Paquet R pour l'estimation d'un mélange de lois de Student multivariées à échelles multiples

National audienceL'utilisation d'un modèle de mélange de lois est une approche statistique classique en classification non-supervisée. Un mélange fréquemment utilisé pour sa simplicité est le mélange gaussien. Cependant, un tel modèle est sensible aux données atypiques. Pour remédier à cela, nous présentons ici le mélange de lois de Student multivariées à échelles multiples, que nous sommes en train d'incorporer au sein d'un paquet R. Ces lois peuvent gérer des queues de lourdeurs différentes selon les directions alors que les lois gaussiennes et les lois de Student multivariées standards sont contraintes à être symétriques

Design considerations for a W-band Josephson junction travelling wave parametric amplifier

Most Josephson junction Travelling Wave Parametric Amplifiers (JTWPAs) developed so far have been focused on operation below 20 GHz, primarily driven by the choice of the qubit resonance frequency used in quantum computation research. Consequently, there is a lack of effort to extend their operation to higher frequency ranges. However, millimetre (mm)- wave JTWPAs could offer potential significant advantages for astronomy, but their operation in this regime is largely unexplored. In this paper, we describe the design considerations for extending JTWPAs operation to the W-band range. We present two JTWPA designs, one with and one without phase matching elements, and we discuss the design methodology of both approaches, before showing their predicted performance respectively

Investigating pin-holes issues in Josephson junction travelling wave parametric amplifiers requiring large area of dielectric layer

Microwave superconducting Josephson Travelling Wave Parametric Amplifiers (JTWPAs) exploit the non-linear inductance of a long superconducting metamaterial line formed by thousands of Josephson junctions to achieve broadband parametric gain with quantum limited added noise. Nevertheless, pin-holes in the dielectric (spacer) layer required for fabricating these superconducting transmission lines (STLs) represent a challenge for JTWPAs fabrication. In this paper, we explore two pin-holes mitigation techniques, which shown promising results with DC characterisation of a suite of test structures at cryogenic temperatures. When implemented for actual JTWPA designs with much longer length, they have shown to improve the fabrication yield albeit some pin-holes still seems to exist over the large wafer area. This indicates that further mitigation effort is required to completely eradicate the pin-holes issue for applications requiring large area of dielectric layer such as microwave JTWPAs

Paquet R pour l'estimation d'un mélange de lois de Student multivariées à échelles multiples

National audienceL'utilisation d'un modèle de mélange de lois est une approche statistique classique en classification non-supervisée. Un mélange fréquemment utilisé pour sa simplicité est le mélange gaussien. Cependant, un tel modèle est sensible aux données atypiques. Pour remédier à cela, nous présentons ici le mélange de lois de Student multivariées à échelles multiples, que nous sommes en train d'incorporer au sein d'un paquet R. Ces lois peuvent gérer des queues de lourdeurs différentes selon les directions alors que les lois gaussiennes et les lois de Student multivariées standards sont contraintes à être symétriques

Multivariate Multi-scaled Student Distributions : brain tumor characterization from multiparametric MRI

International audienceBrain tumor characterization is very useful for patients treatment, but it can be time-consuming for medical experts. Furthermore, the reference method to characterize tissues is biopsy which is a local and invasive technique. Because of this, there is a huge interest for automatic and non-invasive approaches in order to characterize tumor. In this study we use a statistical model-based method to classify multiparametric MRI of brain rat tumors, which allows data quality control with atypical observations detection, and may provide a dictionary of tumor signatures. A previous study, [1], used a Gaussian mixture model to characterize pixels inside tumors. With this model, the observations are gathered into classes resulting from Gaussian distributions. However, this model is sensitive to outliers which degrade the relevance of the obtained groups. And inside a tumor, there could be a huge variability and so a lot of outliers. To account for this biological variability, we propose to use generalized Student distributions : the multivariate multi-scaled Student distributions (MMSD, [2]). The MMSD distribution extends the standard multivariate Student distribution by using the Gaussian scale mixture representation of Student distributions. This representation allows us to introduce multi-dimensional weights, which control different tail thickness of the distribution for each dimension, and provide a way to detect outlier data. In this way, we obtain a finer regulation of the influence of atypical data on the groups shapes, and so a greater flexibility of the clustering model. We use an Expectation-Maximization algorithm (EM) to adjust a MMSD mixture on brain tumor MRI. The number of classes inside the mixture is selected by minimizing the Bayesian information criterion (BIC). Our sample consists of healthy rats (n=8) and 4 groups of rats bearing a brain tumor model (n=8 per group), and 5 quantitative MRI parameter maps for each rat. We adjust a MMSD mixture on the healthy sample to detect tumor area in the tumor sample through the multi-dimensional weights. Then we characterize the tumor areas with another MMSD mixture and build a tumor dictionary which discriminates the 4 tumor

