Pro Deo et Patria: unfolding the hybrid governance and political participation of religious institutions in the Democratic Republic of Congo (DRC)

The nexus between Religion and Politics has shifted in the Democratic Republic of Congo (DRC) since 2016—a year that marked the end of Joseph Kabila’s constitutional two-term limit. This interdisciplinary thesis unfolds the public role played by the Conférence Episcopale Nationale du Congo (CENCO) that regroups all Catholic bishops on a national scale and l’Eglise du Christ au Congo (ECC), which is the confederation of 95 Protestant denominations, in that epochal change. Using qualitative and interpretive approaches, this thesis specifically looks at the interlinkages between those two major religious networks and the State to highlight the shift in the Catholic-Protestant communication, mobilization, and participation toward national Politics. It draws on ten months of fieldwork in Kinshasa, the capital-city, with two additional months of follow-up research in Brussels, Belgium and in Vatican-city, Italy. CENCO and ECC have functioned as hybrid governance institutions and actors by using their social influences to shape political order and to provide basic public services. With regards to previous researches in the expanding literature that explores the intersection of Religion and Politics, this thesis brings a particular and primary contribution in displaying how and why the Catholic-Protestant political engagement in the recent years changed considerably in the DRC. The timeframe of this inquiry (that goes from 2016 to 2019) coincided with a specific moment when religious leaders emerged as active and critical voices. The Catholic bishops of CENCO first stepped up as mediators between Kabila’s administration and opposition leaders and fostered the signature of the Saint-Sylvester peace accord in December 2016. Joined later by their Protestant peers of ECC, they also mobilized societal energies as protest catalysts alongside Christian laity and youth-driven civil movements that paved the road to the DRC’s first and historic peaceful transfer of power since its independence from Belgium. That event was concretized by the outgoing Joseph Kabila and the elected Felix Tshisekedi on 24 January 2019 on the lawn of the Palais de la nation. This work also paid a particular attention to historical, cultural, and theological underpinnings linked to this shift in religious political participation. The research relies on interviews conducted among Congolese religious elites, leaders, and laypersons over a period of two years. Despite its ambivalence as observed in post-2018 elections’ era, Religion served both as a cohesive and transformational political force in the DRC

A Self-management Approach for Dietary Sodium Restriction in Patients With CKD:A Randomized Controlled Trial

Health and self-regulatio

Detecting natural disasters, damage, and incidents in the wild

Responding to natural disasters, such as earthquakes, floods, and wildfires, is a laborious task performed by on-the-ground emergency responders and analysts. Social media has emerged as a low-latency data source to quickly understand disaster situations. While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes. However, no large-scale image datasets for incident detection exists. In this work, we present the Incidents Dataset, which contains 446,684 images annotated by humans that cover 43 incidents across a variety of scenes. We employ a baseline classification model that mitigates false-positive errors and we perform image filtering experiments on millions of social media images from Flickr and Twitter. Through these experiments, we show how the Incidents Dataset can be used to detect images with incidents in the wild. Code, data, and models are available online at http://incidentsdataset.csail.mit.edu.Comment: ECCV 202

Self-monitoring and self-efficacy in patients with chronic kidney disease during low-sodium diet self-management interventions: secondary analysis of the ESMO and SUBLIME trials

Background Patients with chronic kidney disease are often requested to engage in self-monitoring sodium (i.e. salt) intake, but it is currently unknown how self-monitoring would empower them. This study aims to assess: (1) how frequent self-monitoring tools are being used during low-sodium diet self-management interventions; (2) whether self-efficacy (i.e. trust in own capability to manage the chronic disease) is associated with self-monitoring frequency; and (3) whether higher self-monitoring frequency is associated with an improvement in self-efficacy over time.Method Data from two multicenter randomized controlled trials (ESMO [n = 151] and SUBLIME [n = 99]) among adult Dutch patients with chronic kidney disease (eGFR >= 20-25 mL/min/1.73 m(2)) were used. In both studies, routine care was compared to a 3-month low-sodium diet self-management intervention with several self-monitoring tools (online food diary, home blood pressure monitor, and urinary sodium measurement device [only ESMO]). Data was collected on usage frequency of self-monitoring tools. Frequencies during the interventions were compared between low and high baseline self-efficacy groups using the Mann-Whitney U test and T-test and associated with changes in self-efficacy during the interventions using Spearman correlation coefficients.Results Large variations in self-monitoring frequency were observed. In both interventions, usage of self-monitoring tools was highest during the first month with sharp drops thereafter. The online food diary was the most frequently used tool. In the ESMO intervention, low baseline self-efficacy was associated with a higher usage frequency of self-monitoring tools. This finding was not confirmed in the SUBLIME intervention. No significant associations were found between usage frequency of self-monitoring tools and changes in self-efficacy over time.Conclusion Patients with low self-efficacy might benefit most from frequent usage of self-monitoring tools when sufficient guidance and support is provided.Clinical epidemiolog

