A Database of >20 keV Electron Green's Functions of Interplanetary Transport at 1 AU

We use interplanetary transport simulations to compute a database of electron Green's functions, i.e., differential intensities resulting at the spacecraft position from an impulsive injection of energetic (>20 keV) electrons close to the Sun, for a large number of values of two standard interplanetary transport parameters: the scattering mean free path and the solar wind speed. The nominal energy channels of the ACE, STEREO, and Wind spacecraft have been used in the interplanetary transport simulations to conceive a unique tool for the study of near-relativistic electron events observed at 1 AU. In this paper, we quantify the characteristic times of the Green's functions (onset and peak time, rise and decay phase duration) as a function of the interplanetary transport conditions. We use the database to calculate the FWHM of the pitch-angle distributions at different times of the event and under different scattering conditions. This allows us to provide a first quantitative result that can be compared with observations, and to assess the validity of the frequently used term beam-like pitch-angle distribution.

Protein interaction studies point to new functions for Escherichia coli glyceraldehyde-3-phosphate dehydrogenase

Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is considered a multifunctional protein with defined functions in numerous mammalian cellular processes. GAPDH functional diversity depends on various factors such as covalent modifications, subcellular localization, oligomeric state and intracellular concentration of substrates or ligands, as well as protein-protein interactions. In bacteria, alternative GAPDH functions have been associated with its extracellular location in pathogens or probiotics. In this study, new intracellular functions of E. coli GAPDH were investigated following a proteomic approach aimed at identifying interacting partners using in vivo formaldehyde cross-linking followed by mass spectrometry. The identified proteins were involved in metabolic processes, protein synthesis and folding or DNA repair. Some interacting proteins were also identified in immunopurification experiments in the absence of cross-linking. Pull-down experiments and overlay immunoblotting were performed to further characterize the interaction with phosphoglycolate phosphatase (Gph). This enzyme is involved in the metabolism of 2-phosphoglycolate formed in the DNA repair of 3"-phosphoglycolate ends generated by bleomycin damage. We show that interaction between Gph and GAPDH increases in cells challenged with bleomycin, suggesting involvement of GAPDH in cellular processes linked to DNA repair mechanisms.

Executive functions profile in extreme eating/weight conditions: from anorexia nervosa to obesity

Extreme weight conditions (EWC) groups along a continuum may share some biological risk factors and intermediate neurocognitive phenotypes. A core cognitive trait in EWC appears to be executive dysfunction, with a focus on decision making, response inhibition and cognitive flexibility. Differences between individuals in these areas are likely to contribute to the differences in vulnerability to EWC. The aim of the study was to investigate whether there is a common pattern of executive dysfunction in EWC while comparing anorexia nervosa patients (AN), obese subjects (OB) and healthy eating/weight controls (HC).

The Human Mesenchymal Stromal Cell-Derived Osteocyte Capacity to Modulate Dendritic Cell Functions Is Strictly Dependent on the Culture System.

In vitro differentiation of mesenchymal stromal cells (MSC) into osteocytes (human differentiated osteogenic cells, hDOC) before implantation has been proposed to optimize bone regeneration. However, a deep characterization of the immunological properties of DOC, including their effect on dendritic cell (DC) function, is not available. DOC can be used either as cellular suspension (detached, Det-DOC) or as adherent cells implanted on scaffolds (adherent, Adh-DOC). By mimicking in vitro these two different routes of administration, we show that both Det-DOC and Adh-DOC can modulate DC functions. Specifically, the weak downregulation of CD80 and CD86 caused by Det-DOC on DC surface results in a weak modulation of DC functions, which indeed retain a high capacity to induce T-cell proliferation and to generate CD4(+)CD25(+)Foxp3(+) T cells. Moreover, Det-DOC enhance the DC capacity to differentiate CD4(+)CD161(+)CD196(+) Th17-cells by upregulating IL-6 secretion. Conversely, Adh-DOC strongly suppress DC functions by a profound downregulation of CD80 and CD86 on DC as well as by the inhibition of TGF-β production. In conclusion, we demonstrate that different types of DOC cell preparation may have a different impact on the modulation of the host immune system. This finding may have relevant implications for the design of cell-based tissue-engineering strategies.

