Florence data science

13th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2022-03-11 14:00 - 15:00 (Europe/Rome) | Online
The Florence Center for Data Science is happy to present the 13th Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 11th of March 2021, from 2-3 pm.
The seminar on “ Paths and flows for centrality measures in networks” will be held by Daniela Bubboloni from the Department of Mathematics and Computer Science “Ulisse Dini” of the University of Florence.
Register in advance for this webinar:
https://us02web.zoom.us/webinar/register/WN_KFoLkeSfT3-kzWLK2mwHPA
After registering, you will receive a confirmation email containing information about joining the webinar.
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Speaker: Daniela Bubboloni – Department of Mathematics and Computer Science “Ulisse Dini”, University of Florence
Title: Paths and flows for centrality measures in networks
Abstract: Consider the number of paths that must pass through a subset X of vertices of a capacitated network N in a maximum sequence of arc-disjoint paths connecting two vertices y and z. Consider then the difference between the maximum flow value from y to z in N and the maximum flow value from y to z in the network obtained by N by setting to zero the capacities of all the arcs incident to X. When X is a singleton, those quantities are involved in defining and computing the flow betweenness centrality and are commonly identified without any rigorous proof justifying the identification. That surprising gap in the literature is the starting point of our research. On the basis of a deep analysis of the interplay between paths and flows, we prove that, when X is a singleton, those quantities coincide. On the other hand, when X has at least two elements, those quantities may be different from each other. By means of the considered quantities, two conceptually different group centrality measures, respectively based on paths and flows, can be naturally defined. Such group centrality measures both extend the flow betweenness centrality to groups of vertices and satisfy a desirable form of monotonicity.

12th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2022-02-25 14:30 - 16:00 | Online
The Florence Center for Data Science is happy to present the twelfth Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 25th of February 2021, from 2.30-4.00 pm.
The seminar will be held by Marco Pangallo from the Institute of Economics and of the Economics and Management in the era of Data Science (EMbeDS) of Sant’Anna School of Advanced Studies – Pisa and Fiammetta Menchetti from the Department of Statistics, Computer Science, Applications “G. Parenti” of the University of Florence.
Register in advance for this webinar:
https://us02web.zoom.us/webinar/register/WN_W9PHbIB-TQOs98cL7Z0Ilw
After registering, you will receive a confirmation email containing information about joining the webinar.
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Speaker: Marco Pangallo – Institute of Economics and of the Economics and Management in the era of Data Science (EMbeDS) of Sant’Anna School of Advanced Studies – Pisa
Title: Making a housing market agent-based model learnable
Abstract: Agent-Based Models (ABMs) are used in several fields to study the evolution of complex systems based on micro-level assumptions. Often, some of their micro-level variables cannot be observed in empirical data. These latent variables make it difficult to initialize an ABM in order to use it to track and forecast empirical time series. In this paper, we propose a protocol to learn the latent variables of an ABM. We show how a complex ABM can be reduced to a probabilistic model, characterized by a computationally tractable likelihood. This reduction can be abstracted into two general design principles: balance of stochasticity and data availability, and replacement of unobservable discrete choices with differentiable approximations. We showcase our protocol by applying it to an ABM of the housing market, in which agents with different incomes bid higher prices to live in high-income neighborhoods. We show that the obtained model preserves the general behavior of the ABM, and at the same time it allows the estimation of latent variables through the optimization of its likelihood. In synthetic experiments, we show that we can learn the latent variables with good accuracy, and that our estimates make out-of-sample forecasting more precise compared to alternative benchmarks. Our protocol can be seen as an alternative to black-box data assimilation methods, forcing the modeler to lay bare the assumptions of the model, think about the inferential process, and identify potential identification problems.
Speaker: Fiammetta Menchetti – Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence
Title: Combining counterfactual outcomes and ARIMA models for policy evaluation
Abstract: The Rubin Causal Model (RCM) is a framework that allows to define the causal effect of an intervention as a contrast of potential outcomes.In recent years, several methods have been developed under the RCM to estimate causal effects in time series settings. None of these makes use of ARIMA models, which are instead very common in the econometrics literature. We propose a novel approach, Causal-ARIMA (C-ARIMA), to define and estimate the causal effect of an intervention in observational time series settings under the RCM. We first formalize the assumptions enabling the definition, the estimation and the attribution of the effect to the intervention. In the empirical application, we use C-ARIMA to assess the causal effect of a permanent price reduction on supermarket sales. The Causal-ARIMA R package provides an implementation of our proposed approach.

11th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2022-02-11 14:30 - 16:00 | Online
The Florence Center for Data Science is happy to present the eleventh Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 11th of February 2021, from 2.30-4.00 pm.
The seminar will be held by Fabio Corradi and Michela Baccini from the Department of Statistics, Computer Science, Applications “G. Parenti” of the University of Florence.
Register in advance for this webinar:
https://us02web.zoom.us/webinar/register/WN_KFoLkeSfT3-kzWLK2mwHPA
After registering, you will receive a confirmation email containing information about joining the webinar.
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Speaker: Fabio Corradi – Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence
Title: Learning the two parameters of the Poisson-Dirichlet distribution with a forensic application
Abstract: This contribution is motivated by the rare type match problem, a relevant forensic issue. There, difficulties arise to evaluate the likelihood ratio comparing the defense and the prosecution hypotheses since the specific matching characteristic from the suspect and the crime scene is not in the reference database. A recently proposed solution approximates the likelihood ratio by plugging in the parameters MLE of a Poisson Dirichlet distribution, a
Bayesian nonparametric prior modeling probability mass function showing a power-law behavior in the infinite dimensional space. We instead consider how to learn the parameters of a Posson-Dirichlet and we propose two sampling schemes: Monte Carlo Markov Chain and Approximate Bayesian Computation. We demonstrate that the previously employed plug-in solution produces a systematic bias that Bayesian inference avoids entirely. Finally, we evaluate the method using a database of Y-chromosome haplotypes.
Speaker: Michela Baccini – Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence
Title: Combining and comparing regional epidemic dynamics in Italy: Bayesian meta-analysis of compartmental models and model assessment via Global Sensitivity Analysis
Abstract: During autumn 2020, Italy faced a second important SARS-CoV-2 epidemic wave. We explored the time pattern of the instantaneous reproductive number R0(t), and estimated the prevalence of infections by region from August to December calibrating SIRD models on COVID19-related deaths, fixing at values from literature Infection Fatality Rate (IFR) and infection duration. A Global Sensitivity Analysis (GSA) was performed on the regional SIRD models. Then, we used Bayesian meta-analysis and meta-regression to combine and compare the regional results and investigate their heterogeneity. The meta-analytic R0(t) curves were similar in the Northern and Central regions, while a less peaked curve was estimated for the South. The maximum R0(t) ranged from 2.61 (North) to 2.15 (South) with an increase following school reopening and a decline at the end of October. Average temperature, urbanization, characteristics of family medicine and health care system, economic dynamism, and use of public transport could partly explain the regional heterogeneity. The GSA indicated the robustness of the regional R0(t) curves to different assumptions on IFR. The infectious period turned out to have a key role in determining the model results, but without compromising between-region comparisons.

