Unified Bayesian Conditional Autoregressive Risk Measures using the Skew Exponential Power Distribution

Conditional Autoregressive Value-at-Risk and Conditional Autoregressive Expectile have become two popular approaches for direct measurement of market risk. Since their introduction several improvements both in the Bayesian and in the classical framework have been proposed to better account for asymmetry and local non-linearity. Here we propose a unified Bayesian Conditional Autoregressive Risk Measures approach by using the Skew Exponential Power distribution. Further, we extend the proposed models using a semiparametric P-spline approximation answering for a flexible way to consider the presence of non-linearity. To make the statistical inference we adapt the MCMC algorithm proposed in Bernardi et al. (2018) to our case. The effectiveness of the whole approach is demonstrated using real data on daily return of five stock market indices

A Monte-Carlo ab-initio algorithm for the multiscale simulation of compressible multiphase flows

We propose a novel Monte-Carlo based ab-initio algorithm for directly computing the statistics for quantities of interest in an immiscible two-phase compressible flow. Our algorithm samples the underlying probability space and evolves these samples with a sharp interface front-tracking scheme. Consequently, statistical information is generated without resorting to any closure assumptions and information about the underlying microstructure is implicitly included. The proposed algorithm is tested on a suite of numerical experiments and we observe that the ab-initio procedure can simulate a variety of flow regimes robustly and converges with respect of refinement of number of samples as well as number of bubbles per volume. The results are also compared with a state-of-the-art discrete equation method to reveal the inherent limitations of existing macroscopic models

Mobile Traffic Prediction at the Edge through Distributed and Transfer Learning

Traffic prediction represents one of the crucial tasks for smartly optimizing the mobile network. The research in this topic concentrated in making predictions in a centralized fashion, i.e., by collecting data from the different network elements. This translates to a considerable amount of energy for data transmission and processing. In this work, we propose a novel prediction framework based on edge computing which uses datasets obtained on the edge through a large measurement campaign. Two main Deep Learning architectures are designed, based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), and tested under different training conditions. In addition, Knowledge Transfer Learning (KTL) techniques are employed to improve the performance of the models while reducing the required computational resources. Simulation results show that the CNN architectures outperform the RNNs. An estimation for the needed training energy is provided, highlighting KTL ability to reduce the energy footprint of the models of 60% and 90% for CNNs and RNNs, respectively. Finally, two cutting-edge explainable Artificial Intelligence techniques are employed to interpret the derived learning models.Comment: 12 pages, 9 figure

Quantile mixed graphical models with an application to mass public shootings in the United States

Over the last fifty years, the United States have experienced hundreds of mass public shootings that resulted in thousands of victims. Characterized by their frequent occurrence and devastating nature, mass shootings have become a major public health hazard that dramatically impact safety and well-being of individuals and communities. Given the epidemic traits of this phenomenon, there have been concerted efforts to understand the root causes that lead to public mass shootings in order to implement effective prevention strategies. We propose a quantile mixed graphical model for investigating the intricacies of inter- and infra-domain relationships of this complex phenomenon, where conditional relations between discrete and continuous variables are modeled without stringent distributional assumptions using Parzen's definition of mid-quantile. To retrieve the graph structure and recover only the most relevant connections, we consider the neighborhood selection approach in which conditional mid-quantiles of each variable in the network are modeled as a sparse function of all others. We propose a two-step procedure to estimate the graph where, in the first step, conditional mid-probabilities are obtained semi-parametrically and, in the second step, the model parameters are estimated by solving an implicit equation with a LASSO penalty

On the discrete equation model for compressible multiphase fluid flows

The modeling of multi-phase flow is very challenging, given the range of scales as well as the diversity of flow regimes that one encounters in this context. We revisit the discrete equation method (DEM) for two-phase flow in the absence of heat conduction and mass transfer. We analyze the resulting probability coefficients and prove their local convexity, rigorously establishing that our version of DEM can model different flow regimes ranging from the disperse to stratified (or separated) flow. Moreover, we reformulate the underlying mesoscopic model in terms of an one-parameter family of PDEs that interpolates between different flow regimes. We also propose two sets of procedures to enforce relaxation to equilibrium. We perform several numerical tests to show the flexibility of the proposed formulation, as well as to interpret different model components. The one-parameter family of PDEs provides an unified framework for modeling mean quantities for a multiphase flow, while at the same time identifying two key parameters that model the inherent uncertainty in terms of the underlying microstructure