A Gradient based algorithm for blind inversion of Wiener System using multi-variate score functions

A Wiener system is a linear time-invariant filter, followed by an invertible nonlinear distortion. Assuming that the input signal is an independent and identically distributed (iid) sequence, we propose an algorithm for estimating the input signal only by observing the output of the Wiener system. The algorithm is based on minimizing the mutual information of the output samples, by means of a steepest descent gradient approach.

Error models in hydrogeology applications

Notre consommation en eau souterraine, en particulier comme eau potable ou pour l'irrigation, a considérablement augmenté au cours des années. De nombreux problèmes font alors leur apparition, allant de la prospection de nouvelles ressources à la remédiation des aquifères pollués. Indépendamment du problème hydrogéologique considéré, le principal défi reste la caractérisation des propriétés du sous-sol. Une approche stochastique est alors nécessaire afin de représenter cette incertitude en considérant de multiples scénarios géologiques et en générant un grand nombre de réalisations géostatistiques. Nous rencontrons alors la principale limitation de ces approches qui est le coût de calcul dû à la simulation des processus d'écoulements complexes pour chacune de ces réalisations. Dans la première partie de la thèse, ce problème est investigué dans le contexte de propagation de l'incertitude, oú un ensemble de réalisations est identifié comme représentant les propriétés du sous-sol. Afin de propager cette incertitude à la quantité d'intérêt tout en limitant le coût de calcul, les méthodes actuelles font appel à des modèles d'écoulement approximés. Cela permet l'identification d'un sous-ensemble de réalisations représentant la variabilité de l'ensemble initial. Le modèle complexe d'écoulement est alors évalué uniquement pour ce sousensemble, et, sur la base de ces réponses complexes, l'inférence est faite. Notre objectif est d'améliorer la performance de cette approche en utilisant toute l'information à disposition. Pour cela, le sous-ensemble de réponses approximées et exactes est utilisé afin de construire un modèle d'erreur, qui sert ensuite à corriger le reste des réponses approximées et prédire la réponse du modèle complexe. Cette méthode permet de maximiser l'utilisation de l'information à disposition sans augmentation perceptible du temps de calcul. La propagation de l'incertitude est alors plus précise et plus robuste. La stratégie explorée dans le premier chapitre consiste à apprendre d'un sous-ensemble de réalisations la relation entre les modèles d'écoulement approximé et complexe. Dans la seconde partie de la thèse, cette méthodologie est formalisée mathématiquement en introduisant un modèle de régression entre les réponses fonctionnelles. Comme ce problème est mal posé, il est nécessaire d'en réduire la dimensionnalité. Dans cette optique, l'innovation du travail présenté provient de l'utilisation de l'analyse en composantes principales fonctionnelles (ACPF), qui non seulement effectue la réduction de dimensionnalités tout en maximisant l'information retenue, mais permet aussi de diagnostiquer la qualité du modèle d'erreur dans cet espace fonctionnel. La méthodologie proposée est appliquée à un problème de pollution par une phase liquide nonaqueuse et les résultats obtenus montrent que le modèle d'erreur permet une forte réduction du temps de calcul tout en estimant correctement l'incertitude. De plus, pour chaque réponse approximée, une prédiction de la réponse complexe est fournie par le modèle d'erreur. Le concept de modèle d'erreur fonctionnel est donc pertinent pour la propagation de l'incertitude, mais aussi pour les problèmes d'inférence bayésienne. Les méthodes de Monte Carlo par chaîne de Markov (MCMC) sont les algorithmes les plus communément utilisés afin de générer des réalisations géostatistiques en accord avec les observations. Cependant, ces méthodes souffrent d'un taux d'acceptation très bas pour les problèmes de grande dimensionnalité, résultant en un grand nombre de simulations d'écoulement gaspillées. Une approche en deux temps, le "MCMC en deux étapes", a été introduite afin d'éviter les simulations du modèle complexe inutiles par une évaluation préliminaire de la réalisation. Dans la troisième partie de la thèse, le modèle d'écoulement approximé couplé à un modèle d'erreur sert d'évaluation préliminaire pour le "MCMC en deux étapes". Nous démontrons une augmentation du taux d'acceptation par un facteur de 1.5 à 3 en comparaison avec une implémentation classique de MCMC. Une question reste sans réponse : comment choisir la taille de l'ensemble d'entrainement et comment identifier les réalisations permettant d'optimiser la construction du modèle d'erreur. Cela requiert une stratégie itérative afin que, à chaque