@booklet {, title = {Adversarial Training is Not Ready for Robot Learning}, year = {2021}, author = {Mathias Lechner and Ramin Hasani and Radu Grosu and Daniela Rus and Thomas A. Henzinger} } @conference {, title = {Distributed Control for Flocking Maneuvers via Acceleration-Weighted Neighborhooding}, booktitle = {Proceedings of ACC 2021, The 2021 American Control Conference}, year = {2021}, month = {06/2021}, author = {S. Roy and U. Mehmood and R. Grosu and S. A. Smolka and S. D. Stoller and A. Tiwari} } @conference {, title = {A Distributed Simplex Architecture for Multi-Agent Systems}, booktitle = {Proceedings of SETTA 2021, Symposium on Dependable Software Engineering}, year = {2021}, month = {11/2021}, author = {U. Mehmood and S. D. Stoller and R. Grosu and S. Roy and A. Damare and S. D. Smolka} } @conference {, title = {Gershgorin Loss Stabilizes the Recurrent Neural Network Compartment of an End-to-end Robot Learning Scheme}, booktitle = {2020 IEEE International Conference on Robotics and Automation (ICRA)}, year = {2020}, doi = {10.1109/ICRA40945.2020.9196608}, author = {Lechner, Mathias and Hasani, Ramin and Rus, Daniela and Grosu, Radu} } @booklet {, title = {Learning Distributed Controllers for V-Formation}, year = {2020}, author = {Shouvik Roy and Usama Mehmood and Radu Grosu and Scott A. Smolka and Scott D. Stoller and Ashish Tiwari} } @booklet {, title = {Liquid Time-constant Networks}, year = {2020}, author = {Ramin Hasani and Mathias Lechner and Alexander Amini and Daniela Rus and Radu Grosu} } @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} } @article {, title = {Single-variable delay-differential equation approximations of the Fitzhugh-Nagumo and Hodgkin-Huxley models}, journal = {Communications in Nonlinear Science and Numerical Simulation}, volume = {82}, year = {2020}, pages = {105066}, doi = {10.1016/j.cnsns.2019.105066}, url = {https://doi.org/10.1016/j.cnsns.2019.105066}, author = {Raffael Bechara Rameh and ELIZABETH M. CHERRY and Rodrigo Weber dos Santos} } @article {, title = {Electromechanical vortex filaments during cardiac fibrillation}, journal = {Nature}, volume = {555}, year = {2018}, pages = {667+}, abstract = {

The self-organized dynamics of vortex-like rotating waves, which are also known as scroll waves, are the basis of the formation of complex spatiotemporal patterns in many excitable chemical and biological systems. In the heart, filament-like phase singularities5,6\ that are associated with three-dimensional scroll waves7\ are considered to be the organizing centres of life-threatening cardiac arrhythmias. The mechanisms that underlie the onset, maintenance and control14,15,16\ of electromechanical turbulence in the heart are inherently three-dimensional phenomena. However, it has not previously been possible to visualize the three-dimensional spatiotemporal dynamics of scroll waves inside cardiac tissues. Here we show that three-dimensional mechanical scroll waves and filament-like phase singularities can be observed deep inside the contracting heart wall using high-resolution four-dimensional ultrasound-based strain imaging. We found that mechanical phase singularities co-exist with electrical phase singularities during cardiac fibrillation. We investigated the dynamics of electrical and mechanical phase singularities by simultaneously measuring the membrane potential, intracellular calcium concentration and mechanical contractions of the heart. We show that cardiac fibrillation can be characterized using the three-dimensional spatiotemporal dynamics of mechanical phase singularities, which arise inside the fibrillating contracting ventricular wall. We demonstrate that electrical and mechanical phase singularities show complex interactions and we characterize their dynamics in terms of trajectories, topological charge and lifetime. We anticipate that our findings will provide novel perspectives for non-invasive diagnostic imaging and therapeutic applications.