Kick-off meeting of the Master in Data Science and Statistical Learning MD2SL
2022-02-01 16:30 - 18:30 | Online
We are pleased to invite you to the Kick-off meeting of the Master in Data Science and Statistical Learning of the University of Florence in collaboration with the Scuola IMT Alti Studi Lucca.
Here attached you can find the program of the event. The Kick-off meeting will take place online on Tuesday, 1st of February 2022 starting from 4.30 pm.

You can register for the event using this link https://us02web.zoom.us/meeting/register/tZwrfumuqzwrH9JcaHgeMnPFyMdb2iRMS8QT
After the registration, you wilt receive an email with the link to access the zoom meeting.

10th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2022-01-28 14:30 - 16:00 | Onine
The Florence Center for Data Science is happy to present the tenth Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 28th of January 2021, from 2.30-4.00 pm.
The seminar will be held by Lorenzo Seidenari from the Department of Information Engineering of the University of Florence and Francesco Calabrò from the Department of Mathematics and Applications “Renato Caccioppoli” of the University of Naples “Federico II”.
Register in advance for this webinar:
https://us02web.zoom.us/webinar/register/WN_KFoLkeSfT3-kzWLK2mwHPA
After registering, you will receive a confirmation email containing information about joining the webinar.
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Speaker: Lorenzo Seidenari – Department of Information Engineering, University of Florence
Title: Predicting Multiple Future Trajectories for Safe Self-Driving Cars
Abstract: Autonomous navigating agents are becoming a reality. Pedestrians and drivers are expected to safely navigate complex urban environments along with several non-cooperating agents. Autonomous vehicles will soon replicate this capability. Agents must learn a representation of the world and must make decisions ensuring safety for themselves and others. Apart from sensing objects, knowing, and abiding traffic regulations a driving agent must plan a safe path. This requires predicting motion patterns of observed agents for a far enough future. Moreover, with the rise of autonomous cars, a lot of attention is also drawn by the explainability of machine learning models for self-driving cars. In this talk, I will go over our recent contributions in the field of self-driving systems. I will present our recent works on multimodal trajectory prediction exploiting a novel use of memory augmented neural networks. Finally, we will look at simple explainable models for driving and trajectory prediction.
Speaker: Francesco Calabrò – Department of Mathematics and Applications “Renato Caccioppoli”, University of Naples “Federico II”
Title: The use of neural networks for the resolution of Partial Differential Equations
Abstract: In this talk, we present the construction of a Physics-Informed method for the resolution of stationary Partial Differential Equations. Our method relies on the construction of a Feedforward Neural Network (FNN) with a single hidden layer and sigmoidal transfer functions randomly generated, the so-called Extreme Learning Machines (ELM). We use ELM random projection networks as discrete space where to look for the solution of PDEs. Free parameters (N external weights) are fixed by imposing exactness on M (eventually located randomly) points via collocation. In order to obtain accurate solutions, we underdeterminate the collocation equations (N>M). For linear PDEs, the weights are computed by a one-step least-square solution of the linear system. The least-square solution is capable of automatically selecting the important features, i.e. the functions in the space that are more influent for the solution. This leads to a one-shot automatic method and there is no need for adaptive procedures or tuning of the parameters as done when learning in other methods based on FNN. We present results for elliptic benchmark problems both in the linear case [1] and for the resolution and construction of bifurcation diagrams of nonlinear problems [2]. The results are obtained in collaboration with Gianluca Fabiani and Costantinos Siettos.
[1] Calabrò, F., Fabiani, G., & Siettos, C. (2021). Extreme learning machine collocation for the numerical solution of elliptic PDEs with sharp gradients. Computer Methods in Applied Mechanics and Engineering, 387, 114188.
[2] Fabiani, G., Calabrò, F., Russo, L. & Siettos, C. (2021). Numerical solution and bifurcation analysis of nonlinear partial differential equations with extreme learning machines. J Sci Comput 89, 44

IFAC Seminar Series «Artificial Intelligence Applications»
2021-12-20 11:00 - 12:00 | Area di Ricerca CNR, Via Madonna del Piano, 10, Sesto Fiorentino, FI
The Florence Center for Data Science is happy to forward the invitation to a seminar of the seminar series «Artificial Intelligence Applications» organized by Andrea Barucci from IFAC-CNR.
This seminar held by Eng. Lorenzo Python on December 20th 2021 from 11 to 12 am and will be on “On the segmentation and classification of ancient Egyptian hieroglyphs through deep learning techniques”.
Lorenzo has graduated with Andrea Barucci and Prof. Argenti (DINFO-UniFI) in Electronics and Telecommunications Engineering. This seminar aims to show the power of deep learning tools on a specific example. As you know, these methods are transversal and can be adapted to various applications, among which we are sure those of your interest. The seminar will illustrate the implementation of a neural network capable of performing object detection and segmentation tasks, applied to the case of Egyptian hieroglyphs taken from ancient texts.
The seminar will be available in two ways: in-person and remotely (Webex details below). If you are interested in attending it in person, please notify Andrea Barucci (a.barucci@ifac.cnr.it) as soon as possible, given the Covid19 restrictions.