nouvelle simulation d'écoulement, le modèle d'erreur soit amélioré en incorporant les nouvelles informations. Ceci est développé dans la quatrième partie de la thèse, oú cette méthodologie est appliquée à un problème d'intrusion saline dans un aquifère côtier. -- Our consumption of groundwater, in particular as drinking water and for irrigation, has considerably increased over the years and groundwater is becoming an increasingly scarce and endangered resource. Nofadays, we are facing many problems ranging from water prospection to sustainable management and remediation of polluted aquifers. Independently of the hydrogeological problem, the main challenge remains dealing with the incomplete knofledge of the underground properties. Stochastic approaches have been developed to represent this uncertainty by considering multiple geological scenarios and generating a large number of realizations. The main limitation of this approach is the computational cost associated with performing complex of simulations in each realization. In the first part of the thesis, we explore this issue in the context of uncertainty propagation, where an ensemble of geostatistical realizations is identified as representative of the subsurface uncertainty. To propagate this lack of knofledge to the quantity of interest (e.g., the concentration of pollutant in extracted water), it is necessary to evaluate the of response of each realization. Due to computational constraints, state-of-the-art methods make use of approximate of simulation, to identify a subset of realizations that represents the variability of the ensemble. The complex and computationally heavy of model is then run for this subset based on which inference is made. Our objective is to increase the performance of this approach by using all of the available information and not solely the subset of exact responses. Two error models are proposed to correct the approximate responses follofing a machine learning approach. For the subset identified by a classical approach (here the distance kernel method) both the approximate and the exact responses are knofn. This information is used to construct an error model and correct the ensemble of approximate responses to predict the "expected" responses of the exact model. The proposed methodology makes use of all the available information without perceptible additional computational costs and leads to an increase in accuracy and robustness of the uncertainty propagation. The strategy explored in the first chapter consists in learning from a subset of realizations the relationship between proxy and exact curves. In the second part of this thesis, the strategy is formalized in a rigorous mathematical framework by defining a regression model between functions. As this problem is ill-posed, it is necessary to reduce its dimensionality. The novelty of the work comes from the use of functional principal component analysis (FPCA), which not only performs the dimensionality reduction while maximizing the retained information, but also allofs a diagnostic of the quality of the error model in the functional space. The proposed methodology is applied to a pollution problem by a non-aqueous phase-liquid. The error model allofs a strong reduction of the computational cost while providing a good estimate of the uncertainty. The individual correction of the proxy response by the error model leads to an excellent prediction of the exact response, opening the door to many applications. The concept of functional error model is useful not only in the context of uncertainty propagation, but also, and maybe even more so, to perform Bayesian inference. Monte Carlo Markov Chain (MCMC) algorithms are the most common choice to ensure that the generated realizations are sampled in accordance with the observations. Hofever, this approach suffers from lof acceptance rate in high dimensional problems, resulting in a large number of wasted of simulations. This led to the introduction of two-stage MCMC, where the computational cost is decreased by avoiding unnecessary simulation of the exact of thanks to a preliminary evaluation of the proposal. In the third part of the thesis, a proxy is coupled to an error model to provide an approximate response for the two-stage MCMC set-up. We demonstrate an increase in acceptance rate by a factor three with respect to one-stage MCMC results. An open question remains: hof do we choose the size of the learning set and identify the realizations to optimize the construction of the error model. This requires devising an iterative strategy to construct the error model, such that, as new of simulations are performed, the error model is iteratively improved by incorporating the new information. This is discussed in the fourth part of the thesis, in which we apply this methodology to a problem of saline intrusion in a coastal aquifer.

