@article {, title = {Control and anticontrol of chaos in fractional-order models of Diabetes, HIV, Dengue, Migraine, Parkinson{\textquoteright}s and Ebola virus diseases}, journal = {Chaos, Solitons \& Fractals}, volume = {153}, year = {2021}, month = {12/2021}, url = {https://scholar.google.com/citations?view_op=view_citation\&hl=en\&user=jKiNd-0AAAAJ\&sortby=pubdate\&citation_for_view=jKiNd-0AAAAJ:hCrLmN-GePgC}, author = {Manashita Borah and Debanita Das and Antara Gayan and Flavio Fenton and Elizabeth Cherry} } @article {, title = {Interactive 3D Human Heart Simulations on Segmented Human MRI Hearts}, year = {2021}, url = {https://www.cinc.org/2021/Program/accepted/261.pdf}, author = {John P. Berman and FLAVIO H. FENTON and ELIZABETH M. CHERRY and Tinen Iles and Paul A. Iaizzo and Abouzar Kaboudian} } @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} } @article {, title = {Quantifying arrhythmic long QT effects of hydroxychloroquine and azithromycin with whole-heart optical mapping and simulations}, journal = {Heart Rhythm O2}, volume = {2}, year = {2021}, month = {08/2021}, url = {https://scholar.google.com/citations?view_op=view_citation\&hl=en\&user=jKiNd-0AAAAJ\&sortby=pubdate\&citation_for_view=jKiNd-0AAAAJ:Ug5p-4gJ2f0C}, author = {Ilija Uzelac and Abouzar Kaboudian and Shahriar Iravanian and Jimena G Siles-Paredes and James C Gumbart and Hiroshi Ashikaga and Neal Bhatia and Robert F Gilmour Jr and Elizabeth M Cherry and Flavio H Fenton} } @conference {, title = {Safe CPS from Unsafe Controllers}, booktitle = {Proceedings of CAADSPC 2001, the 2021 CPS-IoT Workshop on Computation-Aware Algorithmic Design for Cyber-Physical Systems}, year = {2021}, month = {06/2021}, author = {U. Mehmood and S. Bak and S. A. Smolka and S. D. Stoller} } @article {, title = {Automated Synthesis of Safe Digital Controllers for Sampled-Data Stochastic Nonlinear Systems}, journal = {IEEE Access}, year = {2020}, author = {F. Shmarov and S. Soudjani and N. Paoletti and E. Bartocci and S. Lin and S. A. Smolka and P. Zuliani} } @conference {, title = {Bayesian Neural Predictive Monitoring}, booktitle = {Proceedings of OVERLAY-20, Second Workshop on Formal Methods for AI}, year = {2020}, month = {09/2020}, author = {L. Bortolussi and F. Cairoli and N. Paoletti and S. A. Smolka and S. D. Stoller} } @article {, title = {Excitable dynamics in neural and cardiac systems}, journal = {Communications in Nonlinear Science and Numerical Simulation}, volume = {86}, year = {2020}, month = {07/2020}, url = {https://scholar.google.com/citations?view_op=view_citation\&hl=en\&user=jKiNd-0AAAAJ\&sortby=pubdate\&citation_for_view=jKiNd-0AAAAJ:hMsQuOkrut0C}, author = {Roberto Barrio and Stephen Coombes and Mathieu Desroches and Flavio Fenton and Stefan Luther and Esther Pueyo} } @article {, title = {Fatal arrhythmias: another reason why doctors remain cautious about chloroquine/hydroxychloroquine for treating COVID-19}, journal = {Heart Rhythm}, volume = {17}, year = {2020}, month = {09/2020}, url = {https://scholar.google.com/citations?view_op=view_citation\&hl=en\&user=jKiNd-0AAAAJ\&sortby=pubdate\&citation_for_view=jKiNd-0AAAAJ:tYavs44e6CUC}, author = {Ilija Uzelac and Shahriar Iravanian and Hiroshi Ashikaga and Neal K Bhatia and Conner Herndon and Abouzar Kaboudian and James C Gumbart and Elizabeth M Cherry and Flavio H Fenton} } @inbook {, title = {From Automated MRI Scan to Finite Elements}, year = {2019}, pages = {35-48}, isbn = {978-3-030-31513-9}, doi = {10.1007/978-3-030-31514-6_3}, author = {Glimm, James and Lim, Hyunkyung and Bishop, Martin and Kim, Soojin} } @booklet {, title = {From Reactive Systems to Cyber-physical Systems: Essays Dedicated to Scott A. Smolka on the Occasion of His 65th Birthday}, year = {2019}, author = {Bartocci, Ezio and Cleaveland, Rance and Grosu, Radu and Sokolsky, Oleg} } @article {, title = {An Algebraic Framework for Runtime Verification}, journal = {CoRR}, volume = {abs/1802.03775}, year = {2018}, url = {http://arxiv.org/abs/1802.03775}, author = {Stefan Jaksic and Ezio Bartocci and Radu Grosu and Dejan Nickovic} } @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} } @conference {, title = {Lagrangian Reachabililty}, booktitle = {Computer Aided Verification - 29th International Conference, {CAV} 2017 Proceedings, Part {I}}, year = {2017}, abstract = {

