A stochastic estimated version of the Italian dynamic General Equilibrium Model (IGEM)

We estimate with Bayesian techniques the Italian dynamic General Equilibrium Model (IGEM), which has been developed at the Italian Treasury Department, Ministry of Economy and Finance, to assess the effects of alter-native policy interventions. We analyze and discuss the estimated effects of various shocks on the Italian economy. Compared to the calibrated version used for policy analysis, we find a lower wage rigidity and higher adjustment costs. The degree of prices and wages indexation to past inflation is much smaller than the indexation level assumed in the calibrated model. No substantial difference is found in the estimated monetary parameters. Estimated fiscal multipliers are slightly smaller than those obtained from the calibrated version of the model

The development of monoclonal antibodies to IFITM1 protein using scFv-phage display libraries

BACKGROUND AND AIMS: Immunotherapy using monoclonal antibodies has been proven to be highly effective in the treatment of a variety of cancers and studies are still focusing on the development of new antibodies and new therapeutic combinations involving them. The role that the IFITM1 protein plays in cancers remains unclear, however, the protein has been associated with poor prognosis and progression of many types of cancers and its attenuation leads to the inhibition of growth, invasion and migration of the tumour cells. The IFITM1 protein is a transmembrane receptor whose conformations on the plasma membrane is known to be regulated by interferon signalling. The project examines the use of oligomeric isoforms of IFITM1 protein to isolate novel scFv antibodies from antibody phage libraries. The aim is to identify epitopes specific for the monomer and oligomers of interest that can be used to better understand the role of IFITM1 in cancer and also generate a potential therapeutic agent based on agonist or antagonist actions. Hypothesis: can monoclonal antibodies be designed for all three of the IFITM1 oligo-forms to study conformational changes in human cancer cells? METHODS: Monomeric and oligomeric IFITM1 proteins, purified from a bacterial paste were tested for denaturing resistance in order to confirm preliminary data and annotate the conformationally variants produced bacteria. A naïve phage antibody display library was used in a biopanning process against the purified fractions of the IFITM1 protein. The specificity of the selected clones was assessed by phage ELISA. RESULTS: Western blot results confirm preliminary data defining the purified monomers and oligomers as stable forms of IFITM1 protein. The biopanning rounds showed positive enrichment of the specific epitopes. ELISA assays confirmed binding specificity of the selected clones in both IFITM1 and its purified oligomeric species with some of the isolated scFvs exhibiting high specificity and reactivity. The overall aim of the thesis to develop isoform specific antibodies to the IFITM1 protein was achieved. This will allow further validation of recombinant biologics to the IFITM1 pathway to shed light on IFITM1 functions both in viral and cancer settings

Partial containment control over signed graphs

In this paper, we deal with the containment control problem in presence of antagonistic interactions. In particular, we focus on the cases in which it is not possible to contain the entire network due to a constrained number of control signals. In this scenario, we study the problem of selecting the nodes where control signals have to be injected to maximize the number of contained nodes. Leveraging graph condensations, we find a suboptimal and computationally efficient solution to this problem, which can be implemented by solving an integer linear problem. The effectiveness of the selection strategy is illustrated through representative simulations

Steering opinion dynamics via containment control

In this paper, we model the problem of influencing the opinions of groups of individuals as a containment control problem, as in many practical scenarios, the control goal is not full consensus among all the individual opinions, but rather their containment in a certain range, determined by a set of leaders. As in classical bounded confidence models, we consider individuals affected by the confirmation bias, thus tending to influence and to be influenced only if their opinions are sufficiently close. However, here we assume that the confidence level, modeled as a proximity threshold, is not constant and uniform across the individuals, as it depends on their opinions. Specifically, in an extremist society, the most radical agents (i.e., those with the most extreme opinions) have a higher appeal and are capable of influencing nodes with very diverse opinions. The opposite happens in a moderate society, where the more connected (i.e., influential) nodes are those with an average opinion. In three artificial societies, characterized by different levels of extremism, we test through extensive simulations the effectiveness of three alternative containment strategies, where leaders have to select the set of followers they try to directly influence. We found that, when the network size is small, a stochastic time-varying pinning strategy that does not rely on information on the network topology proves to be more effective than static strategies where this information is leveraged, while the opposite happens for large networks where the relevance of the topological information is prevalent

