449 research outputs found

    Exploring the use of nature as an adjunct to psychological interventions for depression in young populations

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    Depression in adolescence is a global priority and it is critical to identify effective and accessible interventions. This systematic review aimed to synthesise experimental research on nature-based interventions (NBIs), to determine effects on depressive symptoms in young people. The secondary research question sought to understand characteristics of effective NBIs. A comprehensive systematic search was conducted across major and grey literature databases and papers were screened according to specified criteria. Participants’ ages were required to be between 10 and 24 years and studies needed to use an experimental design, including a control group. Experimental conditions were defined by psychotherapeutic interventions with nature exposure and outcomes measured either clinical symptomatology or subjective states of depression. Ten papers were identified, quality assessed and summarised in a narrative synthesis. Thirteen significant effects were reported in nine studies, highlighting the potential for NBIs as effective interventions for depressive symptoms in young people. However, due to methodological biases, such as lack of randomisation or control over group differences and frequent use of passive control groups, there remains considerable uncertainty over the effectiveness of NBIs. Characteristics of effective NBIs are tentatively discussed, however, further work is needed to clarify which aspects specifically contribute to the beneficial effects observed. Future research should seek to address the limitations of small samples, selection biases and test NBIs against more comparable and evidence-based interventions. It is hoped future studies will consider the inclusion of clinical populations, to explore the utility of NBIs as a treatment option for adolescent depression

    Causal models and algorithmic fairness

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    This thesis aims to clarify a number of conceptual aspects of the debate surrounding algorithmic fairness. The particular focus here is the role of causal modeling in defining criteria of algorithmic fairness. In Chapter 1, I argue that in the discussion of algorithmic fairness, two fundamentally distinct notions of fairness have been conflated. Subsequently, I propose that what is usually taken to be the problem of algorithmic fairness should be divided into two subproblems, the problem of predictive fairness, and the problem of allocative fairness. At the core of Chapter 2 is the proof of a theorem that establishes that three of the most popular (predictive) fairness criteria are pairwise incompatible. In particular, I show that under certain conditions, a predictive algorithm that satisfies a criterion called counterfactual fairness will with logical necessity violate two other popular predictive fairness criteria called equalized odds and predictive parity. In Chapter 3, a new predictive fairness criterion is developed using a mathematical framework for causal modeling. This fairness criterion, which I call causal relevance fairness, is a relaxation of another popular fairness criterion, counterfactual fairness, but turns out to be more closely in line with philosophical theories of discrimination. In Chapter 4, another infamous impossibility result in algorithmic fairness is analyzed through the lens of causality. I argue that by using a causal inference method called matching, we can modify the two fairness criteria equalized odds and predictive parity in a way that resolves the impossibility. Lastly, Chapter 5 contains an empirical case study. In it, the fairness of a popular recidivism risk prediction tool is analyzed using the criteria of (predictive) fairness developed earlier

    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!)

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    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!

    Deep generative modelling of the imaged human brain

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    Human-machine symbiosis is a very promising opportunity for the field of neurology given that the interpretation of the imaged human brain is a trivial feat for neither entity. However, before machine learning systems can be used in real world clinical situations, many issues with automated analysis must first be solved. In this thesis I aim to address what I consider the three biggest hurdles to the adoption of automated machine learning interpretative systems. For each issue, I will first elucidate the reader on its importance given the overarching narratives of both neurology and machine learning, and then showcase my proposed solutions to these issues through the use of deep generative models of the imaged human brain. First, I start by addressing what is an uncontroversial and universal sign of intelligence: the ability to extrapolate knowledge to unseen cases. Human neuroradiologists have studied the anatomy of the healthy brain and can therefore, with some success, identify most pathologies present on an imaged brain, even without having ever been previously exposed to them. Current discriminative machine learning systems require vast amounts of labelled data in order to accurately identify diseases. In this first part I provide a generative framework that permits machine learning models to more efficiently leverage unlabelled data for better diagnoses with either none or small amounts of labels. Secondly, I address a major ethical concern in medicine: equitable evaluation of all patients, regardless of demographics or other identifying characteristics. This is, unfortunately, something that even human practitioners fail at, making the matter ever more pressing: unaddressed biases in data will become biases in the models. To address this concern I suggest a framework through which a generative model synthesises demographically counterfactual brain imaging to successfully reduce the proliferation of demographic biases in discriminative models. Finally, I tackle the challenge of spatial anatomical inference, a task at the centre of the field of lesion-deficit mapping, which given brain lesions and associated cognitive deficits attempts to discover the true functional anatomy of the brain. I provide a new Bayesian generative framework and implementation that allows for greatly improved results on this challenge, hopefully, paving part of the road towards a greater and more complete understanding of the human brain

