A reduced algorithm from Faugeras-Bethods theorem in labeling problems

This paper illustrates a new approach to labeling ("object classification") problems, and it targets the simplification of a (computationally) complex algorithm based on Faugeras and Berthod's theorem.

Artificial Neural Networks in Financial Modelling

The study of Artificial Neural Networks derives from first trials to translate in mathematical models the principles of biological processing. An Artificial Neural Network deals with generating, in the fastest times, an implicit and predictive model of the evolution of a system. In particular, it derives from experience its ability to be able to recognize some behaviours or situations and to suggest how to take them into account. This work illustrates an approach to the use of Artificial Neural Networks for Financial Modelling; we aim to explore the structural differences (and implications) between one- and multi- agent and population models. In one-population models, ANNs are involved as forecasting devices with wealth-maximizing agents (in which agents make decisions so as to achieve an utility maximization following non-linear models to do forecasting), while in multipopulation models agents do not follow predetermined rules, but tend to create their own behavioural rules as market data are collected. In particular, it is important to analyze diversities between one-agent and one-population models; in fact, in building one-population model it is possible to illustrate the market equilibrium endogenously, which is not possible in one-agent model where all the environmental characteristics are taken as given and beyond the control of the single agent.artificial neural network, financial modelling, population model, market equilibrium.

A Data Set Generation Algorithm in Combinatorial Auctions

The generation of realistic data sets in a Combinatorial Auction may be a challenging problem. Well-formed data sets are very useful in the evaluation of algorithms trying to solve the winner determination problem. In this paper a general data set generation scheme is presented, both from an algorithmic and economic point of view. As a case study, a possible auction setting is discussed where the goods on sale are connections between points in space.bid, combinatorial auction, data set generation.

Baum-Eagon inequality in probabilistic labeling problems

This work illustrates an approach to the study of labeling, aka 'object classification'. This kind of parallel computing problem well suites to AI applications (pattern recognition, edge detection, etc.) Our target consists in simplifying an overly computationally costly algorithm proposed by Faugeras and Berthod; using Baum-Eagon theorem, we obtained a reduced algorithm which produces results comparable with other more complex approaches.labeling, artificial intelligence, edge detection, probabilistic algorithms, pixel classification

Diagnostic issues faced by a rare disease healthcare network during Covid-19 outbreak: data from the Campania Rare Disease Registry

Background: The aims of this study were: to investigate the capacity of the rare disease healthcare network in Campania to diagnose patients with rare diseases during the outbreak of Covid-19; and to shed light on problematic diagnoses during this period. Methods: To describe the impact of the Covid-19 pandemic on the diagnosis of patients with rare diseases, a retrospective analysis of the Campania Region Rare Disease Registry was performed. A tailored questionnaire was sent to rare disease experts to investigate major issues during the emergency period. Results: Prevalence of new diagnoses of rare disease in March and April 2020 was significantly lower than in 2019 (117 versus 317, P < 0.001 and 37 versus 349, P < 0.001, respectively) and 2018 (117 versus 389, P < 0.001 and 37 versus 282, P < 0.001, respectively). Eighty-two among 98 rare disease experts completed the questionnaire. Diagnostic success (95%), access to diagnosis (80%) and follow-up (72%), lack of Personal Protective Equipment (60%), lack of Covid-19 guidelines (50%) and the need for home therapy (78%) were the most important issues raised during Covid-19 outbreak. Conclusions: This study describes the effects of the Covid-19 outbreak on the diagnosis of rare disease in a single Italian region and investigates potential issues of diagnosis and management during this period