Deriving Inverse Operators for Modal Logic

International audienceSpatial constraint systems are algebraic structures from concurrent constraint programming to specify spatial and epistemic behavior in multi-agent systems. We shall use spatial constraint systems to give an abstract characterization of the notion of normality in modal logic and to derive right inverse/reverse operators for modal languages. In particular, we shall identify the weakest condition for the existence of right inverses and show that the abstract notion of normality corresponds to the preservation of finite suprema. We shall apply our results to existing modal languages such as the weakest normal modal logic, Hennessy-Milner logic, and linear-time temporal logic. We shall discuss our results in the context of modal concepts such as bisimilarity and inconsistency invariance

TkT: Automatic Inference of Timed and Extended Pushdown Automata

To mitigate the cost of manually producing and maintaining models capturing software specifications, specification mining techniques can be exploited to automatically derive up-to-date models that faithfully represent the behavior of software systems. So far, specification mining solutions focused on extracting information about the functional behavior of the system, especially in the form of models that represent the ordering of the operations. Well-known examples are finite state models capturing the usage protocol of software interfaces and temporal rules specifying relations among system events. Although the functional behavior of a software system is a primary aspect of concern, there are several other non-functional characteristics that must be typically addressed jointly with the functional behavior of a software system. Efficiency is one of the most relevant characteristics. In fact, an application delivering the right functionalities inefficiently has a big chance to not satisfy the expectation of its users. Interestingly, the timing behavior is strongly dependent on the functional behavior of a software system. For instance, the timing of an operation depends on the functional complexity and size of the computation that is performed. Consequently, models that combine the functional and timing behaviors, as well as their dependencies, are extremely important to precisely reason on the behavior of software systems. In this paper, we address the challenge of generating models that capture both the functional and timing behavior of a software system from execution traces. The result is the Timed k-Tail (TkT) specification mining technique, which can mine finite state models that capture such an interplay: the functional behavior is represented by the possible order of the events accepted by the transitions, while the timing behavior is represented through clocks and clock constraints of different nature associated with transitions. Our empirical evaluation with several libraries and applications show that TkT can generate accurate models, capable of supporting the identification of timing anomalies due to overloaded environment and performance faults. Furthermore, our study shows that TkT outperforms state-of-the-art techniques in terms of scalability and accuracy of the mined models

Metabolomics in Parkinson's disease

Parkinson ’ s disease (PD) is a multifactorial neurodegenerative disorder in which environmental (lifestyle, dietary, infectious disease) factors as well as genetic make-up play a role. Metabolomics, an evolving research field combining biomarker discovery and pathogenetics, is particularly useful in studying complex pathophysiology in general and Parkinson ’ s disease (PD) specifically. PD, the second most frequent neurodegenerative disorder, is characterized by the loss of dopaminergic neurons in the substantia nigra and the presence of intraneural inclusions of α -synuclein aggregates. Although considered a predominantly movement disorder, PD is also associated with number of non-motor features. Metabolomics has provided useful information regarding this neurodegenerative process with the aim of identifying a disease-specific fingerprint. Unfortunately, many disease variables such as clinical presentation, motor system involvement, disease stage and duration substantially affect biomarker relevance. As such, metabolomics provides a unique approach to studying this multifactorial neurodegenerative disorder