A Verified and Compositional Translation of LTL to Deterministic Rabin Automata

We present a formalisation of the unified translation approach from linear temporal logic (LTL) to omega-automata from [Javier Esparza et al., 2018]. This approach decomposes LTL formulas into "simple" languages and allows a clear separation of concerns: first, we formalise the purely logical result yielding this decomposition; second, we develop a generic, executable, and expressive automata library providing necessary operations on automata to re-combine the "simple" languages; third, we instantiate this generic theory to obtain a construction for deterministic Rabin automata (DRA). We extract from this particular instantiation an executable tool translating LTL to DRAs. To the best of our knowledge this is the first verified translation of LTL to DRAs that is proven to be double-exponential in the worst case which asymptotically matches the known lower bound

Formal Models for Biological Systems

In the last thirty years, formal models have been thoroughly employed in the realm of biological systems for many reasons: (i) preventing those ambiguities that may arise when informal notations are used for system description, (ii) supporting the development of simulators, (iii) supporting the development of tools, such as model checkers, allowing for verifying whether a system satisfies a given behavioural property, (iv) offering several instruments allowing for comparing the behaviour of different systems. The work in this thesis can be divided into two contributions concerning formal models for biological systems. The first contribution is related to the study of the robustness of biochemical networks. In particular, we take inspiration from the notion of alpha-robustness, which, intuitively, verifies how by varying the initial concentration of some species, called conventionally the input species, the concentration of other species of interest, called the output species, varies at steady state. Robustness in our sense captures random effects and temporary effects that are typical of the stochastic model. We will employ: (i) the process calculi approach for specifying systems of interest, (ii) the semantic model of evolution sequences, which, intuitively, model the behaviour of a system as the sequence of probability measures over the attainable configurations, (iii) a formal notion of robustness, defined on the semantic model, and (iv) an algorithm allowing us to estimate the robustness of a system starting from its specification. We validate our approach on three case studies EnvZ/OmpR Osmoregulatory Signaling System in Escherichia Coli, which is an example of the regulatory network, the mechanism of Bacterial Chemotaxis of Escherichia Coli, and an abstract chemical reaction network, called Enzyme Activity at Saturation. We have provided a Python implementation available at https://github.com/dmanicardi/spebnr. Our second contribution is showing how the features of CospanSpan(Graph) can be exploited in modelling biological systems. CospanSpan(Graph) offers an algebraic approach for the compositional description of variable topology networks that has been only partially exploited so far for the formalisation of that kind of systems. In particular, we provide a simplified model of a human heart and a model of a dual-chamber pacemaker that can interact with the model of the heart. Then, we model a gene regulatory network, namely the Lac Operon of Escherichia Coli.In the last thirty years, formal models have been thoroughly employed in the realm of biological systems for many reasons: (i) preventing those ambiguities that may arise when informal notations are used for system description, (ii) supporting the development of simulators, (iii) supporting the development of tools, such as model checkers, allowing for verifying whether a system satisfies a given behavioural property, (iv) offering several instruments allowing for comparing the behaviour of different systems. The work in this thesis can be divided into two contributions concerning formal models for biological systems. The first contribution is related to the study of the robustness of biochemical networks. In particular, we take inspiration from the notion of alpha-robustness, which, intuitively, verifies how by varying the initial concentration of some species, called conventionally the input species, the concentration of other species of interest, called the output species, varies at steady state. Robustness in our sense captures random effects and temporary effects that are typical of the stochastic model. We will employ: (i) the process calculi approach for specifying systems of interest, (ii) the semantic model of evolution sequences, which, intuitively, model the behaviour of a system as the sequence of probability measures over the attainable configurations, (iii) a formal notion of robustness, defined on the semantic model, and (iv) an algorithm allowing us to estimate the robustness of a system starting from its specification. We validate our approach on three case studies EnvZ/OmpR Osmoregulatory Signaling System in Escherichia Coli, which is an example of the regulatory network, the mechanism of Bacterial Chemotaxis of Escherichia Coli, and an abstract chemical reaction network, called Enzyme Activity at Saturation. We have provided a Python implementation available at https://github.com/dmanicardi/spebnr. Our second contribution is showing how the features of CospanSpan(Graph) can be exploited in modelling biological systems. CospanSpan(Graph) offers an algebraic approach for the compositional description of variable topology networks that has been only partially exploited so far for the formalisation of that kind of systems. In particular, we provide a simplified model of a human heart and a model of a dual-chamber pacemaker that can interact with the model of the heart. Then, we model a gene regulatory network, namely the Lac Operon of Escherichia Coli

MaxSAT Evaluation 2018 : Solver and Benchmark Descriptions

Non peer reviewe

Experimental and phenomenological study of persistent photoconductivity in YBa2Cu3O6 thin films

Persistent Photoconductivity in YBa2Cu3O6+x thin films is studied by means of infrared photoconductivity measurements after the films have been illuminated with visible light at low temperature. Experimentally, the effect is characterized by the samples\u27 electrical response to infrared light, either causing the resistance to decrease or increase. A cellular automata model is proposed in explanation of these results and is shown to be consistent with current experimental understanding of this unusual effect, both our results and those of others. The cellular automata model may have application to other unusual optical phenomena exhibited by YBa2Cu3O6+x such as resonant Raman scattering

Building DryVR: A verification and controller synthesis engine for cyber-physical systems and safety-critical autonomous vehicle features