ACCEDI A RIUNIONE WEBEX
https://ifac-cnr.webex.com/ifac-cnr-it/j.php?MTID=m68d26293af558dc566e15300fec76152
Numero riunione (codice di accesso): 2734 341 9533
Password riunione: upJQH8p5M2w

Data Science Xmas Lecture
2021-12-16 15:00 - 16:30 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
The Florence Center for Data Science is happy to present the “Data Science Christmas Lecture” which is going to be held by Marina Vannucci, Noah Harding Professor of Statistics, Rice University
When: 16th December 2021, from 3 to 4.30 pm
Where: Aula 205 (ex 32) – DISIA – Viale Morgagni 59. The participation on site is restricted and you need to register here https://labdisia.disia.unifi.it/reserve205/.
If you decide to attend the event, after registering please send us an email at this email address datascience@unifi.it
The Lecture will be available also online. Please register here to participate https://us02web.zoom.us/webinar/register/WN_0QB9L_b3RlS9Zp5Rix30_g
Speaker: Marina Vannucci – Noah Harding Professor of Statistics, Rice University
Title: Bayesian Models for Microbiome Data with Variable Selection
Abstract: I will describe Bayesian models developed for understanding how the microbiome varies within a population of interest. I will focus on integrative analyses, where the goal is to combine microbiome data with other available information (e.g. dietary patterns) to identify significant associations between taxa and a set of predictors. For this, I will describe a general class of hierarchical Dirichlet-Multinomial (DM) regression models which use spike-and-slab priors for the selection of the significant associations. I will also describe a joint model that efficiently embeds DM regression models and compositional regression frameworks, in order to investigate how the microbiome may affect the relation between dietary factors and phenotypic responses, such as body mass index. I will discuss the advantages and limitations of the proposed methods with respect to current standard approaches used in the microbiome community and will present results on the analysis of real datasets. If time allows, I will also briefly present extensions of the model to mediation analysis.

9th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2021-12-10 15:00 - 16:30 | Onine
The Florence Center for Data Science is happy to present the ninth Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 10th of December 2021, from 3-4.30 pm.
The seminar will be held by Raffaele Guetto and Andrea Marino from the Department of Statistics, Computer Science, Applications “G. Parenti” of the University of Florence.
Register in advance for this webinar:https://us02web.zoom.us/webinar/register/WN_KFoLkeSfT3-kzWLK2mwHPA
After registering, you will receive a confirmation email containing information about joining the webinar.
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Speaker: Raffaele Guetto – Department of Statistics, Computer Science, Applications “G. Parenti” – University of FlorenceTitle: Italy’s lowest-low fertility in times of uncertainty
Abstract: The generalized and relatively homogeneous fertility decline across European countries in the aftermath of the Great Recession poses serious challenges to our knowledge of contemporary low fertility patterns. The rise of economic uncertainty has often been identified, in the sociological and demographic literature, as the main cause of this state of affairs. The forces of uncertainty have been traditionally operationalized through objective indicators of individuals’ actual and past labour market situations. However, this presentation argues that the role of uncertainty needs to be conceptualized and operationalized taking into account that people use works of imagination, producing their own narrative of the future, also influenced by the media. To outline such an approach, I review contemporary drivers of Italy’s lowest-low fertility, placing special emphasis on the role of uncertainty fueled by labour market deregulations and – more recently – the Covid-19 pandemic. I discuss the effects of the objective (labour-market related) and subjective (individuals’ perceptions, including future outlooks) sides of uncertainty on fertility, based on a set of recent empirical findings obtained through a variety of data and methods. In doing so, I highlight the potential contribution of so-called “big data” and techniques of media content analysis and Natural Language Processing for the analysis of the effects of media-conveyed narratives of the economy.
Speaker: Andrea Marino – Department of Statistics, Computer Science, Applications “G. Parenti” – University of FlorenceTitle: Approximating the Neighborhood Function of (Temporal) GraphsAbstract: The average distance in graphs (like, for instance, the Facebook friendship network and the Internet Movie Database collaboration network), often referred to as degrees of separation, has been largely investigated. However, if the number of nodes is very large (millions or billions), computing this measure needs prohibitive time and space costs as it requires to compute for each node the so-called neighbourhood function, i.e. for each vertex v and for each h, how many nodes are within distance h from v. Temporal graphs are a special kind of graphs where edges have temporal labels, specifying their occurring times, in the same way as the connections of the public transportation network of a city are available only at scheduled times. Here, paths make sense only if they correspond to sequences of edges with strictly increasing labels. A possible notion of distance between two nodes in a temporal network is the earliest arrival time of the temporal paths connecting the two nodes. In this case, the temporal neighbourhood function is defined as the number of nodes reachable from a given one in a given time interval, and it is also expensive to compute. We introduce probabilistic counting in order to approximate the size of sets and we show how both plain and temporal neighbourhood functions can be approximated by plugging this technique into a simple dynamic programming algorithm.

8th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2021-11-26 14:00 - 15:30 | Onine
The Florence Center for Data Science is happy to present the eighth Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 26th of November 2021, from 2-3.30 pm.
The seminar will be held by Giorgio Ricchiuti from the Department of Economics and Management and Marco Bertini from the Department of Information Engineering of the University of Florence.Register in advance for this webinar:https://us02web.zoom.us/webinar/register/WN__m4iKOO3R6WBL4uVvT-v4A
After registering, you will receive a confirmation email containing information about joining the webinar.
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Speaker: Giorgio Ricchiuti – Department of Economics and Management, University of Florence
Title: State Space Model to Detect Cycles in Heterogeneous Agents Models (joint work with Filippo Gusella)
Abstract: We propose an empirical test to depict possible endogenous cycles within Heterogeneous Agent Models (HAMs). We consider a 2-type HAM into a standard small-scale dynamic asset pricing framework. On the one hand, fundamentalists base their expectations on the deviation of fundamental value from market price expecting a convergence between them. On the other hand, chartists, subject to self-fulling moods, consider the level of past prices and relate it to the fundamental value acting as contrarians. These pricing strategies, by their nature, cannot be directly observed but can cause the response of the observed data. For this reason, we consider the agents’ beliefs as unobserved state components from which, through a state space model formulation, the heterogeneity of fundamentalist-chartist trader cycles can be mathematically derived and empirically tested. The model is estimated using the S&P500 index, for the period 1990-2020 at different time scales, specifically, daily, monthly, and quarterly.

