Les contraintes phonologiques en lecture en milieu de diglossie créole / français

Le présent article est le résultat d’une sélection de difficultés d’élèves en situation d’apprentissage de la lecture en français, en milieu créolophone guadeloupéen. Les difficultés sont analysées en termes de ‘contraintes phonologiques’, c’est-à-dire à la lumière de traits phonologiques du créole également attestés dans les variétés de français antillais. Des propositions didactiques sont avancées tenant compte des milieux linguistiques et sociolinguistiques des élèves. Ces dernières visent à prévenir et à remédier à celles des difficultés qui sont analysées ci-dessous et qui ne sont pas dues à la dyslexie. Les niveaux préconisés pour la prévention sont ceux de l’école maternelle.The present study is a selection of difficulties met by French-based Creole learners in Guadeloupe while they are taught the mechanisms of Reading in French. The difficulties are viewed as constraints, that is to say, as those phonological traits without which, learning how to read can’t be successful for those profiles. The grill of analysis used is the phonological features of the Creole language and particularly those that present divergences with the French system. Didactic paths are suggested for prevention but also for remediation. Some attempts are made at differentiating between the difficulties met by a learner in diglottic situations and those that turn out to be those of a dyslexic context. The ideal school level recommended for the exercises is kindergarten

Convergent validity of the new form of the DES

p. 101-103The line and circle farms of the Dissociative Experiences Scale (DES I and DES II) were administered to 65 subjects in the general population, 87 subjects with a clinical diagnosis of dissociative identity disorder, and 26 subjects with a diagnosis of chemical dependency. In all three samples the DES II showed excellent validity when compared to the original line form of the DES

Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data

Source separation involves the ill-posed problem of retrieving a set of source signals that have been observed through a mixing operator. Solving this problem requires prior knowledge, which is commonly incorporated by imposing regularity conditions on the source signals, or implicitly learned through supervised or unsupervised methods from existing data. While data-driven methods have shown great promise in source separation, they often require large amounts of data, which rarely exists in planetary space missions. To address this challenge, we propose an unsupervised source separation scheme for domains with limited data access that involves solving an optimization problem in the wavelet scattering covariance representation space\unicode{x2014}an interpretable, low-dimensional representation of stationary processes. We present a real-data example in which we remove transient, thermally-induced microtilts\unicode{x2014}known as glitches\unicode{x2014}from data recorded by a seismometer during NASA's InSight mission on Mars. Thanks to the wavelet scattering covariances' ability to capture non-Gaussian properties of stochastic processes, we are able to separate glitches using only a few glitch-free data snippets.Comment: ICML 202

Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders

Unsupervised source separation involves unraveling an unknown set of source signals recorded through a mixing operator, with limited prior knowledge about the sources, and only access to a dataset of signal mixtures. This problem is inherently ill-posed and is further challenged by the variety of time-scales exhibited by sources in time series data. Existing methods typically rely on a preselected window size that limits their capacity to handle multi-scale sources. To address this issue, instead of operating in the time domain, we propose an unsupervised multi-scale clustering and source separation framework by leveraging wavelet scattering covariances that provide a low-dimensional representation of stochastic processes, capable of distinguishing between different non-Gaussian stochastic processes. Nested within this representation space, we develop a factorial Gaussian-mixture variational autoencoder that is trained to (1) probabilistically cluster sources at different time-scales and (2) independently sample scattering covariance representations associated with each cluster. Using samples from each cluster as prior information, we formulate source separation as an optimization problem in the wavelet scattering covariance representation space, resulting in separated sources in the time domain. When applied to seismic data recorded during the NASA InSight mission on Mars, our multi-scale nested approach proves to be a powerful tool for discriminating between sources varying greatly in time-scale, e.g., minute-long transient one-sided pulses (known as ``glitches'') and structured ambient noises resulting from atmospheric activities that typically last for tens of minutes. These results provide an opportunity to conduct further investigations into the isolated sources related to atmospheric-surface interactions, thermal relaxations, and other complex phenomena

Spectra of High-Redshift Type Ia Supernovae and a Comparison with their Low-Redshift Counterparts

