Special Algorithm for Stability Analysis of Multistable Biological Regulatory Systems

We consider the problem of counting (stable) equilibriums of an important family of algebraic differential equations modeling multistable biological regulatory systems. The problem can be solved, in principle, using real quantifier elimination algorithms, in particular real root classification algorithms. However, it is well known that they can handle only very small cases due to the enormous computing time requirements. In this paper, we present a special algorithm which is much more efficient than the general methods. Its efficiency comes from the exploitation of certain interesting structures of the family of differential equations.Comment: 24 pages, 5 algorithms, 10 figure

Distributed Maple: parallel computer algebra in networked environments

AbstractWe describe the design and use of Distributed Maple, an environment for executing parallel computer algebra programs on multiprocessors and heterogeneous clusters. The system embeds kernels of the computer algebra system Maple as computational engines into a networked coordination layer implemented in the programming language Java. On the basis of a comparatively high-level programming model, one may write parallel Maple programs that show good speedups in medium-scaled environments. We report on the use of the system for the parallelization of various functions of the algebraic geometry library CASA and demonstrate how design decisions affect the dynamic behaviour and performance of a parallel application. Numerous experimental results allow comparison of Distributed Maple with other systems for parallel computer algebra

Toward “smart tubes” using iterative learning control

In the paper, we present our progress toward designing a “smart” high-peak power microwave (HPM) tube. We use iterative learning control (ILC) methodologies in order to control a repetitively pulsed high-power backward-wave oscillator (BWO). The learning-control algorithm is used to drive the error between the actual output and its desired value to zero. The desired output may be a given power level, a given frequency, or a combination of both. The learning-control methodology is then verified in simulation. This methodology is applicable to a wide variety of HPM sources

Simulation and statistical model-checking of logic-based multi-agent system models

This thesis presents SALMA (Simulation and Analysis of Logic-Based Multi- Agent Models), a new approach for simulation and statistical model checking of multi-agent system models. Statistical model checking is a relatively new branch of model-based approximative verification methods that help to overcome the well-known scalability problems of exact model checking. In contrast to existing solutions, SALMA specifies the mechanisms of the simulated system by means of logical axioms based upon the well-established situation calculus. Leveraging the resulting first-order logic structure of the system model, the simulation is coupled with a statistical model-checker that uses a first-order variant of time-bounded linear temporal logic (LTL) for describing properties. This is combined with a procedural and process-based language for describing agent behavior. Together, these parts create a very expressive framework for modeling and verification that allows direct fine-grained reasoning about the agents’ interaction with each other and with their (physical) environment. SALMA extends the classical situation calculus and linear temporal logic (LTL) with means to address the specific requirements of multi-agent simulation models. In particular, cyber-physical domains are considered where the agents interact with their physical environment. Among other things, the thesis describes a generic situation calculus axiomatization that encompasses sensing and information transfer in multi agent systems, for instance sensor measurements or inter-agent messages. The proposed model explicitly accounts for real-time constraints and stochastic effects that are inevitable in cyber-physical systems. In order to make SALMA’s statistical model checking facilities usable also for more complex problems, a mechanism for the efficient on-the-fly evaluation of first-order LTL properties was developed. In particular, the presented algorithm uses an interval-based representation of the formula evaluation state together with several other optimization techniques to avoid unnecessary computation. Altogether, the goal of this thesis was to create an approach for simulation and statistical model checking of multi-agent systems that builds upon well-proven logical and statistical foundations, but at the same time takes a pragmatic software engineering perspective that considers factors like usability, scalability, and extensibility. In fact, experience gained during several small to mid-sized experiments that are presented in this thesis suggest that the SALMA approach seems to be able to live up to these expectations.In dieser Dissertation wird SALMA (Simulation and Analysis of Logic-Based Multi-Agent Models) vorgestellt, ein im Rahmen dieser Arbeit entwickelter Ansatz für die Simulation und die statistische Modellprüfung (Model Checking) von Multiagentensystemen. Der Begriff „Statistisches Model Checking” beschreibt modellbasierte approximative Verifikationsmethoden, die insbesondere dazu eingesetzt werden können, um den unvermeidlichen Skalierbarkeitsproblemen von exakten Methoden zu entgehen. Im Gegensatz zu bisherigen Ansätzen werden in SALMA die Mechanismen des simulierten Systems mithilfe logischer Axiome beschrieben, die auf dem etablierten Situationskalkül aufbauen. Die dadurch entstehende prädikatenlogische Struktur des Systemmodells wird ausgenutzt um ein Model Checking Modul zu integrieren, das seinerseits eine prädikatenlogische Variante der linearen temporalen Logik (LTL) verwendet. In Kombination mit einer prozeduralen und prozessorientierten Sprache für die Beschreibung von Agentenverhalten entsteht eine ausdrucksstarke und flexible Plattform für die Modellierung und Verifikation von Multiagentensystemen. Sie ermöglicht eine direkte und feingranulare Beschreibung der Interaktionen sowohl zwischen Agenten als auch von Agenten mit ihrer (physischen) Umgebung. SALMA erweitert den klassischen Situationskalkül und die lineare temporale Logik (LTL) um Elemente und Konzepte, die auf die spezifischen Anforderungen bei der Simulation und Modellierung von Multiagentensystemen ausgelegt sind. Insbesondere werden cyber-physische Systeme (CPS) unterstützt, in denen Agenten mit ihrer physischen Umgebung interagieren. Unter anderem wird eine generische, auf dem Situationskalkül basierende, Axiomatisierung von Prozessen beschrieben, in denen Informationen innerhalb von Multiagentensystemen transferiert werden – beispielsweise in Form von Sensor- Messwerten oder Netzwerkpaketen. Dabei werden ausdrücklich die unvermeidbaren stochastischen Effekte und Echtzeitanforderungen in cyber-physischen Systemen berücksichtigt. Um statistisches Model Checking mit SALMA auch für komplexere Problemstellungen zu ermöglichen, wurde ein Mechanismus für die effiziente Auswertung von prädikatenlogischen LTL-Formeln entwickelt. Insbesondere beinhaltet der vorgestellte Algorithmus eine Intervall-basierte Repräsentation des Auswertungszustands, sowie einige andere Optimierungsansätze zur Vermeidung von unnötigen Berechnungsschritten. Insgesamt war es das Ziel dieser Dissertation, eine Lösung für Simulation und statistisches Model Checking zu schaffen, die einerseits auf fundierten logischen und statistischen Grundlagen aufbaut, auf der anderen Seite jedoch auch pragmatischen Gesichtspunkten wie Benutzbarkeit oder Erweiterbarkeit genügt. Tatsächlich legen erste Ergebnisse und Erfahrungen aus mehreren kleinen bis mittelgroßen Experimenten nahe, dass SALMA diesen Zielen gerecht wird

