Papers

2021

Naval-Marimont, S. & Tarroni, G. (in press). Implicit field learning for unsupervised anomaly detection in medical images. 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). 27 September - 1 October, Strasbourg, France (virtual).

Ikram, K., Mondragón, E., Alonso, E. & Garcia-Ortiz, M. (in press). HexaJungle: a MARL Simulator to Study the Emergence of Language. Conference on Computer Vision and Pattern Recognition (CVPR 2021), Embodied AI Workshop, 19th-25th June, Nashville, TN (virtual).

Lewis, D., Zugarini, A. & Alonso, E. (in press). Syllable Neural Language Models for English Poem Generation. 12th International Conference on Computational Creativity (ICCC'21), 14th-18th September, Mexico (virtual).

Guizzo, E., Weyde, T. & Tarroni, T. (2021). Anti-Transfer Learning for Task Invariance in Convolutional Neural Networks for Speech Processing. Neural Networks, 142, pp.238-251. doi: 10.1016/j.neunet.2021.05.012

Daviaud, L., Jurdziński, M., Lazić, R., Mazowiecki, F., Pérez, G.A. & Worrell, J. (2021). When are emptiness and containment decidable for probabilistic automata?. Journal of Computer and System Sciences, 119, pp. 78–96. doi:10.1016/j.jcss.2021.01.006.

Najibi, F., Apostolopoulou, D. & Alonso, E. (2021). Enhanced performance Gaussian process regression for probabilistic short-term solar output forecast. International Journal of Electrical Power and Energy Systems, 130, 106916. doi: 10.1016/j.ijepes.2021.106916

Najibi, F., Apostolopoulou, D. & Alonso, E. (in press). Improved Gaussian Process Regression Solar Output Forecast with Pre-clustering Techniques. IEEE PowerTech 2021 Conference, 27 June- 2 July, Madrid, Spain (virtual).

Jankovics, V., Garcia-Ortiz, M. & Alonso, E. (2021). HetSAGE: Heterogenous Graph Neural Network for Relational Learning. The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 35(18), pp. 15803-15804, 2-9 February, Vancouver, Canada (virtual).

Caselles-Dupre, H., Garcia-Ortiz, M. & Filliat, D. (2021). On the Sensory Commutativity of Action Sequences for Embodied Agents. The Twentieth International Conference on Autonomous Agents and Multiagent Systems (AAMAS-21), pp. 1472-1474, 3-7 May, London, UK (virtual).

Naval-Marimont, S. & Tarroni, G. (2021). Anomaly detection through latent space restoration using vector-quantized variational autoencoders. The IEEE International Symposium on Biomedical Imaging (IEEE-ISBI 2021), pp. 1764-1767, 13-16 April, Nice, France (virtual). doi:10.1109/ISBI48211.2021.9433778.

Ter-Sarkisov, A. (2021). Detection and Segmentation of Lesion Areas in Chest CT Scans for the Prediction of COVID-19. Science and Information Technology Letters, 1(2), pp. 92-99.doi:10.31763/sitech.v1i2.202

Ter-Sarkisov, A. (2021). Lightweight Model for the Prediction of COVID-19 through the Detection and Segmentation of Lesions in Chest CT Scans. International Journal of Automation, Artificial Intelligence and Machine Learning, 2(1), pp. 1-15.doi: 10.21203/rs.3.rs-108548/v2

2020

Ananda, A., Karabag, C., Ter-Sarkisov, A., Alonso, E. & Reyes-Aldasoro, C.C. (2020). Radiography Classification: A comparison between Eleven Convolutional Neural Networks. In Proceedings of The International Workshop on Digital Medical Image Processing and Applications (MCNA), (pp. 119 – 125), 19-22 October, Valencia, Spain. doi: 10.1109/MCNA50957.2020.9264285.

Bai, W., Suzuki, H. Huang, J. Francis, C., Wang, S., Tarroni, G., Guitton, F., Aung, N., Fung, K., Petersen, S., Piechnik, S., Neubauer, S., Evangelou, E., Dehghan, A., O'Regan, D., Wilkins, M., Guo, Y., Matthews, P. & Rueckert, D. (2020). A population-based phenome-wide association study of cardiac and aortic structure and function. Nature Medicine, 26, pp.1654–1662. doi:10.1038/s41591-020-1009-y.

Daviaud, L. (2020). Register complexity and determinisation of max-plus automata. ACM SIGLOG News, 7(2), pp. 4–14. doi:10.1145/3397619.3397621.

Lambrechts, A., Cook, J., Ludvig, E.A., Alonso, E., Anns, S., Taylor, M. and Gaigg S.B. (2020). Reward devaluation in autistic and adolescents with complex needs: a feasibility study. Autism Research,13(11), pp. 1915-1928. doi: 10.1002/aur.2388.

Wang, S., Tarroni, G., Qin, C., Mo, Y., Dai, C., Chen, C., Glocker, B., Guo, Y., Rueckert, D. & Bai, W. (2020). Deep Generative Model-based Quality Control for Cardiac MRI Segmentation. In A. L. Martel, P. Abolmaesumi,, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu & L. Joskowicz (Eds.)Proceedings of 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), (pp. 88-97), (LNCS, volume 12264), 4-8 October, Lima, Peru.

Chen, C. Qin. C., Qiu, H., Ouyang, C., Wang, S., Chen, L. Tarroni, G. Bai, W., & Rueckert, D. (2020). Realistic Adversarial Data Augmentation for MR Image Segmentation. In A. L. Martel, P. Abolmaesumi,, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu & L. Joskowicz (Eds.)Proceedings of 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020),(pp. 667-677)(LNCS, volume 12261) 4-8 October, Lima, Peru.

Tarroni, G., Bai, W., Oktay, O., Schuh, A., Suzuki, H., Glocker, B., Matthews, P. M. and Rueckert, D. (2020). Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK Biobank. Scientific Reports, 10(1), 2408. doi:10.1038/s41598-020-58212-2.

Li, S., Won, H., Fu, X., Fairbank, M., Wunsch, D.C. and Alonso, E. (2020), Neural-Network Vector Controller for Permanent-Magnet Synchronous Motor Drives: Simulated and Hardware-Validated Results, IEEE Transactions on Cybernetics, 50(7), pp. 3218 - 3230. doi: 10.1109/TCYB.2019.2897653.

