, 3787–3795.","apa":"Henzinger, T. A., Lechner, M., &#38; Zikelic, D. (2021). Scalable verification of quantized neural networks. In <i>Proceedings of the AAAI Conference on Artificial Intelligence</i> (Vol. 35, pp. 3787–3795). Virtual: AAAI Press.","chicago":"Henzinger, Thomas A, Mathias Lechner, and Dorde Zikelic. “Scalable Verification of Quantized Neural Networks.” In <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, 35:3787–95. AAAI Press, 2021.","ama":"Henzinger TA, Lechner M, Zikelic D. Scalable verification of quantized neural networks. In: <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>. Vol 35. AAAI Press; 2021:3787-3795.","short":"T.A. Henzinger, M. Lechner, D. Zikelic, in:, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, 2021, pp. 3787–3795."},"intvolume":"        35","ec_funded":1,"abstract":[{"lang":"eng","text":"Formal verification of neural networks is an active topic of research, and recent advances have significantly increased the size of the networks that verification tools can handle. However, most methods are designed for verification of an idealized model of the actual network which works over real arithmetic and ignores rounding imprecisions. This idealization is in stark contrast to network quantization, which is a technique that trades numerical precision for computational efficiency and is, therefore, often applied in practice. Neglecting rounding errors of such low-bit quantized neural networks has been shown to lead to wrong conclusions about the network’s correctness. Thus, the desired approach for verifying quantized neural networks would be one that takes these rounding errors\r\ninto account. In this paper, we show that verifying the bitexact implementation of quantized neural networks with bitvector specifications is PSPACE-hard, even though verifying idealized real-valued networks and satisfiability of bit-vector specifications alone are each in NP. Furthermore, we explore several practical heuristics toward closing the complexity gap between idealized and bit-exact verification. In particular, we propose three techniques for making SMT-based verification of quantized neural networks more scalable. Our experiments demonstrate that our proposed methods allow a speedup of up to three orders of magnitude over existing approaches."}],"status":"public","department":[{"_id":"GradSch"},{"_id":"ToHe"}],"acknowledgement":"This research was supported in part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein\r\nAward), 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.\r\n","alternative_title":["Technical Tracks"],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"10665","month":"05","related_material":{"record":[{"id":"11362","relation":"dissertation_contains","status":"public"}]},"oa":1,"title":"Scalable verification of quantized neural networks","publication_status":"published","article_processing_charge":"No","scopus_import":"1","main_file_link":[{"url":"https://ojs.aaai.org/index.php/AAAI/article/view/16496","open_access":"1"}],"publication_identifier":{"isbn":["978-1-57735-866-4"],"issn":["2159-5399"],"eissn":["2374-3468"]},"date_updated":"2025-07-14T09:10:11Z","project":[{"_id":"2564DBCA-B435-11E9-9278-68D0E5697425","name":"International IST Doctoral Program","grant_number":"665385","call_identifier":"H2020"},{"_id":"25F42A32-B435-11E9-9278-68D0E5697425","grant_number":"Z211","name":"The Wittgenstein Prize","call_identifier":"FWF"},{"_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","grant_number":"863818","name":"Formal Methods for Stochastic Models: Algorithms and Applications","call_identifier":"H2020"}],"quality_controlled":"1","page":"3787-3795","date_created":"2022-01-25T15:15:02Z","year":"2021","volume":35,"file":[{"relation":"main_file","checksum":"2bc8155b2526a70fba5b7301bc89dbd1","file_size":137235,"creator":"mlechner","file_name":"16496-Article Text-19990-1-2-20210518 (1).pdf","content_type":"application/pdf","file_id":"10684","date_created":"2022-01-26T07:41:16Z","access_level":"open_access","success":1,"date_updated":"2022-01-26T07:41:16Z"}],"date_published":"2021-05-28T00:00:00Z","conference":{"name":"AAAI: Association for the Advancement of Artificial Intelligence","start_date":"2021-02-02","location":"Virtual","end_date":"2021-02-09"},"external_id":{"arxiv":["2012.08185"]},"oa_version":"Published