Post-transcriptional regulation of circadian gene expression by miRNAs

La grande majorité des organismes vivants ont développé un système d'horloges biologiques internes, appelées aussi horloges circadiennes, contrôlant l'expression de gênes impliqués dans de nombreux processus moléculaires et comportementaux. Au cours de la dernière décennie, des analyses « microarray » et séquençages à haut débit sur divers tissus de mammifères, indiquent que jusqu'à 20% du transcriptome serait sous contrôle circadien. Il était jusqu'à présent admis que la majorité des ARNm ayant une accumulation rythmique était générée par une transcription qui était elle-même rythmique. Toutefois, de récentes études ont suggéré qu'une proportion considérable des ARNm cycliques serait en fait générée par des mécanismes post-transcriptionnelles, incluant une régulation par micro-ARN (miARN). Lorsque j'ai débuté mon travail de thèse, l'influence des miARN sur l'expression des gènes circadiens, au niveau pangénomique, était encore méconnue. Par l'utilisation d'un modèle murin, dont la biogenèse des miARN a été spécifiquement désactivée au niveau des cellules hépatiques (knockout conditionnel pour Dicer), je me suis donc intéressée au rôle que jouaient ces molécules régulatrices sur la rythmicité de l'expression génique dans le foie. Des séquençages sur l'ensemble du transcriptome révèlent que l'horloge interne du foie est étonnement résistante à la perte totale des miARN. Nous avons cependant trouvé que les miARN agissent de façon importante sur la régulation de l'expression des gènes contrôlés par l'horloge moléculaire. La corégulation par les miARN, affectant jusqu'à 30% des gènes transcrits de façon rythmiques, conduit ainsi à une modulation de phase et d'amplitude du rythme de l'abondance des ARNm. En revanche, seuls peu de transcrits dépendent uniquement des miARN pour la rythmicité de leur accumulation. Enfin, mon travail met en évidence plusieurs miARN spécifiques, qui semblent préférentiellement moduler l'expression des gènes cycliques et permet l'identification de voies hépatiques particulièrement sujettes à une double régulation par les miARN et l'horloge biologique interne. La première masse d'analyses a essentiellement porté sur le rôle que jouent les miARN au niveau de l'expression des gènes contrôlés par l'horloge interne. Dans deux études de suivi, je me suis penchée sur deux aspects supplémentaires et complémentaires de la manière dont les miARN et l'oscillation de l'expression des gènes interagissent. Dans les hépatocytes murins, spécifiquement privés de Dicer, je me suis demandée si un phénotype horloge avait pu être masqué, dû à un entraînement stable de l'horloge du foie par l'horloge maîtresse du cerveau. J'ai donc commencé une série d'expériences ambitieuses (impliquant la mesure de la rythmicité du foie in vivo, chez l'animal vivant) afin de déséquilibrer l'entrainement de l'horloge hépatique via l'utilisation d'un protocole nutritionnel spécifique. Les premiers résultats suggèrent que dans des conditions où l'animal subit une restriction alimentaire pendant la journée, les miARN sont importants dans la cinétique d'adaptation des organes périphériques à un nouvel horaire de sustentation. Dans une deuxième ligne de recherche, j'ai plus profondément étudié quels seraient les miARN responsables des rythmes post-transcriptionnels des ARNm, en utilisant le séquençage de « small » ARN sur 24h. L'analyse est en cours et se poursuivra après l'obtention de mon diplôme. De façon générale, mon travail révèle d'importants et nouveaux rôles des miARN dans la modulation de l'expression circadienne des gènes hépatiques. De plus, le set de données générées dans l'étude déjà publiée, peut dorénavant servir de ressource valable pour de prochaines investigations sur le rôle physiologique que les miARN jouent au niveau du foie. -- Most living organisms have developed internal timing systems, called circadian clocks, to drive the rhythmic expression of genes involved in many molecular and behavioral processes. Over the last decade, microarray analyses and high- throughput sequencing from various mammalian tissues have indicated that up to 20% of the transcriptome are under circadian control. It was generally assumed that the majority of rhythmic mRNA accumulation is generated by rhythmic transcription. However, recent studies have suggested that a considerable proportion of mRNA cycling may actually be generated by post-transcriptional mechanisms, including by microRNAs. When I started my thesis work, it was still unknown how miRNAs influence circadian gene expression in a genome-wide fashion. Using a mouse model in which miRNA biogenesis can be inactivated in hepatocytes (conditional Dicer knockout mouse), I have thus addressed the role that these regulatory molecules play in rhythmic gene expression in the liver. Whole transcriptome sequencing revealed that the hepatic core clock was surprisingly resilient to total miRNA loss. However, we found that miRNAs acted as important regulators of clock-controlled gene expression. Co- regulation by miRNAs, which affected up to 30% of rhythmically transcribed genes, thus led to the modulation of phases and amplitudes of mRNA abundance rhythms. By contrast, only very few transcripts were strictly dependent on miRNAs for their rhythmic accumulation. Finally, my work highlights several specific miRNAs that appear to preferentially modulate cyclic gene expression, and identifies pathways in the liver that are particularly prone to dual regulation through miRNAs and the clock. The first bulk of analyses mainly dealt with the role that miRNAs play at the level of rhythmic clock output gene expression. In two follow-up studies I further