{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:54:42Z","timestamp":1740099282199,"version":"3.37.3"},"publisher-location":"Cham","reference-count":51,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030205201"},{"type":"electronic","value":"9783030205218"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","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":[[2019]]},"DOI":"10.1007\/978-3-030-20521-8_1","type":"book-chapter","created":{"date-parts":[[2019,6,4]],"date-time":"2019-06-04T23:02:40Z","timestamp":1559689360000},"page":"3-14","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Deeper Look into \u2018Deep Learning of Aftershock Patterns Following Large Earthquakes\u2019: Illustrating First Principles in Neural Network Physical Interpretability"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2167-7534","authenticated-orcid":false,"given":"Arnaud","family":"Mignan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4058-260X","authenticated-orcid":false,"given":"Marco","family":"Broccardo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,5,16]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436\u2013444 (2015)","journal-title":"Nature"},{"issue":"6245","key":"1_CR2","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","volume":"349","author":"MI Jordan","year":"2015","unstructured":"Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255\u2013260 (2015)","journal-title":"Science"},{"key":"1_CR3","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1126\/science.aag2302","volume":"355","author":"G Carleo","year":"2017","unstructured":"Carleo, G., Troyer, M.: Solving the quantum many-body problem with artificial neural networks. Science 355, 602\u2013606 (2017)","journal-title":"Science"},{"issue":"34","key":"1_CR4","doi-asserted-by":"publisher","first-page":"8505","DOI":"10.1073\/pnas.1718942115","volume":"115","author":"J Han","year":"2018","unstructured":"Han, J., Jentzen, A., Weinan, E.: Solving high-dimensional partial differential equations using deep learning. PNAS 115(34), 8505\u20138510 (2018)","journal-title":"PNAS"},{"key":"1_CR5","doi-asserted-by":"publisher","first-page":"024102","DOI":"10.1103\/PhysRevLett.120.024102","volume":"120","author":"J Pathak","year":"2018","unstructured":"Pathak, J., Hunt, B., Girvan, M., Lu, Z., Ott, E.: Model-free prediction of large spatiotemporally chaotic systems from data: a reservoir computing approach. Phys. Rev. Lett. 120, 024102 (2018)","journal-title":"Phys. Rev. Lett."},{"issue":"1","key":"1_CR6","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1785\/0220180259","volume":"90","author":"Q Kong","year":"2019","unstructured":"Kong, Q., Trugman, D.T., Ross, Z.E., Bianco, M.J., Meade, B.J., Gerstoft, P.: Machine learning in seismology: turning data into insights. Seismol. Res. Lett. 90(1), 3\u201314 (2019)","journal-title":"Seismol. Res. Lett."},{"key":"1_CR7","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1111\/j.1467-8667.2009.00595.x","volume":"24","author":"A Panakkat","year":"2009","unstructured":"Panakkat, A., Adeli, H.: Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators. Comput.-Aided Civ. Infrastruct. Eng. 24, 280\u2013292 (2009)","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"issue":"5306","key":"1_CR8","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1126\/science.275.5306.1616","volume":"275","author":"RJ Geller","year":"1997","unstructured":"Geller, R.J., Jackson, D.D., Kagan, Y.Y., Mulargia, F.: Earthquakes cannot be predicted. Science 275(5306), 1616\u20131617 (1997)","journal-title":"Science"},{"key":"1_CR9","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/S0264-3707(01)00039-4","volume":"32","author":"B Brodi","year":"2001","unstructured":"Brodi, B.: A neural-network model for earthquake occurrence. J. Geodyn. 