On the role of prosody in disambiguating wh-exclamatives and wh-interrogatives in Cosenza Italian

International audienceThis work investigates the role of prosody in the perception wh-exclamatives and (information seeking) wh-interrogatives in Cosenza Italian. We used reaction times (RTs) as a diagnostic of listeners' (in)security in sentence type disambiguation during a two-forced choice identification task. Our results show that listeners identify the two sentence types after the end of the utterance in most of the trials, and not before it. This suggests that prosodic cues that occur before the end of the utterance (e.g., in the prenuclear section of the intonational contour) are not strong enough by themselves guide the pragmatic interpretation of the utterances. Furthermore, our study shows that exclamatives are processed faster than interrogatives, but this effect disappears when segmental duration is taken into account

Artificial intelligence applied to bailout decisions in financial systemic risk management

We describe the bailout of banks by governments as a Markov Decision Process (MDP) where the actions are equity investments. The underlying dynamics is derived from the network of financial institutions linked by mutual exposures, and the negative rewards are associated to the banks' default. Each node represents a bank and is associated to a probability of default per unit time (PD) that depends on its capital and is increased by the default of neighbouring nodes. Governments can control the systemic risk of the network by providing additional capital to the banks, lowering their PD at the expense of an increased exposure in case of their failure. Considering the network of European global systemically important institutions, we find the optimal investment policy that solves the MDP, providing direct indications to governments and regulators on the best way of action to limit the effects of financial crises.Comment: 12 pages, 6 figure

A Closed-Form Filter for Binary Time Series

Non-Gaussian state-space models arise in several applications. Within this framework, the binary time series setting is a source of constant interest due to its relevance in many studies. However, unlike Gaussian state-space models, where filtering, predictive and smoothing distributions are available in closed-form, binary state-space models require approximations or sequential Monte Carlo strategies for inference and prediction. This is due to the apparent absence of conjugacy between the Gaussian states and the likelihood induced by the observation equation for the binary data. In this article we prove that the filtering, predictive and smoothing distributions in dynamic probit models with Gaussian state variables are, in fact, available and belong to a class of unified skew-normals (SUN) whose parameters can be updated recursively in time via analytical expressions. Also the functionals of these distributions depend on known functions, but their calculation requires intractable numerical integration. Leveraging the SUN properties, we address this point via new Monte Carlo methods based on independent and identically distributed samples from the smoothing distribution, which can naturally be adapted to the filtering and predictive case, thereby improving state-of-the-art approximate or sequential Monte Carlo inference in small-to-moderate dimensional studies. A scalable and optimal particle filter which exploits the SUN properties is also developed to deal with online inference in high dimensions. Performance gains over competitors are outlined in a real-data financial application

I learn. You learn. We learn? An experiment in collaborative concept mapping.

International audienc

Assessing COVID-19-Related Excess Mortality Using Multiple Approaches—Italy, 2020–2021

Introduction: Excess mortality (EM) is a valid indicator of COVID-19’s impact on public health. Several studies regarding the estimation of EM have been conducted in Italy, and some of them have shown conflicting values. We focused on three estimation models and compared their results with respect to the same target population, which allowed us to highlight their strengths and limitations. Methods: We selected three estimation models: model 1 (Maruotti et al.) is a Negative-Binomial GLMM with seasonal patterns; model 2 (Dorrucci et al.) is a Negative Binomial GLM epidemiological approach; and model 3 (Scortichini et al.) is a quasi-Poisson GLM time-series approach with temperature distributions. We extended the time windows of the original models until December 2021, computing various EM estimates to allow for comparisons. Results: We compared the results with our benchmark, the ISS-ISTAT official estimates. Model 1 was the most consistent, model 2 was almost identical, and model 3 differed from the two. Model 1 was the most stable towards changes in the baseline years, while model 2 had a lower cross-validation RMSE. Discussion: Presently, an unambiguous explanation of EM in Italy is not possible. We provide a range that we consider sound, given the high variability associated with the use of different models. However, all three models accurately represented the spatiotemporal trends of the pandemic waves in Italy

