Extending the theory of Owicki and Gries with asynchronous message passing

We describe an extension of the theory of Owicki and Gries (1976) to a programming language that supports asynchronous message passing based on unconditional send actions and conditional receive actions. The focus is on exploring the fitness of the extension for distributed program derivation. A number of experiments are reported, based on a running example problem, and with the aim of exploring design heuristics and of streamlining derivations and progress arguments.

Linguistic determinism and theory of mind in deaf children

DeVilliers and DeVilliers (2000, 2005) propose that deaf and hearing children acquire a theory of mind (or the understanding that human behaviour is the product of psychological states like true and false beliefs) as a consequence of their linguistic mastery of a rule of syntax. Specifically, they argue that the syntactic rule for sentential complementation with verbs of speech (e.g., “say”) precedes syntactic mastery of complementation for cognition (e.g., “think”) and both of these developmentally precede and promote conceptual mastery of a theory of mind (ToM), as indexed via success on standard false belief tests. The present study examined this proposition in groups of primary-school-aged deaf children and hearing preschoolers who took false belief tests and a modified memory for complements test that included control questions. Guttman scaling techniques indicated no support either for the prediction that syntactic skill precedes ToM understanding or for the earlier emergence of complementation for “say” than for “think”. Methodological issues and implications for deaf children's ToM development are discussed.

Asymptotic theory of time varying networks with memory and heterogeneous activation pattern

In this thesis work we develop a new generative model of social networks belonging to the family of Time Varying Networks. The importance of correctly modelling the mechanisms shaping the growth of a network and the dynamics of the edges activation and inactivation are of central importance in network science. Indeed, by means of generative models that mimic the real-world dynamics of contacts in social networks it is possible to forecast the outcome of an epidemic process, optimize the immunization campaign or optimally spread an information among individuals. This task can now be tackled taking advantage of the recent availability of large-scale, high-quality and time-resolved datasets. This wealth of digital data has allowed to deepen our understanding of the structure and properties of many real-world networks. Moreover, the empirical evidence of a temporal dimension in networks prompted the switch of paradigm from a static representation of graphs to a time varying one. In this work we exploit the Activity-Driven paradigm (a modeling tool belonging to the family of Time-Varying-Networks) to develop a general dynamical model that encodes fundamental mechanism shaping the social networks' topology and its temporal structure: social capital allocation and burstiness. The former accounts for the fact that individuals does not randomly invest their time and social interactions but they rather allocate it toward already known nodes of the network. The latter accounts for the heavy-tailed distributions of the inter-event time in social networks. We then empirically measure the properties of these two mechanisms from seven real-world datasets and develop a data-driven model, analytically solving it. We then check the results against numerical simulations and test our predictions with real-world datasets, finding a good agreement between the two. Moreover, we find and characterize a non-trivial interplay between burstiness and social capital allocation in the parameters phase space. Finally, we present a novel approach to the development of a complete generative model of Time-Varying-Networks. This model is inspired by the Kaufman's adjacent possible theory and is based on a generalized version of the Polya's urn. Remarkably, most of the complex and heterogeneous feature of real-world social networks are naturally reproduced by this dynamical model, together with many high-order topological properties (clustering coefficient, community structure etc.).