    Learning from complex networks

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    Graph Theory has proven to be a universal language for describing modern complex systems. The elegant theoretical framework of graphs drew the researchers' attention over decades. Therefore, graphs have emerged as a ubiquitous data structure in various applications where a relational characteristic is evident. Graph-driven applications are found, e.g., in social network analysis, telecommunication networks, logistic processes, recommendation systems, modeling kinetic interactions in protein networks, or the 'Internet of Things' (IoT) where modeling billions of interconnected web-enabled devices is of paramount importance. This thesis dives deep into the challenges of modern graph applications. It proposes a robustified and accelerated spectral clustering model in homogeneous graphs and novel transformer-driven graph shell models for attributed graphs. A new data structure is introduced for probabilistic graphs to compute the information flow efficiently. Moreover, a metaheuristic algorithm is designed to find a good solution to an optimization problem composed of an extended vehicle routing problem. The thesis closes with an analysis of trend flows in social media data. Detecting communities within a graph is a fundamental data mining task of interest in virtually all areas and also serves as an unsupervised preprocessing step for many downstream tasks. One most the most well-established clustering methods is Spectral Clustering. However, standard spectral clustering is highly sensitive to noisy input data, and the eigendecomposition has a high, cubic runtime complexity O(n^3). Tackling one of these problems often exacerbates the other. This thesis presents a new model which accelerates the eigendecomposition step by replacing it with a Nyström approximation. Robustness is achieved by iteratively separating the data into a cleansed and noisy part of the data. In this process, representing the input data as a graph is vital to identify parts of the data being well connected by analyzing the vertices' distances in the eigenspace. With the advances in deep learning architectures, we also observe a surge in research on graph representation learning. The message-passing paradigm in Graph Neural Networks (GNNs) formalizes a predominant heuristic for multi-relational and attributed graph data to learn node representations. In downstream applications, we can use the representations to tackle theoretical problems known as node classification, graph classification/regression, and relation prediction. However, a common issue in GNNs is known as over-smoothing. By increasing the number of iterations within the message-passing, the nodes' representations of the input graph align and become indiscernible. This thesis shows an efficient way of relaxing the GNN architecture by employing a routing heuristic in the general workflow. Specifically, an additional layer routes the nodes' representations to dedicated experts. Each expert calculates the representations according to their respective GNN workflow. The definitions of distinguishable GNNs result from k-localized views starting from a central node. This procedure is referred to as Graph Shell Attention (SEA), where experts process different subgraphs in a transformer-motivated fashion. Reliable propagation of information through large communication networks, social networks, or sensor networks is relevant to applications concerning marketing, social analysis, or monitoring physical or environmental conditions. However, social ties of friendship may be obsolete, and communication links may fail, inducing the notion of uncertainty in such networks. This thesis addresses the problem of optimizing information propagation in uncertain networks given a constrained budget of edges. A specialized data structure, called F-tree, addresses two NP-hard subproblems: the computation of the expected information flow and the optimal choice of edges. The F-tree identifies independent components of a probabilistic input graph for which the information flow can either be computed analytically and efficiently or for which traditional Monte-Carlo sampling can be applied independently of the remaining network. The next part of the thesis covers a graph problem from the Operations Research point of view. A new variant of the well-known vehicle routing problem (VRP) is introduced, where customers are served within a specific time window (TW), as well as flexible delivery locations (FL) including capacity constraints. The latter implies that each customer is scheduled in one out of a set of capacitated delivery service locations. Practically, the VRPTW-FL problem is relevant for applications in parcel delivery, routing with limited parking space, or, for example, in the scope of hospital-wide scheduling of physical therapists. This thesis presents a metaheuristic built upon a hybrid Adaptive Large Neighborhood Search (ALNS). Moreover, a backtracking mechanism in the construction phase is introduced to alter unsatisfactory decisions at early stages. In the computational study, hospital data is used to evaluate the utility of flexible delivery locations and various cost functions. In the last part of the thesis, social media trends are analyzed, which yields insights into user sentiment and newsworthy topics. Such trends consist