We consider a general statistical linear inverse problem, where the solution is represented via a known (possibly overcomplete) dictionary that allows its sparse representation. We propose two different approaches. A model selection estimator selects a single model by minimizing the penalized empirical risk over all possible models. By contrast with direct problems, the penalty depends on the model itself rather than on its size only as for complexity penalties. A Q-aggregate estimator averages over the entire collection of estimators with properly chosen weights.

The knowledge production provided by universities is essential to sustaining a country's long-term economic growth and international competitiveness. Many nations are thus driving to create sustainable and effective funding environments. The evaluation of university knowledge, productivity and research quality becomes critical, with ever increasing share of public funding allocated on the basis of research assessment exercises.

We deal with the mathematical model of the incremental degradation of the internal coating (e.g. a polymeric material) of a metallic pipe in which a fluid flows relatively fast. The fluid drags solid impurities so that longitudinal scratches, inaccessible to any direct inspection procedure, are produced on the coating. Time evolution of this kind of defects can be reconstructed from the knowledge of a sequence of temperature maps of the external surface.

Traffic data, automatically collected en masse every day, can be mined to discover information or patterns to support police investigations. Leveraging on domain expertise, in this paper we show how unsupervised clustering techniques can be used to infer trending behaviors for road-users and thus classify both routes and vehicles. We describe a tool devised and implemented upon openly-available scientific libraries and we present a new set of experiments involving three years worth data.

The rapid development of cyber insurance market brings forward the question about the effect of cyber insurance on cyber security. Some researchers believe that the effect should be positive as organisations will be forced to maintain a high level of security in order to pay lower premiums. On the other hand, other researchers conduct a theoretical analysis and demonstrate that availability of cyber insurance may result in lower investments in security. In this paper we propose a mathematical analysis of a cyber-insurance model in a non-competitive market.

As discussed in the recent literature, several innovative car insurance concepts are proposed in order to gain advantages both for insurance companies and for drivers. In this context, the "pay-how-you-drive" paradigm is emerging, but it is not thoroughly discussed and much less implemented. In this paper, we propose an approach in order to identify the driver behavior exploring the usage of unsupervised machine learning techniques. A real-world case study is performed to evaluate the effectiveness of the proposed solution.

Improving strategies for the control and eradication of invasive species is an important aspect of nature conservation, an aspect where mathematical modeling and optimization play an important role. In this paper, we introduce a reaction-diffusion partial differential equation to model the spatiotemporal dynamics of an invasive species, and we use optimal control theory to solve for optimal management, while implementing a budget constraint. We perform an analytical study of the model properties, including the well-posedness of the problem.

We present an extension of recent relativistic Lattice Boltzmann methods based on Gaussian quadratures for the study of fluids in (2+1) dimensions. The new method is applied to the analysis of electron flow in graphene samples subject to electrostatic drive; we show that the flow displays hydro-electronic whirlpools in accordance with recent analytical calculations as well as experimental results.