Vine copula modeling dependence among cyber risks: A dangerous regulatory paradox

Dependence among different cyber risk classes is a fundamentally underexplored topic in the literature. However, disregarding the dependence structure in cyber risk management leads to inconsistent estimates of potential unintended losses. To bridge this gap, this article adopts a regulatory perspective to develop vine copulas to capture dependence. In quantifying the solvency capital requirement gradient for cyber risk measurement according to Solvency II, a dangerous paradox emerges: an insurance company does not tend to provide cyber risk hedging products as they are excessively expensive and would require huge premiums that it would not be possible to find policyholders

SUPER-Net: Trustworthy Medical Image Segmentation with Uncertainty Propagation in Encoder-Decoder Networks

Deep Learning (DL) holds great promise in reshaping the healthcare industry owing to its precision, efficiency, and objectivity. However, the brittleness of DL models to noisy and out-of-distribution inputs is ailing their deployment in the clinic. Most models produce point estimates without further information about model uncertainty or confidence. This paper introduces a new Bayesian DL framework for uncertainty quantification in segmentation neural networks: SUPER-Net: trustworthy medical image Segmentation with Uncertainty Propagation in Encoder-decodeR Networks. SUPER-Net analytically propagates, using Taylor series approximations, the first two moments (mean and covariance) of the posterior distribution of the model parameters across the nonlinear layers. In particular, SUPER-Net simultaneously learns the mean and covariance without expensive post-hoc Monte Carlo sampling or model ensembling. The output consists of two simultaneous maps: the segmented image and its pixelwise uncertainty map, which corresponds to the covariance matrix of the predictive distribution. We conduct an extensive evaluation of SUPER-Net on medical image segmentation of Magnetic Resonances Imaging and Computed Tomography scans under various noisy and adversarial conditions. Our experiments on multiple benchmark datasets demonstrate that SUPER-Net is more robust to noise and adversarial attacks than state-of-the-art segmentation models. Moreover, the uncertainty map of the proposed SUPER-Net associates low confidence (or equivalently high uncertainty) to patches in the test input images that are corrupted with noise, artifacts, or adversarial attacks. Perhaps more importantly, the model exhibits the ability of self-assessment of its segmentation decisions, notably when making erroneous predictions due to noise or adversarial examples

Trustworthy Medical Segmentation with Uncertainty Estimation

Deep Learning (DL) holds great promise in reshaping the healthcare systems given its precision, efficiency, and objectivity. However, the brittleness of DL models to noisy and out-of-distribution inputs is ailing their deployment in the clinic. Most systems produce point estimates without further information about model uncertainty or confidence. This paper introduces a new Bayesian deep learning framework for uncertainty quantification in segmentation neural networks, specifically encoder-decoder architectures. The proposed framework uses the first-order Taylor series approximation to propagate and learn the first two moments (mean and covariance) of the distribution of the model parameters given the training data by maximizing the evidence lower bound. The output consists of two maps: the segmented image and the uncertainty map of the segmentation. The uncertainty in the segmentation decisions is captured by the covariance matrix of the predictive distribution. We evaluate the proposed framework on medical image segmentation data from Magnetic Resonances Imaging and Computed Tomography scans. Our experiments on multiple benchmark datasets demonstrate that the proposed framework is more robust to noise and adversarial attacks as compared to state-of-the-art segmentation models. Moreover, the uncertainty map of the proposed framework associates low confidence (or equivalently high uncertainty) to patches in the test input images that are corrupted with noise, artifacts or adversarial attacks. Thus, the model can self-assess its segmentation decisions when it makes an erroneous prediction or misses part of the segmentation structures, e.g., tumor, by presenting higher values in the uncertainty map

Effect of Covid-19 frailty heterogeneity on the future evolution of mortality by stratified weighting

The starting point of our research is the inadequacy of assuming, in the construction of a model of mortality, that frailty is constant for the individuals comprising a demographic population. This assumption is implicitly made by standard life table techniques. The substantial differences in the individual susceptibility to specific causes of death lead to heterogeneity in frailty, and this can have a material effect on mortality models and projections – specifically a bias due to the underestimation of longevity improvements. Given these considerations, in order to overcome the misrepresentation of the future mortality evolution, we develop a stochastic model based on a stratification weighting mechanism, which takes into account heterogeneity in frailty. Furthermore, the stratified stochastic model has been adapted also to capture Covid-19 frailty heterogeneity, that is a frailty worsening due to the Covid-19 virus. Based on different frailty levels characterising a population, which affect mortality differentials, the analysis allows for forecasting the temporary excess of deaths by the stratification schemes in a stochastic environment

