3IA PhD/Postdoc Seminar #7

Published on September 24, 2021 Updated on October 24, 2022

on the October 1, 2021

from 10:30am to 12:00pm



10:30 - 11:00
Vasiliki Stergiopoulou (CNRS)

COL0RME: Super-Resolution Microscopy Based on the Localization of Sparse Blinking Fluorophores
Abstract: Super-resolution techniques are frequently applied in fluorescence microscopy to overcome the physical barriers caused by light diffraction and to allow the observation of otherwise indistinguishable biological entities. However, state-of-the-art approaches often suffer from low temporal resolution, while further requiring specific and demanding acquisition conditions. By analyzing the stochastic fluctuations of standard fluorescent molecules, a solution to the aforementioned limitations is provided, as sufficiently high spatio-temporal resolution for live-cell imaging can be achieved. Based on this idea, we present COL0RME, a method for COvariance-based L0 super-Resolution Microscopy with intensity Estimation that achieves good spatio-temporal resolution by solving a sparse optimization problem in the covariance domain. The method is composed of two steps: the former where molecules' accurate localization is provided; the latter where real intensity values are computed given the pre-estimated support. Numerical results both on synthetic and real fluorescent microscopy images are available, showing that COL0RME outperforms competing methods exploiting analogously temporal fluctuations; in particular it achieves better localization, reduces background artifacts and avoids fine parameter tuning.

11:00 - 11:30
Ali Ballout (UCA)

Predicting the Possibilistic Score of Atomic Candidate OWL Axioms
Abstract: In the framework of ontology learning, we tackle the challenge of predicting if candidate axioms are acceptable (i.e., compatible with available evidence) or not. The prediction is done using a Machine-learning model, and is based on a semantic similarity measure extracted from the subsumption hierarchy of an ontology. We prove that the proposed method is able to learn the acceptability labels of candidate OWL axioms with high accuracy, and that it can do so for multiple types of OWL axioms.

11:30 - 12:00

Open discussion on the two contributions