To test safety of autonomous vehicles, large corporations have raced to log millions of miles of test driving on public roads. While this can improve confidence in such systems, testing alone cannot establish of absence of failure scenarios. In fact, it has also been reported that the amount of data required to guarantee a probability of 10^-9 fatality per hour of driving would require 10^9 hours of driving [1] [2], which is roughly in the order of thirty billion miles. Formal verification can give guarantees about absence of failures and potentially reduce the amount of testing needed significantly. Simulation based verification is a promising approach to provide formal safety guarantees to Cyber-Physical Systems (CPS). However, existing verification tools rely on the explicit mathematical models of the system. Detailed mathematical models are often not available or are too complex for formal verification tools. To address this issue, the DryVR approach for verification is presented in [3]. DryVR views a cyber-physical system as a combination of a white-box transition graph and a black-box simulator. This alleviates the need for complete mathematical models, but at the same time exploits models when they are available. A verification algorithm for directed acyclic time-dependent transition graph is also presented in [3]. In this thesis, we present the detailed construction of the DryVR tool with several new functionalities, which includes: (a) verification on state-dependent cyclic transition graph with guard and reset functions; (b) controller synthesis that searches transition graph for given reach-avoid specification; (c) interface that allows user to connect DryVR with arbitrary black-box simulators, and (d) integration with Jupyter Notebook [4]. We also present a case study for autonomous vehicle system in this thesis, and DryVR comes with verification and controller synthesis examples to illustrate its capabilities. The evaluation of included examples is presented in later chapter shows that both verification and controller synthesis are promising starting point for DryVR to become a comprehensive verification and synthesis toolbox for practical CPS

A genetic programming system with an epigenetic mechanism for traffic signal control

Traffic congestion is an increasing problem in most cities around the world. It impacts businesses as well as commuters, small cities and large ones in developing as well as developed economies. One approach to decrease urban traffic congestion is to optimize the traffic signal behaviour in order to be adaptive to changes in the traffic conditions. From the perspective of intelligent transportation systems, this optimization problem is called the traffic signal control problem and is considered a large combinatorial problem with high complexity and uncertainty. A novel approach to the traffic signal control problem is proposed in this thesis. The approach includes a new mechanism for Genetic Programming inspired by Epigenetics. Epigenetic mechanisms play an important role in biological processes such as phenotype differentiation, memory consolidation within generations and environmentally induced epigenetic modification of behaviour. These properties lead us to consider the implementation of epigenetic mechanisms as a way to improve the performance of Evolutionary Algorithms in solution to real-world problems with dynamic environmental changes, such as the traffic control signal problem. The epigenetic mechanism proposed was evaluated in four traffic scenarios with different properties and traffic conditions using two microscopic simulators. The results of these experiments indicate that Genetic Programming was able to generate competitive actuated traffic signal controllers for all the scenarios tested. Furthermore, the use of the epigenetic mechanism improved the performance of Genetic Programming in all the scenarios. The evolved controllers adapt to modifications in the traffic density and require less monitoring and less human interaction than other solutions because they dynamically adjust the signal behaviour depending on the local traffic conditions at each intersection

Computerised Modelling for Developmental Biology

Many studies in developmental biology rely on the construction and analysis of models. This research presents a broad view of modelling approaches for developmental biology, with a focus on computational methods. An overview of modelling techniques is given, followed by several case studies. Using 3D reconstructions, the heart development of the turtle is examined, with special attention to heart looping and the development of the outflow tract. Subsequently, an ontology system is presented in which anatomical, developmental and physiological information on the vertebrate heart is modelled. Finally, two Petri net models are discussed, which model the developmental process of gradient formation, both in a qualitative and quantitative manner.LEI Universiteit LeidenImagin

GA SAKe : forecasting landslide activations by a genetic-algorithms-based hydrological model

Abstract. GASAKe is a new hydrological model aimed at forecasting the triggering of landslides. The model is based on genetic algorithms and allows one to obtain thresholds for the prediction of slope failures using dates of landslide activations and rainfall series. It can be applied to either single landslides or a set of similar slope movements in a homogeneous environment. Calibration of the model provides families of optimal, discretized solutions (kernels) that maximize the fitness function. Starting from the kernels, the corresponding mobility functions (i.e., the predictive tools) can be obtained through convolution with the rain series. The base time of the kernel is related to the magnitude of the considered slope movement, as well as to the hydro-geological complexity of the site. Generally, shorter base times are expected for shallow slope instabilities compared to larger-scale phenomena. Once validated, the model can be applied to estimate the timing of future landslide activations in the same study area, by employing measured or forecasted rainfall series. Examples of application of GASAKe to a medium-size slope movement (the Uncino landslide at San Fili, in Calabria, southern Italy) and to a set of shallow landslides (in the Sorrento Peninsula, Campania, southern Italy) are discussed. In both cases, a successful calibration of the model has been achieved, despite unavoidable uncertainties concerning the dates of occurrence of the slope movements. In particular, for the Sorrento Peninsula case, a fitness of 0.81 has been obtained by calibrating the model against 10 dates of landslide activation; in the Uncino case, a fitness of 1 (i.e., neither missing nor false alarms) has been achieved using five activations. As for temporal validation, the experiments performed by considering further dates of activation have also proved satisfactory. In view of early-warning applications for civil protection, the capability of the model to simulate the occurrences of the Uncino landslide has been tested by means of a progressive, self-adaptive procedure. Finally, a sensitivity analysis has been performed by taking into account the main parameters of the model. The obtained results are quite promising, given the high performance of the model against different types of slope instabilities characterized by several historical activations. Nevertheless, further refinements are still needed for application to landslide risk mitigation within early-warning and decision-support systems