We introduce LRT, a new Lagrangian-based ReachTube computation algorithm that conservatively approximates the set of reachable states of a nonlinear dynamical system. LRT makes use of the Cauchy-Green stretching factor (SF), which is derived from an over-approximation of the gradient of the solution flows. The SF measures the discrepancy between two states propagated by the system solution from two initial states lying in a well-defined region, thereby allowing LRT to compute a reachtube with a ball-overestimate in a metric where the computed enclosure is as tight as possible. To evaluate its performance, we implemented a prototype of LRT in C++/Matlab, and ran it on a set of well-established benchmarks. Our results show that LRT compares very favorably with respect to the CAPD and Flow* tools.

}, doi = {10.1007/978-3-319-63387-9_19}, author = {Jacek Cyranka and Md. Ariful Islam and Greg Byrne and Paul L. Jones and Scott A. Smolka and Radu Grosu} } @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 = {{SMT-based Synthesis of Safe and Robust PID Controllers for Stochastic Hybrid Systems}}, booktitle = {Haifa Verification Conference, to appear}, year = {2017}, abstract = {

Abstract. We present a new method for the automated synthesis of safe and robust Proportional-Integral-Derivative (PID) controllers for stochastic hybrid systems. Despite their widespread use in industry, no automated method currently exists for deriving a PID controller (or any other type of controller, for that matter) with safety and performance guarantees for such a general class of systems. In particular, we consider hybrid systems with nonlinear dynamics (Lipschitzcontinuous ordinary differential equations) and random parameters, and we synthesize PID controllers such that the resulting closed-loop systems satisfy safety and performance constraints given as probabilistic bounded reachability properties. Our technique leverages SMT solvers over the reals and nonlinear differential equations to provide formal guarantees that the synthesized controllers satisfy such properties. These controllers are also robust by design since they minimize the probability of reaching an unsafe state in the presence of random disturbances. We apply our approach to the problem of insulin regulation for type 1 diabetes, synthesizing controllers with robust responses to large random meal disturbances, thereby enabling them to maintain blood glucose levels within healthy, safe ranges.

}, author = {Fedor Shmarov and Nicola Paoletti and Ezio Bartocci and Shan Lin and Scott Smolka and Paolo Zuliani} } @conference {, title = {Bifurcation Analysis of Cardiac Alternans Using δ -Decidability.}, booktitle = {CMSB}, year = {2016}, publisher = {Springer}, organization = {Springer}, keywords = {dblp}, isbn = {978-3-319-45176-3}, url = {http://dblp.uni-trier.de/db/conf/cmsb/cmsb2016.html$\#$IslamBKCCFGJS16}, author = {Islam, Md. Ariful and Byrne, Greg and Kong, Soonho and Clarke, Edmund M. and Cleaveland, Rance and Fenton, Flavio H. and Grosu, Radu and Jones, Paul L. and Smolka, Scott A.}, editor = {Bartocci, Ezio and Li{\`o}, Pietro and Paoletti, Nicola} } @conference {, title = {High-level modeling for computer-aided clinical trials of medical devices}, booktitle = {High Level Design Validation and Test Workshop (HLDVT), 2016 IEEE International}, year = {2016}, publisher = {IEEE}, organization = {IEEE}, abstract = {

Medical devices like the Implantable Cardioverter Defibrillator (ICD) are life-critical systems. Malfunctions of the device can cause serious injury or death of the patient. In addition to rigorous testing and verification during the development process, new medical devices often go through clinical trials to evaluate their safety and performance on sample populations. Clinical trials are costly and prone to failure if not planned and executed properly. Evaluating devices on computer models of the relevant physiological systems can provide helpful insights into the safety and efficacy of the device, thus helping to plan and execute a clinical trial. In this paper, we demonstrate how to develop high-level physiological models of cardiac electrophysiology and how to apply them to the Rhythm ID Head to Head Trial (RIGHT), a 5-year long clinical trial for comparing two ICDs. We refer to this as a Computer-Aided Clinical Trial (CACT). We explored two modeling options, a white-box model capturing the mechanisms of the physiological behaviors, and a blackbox model which uses machine learning methods to synthesize physiological input signals. Both models were able to generate physiological inputs to the ICDs and we discuss the challenges and appropriateness of the two modeling options.