The commissaires des classes in the French Royal Navy, 17th-18th centuries

Ce travail étudie une catégorie socio-professionnelle méconnue de l'époque moderne : les commissaires aux classes et ceux qui en faisaient fonction de 1668 à 1795. Il retrace l'évolution de cet office au cours de la période, en mettant au jour les modifications apportées par les différents secrétaires d'État : ceux-ci cherchent à mieux circonscrire les charges dévolues aux commissaires, s'appuyant sur les nombreux rapports, correspondances et mémoires rédigés dans les ports, en temps de guerre comme en temps de paix. Mais au-delà de la fonction, souvent protéiforme (de la levée des classes aux inspections de bâtiments), il y a les hommes mêmes, des hommes de terrain, souvent commissaires de père en fils, qui tissent auprès des gens de mer de véritables réseaux (familiaux, professionnels) qui compensent leur image ambivalente. Critiqués, ils jouent pourtant dans les quartiers qu'ils ont en charge un rôle de modérateur social jusqu’ici largement ignoré. L'étude se propose de montrer comment au fil des ans ces hommes de plume se sont mués en agents d’administration. Elle met également en évidence la naissance de dynasties constituant et s’intégrant à des clientèles plus vastes, conscientes tout autant de leurs devoirs que de leurs prérogatives vis-à-vis de l'épée. Deux études de cas, l’une présentant l’émergence d’une de ces dynasties et l’autre analysant un procès en prévarication, illustrent les thèses avancées. Le volume II présente un dictionnaire biographique de 440 responsables des classes, permettant de mieux se représenter la réalité tant numérique que sociologique de ces cadres de l'administration maritime.This study analyses a widely unknown 18th century socio-professional category: the commissaires des classes. The French government's system of naval conscription created by Colbert in 1668 divided the realm into several districts, each one directed by an officier des classes. Although they are a reliable source on the French seamen, no one ever wondered who they were, what their social background was and how they managed to fulfill the government's requests concerning naval conscription. The Secretaries of State for the Navy tried to turn them from simple clerks to officers of administration. Thus emerged many unofficial functions, such as: social appeasement, financial help for seamen and closer relations with the littoral authorities (municipalities, merchants, ship-owners) than ever suspected. Their image proved to be ambivalent: they were loathed because they embodied the Royal Law but also praised for their social work. Difficulties in wartime forced them to rise to the occasion. The study of their work through their letters and reports to the Ministry, their administrative production (registration rolls) and the up to now widely unused personal files kept in the National Archives also revealed that they built dynasties of administrators intimately linked with clientelist networks within the maritime districts and at the Court. Acting as a lobby group, these families were keen on keeping their privileges and on preserving their interests in spite of the numerous reforms held by the Ministers throughout the 18th century. A biographical dictionary of 440 officers of classes completes this study, revealing the sociological reality of this administrative key group

The AI Race: Why Current Neural Network-based Architectures are a Poor Basis for Artificial General Intelligence

Artificial General Intelligence is the idea that someday an hypothetical agent will arise from artificial intelligence (AI) progresses, and will surpass by far the brightest and most gifted human minds. This idea has been around since the early development of AI. Since then, scenarios on how such AI may behave towards humans have been the subject of many fictional and research works. This paper analyzes the current state of artificial intelligence progresses, and how the current AI race with the ever faster release of impressive new AI methods (that can deceive humans, outperform them at tasks we thought impossible to tackle by AI a mere decade ago, and that disrupt the job market) have raised concerns that Artificial General Intelligence (AGI) might be coming faster that we thought. In particular, we focus on 3 specific families of modern AIs to develop the idea that deep neural networks, which are the current backbone of nearly all artificial intelligence methods, are poor candidates for any AGI to arise due to their many limitations, and therefore that any threat coming from the recent AI race does not lie in AGI but in the limitations, uses, and lack of regulations of our current models and algorithms

Contributions au clustering collaboratif et à ses potentielles applications en imagerie à très haute résolution