delved into two additional, complementary aspects of how miRNAs and gene expression oscillations interact. First, I addressed whether a core clock phenotype in the hepatocyte-specific Dicer knockout could have been masked due to the stable entrainment of the liver clock by the animals' master clock in the brain. I thus started a series of ambitious experiments (involving the in vivo recording of liver rhythms in live animals) to bring the stable entrainment of the liver clock out of equilibrium using specific feeding protocols. My first results suggest that under conditions when animals are challenged by food restriction to daytime, miRNAs are important for the kinetics of adapting to unusual mealtime in peripheral tissue. In a second line of research, I have more carefully investigated which miRNAs are responsible for post- transcriptional mRNA rhythms using small RNA sequencing around-the-clock. The analyses are ongoing and will be continued after my graduation. Overall, my work uncovered important and novel roles of miRNA activity in shaping hepatic circadian gene expression; moreover, the datasets collect in the published studies can serve as a valuable resource for further investigations into the physiological roles that miRNAs play in liver. -- L'alternance du jour et de la nuit dirige depuis longtemps la vie quotidienne des êtres humains et de la plupart des organismes sur terre. Ce cycle de 24 heures façonne beaucoup de changements comportementaux et physiologiques tels que la vigilance, la température corporelle et le sommeil. Les rythmes journaliers, appelés rythmes circadiens, sont dirigés par des horloges biologiques tournant dans presque chaque cellule du corps. Une structure dans le cerveau agit en tant qu'horloge maitresse pour synchroniser les horloges internes entre elles et en fonction des signaux de jour/nuit extérieurs. Dans les cellules "les gènes de l'horloge" sont activés et désactivés une fois par jour ce qui déclenche des cycles dans lesquels des protéines sont produites de manière circadienne. Ces rythmes protéiques sont spécialisés pour chaque tissu ou organe et peuvent les aider à réaliser leurs tâches quotidiennes. Les rythmes circadiens peuvent être générés d'autres manières n'impliquant pas directement les composants des gènes de l'horloge. Les ARN messagers (ARNm) sont des molécules intermédiaires dans la production de protéines à partir d'ADN. Dans le foie des souris jusqu'à 20% des molécules d'ARNm sont produites suivant des rythmes circadiens. Le foie réalise des tâches essentielles dans le contrôle du métabolisme incluant celui des hydrates de carbone, des graisses et du cholestérol. Un timing précis est important afin de traiter les substances nutritives correctement lors des repas il en résulte une variation des quantités de certains ARNm et protéines coïncidant avec les repas. Les microARNs constituent une autre classe de molécules ARN de très petite taille qui régulent l'efficacité de traduction des ARNm en protéines et la stabilité des ARNm. Lors de mon travail de thèse, j'ai exploré de manière approfondie l'influence de ces petits régulateurs sur les rythmes circadiens du foie de souris. Ces expériences qui impliquaient le "Knock-out" d'un gène essentiel à la production de microARNs montrent qu'au lieu de générer les rythmes des ARNm, les microARNs les ajustent pour répondre aux besoins spécifiques du foie comme assurer leur pic au bon moment de la journée. Le ciblage de microARNs spécifiques peut révéler de nouvelles stratégies pour rectifier ces rythmes lorsque par exemple les fonctions métaboliques ne fonctionnent plus normalement. -- The rising and setting of the sun have long driven the daily schedules of humans and most organisms on the earth. This 24-hr cycle shapes many behavioural and physiological changes, such as alertness, body temperature, and sleep. These daily rhythms, which are called circadian rhythms, are dictated by biological clocks that are ticking in almost every single cell of the body. A region in the brain acts as a master clock to synchronize the internal clocks with each other and with the outside light/dark cycles. In cells, "core clock genes" are turned on and off once per day, which triggers cycles that cause some proteins to be produced in a circadian manner. The protein rhythms are specialized to a particular tissue or organ, and may help them to carry out their designated daily tasks. However, circadian rhythms might also be produced by other ways that do not involve these core clock components. Messenger RNAs (mRNAs) are intermediate molecules in the production of proteins from DNA. In the mouse liver, up to 20% of mRNA molecules are produced in circadian cycles. The liver performs essential tasks that control metabolism-including that of carbohydrates, fats, and cholesterol. Precisely timing when certain mRNAs and proteins reach peaks and troughs in their activities to coincide with mealtimes is important for nutrients to be properly processed. Other RNA molecules called microRNAs, i.e. RNAs of very small size, regulate at which rate mRNA molecules are translated into proteins. In my thesis work, I have explored at the influence of these small regulators on circadian rhythms in the mouse liver in greater detail. These experiments, which involved "knocking out" a gene that is essential for the production of microRNAs, show that rather than generating the mRNA rhythms, the microRNAs appear to adjust them to meet the specific needs of the liver, such as ensuring that they peak at the right time-of-day. Targeting specific microRNA molecules may reveal new strategies to tweak these rhythms, which could help to improve conditions when metabolic functions go wrong.

