On cyclotomic primality tests

In 1980, L. Adleman, C. Pomerance, and R. Rumely invented the first cyclotomicprimality test, and shortly after, in 1981, a simplified and more efficient versionwas presented by H.W. Lenstra for the Bourbaki Seminar. Later, in 2008, ReneSchoof presented an updated version of Lenstra\u27s primality test. This thesis presents adetailed description of the cyclotomic primality test as described by Schoof, along withsuggestions for implementation. The cornerstone of the test is a prime congruencerelation similar to Fermat\u27s \little theorem that involves Gauss or Jacobi sumscalculated over cyclotomic fields. The algorithm runs in very nearly polynomial time.This primality test is currently one of the most computationally efficient tests and isused by default for primality proving by the open source mathematics systems Sageand PARI/GP. It can quickly test numbers with thousands of decimal digits

Molecular Pathways of Notch Signaling in Vascular Smooth Muscle Cells

Notch signaling in the cardiovascular system is important during embryonic development, vascular repair of injury, and vascular pathology in humans. The vascular smooth muscle cell (VSMC) expresses multiple Notch receptors throughout its life cycle, and responds to Notch ligands as a regulatory mechanism of differentiation, recruitment to growing vessels, and maturation. The goal of this review is to provide an overview of the current understanding of the molecular basis for Notch regulation of VSMC phenotype. Further, we will explore Notch interaction with other signaling pathways important in VSMC

Moving beyond the cost–loss ratio : economic assessment of streamflow forecasts for a risk-averse decision maker

A large effort has been made over the past 10 years to promote the operational use of probabilistic or ensemble streamflow forecasts. Numerous studies have shown that ensemble forecasts are of higher quality than deterministic ones. Many studies also conclude that decisions based on ensemble rather than deterministic forecasts lead to better decisions in the context of flood mitigation. Hence, it is believed that ensemble forecasts possess a greater economic and social value for both decision makers and the general population. However, the vast majority of, if not all, existing hydro-economic studies rely on a cost–loss ratio framework that assumes a risk-neutral decision maker. To overcome this important flaw, this study borrows from economics and evaluates the economic value of early warning flood systems using the well-known Constant Absolute Risk Aversion (CARA) utility function, which explicitly accounts for the level of risk aversion of the decision maker. This new framework allows for the full exploitation of the information related to a forecasts’ uncertainty, making it especially suited for the economic assessment of ensemble or probabilistic forecasts. Rather than comparing deterministic and ensemble forecasts, this study focuses on comparing different types of ensemble forecasts. There are multiple ways of assessing and representing forecast uncertainty. Consequently, there exist many different means of building an ensemble forecasting system for future streamflow. One such possibility is to dress deterministic forecasts using the statistics of past error forecasts. Such dressing methods are popular among operational agencies because of their simplicity and intuitiveness. Another approach is the use of ensemble meteorological forecasts for precipitation and temperature, which are then provided as inputs to one or many hydrological model(s). In this study, three concurrent ensemble streamflow forecasting systems are compared: simple statistically dressed deterministic forecasts, forecasts based on meteorological ensembles, and a variant of the latter that also includes an estimation of state variable uncertainty. This comparison takes place for the Montmorency River, a small flood-prone watershed in southern central Quebec, Canada. The assessment of forecasts is performed for lead times of 1 to 5 days, both in terms of forecasts’ quality (relative to the corresponding record of observations) and in terms of economic value, using the new proposed framework based on the CARA utility function. It is found that the economic value of a forecast for a risk-averse decision maker is closely linked to the forecast reliability in predicting the upper tail of the streamflow distribution. Hence, post-processing forecasts to avoid overforecasting could help improve both the quality and the value of forecasts

Experimental and theoretical investigations of friction properties of graphite intercalated compounds.

It is classically admitted that the goodfriction properties of lamellar compounds are strongly related to their anisotropic structure and especially to the existence of weak interlayer interactions through the van der Waals gap separating the basal layers. As it is also known, the presence of the van der Waals gap in the structure of lamellar compounds will allow lot of chemical species to be intercalated in the structure leading both to the expansion of structure parameters and inter layer interactions modifications. The present work is concerned with the experimental and theoretical study of friction propertiesof Graphite Intercalated Compounds (GICs) in order to better understand thetribologiclamellar compounds. In order to modulate the interlayer interactions, two types of intercalated species were used, electrophylic species (AlCl3, FeCl3, SbCl5) and nucleophilic species (Li, K, Rb)