This thesis presents several algorithms developed in the context of the ANR COCLICO project and contains two main axis: The first axis is concerned with introducing Markov Random Fields (MRF) based models to provide a semantic rich and suited algorithm applicable to images that are already segmented. This method is based on the Iterated Conditional Modes Algorithm (ICM algorithm) and can be applied to the segments of very high resolution (VHR) satellite pictures. Our proposed method can cope with highly irregular neighborhood dependencies and provides some low level semantic information on the clusters and their relationship within the image. The second axis deals with collaborative clustering methods developed with the goal of being applicable to as many clustering algorithms as possible, including the algorithms used in the first axis of this work. A key feature of the methods proposed in this thesis is that they can deal with either of the following two cases: 1) several clustering algorithms working together on the same data represented in different feature spaces, 2) several clustering algorithms looking for similar clusters in different data sets having similar distributions. Clustering algorithms to which these methods are applicable include the ICM algorithm, the K-Means algorithm, density based algorithms such as DB-scan, all Expectation-Maximization (EM) based algorithms such as the Self-Organizing Maps (SOM) and the Generative Topographic Mapping (GTM) algorithms. Unlike previously introduced methods, our models have no restrictions in term of types of algorithms that can collaborate together, do not require that all methods be looking for the same number of clusters, and are provided with solid mathematical foundations.Cette thèse présente plusieurs algorithmes développés dans le cadre du projet ANR COCLICO et contient deux axes principaux :Le premier axe concerne l'introduction d'un algorithme applicable aux images satellite à très haute résolution, qui est basé sur les champs aléatoires de Markov et qui apporte des notions sémantiques sur les clusters découverts. Cet algorithme est inspiré de l'algorithme Iterated conditional modes (ICM) et permet de faire un clustering sur des segments d'images pré-traitées. La méthode que nous proposons permet de gérer des voisinages irréguliers entre segments et d'obtenir des informations sémantiques de bas niveau sur les clusters de l'image traitée.Le second axe porte sur le développement de méthodes de clustering collaboratif applicables à autant d'algorithmes que possible, ce qui inclut les algorithmes du premier axe. La caractéristique principale des méthodes proposées dans cette thèse est leur applicabilité aux deux cas suivants : 1) plusieurs algorithmes travaillant sur les mêmes objets dans des espaces de représentation différents, 2) plusieurs algorithmes travaillant sur des données différentes ayant des distributions similaires. Les méthodes que nous proposons peuvent s'appliquer à de nombreux algorithmes comme l'ICM, les K-Moyennes, l'algorithme EM, ou les cartes topographiques (SOM et GTM). Contrairement aux méthodes précédemment proposées, notre modèle permet à des algorithmes très différents de collaborer ensemble, n'impose pas de contrainte sur le nombre de clusters recherchés et a une base mathématique solide

Contributions à l'apprentissage non-supervisé moderne: Applications aux cas du clustering multi-vue et de l'apprentissage profond non-supervisé

This document is the manuscript presented in order to obtain the "Habilitation à Diriger des Recherches" of Sorbonne University (France), prepared at ISEP Engineering School where I am currently an Associate Professor. My main professional activities of research but also teaching and administrative work, since after I defended my PhD in November 2016, are described in this document. Since research is a continuum, it may also contain elements and recalls from previous works done between 2013 and 2016.In particular, my main axis of research is unsupervised learning, and in the first part of this manuscript I describe my contributions centered around two sub-axis: Unsupervised learning in multi-view environments, and deep learning applied to image processing (satellite and medical) in cases where no labeled data are available. These two sub-axis form the main chapters of this document and describe contributions both in terms of applications and theoretical findings. Issues such as the notion of confidence in unsupervised learning, weakly supervised learning with unreliable ground-truths, clustering stability, as well as the limitations and future evolutions of unsupervised learning are discussed in this manuscript.Ce manuscrit d'Habilitation à diriger des recherches est la synthèse de mes travaux réalisés depuis 2016 en tant qu'enseignant-chercheur à l'ISEP et chercheur au LIPN dans l'équipe A3-ADA. J'y présente mes travaux autour de mon thème central de recherche : l'apprentissage non-supervisé. Ces travaux s'articulent autour de 2 grands axes : l'apprentissage non-supervisé dans un contexte multi-vue, et l'apprentissage non-supervisé profond. Ces deux axes découlent directement de mes travaux de thèses sur le clustering collaboratif appliqué aux images satellite à haute résolution. Mes travaux en apprentissage multi-vue non-supervisé abordent des thématiques telles que la confiance et la pondération des vues dans les environnements non-supervisés, les données manquantes dans un contexte multi-vue, mais aussi des aspects de modélisation du clustering multi-vue afin de théoriser des éléments tels que la stabilité de ce type de méthodes, mais aussi leur capacité à apporter de la nouveauté tout en gardant une cohérence avec les données locales.Mes travaux sur l'apprentissage profond dans un cadre non-supervisé découlent quant à eux du constat que la majorité des méthodes d'apprentissage profond les plus performantes sont supervisées et nécessitent d'importants volumes de données annotées pour leur entraînement. Or, quand on regarde les applications concrètes en imagerie, on s'aperçoit que dans la plupart des cas, ces données annotées ne sont pas disponibles (ou le sont trop tard), sont coûteuses à produire, et que les modèles une fois entraînés sont difficilement transférables. J'aborde donc dans mes travaux des architectures d'apprentissage profond adaptées à des contextes non-supervisés. A travers des applications en imagerie satellite (étude d'urbanisation, cartographie automatique de dégâts de catastrophes naturelles, etc.), ainsi qu'en imagerie médicale (étude de pathologies de l'oeil), mes travaux se sont intéressés à des architectures originales et ont pu en étudier les points forts et les limites

L'apprentissage non-supervisé et ses contradictions

International audienc