Multivariate Multi-scaled Student Distributions : brain tumor characterization from multiparametric MRI

International audienceBrain tumor characterization is very useful for patients treatment, but it can be time-consuming for medical experts. Furthermore, the reference method to characterize tissues is biopsy which is a local and invasive technique. Because of this, there is a huge interest for automatic and non-invasive approaches in order to characterize tumor. In this study we use a statistical model-based method to classify multiparametric MRI of brain rat tumors, which allows data quality control with atypical observations detection, and may provide a dictionary of tumor signatures. A previous study, [1], used a Gaussian mixture model to characterize pixels inside tumors. With this model, the observations are gathered into classes resulting from Gaussian distributions. However, this model is sensitive to outliers which degrade the relevance of the obtained groups. And inside a tumor, there could be a huge variability and so a lot of outliers. To account for this biological variability, we propose to use generalized Student distributions : the multivariate multi-scaled Student distributions (MMSD, [2]). The MMSD distribution extends the standard multivariate Student distribution by using the Gaussian scale mixture representation of Student distributions. This representation allows us to introduce multi-dimensional weights, which control different tail thickness of the distribution for each dimension, and provide a way to detect outlier data. In this way, we obtain a finer regulation of the influence of atypical data on the groups shapes, and so a greater flexibility of the clustering model. We use an Expectation-Maximization algorithm (EM) to adjust a MMSD mixture on brain tumor MRI. The number of classes inside the mixture is selected by minimizing the Bayesian information criterion (BIC). Our sample consists of healthy rats (n=8) and 4 groups of rats bearing a brain tumor model (n=8 per group), and 5 quantitative MRI parameter maps for each rat. We adjust a MMSD mixture on the healthy sample to detect tumor area in the tumor sample through the multi-dimensional weights. Then we characterize the tumor areas with another MMSD mixture and build a tumor dictionary which discriminates the 4 tumor

Tumor classification and prediction using robust multivariate clustering of multiparametric MRI

International audienceIn neuro-oncology, the use of multiparametric MRI may better characterize brain tumor heterogeneity. To fully exploit multiparametric MRI (e.g. tumor classification), appropriate analysis methods are yet to be developed. In this work, we show on small animals data that advanced statistical learning approaches can help 1) in organizing existing data by detecting and excluding outliers and 2) in building a dictionary of tumor fingerprints from a clustering analysis of their microvascular features

A corpus for studying full answer justification

International audienceQuestion answering (QA) systems aim at retrieving precise information from a large collection of documents. To be considered as reliable by users, a QA system must provide elements to evaluate the answer. This notion of answer justification can also be useful when developing a QA system in order to give criteria for selecting correct answers. An answer justification can be found in a sentence, a passage made of several consecutive sentences or several passages of a document or several documents. Thus, we are interested in pinpointing the set of information that allows verifying the correctness of the answer in a candidate passage and the question elements that are missing in this passage. Moreover, the relevant information is often given in texts in a different form from the question form : anaphora, paraphrases, synonyms. In order to have a better idea of the importance of all the phenomena we underlined, and to provide enough examples at the QA developer’s disposal to study them, we decided to build an annotated corpus