Date: March 14th 2019, 11am
Speaker: Adrien Picquenot (CEA Saclay)
Title: Applying the GMCA to extended sources in X-Ray Astronomy
The Galileon model is a tensor-scalar theory of gravity which offers a theoretically viable explanation to the late acceleration of the Universe expansion and recovers General Relativity in the strong field limit. The main goal is to establish the status of the model from cosmological observations. Though, the multi-messenger observation of GW170817 and its consequences for the Galileon model will be briefly discussed, since most allowed Galileon scenarios have a gravitational wave speed different than the speed of light.
Most constraints obtained so far on Galileon model parameters from cosmological data were derived for the limited subset of tracker solutions and reported tensions between the model and data. We present here an exploration of the general solution of the Galileon model, which is confronted against recent cosmological data.
We find that, while the general solution provides a good fit to CMB spectra, it fails to reproduce cosmological data when extending the comparison to BAO and SNIa data. Tensions remain if the models are extended with an additional free parameter, such as the sum of active neutrino masses or the normalization of the CMB lensing spectrum.
Optimal transport has become a mathematical gem at the interface of probability, analysis and optimization. It is a theory longly developed by the mathematician community, started by Monge and followed by Kantorovich which found applications in several fields like differential geometry, PDEs or gradient flows just to name a few.
Lately, it began to make its way into the machine learning and data treatment community. The optimal transport can be used to define a distance that is very useful when comparing histograms or point clouds, a typical scenario in nowadays applications. Some breakthrough contributions, like the entropic regularization, allowed to convexify and efficiently solve the transport problem opening the doors for many applications like Wasserstein barycenters or dictionary learning for example.
Nevertheless, Optimal Transport has not entered fully into the signal treatment community. One of the obstacles is the fact that the theory is well developed in the space of nonnegative measures but very little work has been done to extend it to signed measures. Considering a machine learning point of view, this presentation will deal with some theoretic aspects of an Optimal Transport based "distance" for signed measures that can be useful for future applications like Blind Source Separation. An algorithm for its efficient calculation will be presented as well.
- The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM- Kostas
- Cosmological Constraints from Multiple Probes in the Dark Energy Survey - Martin
- How to insert the CosmoStat signature in e-mails - Fadi
- Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning - Fadi
- [Highlighted paper]
- Weak-lensing shear estimates with general adaptive moments, and studies of bias by pixellation, PSF distortions, and noise - Morgan
Date: January 14th 2018, 10am
Speaker: Pol del Aguila Pla (KTH Royal Institute of Technology)
Title: Cell detection by functional inverse diffusion and non-negative group sparsity - Biology, physics, math and engineering [slides]
Image-based immunoassays are used every day across the world to develop new drugs, diagnose diseases, and research the workings of the human body. Since August, some of these are analyzed by technology that, at its core, has an algorithm included in my Ph.D. work. In this talk, I will outline the research project that lead to this algorithm and go through the modeling and optimization results we present in  and . This will include, among others, the modeling of complex biochemical assays as systems of partial differential equations, a linear-systems view on diffusion models, investigations in group-sparsity regularization in function spaces, and first-order methods for optimization problems with more than 25 million variables. To conclude the presentation, I will go through the new paths we have started to explore in connecting all this work to deep learning frameworks .
: Pol del Aguila Pla and Joakim Jaldén, “Cell detection by functional inverse diffusion and non-negative group sparsity—Part I: Modeling and Inverse Problems”, IEEE Transactions on Signal Processing, vol. 66, no. 20, pp. 5407–5421, 2018
: Pol del Aguila Pla and Joakim Jaldén, “Cell detection by functional inverse diffusion and non-negative group sparsity—Part II: Proximal optimization and Performance Evaluation”, IEEE Transactions on Signal Processing, vol. 66, no. 20, pp. 5422–5437, 2018
: Pol del Aguila Pla, Vidit Saxena, and Joakim Jaldén, “SpotNet – Learned iterations for cell detection in image-based immunoassays”, Submitted to the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), arXiv: 1810.06132 [eess.SP]
Date: October 16th 2018, 2pm
Speaker: Sylvain Vanneste (LAL)
Title: Detecting CMB B-modes
The non-zero mass of neutrinos suppresses the growth of cosmic structure on small scales. Since the level of suppression depends on the sum of the masses of the three active neutrino species, the evolution of large-scale structure is a promising tool to constrain the total mass of neutrinos and possibly shed light on the mass hierarchy. I will discuss recent progress and future prospects to constrain the neutrino mass sum with cosmology, with a focus on observables in the nonlinear regime.