Daviaud, L., Jurdzinski, M. and Thejaswini, S.K. (2020). The Strahler number of a parity game. Proceedings of the 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020) LIPIcs: Leibniz International Proceedings in Informatics, 168, 123. 8-11 July, Saarbrucken, Germany.

Pozdniakov, K., Alonso, E., Stankovic, V., Tam, K. and Jones K. (2020). Smart Computer Security Audit: Reinforcement Learning with a Deep Neural Network Approximator, In C. Onwubiko , T. Lynn , P. Rosati , A. Erola, X. Bellekens, P. Endo, G. Fox & M. G. Jaatun (Eds.) Cyber Science: Advancing a Multidisciplinary Approach to Cyber Security (Proceedings of Cyber2020), (pp. 135-143), 15-19 June, Dublin, Ireland.

Chen C., Qin, C., Qiu, H., Tarroni, G., Duan, J., Bai, W. and Rueckert, D. (2020). Deep Learning for Cardiac Image Segmentation: A Review. Frontiers in Cardiovascular Medicine, 7(25), pp. 1–33. doi:10.3389/fcvm.2020.00025.

Mello, F. R. de A.F., Apostolopoulou, D. and Alonso, E. (2020). Cost Efficient Distributed Load Frequency Control in Power Systems, In Proceedings of the 21st International Federation of Automatic Control Conference, 12-17 July, Berlin, Germany.

Bojanczyk, M., Daviaud, L., Guillon, B., Penelle, V. and Sreejith, A.V. (2020). Undecidability of a weak version of MSO+U. Logical Methods in Computer Science, 16(1), pp. 12–12. Logical Methods in Computer Science Volume 16, Issue 1, 2020, pp. 12:1–12:15.

Chen, C., Ouyang, C., Tarroni, G., Schlemper, J., Qiu, H., Bai, W. and Rueckert, D. (2020). Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation. In M. Pop, M. Sermesant, O. Camara, X. Zhuang, S. Li, A. Young, T. Mansi and A. Suinesiaputra (Eds.), Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges. STACOM 2019. (pp. 209-219). Cham, Switzerland: Springer. doi:10.1007/978-3-030-39074-7_22.

Rozada, S., Apostolopoulou, D. and Alonso, E. (2020), Load Frequency Control: A Deep Multi-Agent Reinforcement Learning Approach. In Proceedings of The IEEE Power & Energy Society General Meeting, 2-6 August, Montreal, Canada.doi: 10.1109/PESGM41954.2020.9281614

Riaz, A., Asad, M., Alonso, E. and Slabaugh, G. (2020). DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI. Journal of Neuroscience Methods, Vol 335. doi: 10.1016/j.jneumeth.2019.108506.

Biffi, C., Cerrolaza, J. J., Tarroni, G., Bai, W., Marvao, A. de, Oktay, O., Ledig, C., Folgoc, L. L., Kamnitsas, K., Doumou, G., Duan, J., Prasad, S. K., Cook, S. A., O'Regan, D. P. and Rueckert, D. (2020). Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models. IEEE Transactions on Medical Imaging, 39(6), pp. 2088-2099. doi: 10.1109/TMI.2020.2964499.

Ter-Sarkisov, A. (2020). Network of Steel: Neural Font Style Transfer from Heavy Metal to Corporate Logos. In M.De Marsico, G. Sanniti di Baja & A. Fred (Eds.)(Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2020),(pp. 621-629), 22-24 February, Valletta, Malta.

2019

Kokkola, N., Mondragón, E. and Alonso, E. (2019). A Double Error Dynamic Asymptote Model of Associative Learning. Psychological Review, 126(4), pp. 506–549. doi: 10.1037/rev0000147.

Czerwiński, W., Daviaud, L., Fijalkow, N., Jurdziński, M., Lazić, R. and Parys, P. (2019). Universal trees grow inside separating automata: Quasi-polynomial lower bounds for parity games. In T.M. Chan (Ed.), Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA19), (pp. 2333-2349), 6-9 January, San Diego, California, USA. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM). doi: 10.1137/1.9781611975482.142.

Daviaud, L., Jurdzinski, M. and Lehtinen, K. (2019). Alternating weak automata from universal trees. In W. Fokkink and R. van Glabbeek (Eds.), Proceedings of the 30th International Conference on Concurrency Theory (CONCUR 2019), (18:1--18:14), 26-31 August, Amsterdam, The Netherlands. Leibniz International Proceedings in Informatics (LIPIcs), 140. Dagstuhl, Germany: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik. doi: 10.4230/LIPIcs.CONCUR.2019.18.

Tarroni, G., Oktay, O., Bai, W., Schuh, A., Suzuki, H., Passerat-Palmbach, J., De Marvao, A., O’Regan, D. P., Cook, S., Glocker, B., Matthews, P M. and Rueckert, D. (2019). Learning-based quality control for cardiac MR images. IEEE Transactions on Medical Imaging, 38(5), pp. 1127–1138. doi: 10.1109/TMI.2018.2878509.

Biffi, C., Cerrolaza, J.J., Tarroni, G., De Marvao, A., Cook, S.A., O'Regan, D.P. and Rueckert, D. (2019). 3D high-resolution cardiac segmentation reconstruction from 2d views using conditional variational autoencoders. In Proceedings of the IEEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), (pp. 1643–1646), 8-11 April, Venice, Italy. doi: 10.1109/ISBI.2019.8759328.

Chen, C., Biffi, C., Tarroni, G., Petersen, S., Bai, W. and Rueckert, D. (2019). Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images. In D. Shen, T. Liu, T.M. Peters, L.H. Staib, C. Essert, S. Zhou, P.-T. Yap and A. Khan (Eds.), Proceedings of the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019) (pp. 523-531), 13–17 October, Shenzhen, China. Lecture Notes in Computer Science, 11765. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-32245-8_58.

Bai, W., Chen, C., Tarroni, G., Duan, J., Guitton, F., Petersen, S.E., Guo, Y., Matthews, P. M. and Rueckert, D. (2019). Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction. In D. Shen, T. Liu, T.M. Peters, L.H. Staib, C. Essert, S. Zhou, P.-T. Yap and A. Khan (Eds.), Proceedings of the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019) (pp. 541-549), 13–17 October, Shenzhen, China. Lecture Notes in Computer Science, 11765. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-32245-8_60.

