NCube/papyrus huggingface dataset:
Effets de la pleine conscience sur l’empathie, les compétences émotionnelles, le cortisol capillaire, et le stress psychologique des soignants en hémato-oncologie pédiatrique

The healthcare profession requires a variety of professional skills to provide effective clinical care. Empathy underlies professional caregivers’ skills and is essential to a harmonious therapeutic relationship. Empathy has been linked to improved patient health outcomes and better patient satisfaction. However, extended empathic contact with patients’ suffering makes professional caregivers more vulnerable to emotional exhaustion if they are unable to properly regulate their emotions. Professionals working in pediatric oncology face chronic stress that can affect their empathic skills and the quality of the professional-patient relationship. Prolonged chronic stress can manifest itself physiologically and influence professionals’ cortisol levels. It is therefore essential that professional caregivers learn adequate emotional regulation strategies. In this context, three emotional competencies appear essential: identifying one’s own emotions, identifying others’ emotions, and accepting one’s emotions. These emotional competencies could be developed by mindfulness-based interventions such as the Mindfulness-Based Stress Reduction (MBSR) program. The overall objective of this thesis is to evaluate the effects of an MBSR-based stress reduction program on empathy, emotional competencies, hair cortisol, and psychological stress in professional caregivers working in pediatric hematology-oncology. This thesis is composed of three articles. The first article presents a systematic literature review aimed at identifying the range of outcomes measured in studies on the effects of MBSR on professional caregivers, assessing the impact of MBSR on these outcomes, and determining if MBSR has an impact on empathy and emotional skills. The second article aims to evaluate the feasibility and acceptability of an MBSR-based intervention, as well as to evaluate changes in empathy (perspective taking and empathic concern) and emotional competencies (identifying one’s own emotions, identifying emotions in others, and accepting one’s emotions). Two pre-post studies were conducted with psychology students (n = 12) and professionals working in pediatric hematology-oncology (n = 25). The intervention was feasible and acceptable, and significant improvements were observed on all measures except the empathic concern and the identification of others’ emotions. Finally, the third article aims to identify changes in hair cortisol, perceived stress, psychological distress, and burnout. No changes were observed in hair cortisol levels, but significant changes were observed in perceived stress, psychological distress, and burnout. These encouraging results justify moving towards efficacy trials.

Keyphrases: Stress, Empathy, Emotions, Emotional competencies, Emotional acceptance, Stress, Hair cortisol, Burnout, Healthcare providers, Hematology-oncology, Mindfulness, Mindfulness-Based Stress Reduction, MBSR
Étude numérique de la diffusion des défauts ponctuels dans les alliages de nickel

Thanks to its great versatility, nickel is widely used in industry in pure form or more frequently in alloys. These metals have several good physical characteristics such as high mechanical strength, low expansion at high temperature and corrosion resistance involving intensive use in several industrial fields such as aerospacial, car construction, conductive tubes in nuclear power plants and even for electronic components. The study of point defects diffusion in nickel-based alloys is in increase and this since it directly affects their beneficial properties. In this thesis, we describe in detail the kinetics of these defects in nickel and its alloys using the kinetic ART method which is an off-lattice kinetic Monte Carlo method with on-the-fly catalog building. We describe in detail the energy landscape for these defects in pure nickel. Our results show complex diffusion mechanisms even for defects as simple as vacancy aggregates or interstitials. We also studied nickel alloys with a vacancy and interstitial defects, which allows us to find that these defects can cause a structural evolution of such alloys thus pushing them to a disordered phase. We also examine the diffusion of the vacancy in the NiFe alloy which allows us to explore the exact diffusive behavior of this defect depending on the chemical composition of its immediate surroundings of its first neighbors.

Keyphrases: Diffusion, Nickel, Point defects, Kinetic Monte Carlo, Alloys
Analyse et modélisation d’étoiles naines blanches de type DB dans le Sloan Digital Sky Survey et le relevé Gaia