Aberrant early in life stimulation of the stress-response system affects emotional contagion and oxytocin regulation in adult male mice

Results over the last decades have provided evidence suggesting that HPA axis dysfunction is a major risk factor predisposing to the development of psychopathological behaviour. This susceptibility can be programmed during developmental windows of marked neuroplasticity, allowing early-life adversity to convey vulnerability to mental illness later in life. Besides genetic predisposition, also environmental factors play a pivotal role in this process, through embodiment of the mother’s emotions, or via nutrients and hormones transferred through the placenta and the maternal milk. The aim of the current translational study was to mimic a severe stress condition by exposing female CD-1 mouse dams to abnormal levels of corticosterone (80 µg/mL) in the drinking water either during the last week of pregnancy (PreCORT) or the first one of lactation (PostCORT), compared to an Animal Facility Rearing (AFR) control group. When tested as adults, male mice from PostCORT offspring and somewhat less the PreCORT mice exhibited a markedly increased corticosterone response to acute restraint stress, compared to perinatal AFR controls. Aberrant persistence of adolescence-typical increased interest towards novel social stimuli and somewhat deficient emotional contagion also characterised profiles in both perinatal-CORT groups. Intranasal oxytocin (0 or 20.0 µg/kg) generally managed to reduce the stress response and restore a regular behavioural phenotype. Alterations in density of glucocorticoid and mineralocorticoid receptors, oxytocin and µ- and κ-opioid receptors were found. Changes differed as a function of brain areas and the specific age window of perinatal aberrant stimulation of the HPA axis. Present results provided experimental evidence in a translational mouse model that precocious adversity represents a risk factor predisposing to the development of psychopathological behaviour

Smart Welfare

Grazie ai programmi P.I.P.P.I. (Serbati, Milani, 2019) e Reddito di Cittadi-nanza (Milani, Petrella, Colombini, 2019), della cui implementazione siamo incaricati dal Ministero del Lavoro e delle Politiche Sociali, beneficiamo di un osservatorio di particolare ampiezza su ciò che accade nel sistema di wel-fare per l’infanzia e la famiglia nell’intero paese. Nei primi giorni della pan-demia, in particolare dopo la chiusura del 9.3.2020, abbiamo osservato cambiamenti inattesi, repentini e ampi nelle pratiche degli operatori. Cam-biamenti così peculiari da aver fatto emergere, nel tempo di un battito d’ali, una parola nuova, necessaria a descriverli: smart welfare

Diagnostic predictors of immunotherapy response in head and neck squamous cell carcinoma

Programmed cell death ligand-1 (PD-L1) binds PD-1 on CD8+ lymphocytes, inhibiting their cytotoxic action. Its aberrant expression by head and neck squamous cell carcinoma (HNSCC) cells leads to immune escape. Pembrolizumab and nivolumab, two humanized monoclonal antibodies against PD-1, have been approved in HNSCC treatment, but similar to 60% of patients with recurrent or metastatic HNSCC fail to respond to immunotherapy and only 20 to 30% of treated patients have long-term benefits. The purpose of this review is to analyze all the fragmentary evidence present in the literature to identify what future diagnostic markers could be useful for predicting, together with PD-L1 CPS, the response to immunotherapy and its durability. We searched PubMed, Embase, and the Cochrane Register of Controlled Trials and we summarize the evidence collected in this review. We confirmed that PD-L1 CPS is a predictor of response to immunotherapy, but it should be measured across multiple biopsies and repeatedly over time. PD-L2, IFN-gamma, EGFR, VEGF, TGF-beta, TMB, blood TMB, CD73, TILs, alternative splicing, tumor microenvironment, and some macroscopic and radiological features are promising predictors worthy of further studies. Studies comparing predictors appear to give greater potency to TMB and CXCR9