Laflaquiere, A. and Garcia Ortiz, M. (2019). Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox and R. Garnett (Eds.), Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), 8-14 December, Vancouver, Canada. Advances in Neural Information Processing Systems, 32.

Caselles-Dupré, H., Garcia Ortiz, M. and Filliat, D. (2019). Symmetry-Based Disentangled Representation Learning requires Interaction with Environments. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox and R. Garnett (Eds.), Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), 8-14 December, Vancouver, Canada. Advances in Neural Information Processing Systems, 32.

Caselles-Dupré, H., Garcia Ortiz, M. and Filliat, D. (2019). S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay. Presented at NeurIPS 2018 Workshop on Continual Learning, 7 Dec, Montréal, Canada.

Bauer, J., Broom, M. and Alonso, E. (2019). The stabilization of equilibria in evolutionary game dynamics through mutation: mutation limits in evolutionary games. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 475(2231), pp. 20190355–20190355. doi: 10.1098/rspa.2019.0355.

Carrera, A., Alonso, E. and Iglesias, C.A. (2019). A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks. Sensors, 19(15). doi: 10.3390/s19153408.

Lesort T, Caselles-Dupré H, Garcia Ortiz M, Stoian A, Filliat D. (2019). Generative Models from the perspective of Continual Learning. Proceedings of the International Joint Conference on Neural Networks (IJCNN 2019), (pp. 1-8), 14-19 July, Budapest, Hungary. doi: 10.1109/IJCNN.2019.8851986.

2018

Daviaud, L. and Paperman, C. (2018). Classes of languages generated by the Kleene star of a word. Information and Computation, 262, pp. 90–109. doi: 10.1016/j.ic.2018.07.002.

Daviaud, L., Johnson, M. and Kambites, M. (2018). Identities in upper triangular tropical matrix semigroups and the bicyclic monoid. Journal of Algebra, 501, pp. 503–525. doi:10.1016/j.jalgebra.2017.12.032.

Daviaud, L., Jurdzinski, M., Lazic, R., Mazowiecki, F., Pérez, G.A. and Worrell, J. (2018). When is containment decidable for probabilistic automata?. In I. Chatzigiannakis, C. Kaklamanis, D. Marx and D. Sannella (Eds.), Proceedings of the 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018) (pp. 121:1--121:14), 9-13 July, Prague, Czech Republic. Leibniz International Proceedings in Informatics (LIPIcs), 107. Dagstuhl, Germany: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik. doi: 10.4230/LIPIcs.ICALP.2018.121.

Bojanczyk, M., Daviaud, L. and Krishna, S. (2018). Regular and first-order list functions. In Proceedings 33th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS'18), (pp. 125-134), 9-12 July, Oxford, England. New York, NY, USA: ACM. doi: 10.1145/3209108.3209163.

Daviaud, L., Jurdzinski, M. and Lazic, R. (2018). A pseudo-quasi-polynomial algorithm for mean-payoff parity games. In Proceedings 33th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS'18), (pp. 325-334), 9-12 July, Oxford. New York, NY, USA: ACM. doi: 10.1145/3209108.3209162.

Caselles-Dupré H., Garcia Ortiz, M. and Filliat, D. (2018). Continual State Representation Learning for Reinforcement Learning using Generative Replay. Presented at NeurIPS 2018 Workshop on Continual Learning, 7 Dec, Montréal, Canada.

Annabi, L. and Garcia Ortiz, M. (2018). State representation learning with recurrent capsule networks. Presented at NeurIPS 2018 Workshop on Modeling the Physical World: Perception, Learning, and Control, 7 Dec, Montréal, Canada.

Kulak, T. and Garcia Ortiz, M. (2018). Emergence of Sensory Representations Using Prediction in Partially Observable Environments. In V. Kůrková, Y., Manolopoulos, B., Hammer, L., Iliadis and I. Maglogiannis (Eds.), Proceedings of the 27th International Conference on Artificial Neural Networks and Machine Learning (ICANN 2018), (pp. 489-498), 4-7 October, Rhodes, Greece. Lecture Notes in Computer Science, 11140, Cham, Switzerland: Springer. doi: 10.1007/978-3-030-01421-6_47.

Garcia Ortiz, M. and Laflaquiere, A. (2018). Learning Representations of Spatial Displacement through Sensorimotor Prediction. In Proceedings of the Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob 2018), (pp. 7-12), 17 September, Tokyo, Japan. doi: 10.1109/DEVLRN.2018.8761034.

Caselles-Dupre, H., Annabi, L., Hagen, O., Garcia Ortiz, M. and Filliat, D. (2018). Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning. Presented at the Workshop on Continual Unsupervised Sensorimotor Learning, The Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob 2018), 17 September, Tokyo, Japan.

Ter-Sarkisov, A., Kelleher, J.D., Earley, B., Keane, M. and Ross, R.J. (2018). Beef Cattle Instance Segmentation Using Fully Convolutional Neural Network. Presented at the 29th British Machine Vision Conference (BMVC 2018), 3-6 September, Newcastle upon Tyne, UK.

Bai, W., Sinclair, M., Tarroni, G., Oktay, O., Rajchl, M., Vaillant, G., Lee, A.M., Aung, N., Lukaschuk, E., Sanghvi, M.M., Zemrak, F., Fung, K., Paiva, J.M., Carapella, V., Kim, Y.J., Suzuki, H., Kainz, B., Matthews, P.M., Petersen, S.E., Piechnik, S.K., Neubauer, S. Glocker, B. and Rueckert, D. (2018). Automated cardiovascular magnetic resonance image analysis with fully convolutional networks . Journal of Cardiovascular Magnetic Resonance, 20(1). doi: 10.1186/s12968-018-0471-x .

Tarroni, G., Oktay, O., Sinclair, M., Bai, W., Schuh, A., Suzuki, H., de Marvao, A., O’Regan, D., Cook, S. and Rueckert, D. (2018). A comprehensive approach for learning-based fully-automated inter-slice motion correction for short-axis cine cardiac MR image stacks. In A.F. Frangi, J.A., Schnabel, C., Davatzikos, C. López Alberola and G. Fichtinger (Eds.), Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018), (pp. 268-276), 16-20 September, Granada, Spain. Lecture Notes in Computer Science, 11070. doi: 10.1007/978-3-030-00928-1_31.

