214 research outputs found
Prediction of scientific collaborations through multiplex interaction networks
Link prediction algorithms can help to understand the structure and dynamics
of scientific collaborations and the evolution of Science. However, available
algorithms based on similarity between nodes of collaboration networks are
bounded by the limited amount of links present in these networks. In this work,
we reduce the latter intrinsic limitation by generalizing the Adamic-Adar
method to multiplex networks composed by an arbitrary number of layers, that
encode diverse forms of scientific interactions. We show that the new metric
outperforms other single-layered, similarity-based scores and that scientific
credit, represented by citations, and common interests, measured by the usage
of common keywords, can be predictive of new collaborations. Our work paves the
way for a deeper understanding of the dynamics driving scientific
collaborations, and provides a new algorithm for link prediction in multiplex
networks that can be applied to a plethora of systems
Monitoring Gender Gaps via LinkedIn Advertising Estimates: the case study of Italy
Women remain underrepresented in the labour market. Although significant
advancements are being made to increase female participation in the workforce,
the gender gap is still far from being bridged. We contribute to the growing
literature on gender inequalities in the labour market, evaluating the
potential of the LinkedIn estimates to monitor the evolution of the gender gaps
sustainably, complementing the official data sources. In particular, assessing
the labour market patterns at a subnational level in Italy. Our findings show
that the LinkedIn estimates accurately capture the gender disparities in Italy
regarding sociodemographic attributes such as gender, age, geographic location,
seniority, and industry category. At the same time, we assess data biases such
as the digitalisation gap, which impacts the representativity of the workforce
in an imbalanced manner, confirming that women are under-represented in
Southern Italy. Additionally to confirming the gender disparities to the
official census, LinkedIn estimates are a valuable tool to provide dynamic
insights; we showed an immigration flow of highly skilled women, predominantly
from the South. Digital surveillance of gender inequalities with detailed and
timely data is particularly significant to enable policymakers to tailor
impactful campaigns.Comment: 10 page
Developing Real Estate Automated Valuation Models by Learning from Heterogeneous Data Sources
In this paper we propose a data acquisition methodology, and a Machine Learning solution for the partially automated evaluation of real estate properties. The novelty and importance of the approach lies in two aspects: (1) when compared to Automated Valuation Models (AVMs) as available to real estate operators, it is highly adaptive and non-parametric, and integrates diverse data sources; (2) when compared to Machine Learning literature that has addressed real estate applications, it is more directly linked to the actual business processes of appraisal companies: in this context prices that are advertised online are normally not the most relevant source of information, while an appraisal document must be proposed by an expert and approved by a validator, possibly with the help of technological tools. We describe a case study using a set of 7988 appraisal documents for residential properties in Turin, Italy. Open data were also used, including location, nearby points of interest, comparable property prices, and the Italian revenue service area code. The observed mean error as measured on an independent test set was around 21 K€, for an average property value of about 190 K€. The AVM described here can help the stakeholders in this process (experts, appraisal company) to provide a reference price to be used by the expert, to allow the appraisal company to validate their evaluations in a faster and cheaper way, to help the expert in listing a set of comparable properties, that need to be included in the appraisal document
Bistable Clustering in Driven Granular Mixtures
The behavior of a bidisperse inelastic gas vertically shaken in a
compartmentalized container is investigated using two different approaches: the
first is a mean-field dynamical model, which treats the number of particles in
the two compartments and the associated kinetic temperatures in a
self-consistent fashion; the second is an event-driven numerical simulation.