Version","ddc":["000"],"author":[{"orcid":"0000-0002-2985-7724","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas A","full_name":"Henzinger, Thomas A","last_name":"Henzinger"},{"last_name":"Lechner","first_name":"Mathias","full_name":"Lechner, Mathias","id":"3DC22916-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Zikelic","first_name":"Dorde","full_name":"Zikelic, Dorde","id":"294AA7A6-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4681-1699"}],"issue":"5A","publication":"Proceedings of the AAAI Conference on Artificial Intelligence","publisher":"AAAI Press","file_date_updated":"2022-01-26T07:41:16Z","type":"conference","arxiv":1,"language":[{"iso":"eng"}]},{"citation":{"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.","ama":"Lechner M, Hasani R, Grosu R, Rus D, Henzinger TA. Adversarial training is not ready for robot learning. In: <i>2021 IEEE International Conference on Robotics and Automation</i>. ICRA. ; 2021:4140-4147. doi:<a href=\"https://doi.org/10.1109/ICRA48506.2021.9561036\">10.1109/ICRA48506.2021.9561036</a>","chicago":"Lechner, Mathias, Ramin Hasani, Radu Grosu, Daniela Rus, and Thomas A Henzinger. “Adversarial Training Is Not Ready for Robot Learning.” In <i>2021 IEEE International Conference on Robotics and Automation</i>, 4140–47. ICRA, 2021. <a href=\"https://doi.org/10.1109/ICRA48506.2021.9561036\">https://doi.org/10.1109/ICRA48506.2021.9561036</a>.","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.","apa":"Lechner, M., Hasani, R., Grosu, R., Rus, D., &#38; Henzinger, T. A. (2021). Adversarial training is not ready for robot learning. In <i>2021 IEEE International Conference on Robotics and Automation</i> (pp. 4140–4147). Xi’an, China. <a href=\"https://doi.org/10.1109/ICRA48506.2021.9561036\">https://doi.org/10.1109/ICRA48506.2021.9561036</a>","mla":"Lechner, Mathias, et al. “Adversarial Training Is Not Ready for Robot Learning.” <i>2021 IEEE International Conference on Robotics and Automation</i>, 2021, pp. 4140–47, doi:<a href=\"https://doi.org/10.1109/ICRA48506.2021.9561036\">10.1109/ICRA48506.2021.9561036</a>.","ieee":"M. Lechner, R. Hasani, R. Grosu, D. Rus, and T. A. Henzinger, “Adversarial training is not ready for robot learning,” in <i>2021 IEEE International Conference on Robotics and Automation</i>, Xi’an, China, 2021, pp. 4140–4147."},"has_accepted_license":"1","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."}],"status":"public","department":[{"_id":"GradSch"},{"_id":"ToHe"}],"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).","publication_status":"published","related_material":{"record":[{"id":"11362","relation":"dissertation_contains","status":"public"}]},"title":"Adversarial training is not ready for robot learning","oa":1,"_id":"10666","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2103.08187"}],"publication_identifier":{"eisbn":["978-1-7281-9077-8"],"isbn":["978-1-7281-9078-5"],"eissn":["2577-087X"],"issn":["1050-4729"]},"article_processing_charge":"No","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode","image":"/images/cc_by_nc_nd.png","name":"Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0)","short":"CC BY-NC-ND (3.0)"},"isi":1,"project":[{"call_identifier":"FWF","name":"The Wittgenstein 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Prize","grant_number":"Z211","call_identifier":"FWF"}],"quality_controlled":"1","date_created":"2022-01-25T15:45:58Z","year":"2021","file":[{"access_level":"open_access","date_updated":"2022-01-26T07:39:59Z","success":1,"content_type":"application/pdf","date_created":"2022-01-26T07:39:59Z","file_id":"10682","file_name":"infinite_time_horizon_safety_o.pdf","creator":"mlechner","file_size":452492,"checksum":"0fc0f852525c10dda9cc9ffea07fb4e4","relation":"main_file"}],"department":[{"_id":"GradSch"},{"_id":"ToHe"},{"_id":"KrCh"}],"acknowledgement":"This research was supported in part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award), 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.","alternative_title":[" Advances in Neural Information Processing Systems"],"user_id":"2EBD1598-F248-11E8-B48F-1D18A9856A87","_id":"10667","month":"12","related_material":{"record":[{"status":"public","relation":"dissertation_contains","id":"11362"}]},"title":"Infinite