Structure matters: Assessing the statistical significance of network topologies.

Network analysis has found widespread utility in many research areas. However, assessing the statistical significance of observed relationships within networks remains a complex challenge. Traditional node permutation tests are often insufficient in capturing the effect of changing network topology by creating reliable null distributions.

Modeling financial distress propagation on customer-supplier networks.

Financial networks have been the object of intense quantitative analysis during the last few decades. Their structure and the dynamical processes on top of them are of utmost importance to understand the emergent collective behavior behind economic and financial crises. In this paper, we propose a stylized model to understand the "domino effect" of distress in client-supplier networks.

Differential Replication for Credit Scoring in Regulated Environments.

Differential replication is a method to adapt existing machine learning solutions to the demands of highly regulated environments by reusing knowledge from one generation to the next. Copying is a technique that allows differential replication by projecting a given classifier onto a new hypothesis space, in circumstances where access to both the original solution and its training data is limited. The resulting model replicates the original decision behavior while displaying new features and characteristics.

Environmental Adaptation and Differential Replication in Machine Learning.

When deployed in the wild, machine learning models are usually confronted with an environment that imposes severe constraints. As this environment evolves, so do these constraints. As a result, the feasible set of solutions for the considered need is prone to change in time.

Risk mitigation in algorithmic accountability: The role of machine learning copies.

Machine learning plays an increasingly important role in our society and economy and is already having an impact on our daily life in many different ways. From several perspectives, machine learning is seen as the new engine of productivity and economic growth. It can increase the business efficiency and improve any decision-making process, and of course, spawn the creation of new products and services by using complex machine learning algorithms.

The keys to control a COVID-19 outbreak in a haemodialysis unit.

Background: The high rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spreading represents a challenge to haemodialysis (HD) units. While fast isolation of suspected cases plays an essential role to avoid disease outbreaks, significant rates of asymptomatic cases have recently been described. After detecting an outbreak in one of our HD clinics, wide SARS-CoV-2 screening and segregation of confirmed cases were performed.