}, doi = {https://doi.org/10.1038/nature26001}, author = {Jan Christoph and M. Chebbok and Carl Richter and Johannes Schr{\"o}der-Schetelig and Philip Bittihn and Seth Stein and Ilija Uzelac and FLAVIO H. FENTON and Gerd Hasenfuss and Robert F. Gilmour and Stefan Luther} } @conference {, title = {OpenUAV: A UAV Testbed for the CPS and Robotics Community}, booktitle = {Proceedings of the 9th ACM/IEEE International Conference on Cyber-Physical Systems}, year = {2018}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Piscataway, NJ, USA}, isbn = {978-1-5386-5301-2}, doi = {10.1109/ICCPS.2018.00021}, url = {https://doi.org/10.1109/ICCPS.2018.00021}, author = {Schmittle, Matt and Lukina, Anna and Vacek, Lukas and Das, Jnaneshwar and Buskirk, Christopher P. and Rees, Stephen and Sztipanovits, Janos and Grosu, Radu and Kumar, Vijay} } @article {, title = {An Optimization-Based Algorithm for the Construction of Cardiac Purkinje Network Models}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {65}, year = {2018}, pages = {2760{\textendash}2768}, doi = {10.1109/tbme.2018.2815504}, url = {https://doi.org/10.1109/tbme.2018.2815504}, author = {Jesuliana N. Ulysses and Lucas A. Berg and ELIZABETH M. CHERRY and Ben R. Liu and Rodrigo W. dos Santos and Bruno G. de Barros and Bernardo M. Rocha and Rafael A. B. de Queiroz} } @inbook {, title = {Process Algebra and Model Checking}, booktitle = {Handbook of Model Checking.}, year = {2018}, pages = {1149{\textendash}1195}, doi = {10.1007/978-3-319-10575-8\_32}, url = {https://doi.org/10.1007/978-3-319-10575-8\_32}, author = {Rance Cleaveland and A. W. Roscoe and Scott A. Smolka} } @conference {, title = {Data assimilation for cardiac electrical dynamics}, booktitle = {Proceedings of the 5th {International} {Conference} on {Computational} and {Mathematical} {Biomedical} {Engineering}}, year = {2017}, address = {Pittsburgh, PA}, abstract = {

New insights into the mechanisms underlying cardiac arrhythmias may be possible from a greater understanding of variables unobserved in experimental settings. When experimental observations are sparse, data assimilation can be effective at recovering a more complete system state estimate. We have recently shown using synthetic observations of one- and three-dimensional cardiac tissue that data assimilation can be used to recover state variables observed sparsely in space and time or unobserved. Here we extend these findings to show that data assimilation remains effective in reconstructing cardiac states with underlying system spatial heterogeneity and in the presence of significant model error.

}, author = {Cherry, E. M. and Cairns, D. I. and Holt, N. and LaVigne, N. S. and Fenton, F. H. and Hoffman, M. J.}, editor = {Nithiarasu, P. and Robertson, A. M.} } @article {, title = {Modeling bipolar stimulation of cardiac tissue}, journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science}, volume = {27}, year = {2017}, pages = {093920}, abstract = {

Unipolar stimulation of cardiac tissue is often used in the design of cardiac pacemakers because of the low current required to depolarize the surrounding tissue at rest. However, the advantages of unipolar over bipolar stimulation are not obvious at shorter coupling intervals when the tissue near the pacing electrode is relatively refractory. Therefore, this paper analyzes bipolar stimulation of cardiac tissue. The strength-interval relationship for bipolar stimulation is calculated using the bidomain model and a recently developed parsimonious ionic current model. The strength-interval curves obtained using different electrode separations and arrangements (electrodes placed parallel to the fibers versus perpendicular to the fibers) indicate that bipolar stimulation results in more complex activation patterns compared to unipolar stimulation. An unusually low threshold stimulus current is observed when the electrodes are close to each other (a separation of 1 mm) because of break excitation. Unlike for unipolar stimulation, anode make excitation is not present during bipolar stimulation, and an abrupt switch from anode break to cathode make excitation can cause dramatic changes in threshold with very small changes in the interval. These results could impact the design of implantable pacemakers and defibrillators.