32, 289\u2013310 (2001)","journal-title":"J. Geodyn."},{"key":"1_CR10","doi-asserted-by":"publisher","first-page":"15032","DOI":"10.1016\/j.eswa.2011.05.043","volume":"38","author":"M Moustra","year":"2011","unstructured":"Moustra, M., Avraamides, M., Christodoulou, C.: Artificial neural networks for earthquake prediction using time series magnitude data or seismic electric signals. Expert Syst. Appl. 38, 15032\u201315039 (2011)","journal-title":"Expert Syst. Appl."},{"key":"1_CR11","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1038\/s41586-018-0438-y","volume":"560","author":"PMR DeVries","year":"2018","unstructured":"DeVries, P.M.R., Vi\u00e9gas, F., Wattenberg, M., Meade, B.J.: Deep learning of aftershock patterns following large earthquakes. Nature 560, 632\u2013634 (2018)","journal-title":"Nature"},{"key":"1_CR12","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1007\/s00024-004-2659-2","volume":"162","author":"D Vere-Jones","year":"2005","unstructured":"Vere-Jones, D., Ben-Zion, Y., Zuniga, R.: Statistical seismology. Pure Appl. Geophys. 162, 1023\u20131026 (2005)","journal-title":"Pure Appl. Geophys."},{"key":"1_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.tecto.2011.03.010","volume":"505","author":"A Mignan","year":"2011","unstructured":"Mignan, A.: Retrospective on the Accelerating Seismic Release (ASR) hypothesis: controversy and new horizons. Tectonophysics 505, 1\u201316 (2011)","journal-title":"Tectonophysics"},{"key":"1_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-33182-4","volume-title":"Critical Phenomena in Natural Sciences, Chaos, Fractals, Selforganization and Disorder: Concepts and Tools","author":"D Sornette","year":"2009","unstructured":"Sornette, D.: Critical Phenomena in Natural Sciences, Chaos, Fractals, Selforganization and Disorder: Concepts and Tools. Springer, New York (2009). https:\/\/doi.org\/10.1007\/3-540-33182-4"},{"key":"1_CR15","doi-asserted-by":"publisher","first-page":"L21308","DOI":"10.1029\/2012GL053946","volume":"39","author":"A Mignan","year":"2012","unstructured":"Mignan, A.: Seismicity precursors to large earthquakes unified in a stress accumulation framework. Geophys. Res. Lett. 39, L21308 (2012)","journal-title":"Geophys. Res. Lett."},{"key":"1_CR16","doi-asserted-by":"publisher","first-page":"107","DOI":"10.5194\/npg-23-107-2016","volume":"23","author":"A Mignan","year":"2016","unstructured":"Mignan, A.: Static behaviour of induced seismicity. Nonlin. Process. Geophys. 23, 107\u2013113 (2016)","journal-title":"Nonlin. Process. Geophys."},{"key":"1_CR17","doi-asserted-by":"publisher","first-page":"241","DOI":"10.5194\/npg-25-241-2018","volume":"25","author":"A Mignan","year":"2018","unstructured":"Mignan, A.: Utsu aftershock productivity law explained from geometric operations on the permanent static stress field of mainshocks. Nonlin. Process. Geophys. 25, 241\u2013250 (2018)","journal-title":"Nonlin. Process. Geophys."},{"key":"1_CR18","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.tecto.2011.08.019","volume":"522\u2013523","author":"KF Tiampo","year":"2012","unstructured":"Tiampo, K.F., Shcherbakov, R.: Seismicity-based earthquake forecasting techniques: ten years of progress. Tectonophysics 522\u2013523, 89\u2013121 (2012)","journal-title":"Tectonophysics"},{"key":"1_CR19","doi-asserted-by":"publisher","first-page":"9726","DOI":"10.1002\/2015GL066232","volume":"42","author":"A Mignan","year":"2015","unstructured":"Mignan, A.: Modeling aftershocks as a stretched exponential relaxation. Geophys. Res. Lett. 42, 9726\u20139732 (2015)","journal-title":"Geophys. Res. Lett."