Neural Networks retrieving Boolean patterns in a sea of Gaussian ones

Restricted Boltzmann Machines are key tools in Machine Learning and are described by the energy function of bipartite spin-glasses. From a statistical mechanical perspective, they share the same Gibbs measure of Hopfield networks for associative memory. In this equivalence, weights in the former play as patterns in the latter. As Boltzmann machines usually require real weights to be trained with gradient descent like methods, while Hopfield networks typically store binary patterns to be able to retrieve, the investigation of a mixed Hebbian network, equipped with both real (e.g., Gaussian) and discrete (e.g., Boolean) patterns naturally arises. We prove that, in the challenging regime of a high storage of real patterns, where retrieval is forbidden, an extra load of Boolean patterns can still be retrieved, as long as the ratio among the overall load and the network size does not exceed a critical threshold, that turns out to be the same of the standard Amit-Gutfreund-Sompolinsky theory. Assuming replica symmetry, we study the case of a low load of Boolean patterns combining the stochastic stability and Hamilton-Jacobi interpolating techniques. The result can be extended to the high load by a non rigorous but standard replica computation argument.Comment: 16 pages, 1 figur

Evaluating the Empirical Performance of Alternative Econometric Models for Oil Price Forecasting

The relevance of oil in the world economy explains why considerable effort has been devoted to the development of different types of econometric models for oil price forecasting. Several specifications have been proposed in the economic literature. Some are based on financial theory and concentrate on the relationship between spot and futures prices (“financial” models). Others assign a key role to variables explaining the characteristics of the physical oil market (“structural” models). The empirical literature is very far from any consensus about the appropriate model for oil price forecasting that should be implemented. Relative to the previous literature, this paper is novel in several respects. First of all, we test and systematically evaluate the ability of several alternative econometric specifications proposed in the literature to capture the dynamics of oil prices. Second, we analyse the effects of different data frequencies on the coefficient estimates and forecasts obtained using each selected econometric specification. Third, we compare different models at different data frequencies on a common sample and common data. Fourth, we evaluate the forecasting performance of each selected model using static and dynamic forecasts, as well as different measures of forecast errors. Finally, we propose a new class of models which combine the relevant aspects of the financial and structural specifications proposed in the literature (“mixed” models). Our empirical findings can be summarized as follows. Financial models in levels do not produce satisfactory forecasts for the WTI spot price. The financial error correction model yields accurate in-sample forecasts. Real and strategic variables alone are insufficient to capture the oil spot price dynamics in the forecasting sample. Our proposed mixed models are statistically adequate and exhibit accurate forecasts. Different data frequencies seem to affect the forecasting ability of the models under analysis.Oil Price, WTI Spot And Futures Prices, Forecasting, Econometric Models

Adaptation of an i-voting scheme to Italian Elections for Citizens Abroad

We adapt the Ara´ujo-Traor´e protocol to Italian elections, with emphasis on anti-coercion measures. In this short paper we focus on a new method for managing anti-coercion credentials for each voter

Rethinking social housing: behavioural patterns and technological innovations

The building sector accounts for 40% of energy use and 25% of CO2 emissions, mainly due to inefficient building practices and energy consumption during the operational phase of buildings. Social housing accounts for a significant proportion of the European building stock and about 50% of the existing buildings are likely to require large-scale renovations in the coming years, meeting the current EPBD directive. This could represent an opportunity to renovate the affordable building stock, often characterized by premature disrepair, resulting in a bad perception from inhabitants and community. Significant European experiences have already shown the importance of an integrated approach finalized to the construction or renovation of social housing, leveraging on environmental sustainability, creating urban identity, adopting measures to face social disadvantage, offering at the same time quality housing standard. In this regard, it seems necessary to match technological advancements and knowledge in energy retrofitting with social needs and habits. The implementation of energy-efficiency improvements in social housing requests support and participation of the final energy consumer. The paper investigates how to deal with knowledge gaps in the relationship between retrofit technologies and users\u2019 behaviour and possible strategic measures to increase awareness between tenants through two case studies

Aristofane e la legge sulla’eisangelia

On the sources (exp. Aristophanes Ran. 358-362) of the law, which was promulgated in 411 and revised soon after 403 B.C