of bursts of messages concerning a particular topic within a time frame, significantly deviating from the average appearance frequency of the same subject. This thesis presents a method to classify trend archetypes to predict future dissemination by investigating the dissemination of such trends in space and time. Generally, with the ever-increasing scale and complexity of graph-structured datasets and artificial intelligence advances, AI-backed models will inevitably play an important role in analyzing, modeling, and enhancing knowledge extraction from graph data.Die Graphentheorie hat sich zur einer universellen Sprache entwickelt, mit Hilfe derer sich moderne und komplexe Systeme und ZusammenhĂ€nge beschreiben lassen. Diese theoretisch elegante und gut fundierte Rahmenstruktur attrahierte ĂŒber Dekaden hinweg die Aufmerksamkeit von Wissenschaftlern/-innen. In der heutigen Informationstechnologie-Landschaft haben sich Graphen lĂ€ngst zu einer allgegenwĂ€rtigen Datenstruktur in Anwendungen etabliert, innerhalb derer charakteristische Zusammenhangskomponenten eine zentrale Rolle spielen. Anwendungen, die ĂŒber Graphen unterstĂŒtzt werden, finden sich u.a. in der Analyse von sozialen Netzwerken, Telekommunikationsnetwerken, logistische Prozessverwaltung, Analyse von Empfehlungsdiensten, in der Modellierung kinetischer Interaktionen von Proteinstrukturen, oder auch im "Internet der Dinge" (engl.: 'Internet Of Things' (IoT)), welches das Zusammenspiel von abermillionen web-unterstĂŒtzte EndgerĂ€te abbildet und eine prĂ€dominierende Rolle fĂŒr große IT-Unternehmen spielt. Diese Dissertation beleuchtet die Herausforderungen moderner Graphanwendungen. Im Bereich homogener Netzwerken wird ein beschleunigtes und robustes spektrales Clusteringverfahren, sowie ein Modell zur Untersuchung von Teilgraphen mittels Transformer-Architekturen fĂŒr attribuierte Graphen vorgestellt. Auf wahrscheinlichkeitsbasierten homogenen Netzwerken wird eine neue Datenstruktur eingefĂŒhrt, die es erlaubt einen effizienten Informationsfluss innerhalb eines Graphen zu berechnen. DarĂŒber hinaus wird ein Optimierungsproblem in Transportnetzwerken beleuchtet, sowie eine Untersuchung von TrendflĂŒssen in sozialen Medien diskutiert. Die Untersuchung von VerbĂŒnden (engl.: 'Clusters') von Graphdaten stellt einen Eckpfeiler im Bereich der Datengewinnung dar. Die Erkenntnisse sind nahezu in allen praktischen Bereichen von Relevanz und dient im Bereich des unĂŒberwachten Lernens als Vorverarbeitungsschritt fĂŒr viele nachgeschaltete Aufgaben. Einer der weit verbreitetsten Methodiken zur Verbundanalyse ist das spektrale Clustering. Die QualitĂ€t des spektralen Clusterings leidet, wenn die Eingabedaten sehr verrauscht sind und darĂŒber hinaus ist die Eigenwertzerlegung mit O(n^3) eine teure Operation und damit wesentlich fĂŒr die hohe, kubische LaufzeitkomplexitĂ€t verantwortlich. Die Optimierung von einem dieser Kriterien exazerbiert oftmals das verbleibende Kriterium. In dieser Dissertation wird ein neues Modell vorgestellt, innerhalb dessen die Eigenwertzerlegung ĂŒber eine Nyström AnnĂ€herung beschleunigt wird. Die Robustheit wird ĂŒber ein iteratives Verfahren erreicht, das die gesĂ€uberten und die verrauschten Daten voneinander trennt. Die Darstellung der Eingabedaten ĂŒber einen Graphen spielt hierbei die zentrale Rolle, die es erlaubt die dicht verbundenen Teile des Graphen zu identifizieren. Dies wird ĂŒber eine Analyse der Distanzen im Eigenraum erreicht. Parallel zu neueren Erkenntnissen im Bereich des Deep Learnings lĂ€sst sich auch ein Forschungsdrang im reprĂ€sentativen Lernen von Graphen erkennen. Graph Neural Networks (GNN) sind eine neue Unterform von kĂŒnstlich neuronalen Netzen (engl.: 'Artificial Neural Networks') auf der Basis von Graphen. Das Paradigma des sogenannten 'message-passing' in neuronalen Netzen, die auf Graphdaten appliziert werden, hat sich hierbei zur prĂ€dominierenden Heuristik entwickelt, um Vektordarstellungen von Knoten aus (multi-)relationalen, attribuierten Graphdaten zu lernen. Am Ende der Prozesskette können wir somit theoretische Probleme angehen und lösen, die sich mit Fragestellungen ĂŒber die Klassifikation von Knoten oder Graphen, ĂŒber regressive Ausdrucksmöglichkeiten bis hin zur Vorhersage von relationaler Verbindungen beschĂ€ftigen. Ein klassisches Problem innerhalb graphischer neuronaler Netze ist bekannt unter der Terminologie des 'over-smoothing' (dt.: 'ÜberglĂ€ttens'). Es beschreibt, dass sich mit steigender Anzahl an Iterationen des wechselseitigen Informationsaustausches, die KnotenreprĂ€sentationen im vektoriellen Raum angleichen und somit nicht mehr unterschieden werden können. In dieser Forschungsarbeit wird eine effiziente Methode vorgestellt, die die klassische GNN Architektur aufbricht und eine Vermittlerschicht in den herkömmlichen Verarbeitungsfluss einarbeitet. Konkret gesprochen werden hierbei KnotenreprĂ€sentationen an ausgezeichnete Experten geschickt. Jeder Experte verarbeitet auf idiosynkratischer Basis die Knoteninformation. Ausgehend von einem Anfrageknoten liegt das Kriterium fĂŒr die Unterscheidbarkeit von Experten in der restriktiven Verarbeitung lokaler Information. Diese neue Heuristik wird als 'Graph Shell Attention' (SEA) bezeichnet und beschreibt die Informationsverarbeitung unterschiedlicher Teilgraphen von Experten unter der Verwendung der Transformer-technologie. Eine zuverlĂ€ssige Weiterleitung von Informationen ĂŒber grĂ¶ĂŸere Kommunikationsnetzwerken, sozialen Netzwerken oder Sensorennetzwerken spielen eine wichtige Rolle in Anwendungen der Marktanalyse, der Analyse eines sozialen GefĂŒges, oder der Überwachung der physischen und umweltorientierten Bedingungen. Innerhalb dieser Anwendungen können FĂ€lle auftreten, wo Freundschaftsbeziehungen nicht mehr aktuell sind, wo die Kommunikation zweier Endpunkte zusammenbricht, welches mittels einer Unsicherheit des Informationsaustausches zweier Endpunkte ausgedrĂŒckt werden kann. Diese Arbeit untersucht die Optimierung des Informationsflusses in Netzwerken, deren