Frailty-based Lee–Carter family of stochastic mortality models

In the actuarial literature, frailty is defined to be the unobserved variable which encompasses all the factors affecting human mortality other than gender and age. Heterogeneity in individual frailty can play a significant role in population mortality dynamics. In the present paper, we identify the main latent factors that explain the frailty component, in order to clarify its role in mortality projections. We show, using longitudinal survey data, that frailty is mainly due to co-morbidities that impact on the process of deterioration in terms of the human body’s physiological capacity. Accordingly, we provide frailty-based stochastic models for projecting mortality based on the Lee–Carter family of models. We propose several versions that consider frailty both as an age-dependent and a time-dependent factor and also combining the interaction effects of age and time in comparison with the general level of mortality, and compare the resulting mortality projections using data from England

Automated control procedures and first results from the temporary seismic monitoring of the 2012 Emilia sequence

After moderate to strong earthquakes in Italy or in the surrounding areas, the Istituto Nazionale di Geofisica e Vulcanologia (INGV; National Institute for Geophysics and Volcanology) activates a temporary seismic network infrastructure. This is devoted to integration with the Italian National Seismic Network (RSN) [Delladio 2011] in the epicentral area, thus improving the localization of the aftershocks distribution after a mainshock. This infrastructure is composed of a stand-alone, locally recording part (Re.Mo.) [Moretti et al. 2010] and a real-time telemetered part (Re.Mo.Tel.) [Abruzzese et al. 2011a, 2011b] that can stream data to the acquisition centers in Rome and Grottaminarda. After the May 20, 2012, Ml 5.9 earthquake in the Emilia region (northern Italy), the temporary network was deployed in the epicentral area; in particular, 10 telemetered and 12 stand-alone stations were installed [Moretti et al. 2012, this volume]. Using the dedicated connection between the acquisition center in Rome and the Ancona acquisition sub-center [Cattaneo et al. 2011], the signals of the real-time telemetered stations were acquired also in this sub-center. These were used for preliminary quality control, by adopting the standard procedures in use here (see next paragraph, and Monachesi et al. [2011]). The main purpose of the present study is a first report on this quality check, which should be taken into account for the correct use of these dat

Esperienze di monitoraggio integrato: il caso della Rete Sismometrica dell’Italia centro orientale e dei suoi servizi

Viene presentata l’esperienza maturata dagli operatori della sede di Ancona dell’INGV (INGV-AN) nell’ambito delle collaborazioni tra l’Istituto Nazionale di Geofisica e Vulcanologia (INGV) e la Regione Marche per il miglioramento delle attività di monitoraggio sismico. L’attività dell’INGV-AN aveva due scopi: migliorare le conoscenze sulla sismicità regionale a fini scientifici e perfezionare il servizio di informazione svolto per il Dipartimento per le Politiche Integrate di Sicurezza e per la Protezione Civile (DPISPC). Per il raggiungimento degli scopi si è proceduto all’incremento del numero di stazioni, alla trasformazione in real- time della vecchia rete dial-up, alla installazione di nuove stazioni accelerometriche, e all’utilizzo del complesso dei dati raccolti dalle stazioni accelerometriche e velocimetriche in funzione nel territorio regionale e in quelli immediatamente limitrofi, nonché allo scambio dati tra la sede INGV di Ancona e quella di Roma. I costi dell’intera operazione sono stati contenuti grazie all’utilizzo delle infrastrutture radio wireless della Regione Marche, della economica trasmissione UMTS, di acquisitori GAIA sviluppati dall’INGV e di economici ma efficaci accelerometri MEMS SF3000L della Colybris. Gli obiettivi raggiunti sono i presupposti per il proseguimento della collaborazione tra i due enti rivolta alla copertura più ampia possibile del territorio regionale con reti di rilevamento accelerometrico a basso costo e alla realizzazione di servizi sempre più finalizzati all’emergenza sismica

A Geological Itinerary Through the Southern Apennine Thrust-Belt (Basilicata—Southern Italy)

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BEACH BALLS IN ITALIA CENTRO-ORIENTALE

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