Appellation d’origine ou appellation générique: le cas du fromage Cotija au Mexique

Les effets positifs des appellations d’origine pour le développement territorial dans certaines régions européennes ont attiré l’attention de producteurs et de promoteurs du développement dans les pays du Sud. Ainsi, les producteurs de fromage Cotija au Mexique ont sollicité une appellation d’origine en 2004. Elle leur a été refusée, l’administration estimant que la dénomination Cotija constituait un terme générique. Au-delà du débat sur le caractère générique ou non d’une appellation, ce cas est très instructif sur les limites du cadre légal et institutionnel mexicain, plus de 30 ans après que la Tequila soit devenue la première appellation reconnue au Mexique. L’absence d’un objectif politique clair et explicite pour les appellations d’origine se traduit dans l’incertitude et la faiblesse des institutions mises en place. Ce contexte n’est pas favorable pour la reconnaissance d’appellations d’origine et remet donc en cause leur utilisation comme outil de développement territorial.Positive effects of designations of origin for territorial development in several European regions have attracted the attention of producers and promoters of development in developing countries. Thus, Cotija cheese producers in Mexico have requested a designation of origin in 2004. It was denied, the administration considering Cotija as a generic term. Beyond the debate on whether a name is generic or not, this case is very instructive about the limits of the Mexican legal and institutional framework, 30 years after Tequila became the first designation of origin recognized in Mexico. The lack of clear and explicit political objectives for designations of origin results in uncertainty and weak institutions. This context is not favorable for the development of designations of origin and its use as a tool for territorial development

Les limites de l'action collective dans deux bassins laitiers mexicains

SAD CT3This study focuses on coordination and collective action problems in two Mexican dairy basins contemplated from the viewpoint of the Localized Agrifood System approach. Beyond structural differences between these two cases, weakness of collective action and preponderance of individualistic and opportunistic behaviours constitute a common denominator. The analysis emphasizes that homogeneity among actors and geographical proximity are not sufficient to generate confidence and cooperation at the horizontal level. At the vertical level, competitiveness is almost exclusively based on costs, exacerbating competition and opportunism. To break the vicious cycle of mistrust and individualism, the reinforcement of relations among actors must be fostered on the basis of collective projects and shared aims.Cet article s'intéresse aux problèmes de coordination et d'action collective dans deux bassins laitiers mexicains, étudiés à partir de l'approche des Systèmes agroalimentaires localisés. Au-delà des différences structurelles entre ces deux cas, la faiblesse de l'action collective et la prépondérance de comportements individualistes et opportunistes constituent un dénominateur commun. L'analyse souligne que l'homogénéité des acteurs et la proximité géographique ne sont pas suffisantes pour générer confiance et coopération au niveau horizontal. Au niveau vertical, la compétitivité se base presque exclusivement sur les coûts, exacerbant la concurrence et l'opportunisme. Pour rompre le cercle vicieux de la méfiance et de l'individualisme, le renforcement des relations entre acteurs devrait se fonder sur des projets collectifs, des buts communs

Transfer Learning with Mixtures of Manifolds

Advances in scientific instrumentation technology have increased the speed of data acquisition and the precision of sampling, creating an abundance of high-dimensional data sets. The ability to combine these disparate data sets and to transfer information between them is critical to accurate scientific analysis. Many modern-day instruments can record data at many thousands of channels, far greater than the actual degrees of freedom in the sample data. This makes manifold learning, a class of methods that exploit the observation that high-dimensional data tend to lie on lower-dimensional manifolds, especially well-suited to this transfer learning task. Existing manifold-based transfer learning methods can align related data sets in differing feature representations, but their inherent single manifold assumption causes them to fail in the presence of complex mixtures of manifolds. In this dissertation, a new class of transfer learning algorithms is developed for high-dimensional data sets that intrinsically lie on multiple low-dimensional manifolds. With a more realistic mixture of manifolds assumption, this class of algorithms allows for accurate and efficient transfer of information between data sets by aligning their complex underlying geometries. In this dissertation, algorithms are presented that leverage corresponding samples between data sets and available label information, continuous or categorical. The two primary tasks are aligning mixtures of manifolds and heterogeneous domain adaptation of multi-manifold data sets. Linear, non-linear, and robust versions of the algorithm are described, as well as a method for actively selecting cross-data set correspondences. To show the practical effectiveness of these algorithms, they are compared across a number of synthetic and real-world domains, but most notably to align data recorded by spectroscopic instruments during space exploration, a new domain for transfer learning