},{"key":"1_CR20","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/nature09402","volume":"467","author":"K Richards-Dinger","year":"2010","unstructured":"Richards-Dinger, K., Stein, R.S., Toda, S.: Decay of aftershock density with distance does not indicate triggering by dynamic stress. Nature 467, 583\u2013586 (2010)","journal-title":"Nature"},{"key":"1_CR21","doi-asserted-by":"publisher","first-page":"B11311","DOI":"10.1029\/2010JB007473","volume":"115","author":"S Hainzl","year":"2010","unstructured":"Hainzl, S., Brietzke, G.B., Z\u00f6ller, G.: Quantitative earthquake forecasts resulting from static stress triggering. J. Geophys. Res. 115, B11311 (2010)","journal-title":"J. Geophys. Res."},{"issue":"6","key":"1_CR22","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1016\/0040-1951(65)90003-X","volume":"2","author":"M B\u00e5th","year":"1965","unstructured":"B\u00e5th, M.: Lateral inhomogeneities of the upper mantle. Tectonophysics 2(6), 483\u2013514 (1965)","journal-title":"Tectonophysics"},{"key":"1_CR23","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1038\/nature03622","volume":"435","author":"MC Gerstenberger","year":"2005","unstructured":"Gerstenberger, M.C., Wiemer, S., Jones, L.M., Reasenberg, P.A.: Real-time forecasts of tomorrow\u2019s earthquakes in California. Nature 435, 328\u2013331 (2005)","journal-title":"Nature"},{"key":"1_CR24","unstructured":"Lakkos, S., Hadjiprocopis, A., Compley, R., Smith, P.: A neural network scheme for earthquake prediction based on the seismic electric signals. In: Proceedings of the IEEE Conference on Neural Networks and Signal Processing, pp. 681\u2013689. IEEE, Ermioni (1994)"},{"key":"1_CR25","first-page":"51","volume":"117","author":"EI Alves","year":"1994","unstructured":"Alves, E.I.: Notice on the predictability of earthquake occurrences. Mem\u00f3rias e Not\u00edcias 117, 51\u201361 (1994)","journal-title":"Mem\u00f3rias e Not\u00edcias"},{"key":"1_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1007\/978-3-540-28648-6_153","volume-title":"Advances in Neural Networks - ISNN 2004","author":"Y Liu","year":"2004","unstructured":"Liu, Y., Wang, Y., Li, Y., Zhang, B., Wu, G.: Earthquake prediction by RBF neural network ensemble. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 962\u2013969. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-28648-6_153"},{"key":"1_CR27","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/s11071-006-2018-1","volume":"44","author":"EI Alves","year":"2006","unstructured":"Alves, E.I.: Earthquake forecasting using neural networks: results and future work. Nonlin. Dyn. 44, 341\u2013349 (2006)","journal-title":"Nonlin. Dyn."},{"issue":"1","key":"1_CR28","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1142\/S0129065707000890","volume":"17","author":"A Panakkat","year":"2007","unstructured":"Panakkat, A., Adeli, H.: Neural network models for earthquake magnitude prediction using multiple seismicity indicators. Int. J. Neural Syst. 17(1), 13\u201333 (2007)","journal-title":"Int. J. Neural Syst."},{"key":"1_CR29","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.knosys.2013.06.011","volume":"50","author":"F Mart\u00ednez-\u00c1lvarez","year":"2013","unstructured":"Mart\u00ednez-\u00c1lvarez, F., Reyes, J., Morales-Esteban, A., Rubio-Escudero, C.: Determining the best set of seismicity indicators to predict earthquakes. Two case studies Chile and the Iberian Peninsula. Knowl.-Based Syst. 50, 198\u2013210 (2013)","journal-title":"Knowl.-Based Syst."},{"key":"1_CR30","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.knosys.2016.02.014","volume":"101","author":"G Asencio-Cort\u00e9s","year":"2016","unstructured":"Asencio-Cort\u00e9s, G., Mart\u00ednez-\u00c1lvarez, F., Morales-Esteban, A., Reyes, J.: A sensitivity study of seismicity indicators in supervised learning to improve earthquake prediction. Knowl.-Based Syst. 101, 15\u201330 (2016)","journal-title":"Knowl.-Based Syst."