{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T17:17:32Z","timestamp":1760203052755,"version":"3.40.3"},"publisher-location":"Cham","reference-count":9,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030598532"},{"type":"electronic","value":"9783030598549"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59854-9_4","type":"book-chapter","created":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T23:02:42Z","timestamp":1604358162000},"page":"27-32","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["StochNetV2: A Tool for Automated Deep Abstractions for Stochastic Reaction Networks"],"prefix":"10.1007","author":[{"given":"Denis","family":"Repin","sequence":"first","affiliation":[]},{"given":"Nhat-Huy","family":"Phung","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9041-0905","authenticated-orcid":false,"given":"Tatjana","family":"Petrov","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"4_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1007\/978-3-030-30281-8_15","volume-title":"Quantitative Evaluation of Systems","author":"L Bortolussi","year":"2019","unstructured":"Bortolussi, L., Cairoli, F.: Bayesian abstraction of Markov population models. In: Parker, D., Wolf, V. (eds.) QEST 2019. LNCS, vol. 11785, pp. 259\u2013276. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30281-8_15"},{"key":"4_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-99429-1_2","volume-title":"Computational Methods in Systems Biology","author":"L Bortolussi","year":"2018","unstructured":"Bortolussi, L., Palmieri, L.: Deep abstractions of chemical reaction networks. In: \u010ce\u0161ka, M., \u0160afr\u00e1nek, D. (eds.) CMSB 2018. LNCS, vol. 11095, pp. 21\u201338. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-99429-1_2"},{"key":"4_CR3","unstructured":"Cai, H., Zhu, L., Han, S.: ProxylessNAS: direct neural architecture search on target task and hardware. CoRR abs\/1812.00332 (2018). http:\/\/arxiv.org\/abs\/1812.00332"},{"issue":"3","key":"4_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1006869","volume":"16","author":"CN Davis","year":"2020","unstructured":"Davis, C.N., Hollingsworth, T.D., Caudron, Q., Irvine, M.A.: The use of mixture density networks in the emulation of complex epidemiological individual-based models. PLoS Comput. Biol. 16(3), 1\u201316 (2020). https:\/\/doi.org\/10.1371\/journal.pcbi.1006869","journal-title":"PLoS Comput. Biol."},{"key":"4_CR5","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.tcs.2011.12.059","volume":"431","author":"J Feret","year":"2012","unstructured":"Feret, J., Henzinger, T., Koeppl, H., Petrov, T.: Lumpability abstractions of rule-based systems. Theoret. Comput. Sci. 431, 137\u2013164 (2012)","journal-title":"Theoret. Comput. Sci."},{"key":"4_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/978-3-030-28042-0_10","volume-title":"Hybrid Systems Biology","author":"M Hajnal","year":"2019","unstructured":"Hajnal, M., Nouvian, M., \u0160afr\u00e1nek, D., Petrov, T.: Data-informed parameter synthesis for population Markov chains. In: \u010ce\u0161ka, M., Paoletti, N. (eds.) HSB 2019. LNCS, vol. 11705, pp. 147\u2013164. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-28042-0_10"},{"key":"4_CR7","unstructured":"Liu, H., Simonyan, K., Yang, Y.: DARTS: differentiable architecture search. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=S1eYHoC5FX"},{"key":"4_CR8","unstructured":"Petrov, T., Repin, D.: Automated deep abstractions for stochastic chemical reaction networks. arXiv preprint arXiv:2002.01889 (2020)"},{"issue":"5","key":"4_CR9","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1017\/S0956792518000517","volume":"30","author":"T Plesa","year":"2019","unstructured":"Plesa, T., Erban, R., Othmer, H.G.: Noise-induced mixing and multimodality in reaction networks. Eur. J. Appl. Math. 30(5), 887\u2013911 (2019)","journal-title":"Eur. J. Appl. Math."}],"container-title":["Lecture Notes in Computer Science","Quantitative Evaluation of Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59854-9_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,17]],"date-time":"2020-12-17T16:09:23Z","timestamp":1608221363000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-59854-9_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030598532","9783030598549"],"references-count":9,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59854-9_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"3 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"QEST","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Quantitative Evaluation of Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vienna","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"qest2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.qest.org\/qest2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"12","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,10","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4,06","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}