Meta-Prior: Meta learning for Adaptive Inverse Problem Solvers

Deep neural networks have become a foundational tool for addressing imaging inverse problems. They are typically trained for a specific task, with a supervised loss to learn a mapping from the observations to the image to recover. However, real-world imaging challenges often lack ground truth data, rendering traditional supervised approaches ineffective. Moreover, for each new imaging task, a new model needs to be trained from scratch, wasting time and resources. To overcome these limitations, we introduce a novel approach based on meta-learning. Our method trains a meta-model on a diverse set of imaging tasks that allows the model to be efficiently fine-tuned for specific tasks with few fine-tuning steps. We show that the proposed method extends to the unsupervised setting, where no ground truth data is available. In its bilevel formulation, the outer level uses a supervised loss, that evaluates how well the fine-tuned model performs, while the inner loss can be either supervised or unsupervised, relying only on the measurement operator. This allows the meta-model to leverage a few ground truth samples for each task while being able to generalize to new imaging tasks. We show that in simple settings, this approach recovers the Bayes optimal estimator, illustrating the soundness of our approach. We also demonstrate our method's effectiveness on various tasks, including image processing and magnetic resonance imaging

Transformations et inerties du bénévolat associatif sur la période 1982-2002

International audienceBien que le discours actuel tende à souligner un certain essoufflement de l'engagement militant, le bénévolat aujourd'hui se porte bien. C'est en tout cas ce que rapportent des enquêtes récentes qui soulignent le dynamisme du tissu associatif et de ceux qui participent à son développement qu'ils soient simples adhérents ou dirigeant

A License-Based Search Engine

International audienceThe reuse of licensed resources to produce new ones is very common and encouraged on the Web. But producing resources whose licenses are compliant with all reused resource licenses is not easy. It is necessary to know (1) the set of licenses with which the license of the produced resource is compliant and (2) what are the available resources whose licenses are part of this set. With CaLi, we provide an answer to the first concern. CaLi is a lattice-based model that partially orders licenses in terms of compatibility and compliance. In this demonstration, we illustrate the usability of CaLi through a prototype for the second concern. That is, based on a CaLi ordering of licenses we implement a license-based search engine which can answer questions such as "find licensed resources that can be reused under a given license" or "find licensed resources that can reuse a resource that has a particular license"

Computing combustion noise by combining Large Eddy Simulation with analytical models for the propagation of waves through turbine blades

Two mechanisms control combustion noise generation as shown by Marble and Candel [1]: direct noise, in which acoustic waves propagate through the turbine stages and indirect noise, in which vorticity and/or entropy waves generate noise as they are convected through turbine stages. A method to calculate combustion-generated noise has been implemented in a tool called CHORUS. The method uses the Large eddy simulations of the combustion chamber obtained with the unstructured solver AVBP developed at CERFACS [2] and analytical models for the propagation through turbine stages. The propagation models [3] use the compact row hypothesis to write matching conditions between the inlet and the outlet of a turbine stage. Using numerical simulations, the validity of the analytical methods is studied and the errors made quantified

Understanding approximate and unrolled dictionary learning for pattern recovery

International audienceDictionary learning consists of finding a sparse representation from noisy data and is a common way to encode data-driven prior knowledge on signals. Alternating minimization (AM) is standard for the underlying optimization, where gradient descent steps alternate with sparse coding procedures. The major drawback of this method is its prohibitive computational cost, making it unpractical on large real-world data sets. This work studies an approximate formulation of dictionary learning based on unrolling and compares it to alternating minimization to find the best trade-off between speed and precision. We analyze the asymptotic behavior and convergence rate of gradients estimates in both methods. We show that unrolling performs better on the support of the inner problem solution and during the first iterations. Finally, we apply unrolling on pattern learning in magnetoencephalography (MEG) with the help of a stochastic algorithm and compare the performance to a state-of-the-art method

Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals

When dealing with electro or magnetoencephalography records, many supervised prediction tasks are solved by working with covariance matrices to summarize the signals. Learning with these matrices requires using Riemanian geometry to account for their structure. In this paper, we propose a new method to deal with distributions of covariance matrices and demonstrate its computational efficiency on M/EEG multivariate time series. More specifically, we define a Sliced-Wasserstein distance between measures of symmetric positive definite matrices that comes with strong theoretical guarantees. Then, we take advantage of its properties and kernel methods to apply this distance to brain-age prediction from MEG data and compare it to state-of-the-art algorithms based on Riemannian geometry. Finally, we show that it is an efficient surrogate to the Wasserstein distance in domain adaptation for Brain Computer Interface applications

Modeling crowd dynamics through coarse-grained data analysis

Understanding and predicting the collective behaviour of crowds is essential to improve the efficiency of pedestrian flows in urban areas and minimize the risks of accidents at mass events. We advocate for the development of crowd traffic management systems, whereby observations of crowds can be coupled to fast and reliable models to produce rapid predictions of the crowd movement and eventually help crowd managers choose between tailored optimization strategies. Here, we propose a Bi-directional Macroscopic (BM) model as the core of such a system. Its key input is the fundamental diagram for bi-directional flows, i.e. the relation between the pedestrian fluxes and densities. We design and run a laboratory experiments involving a total of 119 participants walking in opposite directions in a circular corridor and show that the model is able to accurately capture the experimental data in a typical crowd forecasting situation. Finally, we propose a simple segregation strategy for enhancing the traffic efficiency, and use the BM model to determine the conditions under which this strategy would be beneficial. The BM model, therefore, could serve as a building block to develop on the fly prediction of crowd movements and help deploying real-time crowd optimization strategies

QTLs and candidate genes for desiccation and abscisic acid content in maize kernels

<p>Abstract</p> <p>Background</p> <p>Kernel moisture at harvest is an important trait since a low value is required to prevent unexpected early germination and ensure seed preservation. It is also well known that early germination occurs in viviparous mutants, which are impaired in abscisic acid (ABA) biosynthesis. To provide some insight into the genetic determinism of kernel desiccation in maize, quantitative trait loci (QTLs) were detected for traits related to kernel moisture and ABA content in both embryo and endosperm during kernel desiccation. In parallel, the expression and mapping of genes involved in kernel desiccation and ABA biosynthesis, were examined to detect candidate genes.</p> <p>Results</p> <p>The use of an intermated recombinant inbred line population allowed for precise QTL mapping. For 29 traits examined in an unreplicated time course trial of days after pollination, a total of 78 QTLs were detected, 43 being related to kernel desiccation, 15 to kernel weight and 20 to ABA content. Multi QTL models explained 35 to 50% of the phenotypic variation for traits related to water status, indicating a large genetic control amenable to breeding. Ten of the 20 loci controlling ABA content colocated with previously detected QTLs controlling water status and ABA content in water stressed leaves. Mapping of candidate genes associated with kernel desiccation and ABA biosynthesis revealed several colocations between genes with putative functions and QTLs. Parallel investigation via RT-PCR experiments showed that the expression patterns of the ABA-responsive <it>Rab17 </it>and <it>Rab28 </it>genes as well as the late embryogenesis abundant <it>Emb5 </it>and aquaporin genes were related to desiccation rate and parental allele effect. Database searches led to the identification and mapping of two <it>zeaxanthin epoxidase </it>(<it>ZEP</it>) and five novel <it>9-cis-epoxycarotenoid dioxygenase </it>(<it>NCED</it>) related genes, both gene families being involved in ABA biosynthesis. The expression of these genes appeared independent in the embryo and endosperm and not correlated with ABA content in either tissue.</p> <p>Conclusions</p> <p>A high resolution QTL map for kernel desiccation and ABA content in embryo and endosperm showed several precise colocations between desiccation and ABA traits. Five new members of the maize <it>NCED </it>gene family and another maize <it>ZEP </it>gene were identified and mapped. Among all the identified candidates, aquaporins and members of the <it>Responsive to ABA </it>gene family appeared better candidates than <it>NCEDs </it>and <it>ZEPs</it>.</p