Both approaches reveal a non-stationary regime, which has no counterpart in the
case of monodisperse granular gases. Specifically, when the mass difference
between the two species exceeds a certain threshold the populations display a
bistable behavior, with particles of each species switching back and forth
between compartments. The reason for such an unexpected behavior is attributed
to the interplay of kinetic energy non-equipartition due to inelasticity with
the energy redistribution induced by collisions. The mean-field model and
numerical simulation are found to agree qualitatively.Comment: 23 pages, 12 figure
News and the city: understanding online press consumption patterns through mobile data
The always increasing mobile connectivity affects every aspect of our daily
lives, including how and when we keep ourselves informed and consult news
media. By studying a DPI (deep packet inspection) dataset, provided by one of
the major Chilean telecommunication companies, we investigate how different
cohorts of the population of Santiago De Chile consume news media content
through their smartphones. We find that some socio-demographic attributes are
highly associated to specific news media consumption patterns. In particular,
education and age play a significant role in shaping the consumers behaviour
even in the digital context, in agreement with a large body of literature on
off-line media distribution channels
The Impact of Disinformation on a Controversial Debate on Social Media
In this work we study how pervasive is the presence of disinformation in the
Italian debate around immigration on Twitter and the role of automated accounts
in the diffusion of such content. By characterising the Twitter users with an
\textit{Untrustworthiness} score, that tells us how frequently they engage with
disinformation content, we are able to see that such bad information
consumption habits are not equally distributed across the users; adopting a
network analysis approach, we can identify communities characterised by a very
high presence of users that frequently share content from unreliable news
sources. Within this context, social bots tend to inject in the network more
malicious content, that often remains confined in a limited number of clusters;
instead, they target reliable content in order to diversify their reach. The
evidence we gather suggests that, at least in this particular case study, there
is a strong interplay between social bots and users engaging with unreliable
content, influencing the diffusion of the latter across the network
Thermal convection in mono-disperse and bi-disperse granular gases: A simulation study
We present results of a simulation study of inelastic hard-disks vibrated in
a vertical container. An Event-Driven Molecular Dynamics method is developed
for studying the onset of convection. Varying the relevant parameters
(inelasticity, number of layers at rest, intensity of the gravity) we are able
to obtain a qualitative agreement of our results with recent hydrodynamical
predictions. Increasing the inelasticity, a first continuous transition from
the absence of convection to one convective roll is observed, followed by a
discontinuous transition to two convective rolls, with hysteretic behavior. At
fixed inelasticity and increasing gravity, a transition from no convection to
one roll can be evidenced. If the gravity is further increased, the roll is
eventually suppressed. Increasing the number of monolayers the system
eventually localizes mostly at the bottom of the box: in this case multiple
convective rolls as well as surface waves appear. We analyze the density and
temperature fields and study the existence of symmetry breaking in these fields
in the direction perpendicular to the injection of energy. We also study a
binary mixture of grains with different properties (inelasticity or diameters).
The effect of changing the properties of one of the components is analyzed,
together with density, temperature and temperature ratio fields.
Finally, the presence of a low-fraction of quasi-elastic impurities is shown
to determine a sharp transition between convective and non-convective steady
states.Comment: 11 pages, 12 figures, accepted for publication on Physical Review
Conservation of Landrace: The Key Role of the Value for Agrobiodiversity Conservation. An Application on Ancient Tomatoes Varieties
Abstract Agricultural biological diversity (agrobiodiversity), is a small component of biodiversity, and presents two levels: genetic resources for food and agriculture and ecological services. All the components contribute to sustain the key functions of agro-ecosystems. It is commonly acknowledged that biodiversity is jeopardized by erosion, whereas there is less awareness about agrobiodiversity loss, although this has very negative short and long-term consequences for producers and consumers. In particular, important for conserving agrobiodiversity is the protection of landraces (LRs). The disappearance of LRs, also called by the farmers local or primitive varieties, means both genetic and cultural erosion. For this reason, in-situ LRs conservation is essential, as well as the ex situ one. The main objective of the present work is the evaluation of agrobiodiversity and of its role for the local community, by means of the Contingent Valuation. The attention is focused on the tomatoes landrace "Pomodoro di Mercatello", a variety once widely cultivated in some areas within the province of Perugia and now kept alive by a farmer who still grows and sells it
Towards a data-driven characterization of behavioral changes induced by the seasonal flu
In this work, we aim to determine the main factors driving self-initiated behavioral changes during the seasonal flu. To this end, we designed and deployed a questionnaire via Influweb, a Web platform for participatory surveillance in Italy, during the 2017 − 18 and 2018 − 19 seasons. We collected 599 surveys completed by 434 users. The data provide socio-demographic information, level of concerns about the flu, past experience with illnesses, and the type of behavioral changes voluntarily implemented by each participant. We describe each response with a set of features and divide them in three target categories. These describe those that report i) no (26%), ii) only moderately (36%), iii) significant (38%) changes in behaviors. In these settings, we adopt machine learning algorithms to investigate the extent to which target variables can be predicted by looking only at the set of features. Notably, 66% of the samples in the category describing more significant changes in behaviors are correctly classified through Gradient Boosted Trees. Furthermore, we investigate the importance of each feature in the classification task and uncover complex relationships between individuals’ characteristics and their attitude towards behavioral change. We find that intensity, recency of past illnesses, perceived susceptibility to and perceived severity of an infection are the most significant features in the classification task and are associated to significant changes in behaviors. Overall, the research contributes to the small set of empirical studies devoted to the data-driven characterization of behavioral changes induced by infectious disease
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