Biffi, C., Oktay, O., Tarroni, G., Bai, W., De Marvao, A., Doumou, G., Rajchl, M., Bedair, R., Prasad, S., Cook, S., O'Regan, D. and (2018). Learning interpretable anatomical features through deep generative models: Application to cardiac remodelling . In A.F. Frangi, J.A., Schnabel, C., Davatzikos, C. López Alberola and G. Fichtinger (Eds.), Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018), (pp. 464-471), 16-20 September, Granada, Spain. Lecture Notes in Computer Science, 11071. doi: 10.1007/978-3-030-00934-2_52.

Bai, W., Suzuki, H., Qin, C., Tarroni, G., Oktay, O., Matthews, P.M. and Rueckert, D. (2018). Recurrent Neural Networks for Aortic Image Sequence Segmentation with Sparse Annotations. In A.F. Frangi, J.A., Schnabel, C., Davatzikos, C. López Alberola and G. Fichtinger (Eds.), Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018), (pp. 586-594), 16-20 September, Granada, Spain. Lecture Notes in Computer Science, 11073. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-00937-3_67.

Riaz, A., Asad, M., Alonso, E. and Slabaugh, G.G. (2018). Fusion of fMRI and Non-Imaging Data for ADHD Classification. Computerized Medical Imaging and Graphics, 65, pp. 115–128. doi: 10.1016/j.compmedimag.2017.10.002.

Basaru, R.R., Child, C., Alonso, E. and Slabaugh, G. (2018). Data-driven Recovery of Hand Depth using Conditional Regressive Random Forest on Stereo Images . IET Computer Vision, 12(5). doi: 10.1049/iet-cvi.2017.0227.

Olliverre, N.J., Yang, G., Slabaugh, G.G., Reyes-Aldasoro, C.C. and Alonso, E. (2018). Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models. In A. Gooya, O. Goksel, I. Oguz and N. Burgos (Eds.), Proceedings of the International Workshop on Simulation and Synthesis in Medical Imaging (SASHIMI 2018), (pp. 130-138), 16-22 September, Granada, Spain. Lecture Notes in Computer Science, 11037. doi: 10.1007/978-3-030-00536-8_14.

Najibi, F., Alonso, E. and Apostolopoulou, D. (2018). Optimal Dispatch of Pumped Storage Hydro Cascade under Uncertainty. In Proceedings of the 12th International UKACC Conference on Control (CONTROL 2018), (pp. 187-192), 5-7 September, Sheffield, England. doi: 10.1109/CONTROL.2018.8516823 .

Riaz, A., Asad, M., Al-Arif, S.M.M.R., Alonso, E., Dima, D., Corr, P., and Slabaugh, G. (2018). DeepFMRI: And End-to-End Deep Network for Classification of FRMI Data. In Proceedings of the 15th IEEE International Symposium on Biomedical Imaging (ISBI 2018), (pp. 1419-1422), 4-7 April, Washington DC, USA. doi: 10.1109/ISBI.2018.8363838 .

2017

Tarroni, G., Oktay, O., Bai, W., Schuh, A., Suzuki, H., Passerat-Palmbach, J., Glocker, B., de Marvao, A., O'Regan, D.P., Cook, S.A., and Rueckert, D. (2017). Learning-Based Heart Coverage Estimation for Short-Axis Cine Cardiac MR Images. In: Pop, M. and Wright, G.A., Proceedings of The 9th International Conference on Functional Imaging and Modelling of the Heart (FIMH 2017), (pp. 73-82), June 11-13, Toronto, ON, Canada. doi: 10.1007/978-3-319-59448-4_8.

Daviaud, L., Guillon, P. and Merlet, G. (2017). Comparison of max-plus automata and joint spectral radius of tropical matrices. In K.G. Larsen, H.L. Bodlaender and J.-. Raskin (Eds.), Proceedings of the 42nd International Symposium on Mathematical Foundations of Computer Science (MFCS 2017), (pp. 19:1-19:14), 21-25 August, Aalborg, Denmark. Leibniz International Proceedings in Informatics (LIPIcs), 83. Dagstuhl, Germany: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik. doi: 10.4230/LIPIcs.MFCS.2017.19.

Daviaud, L. and Johnson, M. (2017). The shortest identities for max-plus automata with two states. In K.G. Larsen, H.L. Bodlaender and J.-. Raskin (Eds.), Proceedings of the 42nd International Symposium on Mathematical Foundations of Computer Science (MFCS 2017), (pp. 48:1-48:13), 21-25 August, Aalborg, Denmark. Leibniz International Proceedings in Informatics (LIPIcs), 83. Dagstuhl, Germany: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik. doi: 10.4230/LIPIcs.MFCS.2017.48.

Bojanczyk, M., Daviaud, L., Guillon, B. and Penelle, V. (2017). Which classes of origin graphs are generated by transducers? In I. Chatzigiannakis, P. Indyk, F. Kuhn, and A. Muscholl (Eds.), Proceedings of the 44th International Colloquium on Automata, Languages, and Programming (ICALP 2017), (pp. 114:1--114:13,10-14), 10-14 July, Warsaw, Poland. Leibniz International Proceedings in Informatics (LIPIcs), 80. Dagstuhl, Germany: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik. doi: 10.4230/LIPIcs.ICALP.2017.114.

Daviaud, L., Jecker, I., Reynier, P.A. and Villevalois, D. (2017). Degree of sequentiality of weighted automata. In J. Esparza and A.S. Murawski (eds.), Proceedings of the 20th International Conference on Foundations of Software Science and Computation Structures (FoSSaCS 2017), (pp. 215-230), 22-29 April, Uppsala, Sweden. doi: 10.1007/978-3-662-54458-7_13.

Ter-Sarkisov, A. and Marsland, S. (2017). K-Bit-Swap: a new operator for real-coded evolutionary algorithms. Soft Computing, 21(20), pp. 6133–6142. doi: 10.1007/s00500-016-2170-6.

Ter-Sarkisov, A., Ross, R. and Kelleher, J. (2017). Bootstrapping Labelled Dataset Construction for Cow Tracking and Behavior Analysis. In Proceedings of the 14th Conference on Computer and Robot Vision (CRV 2017), (pp. 277-284), 16-19 May, Edmonton, Canada. doi: 10.1109/CRV.2017.25.