}, author = {Abbas, Houssam and Jiang, Zhihao and Jang, Kuk Jin and Beccani, Marco and Liangy, Jackson and Mangharam, Rahul} } @conference {, title = {In-silico pre-clinical trials for implantable cardioverter defibrillators}, booktitle = {Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the}, year = {2016}, publisher = {IEEE}, organization = {IEEE}, abstract = {

Regulatory authorities require that the safety and efficacy of a new high-risk medical device be proven in a Clinical Trial (CT), in which the effects of the device on a group of patients are compared to the effects of the current standard of care. Phase III trials can run for several years, cost millions of dollars, and expose patients to an unproven device. In this paper, we demonstrate how to use a large group of synthetic patients based on computer modeling to improve the planning of a CT so as to increase the chances of a successful trial for implantable cardioverter defibrillators (ICDs). We developed a computer model of the electrical generation and propagation in the heart. This model was used to generate a large group of heart instances capable of producing episodes of 19 different arrhythmias. We also implemented two arrhythmia detection algorithms from the literature: Rhythm ID from Boston Scientific and PR Logic + Wavelet from Medtronic. Using this setup, we conducted multiple in-silico trials to compare the ability of the two algorithms to appropriately discriminate between potentially fatal Ventricular Tachy-arrhythmias (VT) and nonfatal Supra-Ventricular Tachy-arrhythmias (SVTs). The results of our in-silico trial indicate that Rhythm ID was less able to discriminate between SVT and VT and so may lead to more cases of inappropriate therapy. This corroborates the findings of the Rhythm ID Going Head to Head Trial (RIGHT), a clinical trial that compared the two algorithms in patients. We further demonstrated that the result continues to hold if we vary the distribution of arrhythmias in the synthetic population. We also used the same in-silico cohort to explore the sensitivity of the outcome to different parameter settings of the device algorithms, which is not feasible in a real clinical trial. In-silico trials can provide early insight into the factors which affect the outcome of a CT at a fraction of the cost and duration and without the ethical issues.

}, author = {Jiang, Zhihao and Abbas, Houssam and Jang, Kuk Jin and Beccani, Marco and Liang, Jackson and Dixit, Sanjay and Mangharam, Rahul} } @article {, title = {Abstract Model Repair}, journal = {Logical Methods in Computer Science}, volume = {3:11}, year = {2015}, author = {G. Chatzieleftheriou and B. Bonakdarpour and P. Katsaros and S. A. Smolka} } @article {, title = {Model-Order Reduction of Ion Channel Dynamics Using Approximate Bisimulation}, journal = {Theoretical Computer Science}, volume = {599C}, year = {2015}, month = {09/2015}, chapter = {34}, author = {Md. A. Islam and A. Murthy and E. Bartocci and E. M. Cherry and F. H. Fenton and J. Glimm and S. A. Smolka and R. Grosu} } @conference {21, title = {SpaTeL: A Novel Spatial-temporal Logic and Its Applications to Networked Systems}, booktitle = {Proceedings of the 18th International Conference on Hybrid Systems: Computation and Control}, year = {2015}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, keywords = {networked dynamical systems, parameter synthesis, spatial temporal logic, statistical model checking}, isbn = {978-1-4503-3433-4}, doi = {10.1145/2728606.2728633}, url = {http://doi.acm.org/10.1145/2728606.2728633}, author = {Haghighi, Iman and Jones, Austin and Kong, Zhaodan and Bartocci, Ezio and Grosu, Radu and Belta, Calin} } @article {, title = {System design of stochastic models using robustness of temporal properties}, journal = {Theoretical Computer Science}, volume = {587}, year = {2015}, pages = {3 - 25}, abstract = {

Abstract Stochastic models such as Continuous-Time Markov Chains (CTMC) and Stochastic Hybrid Automata (SHA) are powerful formalisms to model and to reason about the dynamics of biological systems, due to their ability to capture the stochasticity inherent in biological processes. A classical question in formal modelling with clear relevance to biological modelling is the model checking problem, i.e. calculate the probability that a behaviour, expressed for instance in terms of a certain temporal logic formula, may occur in a given stochastic process. However, one may not only be interested in the notion of satisfiability, but also in the capacity of a system to maintain a particular emergent behaviour unaffected by the perturbations, caused e.g. from extrinsic noise, or by possible small changes in the model parameters. To address this issue, researchers from the verification community have recently proposed several notions of robustness for temporal logic providing suitable definitions of distance between a trajectory of a (deterministic) dynamical system and the boundaries of the set of trajectories satisfying the property of interest. The contributions of this paper are twofold. First, we extend the notion of robustness to stochastic systems, showing that this naturally leads to a distribution of robustness degrees. By discussing three examples, we show how to approximate the distribution of the robustness degree and the average robustness. Secondly, we show how to exploit this notion to address the system design problem, where the goal is to optimise some control parameters of a stochastic model in order to maximise robustness of the desired specifications.

}, keywords = {Temporal logic}, issn = {0304-3975}, doi = {http://dx.doi.org/10.1016/j.tcs.2015.02.046}, url = {http://www.sciencedirect.com/science/article/pii/S0304397515002224}, author = {Ezio Bartocci and Luca Bortolussi and Laura Nenzi and Guido Sanguinetti} }