As the amount of biological data in the public domain grows, so does the range of modeling
and analysis techniques employed in systems biology. In recent years, a number of …

Machine learning (ML), a branch of artificial intelligence, where machines learn from big
data, is at the crest of a technological wave of change swee** society. Cardiovascular …

Abstract The term Cyber-Physical Systems (CPS) typically refers to engineered, physical
and biological systems monitored and/or controlled by an embedded computational core …

This paper introduces a framework for inference of timed temporal logic properties from data.
The dataset is given as a finite set of pairs of finite-time system traces and labels, where the …

Networked dynamical systems are increasingly used as models for a variety of processes
ranging from robotic teams to collections of genetically engineered living cells. As the …

In this article, we focus on inferring high-level descriptions of a system from its execution
traces. Specifically, we consider a classification problem where system behaviors are …

We present a novel approach to learn logical formulae characterising the emergent
behaviour of a dynamical system from system observations. At a high level, the approach …

Recent research on spatial and spatio-temporal model checking provides novel image
analysis methodologies, rooted in logical methods for topological spaces. Medical imaging …

Cyber-Physical Systems (CPS) consist of collaborative, networked and tightly intertwined
computational (logical) and physical components, each operating at different spatial and …

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 …