Verbindungen unsicher sind, hinsichtlich der Bedingung, dass nur ein Bruchteil der möglichen Kanten fĂŒr den Informationsaustausch benutzt werden dĂŒrfen. Eine eigens entwickelte Datenstruktur - der F-Baum - wird eingefĂŒhrt, die 2 NP-harte Teilprobleme auf einmal adressiert: zum einen die Berechnung des erwartbaren Informationsflusses und zum anderen die Auswahl der optimalen Kanten. Der F-Baum unterscheidet hierbei unabhĂ€ngige Zusammenhangskomponenten der wahrscheinlichkeitsbasierten Eingabedaten, deren Informationsfluss entweder analytisch korrekt und effizient berechnet werden können, oder lokal ĂŒber traditionelle Monte-Carlo sampling approximiert werden können. Der darauffolgende Abschnitt dieser Arbeit befasst sich mit einem Graphproblem aus Sicht der Optimierungsforschung angewandter Mathematik. Es wird eine neue Variante der Tourenplanung vorgestellt, welches neben kundenspezifischer Zeitfenster auch flexible Zustellstandorte beinhaltet. DarĂŒber hinaus obliegt den Zielorten, an denen Kunden bedient werden können, weiteren KapazitĂ€tslimitierungen. Aus praktischer Sicht ist das VRPTW-FL (engl.: "Vehicle Routing Problem with Time Windows and Flexible Locations") eine bedeutende Problemstellung fĂŒr Paketdienstleister, Routenplanung mit eingeschrĂ€nkten StellplĂ€tzen oder auch fĂŒr die praktische Planung der Arbeitsaufteilung von behandelnden Therapeuten/-innen und Ärzten/-innen in einem Krankenhaus. In dieser Arbeit wird fĂŒr die BewĂ€ltigung dieser Problemstellung eine Metaheuristik vorgestellt, die einen hybriden Ansatz mit der sogenannten Adaptive Large Neighborhood Search (ALNS) impliziert. DarĂŒber hinaus wird als Konstruktionsheuristik ein 'Backtracking'-Mechanismus (dt.: RĂŒckverfolgung) angewandt, um initiale Startlösungen aus dem Lösungssuchraum auszuschließen, die weniger vielversprechend sind. In der Evaluierung dieses neuen Ansatz werden Krankenhausdaten untersucht, um auch die NĂŒtzlichkeit von flexiblen Zielorten unter verschiedenen Kostenfunktionen herauszuarbeiten. Im letzten Kapitel dieser Dissertation werden Trends in sozialen Daten analysiert, die Auskunft ĂŒber die Stimmung der Benutzer liefern, sowie Einblicke in tagesaktuelle Geschehnisse gewĂ€hren. Ein Kennzeichen solcher Trends liegt in dem Aufbraußen von inhaltsspezifischen Themen innerhalb eines Zeitfensters, die von der durchschnittlichen ErscheinungshĂ€ufigkeit desselben Themas signifikant abweichen. Die Untersuchung der Verbreitung solches Trends ĂŒber die zeitliche und örtliche Dimension erlaubt es, Trends in Archetypen zu klassifizieren, um somit die Ausbreitung zukĂŒnftiger Trends hervorzusagen. Mit der immerwĂ€hrenden Skalierung von Graphdaten und deren KomplexitĂ€t, und den Fortschritten innerhalb der kĂŒnstlichen Intelligenz, wird das maschinelle Lernen unweigerlich weiterhin eine wesentliche Rolle spielen, um Graphdaten zu modellieren, analysieren und schlussendlich die Wissensextraktion aus derartigen Daten maßgeblich zu fördern.La thĂ©orie des graphes s'est rĂ©vĂ©lĂ©e ĂȘtre une langue universel pour dĂ©crire les systĂšmes complexes modernes. L'Ă©lĂ©gant cadre thĂ©orique des graphes a attirĂ© l'attention des chercheurs pendant des dĂ©cennies. Par consĂ©quent, les graphes sont devenus une structure de donnĂ©es omniprĂ©sente dans diverses applications oĂč une caractĂ©ristique relationnelle est Ă©vidente. Les applications basĂ©es sur les graphes se retrouvent, par exemple, dans l'analyse des rĂ©seaux sociaux, les rĂ©seaux de tĂ©lĂ©communication, les processus logistiques, les systĂšmes de recommandation, la modĂ©lisation des interactions cinĂ©tiques dans les rĂ©seaux de protĂ©ines, ou l'"Internet des objets" (IoT) oĂč la modĂ©lisation de milliards de dispositifs interconnectĂ©s basĂ©s sur le web est d'une importance capitale. Cette thĂšse se penche sur les dĂ©fis posĂ©s par les applications modernes des graphes. Elle propose un modĂšle de regroupement spectral robuste et accĂ©lĂ©rĂ© dans les graphes homogĂšnes et de nouveaux modĂšles d'enveloppe de graphe pilotĂ©s par transformateur pour les graphes attribuĂ©s. Une nouvelle structure de donnĂ©es est introduite pour les graphes probabilistes afin de calculer efficacement le flux d'informations. De plus, un algorithme mĂ©taheuristique est conçu pour trouver une bonne solution Ă  un problĂšme d'optimisation composĂ© d'un problĂšme Ă©tendu de routage de vĂ©hicules. La thĂšse se termine par une analyse des flux de tendances dans les donnĂ©es des mĂ©dias sociaux. La dĂ©tection de communautĂ©s au sein d'un graphe est une tĂąche fondamentale d'exploration de donnĂ©es qui prĂ©sente un intĂ©rĂȘt dans pratiquement tous les domaines et sert Ă©galement d'Ă©tape de prĂ©traitement non supervisĂ© pour de nombreuses tĂąches en aval. L'une des mĂ©thodes de regroupement les mieux Ă©tablies est le regroupement spectral. Cependant, le regroupement spectral standard est trĂšs sensible aux donnĂ©es d'entrĂ©e bruitĂ©es, et l'eigendecomposition a une complexitĂ© d'exĂ©cution cubique Ă©levĂ©e O(n^3). S'attaquer Ă  l'un de ces problĂšmes exacerbe souvent l'autre. Cette thĂšse prĂ©sente un nouveau modĂšle qui accĂ©lĂšre l'Ă©tape d'eigendecomposition en la remplaçant par une approximation de Nyström. La robustesse est obtenue en sĂ©parant itĂ©rativement les donnĂ©es en une partie nettoyĂ©e et une partie bruyante. Dans ce processus, la reprĂ©sentation des donnĂ©es d'entrĂ©e sous forme de graphe est essentielle pour identifier les parties des donnĂ©es qui sont bien connectĂ©es en analysant les distances des sommets dans l'espace propre. Avec les progrĂšs des architectures de Deep Learning, nous observons Ă©galement une poussĂ©e de la recherche sur l'apprentissage de la reprĂ©sentation graphique. Le paradigme du passage de messages dans les rĂ©seaux neuronaux graphiques (GNN) formalise une heuristique prĂ©dominante pour les donnĂ©es graphiques multi-relationnelles et attribuĂ©es afin d'apprendre les reprĂ©sentations des nƓuds. Dans les applications en aval, nous pouvons utiliser les reprĂ©sentations pour rĂ©soudre des problĂšmes