Aggregation models on hypergraphs

Following a newly introduced approach by Rasetti and Merelli we investigate the possibility to extract topological information about the space where interacting systems are modelled. From the statistical datum of their observable quantities, like the correlation functions, we show how to reconstruct the activities of their constitutive parts which embed the topological information. The procedure is implemented on a class of polymer models on hypergraphs with hard-core interactions. We show that the model fulfils a set of iterative relations for the partition function that generalise those introduced by Heilmann and Lieb for the monomer-dimer case. After translating those relations into structural identities for the correlation functions we use them to test the precision and the robustness of the inverse problem. Finally the possible presence of a further interaction of peer-to-peer type is considered and a criterion to discover it is identified.Comment: Improved version, 12 pages, 5 figure

Segmenting toroidal time series by nonhomogeneous hidden semi-Markov models

Motivated by classification issues in marine studies, we propose a hidden semi-Markov model to segment toroidal time series according to a finite number of latent regimes. The time spent in a given regime and the chances of a regime switching event are separately modeled by a battery of regression models that depend on time-varying covariates

Adversarial Out-domain Examples for Generative Models

Deep generative models are rapidly becoming a common tool for researchers and developers. However, as exhaustively shown for the family of discriminative models, the test-time inference of deep neural networks cannot be fully controlled and erroneous behaviors can be induced by an attacker. In the present work, we show how a malicious user can force a pre-trained generator to reproduce arbitrary data instances by feeding it suitable adversarial inputs. Moreover, we show that these adversarial latent vectors can be shaped so as to be statistically indistinguishable from the set of genuine inputs. The proposed attack technique is evaluated with respect to various GAN images generators using different architectures, training processes and for both conditional and not-conditional setups.Comment: accepted in proceedings of the Workshop on Machine Learning for Cyber-Crime Investigation and Cybersecurit

The Struggle against Social Exclusion at the Local Level: Diversity and Convergence in European Cities

This article is based on the results of the European comparative research project ESOPO (Evaluation of Social Policies at the Local Urban Level: Income Support for the Able-Bodied) directed by Chiara Saraceno (Saraceno, 2002). The research explored the  configuration and impact of income support programmes in favour of able-bodied individuals in 13 cities of 6 European countries.2 In the face of rising unemployment and for a growing section of the population the difficulty of finding a steady job, most European countries have adopted anti-poverty strategies. Minimum income benefit in various forms constitutes a central element of income support for disadvantaged populations. Although its stated objective is often the same – to combat exclusion – there is a fairly large degree of heterogeneity in the way this policy is organized at local level, even in strongly centralized countries. Beyond simply revealing institutional differences, the comparative study of local experiences gives us a closer understanding of the rationale according to which each city – with its own mode of development, political and social history, culture, associative or community resources and, more broadly, the characteristics of its civil society – structures its anti-poverty strategies. Comparative analysis of local situations has the advantage of highlighting the different complexity of the processes at work, as well as of the local configurations which result from them. These may involve arrangements and relationships between public institutions, intermediate organizations, the Church, family networks and local community. Moreover, such an approach allows us to discern both the diversified forms and levels of intervention of these various actors and the principles involved by looking at the interaction between people and institutions. In drawing on this research which is focused on a comparative study of the models of anti-poverty social policies, we will discuss some important issues. First, we will argue that poverty cannot be separated from the social conditions which generate it and from the social structures in which it is embedded. Second, we will demonstrate that the comparative study of anti-poverty models enables us to define more precisely the systems that mobilize resources other than those implemented on the basis of well known and formalized criteria. In fact they are sometimes very localized and based on particular arrangements between the public sphere and the civil society. Finally, we will show how these local systems that implement anti-poverty social policies are not necessarily leading to strong institutionalization and public regulation through a linear process of modernization. Although the challenge of social integration is driving all countries towards greater intervention, it is also obliging them to introduce new and more flexible forms of regulations

The Struggle against Social Exclusion at the Local Level: Diversity and Convergence in European Cities

