@article {, title = {A Network-based Cardiac Electrophysiology Simulator with Realistic Signal Generation and Response to Pacing Maneuvers}, journal = {Computing in Cardiology}, year = {2021}, url = {https://www.cinc.org/2021/Program/accepted/294_Preprint.pdf}, author = {Shahriar Iravanian and Ilija Uzelac and Abouzar Kaboudian and Jonathan Langberg and Flavio Fenton} } @article {, title = {Neural Predictive Monitoring and a Comparison of Frequentist and Bayesian Approaches}, journal = {International Journal on Software Tools for Technology Transfer}, year = {2021}, month = {05/2021}, author = {L. Bortolussi and F. Cairoli and N. Paoletti and S. A. Smolka and S. D. Stoller} } @conference {, title = {A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, year = {2020}, month = {13{\textendash}18 Jul}, publisher = {PMLR}, organization = {PMLR}, abstract = {

We propose a neural information processing system obtained by re-purposing the function of a biological neural circuit model to govern simulated and real-world control tasks. Inspired by the structure of the nervous system of the soil-worm, C. elegans, we introduce ordinary neural circuits (ONCs), defined as the model of biological neural circuits reparameterized for the control of alternative tasks. We first demonstrate that ONCs realize networks with higher maximum flow compared to arbitrary wired networks. We then learn instances of ONCs to control a series of robotic tasks, including the autonomous parking of a real-world rover robot. For reconfiguration of the purpose of the neural circuit, we adopt a search-based optimization algorithm. Ordinary neural circuits perform on par and, in some cases, significantly surpass the performance of contemporary deep learning models. ONC networks are compact, 77\% sparser than their counterpart neural controllers, and their neural dynamics are fully interpretable at the cell-level.

}, url = {http://proceedings.mlr.press/v119/hasani20a.html}, author = {Hasani, Ramin and Lechner, Mathias and Amini, Alexander and Rus, Daniela and Grosu, Radu}, editor = {Hal Daum{\'e} III and Aarti Singh} } @booklet {, title = {Neural Flocking: MPC-based Supervised Learning of Flocking Controllers}, year = {2020}, author = {Shouvik Roy and Usama Mehmood and Radu Grosu and Scott A. Smolka and Scott D. Stoller and Ashish Tiwari} } @booklet {, title = {Neural Simplex Architecture}, year = {2020}, author = {Dung T. Phan and Radu Grosu and Nils Jansen and Nicola Paoletti and Scott A. Smolka and Scott D. Stoller} } @conference {, title = {Next-Generation Lagrangian Reachtubes}, booktitle = {Proceedings of CDC 2020, 59th IEEE Conference on Decision and Control}, year = {2020}, month = {12/2020}, author = {S. Gruenbacher and J. Cyranka and M. Lechner and Md. A. Islam and S. A. Smolka and R. Grosu} } @conference {, title = {Neural State Classification for Hybrid Systems}, booktitle = {Proc.\ 16th International Symposium on Automated Technology for Verification and Analysis (ATVA 2018)}, year = {2018}, publisher = {Springer-Verlag}, organization = {Springer-Verlag}, author = {Dung Phan and Nicola Paoletti and Timothy Zhang and Radu Grosu and Scott A. Smolka and Scott D. Stoller} } @conference {, title = {A novel ICD morphology discriminator to improve discrimination between Ventricular and Supraventricular tachycardias}, booktitle = {In Heart Rhythm Scientific Sessions, Chicago}, year = {2017}, month = {05/2017}, abstract = {

Implantable\ \  \ Cardioverter\ \  \ Defibrillator\ \  \ (ICD)\ \  \ morphology\ \  \ discriminators\ \  \ (MDs)\ \  \ discriminate\ \  \ ventricular\ \  \ (VT)\ \  \ from\ \  \ supraventricular\ \  \ tachycardias\ \  \ (SVT)\ \  \ by\ \  \ comparing\ \  \ single-beat\ \  \ electrogram\ \  \ (EGM)\ \  \ shape\ \  \ during\ \  \ tachycardia\ \  \ versus\ \  \ sinus\ \  \ rhythm\ \  \ (Fig.\ \  \ A)\ \  \ and\ \  \ counting\ \  \ the\ \  \ matches.\ \  \ \ \  \ 
Objective

}, author = {H. Abbas and K.J. Jang and J. Liang and S. Dixit and R. Mangharam} }