Speaker: Marco Bertini – Department of Information Engineering, University of Florence
Title: High quality video experience using deep neural networks
Abstract: Lossy image and video compression algorithms are the enabling technology for a large variety of multimedia applications, reducing the bandwidth required for image transmission and video streaming. However, lossy image and video compression codecs decrease the perceived visual quality, eliminate higher frequency details and in certain cases add noise or small image structures. There are two main drawbacks of this phenomenon. First, images and videos appear much less pleasant to the human eye, reducing the quality of experience. Second, computer vision algorithms such as object detectors may be hindered and their performance reduced. Removing such artefacts means recovering the original image from a perturbed version of it. This means that one ideally should invert the compression process through a complicated non-linear image transformation. In this talk, I’ll present our most recent works based on the GAN framework that allows us to produce images with photorealistic details from highly compressed inputs.

DISIA Seminar: Mortality and morbidity drivers of the global distribution of health
2021-11-25 12:00 - 13:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Title: Mortality and morbidity drivers of the global distribution of health
Speaker: Inaki Permanyer (Centre d’Estudis Demogràfics, Barcelona)
Location: Aula 205 (ex 32) – DISIA – Viale Morgagni 59 The participation on site is restricted to max 20 people (need to register here https://labdisia.disia.unifi.it/reserve205/). However, the seminar will be also online and you can participate at the following link: http://meet.google.com/aio-dhut-nah
You can find the abstract here: Abstract

DISIA Seminar: Innovation on methods for the evaluation of the short-term health effects of environmental hazards
2021-11-24 16:00 - 17:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Title: Innovation on methods for the evaluation of the short-term health effects of environmental hazards.
Speaker: Francesco Sera (DiSIA) e Dominic Royé (University of Santiago de Compostela)
Location: Aula 205 (ex 32) – DISIA – Viale Morgagni 59 The participation on site is restricted to max 20 people (need to register here https://labdisia.disia.unifi.it/reserve205/). However, the seminar will be also online and you can participate at the following link: http://meet.google.com/aio-dhut-nah
You can find the abstract here: Abstract

Seminar for "Nobel Prize for Economics 2021"
2021-11-23 11:30 - 12:30 | D6 Via delle Pandette 9, Via delle Pandette 9, Firenze, Italy
The Florence Center for Data Science is happy to invite you to the Seminar for “Nobel Prize for Economics 2021” organized by the DISEi and the School of Economics and Management of the University of Florence that will take place Tuesday 23rd of November 2021.
Click here to book your seat in advance. Soon we will provide the link for the live streaming.
This event will be in Italian.

7th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2021-11-12 14:00 - 15:30 | Onine
The Florence Center for Data Science is happy to present the seventh Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 12th of November 2021, from 2-3.30 pm.
The seminar will be held by Luigi Brugnano from the Department of Mathematics and Computer Science “Ulisse Dini” and Veronica Ballerini from the Department of Statistics, Computer Science and Applications “G. Parenti” of the University of Florence.
Register in advance for this webinar:https://us02web.zoom.us/webinar/register/WN_hjjYNaS1Tmqz1debdP-BQA
After registering, you will receive a confirmation email containing information about joining the webinar.

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Speaker: Luigi Brugnano – Department of Mathematics and Computer Science “Ulisse Dini”, University of Florence
Title: Recent advances in bibliometric indexes and their implementation
Abstract: Bibliometric indexes are nowadays very commonly used for assessing scientific production, research groups, journals, etc. It must be stressed that such indexes cannot substitute to enter the merit of the specific research but, nonetheless, they can provide a gross evaluation of its impact on the scientific community. That premise, the currently used indexes often have drawbacks and/or sensibly vary for different subjects of investigation. For this reason, in [1] an alternative index has been proposed, based on an idea akin to that of the Google PageRank. Its actual implementation has been recently done in the Scopus database [2]. In this talk, the basic facts and results of this approach will be recalled.
[1] P.Amodio, L.Brugnano. Recent advances in bibliometric indexes and the PaperRank problem. Journal of Computational and Applied Mathematics 267 (2014) 182-194. http://doi.org/10.1016/j.cam.2014.02.018
[2] P.Amodio, L.Brugnano, F.Scarselli. Implementation of the PaperRank and AuthorRank indices in the Scopus database. Journal of Infometrics 15 (2021) 101206. https://doi.org/10.1016/j.joi.2021.101206

Speaker: Veronica Ballerini – Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence
Title: Fisher’s Noncentral Hypergeometric Distribution for the Size Estimation of Unemployed Graduates in Italy (joint work with Brunero Liseo, University Sapienza di Roma)
Abstract: To quantify unemployment among those who have never been employed is often tough. The lack of an administrative data flow attributable to such individuals makes them an elusive population. Hence, one must rely on surveys. However, individuals’ response rates to questions on their occupation may differ according to their employment status, implying a not-at-random missing data generation mechanism. We exploit the underused Fisher’s noncentral hypergeometric distribution (FNCH) to solve such a biased urn experiment. FNCH has been underemployed in the statistical literature mainly because of the computational burden given by its probability mass function. Indeed, as the number of draws and the number of different categories in the population increases, any method involving the evaluation of the likelihood is practically unfeasible. Firstly, we present a methodology that allows the approximation of the posterior distribution of the population size via MCMC and ABC methods. Then, we apply such methodology to the case of graduated unemployed in Italy, exploiting information from different data sources.

