1,720,972 research outputs found
Relative privacy threats and learning from anonymized data
No full textWe consider group-based anonymization schemes, a popular approach to data publishing. This approach aims at protecting privacy of the individuals involved in a dataset, by releasing an obfuscated version of the original data, where the exact correspondence between individuals and attribute values is hidden. When publishing data about individuals, one must typically balance the learner's utility against the risk posed by an attacker, potentially targeting individuals in the dataset. Accordingly, we propose a unified Bayesian model of group-based schemes and a related MCMC methodology to learn the population parameters from an anonymized table. This allows one to analyze the risk for any individual in the dataset to be linked to a specific sensitive value, when the attacker knows the individual's nonsensitive attributes, beyond what is implied for the general population. We call this relative threat analysis. Finally, we illustrate the results obtained with the proposed methodology on a real-world dataset
The first wave of the SARS-CoV-2 epidemic in Tuscany (Italy): A SI2R2D compartmental model with uncertainty evaluation
No full textWith the aim of studying the spread of the SARS-CoV-2 infection in the Tuscany region of Italy during the first epidemic wave (February-June 2020), we define a compartmental model that accounts for both detected and undetected infections and assumes that only notified cases can die. We estimate the infection fatality rate, the case fatality rate, and the basic reproduction number, modeled as a time-varying function, by calibrating on the cumulative daily number of observed deaths and notified infected, after fixing to plausible values the other model parameters to assure identifiability. The confidence intervals are estimated by a parametric bootstrap procedure and a Global Sensitivity Analysis is performed to assess the sensitivity of the estimates to changes in the values of the fixed parameters. According to our results, the basic reproduction number drops from an initial value of 6.055 to 0 at the end of the national lockdown, then it grows again, but remaining under 1. At the beginning of the epidemic, the case and the infection fatality rates are estimated to be 13.1% and 2.3%, respectively. Among the parameters considered as fixed, the average time from infection to recovery for the not notified infected appears to be the most impacting one on the model estimates. The probability for an infected to be notified has a relevant impact on the infection fatality rate and on the shape of the epidemic curve. This stresses the need of collecting information on these parameters to better understand the phenomenon and get reliable predictions
Il processo di certificazione per la redazione del manuale di manutenzione. Il sistema dei mulini di Ottati
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Development of a model for the prediction of mechanical properties for Al-Si-Mg castings
No full textA356 alloy is widely used to produce structural components by means of Low Pressure Die Casting (LPDC) process. Generally, a T6 heat treatment (solution, quenching and aging treatment) is carried out to improve the strength of the casting. Nowadays, software simulation of casting processes and solidification phenomena is a common practice for designing sound components even if mechanical strength values and their correlation with microstructure parameters are not given. However, the possibility to predict material behaviour before producing the castings would represent an additional precious tool for exploiting material properties. In the present study, a model for the estimation of tensile as-cast properties based on casting simulation was validated on a 22'' wheel obtained by LPDC. Microstructural and mechanical properties were investigated on the component both in as-cast and T6 condition. First, areas with different thicknesses and cooling conditions were analysed and secondary dendrite arm spacing (SDAS) measurements were carried out. Subsequently, tensile tests were performed on specimens from rim and spokes. Experimental data were used to verify the reliability of simulation results and to validate the as-cast model. Based on additional information provided by simulation software and experimental data, a mathematical model to predict the mechanical properties after T6 heat treatment was also proposed
Approximate Bayesian Computation for structural identification of ancient tie-rods using noisy modal data
No full textMasonry arches and vaults are common historic structural elements that frequently experience asymmetric loading due to seismic action or abutment settlements. Over the past few decades, numerous studies have sought to enhance our understanding of the structural behavior of these elements for the purpose of preventive conservation. The assessment of the structural performance of existing constructions typically relies on effective numerical models guided by a set of unknown input parameters, including geometry, mechanical characteristics, physical properties, and boundary conditions. These parameters can be estimated through deterministic optimization functions aimed at minimizing the discrepancy between the output of a numerical model and the measured dynamic and/or static structural response. However, deterministic approaches overlook uncertainties associated with both input parameters and measurements. In this context, the Bayesian approach proves valuable for estimating unknown numerical model parameters and their associated uncertainties (posterior distributions). This involves updating prior knowledge of model parameters (prior distributions) based on current measurements and explicitly considering all sources of uncertainties affecting observed quantities through likelihood functions. However, two significant challenges arise: the likelihood function may be unknown or too complex to evaluate, and the computational costs for approximating the posterior distribution can be prohibitive. This study addresses these challenges by employing Approximate Bayesian Computation (ABC) to handle the intractable likelihood function. Additionally, the computational burden is mitigated through the use of accurate surrogate models such as Polynomial Chaos Expansions (PCE) and Artificial Neural Networks (ANN). The research focuses on setting up numerical models for simple structural systems (tie-rods) and inferring unknown input parameters, such as mechanical properties and boundary conditions, through Bayesian updating based on observed structural responses (modal data, strains, displacements). The main novelties of this research regard, on the one hand, the proposal of a methodology for obtaining a reliable estimate of the axial force in ancient tie-rods accounting for different sources of uncertainty and, on the other hand, the application of ABC to obtain the structural identification inverse problem solution
Bayesian-based model updating using natural frequency data for historic masonry towers
No full textModel updating procedures are commonly used to identify numerical models of a structure to be subsequently used for reliable assessment of its behaviour under environmental loads. In the case of historic masonry buildings, the uncertainties that are involved in the knowledge process (material properties, geometry, boundary conditions, etc.) can severely affect the matching between the experimental data and the corresponding model output. To account for the different sources of uncertainties that are involved in the model updating procedure for historic confined masonry towers, this paper proposes an application of the Bayesian paradigm. Effects of parameter uncertainty, observation errors and model inadequacy are explored by comparing the output of the numerical model against real measured modal data. The proposed methodology aims at obtaining the posterior distribution of unknown quantities to estimate their uncertainty and to identify values of the parameters to be used in the numerical model for subsequent analyses. The comparison among the updated distributions related to different initial probabilistic modelling assumptions (prior distributions, measurement errors and modelling uncertainties) shows significant improvements of the predictive capabilities with a considerable reduction of the initial uncertainties, which confirm the potential of the proposed approach
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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