time horizon safety of Bayesian neural networks","oa":1,"publication_status":"published","has_accepted_license":"1","day":"01","citation":{"short":"M. Lechner, Ð. Žikelić, K. Chatterjee, T.A. Henzinger, in:, 35th Conference on Neural Information Processing Systems, 2021.","ama":"Lechner M, Žikelić Ð, Chatterjee K, Henzinger TA. Infinite time horizon safety of Bayesian neural networks. In: <i>35th Conference on Neural Information Processing Systems</i>. ; 2021. doi:<a href=\"https://doi.org/10.48550/arXiv.2111.03165\">10.48550/arXiv.2111.03165</a>","chicago":"Lechner, Mathias, Ðorđe Žikelić, Krishnendu Chatterjee, and Thomas A Henzinger. “Infinite Time Horizon Safety of Bayesian Neural Networks.” In <i>35th Conference on Neural Information Processing Systems</i>, 2021. <a href=\"https://doi.org/10.48550/arXiv.2111.03165\">https://doi.org/10.48550/arXiv.2111.03165</a>.","apa":"Lechner, M., Žikelić, Ð., Chatterjee, K., &#38; Henzinger, T. A. (2021). Infinite time horizon safety of Bayesian neural networks. In <i>35th Conference on Neural Information Processing Systems</i>. Virtual. <a href=\"https://doi.org/10.48550/arXiv.2111.03165\">https://doi.org/10.48550/arXiv.2111.03165</a>","ista":"Lechner M, Žikelić Ð, Chatterjee K, Henzinger TA. 2021. Infinite time horizon safety of Bayesian neural networks. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems,  Advances in Neural Information Processing Systems, .","ieee":"M. Lechner, Ð. Žikelić, K. Chatterjee, and T. A. Henzinger, “Infinite time horizon safety of Bayesian neural networks,” in <i>35th Conference on Neural Information Processing Systems</i>, Virtual, 2021.","mla":"Lechner, Mathias, et al. “Infinite Time Horizon Safety of Bayesian Neural Networks.” <i>35th Conference on Neural Information Processing Systems</i>, 2021, doi:<a href=\"https://doi.org/10.48550/arXiv.2111.03165\">10.48550/arXiv.2111.03165</a>."},"ec_funded":1,"abstract":[{"lang":"eng","text":"Bayesian neural networks (BNNs) place distributions over the weights of a neural network to model uncertainty in the data and the network's prediction. We consider the problem of verifying safety when running a Bayesian neural network policy in a feedback loop with infinite time horizon systems. Compared to the existing sampling-based approaches, which are inapplicable to the infinite time horizon setting, we train a separate deterministic neural network that serves as an infinite time horizon safety certificate. In particular, we show that the certificate network guarantees the safety of the system over a subset of the BNN weight posterior's support. Our method first computes a safe weight set and then alters the BNN's weight posterior to reject samples outside this set. Moreover, we show how to extend our approach to a safe-exploration reinforcement learning setting, in order to avoid unsafe trajectories during the training of the policy. We evaluate our approach on a series of reinforcement learning benchmarks, including non-Lyapunovian safety specifications."}],"status":"public"},{"month":"07","_id":"10668","user_id":"2EBD1598-F248-11E8-B48F-1D18A9856A87","publication_status":"published","oa":1,"title":"On-off center-surround receptive fields for accurate and robust image classification","acknowledgement":"Z.B. is supported by the Doctoral College Resilient Embedded Systems, which is run jointly by the TU Wien’s Faculty of Informatics and the UAS Technikum Wien. R.G. is partially supported by the Horizon 2020 Era-Permed project Persorad, and ECSEL Project grant no. 783163 (iDev40). R.H and D.R were partially supported by Boeing and MIT. M.L. is supported in part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award).","department":[{"_id":"GradSch"},{"_id":"ToHe"}],"alternative_title":["PMLR"],"status":"public","abstract":[{"lang":"eng","text":"Robustness to variations in lighting conditions is a key objective for any deep vision system. To this end, our paper extends the receptive field of convolutional neural networks with two residual components, ubiquitous in the visual processing system of vertebrates: On-center and off-center pathways, with an excitatory center and inhibitory surround; OOCS for short. The On-center pathway is excited by the presence of a light stimulus in its center, but not