The study of white dwarf stars relies heavily on the determination of their atmospheric and physical parameters, such as effective temperature, surface gravity, chemical composition, mass, age and luminosity. Thanks to surveys like the Sloan Digital Sky Survey (SDSS) or Gaia, photometric and spectroscopic  data, as well as trigonometric parallaxes were made publicly available for a large number of white dwarfs, making it possible to determine the parameters listed above for very large samples. A comparative analysis of the atmospheric parameters of type DA and type DB white dwarfs from the SDSS and Gaia is presented in this thesis. This analysis reveals that the Stark broadening profiles in pure hydrogen model atmospheres as well as the van der Waals broadening treatment in helium-rich atmospheres need to be revisited. The comparison of photometric and spectroscopic effective temperatures and masses unveils the presence of DA+DA, DA+DC, DA+DB, as well as DB+DB unresolved double degenerate systems.  The photometric and spectroscopic techniques can only determine a few parameters directly. To obtain the other quantities of interest, one must rely on evolutionary models and the mass-radius relation. The exquisite Gaia trigonometric parallaxes offer the opportunity to put this relation to the test for a large number of white dwarfs. It is found that the theoretical mass-radius relation is valid.       The DB white dwarfs are less well understood than their hydrogen-rich counterpart since they are rarer. Therefore, a thorough analysis of the DB white dwarf population is presented. The analysis of the surface gravities and mass distribution reveals the presence of several objects of particular interest, massive or magnetic DB stars for example. The hydrogen abundance distribution also reveals that white dwarfs have very different quantities of that element in their atmosphere, in the same temperature range, suggesting that they must have followed different evolutionary channels.  The fraction of DB stars remains somewhat constant between 40,000 K and 25,000 K and steadily increases below this temperature, reaching its peak value around 15,000 K. This is in line with the two evolutionary channels and the convective dilution scenario, where some DA transform into DB stars when the thin hydrogen layer at the surface is mixed in the underlying helium convection zone, while some DB stars retain their helium-rich atmosphere throughout their evolution.  The origin of this photospheric hydrogen, however, is still up for debate. Most DBA white dwarfs have hydrogen abundances way too high to have a residual origin resulting from the convective dilution scenario. One possible solution to this dilemma is that a small residual hydrogen layer still floats on the surface, thus giving the illusion of a higher hydrogen content. This hypothesis is tested with new chemically stratified model atmospheres.

Keyphrases: Stars: fundamental parameters, Stars: evolution, White dwarfs, Techniques: photometric and spectroscopic
Modélisation des effets de haute densité à la photosphère des naines blanches froides

This thesis presents a new generation of cool white dwarf atmosphere models. Those stars, being among the oldest in the Milky Way, are especially useful for age dating stellar populations and improving our understanding of the evolutionary history of our galaxy. Age dating of those objects however, relies on the determination of their atmospheric parameters, which are uncertain given the difficulties inherent in modeling their atmospheres. Indeed, the atmospheres of cool white dwarfs are particularly dense, which seriously complicates their modeling because of the importance of interactions between particles.  We use modern simulation techniques (density functional theory, molecular dynamics and ab initio methods of quantum chemistry) to improve the constitutive physics of atmosphere models. In particular, we show that the collision-induced absorption profiles are distorted under the physical conditions found in cool white dwarf atmospheres, we quantify the pressure ionization of heavy elements in a dense helium medium and we compute spectral line profiles beyond the impact approximation. By integrating these new calculations into an existing atmosphere code (and also by adding a new equation of state and continuum opacities suitable for high densities), we have developed the most accurate cool white dwarf atmosphere code to date.  To test the accuracy of our models, we have evaluated their ability to reproduce the spectra of cool, metal-polluted white dwarfs. Those cool white dwarfs are the only ones to show atomic spectral lines and thus represent ideal benchmarks to observationally validate our models. Our model atmosphere code passes the test: it manages to reproduce the spectra of stars that previous codes were struggling to model, which demonstrates its superiority and validates its constitutive physics.  In this thesis, this new tool finds several applications. In particular, it allows to reveal the strange atmospheric composition of white dwarf WD J2356-209. Our analysis shows that this star has accreted a sodium-rich planetesimal, for which no Solar System analogue is known. Moreover, by combining our models with recent observations from the Pan-STARRS survey and the Gaia mission, we paint the most accurate picture to date of the spectral evolution of cool white dwarfs. More specifically, we identify a decrease of the fraction of stars that are hydrogen-rich between 7500 and 6250 K, which we interpret as being due to convective mixing. Between 6250 and 5000 K, we find that this fraction increases, a phenomenon for which no physical explanation exists at the moment. At lower temperatures, we show that the fraction of hydrogen-rich objects does not increase, thus ruling out the scenario according to which accretion of hydrogen from the interstellar medium dominates the spectral evolution of cool white dwarfs.

