output
20162022
most citedFederated Learning Enables Big Data for Rare Cancer Boundary Detection

390 citations

7 papers

cs.LG2022★ 390 cited

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Sarthak Pati, Ujjwal Baid, Brandon Edwards +276

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…

nlin.CD2021★ 1 cited

Chaos synchronization with coexisting global fields

O. Alvarez-Llamoza, M. G. Cosenza

We investigate the phenomenon of chaos synchronization in systems subject to coexisting autonomous and external global fields by employing a simple model of coupled maps. Two state…

eess.IV2021★ 16 cited

The Federated Tumor Segmentation (FeTS) Challenge

Sarthak Pati, Ujjwal Baid, Maximilian Zenk +29

This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021. International challenges have become the standar…

nlin.AO2021

Chimeras and clusters emerging from robust-chaos dynamics

M. G. Cosenza, O. Alvarez-Llamoza, A. V. Cano

We show that dynamical clustering, where a system segregates into distinguishable subsets of synchronized elements, and chimera states, where differentiated subsets of synchronized…

physics.soc-ph2020

Leaders and obstacles raise cultural boundaries

M. G. Cosenza, O. Alvarez-Llamoza, C. Echeverría +1

We employ an agent-based model for cultural dynamics to investigate the effects of spatial heterogeneities on the collective behavior of a social system. We introduce heterogeneity…

physics.chem-ph2018★ 1 cited

Damping and clustering into crowded environment of catalytic chemical oscillators

Carlos Echeverria, José L. Herrera, Kay Tucci +2

A system formed by a crowded environment of catalytic obstacles and complex oscillatory chemical reactions is inquired. The obstacles are static spheres of equal radius, which are…

nlin.AO2016★ 1 cited

A mesoscopic model for binary fluids

C. Echeverria, K. Tucci, O. Alvarez-Llamoza +3

We propose a model to study symmetric binary fluids, based in the mesoscopic molecular simulation technique known as multiparticle collision, where space and state variables are co…