Beyond shared memory loop parallelism in the polyhedral model

2013 Spring.Includes bibliographical references.With the introduction of multi-core processors, motivated by power and energy concerns, parallel processing has become main-stream. Parallel programming is much more difficult due to its non-deterministic nature, and because of parallel programming bugs that arise from non-determinacy. One solution is automatic parallelization, where it is entirely up to the compiler to efficiently parallelize sequential programs. However, automatic parallelization is very difficult, and only a handful of successful techniques are available, even after decades of research. Automatic parallelization for distributed memory architectures is even more problematic in that it requires explicit handling of data partitioning and communication. Since data must be partitioned among multiple nodes that do not share memory, the original memory allocation of sequential programs cannot be directly used. One of the main contributions of this dissertation is the development of techniques for generating distributed memory parallel code with parametric tiling. Our approach builds on important contributions to the polyhedral model, a mathematical framework for reasoning about program transformations. We show that many affine control programs can be uniformized only with simple techniques. Being able to assume uniform dependences significantly simplifies distributed memory code generation, and also enables parametric tiling. Our approach implemented in the AlphaZ system, a system for prototyping analyses, transformations, and code generators in the polyhedral model. The key features of AlphaZ are memory re-allocation, and explicit representation of reductions. We evaluate our approach on a collection of polyhedral kernels from the PolyBench suite, and show that our approach scales as well as PLuTo, a state-of-the-art shared memory automatic parallelizer using the polyhedral model. Automatic parallelization is only one approach to dealing with the non-deterministic nature of parallel programming that leaves the difficulty entirely to the compiler. Another approach is to develop novel parallel programming languages. These languages, such as X10, aim to provide highly productive parallel programming environment by including parallelism into the language design. However, even in these languages, parallel bugs remain to be an important issue that hinders programmer productivity. Another contribution of this dissertation is to extend the array dataflow analysis to handle a subset of X10 programs. We apply the result of dataflow analysis to statically guarantee determinism. Providing static guarantees can significantly increase programmer productivity by catching questionable implementations at compile-time, or even while programming

Simulation and statistical model-checking of logic-based multi-agent system models