This article is based on the results of the European comparative research project ESOPO (Evaluation of Social Policies at the Local Urban Level: Income Support for the Able-Bodied) directed by Chiara Saraceno (Saraceno, 2002). The research explored the  configuration and impact of income support programmes in favour of able-bodied individuals in 13 cities of 6 European countries.2 In the face of rising unemployment and for a growing section of the population the difficulty of finding a steady job, most European countries have adopted anti-poverty strategies. Minimum income benefit in various forms constitutes a central element of income support for disadvantaged populations. Although its stated objective is often the same – to combat exclusion – there is a fairly large degree of heterogeneity in the way this policy is organized at local level, even in strongly centralized countries. Beyond simply revealing institutional differences, the comparative study of local experiences gives us a closer understanding of the rationale according to which each city – with its own mode of development, political and social history, culture, associative or community resources and, more broadly, the characteristics of its civil society – structures its anti-poverty strategies. Comparative analysis of local situations has the advantage of highlighting the different complexity of the processes at work, as well as of the local configurations which result from them. These may involve arrangements and relationships between public institutions, intermediate organizations, the Church, family networks and local community. Moreover, such an approach allows us to discern both the diversified forms and levels of intervention of these various actors and the principles involved by looking at the interaction between people and institutions. In drawing on this research which is focused on a comparative study of the models of anti-poverty social policies, we will discuss some important issues. First, we will argue that poverty cannot be separated from the social conditions which generate it and from the social structures in which it is embedded. Second, we will demonstrate that the comparative study of anti-poverty models enables us to define more precisely the systems that mobilize resources other than those implemented on the basis of well known and formalized criteria. In fact they are sometimes very localized and based on particular arrangements between the public sphere and the civil society. Finally, we will show how these local systems that implement anti-poverty social policies are not necessarily leading to strong institutionalization and public regulation through a linear process of modernization. Although the challenge of social integration is driving all countries towards greater intervention, it is also obliging them to introduce new and more flexible forms of regulations

Bayesian hierarchical modeling and analysis for physical activity trajectories using actigraph data

Rapid developments in streaming data technologies are continuing to generate increased interest in monitoring human activity. Wearable devices, such as wrist-worn sensors that monitor gross motor activity (actigraphy), have become prevalent. An actigraph unit continually records the activity level of an individual, producing a very large amount of data at a high-resolution that can be immediately downloaded and analyzed. While this kind of \textit{big data} includes both spatial and temporal information, the variation in such data seems to be more appropriately modeled by considering stochastic evolution through time while accounting for spatial information separately. We propose a comprehensive Bayesian hierarchical modeling and inferential framework for actigraphy data reckoning with the massive sizes of such databases while attempting to offer full inference. Building upon recent developments in this field, we construct Nearest Neighbour Gaussian Processes (NNGPs) for actigraphy data to compute at large temporal scales. More specifically, we construct a temporal NNGP and we focus on the optimized implementation of the collapsed algorithm in this specific context. This approach permits improved model scaling while also offering full inference. We test and validate our methods on simulated data and subsequently apply and verify their predictive ability on an original dataset concerning a health study conducted by the Fielding School of Public Health of the University of California, Los Angeles

Finite mixtures in capture-recapture surveys for modelling residency patterns in marine wildlife populations

In this work, the goal is to estimate the abundance of an animal population using data coming from capture-recapture surveys. We leverage the prior knowledge about the population's structure to specify a parsimonious finite mixture model tailored to its behavioral pattern. Inference is carried out under the Bayesian framework, where we discuss suitable priors' specification that could alleviate label-switching and non-identifiability issues affecting finite mixtures. We conduct simulation experiments to show the competitive advantage of our proposal over less specific alternatives. Finally, the proposed model is used to estimate the common bottlenose dolphins' population size at the Tiber River estuary (Mediterranean Sea), using data collected via photo-identification from 2018 to 2020. Results provide novel insights on the population's size and structure, and shed light on some of the ecological processes governing the population dynamics

Nowcasting COVID-19 incidence indicators during the Italian first outbreak

A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replicate the results are provided.publishedVersio