Estimated effectiveness of a primary cycle of protein recombinant vaccine nvx-cov2373 against COVID-19

Importance: Protein recombinant vaccine NVX-CoV2373 (Novavax) against COVID-19 was authorized for its use in adults in late 2021, but evidence on its estimated effectiveness in a general population is lacking. Objective: To estimate vaccine effectiveness of a primary cycle with NVX-CoV2373 against SARS-CoV-2 infection and symptomatic COVID-19. Design, setting, and participants: Retrospective cohort study linking data from the national vaccination registry and the COVID-19 surveillance system in Italy during a period of Omicron predominance. All adults starting a primary vaccination with NVX-CoV2373 between February 28 and September 4, 2022, were included, with follow-up ending on September 25, 2022. Data were analyzed in February 2023. Exposures: Partial (1 dose only) vaccination and full vaccination (2 doses) with NVX-CoV-2373. Main outcomes and measures: Notified SARS-CoV-2 infection and symptomatic COVID-19. Poisson regression models were used to estimate effectiveness against both outcomes. Adjusted estimated vaccine effectiveness was calculated as (1 - incidence rate ratio) × 100. Results: The study included 20 903 individuals who started the primary cycle during the study period. Median (IQR) age of participants was 52 (39-61) years, 10 794 (51.6%) were female, and 20 592 participants (98.5%) had no factors associated with risk for severe COVID-19. Adjusted estimated vaccine effectiveness against notified SARS-CoV-2 infection in those partially vaccinated with NVX-CoV2373 was 23% (95% CI, 13%-33%) and was 31% (95% CI, 22%-39%) in those fully vaccinated. Estimated vaccine effectiveness against symptomatic COVID-19 was 31% (95% CI, 16%-44%) in those partially vaccinated and 50% (95% CI, 40%-58%) in those fully vaccinated. Estimated effectiveness during the first 4 months after completion of the primary cycle decreased against SARS-CoV-2 infection but remained stable against symptomatic COVID-19. Conclusions and relevance: This cohort study found that, in an Omicron-dominant period, protein recombinant vaccine NVX-CoV2373 was associated with protection against SARS-CoV-2 infection and symptomatic COVID-19. The use of this vaccine could remain an important element in reducing the impact of the SARS-CoV-2 pandemic

Methods in prosody: A Romance language perspective

This book presents a collection of pioneering papers reflecting current methods in prosody research with a focus on Romance languages. The rapid expansion of the field of prosody research in the last decades has given rise to a proliferation of methods that has left little room for the critical assessment of these methods. The aim of this volume is to bridge this gap by embracing original contributions, in which experts in the field assess, reflect, and discuss different methods of data gathering and analysis. The book might thus be of interest to scholars and established researchers as well as to students and young academics who wish to explore the topic of prosody, an expanding and promising area of study

Methods in prosody: A Romance language perspective

This book presents a collection of pioneering papers reflecting current methods in prosody research with a focus on Romance languages. The rapid expansion of the field of prosody research in the last decades has given rise to a proliferation of methods that has left little room for the critical assessment of these methods. The aim of this volume is to bridge this gap by embracing original contributions, in which experts in the field assess, reflect, and discuss different methods of data gathering and analysis. The book might thus be of interest to scholars and established researchers as well as to students and young academics who wish to explore the topic of prosody, an expanding and promising area of study

Methods in prosody: A Romance language perspective

This book presents a collection of pioneering papers reflecting current methods in prosody research with a focus on Romance languages. The rapid expansion of the field of prosody research in the last decades has given rise to a proliferation of methods that has left little room for the critical assessment of these methods. The aim of this volume is to bridge this gap by embracing original contributions, in which experts in the field assess, reflect, and discuss different methods of data gathering and analysis. The book might thus be of interest to scholars and established researchers as well as to students and young academics who wish to explore the topic of prosody, an expanding and promising area of study