thĂ©oriques tels que la classification des nƓuds, la classification/rĂ©gression des graphes et la prĂ©diction des relations. Cependant, un problĂšme courant dans les GNN est connu sous le nom de lissage excessif. En augmentant le nombre d'itĂ©rations dans le passage de messages, les reprĂ©sentations des nƓuds du graphe d'entrĂ©e s'alignent et deviennent indiscernables. Cette thĂšse montre un moyen efficace d'assouplir l'architecture GNN en employant une heuristique de routage dans le flux de travail gĂ©nĂ©ral. Plus prĂ©cisĂ©ment, une couche supplĂ©mentaire achemine les reprĂ©sentations des nƓuds vers des experts spĂ©cialisĂ©s. Chaque expert calcule les reprĂ©sentations en fonction de son flux de travail GNN respectif. Les dĂ©finitions de GNN distincts rĂ©sultent de k vues localisĂ©es Ă  partir d'un nƓud central. Cette procĂ©dure est appelĂ©e Graph Shell Attention (SEA), dans laquelle les experts traitent diffĂ©rents sous-graphes Ă  l'aide d'un transformateur. La propagation fiable d'informations par le biais de grands rĂ©seaux de communication, de rĂ©seaux sociaux ou de rĂ©seaux de capteurs est importante pour les applications concernant le marketing, l'analyse sociale ou la surveillance des conditions physiques ou environnementales. Cependant, les liens sociaux d'amitiĂ© peuvent ĂȘtre obsolĂštes, et les liens de communication peuvent Ă©chouer, induisant la notion d'incertitude dans de tels rĂ©seaux. Cette thĂšse aborde le problĂšme de l'optimisation de la propagation de l'information dans les rĂ©seaux incertains compte tenu d'un budget contraint d'arĂȘtes. Une structure de donnĂ©es spĂ©cialisĂ©e, appelĂ©e F-tree, traite deux sous-problĂšmes NP-hard: le calcul du flux d'information attendu et le choix optimal des arĂȘtes. L'arbre F identifie les composants indĂ©pendants d'un graphe d'entrĂ©e probabiliste pour lesquels le flux d'informations peut ĂȘtre calculĂ© analytiquement et efficacement ou pour lesquels l'Ă©chantillonnage Monte-Carlo traditionnel peut ĂȘtre appliquĂ© indĂ©pendamment du reste du rĂ©seau. La partie suivante de la thĂšse couvre un problĂšme de graphe du point de vue de la recherche opĂ©rationnelle. Une nouvelle variante du cĂ©lĂšbre problĂšme d'acheminement par vĂ©hicule (VRP) est introduite, oĂč les clients sont servis dans une fenĂȘtre temporelle spĂ©cifique (TW), ainsi que des lieux de livraison flexibles (FL) incluant des contraintes de capacitĂ©. Ces derniĂšres impliquent que chaque client est programmĂ© dans l'un des emplacements de service de livraison Ă  capacitĂ©. En pratique, le problĂšme VRPTW-FL est pertinent pour des applications de livraison de colis, d'acheminement avec un espace de stationnement limitĂ© ou, par exemple, dans le cadre de la programmation de kinĂ©sithĂ©rapeutes Ă  l'Ă©chelle d'un hĂŽpital. Cette thĂšse prĂ©sente une mĂ©taheuristique construite sur une recherche hybride de grands voisinages adaptatifs (ALNS). En outre, un mĂ©canisme de retour en arriĂšre dans la phase de construction est introduit pour modifier les dĂ©cisions insatisfaisantes Ă  des stades prĂ©coces. Dans l'Ă©tude computationnelle, des donnĂ©es hospitaliĂšres sont utilisĂ©es pour Ă©valuer l'utilitĂ© de lieux de livraison flexibles et de diverses fonctions de coĂ»t. Dans la derniĂšre partie de la thĂšse, les tendances des mĂ©dias sociaux sont analysĂ©es, ce qui donne un aperçu du sentiment des utilisateurs et des sujets d'actualitĂ©. Ces tendances consistent en des rafales de messages concernant un sujet particulier dans un laps de temps donnĂ©, s'Ă©cartant de maniĂšre significative de la frĂ©quence moyenne d'apparition du mĂȘme sujet. Cette thĂšse prĂ©sente une mĂ©thode de classification des archĂ©types de tendances afin de prĂ©dire leur diffusion future en Ă©tudiant la diffusion de ces tendances dans l'espace et dans le temps. D'une maniĂšre gĂ©nĂ©rale, avec l'augmentation constante de l'Ă©chelle et de la complexitĂ© des ensembles de donnĂ©es structurĂ©es en graphe et les progrĂšs de l'intelligence artificielle, les modĂšles soutenus par l'IA joueront inĂ©vitablement un rĂŽle important dans l'analyse, la modĂ©lisation et l'amĂ©lioration de l'extraction de connaissances Ă  partir de donnĂ©es en graphe

    METROPOLITAN ENCHANTMENT AND DISENCHANTMENT. METROPOLITAN ANTHROPOLOGY FOR THE CONTEMPORARY LIVING MAP CONSTRUCTION

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    We can no longer interpret the contemporary metropolis as we did in the last century. The thought of civil economy regarding the contemporary Metropolis conflicts more or less radically with the merely acquisitive dimension of the behaviour of its citizens. What is needed is therefore a new capacity for imagining the economic-productive future of the city: hybrid social enterprises, economically sustainable, structured and capable of using technologies, could be a solution for producing value and distributing it fairly and inclusively. Metropolitan Urbanity is another issue to establish. Metropolis needs new spaces where inclusion can occur, and where a repository of the imagery can be recreated. What is the ontology behind the technique of metropolitan planning and management, its vision and its symbols? Competitiveness, speed, and meritocracy are political words, not technical ones. Metropolitan Urbanity is the characteristic of a polis that expresses itself in its public places. Today, however, public places are private ones that are destined for public use. The Common Good has always had a space of representation in the city, which was the public space. Today, the Green-Grey Infrastructure is the metropolitan city's monument that communicates a value for future generations and must therefore be recognised and imagined; it is the production of the metropolitan symbolic imagery, the new magic of the city

    The Politics of Racial Translation : Negotiating Foreignness and Authenticity in Russophone Intersectional Feminism and Timati's Hip-hop (2012-2018)