Sidelobe elimination for generalized synthetic discriminant functions by a two-filter correlation and subsequent postprocessing of the intensity distributions

One of the most important problems in optical pattern recognition by correlation is the appearance of sidelobes in the correlation plane, which causes false alarms. We present a method that eliminate sidelobes of up to a given height if certain conditions are satisfied. The method can be applied to any generalized synthetic discriminant function filter and is capable of rejecting lateral peaks that are even higher than the central correlation. Satisfactory results were obtained in both computer simulations and optical implementation.

Biological interpretation of genome-wide association studies using predicted gene functions.

The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.

Implementing Role Based Access Control into the Enterprise Environment to Support Business and IT Functions

The thesis studies role based access control and its suitability in the enterprise environment. The aim is to research how extensively role based access control can be implemented in the case organization and how it support organization’s business and IT functions. This study points out the enterprise’s needs for access control, factors of access control in the enterprise environment and requirements for implementation and the benefits and challenges it brings along. To find the scope how extensively role based access control can be implemented into the case organization, firstly is examined the actual state of access control. Secondly is defined a rudimentary desired state (how things should be) and thirdly completed it by using the results of the implementation of role based access control application. The study results the role model for case organization unit, and the building blocks and the framework for the organization wide implementation. Ultimate value for organization is delivered by facilitating the normal operations of the organization whilst protecting its information assets.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

A digital holography technique for generating beams with arbitrary polarization and shape

A method for generating beams with arbitrary polarization and shape is proposed. Our design requires the use of a Mach-Zehnder set-up combined with translucent liquid crystal displays in each arm of the interferometer; in this way, independent manipulation of each transverse beam components is possible. The target of this communication is to develop a numerical procedure for calculating the holograms required for dynamically encode any amplitude value and polarization state in each point of the wavefront. Several examples demonstrating the capabilities of the method are provided.