Tarroni, G., Oktay, O., Bai, W., Schuh, A., Suzuki, H., Passerat-Palmbach, J., De Marvao, A., O’Regan, D. P., Cook, S., Glocker, B., Matthews, P M. and Rueckert, D. (2017). Learning-Based Quality Control for Cardiac MR Images. IEEE Transactions on Medical Imaging, 38(5), pp. 1127-1138. doi: 10.1109/TMI.2018.2878509 .

Bai, W., Oktay, O., Sinclair, M., Suzuki, H., Rajchl, M., Tarroni, G., Glocker, B., King, A., Matthews, P. M. and Rueckert, D. (2017). Semi-supervised learning for network-based cardiac MR image segmentation. In M. Descoteaux, L. Maier-Hein, A. Franz, P. Jannin, D.L. Collins and S. Duchesne (Eds.), Proceedinsg of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2017) , (pp. 253-260), 11-13 September, Quebec City, Canada. Lecture Notes in Computer Science, 10434. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-66185-8_29.

Garcia Ortiz, M. (2017). Sensorimotor Prediction with Neural Networks on Continuous Spaces. In A. Lintas, S. Rovetta, P.F.M.J. Verschure and A.E.P. Villa (Eds.), Proceedings of the 26th International Conference on Artificial Neural Networks , Artificial Neural Networks and Machine Learning (ICANN 2017), (pp. 76-83), 11-14 September, Alghero, Italy. Lecture Notes in Computer Science (LNCS), 10613. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-68600-4_10.

Luzardo, A., Rivest, F., Alonso, E. and Ludvig, E. (2017). A Drift-Diffusion Model of Interval Timing in the Peak Procedure. Journal of Mathematical Psychology, 77, pp. 111–123. doi: 10.1016/j.jmp.2016.10.002.

Mondragón, E., Alonso, E. and Kokkola, K. (2017). Associative Learning Should Go Deep. Trends in Cognitive Sciences, 21(11), pp. 822–825. doi: 10.1016/j.tics.2017.06.001.

Albrecht, T., Slabaugh, G., Alonso, E. and Al-Arif, M.R. (2017). Deep Learning for Single-Molecule Science. Nanotechnology, 28(42), pp. 423001–423001. doi: 10.1088/1361-6528/aa8334.

Luzardo, A., Alonso, E. and Mondragón, E. (2017). A Rescorla-Wagner Drift-Diffusion Model of Conditioning and Timing. PLoS Computational Biology, 13(11). doi: 10.1371/journal.pcbi.1005796.

Guimera Busquets, J., Alonso, E. and Evans, A. (2017). Air Itinerary Shares Estimation Using Multinomial Logit Models. Transportation Planning and Technology, 41(1), pp. 3–16. doi: 10.1080/03081060.2018.1402742.

Basaru, R., Child, C., Alonso, E. and Slabaugh, G.G. (2017). Conditional Regressive Random Forest Stereo-based Hand Depth Recovery. In Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW 2018), (pp. 614-622), 22-29 October, Venice, Italy. doi: 10.1109/ICCVW.2017.78.

Basaru, R., Child, C., Alonso, E. and Slabaugh, G.G. (2017). Hand Pose Estimation Using Deep Stereovision and Markov-chain Monte Carlo. In Proceedings of the Workshop on Observing and Understanding Hands in Action (HANDS 2017) at the IEEE International Conference on Computer Vision (ICCV'17), (pp. 595-603), 22-19 October, Venice, Italy. doi: 10.1109/ICCVW.2017.76.

Teichmann, J., Alonso, E. and Broom, M. (2017). Reinforcement Learning as a Model of Aposematism. In E. Lutton, P. Legrand, P. Parrend, N. Monmarché and M. Schoenauer (Eds.), Proceedings of the 13th International Conference on Artificial Evolution, (pp. 217-230), 25-27 October, Paris, France.

Teichmann, J., Alonso, E. and Broom, M. (2017). Reinforcement Learning is an Effective Strategy to Create Phenotypic Variation and a Potential Mechanism for the Initial Evolution of Learning. In E. Lutton, P. Legrand, P. Parrend, N. Monmarché and M. Schoenauer (Eds.), Proceedings of the 13th International Conference on Artificial Evolution, (pp. 246-252), 25-27 October, Paris, France.

Riaz, A., Asad, M., Al-Arid, S.M.M.R., Alonso, E., Dima, D., Corr, P. and Slabaugh, G. (2017). FCNet: A Convolutional Neural Network for Calculating Functional Connectivity from functional MRI. In G. Wu, P. Laurienti, L. Bonilha and B.C. Munsell (Eds.), Proceedings of the 1st International Workshop on Connectomics in NeuroImaging (CNI 2017), (pp. 70-78), 14 September, Quebec City, Canada. Lecture Notes on Computer Science, 10511. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-67159-8_9.

2016

Gepperth A. R. T., Garcia Ortiz, M., Sattarov, E., and Heisele B. (2016). Dynamic attention priors: a new and efficient concept for improving object detection. Neurocomputing, 197, pp. 14-28. doi: 10.1016/j.neucom.2016.01.036 .

Riaz, A., Alonso, E. and Slabaugh, G. (2016). Phenotypic Integrated Framework for Classification of ADHD using fMRI. In A. Campilho and F. Karray (Eds.), Proceedings of the International Conference on Image Analysis and Recognition (ICIAR 2016), (pp. 217-225), 13-15 July, Póvoa de Varzim, Portugal. Lecture Notes in Computer Science (LNCS), 9730. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-41501-7_25.

Busquets, J.G., Alonso, E. and Evans, A. (2016). Predicting Aggregate Air Itinerary Shares Using Discrete Choice Modeling. In Proceedings 16th AIAA Aviation Technology, Integration, and Operations Conference, (pp. 1537-1552), 13-17 June, Washington, D.C., USA doi: 10.2514/6.2016-4076.

Basaru, R.R., Slabaugh, G., Child, C. and Alonso, E. (2016). HandyDepth: Example-based Stereoscopic Hand Depth Estimation using Eigen Leaf Node Features. In Proceedings of the International Conference on Systems, Signals and Image Processing (IWSSIP 2016), (pp. 33-36), 23-25 May, Bratislava, Slovakia. doi: 10.1109/IWSSIP.2016.7502698.

