Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning

Within a supervised classification framework, labeled data are used to learn classifier parameters. Prior to that, it is generally required to perform dimensionality reduction via feature extraction. These preprocessing steps have motivated numerous research works aiming at recovering latent variables in an unsupervised context. This paper proposes a unified framework to perform classification and low-level modeling jointly. The main objective is to use the estimated latent variables as features for classification and to incorporate simultaneously supervised information to help latent variable extraction. The proposed hierarchical Bayesian model is divided into three stages: a first low-level modeling stage to estimate latent variables, a second stage clustering these features into statistically homogeneous groups and a last classification stage exploiting the (possibly badly) labeled data. Performance of the model is assessed in the specific context of hyperspectral image interpretation, unifying two standard analysis techniques, namely unmixing and classification

True Lies: The Double Life of the Nucleotide Excision Repair Factors in Transcription and DNA Repair

Nucleotide excision repair (NER) is a major DNA repair pathway in eukaryotic cells. NER removes structurally diverse lesions such as pyrimidine dimers, arising upon UV irradiation or bulky chemical adducts, arising upon exposure to carcinogens and some chemotherapeutic drugs. NER defects lead to three genetic disorders that result in predisposition to cancers, accelerated aging, neurological and developmental defects. During NER, more than 30 polypeptides cooperate to recognize, incise, and excise a damaged oligonucleotide from the genomic DNA. Recent papers reveal an additional and unexpected role for the NER factors. In the absence of a genotoxic attack, the promoters of RNA polymerases I- and II-dependent genes recruit XPA, XPC, XPG, and XPF to initiate gene expression. A model that includes the growth arrest and DNA damage 45α protein (Gadd45α) and the NER factors, in order to maintain the promoter of active genes under a hypomethylated state, has been proposed but remains controversial. This paper focuses on the double life of the NER factors in DNA repair and transcription and describes the possible roles of these factors in the RNA synthesis process

Effect of Corrosion on the Low-cycle Fatigue Strength of Steels used in Frequent Start-up Power Generation Steam Turbine

AbstractThe practical importance of fatigue failure in steam turbine materials has directed many experimental research towards assessing the physical reason for material sensitivity to corrosion fatigue and providing design rules for engineers. Metallic materials used in steam turbine are exposed to cyclic loading at high temperature and steam environment, during their service life. In this study, an original fatigue testing setup was developed to investigate the effect of aqueous solutions and temperature on the fatigue strength on the martensitic stainless steel X19CrMoVNbN11-1 used for rotating blades and (ii) a cast G17CrMoV5-10 steel used for casing. Fatigue tests were carried out in two environments: (i) in air at 90°C and (ii) in distilled water at 90°C (pH = 7.2 and 02 = 3 ppm) at a loading frequency of 1Hz. Internal and surface crack initiation are observed in air at 90°C, whereas in purified water at 90°C, the crack initiated in the surface at corrosion defects. The decrease observed in the corrosion fatigue strength of specimens was more important at high plastic strain level of that on similar specimens tested in air. Based on fractography analysis, fatigue crack initiation mechanisms in air and in distilled water were identified. Two different scenarios for fatigue damage depending on the microstructure are proposed and will be discussed in this paper

Development of a hybridized discontinuous Galerkin solver for inductively coupled plasma.

In this work, the early-stages of the development of a 3D hybridized discontinuous Galerkin solver for inductively coupled plasma are presented. The axisymmetric steady-state version of the code is presented with some basic verification cases.9. Industry, innovation and infrastructur

Comparison of convolutional neural networks for cloudy optical images reconstruction from single or multitemporal joint SAR and optical images

With the increasing availability of optical and synthetic aperture radar (SAR) images thanks to the Sentinel constellation, and the explosion of deep learning, new methods have emerged in recent years to tackle the reconstruction of optical images that are impacted by clouds. In this paper, we focus on the evaluation of convolutional neural networks that use jointly SAR and optical images to retrieve the missing contents in one single polluted optical image. We propose a simple framework that ease the creation of datasets for the training of deep nets targeting optical image reconstruction, and for the validation of machine learning based or deterministic approaches. These methods are quite different in terms of input images constraints, and comparing them is a problematic task not addressed in the literature. We show how space partitioning data structures help to query samples in terms of cloud coverage, relative acquisition date, pixel validity and relative proximity between SAR and optical images. We generate several datasets to compare the reconstructed images from networks that use a single pair of SAR and optical image, versus networks that use multiple pairs, and a traditional deterministic approach performing interpolation in temporal domain.Comment: 17 page

Modèle bayésien hiérarchique pour le démélange et la classification robuste d'images hyperspectrales

L’interprétation des images hyperspectrales demeure un problème complexe qui a été abordée sous différents paradigmes. En particulier, les techniques de classification supervisée et de démélange spectral sont deux familles de méthodes d’interprétation largement utilisées. Ces deux approches offrent des analyses complémentaires : le démélange spectral propose une modélisation basée sur une interprétation physique des images hyperspectrales, en supposant que chaque pixel est un mélange de spectres purs associés aux divers matériaux présents dans la scène, tandis que la classification supervisée cherche à identifier une classe unique par pixel en se basant sur un ensemble de classes sémantiques définies par l’utilisateur et sur un ensemble de données, labellisées par un expert, lui servant d’exemple. Si ces deux techniques ont été largement discutées dans la littérature, elles ont été rarement utilisées conjointement

Measuring the Effect of Organizational Climate on the Employees’ Work Performance as Perceived by the Employees

The study aimed to examine the effect of organizational climate on the individual work performance of the e mployees. To deepen the understanding of the concepts of the study, the literature was reviewed. The study used descriptive assessment and correlational research design and used descriptive and inferential statistics to analyze the data. The population was all employees of the Divine Word College of Laoag and therefore total enumeration was applied. The study found that all dimensions of organizational climate were high, but not very high and this is the same true with individual work performance. Analysis of Variance suggests that there is a significant correlation between organizational climate and individual work performance. &nbsp

Motion-based ground reaction forces and moments prediction method in a moving frame: a pilot study

International audienceA motion-based method to predict ground reaction forces and moments (GRF&M) in a moving and/or non-horizontal frame has been developed. The motion of a subject located on a moving hand pallet truck has been recorded. The moving structure has been equipped with a force platform to compare predicted and measured GRF&M