49 research outputs found
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