},{"issue":"3","key":"1_CR31","first-page":"111","volume":"11","author":"R Madahizadeh","year":"2009","unstructured":"Madahizadeh, R., Allamehzadeh, M.: Prediction of aftershocks distribution using artificial neural networks and its application on the May 12, 2008 Sichuan earthquake. JSEE 11(3), 111\u2013120 (2009)","journal-title":"JSEE"},{"key":"1_CR32","doi-asserted-by":"publisher","first-page":"9276","DOI":"10.1002\/2017GL074677","volume":"44","author":"B Rouet-Leduc","year":"2017","unstructured":"Rouet-Leduc, B., Hulbert, C., Lubbers, N., Barros, K., Humphreys, C.J., Johnson, P.A.: Machine learning predicts laboratory earthquakes. Geophys. Res. Lett. 44, 9276\u20139282 (2017)","journal-title":"Geophys. Res. Lett."},{"key":"1_CR33","unstructured":"Leach, R., Dowla, F.: Earthquake early warning system using real-time signal processing. In: Proceedings of the 1996 IEEE Signal Processing Society Workshop, pp. 463\u2013472. IEEE, Kyoto (1996)"},{"key":"1_CR34","doi-asserted-by":"publisher","first-page":"e1501055","DOI":"10.1126\/sciadv.1501055","volume":"2","author":"Q Kong","year":"2016","unstructured":"Kong, Q., Allen, R.M., Schreier, L., Kwon, Y.-W.: MyShake: a smartphone seismic network for earthquake early warning and beyond. Sci. Adv. 2, e1501055 (2016)","journal-title":"Sci. Adv."},{"key":"1_CR35","doi-asserted-by":"publisher","first-page":"e1700578","DOI":"10.1126\/sciadv.1700578","volume":"4","author":"T Perol","year":"2018","unstructured":"Perol, T., Gharbi, M., Denolle, M.: Convolutional neural network for earthquake detection and location. Sci. Adv. 4, e1700578 (2018)","journal-title":"Sci. Adv."},{"key":"1_CR36","doi-asserted-by":"publisher","first-page":"5120","DOI":"10.1029\/2017JB015251","volume":"123","author":"ZE Ross","year":"2018","unstructured":"Ross, Z.E., Meier, M.-A., Hauksson, E.: P wave arrival picking and first-motion polarity determination with deep learning. J. Geophys. Res. Solid Earth 123, 5120\u20135129 (2018)","journal-title":"J. Geophys. Res. Solid Earth"},{"issue":"1","key":"1_CR37","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1029\/2018jb016674","volume":"124","author":"Zachary E. Ross","year":"2019","unstructured":"Ross, Z.E., Yue, Y., Meier, M.-A., Hauksson, E.: Phaselink: a deep learning approach to seismic phase association. J. Geophys. Res. Solid Earth (2019). https:\/\/doi.org\/10.1029\/2018jb016674","journal-title":"Journal of Geophysical Research: Solid Earth"},{"issue":"6019","key":"1_CR38","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1126\/science.1197341","volume":"331","author":"M Bouchon","year":"2011","unstructured":"Bouchon, M., Karabulut, H., Aktar, M., \u00d6zalaybey, S., Schmittbuhl, J., Bouin, M.P.: Extended nucleation of the 1999 Mw 7.6 Izmit earthquake. Science 331(6019), 877\u2013880 (2011)","journal-title":"Science"},{"key":"1_CR39","doi-asserted-by":"publisher","first-page":"4099","DOI":"10.1038\/srep04099","volume":"4","author":"A Mignan","year":"2014","unstructured":"Mignan, A.: The debate on the prognostic value of earthquake foreshocks: a meta-analysis. Sci. Rep. 4, 4099 (2014)","journal-title":"Sci. Rep."},{"key":"1_CR40","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/978-3-030-17798-0_4","volume-title":"Advances in Computer Vision","author":"A Mignan","year":"2019","unstructured":"Mignan, A.: Asymmetric Laplace mixture modelling of incomplete power-law distributions: application to \u2018seismicity vision\u2019. In: Arai, K., Kapoor, S. (eds.) CVC 2019. AISC, vol. 944, pp. 30\u201343. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-17798-0_4"},{"key":"1_CR41","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1111\/j.1365-246X.2012.05429.x","volume":"189","author":"AP Valentine","year":"2012","unstructured":"Valentine, A.P., Trampert, J.: Data space reduction, quality assessment and searching of seismograms: autoencoder networks for waveform data. Geophys. J. Int. 189, 1183\u20131202 (2012)","journal-title":"Geophys. J. Int."