A Fast Multi-Layer Approximation to Semi-Discrete Optimal Transport

International audienceThe optimal transport (OT) framework has been largely used in inverse imaging and computer vision problems, as an interesting way to incorporate statistical constraints or priors. In recent years, OT has also been used in machine learning, mostly as a metric to compare probability distributions. This work addresses the semi-discrete OT problem where a continuous source distribution is matched to a discrete target distribution. We introduce a fast stochastic algorithm to approximate such a semi-discrete OT problem using a hierarchical multi-layer transport plan. This method allows for tractable computation in high-dimensional case and for large point-clouds, both during training and synthesis time. Experiments demonstrate its numerical advantage over multi-scale (or multi-level) methods. Applications to fast exemplar-based texture synthesis based on patch matching with two layers, also show stunning improvements over previous single layer approaches. This shallow model achieves comparable results with state-of-the-art deep learning methods, while being very compact, faster to train, and using a single image during training instead of a large dataset

Controlling the Biological Effects of Spermine Using a Synthetic Receptor

Polyamines play an important role in biology, yet their exact function in many processes is poorly understood. Artificial host molecules capable of sequestering polyamines could be useful tools for studying their cellular function. However, designing synthetic receptors with affinities sufficient to compete with biological polyamine receptors remains a huge challenge. Binding affinities of synthetic hosts are typically separated by a gap of several orders of magnitude from those of biomolecules. We now report that a dynamic combinatorial selection approach can deliver a synthetic receptor that bridges this gap. The selected receptor binds spermine with a dissociation constant of 22 nM, sufficient to remove it from its natural host DNA and reverse some of the biological effects of spermine on the nucleic acid. In low concentrations, spermine induces the formation of left-handed DNA, but upon addition of our receptor, the DNA reverts back to its right-handed form. NMR studies and computer simulations suggest that the spermine complex has the form of a pseudo-rotaxane. The spermine receptor is a promising lead for the development of therapeutics or molecular probes for elucidating spermine’s role in cell biology.

Valorisation du CO2 par carbonatation minérale avec le procédé d'attrition lixiviante

La présentation porte sur l'Analyse de Cycle de Vie (ACV) du procédé de carbonatation minérale par attrition lixiviante. Cette présentation a été donnée dans le cadre de la "Journée ACV pour la valorisation du CO2" organisée par l'ADEME et le Club CO2 le 30 mars 2018

Design of a hybrid leaching process for mineral carbonation of magnesium silicates: learnings and issues raised from combined experimental and geochemical modelling approaches

The formation of passivation layers is the major obstacle to the development of direct aqueous mineral carbonation of magnesium silicates, one of the most promising routes for large-scale CO2 mitigation. This observation has led to considerable research efforts to prevent the formation of these layers and/or to exfoliate them continuously during the reaction. This keynote gives an account of different solutions investigated by a French research consortium initiated within the framework of several collaborative projects, including the methodology developed to optimize process efficiency and cost. Chemical-based solutions using chelating agents were first examined, but the variety of minerals and also the complex nature of leached surface layers for a given ore demonstrated the need for highly versatile and tunable chemical systems (able to modulate the precipitation of silica or phyllosilicates without sequestrating alkaline metals) and thus the need for a more robust solution. Moving to mechanical exfoliation, it was found that concomitant attrition and leaching could drastically enhance the conversion of silicate ores under mild conditions (20 bar of CO2, 180°C). This was achieved inside the favourable environment of a stirred ball mill. Without the need for thermal pre-treatment, carbonation yields in excess of 80% were obtained after 24 hours starting with variously serpentinised ores or mining residues of minus 100 µm size fraction. Under these conditions, the performance of the attrition-leaching process is driven by the dissolution rate of continuously refreshed ore surfaces and thermodynamic equilibria. Coupled geochemical modelling and experimentation, including thorough product characterization proved the soundness of this concept. Geochemical modelling also guided the selection of suitable operating conditions and process inputs, including the choice of grinding media and the influence of minor elements (Al, M, Ca) on carbonation yield. In particular, the model predicted the existence of a CO2 partial pressure dependent threshold temperature above which precipitation of talc-like phases is favoured at the expense of carbonates. Current research developments are exploring the possibility of combining the attrition-leaching process with CO2-transfering agents, such as polyamines. These additives are expected to expand the process operating window to diluted flue gases (without pre-capture step) and milder temperatures, by (reversibly) concentrating CO2 in solution and by catalysing mineralisation. Other focus points of the work include process scale-up and operation in continuous mode, valorisation of the mineralisation products into construction materials and selective co-extraction of valuable metals

A Fast Multi-Layer Approximation to Semi-Discrete Optimal Transport

The optimal transport (OT) framework has been largely used in inverse imaging and computer vision problems, as an interesting way to incorporate statistical constraints or priors. In recent years, OT has also been used in machine learning, mostly as a metric to compare probability distributions. This work addresses the semi-discrete OT problem where a continuous source distribution is matched to a discrete target distribution. We introduce a fast stochastic algorithm to approximate such a semi-discrete OT problem using a hierarchical multi-layer transport plan. This method allows for tractable computation in high-dimensional case and for large point-clouds, both during training and synthesis time. Experiments demonstrate its numerical advantage over multi-scale (or multi-level) methods. Applications to fast exemplar-based texture synthesis based on patch matching with two layers, also show stunning improvements over previous single layer approaches. This shallow model achieves comparable results with state-of-the-art deep learning methods, while being very compact, faster to train, and using a single image during training instead of a large dataset.Generalized Optimal Transport Models for Image processin

A Multi-layer Approach to Semi-Discrete Optimal Transport with Applications to Texture Synthesis and Style Transfer

This paper investigates a new approach to approximate semi-discrete optimal transport for large-scale problem, i.e. in high dimension and for a large number of points. The proposed technique relies on a hierarchical decomposition of the target discrete distribution and the transport map itself. A stochastic optimization algorithm is derived to estimate the parameters of the corresponding multi-layer weighted nearest neighbor model. This model allows for fast evaluation during synthesis and training, for which it exhibits faster empirical convergence. Several applications to patch-based image processing are investigated: texture synthesis, texture inpainting, and style transfer. The proposed models compare favorably to the state of the art, either in terms of image quality, computation time, or regarding the number of parameters. Additionally, they do not require any pixel-based optimization or training on a large dataset of natural images

A Stochastic Multi-layer Algorithm for Semi-Discrete Optimal Transport with Applications to Texture Synthesis and Style Transfer

This paper investigates a new stochastic algorithm to approximate semi-discrete optimal transport for large-scale problem, i.e. in high dimension and for a large number of points. The proposed technique relies on a hierarchical decomposition of the target discrete distribution and the transport map itself. A stochastic optimization algorithm is derived to estimate the parameters of the corresponding multi-layer weighted nearest neighbor model. This model allows for fast evaluation during synthesis and training, for which it exhibits faster empirical convergence. Several applications to patch-based image processing are investigated: texture synthesis, texture inpainting, and style transfer. The proposed models compare favorably to the state of the art, either in terms of image quality, computation time, or regarding the number of parameters. Additionally, they do not require any pixel-based optimization or training on a large dataset of natural images.Models, Inference and Synthesis for Texture In ColorGeneralized Optimal Transport Models for Image processingRepenser la post-production d'archives avec des méthodes à patch, variationnelles et par apprentissag

Fonctionnalisation sur mesure de dendrimères phosphorés (de l'interface chimie-biologie aux matériaux hybrides)

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