Keyphrases: Dense matter, Equation of state, Line: profiles, Opacity, Planetary systems, Stars: abundances, Stars: atmospheres, Stars: evolution, White dwarfs
Recherche de compagnons de type Jupiter à très grandes séparations autour d’étoiles jeunes dans le voisinage solaire

The main goal of this PhD thesis is to search for exoplanets on wide orbits around young stars using the direct-imaging technique. These exoplanets are of high interest for several reasons. Firstly, as they are far from their host star, they can be studied as if they were isolated objects without the need for sophisticated imaging and data analysis techniques. In some cases, a high resolution spectrum can be acquired to learn more about them. Also, contrary to isolated objects of similar masses, their age and distance can be easily inferred from those of their host star. It is thus interesting to probe around stars for which those characteristics are well known, as they are crucial to infer the masses of exoplanets. As a first step, the results from a search for giant planets on wide orbits are described. A sample of 177 stars member of known young associations inside 70 pc of the Sun were observed using the Canada-France-Hawaii telescope, the Gemini-Sud observatory and the space telescope Spitzer. The observations reached a good completeness down to masses as low as 2 MJup at separations between 1000 and 5000 AU. Four candidate planets were detected, but they were identified as background objects using follow-up observations. A frequency of planets per star was inferred from the survey, such that less than 3% of the stars have at least one planet with masses 1–13MJup at separations of 1000–5000 AU, with a 95% confidence level.  The next step was to combine several archival direct-imaging surveys to the study presented above. A sample of 344 unique stars all confirmed members of young associations was obtained. A Bayesian and Markov chain Monte Carlo analysis was realised to constrain the frequency of companions as well as the distribution of giant planets with masses between 1 and 20 MJup at separations 5-5000 AU. It was inferred from this analysis that 2.17+6.85−1.40%, 0.3+2.6 −0.1%, and 2.61+6.97−1.00% of stars have at least a planet of mass 1-20MJup at separations of 20–1000 AU, 1000–5000 AU and 20–5000 AU, respectively. Furthermore, assuming that the mass and orbital distribution of giant planets follows a power-law such as d2n ∝ fM^αa^βdMda, the parameters can be constrained to α = −0.08+0.75−0.63 and β = −1.41+0.22−0.24, with a 68% confidence level, with a corresponding planetary fraction per stars f = 0.12+0.11−0.06. A dependency over the mass of the host star was then added to the distribution such as d2n ∝ M^αa^β(M⋆/M⊙)^γdMda. In this case, α = −0.18+0.77−0.65, β = −1.43+0.23−0.24, γ = 0.62+0.56−0.50 and f = 0.11+0.11−0.05, with a 68% confidence level.

Keyphrases: Brown dwarf, Exoplanet, Young star, Imaging
On abstraction in a Carnapian system

Rudolf Carnap (1891-1970) rejects two philosophical distinctions that have been made and admitted by Gottlob Frege (1848-1925), namely the object-concept and the sense-reference distinctions. In the analytic tradition and upon these distinctions, a family of analytic systems have been constructed and developed (which we call Fregean systems), within which a number of notions have been employed including the notion of abstraction. It has been claimed (by Neo- Fregeans) that the Fregean notion of abstraction has been captured by what is commonly known as the “principle of abstraction”. The goal of this dissertation is to present the notion of Carnapian abstraction, in particular, and the Carnapian system, in general, in distinction to the Fregean counterparts. We will argue that the admission and rejection of these distinctions will entail fundamentally different analytic systems. Hence, we will show how each system undertakes a different notion of abstraction. Abstraction in a Fregean system will be characterized as a mind-independent process subject to its own rules, whereas in a Carnapian system, abstraction will be characterized as a defined process of distancing from meaning in a linguistic framework. We will conclude that the Carnapian system has advantages over the Fregean one (among which is its simplicity), and that its technical aspect is yet to be developed.

Keyphrases: Analytic Philosophy, Philosophy of Language, Philosophy of Science, Logic, Frege, Carnap, Abstraction, Linguistic Framework, Object-Concept Distinction, Sense-Reference Distinction
On the conceptual and methodological aspects of (political) journalistic translation research

The significance of translation in news production contexts has been well documented in translation studies (e.g., Schäffner and Bassnett 2010; Bielsa and Bassnett 2009). However, the conceptual and methodological aspects of journalistic translation research still need improvements. The first aim of this research is to provide a more comprehensive understanding of journalistic translation processes and the professional behaviour of the individuals who translate in news organizations. In this regard, following Vuorinen (1997), we draw on news gatekeeping theory, in the domain of mass communication and journalism studies. One of the most efficient models of news gatekeeping is that of Shoemaker and Vos (2009), which describes news production processes and the behaviour of newsmen in a comprehensive and generalizable way. Hence, it is a useful theoretical ground for explaining how translation and the individual who translate can be conceptualized in news production contexts. The second aim of this research is to propose a method of textual analysis for describing political journalistic translations. In this regard, we argue for describing political journalistic translations in terms of their relationships with dominant political norms and ideologies in the target society (i.e. the norms and ideologies of political elite groups who are in charge of policy making). This relationship means that, depending on the ideological independence of news institutions, dominant political norms and ideologies may influence the production of political journalistic translations. Also, political journalistic translations can contribute to or challenge the social legitimacy of dominant political norms and ideologies. We use ‘norm life cycle’ theory (Finnemore and Sikkink 1998), proposed in the domain of political science, to explain theoretically why political elite groups need to control the flow of news information, and how political journalistic translations can influence the social legitimacy of the norms and ideologies of leading politicians. After the theoretical discussions, we propose a method of textual analysis, which enables describing the relationship between dominant political norms and ideologies and political journalistic translations at an empirical level. Finally, we apply the proposed method of textual analysis to a case study of political journalistic translations to test the applicability of the method. The corpus, constructed for the purpose of this case study, includes 126 political news pieces produced by ISNA (an Iranian news institution) in Persian and their English translations by BBC.

