anniversary – PhD Seminars

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PhD Seminars Anniversary Event

PhD Seminars Anniversary Event

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2:00 pm: PhD Seminars VII

2:00 pm: PhD Seminars VIII

2:00 pm: PhD Seminars IX

Upcoming Seminars

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2:00 pm: PhD Seminars VII

2:00 pm: PhD Seminars VIII

2:00 pm: PhD Seminars IX

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Talk 1

Talk 1

Talk 1

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Talk 1

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Nathalie GAYRAUD 2017/09/07 2019/10/16Seminars, Special Editions

This year’s PhD seminars concluded with an anniversary event that marked both the 2 years since their creation and the end of the Digital Health, Biology and Earth chapter. Program 14:00 Event opening by Denis Talay and Gérard Giraudon 14:30 2 Years of Phd Seminars: Key numbers by Konstantinos Mavreas…

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Upcoming Seminars
- 2:00 pm – 4:00 pm, April 26, 2021 – PhD Seminars IX
Subjects of interest
applied mathematics asteroid exploration biological systems brain combinatorial optimization communication conductivity estimation dynamic dynamics EEG fixed point loops fluid dynamics functional emmbeddings fusion plasmas Galerkin time domain methods git health-biology infection spread invariant manifolds mapping marketing mesh modeling nanophotonics networks networks-systems-distributed-computing neuronal cells numerical simulations optimal transport optimisation optimization pattern recognition PDE protein function radar applications regularization retina routing applications scientific dissemination sed shape matching source localization stochastic gradient algorithm variability workshop
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12 2:00 pm: PhD Seminars VIII April 12, 2021 2:00 pm – 4:00 pm Talk 1 Speaker Maroua Tikat (WIMMICS) Title Interactive multimedia visualization for exploring and fixing a multi-dimensional metadata base of popular music Abstract This PHD thesis is concerned by the use of information visualization techniques as a mean to allow the exploration of a large dataset of music metadata. In this paper we review some of the major music datasets available, the data they contain, and how information visualization techniques have been used to explore such data. As we shall see, music is a complex entity that can be described as a multitude of multimedia attributes (ex. lyrics, chords, audio, graphics describing sound analysis, etc.). Thus, music datasets are often created by collecting data from specialized datasets. The integration of data from diverse sources might create problems of data quality (ex. ambiguities, imprecision, incomplete sources, conflicts, etc.). Traditionally, information visualization techniques are used to understand the corpus of data and identify causal relationships, trends, patterns of data concentrations. Nonetheless, we suggest that information visualization techniques be used to inspect the data quality of multivariate data sets and highlight the parts of the data sets that need to be fixed/improved. Moreover, using interactive techniques, we suggest that information visualization techniques could be used as entry point for repairing the data set. In the context of this PhD thesis, the research questions are: how to communicate problems related to data quality to the users, and how to visually represent the outcomes of methods used for data completion and correction (such as crowdsourcing, matrix vectorization, graph reasoning, among other). More

26 2:00 pm: PhD Seminars IX April 26, 2021 2:00 pm – 4:00 pm Talk 1 Speaker Sara Sedlar (Athena) Title A Fourier domain spherical convolutional neural network for brain tissue microstructure imaging via diffusion MRI Abstract Diffusion Magnetic Resonance Imaging (dMRI) is a non-invasive and in-vivo imaging technique tailored for tissue examination at a microscopic scale. Consequently, it is essential in the analysis of tissue microstructures of the central nervous system. To explain the measured signals, a number of biophysically inspired multi-compartment (MC) models have been proposed. They represent dMRI data as a linear combination of signals coming from different tissue compartments such as intra- and extra-axonal spaces, gray matter, cerebrospinal fluid, tumorous cells, etc. Multiple studies have shown that the parameters associated with some of these models have potential in the evaluation of several neurological diseases and in the characterization of early age brain development. However, estimation of these parameters via standard non-linear optimizers which include Levenberg-Marquardt and Gauss-Newton algorithms, often require a high number of sampling points and/or are computationally demanding, which limits their clinical application. Since in our work, we are considering dMRI signals acquired on spheres, to address the problem of microstructure parameter estimation, we propose a spherical CNN model with fully spectral domain convolutional and non-linear layers and with rotation invariant power spectrum features. In addition, the model takes into account the real nature of dMRI signals, uniform random distribution of sampling points and important noise which affects these signals. The proposed model is evaluated quantitatively and qualitatively on the problem of Neurite Orientation Dispersion and Density Imaging (NODDI) and Spherical Mean Technique (SMT) parameter estimation. The model is positively evaluated on the real data from Human Connectome Project (HCP) database and on the synthetic data generated by dmipy toolbox. More