Persistence of Politicians and Firms'Innovation

We empirically investigate whether the persistence of politicians in political institutions affects the innovation activity of firms. We use 12,000 firm-level observations from three waves of the Italian Observatory over Small and Medium Enterprises, and introduce a measure of political persistence defined as the average length of individual political careers in political institutions of Italian municipalities. Standard OLS shows no raw correlation between political persistence and firms' innovation activity. However, once the causal effect is isolated by means of instrumental variables, using death of politicians as an exogenous source of variation of political persistence, we find a robust negative relation between political persistence and the probability of process innovation. This finding is consistent with the view that political stability may hinder firms' incentive to innovate to maintain their competitiveness, as long as they can extract rents from long-term connections with politicians.

Transmission Power Measurements for Wireless Sensor Nodes and their Relationship to the Battery Level

In this work we focus on the new generation EYESIFXv2 [1] wireless sensor nodes by carrying out experimental measurements on power related quantities. In particular, our aim is to characterize the relationship between the level of the battery and the transmission power radiated by the node. The present results point out the non linear and non trivial effects due to the output potentiometer which can be used to tune the transmission power. It shall be observed that a thorough study of how battery and/or potentiometer settings translate to actual transmitted power levels is crucial to e.g. design correct power control algorithms, which can effectively operate under any operational condition of the wireless sensor device

Sets with constant normal in Carnot groups: properties and examples

We analyze subsets of Carnot groups that have intrinsic constant normal, as they appear in the blowup study of sets that have finite subRiemannian perimeter. The purpose of this paper is threefold. First, we prove some mild regularity and structural results in arbitrary Carnot groups. Namely, we show that for every constant-normal set in a Carnot group its subRiemannian-Lebesgue representative is regularly open, contractible, and its topological boundary coincides with the reduced boundary and with the measure-theoretic boundary. We infer these properties from a metric cone property. Such a cone will be a semisubgroup with nonempty interior that is canonically associated with the normal direction. We characterize the constant-normal sets exactly as those that are arbitrary unions of translations of such semisubgroups. Second, making use of such a characterization, we provide some pathological examples in the specific case of the free-Carnot group of step 3 and rank 2. Namely, we construct a constant normal set that, with respect to any Riemannian metric, is not of locally finite perimeter; we also construct an example with non-unique intrinsic blowup at some point, showing that it has different upper and lower subRiemannian density at the origin. Third, we show that in Carnot groups of step 4 or less, every constant-normal set is intrinsically rectifiable, in the sense of Franchi, Serapioni, and Serra Cassano

Online Model-Based Testing under Uncertainty

Modern software systems are required to operate in a highly uncertain and changing environment. They have to control the satisfaction of their requirements at run-time, and possibly adapt and cope with situations that have not been completely addressed at design-time. Software engineering methods and techniques are, more than ever, forced to deal with change and uncertainty (lack of knowledge) explicitly. For tackling the challenge posed by uncertainty in delivering more reliable systems, this paper proposes a novel online Model-based Testing technique that complements classic test case generation based on pseudo-random sampling strategies with an uncertainty-aware sampling strategy. To deal with system uncertainty during testing, the proposed strategy builds on an Inverse Uncertainty Quantification approach that is related to the discrepancy between the measured data at run-time (while the system executes) and a Markov Decision Process model describing the behavior of the system under test. To this purpose, a conformance game approach is adopted in which tests feed a Bayesian inference calibrator that continuously learns from test data to tune the system model and the system itself. A comparative evaluation between the proposed uncertainty-aware sampling policy and classical pseudo-random sampling policies is also presented using the Tele Assistance System running example, showing the differences in achieved accuracy and efficiency

Situated Learning with Bebras Tasklets

A Bebras short task, a tasklet, is designed to provide a source for exploring a computational thinking concept: at the end of the contest it could be used as a starting point to delve deeper into a computing topic. In this paper we report an experience which aims at taking full advantage of the potential of Bebras tasklets. A math teacher asked her pupils to act as Bebras \u201ctrainers\u201d for younger mates. The pupils, in pairs, were assigned to design and prepare a tangible game inspired by a Bebras tasklet, devised for the younger pupils to practice. They also had to explain the game to the younger pupils, make them play and support them in solving it. In carrying out this assignment the pupils acting as trainers had to deeply explore the Bebras tasklet and face its computational thinking challenge, and also practiced soft skills as collaborating with peers towards a common goal, adapting language and communicative style to engage with younger mates, devising and designing a tangible object, and planning its creation. The experience proved that using Bebras tasklets as the social and cultural context for situated learning of computational thinking competencies is indeed quite productive