Colcombet, T. and Daviaud, L. (2016). Approximate Comparison of Functions Computed by Distance Automata. Theory of Computing Systems, 58(4), pp. 579–613. doi: 10.1007/s00224-015-9643-3.

Daviaud, L., Reynier, P.-A. and Talbot, J.-.M. (2016).A Generalised Twinning Property for Minimisation of Cost Register Automata. In Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer Science (LICS'16), (pp. 857-866), 5-8 July, New York, NY, USA. New York, NY, USA: ACM. doi: 10.1145/2933575.2934549.

Daviaud, L., Kuperberg, D. and Pin, J.-.E. (2016).Varieties of cost functions. In N. Ollinger and H. Vollmer (Eds.), Proceedings of the 33rd International Symposium on Theoretical Aspects of Computer Science (STACS 2016), (pp.30:1--30:14), 17-20 February, Orléans, France. Leibniz International Proceedings in Informatics (LIPIcs), 47. Dagstuhl, Germany: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik. doi: 10.4230/LIPIcs.STACS.2016.30.

Narang, A., Mor-Avi, V., Bhave, N.M., Tarroni, G., Corsi, C., Davidson, M.H., Roberto, M., Lang, M.D. and Patel, A.R. (2016). Large high-density lipoprotein particle number is independently associated with microvascular function in patients with well-controlled low-density lipoprotein concentration: A vasodilator stress magnetic resonance perfusion study. Journal of Clinical Lipidology, 10(2), pp. 314–322. doi:10.1016/j.jacl.2015.12.006.

Oktay, O., Tarroni, G., Bai, W., De Marvao, A., O'Regan, D., Cook, S. and Rueckert, D. (2016). Respiratory motion correction for 2D cine cardiac MR images using probabilistic edge maps. In Proceedings of the 2016 Computing in Cardiology Conference (CinC 2016), 11-14 September, Vancouver, BC, Canada. doi: 10.22489/CinC.2016.041-323.

2015

Li, S., Fu, X., Alonso, E., Fairbank, M. and Wunsch, D.C. (2015). Neural-network based vector control of VSC-HVDC transmission systems. In Proceedings of the 4th International Conference on Renewable Energy Research and Applications (ICRERA), (pp. 173-180), 22-15 November, Palermo, Italy. doi: 10.1109/ICRERA.2015.7418673.

Bonardi, C., Mondragón, E., Brilot, B. and Jennings, D.J. (2015). Overshadowing by fixed- and variable-duration stimuli. Quarterly Journal of Experimental Psychology, 68(3), pp. 523–542. doi: 10.1080/17470218.2014.960875.

Mondragón, E. and Hall, G. (2015). Analysis of the role of stimulus comparison in discrimination learning in Pigeons. Learning and Motivation,, 49, pp. 14–22. doi: 10.1016/j.lmot.2015.01.003.

Daviaud, L. and Paperman, C. (2015). Classes of languages generated by the kleene star of a word. In G.F., Italiano, G. Pighizzini and D.T. Sannella (Eds.), Proceedings of the 40th International Sympoisum on Mathematical Foundations of Computer Science (MFCS 2015), (pp. 161-178), 24-28 August, Milan, Italy. Lecture Notes in Computer Science (LNCS), 9234. Cham, Switzerland: Springer. doi: 10.1007/978-3-662-48057-1_13.

Fu, X., Li, S., Fairbank, M., Wunsch, D. and Alonso, E. (2015). Training Recurrent Neural Networks with the Levenberg–Marquardt Algorithm for Optimal Control of a Grid-Connected Converter. IEEE Transactions on Neural Networks and Learning Systems, 26(9), pp. 1900–1912. doi: 10.1109/TNNLS.2014.2361267.

Alonso, E., Fairbank, M. and Mondragon, E. (2015). Back to Optimality: A Formal Framework to Express the Dynamics of Learning Optimal Behavior. Adaptive Behavior, 23(4), pp. 206–215. doi: 10.1177/1059712315589355.

Li, S., Fu, X., Jaithwa, I., Alonso, E., Fairbank, M. and C. Wunsch, D. (2015). Control of Three-Phase Grid-Connected Microgrids using Artificial Neural Networks. In A. Rosa, J.J. Merelo, A. Dourado, J.M. Cadenas, K. Madani, A. Ruano and J. Filipe (Eds.), Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3 NCTA, (pp. 58–69), 12-14 November, Lisbon, Portugal. doi: 10.5220/0005607900580069.

Teichmann, J., Alonso, E. and Broom, M. (2015). A reward-driven model of Darwinian fitness. In A. Rosa, J.J. Merelo, A. Dourado, J.M. Cadenas, K. Madani, A. Ruano and J. Filipe (Eds.)Proceedings of the 7th International Joint Conference on Computational Intelligence (IJCCI) - Volume 1 ECTA, (pp. 174-179), 12-14 November, Lisbon, Portugal. doi:10.5220/0005591501740179

Busquets, J.G., Alonso, E. and Evans, A. (2015).Application of Data Mining in Air Traffic Forecasting. In Proceedings of the 15th AIAA Aviation Technology, Integration, and Operations Conference, 22-26 June, Dallas, TX, USA. doi: 10.2514/6.2015-2732.

Li, S., Alonso, E., Fu, X., Fairbank, M., Jaithwa, I. and Wunsch, D.C. (2015). Hardware Validation for Control of Three-Phase Grid-Connected Microgrids Using Artificial Neural Networks. Presented at the 12th International Conference on Applied Computing, 24-26 October, Maynooth, Ireland.

Karcanias, N., Hessami, A.G. and Alonso, E. (2015). Complexity of Multi-Modal Transportation and Systems of Systems. Presented at the 47th Annual Universities’ Transport Study Group Conference (UTSG 2015), 24 December-5 January, London, UK.

Ter-Sarkisov, A., Schwenk, H., Bougares, F. and Barrault, L. (2015). Incremental Adaptation Strategies for Neural Network Language Models. In Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality (CVSC), (pp. 48-56), 26-31 July, Beijing, China. Stroudsburg, PA, USA: Association for Computational Linguistics (ACL).doi: 10.18653/v1/W15-4006.

