Multitasking associative networks

We introduce a bipartite, diluted and frustrated, network as a sparse restricted Boltzman machine and we show its thermodynamical equivalence to an associative working memory able to retrieve multiple patterns in parallel without falling into spurious states typical of classical neural networks. We focus on systems processing in parallel a finite (up to logarithmic growth in the volume) amount of patterns, mirroring the low-level storage of standard Amit-Gutfreund-Sompolinsky theory. Results obtained trough statistical mechanics, signal-to-noise technique and Monte Carlo simulations are overall in perfect agreement and carry interesting biological insights. Indeed, these associative networks pave new perspectives in the understanding of multitasking features expressed by complex systems, e.g. neural and immune networks.Comment: to appear on Phys.Rev.Let

Parallel processing in immune networks

In this work we adopt a statistical mechanics approach to investigate basic, systemic features exhibited by adaptive immune systems. The lymphocyte network made by B-cells and T-cells is modeled by a bipartite spin-glass, where, following biological prescriptions, links connecting B-cells and T-cells are sparse. Interestingly, the dilution performed on links is shown to make the system able to orchestrate parallel strategies to fight several pathogens at the same time; this multitasking capability constitutes a remarkable, key property of immune systems as multiple antigens are always present within the host. We also define the stochastic process ruling the temporal evolution of lymphocyte activity, and show its relaxation toward an equilibrium measure allowing statistical mechanics investigations. Analytical results are compared with Monte Carlo simulations and signal-to-noise outcomes showing overall excellent agreement. Finally, within our model, a rationale for the experimentally well-evidenced correlation between lymphocytosis and autoimmunity is achieved; this sheds further light on the systemic features exhibited by immune networks.Comment: 21 pages, 9 figures; to appear in Phys. Rev.

Analogue neural networks on correlated random graphs

We consider a generalization of the Hopfield model, where the entries of patterns are Gaussian and diluted. We focus on the high-storage regime and we investigate analytically the topological properties of the emergent network, as well as the thermodynamic properties of the model. We find that, by properly tuning the dilution in the pattern entries, the network can recover different topological regimes characterized by peculiar scalings of the average coordination number with respect to the system size. The structure is also shown to exhibit a large degree of cliquishness, even when very sparse. Moreover, we obtain explicitly the replica symmetric free energy and the self-consistency equations for the overlaps (order parameters of the theory), which turn out to be classical weighted sums of 'sub-overlaps' defined on all possible sub-graphs. Finally, a study of criticality is performed through a small-overlap expansion of the self-consistencies and through a whole fluctuation theory developed for their rescaled correlations: Both approaches show that the net effect of dilution in pattern entries is to rescale the critical noise level at which ergodicity breaks down.Comment: 34 pages, 3 figure

Modelling a change of classification in economic time series data

The change of classification problem for economic sectoral time series data is examined by a approach. State space representations are proposed both for data reconstruction and modelling a change of classification. The Doran (1992) methodology of constraining the Kalman filter to satisfy time varying restrictions is applied to show how to handle both limited information and aggregation constraints. We explore the implications of this approach for what will be, perhaps, the most important change of classification in sectoral data: the new National Accounts for European Unification. Results of an experimental application to Italian Quarterly Accounts are provide

Temporal disaggregation and the adjustment of quarterly national accounts for seasonal and calendar effects

The statistical treatment of seasonality and calendar effects in the estimation of quarterly national accounts raises a number of issues that bear important consequences for the assessment of current economic conditions. In many European countries, the quarterly national accounts are constructed by national statistical institutes by disaggregating the original annual measurements using related monthly indicators. In this article we propose and evaluate an alternative approach that hinges upon the estimation of a bivariate basic structural time series model at the monthly frequency, accounting for the presence of seasonality and calendar components. Its main virtue is to enable the adjustment and temporal disaggregation to be carried out simultaneously. The proposed methodology also complies with the recommendations made by the Eurostat - European Central Bank task force on the seasonal adjustment of quarterly national accounts. The overall conclusion is that the identification and consequently the separation of seasonal and calendar effects from aggregate data is highly controversial

Temporal disaggregation and the adjustment of quarterly national accounts for seasonal and calendar effects

The statistical treatment of seasonality and calendar effects in the estimation of quarterly national accounts raises a number of issues that bear important consequences for the assessment of current economic conditions. In many European countries, the quarterly national accounts are constructed by national statistical institutes by disaggregating the original annual measurements using related monthly indicators. In this article we propose and evaluate an alternative approach that hinges upon the estimation of a bivariate basic structural time series model at the monthly frequency, accounting for the presence of seasonality and calendar components. Its main virtue is to enable the adjustment and temporal disaggregation to be carried out simultaneously. The proposed methodology also complies with the recommendations made by the Eurostat - European Central Bank task force on the seasonal adjustment of quarterly national accounts. The overall conclusion is that the identification and consequently the separation of seasonal and calendar effects from aggregate data is highly controversial

Indirect estimation of the monthly transport turnover indicator in Italy

The paper discusses the results of a selection of a set of monthly indicators to be used as predictors of the quarterly index of Italian service turnover. A mixed frequency approach based on sparse temporal disaggregation is used, which outperforms the classical methods of the Chow and Lin family, allowing both a high number of regressors by the LASSO method and stable estimates. The application refers to the turnover in transport, a sector strongly affected in 2020 by the dramatic movements due to the COVID-19 pandemic and the resurgence of inflation at the end of 2021. The monthly indicators are selected from 143 time series: 56 series of business surveys in transport about both the climate and frequency of the answers; 18 series from Assaeroporti about both passengers and cargo flights split by national and international routes; 69 series of monthly turnover in industry split by both sector of economic activity and reference market. The sample spans the months from January 2010 to December 2021 for both seasonally adjusted and unadjusted data. Several aspects of the estimation are considered: the stability of selected indicators over the quarters 2017–2021; their forecasting performance; the reliability of the estimates in terms of their monthly pattern