We present spectra for 14 high-redshift (0.17 < z < 0.83) supernovae, which were discovered by the Supernova Cosmology Project as part of a campaign to measure cosmological parameters. The spectra are used to determine the redshift and classify the supernova type, essential information if the supernovae are to be used for cosmological studies. Redshifts were derived either from the spectrum of the host galaxy or from the spectrum of the supernova itself. We present evidence that these supernovae are of Type Ia by matching to spectra of nearby supernovae. We find that the dates of the spectra relative to maximum light determined from this fitting process are consistent with the dates determined from the photometric light curves, and moreover the spectral time-sequence for SNe Type Ia at low and high redshift is indistinguishable. We also show that the expansion velocities measured from blueshifted CaHK are consistent with those measured for low-redshift Type Ia supernovae. From these first-level quantitative comparisons we find no evidence for evolution in SNIa properties between these low- and high-redshift samples. Thus even though our samples may not be complete, we conclude that there is a population of SNe Ia at high redshift whose spectral properties match those at low redshift.Comment: Accepted for publication in AJ. Also available at http://supernova.lbl.gov

Extracting clean supernova spectra

We use a new technique to extract the spectrum of a supernova from that of the contaminating background of its host galaxy, and apply it to the specific case of high-redshift Type Ia supernova (SN Ia) spectroscopy. The algorithm is based on a two-channel iterative technique employing the Richardson-Lucy restoration method and is implemented in the IRAF code 'specinholucy'. We run the code both on simulated (SN Ia at z=0.5 embedded in a bright host galaxy) and observed (SNe Ia at various phases up to z=0.236) data taken with VLT+FORS1 and show the advantages of using such a deconvolution technique in comparison with less elaborate methods. This paper is motivated by the need for optimal supernova spectroscopic data reduction in order to make meaningful comparisons between the low and high-redshift SN Ia samples. This may reveal subtle evolutionary and systematic effects that could depend on redshift and bias the cosmological results derived from comparisons of local and high-z SNe Ia in recent years. We describe the various aspects of the extraction in some detail as guidelines for the first-time user and present an optimal observing strategy for successful implementation of this method in future high-z SN Ia spectroscopic follow-up programmes.Comment: 15 pages, 14 figures, accepted for publication in A&

Restframe I-band Hubble diagram for type Ia supernovae up to redshift z ~0.5

We present a novel technique for fitting restframe I-band light curves on a data set of 42 Type Ia supernovae (SNe Ia). Using the result of the fit, we construct a Hubble diagram with 26 SNe from the subset at 0.01< z<0.1. Adding two SNe at z~0.5 yields results consistent with a flat Lambda-dominated``concordance universe'' ($\Omega_M,\Omega_\Lambda$)=(0.25,0.75). For one of these, SN 2000fr, new near infrared data are presented. The high redshift supernova NIR data are also used to test for systematic effects in the use of SNe Ia as distance estimators. A flat, Lambda=0, universe where the faintness of supernovae at z~0.5 is due to grey dust homogeneously distributed in the intergalactic medium is disfavoured based on the high-z Hubble diagram using this small data-set. However, the uncertainties are large and no firm conclusion may be drawn. We explore the possibility of setting limits on intergalactic dust based on B-I and B-V colour measurements, and conclude that about 20 well measured SNe are needed to give statistically significant results. We also show that the high redshift restframe I-band data points are better fit by light curve templates that show a prominent second peak, suggesting that they are not intrinsically underluminous.Comment: Accepted for publication in A&A (01/04/2005

Spectroscopic observations of eight supernovae at intermediate redshift

We present spectra of six Type Ia and two Type II supernovae obtained in June 2002 at the William Herschel Telescope during a search for Type Ia supernovae (SNIa) at intermediate redshift. Supernova type identification and phase determination are performed using a fitting technique based on a Xi2 minimization against a series of model templates. The spectra range from z=0.033 to z=0.328, including one spectroscopically underluminous SNIa at z=0.033. This set of spectra significantly increases the sample of well-observed type SNIa supernovae available in the range 0.15< z <0.35. Together with the twelve supernovae observed by our team in 1999 in the same redshift range, they form an homogeneous sample of seventeen type Ia supernovae with comparable signal-to-noise ratio and regular phase sampling in a still largely unexplored region of the redshift space.Comment: 30 pages, 15 figures. Published in A&