Ricerc@Sapienza

We systematically reviewed data about the effect of exogenous estrogens and progestogens on the course of migraine during reproductive age. Thereafter a consensus procedure among international experts was undertaken to develop statements to support clinical decision making, in terms of possible effects on migraine course of exogenous estrogens and progestogens and on possible treatment of headache associated with the use or with the withdrawal of hormones. Overall, quality of current evidence is low.

Il contributo analizza le influenze della tutela multilivello dei diritti fondamentali sulla protezione delle coppie omosessuali nell’ambito europeo, con una particolare attenzione sul sistema della Convenzione europea dei diritti dell’uomo. Nello specifico, vengono esplorate tre tematiche potenzialmente idonee a contribuire allo sviluppo della giurisprudenza e all’evoluzione dello strumento convenzionale.

The aim of this work is to develop a fully-distributed algorithmic framework for training graph convolutional networks (GCNs). The proposed method is able to exploit the meaningful relational structure of the input data, which are collected by a set of agents that communicate over a sparse network topology. After formulating the centralized GCN training problem, we first show how to make inference in a distributed scenario where the underlying data graph is split among different agents. Then, we propose a distributed gradient descent procedure to solve the GCN training problem.

Ovarian cancer (OC) remains relatively rare, although it is among the top 4 causes of cancer death for women younger than 50. The aggressive nature of the disease and its often late diagnosis with peritoneal involvement have an impact on prognosis. The current scientific literature presents ambiguous or uncertain indications for management of peritoneal carcinosis (PC) from OC, both owing to the lack of sufficient scientific data and their heterogeneity or lack of consistency.

We study distributed stochastic nonconvex optimization in multi-agent networks. We introduce a novel algorithmic framework for the distributed minimization of the sum of the expected value of a smooth (possibly nonconvex) function-the agents' sum-utility-plus a convex (possibly nonsmooth) regularizer. The proposed method hinges on successive convex approximation (SCA) techniques, leveraging dynamic consensus as a mechanism to track the average gradient among the agents, and recursive averaging to recover the expected gradient of the sumutility function.