FDS Seminar: Giulia Iori from the City University of London
2021-11-05 14:30 - 15:30 | Onine
Due to technical problems during the 6th seminar, we were not able to listen to the seminar of Giulia Iori. That’s why the Florence Center for Data Science is happy to invite you again to the seminar on “Performance-based research funding: Evidence from the largest natural experiment worldwide” which is going to be held online Friday 5th of November 2021, from 2.30-3.30 pm.
The seminar will be held by Giulia Iori from the Department of Economics of the City University of London.
Register for this webinar: https://us02web.zoom.us/webinar/register/WN_M2w6xh0yT9OMKGcqNztFpw

Speaker: Giulia Iori – Department of Economics of the City University of London
Title: Performance-based research funding: Evidence from the largest natural experiment worldwide
Abstract: The Research Excellence Framework (REF) is the main UK government policy on public research in the last 30 years. The primary aim of this policy is to promote and reward research excellence through competition for scarce research resources. Surprisingly, and despite the severe criticisms, little has been done to systematically evaluate its effects. In this paper, we evaluate the impact of the REF 2014. We exploit a large database that contains all publications in Economics, Business, Management, and Finance available in Scopus since 2001. We use a synthetic control method to compare the performance of each of the universities from the UK with counter-factual similar units in terms of past research constructed using data for US universities. We find a significant positive increase, relative to the control group, in the number of published papers, and in the proportion of papers published in highly ranked journals within the Economics/Econometrics area and the Business, Management and Finance area. Both Russell and non-Russell Group universities benefited from the REF, with the Russell Group universities experiencing an overall significant increase in the number of publications and number of publications in top journals, and the non-Russell group experiencing a significant increase in the proportion of publications in top journals in all areas. Interestingly, the non-Russell group experienced a comparatively stronger increase in the proportion of top publications in Economics/Econometrics while the Russell Group experienced a comparatively stronger increase in the proportion of top publications in Business, Management and Finance. However, we see an insignificant effect when we focus on per-author output measures indicating that growth in output was mostly achieved by an increase in the number of research active academics rather than an overall increase in research productivity.

6th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2021-10-29 14:00 - 15:30 | Online
The Florence Center for Data Science is happy to present the sixth Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 29th of October 2021, from 2.30-4 pm.
The seminar will be held by Giulia Iori from the Department of Economics, School of Social Science of the City University of London and Massimo Fornasier from the Department of Mathematics of the Technical University of Munich.
Register in advance for this webinar:https://us02web.zoom.us/webinar/register/WN_B20LTH2CR1SrVYCcD0Eqlw
After registering, you will receive a confirmation email containing information about joining the webinar.
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Speaker: Giulia Iori – Department of Economics, School of Social Science of the City University of London
Title: Performance-based research funding: Evidence from the largest natural experiment worldwide
Abstract: The Research Excellence Framework (REF) is the main UK government policy on public research in the last 30 years. The primary aim of this policy is to promote and reward research excellence through competition for scarce research resources. Surprisingly, and despite the severe criticisms, little has been done to systematically evaluate its effects. In this paper, we evaluate the impact of the REF 2014. We exploit a large database that contains all publications in Economics, Business, Management, and Finance available in Scopus since 2001. We use a synthetic control method to compare the performance of each of the universities from the UK with counter-factual similar units in terms of past research constructed using data for US universities. We find a significant positive increase, relative to the control group, in the number of published papers, and in the proportion of papers published in highly ranked journals within the Economics/Econometrics area and the Business, Management and Finance area. Both Russell and non-Russell Group universities benefited from the REF, with the Russell Group universities experiencing an overall significant increase in the number of publications and number of publications in top journals, and the non-Russell group experiencing a significant increase in the proportion of publications in top journals in all areas. Interestingly, the non-Russell group experienced a comparatively stronger increase in the proportion of top publications in Economics/Econometrics while the Russell Group experienced a comparatively stronger increase in the proportion of top publications in Business, Management and Finance. However, we see an insignificant effect when we focus on per-author output measures indicating that growth in output was mostly achieved by an increase in the number of research active academics rather than an overall increase in research productivity.
Speaker: Massimo Fornasier – Department of Mathematics of the Technical University of Munich
Title: Consensus-based optimization
Abstract: Consensus-based optimization (CBO) is a multi-agent metaheuristic derivative-free optimization method that can globally minimize nonconvex nonsmooth functions and is amenable to theoretical analysis. In fact, optimizing agents (particles) move on the optimization domain driven by a drift towards an instantaneous consensus point, which is computed as a convex combination of particle locations, weighted by the cost function according to Laplace’s principle, and it represents an approximation to a global minimizer. The dynamics are further perturbed by a random vector field to favor exploration, whose variance is a function of thedistance of the particles to the consensus point. Based on an experimentally supported intuition that CBO always performs a gradient descent of the squared Euclidean distance to the global minimizer, we show a novel technique for proving the global convergence to the global minimizer in mean-field law for a rich class of objective functions. We further present formulations of CBO over compact hypersurfaces. We conclude the talk with a few numerical experiments, which show that CBO scales well with the dimension and is extremely versatile.
https://arxiv.org/pdf/2103.15130.pdf to appear in Found. Comput. Math.https://arxiv.org/pdf/2001.11994.pdf appeared in M3AShttps://arxiv.org/pdf/2001.11988.pdf appeared in J. Mach. Learn. Rechhttps://arxiv.org/pdf/2104.00420.pdf to appear in SIAM J. Opt.