}, author = {Galappaththige, Suran K and Gray, Richard A and Roth, Bradley J} } @conference {, title = {Opacity for switched linear systems: Notions and characterization}, booktitle = {2017 IEEE 56th Annual Conference on Decision and Control (CDC)}, year = {2017}, month = {Dec}, keywords = {allowed switching sequences, directed graph, directed graphs, discrete time systems, discrete-time switched linear systems, linear systems, Mathematical model, Power systems, switched linear systems, Switched systems, Switches, switching systems (control)}, doi = {10.1109/CDC.2017.8264445}, author = {B. Ramasubramanian and R. Cleaveland and S. I. Marcus} } @conference {, title = {Quantitative Regular Expressions for Arrhythmia Detection Algorithms}, booktitle = {International Conference on Computational Methods in Systems Biology}, year = {2017}, month = {09/2017}, publisher = {Springer, Cham}, organization = {Springer, Cham}, address = {Darmstadt, Germany }, abstract = {

Motivated by the problem of verifying the correctness of arrhythmia-detection algorithms, we present a formalization of these algorithms in the language of Quantitative Regular Expressions. QREs are a flexible formal language for specifying complex numerical queries over data streams, with provable runtime and memory consumption guarantees. The medical-device algorithms of interest include peak detection (where a peak in a cardiac signal indicates a heartbeat) and various discriminators, each of which uses a feature of the cardiac signal to distinguish fatal from non-fatal arrhythmias. Expressing these algorithms{\textquoteright} desired output in current temporal logics, and implementing them via monitor synthesis, is cumbersome, error-prone, computationally expensive, and sometimes infeasible.

In contrast, we show that a range of peak detectors (in both the time and wavelet domains) and various discriminators at the heart of today{\textquoteright}s arrhythmia-detection devices are easily expressible in QREs. The fact that one formalism (QREs) is used to describe the desired end-to-end operation of an arrhythmia detector opens the way to formal analysis and rigorous testing of these detectors{\textquoteright} correctness and performance. Such analysis could alleviate the regulatory burden on device developers when modifying their algorithms. The performance of the peak-detection QREs is demonstrated by running them on real patient data, on which they yield results on par with those provided by a cardiologist.

}, isbn = {978-3-319-67471-1}, doi = {https://doi.org/10.1007/978-3-319-67471-1_2}, author = {Houssam Abbas and Alena Rodionova and Ezio Bartocci and Scott A. Smolka and Radu Grosu} } @conference {, title = {A framework for decentralized opacity in linear systems}, booktitle = {2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton)}, year = {2016}, month = {Sept}, keywords = {adversarial observer, colluding adversary, Computers, cyber-physical system, Cyber-physical systems, decentralized opacity, directed graph, directed graphs, ISO, linear system, linear systems, observers, Power systems, Trajectory}, doi = {10.1109/ALLERTON.2016.7852240}, author = {B. Ramasubramanian and R. Cleaveland and S. I. Marcus} } @conference {, title = {A framework for opacity in linear systems}, booktitle = {2016 American Control Conference, {ACC} 2016, Boston, MA, USA, July 6-8, 2016}, year = {2016}, doi = {10.1109/ACC.2016.7526666}, url = {https://doi.org/10.1109/ACC.2016.7526666}, author = {Bhaskar Ramasubramanian and Rance Cleaveland and Steven I. Marcus} } @article {, title = {Security assurance cases for medical cyber{\textendash}physical systems}, journal = {IEEE Design \& Test}, volume = {32}, year = {2015}, pages = {56{\textendash}65}, author = {Ray, Arnab and Cleaveland, Rance} }