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    Hva skjer med rase-, kjĂžnns- og seksualitetspolitikk i interseksjonell feminisme og hiphop nĂ„r de forflytter seg Ăžstover til postsovjetiske, russisksprĂ„klige kontekster? Denne avhandlingen utforsker oversettelsens betydning i sirkulasjonen av det amerikanske, engelsksprĂ„klige idiomet ‘rase som motstand’ i russisksprĂ„klige tilpasninger av hiphop og interseksjonalitet i perioden 2012-2018. Ved hjelp av digital etnografi, diskursanalyse og nĂŠrlesing av et utvalg musikkvideoer, analyseres to oversettelsesprosjekter empirisk: en russisksprĂ„klig grasrotside for interseksjonell feminisme, FIO (Feminist Intersectionality Against Oppression), og den – i Russland kontroversielle – tatarisk-jĂždiske hiphop-entreprenĂžren Timati. Denne avhandlingen, som befinner seg i skjĂŠringspunktet mellom kjĂžnns- og seksualitetsstudier, rase- og etnisitetsforskning og postsovjetiske kulturstudier, framhever oversettelse bĂ„de i sitt teoretiske rammeverk og i metodologisk forskningsdesign. Teoretisk er avhandlingens tilnĂŠrming til oversettelse inspirert av feministisk teori, feministisk antropologi og oversettelsesstudier, samt av transnasjonale perspektiver pĂ„ rasialisering og den voksende forskningen pĂ„ rase i Russland. Metodologisk trekker avhandlingen pĂ„ verktĂžy fra oversettelsesstudier og sosiolingvistikk. Ved Ă„ begrepsligjĂžre oversettelse som en forutsetning for geografisk forflytning av ideer, som et sted for forhandling der ting produseres som ‘fremmede’, og som en generativ snarere enn imiterende prosess, spĂžr den hva raseoversettelse frambringer og hvorfor noen oversettelsesprosjekter framstilles som mer fremmede enn andre. Metoden er Ă„ spore hvordan fremmedhet tilskrives ulike objekter, samt Ă„ undersĂžke de oversettelsesstrategiene som brukes i de to nevnte empiriske tilfellene nĂ„r de gjengir engelsksprĂ„klige rasekategorier som ‘white’, ‘black’, ‘people of color’ og ‘women of color’. I tillegg til Ă„ undersĂžke hvilke generative effekter oversettelsene har, bruker studien en multimodal tilnĂŠrming som overskrider de semantiske grensene til etnorasekategorier og muliggjĂžr utforsking av rasemessig oversettelse pĂ„ tvers av flere semiotiske moduser. Avhandlingen begrepsligjĂžr rasemessig oversettelse som gjennomsyret av kjĂžnns- og seksualitetsdynamikk, innebygd i geopolitiske konfrontasjoner og informert av russisk imperial arv, og argumenterer for at rasemessig oversettelse er generativ pĂ„ flere sentrale mĂ„ter. Den rasemessige oversettelsen skaper nye og moderne formsprĂ„k (russofon interseksjonell feminisme og russisk kommersiell hiphop), kronotopisk plasserte former for personlighet knyttet til bestemte oversettelsesstrategier eller motstand mot dem, samt affektive responser og bestridelser av oversettelsesvalg og oversettelsesprosjekter som stigmatiseres eller verdsettes som ‘fremmede’. De som oversetter, tilnĂŠrmer seg det engelsksprĂ„klige formsprĂ„ket ‘rase som motstand’ som ‘fremmed og moderne’, og indekserer en avansert tid og et avansert sted, USA, for Ă„ styrke, reparere og modernisere russisksprĂ„klig feminisme og russiske musikkscener. Begge oversettelsesprosjektene posisjonerer fremmedgjĂžring som en kilde til modernisering, noe som forklarer predisposisjonen for fremmedgjĂžrende oversettelsesstrategier. FremmedgjĂžrende oversettelsesstrategier og motviljen mot bokstavtrohet i postsovjetiske, russisksprĂ„klige kontekster, kan imidlertid gjĂžre oversetterne selv fremmede, noe som fremmer jakten pĂ„ originalitet, forsĂžk pĂ„ Ă„ forhandle oversettelsens stigma og Ă„ reparere kronisk inautentisitet. PĂ„ nettsidene til det russisksprĂ„klige, interseksjonelle feministiske digitale fellesskapet, skaper moderatorenes tilbĂžyelighet til translitterasjoner en form for uro, beskyldninger om uforstĂ„elighet og en slags motvilje for sprĂ„kblanding. Moderatorenes rolle som en kosmopolitisk, tosprĂ„klig, feministisk elite skiller seg fra vanlige russisksprĂ„klige lesere. Interseksjonalitet sirkulerer som en feministisk kronotop, som FIO ser for seg som en feministisk fremtid, et botemiddel mot den russisksprĂ„klige feminismens rasisme, homofobi og transfobi. Feministiske kronotoper blir brukt i diskusjoner rundt oversettelse og blant annet utnyttet i lokale kronotopiske forestillinger om russisk bakstreverskhet og slavofil trangsynthet. Den bokstavelige oversettelsen av kategorien ‘hvit’ genererer figurer som ‘hvit mann’ og ‘hvite kvinner’ og inngĂ„r i det russisksprĂ„klige interseksjonelle feministiske formsprĂ„ket. Produksjonen av modererende antirasistisk hvithet-i-oversettelse mobiliserer kronotopiske figurer av tilbakestĂ„ende rasistiske andre som komparativ kontrast til mer moderne kunnskap. Den driver ogsĂ„ frem spĂžrsmĂ„l om grensene for oversettbarheten til kategorien ‘hvit’ for den situerte antirasistiske post-sovjetiske praksisen i kommentartrĂ„dene, inkludert den skiftende kategorien КаĐČĐșазцы/Kaukasiere. Manglende oversettelse av kategoriene ‘people of color’ og ‘women of color’ genererer blant annet sĂžken etter en postsovjetisk ‘woman of color’ som et russisksprĂ„klig interseksjonelt feministisk subjekt. Slik kanoniseres “rĂ„â€ oversettelse, den transsprĂ„klige spredningen av etno-rasistiske kategorier og instrumentaliseringen av figuren ‘trans woman of color’ av russisksprĂ„klige radfem som et tegn pĂ„ den russisksprĂ„klige interseksjonalitetens totale fremmedhet. Begrepet realia brukes i materialet som en sĂžken etter Ă„ lokalisere det amerikanske raseidiomet og for Ă„ belyse sĂŠrtrekkene ved den post-sovjetiske etnorasiske maktdynamikken. I undersĂžkelsen av raseoversettelse i Timatis hiphop, vises det hvordan Timati, ved Ă„ kombinere amerikansk svart hiphop-estetikk med russisk glamour pĂ„ 2000-tallet, brukte uoversatt amerikansk svarthet som en kosmopolitisk kulturell kapital for Ă„ motvirke volden i den post-sovjetiske rasialiseringen. Ved Ă„ nĂŠrme seg amerikansk svart estetikk gjennom direkte erfaring med ‘kilden’, fikk Timati transnasjonal suksess med sin etnorasiale formbarhet og evne til Ă„ formidle mellom postsovjetiske markeder, lokale Ăžkonomiske