DISIA Seminar: Uncertainty across the “Contact Line”: armed conflict, COVID-19, and perceptions of fertility decline in Eastern Ukraine
2021-10-28 12:00 - 13:30 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Title: Uncertainty across the “Contact Line”: armed conflict, COVID-19, and perceptions of fertility decline in Eastern Ukraine
Speaker: Brienna Perelli-Harris (University of Southampton)
Location: Aula 205 (ex 32) – DISIA – Viale Morgagni 59 The participation on site is restricted to max 15 people (need to contact raffaele.guetto@unifi.it). However, the seminar will be also online and you can participate at the following link: meet.google.com/atb-qtxf-nvb You can find the abstract here: Abstract

5th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2021-10-15 14:00 - 15:30 | Onine
The Florence Center for Data Science is happy to present the fifth Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 15th of October 2021, from 2-3.30 pm.
The seminar will be held by Leonardo Bargigli from the Department of Economics and Management of the University of Florence and Chiara Marzi from the Institute of Applied Physics ‘Nello Carrara’ (IFAC) – National Research Council (CNR).
Register in advance for this webinar:https://us02web.zoom.us/webinar/register/WN_3tq7BbbwTSeA956KFQIK0w
After registering, you will receive a confirmation email containing information about joining the webinar.
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Speaker: Leonardo Bargigli – Department of Economics and Management, University of Florence
Title: Endogenous and Exogenous Volatility in the Foreign Exchange Market (with G. Cifarelli)
Abstract: We study two sources of heteroscedasticity in high-frequency financial data. The first, endogenous, source is the behaviour of bounded rational market participants. The second, exogenous, source is the flow of market relevant information. We estimate the impact of the two sources jointly by means of a Markov switching (MS) SVAR model. Following the original intuition of Rigobon (2003), we achieve identification for all coefficients by assuming that the structural errors of the MS-SVAR model follow a GARCH-DCC process. Using transaction data of the EUR/USD interdealer market in 2016, we firstly detect three regimes of endogenous volatility. Then we show that both kinds of volatility matter for the transmission of shocks, and that the exogenous information is channelled to the market mostly through price variations. This suggests that, on the FX market, liquidity providers are better informed than liquidity takers, who act mostly as feedback traders. The latter are able to profit from trade because, unlike noise traders, they respond immediately to the informative price shocks.
Speaker: Chiara Marzi – Institute of Applied Physics ‘Nello Carrara’ (IFAC) – National Research Council (CNR)
Title: Artificial Intelligence in Neuroimaging
Abstract: Life sciences data coupled with Artificial Intelligence (AI) techniques can help researchers accurately pinpoint novel biomarkers. AI can propose new indices as potential biomarkers while simultaneously aiding in searching for hidden patterns among “well-established” indices. In this webinar, we will take a brief journey through some applications of Machine Learning in neuroimaging. In the first part of the webinar, we will talk about the not so easy “marriage” between AI and clinical data, focusing on Big Data from Radiology Imaging and related issues. In the second part, we will see how we can transfer mathematical, physical, and statistical ideas to the Neuroimaging domain and how AI can help this transfer. An example is the study of the structural complexity of the brain starting from MRI images, using fractal analysis. The Fractal Dimension (FD) can be considered a measure of morphological changes due to healthy ageing and/or the onset of neurological diseases. The use of ML techniques can promote the candidature of FD as a biomarker for many neurological diseases.

DISEI Seminar: Istituzioni e Sviluppo Economico nel capitalismo contemporaneo
2021-10-12 14:00 - 16:00 | Online
Title: Istituzioni e Sviluppo Economico nel capitalismo contemporaneo
Speaker: Elisabetta Basile, Luca Bortolotti, Mario Biggeri, Claudio Cecchi, Franco Volpi The seminar will be held online.
You can find more information at the following link: https://www.disei.unifi.it/vp-360-seminari.html

DISEI Seminar: Global search for science: International knowledge flows between multinational companies and universities
2021-10-05 14:00 - 16:00 | Onine
Title: Global search for science: International knowledge flows between multinational companies and universities
Speaker: Claudio Fassio (Lund University)
The seminar will be held online. You can find more information at the following link: https://www.disei.unifi.it/vp-360-seminari.html

DISIA Seminar: Business Excellence 5.0
2021-10-04 17:00 - 18:00 | Onine
Title: Business Excellence 5.0
Speaker: Gabriele Arcidiacono (DIIE – Università Guglielmo Marconi, Roma)
The Seminar will be held online on the Gmeet platform. To register send an email at the following email address: centro.steering@disia.unifi.it
You can find the abstract here: Abstract

DISIA Seminar: Ethnicity and Governing Party Support in Africa
2021-09-30 15:30 - 17:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Title: Ethnicity and Governing Party Support in Africa
Speaker: Carlos G. Rivero (Valencia University & Centre for International and Comparative Politics, Stellenbosch University)
Location: Aula 205 (ex 32) – DISIA – Viale Morgagni 59
The participation on site is restricted to max 15 people (need to contact raffaele.guetto@unifi.it). However, the seminar will be also online and you can participate at the following link: https://meet.google.com/hfw-mdvm-ity
You can find the abstract here: Abstract