The evolving cobweb of relations among partially rational investors

To overcome the limitations of neoclassical economics, researchers have leveraged tools of statistical physics to build novel theories. The idea was to elucidate the macroscopic features of financial markets from the interaction of its microscopic constituents, the investors. In this framework, the model of the financial agents has been kept separate from that of their interaction. Here, instead, we explore the possibility of letting the interaction topology emerge from the model of the agents' behavior. Then, we investigate how the emerging cobweb of relationship affects the overall market dynamics. To this aim, we leverage tools from complex systems analysis and nonlinear dynamics, and model the network of mutual influence as the output of a dynamical system describing the edge evolution. In this work, the driver of the link evolution is the relative reputation between possibly coupled agents. The reputation is built differently depending on the extent of rationality of the investors. The continuous edge activation or deactivation induces the emergence of leaders and of peculiar network structures, typical of real influence networks. The subsequent impact on the market dynamics is investigated through extensive numerical simulations in selected scenarios populated by partially rational investors

Overconfident agents and evolving financial networks

In this paper, we investigate the impact of agent personality on the complex dynamics taking place in financial markets. Leveraging recent findings, we model the artificial financial market as a complex evolving network: we consider discrete dynamics for the node state variables, which are updated at each trading session, while the edge state variables, which define a network of mutual influence, evolve continuously with time. This evolution depends on the way the agents rank their trading abilities in the network. By means of extensive numerical simulations in selected scenarios, we shed light on the role of overconfident agents in shaping the emerging network topology, thus impacting on the overall market dynamics

Categorical Foundations of Explainable AI

Explainable AI (XAI) aims to address the human need for safe and reliable AI systems. However, numerous surveys emphasize the absence of a sound mathematical formalization of key XAI notions -- remarkably including the term ``\textit{explanation}'' which still lacks a precise definition. To bridge this gap, this paper presents the first mathematically rigorous definitions of key XAI notions and processes, using the well-funded formalism of Category theory. We show that our categorical framework allows to: (i) model existing learning schemes and architectures, (ii) formally define the term ``explanation'', (iii) establish a theoretical basis for XAI taxonomies, and (iv) analyze commonly overlooked aspects of explaining methods. As a consequence, our categorical framework promotes the ethical and secure deployment of AI technologies as it represents a significant step towards a sound theoretical foundation of explainable AI

A wearable sensor to monitor localized sweat rate as support tool for monitoring athletes' performances

Objectives We developed a wearable sensor for the real time measurement of sweat rate in localized areas of the human body. This sensor represents the first step in the development of a wearable sensor network capable to estimate the global sweat rate via an ad hoc algorithm. Such device would be used to monitor athletes' hydration status during training and improve their performances. Equipment and Methods For this study, we tested our sensor on thirteen football players during a cycling test on a cycle ergometer. The sweat rate sensor was compared to a medical device that, although measuring a different physiological process, provides discrete data based on the same working principle, i.e. the diffusion of the water vapour emitted from the skin. Results Our sensor has a working range up to 400 g/m2·h. The statistical analysis and the Bland-Altman plot proved that our sensor is comparable to the medical device used as gold standard. At low sweat rate, the bias is 3.4 g/m2·h with a standard deviation of 7.6 g/m2·h. At maximum sweat rates, the bias is 2.3 g/m2·h with a standard deviation 6.9 g/m2·h. The p values for the Bland-Altman plots at low and maximum sweat rate (0.1331 and 0.2477 obtained by Kolmogorov-Smirnov test, respectively) allow the hypothesis that there is a significant difference between our sweat rate sensor and the medical device to be rejected. Conclusion We presented a prototype of a wearable sweat rate sensor for localized measurements. The trials on thirteen athletes proved that the performance of our sensor is comparable to that of a commercial medical device. This sweat rate sensor can provide valuable information on athletes' hydration status