eliter, amerikansk raseautentisitet og amerikanske hiphop-kjendiser. Han bidro derimot neppe til Ă„ avhjelpe hiphopens kroniske inautentisitet i Russland. ForsĂžk pĂ„ Ă„ hĂ„ndtere stigmaet fremmedhet og imitasjon, og Ă„ oppnĂ„ sterkere hiphop-autentisitet pĂ„ hjemmebane, markerte en reorientering av Timatis prosjekt for rasemessig oversettelse. Fra 2012-2013 figurerer Kaukasus og kaukasiske maskuliniteter som ‘regionale originaler’, noe som gjĂžr det mulig for Timati Ă„ oversette den amerikanske svarte maskuliniteten som ligger til grunn for amerikansk hiphop-autentisitet. Han tar i bruk ulike strategier, som ‘vikarierende autentisitet’, memetikk og hiphop-homofobi, og skaper en homofobisk hiphop-meykhana-cipher og visuelle, imperiale troper av et kaukasisk sublim. Skjegget som rasemessig/seksuell metonymi er sentralt for Ă„ forstĂ„ Timatis prosjekt for rasemessig oversettelse i 2014-2015, som er involvert i seksuell geopolitikk mellom Ăžst og vest. Gjennom hiphop-modernitet og tvillingfantasien om etnisk raseblanding og sentralasiatiske migranters Ăžkonomiske oppsving i Russland, oppvurderes den etniske andre fra Ă„ vĂŠre symbol pĂ„ en sikkerhetstrussel (skjeggete terrorister) til Ă„ bli moderne, homofob og kul – og slik i stand til Ă„ sikre Russlands organiske og multinasjonale fremtid mot et angivelig korrupt og seksuelt perverst Europa. HĂžydepunktet i Timatis prosjekt for rasemessig oversettelse i 2014-2016 resulterte i oppfinnelsen av en bevisst memetisk karakter, Teymuraz, som iscenesetter rasistiske klisjeer av kaukasiske og sentralasiatiske menn. Teymuraz, som en del av Russlands hybridkulturelle trend med estetisk populisme, resirkulerer elementer fra New East gopnik-stil, sovjetiske og postsovjetiske komedier om menn fra Kaukasus og Sentral-Asia, blander elementer av samfunnskritikk og tilegner seg de subalternes stemmer. Han utgjĂžr med dette et sĂŠregent patriotisk, antirasistisk prosjekt med motstridende implikasjoner. Timatis lekne fremfĂžringer av patriotisme og performativ disidentifikasjon fra USA som kilde til hiphop i 2015 forstĂ„r hiphop-patriotisme som en strategi for Ă„ lokalisere hiphop i den skiftende geopolitiske konteksten av Russlands vending bort fra Vesten. Russiske diskurser om hiphop-autentisitet, som er opptatt av Ă„ oppdage og avslĂžre plagiat i Timatis arbeid, gir stemme til et sĂŠregent arbeid med Ă„ skille ‘ekte’ hiphop fra ‘falsk’ hiphop. Dette arbeidet bygger pĂ„ rasistiske diskurser om handel og umoral, og markerer Timati som ‘fremmed’ - bĂ„de utenfor sjangerens og den russiske nasjonens grenser – og avslĂžrer en russisk etnonasjonal skjevhet som ligger til grunn for russiske diskurser om hiphop-autentisitet. NĂžkkelord: oversettelse, interseksjonalitet, rase, etnorasisk, postsovjetisk, russisk hiphop, rap, russisk rap, grasrotfeminisme, digital feminisme, feministiske kronotoper, feministisk oversettelse, feministisk aktivisme, populĂŠrmusikk, Russland, russofon, oversettelsesstrategier, litteralisme, translitterasjon, homofobi, transfobi, vikarierende autentisitet, memetikk, realia, rasemessig/seksuell metonymi, indeksikalitet, multimodalitet, den kalde krigen, russisk populĂŠrkultur, digital aktivisme, autentisitet, hiphop-maskuliniteter, rasialisering, Kaukasus, Sentral-Asia.What happens to race, gender, and sexuality politics of intersectional feminism and hip-hop when they travel eastwards into post-Soviet Russophone contexts? This thesis explores the role of translation in the circulations of the US Anglophone idiom ‘race as resistance’ in 2012-2018 Russophone adaptations of hip-hop and intersectionality. Drawing on the digital ethnography, discourse analysis, and close reading of a selection of music videos, it empirically analyzes two translation projects: a grassroots Russophone translation-based intersectional feminist page, FIO (Feminist Intersectionality Against Oppression) and – a controversial in Russia – Tatar-Jewish hip-hop entrepreneur, Timati. This thesis, situated at the intersection of gender and sexuality studies, race and ethnicity research, and post-Soviet cultural studies, foregrounds translation in its theoretical framework and methodological research design. Theoretically, the dissertation’s approach to translation is inspired by feminist theory, feminist anthropology, and translation studies, as well as by transnational perspectives on racialization and the growing scholarship on race in Russia. Methodologically it draws on the tools from translation studies and sociolinguistics. Conceptualizing translation as a precondition for travel, as a site of negotiation where things are produced as ‘foreign’ and as a generative rather than imitative process, this thesis asks what racial translation generates and why some translation projects are rendered as more foreign than others. The method is to track the attributions of foreignness and examine the translation strategies used by the two projects mentioned above when rendering English-language racial categories such as ‘white,’ ‘black,’ ‘people of color,’ and ‘women of color.’ The study also uses the multimodal approach that goes beyond the limits of the semantics of ethnoracial categories, allowing for the exploration of racial translation across multiple semiotic modes. Conceptualizing racial translation as saturated in gender and sexuality dynamics, embedded in geopolitical confrontations, and informed by Russian imperial legacies, the thesis argues that racial translation is generative in several central ways. It creates novel and modern idioms (Russophone intersectional feminism and Russian commercial hip-hop), chronotopically positioned types of personhood associated with particular translation strategies or resistance to them, as well as affective responses and contestations of translation choices and translation projects stigmatized or valorized as ‘foreign.’ The translators approach the Anglophone idiom of ‘race as resistance’ as ‘foreign and modern,’ indexing advanced time and place, the USA, brought up to invigorate, repair, and modernize Russophone feminisms and Russian music scenes. Both translation projects position foreignization as a source of modernization, explaining the predisposition for foreignizing translation strategies. However, foreignizing translation strategies and the aversion to literalism within post-Soviet Russophone contexts may render translators themselves foreign, propelling the search for originality, the attempts to negotiate the stigma of translation, and to repair chronic inauthenticity. On the pages of the Russophone intersectional feminist community, the moderators’ predisposition to transliterations generates visceral unease, accusations of unintelligibility, anxieties about the language mixing, and the roles of moderators as cosmopolitan bilingual feminist elites different from ordinary Russophone readers. Intersectionality circulates as a feminist chronotope, envisioned by FIO as a feminist future, a remedy against exclusions within Russophone feminisms such as racism, homophobia, and transphobia. Circulating feminist chronotopes are used in contestations around translation, harnessed on local chronotopic imaginaries such as Russian peasant backwardness and Slavophile parochialism. The literal translation of the category ‘white’ generates figures such as ‘white man’ and ‘white women,’ which enter the Russophone intersectional feminist idiom. The production of anti-racist whiteness-in-translation mobilizes chronotopic figures of backward racist others in comparative contrast with more modern bodies of knowledge and types of personhood. It also propels the questioning of the limits of the translatability of the category ‘white’ for the situated anti-racist post-Soviet praxis in the comment threads, including the shifting category КаĐČĐșазцы/Caucasians. Non-translation of the categories ‘people of color’ and ‘women of color’ generates, amongst other things, the search for a post-Soviet ‘woman of color’ as a Russophone intersectional feminist subject, the canonization of raw translation, the translingual proliferation of ethnoracial categories and the instrumentalization of the figure of ‘trans woman of color’ by Russophone radfem as a mark of Russophone intersectionality’s total foreignness. Finally, the term realia emerged in the empirical materials as the quest for localizing the US idiom of race, adjusting intersectionality to the specificities of post-Soviet ethnoracial power dynamics. Within the project of racial translation in Timati’s hip-hop, I show how Timati marrying of US black hip-hop aesthetics and Russian glamour in the 2000s deployed untranslated foreign US blackness as a cosmopolitan cultural capital to alleviate the violence of post-Soviet racialization. Approximating US black aesthetics through the direct experience of ‘the source,’ Timati’s ethnoracial malleability and capacity to mediate between post-Soviet markets, local economic elites, US racial authenticity, and US hip-hop celebrities brought him international success, yet hardly helped alleviate chronic hip-hop inauthenticity in Russia. Attempts to manage the stigma of foreignness and imitation and hence achieve a stronger hip-hop authenticity domestically marked a reorientation of Timati’s project of racial translation. From 2012-2013, the Caucasus and Caucasian masculinities figure as ‘regional originals,’ allowing Timati to translate the US black masculinity underpinning US hip-hop authenticity through the strategies of vicarious realness, memetics, and hip-hop homophobia in a gay bashing hip-hop meykhana cipher and visual Imperial tropes of the Caucasian sublime. The beard as racial/sexual metonymy is central for understanding Timati’s project of racial translation involved in East-West sexual geopolitics in the period of 2014-2015: through hip-hop modernity and the twin fantasy of ethnoracial mixing and Central Asian migrant economic uplift in Russia, the ethnoracial other is revalorized away from the image of the security threat (bearded terrorist) as modern, homophobic and cool, able to secure Russia’s organic and multinational future against corrupt and sexually perverse Europe. The pinnacle of Timati’s project of racial translation in 2014-2016 resulted in the invention of a deliberately memetic character, Teymuraz, enacting racialized cliches of Caucasian and Central Asian men. Teymuraz, as part of Russia’s hybrid cultural trend of aesthetic populism, recycles the elements of New East gopnik style, Soviet and post-Soviet comedies about men from the Caucasus and Central Asia, mixes elements of social critique, and appropriates the voices of the subaltern, performing a peculiar patriotic anti-racist project with contradictory implications. Timati’s 2015 ludic performances of hip-hop patriotism, coupled with performative disidentifications from the USA as a source of hip-hop, are read as a strategy of hip-hop localization in the changing geopolitical context of Russia’s turn away from the West. Russian discourses of hip-hop authenticity, preoccupied with detecting and exposing plagiarism in Timati’s work, serve to discern ‘real’ hip-hop from ‘fake’ hip-hop. These acts of discernment draw on the racialized discourses of commerce and immorality, marking Timati as ‘foreign’ - both outside the borders of the genre and the Russian nation – thus exposing the Russian ethnonational bias underpinning Russian discourses of hip-hop authenticity. Keywords: translation, intersectionality, race, ethnoracial, post-Soviet, Russian hip-hop, hip-hop, rap, Russian rap, grassroots feminism, digital feminism, feminist chronotopes, feminist translation, feminist activism, popular music, Russia, Russophone, translation strategies, literalism, transliteration, homophobia, transphobia, vicarious realness, memetics, realia, racial/sexual metonymy, indexicality, multimodality, Cold War, Russian popular culture, digital activism, authenticity, hip-hop masculinities, racialization, Caucasus, Central Asia.Doktorgradsavhandlin
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