4th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2021-07-02 14:30 - 15:30 | Onine
The Florence Center for Data Science is happy to present the fourth Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 2nd of July 2021, from 2-3.30 pm.
The seminar will be held by Anna Gottard from the Department of Statistics, Computer Science, Applications “G. Parenti” and Costanza Conti from the Department of Industrial Engineering of the University of Florence.
Register in advance for this webinar:
https://us02web.zoom.us/webinar/register/WN_WkYeStnjRK6cincfDPXUZg
After registering, you will receive a confirmation email containing information about joining the webinar.
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Speaker: Anna Gottard – Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence
Title: Circular data, conditional independence & graphical models
Abstract: Circular variables, arising in several contexts and fields, are characterized by periodicity. Models for studying the dependence/independence structure of circular variables are under-explored. We will discuss three multivariate circular distributions, the von Mises, the Wrapped Normal and the Inverse Stereographic distributions, focusing on their properties concerning conditional independence. For each of these distributions, we examine the main properties related to conditional independence and introduce suitable classes of graphical models. The usefulness of the proposal is shown by modelling the conditional independence among dihedral angles characterizing the three-dimensional structure of some proteins.
Speaker: Costanza Conti – Department of Industrial Engineering, University of Florence
Title: Penalized hyperbolic-polynomial splines
(Joint work with: Rosanna Campagna, Universit`a degli Studi della Campania “L. Vanvitelli”)
Abstract: The advent of P-splines, first introduced by Eilers and Marx in 2010 (see [4]), has led to important developments in data regression through splines. With the aim of generalizing polynomial P-splines, in [1] we have recently defined a model of penalized regression spline, called HP-spline, in which polynomial B-spline functions are replaced by Hyperbolic-Polynomial bell-shaped basis functions. HP-splines are defined as a solution to a minimum problem characterized by a discrete penalty term. They inherit from P-splines the advantages of the model, like the separation of the data from the spline nodes, so avoiding the problems of overfitting and the consequent oscillations at the edges. HP-splines are particularly interesting in different applications that require analysis and forecasting of data with exponential trends. Indeed, the starting idea is the definition of a polynomial-exponential smoothing spline model to be used in the framework of the Laplace transform inversion as done in [2,3]. We present some recent results on the existence, uniqueness, and reproduction properties of HP-splines, also with the aim of extending their usage to data analysis.
[1] C. Conti, R. Campagna, Penalized exponential-polynomial splines, Appl. Math. Letters, 118, (2021) 107–159
[2] R. Campagna, C. Conti, S. Cuomo, Computational Error Bounds for Laplace Transform Inversion Based on Smoothing Splines, Appl. Math. Comput., 383, (2020) 125–376
[3] R. Campagna, C. Conti, S. Cuomo, Smoothing exponential-polynomial splines for multiexponential decay data, Dolomites Research note on Approximation (2019) 86–10
[4] P.H.C. Eilers and B.D.Marx, Splines, knots, and penalties, WIREs Comp. Stat., 2, (2010) 637-653.

DISEI Seminar: Assessing and designing research, projects and policies in energy, climate and AI
2021-06-29 16:00 - 17:00
DETAILS
Title: Assessing and designing research, projects and policies in energy, climate and AI
Speaker: Francesco Fuso-Nerini (KTH)
https://www.disei.unifi.it/vp-360-seminari.html
Meeting Link: https://unifirenze.webex.com/unifirenze/j.php?MTID=m3908103502626beac5efee7bf699f135
Meeting Number: 121 695 9503
Password: [REDACTED]

DISEI Seminar: A soft introduction to applied Machine Learning for Social Sciences
2021-06-29 14:00 - 15:00
THE SEMINAR HAS BEEN CANCELLED

DETAILS
Title: A soft introduction to applied Machine Learning for Social Sciences
Speaker: Rafael Boix, Universidad de Valencia
https://www.disei.unifi.it/vp-360-seminari.html
Meeting Link: https://unifirenze.webex.com/unifirenze/j.php?MTID=m3908103502626beac5efee7bf699f135
Meeting Number: 121 695 9503
Password: [REDACTED]

3rd Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2021-06-18 14:00 - 15:30 | Online
The Florence Center for Data Science is happy to present the third Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 18th of June 2021, from 2-3.30 pm.

The Seminar will be held by two new Fellows of the Center: the research unit COPERNICUS Earth Observation and Spatial Analysis and some researchers of the Magnetic Resonance Center (CERM). The speakers are Gherardo Chirici from the Department of Agriculture, Food, Environment and Forestry (University of Florence) and Enrico Ravera from the Magnetic Resonance Center (CERM) and the Department of Chemistry (University of Florence).

Register in advance for the seminar (free of charge): https://us02web.zoom.us/webinar/register/WN_I9bCjz_cQ_K_8nOP94usTw

After registering, you will receive a confirmation email containing information about joining the webinar.

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Speaker: Enrico Ravera – Magnetic Resonance Center (CERM) and Department of Chemistry, University of Florence

Title: From algebra to biology: what does the math of ensemble averaging methods can tell us

Abstract: Our work aims at a quantitative comparison of different methods for reconstructing conformational ensembles of biological macromolecules integrating molecular simulations and experimental data. This field has evolved over the years reflecting the evolution of computational power and sampling schemes, and a plethora of different methods have been proposed. These methods can vary extensively in terms of how the prior information from the simulation is used to reproduce the experimental data, but can be coarsely attributed to two categories: Maximum Entropy or Maximum Parsimony. In any case, the problem is severely underdetermined and therefore additional information needs to be provided on the basis of the chemical knowledge about the system under investigation. Maximum entropy looks for the minimal perturbation of the prior distribution, whereas Maximum Parsimony looks for the smallest possible ensemble that can explain in full the experimental data. On these grounds, one can expect radically different solutions in the reconstruction, but surprises are still possible – and can be justified by a rigorous geometrical description of the different methods.

Speaker: Gherardo Chirici – COPERNICUS Earth Observation and Spatial Analysis and Department of Agriculture, Food, Environment and Forestry, University of Florence

Title: Big data from space. Recent Advances in Remote Sensing Technologies

Abstract: Since the 1970s, remote sensing technologies for terrestrial observation have generated a constant flow of data from different platforms, in different formats and with different purposes. From these, through successive steps, spatial information is generated to support the Earth resources monitoring and planning. Indispensable in various sectors: from urban planning to geology, from agriculture to forest monitoring and, more generally, any type of information to support environmental monitoring. For this reason, remotely sensed information is recognized as a typical example of big data ante litteram. Today the new cloud computing technologies (such as Google Earth Engine) allow facing the complex problem of data management and processing of big data from remote sensing with new strategies that have revolutionized these data sources are used. From experiments on small areas, today we have moved to the possibility of operationally processing vast multidimensional and multitemporal datasets on a global scale. The increased availability of information from space is exemplified by the numerous services offered by the European Copernicus program. The presentation, starting from a brief introduction to remote sensing techniques, illustrates some examples of applications developed within the geoLAB – Geomatics Laboratory of the Department of Agriculture, Food, Environment and Forestry (DAGRI) and the UNIFI COPERNICUS Research Unit.