Tarroni, G., Visentin, S., Cosmi, E. and Grisan, E. (2015). A fully automated approach to aortic distensibility quantification from fetal ultrasound images. In Proceedings of the 2015 Computing in Cardiology Conference (CinC 2015), (pp, 729-732), 6-7 September, Nice, France. doi: 10.1109/CIC.2015.7411014.

Tarroni, G., Visentin, S., Cosmi, E. and Grisan, E. (2015). A novel approach to aortic intima-media thickness quantification from fetal ultrasound images. In Proceedings of the IEEE 12th International Symposium on Biomedical Imaging (ISBI'15), (pp. 858-861), 16-19 April, New York, NY, USA. doi: 10.1109/ISBI.2015.7164006.

Tarroni, G., Castellaro, M., Boffano, C., Bruzzone, M.G., Bertoldo, A. and Grisan, E. (2015). A novel approach to motion correction for ASL images based on brain contours. In B. Gimi and R.C. Molthen (Eds.), Proceedings of Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging (Vol. 9417), 21-26 February, Orlando, FL, USA. doi: 10.1117/12.2081784.

Tarroni, G., Visentin, S., Cosmi, E., Grisan, E. (2015). Fully-automated identification and segmentation of aortic lumen from fetal ultrasound images. In Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015), (pp. 153-156), 25-29 August, Milan, Italy. doi: 10.1109/EMBC.2015.7318323.

Grisan, E., Cantisani, G., Tarroni, G., Yoon, S.K. and Rossi, M. (2015). A supervised learning approach for the robust detection of heart beat in plethysmographic data. In Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015), (pp. 5825-5828), 25-29 August, Milan, Italy. doi: 10.1109/EMBC.2015.7319716.

Boschetto, D., Mirzaei, H., Leong, R.W.L., Tarroni, G. and Grisan, E. (2015). Semiautomatic detection of villi in confocal endoscopy for the evaluation of celiac disease.Proceedings 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015), (pp. 8143-8146), 25-29 August, Milan, Italy. doi: 10.1109/EMBC.2015.7320284.

2014

Mondragon, E., Gray, J., Alonso, E., Bonardi, C. and Jennings, D. (2014). SSCC TD: A Serial and Simultaneous Configural-Cue Compound Stimuli Representation for Temporal Difference Learning. PLoS ONE, 9(7): e102469. doi: 10.1371/journal.pone.0102469.

Alonso, E. and Mondragón, E. (2014). What Have Computational Models Ever Done for Us?: A Case Study in Classical Conditioning. International Journal of Artificial Life Research (IJALR), 4(1), pp. 1–12. doi: 10.4018/ijalr.2014010101.

Colcombet, T., Daviaud, L. and Zuleger, F. (2014). Size-change abstraction and max-plus automata. In E. Csuhaj-Varjú, M. Dietzfelbinger and Z. Ésik (Eds.), Proceedings of the 39th International Symposium on Mathematical Foundations of Computer Science (MFCS 2014), (pp. 208-219), 25-29 August, Budapest, Hungary. Lecture Notes in Computer Science (LNCS), 8634. Cham, Switzerland: Springer. doi:10.1007/978-3-662-44522-8_18.

Fairbank, M., Li, S., Fu, X., Alonso, E. and Wunsch, D. (2014). An Adaptive Recurrent Neural-Network Controller using a Stabilization Matrix and Predictive Inputs to Solve the Tracking Problem under Disturbances. Neural Networks, 49, pp. 74–86. doi: 10.1016/j.neunet.2013.09.010.

Fairbank, M., Prokhorov, D. and Alonso, E. (2014).Clipping in Neurocontrol by Adaptive Dynamic Programming. IEEE Transactions on Neural Networks and Learning Systems, 25(10), pp. 1909–1920. doi: 10.1109/TNNLS.2014.2297991.

Teichmann, J., Broom, M. and Alonso, E. (2014). The Evolutionarily Dynamics of Aposematism: a Numerical Analysis of Co-Evolution in Finite Populations. Mathematical Modelling of Natural Phenomena (MMNP), 9(3), pp. 148–164. doio: 10.1051/mmnp/20149310.

Li, S., Fairbank, M., Johnson, C., Wunsch, D.C., Alonso, E. and Proano, J.L. (2014). Artificial Neural Networks for Control of a Grid-Connected Rectifier/Inverter under Disturbance, Dynamic and High Frequency Switching Conditions. IEEE Transactions on Neural Networks and Learning Systems, 25(4), pp. 738–750. doi: 10.1109/TNNLS.2013.2280906.

Basaru, R.R., Child, C., Alonso, E. and Slabaugh, G. (2014). Quantized Census for Stereoscopic Image Matching. In Proceedings of the 2nd International Conference on 3D Vision (3DV 2014), (pp. 22-29), 8-11 December, Tokyo, Japan. doi: 10.1109/3DV.2014.83.

Alonso, E., Sahota, P. and Mondragón, E. (2014). Computational Models of Classical Conditioning - A Qualitative Evaluation and Comparison. In B. Duval, J. van den Herik, S. Loiseau and J. Filipe (Eds.), Proceedings of the 6th International Conference on Agents and Artificial Intelligence (ICAART 2014). (pp. 544-547), 6-8 March, Angers, France. doi: 10.5220/0004903105440547.

Alonso, E. and Mondragón, E. (2014). Quantum Probability in Operant Conditioning - Behavioral Uncertainty in Reinforcement Learning. In B. Duval, J. van den Herik, S. Loiseau and J. Filipe (Eds.), Proceedings of the 6th International Conference on Agents and Artificial Intelligence (ICAART 2014), (pp. 548-551), 6-8 March, Angers, France. doi: 10.5220/0004903205480551.

Weller, P., Fernandez, A. and Alonso, E. (2014). Towards a Personalised Health System. In M. Bienkiewicz, C. Verdier, G. Plantier, T. Schultz, A. Fred and H. Gamboa (Eds.), Proceedings of the 7th International Conference on Health Informatics (HEALTHINF 2014), (pp. 256-261), 3-6 March, Angers, France. doi: 10.5220/0004749702560261.

