Cosmology with Supernovae 1A

SupernovaeThe basic relations between the luminosity distance, the matter density $\Omega_M$, and the cosmological constant $\Omega_{\Lambda}$ are derived. The universal character of the luminosity of SN1a is described, and the experimental status of the determinations of the Hubble constant, $\Omega_M$, and $\Omega_{\Lambda}$ are recalled. The large supernovae surveys foreseen in the near future, and their expected performances are reviewed. Some aspects of the supernovae explosions are briefly summarise

Compte rendu de : Illustrated atlas of the Himalaya / David Zurick, Julsun Pacheco, Lexington : University Press of Kentucky, 2006

In EBHR, n°3

Microcoding the Lexicon with Co-occurrence Knowledge

Neither syntax nor semantics can justify the use of a certain class of English word combinations. This class contains word pairs that often appear together in a given context of meaning. Such pairs are called co-occurrence relations or idiosyncratic collocations [3]. To correctly understand or produce natural language, such lexical relations need to be specifically encoded in lexicons [6]. [10]. [1]. In this paper, we show how word-based lexicons can be enriched with automatically acquired lexical relations. We call this process microcoding the lexicon, since it corresponds to the addition of lexical associations in a regular lexicon. We are using our enriched lexicon for language generation. Co-occurrence knowledge is particularly important for language generation, without it, awkward or incorrect sentences could be produced. In previous natural language work, co-occurrence knowledge was ignored or hand encoded. In contrast, we acquire it automatically from the analysis of large textual corpora. We describe the acquisition method based on EXTRACT [12], a co-occurrence compiler that retrieves lexical relations from the statistical analysis of a large corpus. We indicate how these lexical associations are entered in a word-based lexicon in a useful and coherent way for language generators. We then show how this information is used in COOK, a functional unification based language based generator that correctly handles collocation ally restricted sentences. Whenever possible, we use examples taken from the bank and stock market domains

Lexical Co-occurrence: The Missing Link

Aside from syntax, linguistic knowledge can be separated into two distinct parts, encyclopedic knowledge and dictionary knowledge. Encyclopedic knowledge describes the world whereas the dictionary describes individual word features, thus capturing lexical knowledge. Among the various types of lexical knowledge, one has generally been overlooked and should bring new results in computational linguistics: co-occurrence knowledge. Co-occurrence knowledge stands for the extent to which an item is specified by its environment independently of syntactic or semantic reasons. The basic concept is that of a lexical relation due to Saussure [49]. A lexical relation between two units of language stands for a correlation of common appearance of the two units in the utterances of the language

Dictionaries for Language Generation Accounting for Co-occurrence Knowledge

Many wording choices in English sentences cannot be accounted for on semantic or syntactic grounds. They can only be expressed in terms of co-occurrence relations. Co-occurrence knowledge has been traditionally overlooked in the past, but should be included in lexicons as it is an inherent part of the language. In this paper, we demonstrate the importance of co-occurrence knowledge for language generation and we show how to include it in computational dictionaries. Using co-occurrence knowledge in the dictionary provides the generator with the information necessary for handling many lexical decisions that were previously ignored. We focus here on the process of building the dictionary, and we show how co-occurrence knowledge can be systematically entered in lexicons. Lexical relations are first identified by a co-occurrence compiler, EXTRACT. Then, domain specific semantic information is used as a criterion for classifying them. We exemplify our approach in the banking domain and we explain how it can be used by a natural language generator

Coupling, Reinforcement, and Speciation

During the process of speciation, populations may diverge for traits and at their underlying loci that contribute barriers to gene flow. These barrier traits and barrier loci underlie individual barrier effects, by which we mean the contribution that a barrier locus or trait-or some combination of barrier loci or traits-makes to overall isolation. The evolution of strong reproductive isolation typically requires the origin of multiple barrier effects. Critically, it also requires the coincidence of barrier effects; for example, two barrier effects, one due to assortative mating and the other due to hybrid inviability, create a stronger overall barrier to gene flow if they coincide than if they distinguish independent pairs of populations. Here, we define "coupling" as any process that generates coincidence of barrier effects, resulting in a stronger overall barrier to gene flow. We argue that speciation research, both empirical and theoretical, needs to consider both the origin of barrier effects and the ways in which they are coupled. Coincidence of barrier effects can occur either as a by-product of selection on individual barrier effects or of population processes, or as an adaptive response to indirect selection. Adaptive coupling may be accompanied by further evolution that enhances individual barrier effects. Reinforcement, classically viewed as the evolution of prezygotic barriers to gene flow in response to costs of hybridization, is an example of this type of process. However, we argue for an extended view of reinforcement that includes coupling processes involving enhancement of any type of additional barrier effect as a result of an existing barrier. This view of coupling and reinforcement may help to guide development of both theoretical and empirical research on the process of speciation

Expression of Protease-Activated Receptor 1 and 2 and Anti-Tubulogenic Activity of Protease-Activated Receptor 1 in Human Endothelial Colony-Forming Cells

Endothelial colony-forming cells (ECFCs) are obtained from the culture of human peripheral blood mononuclear cell (hPBMNC) fractions and are characterised by high proliferative and pro-vasculogenic potential, which makes them of great interest for cell therapy. Here, we describe the detection of protease-activated receptor (PAR) 1 and 2 amongst the surface proteins expressed in ECFCs. Both receptors are functionally coupled to extracellular signal-regulated kinase (ERK) 1 and 2, which become activated and phosphorylated in response to selective PAR1- or PAR2-activating peptides. Specific stimulation of PAR1, but not PAR2, significantly inhibits capillary-like tube formation by ECFCs in vitro, suggesting that tubulogenesis is negatively regulated by proteases able to stimulate PAR1 (e.g. thrombin). The activation of ERKs is not involved in the regulation of tubulogenesis in vitro, as suggested by use of the MEK inhibitor PD98059 and by the fact that PAR2 stimulation activates ERKs without affecting capillary tube formation. Both qPCR and immunoblotting showed a significant downregulation of vascular endothelial growth factor 2 (VEGFR2) in response to PAR1 stimulation. Moreover, the addition of VEGF (50–100 ng/ml) but not basic Fibroblast Growth Factor (FGF) (25–100 ng/ml) rescued tube formation by ECFCs treated with PAR1-activating peptide. Therefore, we propose that reduction of VEGF responsiveness resulting from down-regulation of VEGFR2 is underlying the anti-tubulogenic effect of PAR1 activation. Although the role of PAR2 remains elusive, this study sheds new light on the regulation of the vasculogenic activity of ECFCs and suggests a potential link between adult vasculogenesis and the coagulation cascade

Translating Collocations for Bilingual Lexicons: A Statistical Approach

Collocations are notoriously difficult for non-native speakers to translate, primarily because they are opaque and cannot be translated on a word-by-word basis. We describe a program named Champollion which, given a pair of parallel corpora in two different languages and a list of collocations in one of them, automatically produces their translations. Our goal is to provide a tool for compiling bilingual lexical information above the word level in multiple languages, for different domains. The algorithm we use is based on statistical methods and produces p-word translations of n-word collocations in which n and p need not be the same. For example, Champollion translates make...decision, employment equity, and stock market into prendre...décision, équité en matière d'emploi, and bourse respectively. Testing Champollion on three years' worth of the Hansards corpus yielded the French translations of 300 collocations for each year, evaluated at 73% accuracy on average. In this paper, we describe the statistical measures used, the algorithm, and the implementation of Champollion, presenting our results and evaluation