2nd Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2021-06-04 14:00 - 15:30 | Online
The Florence Center for Data Science is happy to present the second Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 4th of June 2021, from 2-3.30 pm.
The seminar will be held by Assistant Professor Cesare Bracco, PhD from the Department of Mathematics and Computer Science “Ulisse Dini” and Professor Leonardo Boncinelli from the Department of Economics and Management of the University of Florence.
Register her for the event (free of charge): Registration 2nd Seminar

Speaker: PhD Cesare Bracco
Department of Mathematics and Computer Science “Ulisse Dini”, University of Florence
Title: Scattered data: surface reconstruction and fault detection (joint work with O. Davydov, C. Giannelli, D. Großmann, S. Imperatore, D. Mokriš, A. Sestini)
Abstract: We will consider two aspects concerning scattered data approximation. The first is reconstructing a (parametrized) surface from a set of scattered points: the lack of structure in the data requires approximation methods that automatically adapt to the distribution and shape of the data themselves. We will discuss an effective approach to this issue based on hierarchical spline spaces, which can be locally refined, and therefore naturally lead to adaptive algorithms. The reconstruction problem contains another interesting problem: detecting the discontinuities the surface may have in order to reproduce them. Finding the discontinuity curves, usually called faults (or gradient faults when gradient discontinuities are considered), is actually an important issue in itself, with several applications, for example in image processing and geophysics. I will present a method to determine which points in the scattered data set lie close to a (gradient) fault, based on indicators obtained by using numerical differentiation formulas.
Speaker: Professor Leonardo Boncinelli
Department of Economics and Management, University of Florence
Title: Game-based education promotes sustainable water use
Abstract: In this study, we estimate the impact of a game-based educational program aimed at promoting sustainable water usage among 2nd-4th grade students and their families living in the municipality of Lucca, Italy. To this purpose, we exploited unique data from a quasi-experiment involving about two thousand students, one thousand participating (the treatment group), and one thousand not participating (the control group) in the program. Data were collected by means of a survey that we specifically designed and implemented for collecting students’ self-reported behaviours. Our estimates indicate that the program has been successful: the students in the program reported an increase in efficient water usage and an increase in the frequency of discussions with their parents about water usage; moreover, positive effects were still observed after six months. Our findings suggest that game-based educational programs can be an effective instrument to promote sustainable water consumption behaviors in children and their parents.

1st Seminar of the "D2 Seminar Series" - Florence Center for Data Science
2021-05-21 14:00 - 15:30 | Online
The Florence Center for Data Science is happy to present the first Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 21st of May 2021, from 2-3.30 pm.
Here the link for the Registration: https://forms.gle/1AvggjTGfKs1Xxy3A
Speaker: Professor Fabrizia Mealli
Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence
Title: Assessing causality under interference
Abstract: Causal inference from non-experimental data is challenging; it is even more challenging when units are connected through a network. Interference issues may arise, in that potential outcomes of a unit depend on its treatment as well as on the treatments of other units, such as their neighbours in the network. In addition, the typical unconfoundedness assumption must be extended—say, to include the treatment of neighbours, and individual and neighbourhood covariates—to guarantee identification and valid inference. These issues will be discussed, new estimands introduced to define treatment and interference effects and the bias of a naive estimator that wrongly assumes away interference will be shown. A covariate-adjustment method leading to valid estimates of treatment and interference effects in observational studies on networks will be introduced and applied to a problem of assessing the effect of air quality regulations (installation of scrubbers on power plants) on health in the USA.
Speaker: Professor Andrew Bagdanov
Department of Information Engineering, University of Florence
Title: Lifelong Learning at the end of the (new) Early Years
Abstract: Lifelong learning, also often referred to as continual or incremental learning, refers to the training of artificially intelligent systems able to continuously learn to address new tasks from new data while preserving knowledge learned from previously learned ones. Lifelong learning is currently enjoying a sort of renaissance due to renewed interest from the Deep Learning community. In this seminar, I will introduce the overall framework of continual learning, discuss the fundamental role played by the stability-plasticity dilemma in understanding catastrophic forgetting in lifelong learning systems, and present a broad panorama of recent results in class-incremental learning. I will conclude the discussion with a look at current trends, open problems, and low-hanging opportunities in this area.

Kick-off Meeting - Master II Livello DATA SCIENCE AND STATISTICAL LEARNING - MD2SL
2021-02-15 17:00 - 18:30
We are pleased to announce the kick-off meeting for the MD2SL Master, which will be held on February 15th at 17:00.
Further details are available at this link.

CEN-IBS/GMDS Invited Session on "Causal Inference and Machine Learning"
2020-09-28 15:00 - 17:00
“Causal Inference and Machine Learning” – a virtual
satellite invited session of the 2020 joint conference of the GMDS &
CEN-IBS – 28 September, 3-5pm (CEST, Berlin time).
More information on the website https://www.eventbrite.de/e/cen-ibsgmds-invited-session-on-causal-inference-and-machine-learning-tickets-116222778459
Note: the event is free, but you need to register via this eventbrite site https://www.eventbrite.de/e/cen-ibsgmds-invited-session-on-causal-inference-and-machine-learning-tickets-116222778459

2020 Applied Bayesian Statistics Summer School: BAYESIAN CAUSAL INFERENCE
2020-06-08 08:00 - 2020-06-12 17:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
The Applied Bayesian Statistics summer school has been running since 2004. Since 2012 it is organised by:

IMATI CNR Istituto di Matematica Applicata e Tecnologie Informatiche, Consiglio Nazionale delle Ricerche, Milano
Dipartimento di Scienze Statistiche Università Cattolica, Milano

The 2020 Applied Bayesian Statistics Summer School (17th edition) will be held in Florence, Italy. The topic chosen for the 2020 school is
Bayesian Causal Inference. The lecturer is Prof. Fan Li (Department of Statistical Science, Duke University, Durham, NC, USA).
Further details are provided on the conference webpage.
Local contributing organization:
Florence Center for Data Science (FDS)
Department of Statistics, Computer Science, Applications (DISIA), University of Florence