Veronese, E., Tarroni, G., Visentin, S., Cosmi, E., Linguraru, M.G. and Grisan, E. (2014). Estimation of prenatal aorta intima-media thickness from ultrasound examination. Physics in Medicine and Biology, 59(21), pp. 6355–6371. doi: 10.1088/0022-3727/59/21/6355.

Tarroni, G., Visentin, S., Cosmi, E. and Grisan, E. (2014). Automated estimation of aortic intima-media thickness from fetal ultrasound. In M.G. Linguraru, C. Oyarzun Laura, R. Shekhar, S. Wesarg, M.A. González Ballester, K. Drechsler, Y. Sato and M. Erdt (Eds.), Proceedings of the Third International Workshop on Clinical Image-Based Procedures. Translational Research in Medical Imaging (CLIP 2014), (pp. 33-40), 14 September, Boston, MA, USA. Lecture Notes in Computer Science (LNCS), 8680. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-13909-8_5.

Marino, M., Veronesi, F., Tarroni, G., Mor-Avi, V., Patel, A.R. and Corsi, C. (2014). Fully automated assessment of left ventricular volumes, function and mass from cardiac MRI. In Proceedings of the 2014 Computing in Cardiology Conference (CinC 2014), (pp. 109-112), 7-10 September, Cambridge, MA, USA.

Tarroni, G., Visentin, S., Cosmi, E. and Grisan, E. (2014). Near-automated quantification of prenatal aortic intima-media thickness from ultrasound images. In Proceedings of the 2014 Computing in Cardiology Conference (CinC 2014), (pp. 313-316), 7-10 September, Cambridge, MA, USA.

Kawaji, K., Marino, M., Tanaka, A., Tarroni, G., Ota, T., Lang, R.M. and Patel, A.R. (2014). A Novel Technique for Respiratory Motion Correction in Rapid Left Ventricular Myocardial T1 Mapping and Quantitative Analysis of Myocardial Fibrosis.Circulation, 30(25), 130:A17448.

2013

Teichmann, J., Broom, M. and Alonso, E. (2013). The Application of Temporal Difference Learning in Optimal Diet Models. Journal of Theoretical Biology, 340(7), pp. 11–16. doi: 10.1016/j.jtbi.2013.08.036.

Jennings, D., Alonso, E., Mondragon, E., Frassen, M. and Bonardi, C. (2013). The Effect of Stimulus Distribution Form on the Acquisition and Rate of Conditioned Responding: Implications for Theory. Journal of Experimental Psychology: Animal Behavior Processes, 39(3), pp. 233–248. doi: 10.1037/a0032151.

Mondragon, E., Gray, J. and Alonso, E. (2013). A Complete Serial Compound Temporal Difference Simulator for Compound stimuli, Configural cues and Context representation. NeuroInformatics, 11(2), pp. 259–261. doi: 10.1007/s12021-012-9172-z.

Mondragon, E., Alonso, E., Fernandez, A. and Gray, J. (2013). An Extension of the Rescorla and Wagner simulator for Context Conditioning. Computer Methods and Programs in Biomedicine, 110(2), pp. 226–230. doi: 10.1016/j.cmpb.2013.01.016.

Fairbank, M., Alonso, E. and Prokhorov, D. (2013). An Equivalence Between Adaptive Dynamic Programming With a Critic and Backpropagation Through Time. IEEE Transactions on Neural Networks and Learning Systems, 24(12), pp. 2088–2100. doi: 10.1109/TNNLS.2013.2271778.

Alonso, E. and Mondragón, E. (2013). Associative reinforcement learning: A proposal to build truly adaptive agents and multi-agent systems. In S. Loiseau, J. Filipe, B. Duval and J. van den Herik (Eds.), Proceedings 5th of the International Conference on Agents and Artificial Intelligence (ICAART 2015), (pp.141-146), 15-18 February, Barcelona, Spain. doi: 10.5220/0004175601410146.

Alonso, E. and Fairbank, M. (2013). Emergent and Adaptive Systems of Systems. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC'13), (pp. 1721-1725), 13-16 October, Manchester, England. doi: 10.1109/SMC.2013.296.

Li, S., Fairbank, M., Fu, X., Wunsch, D. and Alonso, E. (2013). Nested-Loop Neural Network Vector Control of Permanent Magnet Synchronous Motors. In Proceedings of the IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2013), (pp. 2999-3006), 4-9 August, Dallas, TX, USA. doi: 10.1109/IJCNN.2013.6707124.

Alonso, E., Karcanias, N. and Hessami, A. (2013). Symmetries, groups and groupoids for Systems of Systems. In Proceedings of the 7th Annual IEEE International Systems Conference (SysCon 2013), (pp. 244-250), 15-18 April, Orlando, FL, USA. doi: 10.1109/SysCon.2013.6549889.

Alonso, E., Karcanias, N. and Hessami, A. (2013). Multi-Agent Systems: A new paradigm for Systems of Systems. In R. Ege (Ed.), Proceedings of the Eighth International Conference on Systems (ICONS 2013), (pp. 8-12) January, Seville, Spain.

Colcombet, T. and Daviaud, L. (2013). Approximate comparison of distance automata. In N. Portier and T. Wilke (Eds.), Proceedings of the 30th International Symposium on Theoretical Aspects of Computer Science (STACS 2013), (pp. 574-585), 27 Feb-2 Mar 2013, Kiel, Germany. Leibniz International Proceedings in Informatics (LIPIcs), 20. Dagstuhl, Germany: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik. doi: 10.4230/LIPIcs.STACS.2013.574.

Tarroni, G., Marsili, D., Veronesi, F., Corsi, C., Patel, A.R., Mor-Avi, V and Lamberti, C. (2013). Automated MRI-based biventricular segmentation using 3D narrow-band statistical level-sets. In Murray A. (Ed.), Proceedings of the Computing in Cardiology Conference (CinC 2013), (pp. 627-630), 22-25 September, Zaragoza, Spain.

Tarroni, G., Marsili, D., Veronesi, F., Corsi, C., Lamberti, C. and Sanguinetti, G. (2013). Near-automated 3D segmentation of left and right ventricles on magnetic resonance images. In Proceedings of the 8th International Symposium on Image and Signal Processing and Analysis (ISPA 2013), (pp. 522-527), 4-6 September, Trieste, Italy. doi: 10.1109/ISPA.2013.6703796.