},{"key":"1_CR42","doi-asserted-by":"publisher","first-page":"4773","DOI":"10.1029\/2018GL077870","volume":"45","author":"Z Li","year":"2018","unstructured":"Li, Z., Meier, M.-A., Hauksson, E., Zhan, Z., Andrews, J.: Machine learning seismic wave discrimination: application to earthquake early warning. Geophys. Res. Lett. 45, 4773\u20134779 (2018)","journal-title":"Geophys. Res. Lett."},{"key":"1_CR43","unstructured":"International Seismological Center. http:\/\/www.isc.ac.uk\/ . Accessed 29 Jan 2019"},{"key":"1_CR44","unstructured":"Finite-Source Rupture Model Database. http:\/\/equake-rc.info\/SRCMOD\/ . Accessed 29 Jan 2019"},{"issue":"4","key":"1_CR45","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1785\/BSSA0750041135","volume":"75","author":"Y Okada","year":"1985","unstructured":"Okada, Y.: Surface deformation due to shear and tensile faults in a half-space. Bull. Seismol. Soc. Am. 75(4), 1135\u20131154 (1985)","journal-title":"Bull. Seismol. Soc. Am."},{"key":"1_CR46","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/B978-044452748-6\/00069-9","volume":"4","author":"GCP King","year":"2007","unstructured":"King, G.C.P.: Fault interaction, earthquake stress changes, and the evolution of seismicity. Treatise Geophys. 4, 225\u2013255 (2007)","journal-title":"Treatise Geophys."},{"key":"1_CR47","unstructured":"Nature News: Artificial intelligence nails predictions of earthquake aftershocks. https:\/\/www.nature.com\/articles\/d41586-018-06091-z . Accessed 29 Jan 2019"},{"key":"1_CR48","unstructured":"The New York Times: A.I. is Helping Scientists Predict When and Where the Next Big Earthquake Will Be. https:\/\/www.nytimes.com\/2018\/10\/26\/technology\/earthquake-predictions-artificial-intelligence.html . Accessed 29 Jan 2019"},{"key":"1_CR49","unstructured":"Futurism: Google\u2019s AI can help predict where earthquake aftershocks are most likely. https:\/\/futurism.com\/the-byte\/aftershocks-earthquake-prediction . Accessed 29 Jan 2019"},{"key":"1_CR50","unstructured":"The Verge: Google and Harvard team up to use deep learning to predict earthquake aftershocks. https:\/\/www.theverge.com\/2018\/8\/30\/17799356\/ai-predict-earthquake-aftershocks-google-harvard . Accessed 29 Jan 2019"},{"key":"1_CR51","doi-asserted-by":"publisher","first-page":"11409","DOI":"10.1002\/2017GL075875","volume":"44","author":"BJ Meade","year":"2017","unstructured":"Meade, B.J., DeVries, P.M.R., Faller, J., Viegas, F., Wattenberg, M.: What is better than coulomb failure stress? A ranking of scalar static stress triggering mechanisms from 105 mainshock-aftershock pairs. Geophys. Res. Lett. 44, 11409\u201311416 (2017)","journal-title":"Geophys. Res. Lett."}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-20521-8_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T13:30:27Z","timestamp":1663594227000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-20521-8_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030205201","9783030205218"],"references-count":51,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-20521-8_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"16 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gran Canaria","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 June 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iwann.uma.es\/","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"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"210","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"150","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"71% - 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"}},{"value":"2,9","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"2,5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}