This thesis presents SALMA (Simulation and Analysis of Logic-Based Multi- Agent Models), a new approach for simulation and statistical model checking of multi-agent system models. Statistical model checking is a relatively new branch of model-based approximative verification methods that help to overcome the well-known scalability problems of exact model checking. In contrast to existing solutions, SALMA specifies the mechanisms of the simulated system by means of logical axioms based upon the well-established situation calculus. Leveraging the resulting first-order logic structure of the system model, the simulation is coupled with a statistical model-checker that uses a first-order variant of time-bounded linear temporal logic (LTL) for describing properties. This is combined with a procedural and process-based language for describing agent behavior. Together, these parts create a very expressive framework for modeling and verification that allows direct fine-grained reasoning about the agents’ interaction with each other and with their (physical) environment. SALMA extends the classical situation calculus and linear temporal logic (LTL) with means to address the specific requirements of multi-agent simulation models. In particular, cyber-physical domains are considered where the agents interact with their physical environment. Among other things, the thesis describes a generic situation calculus axiomatization that encompasses sensing and information transfer in multi agent systems, for instance sensor measurements or inter-agent messages. The proposed model explicitly accounts for real-time constraints and stochastic effects that are inevitable in cyber-physical systems. In order to make SALMA’s statistical model checking facilities usable also for more complex problems, a mechanism for the efficient on-the-fly evaluation of first-order LTL properties was developed. In particular, the presented algorithm uses an interval-based representation of the formula evaluation state together with several other optimization techniques to avoid unnecessary computation. Altogether, the goal of this thesis was to create an approach for simulation and statistical model checking of multi-agent systems that builds upon well-proven logical and statistical foundations, but at the same time takes a pragmatic software engineering perspective that considers factors like usability, scalability, and extensibility. In fact, experience gained during several small to mid-sized experiments that are presented in this thesis suggest that the SALMA approach seems to be able to live up to these expectations.In dieser Dissertation wird SALMA (Simulation and Analysis of Logic-Based Multi-Agent Models) vorgestellt, ein im Rahmen dieser Arbeit entwickelter Ansatz für die Simulation und die statistische Modellprüfung (Model Checking) von Multiagentensystemen. Der Begriff „Statistisches Model Checking” beschreibt modellbasierte approximative Verifikationsmethoden, die insbesondere dazu eingesetzt werden können, um den unvermeidlichen Skalierbarkeitsproblemen von exakten Methoden zu entgehen. Im Gegensatz zu bisherigen Ansätzen werden in SALMA die Mechanismen des simulierten Systems mithilfe logischer Axiome beschrieben, die auf dem etablierten Situationskalkül aufbauen. Die dadurch entstehende prädikatenlogische Struktur des Systemmodells wird ausgenutzt um ein Model Checking Modul zu integrieren, das seinerseits eine prädikatenlogische Variante der linearen temporalen Logik (LTL) verwendet. In Kombination mit einer prozeduralen und prozessorientierten Sprache für die Beschreibung von Agentenverhalten entsteht eine ausdrucksstarke und flexible Plattform für die Modellierung und Verifikation von Multiagentensystemen. Sie ermöglicht eine direkte und feingranulare Beschreibung der Interaktionen sowohl zwischen Agenten als auch von Agenten mit ihrer (physischen) Umgebung. SALMA erweitert den klassischen Situationskalkül und die lineare temporale Logik (LTL) um Elemente und Konzepte, die auf die spezifischen Anforderungen bei der Simulation und Modellierung von Multiagentensystemen ausgelegt sind. Insbesondere werden cyber-physische Systeme (CPS) unterstützt, in denen Agenten mit ihrer physischen Umgebung interagieren. Unter anderem wird eine generische, auf dem Situationskalkül basierende, Axiomatisierung von Prozessen beschrieben, in denen Informationen innerhalb von Multiagentensystemen transferiert werden – beispielsweise in Form von Sensor- Messwerten oder Netzwerkpaketen. Dabei werden ausdrücklich die unvermeidbaren stochastischen Effekte und Echtzeitanforderungen in cyber-physischen Systemen berücksichtigt. Um statistisches Model Checking mit SALMA auch für komplexere Problemstellungen zu ermöglichen, wurde ein Mechanismus für die effiziente Auswertung von prädikatenlogischen LTL-Formeln entwickelt. Insbesondere beinhaltet der vorgestellte Algorithmus eine Intervall-basierte Repräsentation des Auswertungszustands, sowie einige andere Optimierungsansätze zur Vermeidung von unnötigen Berechnungsschritten. Insgesamt war es das Ziel dieser Dissertation, eine Lösung für Simulation und statistisches Model Checking zu schaffen, die einerseits auf fundierten logischen und statistischen Grundlagen aufbaut, auf der anderen Seite jedoch auch pragmatischen Gesichtspunkten wie Benutzbarkeit oder Erweiterbarkeit genügt. Tatsächlich legen erste Ergebnisse und Erfahrungen aus mehreren kleinen bis mittelgroßen Experimenten nahe, dass SALMA diesen Zielen gerecht wird

Tools and Algorithms for the Construction and Analysis of Systems

This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems

Tools and Algorithms for the Construction and Analysis of Systems

This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems

Tools and Algorithms for the Construction and Analysis of Systems

This open access two-volume set constitutes the proceedings of the 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2021, which was held during March 27 – April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The total of 41 full papers presented in the proceedings was carefully reviewed and selected from 141 submissions. The volume also contains 7 tool papers; 6 Tool Demo papers, 9 SV-Comp Competition Papers. The papers are organized in topical sections as follows: Part I: Game Theory; SMT Verification; Probabilities; Timed Systems; Neural Networks; Analysis of Network Communication. Part II: Verification Techniques (not SMT); Case Studies; Proof Generation/Validation; Tool Papers; Tool Demo Papers; SV-Comp Tool Competition Papers