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I processi basati sulla biomassa rinnovabile potranno contribuire alla futura richiesta di combustibili, prodotti chimici e materiali date le prospettive di compatibilità ambientale e la riduzione dell'uso di prodotti petrolchimici. L'uso di microrganismi per convertire la biomassa lignocellulosica nei prodotti desiderati è un'opportunità promettente. La combinazione della diversa natura delle biomasse di scarto, insieme alla biodiversità dei microrganismi, rappresentano un’ottimo punto di partenza per la produzione di un'ampia gamma di molecole di interesse industriale. Tuttavia, molti processi di produzione microbica non riescono a soddisfare i requisiti dei processi industriali a causa dell'inibizione della crescita cellulare. Composti inibitori, come gli acidi organici, rilasciati dal pretrattamento della materia prima, come le biomasse lignocellulosiche, oppure prodotti durante la fermentazione interferiscono negativamente con il metabolismo dei microrganismi, compromettendo il processo fermentativo. Lo scopo della ricerca descritta e ́ quello di valutare la possibilità di modificare la composizione lipidica della membrana di Saccharomyces cerevisiae per ottenere mutanti con una maggiore tolleranza agli acidi organici ed anche una migliore capacità di produzione di questi ultimi. Ci siamo concentrati sull'ergosterolo, un componente essenziale della membrana che contribuisce alla sua integrità, fluidità e permeabilità, nonché allo smistamento dei rafts lipidici, dei cluster arricchiti di steroli e sfingolipidi che ospitano diverse proteine di membrana, tra cui la pompa protonica ATPasica (Pma1). Dalla combinazione di saggi complementari in vitro e in vivo, abbiamo visto che la modulazione del contenuto di ergosterolo è fondamentale in S. cerevisiae per ottenere una maggiore tolleranza verso gli acidi organici. Nel saggio in vitro, abbiamo preparato vescicole lipidiche sintetiche contenenti diverse quantità di ergosterolo. I liposomi sono stati riempi, Renewable biomass-based processes are expected to contribute to the future supply of fuels, chemicals and materials due to the prospects of environmental friendliness and reducing the use of petrochemicals. The use of microorganisms to convert lignocellulosic biomass into desired products is a promising opportunity. The diverse nature of the biomass feedstocks, together with the biodiversity of microorganisms, has the potential to lead to the production of a wide array of molecules of industrial interest. Nonetheless, many microbial production processes fail to reach the requirements of industrial processes due to cellular inhibition. Inhibitory compounds released from the feedstock pretreatment or produced during fermentation can negatively interfere with the microbial cell factory metabolism and compromise the fermentation process. Organic acids often represent a major hurdle in industrial bio-based microbial processes. They can be released from lignocellulosic feedstocks pretreatment, inhibiting the fermentative processes, and, can also be the desirable products obtained by microbial fermentation with applications in different industrial sectors. However, when high titers are reached the product itself becomes an additional stress factor. The aim of the research described in this thesis was to evaluate the possibility of modifying the membrane lipid composition of Saccharomyces cerevisiae to obtain mutants with increased organic acid tolerance and also with improved for their production. We focused on ergosterol, an essential component of the plasma membrane and contributes to membrane integrity, fluidity and permeability, as well as to the sorting of lipid rafts, which are sterols and sphingolipids-enriched clusters that house several membrane proteins, among which the proton pump H+-ATPase Pma1. By combining in vitro and in vivo complementary assays, we demonstrated that modulation of ergosterol content is crucial for S. cerevisiae to gain tolerance towards org

Perspective-n-Point-and-Line (PnPL) algorithms aim at fast, accurate, and robust camera localization with respect to a 3D model from 2D-3D feature correspondences, being a major part of modern robotic and AR/VR systems. Current point-based pose estimation methods use only 2D feature detection uncertainties, and the line-based methods do not take uncertainties into account. In our setup, both 3D coordinates and 2D projections of the features are considered uncertain. We propose PnP(L) solvers based on EPnP and DLS for the uncertainty-aware pose estimation. We also modify motion-only bundle adjustment to take 3D uncertainties into account. We perform exhaustive synthetic and real experiments on two different visual odometry datasets. The new PnP(L) methods outperform the state-of-the-art on real data in isolation, showing an increase in mean translation accuracy by 18% on a representative subset of KITTI, while the new uncertain refinement improves pose accuracy for most of the solvers, e.g. decreasing mean translation error for the EPnP by 16% compared to the standard refinement on the same dataset.

Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e.g. SIFT. In this paper we use Convolutional Neural Networks (CNNs) to learn discriminant patch representations and in particular train a Siamese network with pairs of (non)corresponding patches. We deal with the large number of potential pairs with the combination of a stochastic sampling of the training set and an aggressive mining strategy biased towards patches that are hard to classify. By using the L2 distance during both training and test- ing we develop 128-D descriptors whose euclidean distances reflect patch similarity, and which can be used as a drop-in replacement for any task involving SIFT. We demonstrate consistent performance gains over the state of the art, and generalize well against scaling and rotation, perspective transformation, non-rigid deformation, and illumination changes. Our descriptors are efficient to compute and amenable to modern GPUs, and are publicly available., Peer Reviewed, Postprint (author's final draft)

Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e.g. SIFT. In this paper we use Convolutional Neural Networks (CNNs) to learn discriminant patch representations and in particular train a Siamese network with pairs of (non-)corresponding patches. We deal with the large number of potential pairs with the combination of a stochastic sampling of the training set and an aggressive mining strategy biased towards patches that are hard to classify. By using the L2 distance during both training and testing we develop 128-D descriptors whose euclidean distances reflect patch similarity, and which can be used as a drop-in replacement for any task involving SIFT. We demonstrate consistent performance gains over the state of the art, and generalize well against scaling and rotation, perspective transformation, non-rigid deformation, and illumination changes. Our descriptors are efficient to compute and amenable to modern GPUs, and are publicly available.

We propose a robust and efficient method to estimate the pose of a camera with respect to complex 3D textured models of the environment that can potentially contain more than 100, 000 points. To tackle this problem we follow a top down approach where we combine high-level deep network classifiers with low level geometric approaches to come up with a solution that is fast, robust and accurate. Given an input image, we initially use a pre-trained deep network to compute a rough estimation of the camera pose. This initial estimate constrains the number of 3D model points that can be seen from the camera viewpoint. We then establish 3D-to-2D correspondences between these potentially visible points of the model and the 2D detected image features. Accurate pose estimation is finally obtained from the 2D-to-3D correspondences using a novel PnP algorithm that rejects outliers without the need to use a RANSAC strategy, and which is between 10 and 100 times faster than other methods that use it. Two real experimentsdealing with very large and complex 3D models demonstrate the effectiveness of the approach.

We propose a real-time, robust to outliers and accurate solution to the Perspective-n-Point (PnP) problem. The main advantages of our solution are twofold: first, it integrates the outlier rejection within the pose estimation pipeline with a negligible computational overhead; and second, its scalability to arbitrarily large number of correspondences. Given a set of 3D-to-2D matches, we formulate pose estimation problem as a low-rank homogeneous system where the solution lies on its 1D null space. Outlier correspondences are those rows of the linear system which perturb the null space and are progressively detected by projecting them on an iteratively estimated solution of the null space. Since our outlier removal process is based on an algebraic criterion which does not require computing the full-pose and reprojecting back all 3D points on the image plane at each step, we achieve speed gains of more than 100× compared to RANSAC strategies. An extensive experimental evaluation will show that our solution yields accurate results in situations with up to 50% of outliers, and can process more than 1000 correspondences in less than 5ms.

We propose a real-time and accurate solution to the Perspective-n-Point (PnP) problem --estimating the pose of a calibrated camera from n 3D-to-2D point correspondences-- that exploits the fact that in practice the 2D position of not all 2D features is estimated with the same accuracy. Assuming a model of such feature uncertainties is known in advance, we reformulate the PnP problem as a maximum likelihood minimization approximated by an unconstrained Sampson error function, which naturally penalizes the most noisy correspondences. The advantages of this approach are clearly demonstrated in synthetic experiments where feature uncertainties are exactly known. Pre-estimating the features uncertainties in real experiments is, though, not easy. In this paper we model feature uncertainty as 2D Gaussian distributions representing the sensitivity of the 2D feature detectors to different camera viewpoints. When using these noise models with our PnP formulation we still obtain promising pose estimation results that outperform the most recent approaches.

El siguiente documento presenta un método robusto y eficiente para estimar la pose de una cámara. El método propuesto asume el conocimiento previo de un modelo 3D del entorno, y compara una nueva imagen de entrada únicamente con un conjunto pequeño de imágenes similares seleccionadas previamente por un algoritmo de >Bag of Visual Words>. De esta forma se evita el alto coste computacional de calcular la correspondencia de los puntos 2D de la imagen de entrada contra todos los puntos 3D de un modelo complejo, que en nuestro caso contiene más de 100,000 puntos. La estimación de la pose se lleva a cabo a partir de estas correspondencias 2D-3D utilizando un novedoso algoritmo de PnP que realiza la eliminación de valores atípicos (outliers) sin necesidad de utilizar RANSAC, y que es entre 10 y 100 veces más rápido que los métodos que lo utilizan.