Patient Information Leaflets (PILs) provide essential information about medication usage, side effects, precautions, and interactions, making them a valuable resource for Question Answering (QA) systems in healthcare. However, no dedicated benchmark currently exists to evaluate QA systems specifically on PILs, limiting progress in this domain. To address this gap, we introduce a fact-supported synthetic benchmark composed of multiple-choice questions and answers generated from real PILs. We construct the benchmark using a fully automated pipeline that leverages multiple Large Language Models (LLMs) to generate diverse, realistic, and contextually relevant question-answer pairs. The benchmark is publicly released as a standardized evaluation framework for assessing the ability of LLMs to process and reason over PIL content. To validate its effectiveness, we conduct an initial evaluation with state-of-the-art LLMs, showing that the benchmark presents a realistic and challenging task, making it a valuable resource for advancing QA research in the healthcare domain.

Context aware computing is a computational paradigm that has faced a rapid growth in the last few years, especially in the field of mobile devices. One of the promises of context-awareness in this field is the possibility of automatically adapting the functioning mode of mobile devices to the environment and the current situation the user is in, with the aim of improving both their efficiency (using the scarce resources in a more efficient way) and effectiveness (providing better services to the user). We propose a novel approach for providing a basic infrastructure for context-aware applications on mobile devices, in which AI techniques (namely a principled combination of rule-based systems, Bayesian networks, and ontologies) are applied to context inference. The aim is to devise a general inferential framework to easier the development of context-aware applications by integrating the information coming from physical and logical sensors (e.g., position, agenda) and reasoning about this information in order to infer new and more abstract contexts. In previous contextaware applications, most researches focused almost exclusively on time and/or location and other few data, while the same contexts inference was limited to preconceived values. Our approach differs from previous works since we do not focus on particular contextual values, but rather we have developed an architecture where managed contexts can be easily replaced by new contexts, depending on the different needs. Moreover, the inferential infrastructure we designed is able to work in a more general way and can be easily adapted to different models of applications distribution. We show some concrete examples of applications built upon the inferential infrastructure and we discuss its strengths and limitations.

The so-called light logics have been introduced as logical systems enjoying quite remarkable normalization properties. Designing a type assignment system for pure lambda calculus from these logics, however, is problematic. In this paper we show that shifting from usual call-by-name to call-by-value lambda calculus allows regaining strong connections with the underlying logic. This will be done in the context of Elementary Affine Logic (EAL), designing a type system in natural deduction style assigning EAL formulae to lambda terms.

Typing of lambda-terms in elementary and light affine logic (EAL , LAL resp.) has been studied for two different reasons: on the one hand the evaluation of typed terms using LAL (EAL resp.) proof-nets admits a guaranteed polynomial (elementary, resp.) bound; on the other hand these terms can also be evaluated by optimal reduction using the abstract version of Lamping's algorithm. The first reduction is global while the second one is local and asynchronous. We prove that for LAL (EAL resp.) typed terms, Lamping's abstract algorithm also admits a polynomial (elementary, resp.) bound. We also show its soundness and completeness (for EAL and LAL with type fixpoints), by using a simple geometry of interaction model (context semantics).

We propose a type inference algorithm for lambda terms in elementary affine logic (EAL). The algorithm decorates the syntax tree of a simple typed lambda term and collects a set of linear constraints. The result is a parametric elementary type that can be instantiated with any solution of the set of collected constraints.We point out that the typeability of lambda terms in EAL has a practical counterpart, since it is possible to reduce any EAL-typeable lambda terms with the Lamping's abstract algorithm obtaining a substantial increase of performances.We show how to apply the same techniques to obtain decorations of intuitionistic proofs into linear logic proofs.

In 1998 Asperti and Mairson proved that the cost of reducing a lambda-term using an optimal lambda-reducer (a la Lévy) cannot be bound by any elementary function in the number of shared-beta steps. We prove in this paper that an analogous result holds for Lamping’s abstract algorithm. That is, there is no elementary function in the number of shared beta steps bounding the number of duplication steps of the optimal reducer. This theorem vindicates the oracle of Lamping’s algorithm as the culprit for the negative result of Asperti and Mairson. The result is obtained using as a technical tool Elementary Affine Logic. Key words: complexity, elementary affine logic, graph rewriting, optimal reduction