in its surround, whereas the Off-center pathway is excited by the absence of a light stimulus in its center, but not in its surround. We design OOCS pathways via a difference of Gaussians, with their variance computed analytically from the size of the receptive fields. OOCS pathways complement each other in their response to light stimuli, ensuring this way a strong edge-detection capability, and as a result an accurate and robust inference under challenging lighting conditions. We provide extensive empirical evidence showing that networks supplied with OOCS pathways gain accuracy and illumination-robustness from the novel edge representation, compared to other baselines."}],"day":"01","has_accepted_license":"1","intvolume":"       139","citation":{"chicago":"Babaiee, Zahra, Ramin Hasani, Mathias Lechner, Daniela Rus, and Radu Grosu. “On-off Center-Surround Receptive Fields for Accurate and Robust Image Classification.” In <i>Proceedings of the 38th International Conference on Machine Learning</i>, 139:478–89. ML Research Press, 2021.","ista":"Babaiee Z, Hasani R, Lechner M, Rus D, Grosu R. 2021. On-off center-surround receptive fields for accurate and robust image classification. Proceedings of the 38th International Conference on Machine Learning. ML: Machine Learning, PMLR, vol. 139, 478–489.","apa":"Babaiee, Z., Hasani, R., Lechner, M., Rus, D., &#38; Grosu, R. (2021). On-off center-surround receptive fields for accurate and robust image classification. In <i>Proceedings of the 38th International Conference on Machine Learning</i> (Vol. 139, pp. 478–489). Virtual: ML Research Press.","ieee":"Z. Babaiee, R. Hasani, M. Lechner, D. Rus, and R. Grosu, “On-off center-surround receptive fields for accurate and robust image classification,” in <i>Proceedings of the 38th International Conference on Machine Learning</i>, Virtual, 2021, vol. 139, pp. 478–489.","mla":"Babaiee, Zahra, et al. “On-off Center-Surround Receptive Fields for Accurate and Robust Image Classification.” <i>Proceedings of the 38th International Conference on Machine Learning</i>, vol. 139, ML Research Press, 2021, pp. 478–89.","short":"Z. Babaiee, R. Hasani, M. Lechner, D. Rus, R. Grosu, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 478–489.","ama":"Babaiee Z, Hasani R, Lechner M, Rus D, Grosu R. On-off center-surround receptive fields for accurate and robust image classification. In: <i>Proceedings of the 38th International Conference on Machine Learning</i>. Vol 139. ML Research Press; 2021:478-489."},"publisher":"ML Research Press","language":[{"iso":"eng"}],"type":"conference","file_date_updated":"2022-01-26T07:38:32Z","conference":{"location":"Virtual","end_date":"2021-07-24","name":"ML: Machine Learning","start_date":"2021-07-18"},"oa_version":"Published Version","date_published":"2021-07-01T00:00:00Z","publication":"Proceedings of the 38th International Conference on Machine Learning","author":[{"first_name":"Zahra","full_name":"Babaiee, Zahra","last_name":"Babaiee"},{"first_name":"Ramin","full_name":"Hasani, Ramin","last_name":"Hasani"},{"id":"3DC22916-F248-11E8-B48F-1D18A9856A87","last_name":"Lechner","first_name":"Mathias","full_name":"Lechner, Mathias"},{"full_name":"Rus, Daniela","first_name":"Daniela","last_name":"Rus"},{"first_name":"Radu","full_name":"Grosu, Radu","last_name":"Grosu"}],"ddc":["000"],"year":"2021","date_created":"2022-01-25T15:46:33Z","page":"478-489","quality_controlled":"1","project":[{"call_identifier":"FWF","grant_number":"Z211","name":"The Wittgenstein Prize","_id":"25F42A32-B435-11E9-9278-68D0E5697425"}],"date_updated":"2022-05-04T15:02:27Z","file":[{"content_type":"application/pdf","date_created":"2022-01-26T07:38:32Z","file_id":"10681","access_level":"open_access","success":1,"date_updated":"2022-01-26T07:38:32Z","file_name":"babaiee21a.pdf","file_size":4246561,"creator":"mlechner","relation":"main_file","checksum":"d30eae62561bb517d9f978437d7677db"}],"volume":139,"article_processing_charge":"No","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode","image":"/images/cc_by_nc_nd.png","name":"Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0)","short":"CC BY-NC-ND (3.0)"},"publication_identifier":{"issn":["2640-3498"]},"main_file_link":[{"url":"https://proceedings.mlr.press/v139/babaiee21a","open_access":"1"}]}]