Coarse-Grained Molecular Dynamics with Normalizing Flows.

We propose a sampling algorithm relying on a collective variable (CV) of midsize dimension modeled by a normalizing flow and using nonequilibrium dynamics to propose full configurational moves from the proposition of a refreshed value of the CV made by the flow. The algorithm takes the form of a Markov chain with nonlocal updates, allowing jumps through energy barriers across metastable states. The flow is trained throughout the algorithm to reproduce the free energy landscape of the CV.

Intrinsic dimension as a multi-scale summary statistics in network modeling.

Complex networks are powerful mathematical tools for modelling and understanding the behaviour of highly interconnected systems. However, existing methods for analyzing these networks focus on local properties (e.g.

Maximally informative feature selection using Information Imbalance: Application to COVID-19 severity prediction.

Clinical databases typically include, for each patient, many heterogeneous features, for example blood exams, the clinical history before the onset of the disease, the evolution of the symptoms, the results of imaging exams, and many others. We here propose to exploit a recently developed statistical approach, the Information Imbalance, to compare different subsets of patient features and automatically select the set of features that is maximally informative for a given clinical purpose, especially in minority classes. We adapt the Information Imbalance approach to work in a clinical framework, where patient features are often categorical and are generally available only for a fraction of the patients.

Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks.

We introduce an approach which allows detecting causal relationships between variables for which the time evolution is available. Causality is assessed by a variational scheme based on the Information Imbalance of distance ranks, a statistical test capable of inferring the relative information content of different distance measures. We test whether the predictability of a putative driven system Y can be improved by incorporating information from a potential driver system X, without explicitly modeling the underlying dynamics and without the need to compute probability densities of the dynamic variables.

Improving acute stroke assessment in non-enhanced computed tomography: automated tool for early ischemic lesion volume detection.

Background And Objectives: ASPECTs is a widely used marker to identify early stroke signs on non-enhanced computed tomography (NECT), yet it presents interindividual variability and it may be hard to use for non-experts. We introduce an algorithm capable of automatically estimating the NECT volumetric extension of early acute ischemic changes in the 3D space. We compared the power of this marker with ASPECTs evaluated by experienced practitioner in predicting the clinical outcome.