Keyphrases: translation, journalism, news institutions, news gatekeeping, political norms, norms life cycle, social internalization of norms
Recurrent neural models and related problems in natural language processing

The recurrent neural network (RNN) is one of the most powerful machine learning models specialized in capturing temporal variations and dependencies of sequential data. Thanks to the resurgence of deep learning during the past decade, we have witnessed plenty of novel RNN structures being invented and applied to various practical problems especially in the field of natural language processing (NLP). This thesis follows a similar direction, in which we offer new insights about RNNs’ structural properties and how the recently proposed RNN models may stimulate the formation of new open problems in NLP. The scope of this thesis is divided into two parts: model analysis and new open problems. In the first part, we explore two important aspects of RNNs: their connecting architectures and basic operations in their transition functions. Specifically, in the first article, we define several rigorous measurements for evaluating the architectural complexity of any given recurrent architecture with arbitrary network topology. Thoroughgoing experiments on these measurements demonstrate their theoretical validity and utility of guiding the RNN architecture design. In the second article, we propose a novel module to combine different information flows multiplicatively in RNNs’ basic transition functions. RNNs equipped with the new module are empirically showed to have better gradient properties and stronger generalization capacities without extra computational and memory cost. The second part focuses on two open problems in NLP: how to perform advanced multi-hop reasoning in machine reading comprehension and how to encode personalities into chitchat dialogue systems. We collect two different large scale datasets aiming to motivate the methodological progress on these two problems. Particularly, in the third article we introduce HotpotQA dataset containing over 113k Wikipedia based question-answer pairs. Most of the questions in HotpotQA are answerable only through accurate multi-hop reasoning over multiple documents. Supporting facts required for reasoning are also provided to help the model to make explainable predictions. The fourth article tackles the problem of the lack of personality in chatbots. The proposed persona-chat dataset encourages more engaging and consistent conversations by forcing dialog partners conditioning on given personas. We show that baseline models trained on persona-chat are able to express consistent personalities and to respond in more captivating ways by concentrating on personas of both themselves and other interlocutors.

Keyphrases: recurrent neural networks, deep learning, natural language processing, reading comprehension, dialogue system
Détection de changement en imagerie satellitaire multimodale

The purpose of this research is to study the detection of temporal changes between two (or more) multimodal images satellites, i.e., between two different imaging modalities acquired by two heterogeneous sensors, giving for the same scene two images encoded differently and depending on the nature of the sensor used for each acquisition. The two (or multiple) multimodal satellite images are acquired and coregistered at two different dates, usually before and after an event. In this study, we propose new models belonging to different categories of multimodal change detection in remote sensing imagery. As a first contribution, we present a new constraint scenario expressed on every pair of pixels existing in the before and after image change. A second contribution of our work is to propose a spatio-temporal textural gradient operator expressed with complementary norms and also a new filtering strategy of the difference map resulting from this operator. Another contribution consists in constructing an observation field from a pair of pixels and to infer a solution maximum a posteriori sense. A fourth contribution is proposed which consists to build a common feature space for the two heterogeneous images. Our fifth contribution lies in the modeling of patterns of change by anomalies and on the analysis of reconstruction errors which we propose to learn a non-supervised model from a training base consisting only of patterns of no-change in order that the built model reconstruct the normal patterns (non-changes) with a small reconstruction error. In the sixth contribution, we propose a pairwise learning architecture based on a pseudosiamese CNN network that takes as input a pair of data instead of a single data and constitutes two partly uncoupled CNN parallel network streams (descriptors) followed by a decision network that includes fusion layers and a loss layer in the sense of the entropy criterion. The proposed models are enough flexible to be used effectively in the monomodal change detection case.

Keyphrases: Fastmap, Deep learning, Radar, Multimodal satellite images, Heterogeneous images, Optical, Change detection, Pairwise pixels, Convolutional neural networks, Invariant operator, Anomaly detection