Two questions are addressed: 1) whether a dream is meaningful as a whole, or whether the scenes are separate and unconnected, and 2) whether dream images are an epiphenomenon of a functional physiologicaL process of REM sleep, or whether they are akin to waking thought. Theories of REM sleep as a period of information-processing are reviewed. This is Linked with work on the relationship between dreaming and creativity, and between memory and imagery. Because of the persuasive evidence that REM sleep is implicated in the consolidation of memories there is a review of recent work on neural associative network models of memory. Two theories of dreams based on these models are described, and predictions with regard to the above two questions are made. Psychological evidence of relevance to the neural network theories is extensively reviewed. These predictions are compared with those of the recent application of structuralism to the study of dreams, which is an extension from its usual field of mythology and anthropology. The different theories are tested against four nights of dreams recorded in a sleep Lab. The analysis shows that not only do dreams concretise waking concerns as metaphors but that these concerns are depicted in oppositional terms, such as, for example, inside/outside or revolving/static. These oppositions are then permuted from one dream to the next until a resolution of the initial concern is achieved at the end of the night. An account of the use of the single case-study methodology in psychology is given, in addition to a replication of the analysis of one night's dreams by five independent judges. There is an examination of objections to the structuralist methodology, and of objections to the paradigm of multiple dream awakenings. The conclusion is drawn that dreams involve the unconscious dialectical step-by-step resolution of conflicts which to a great extent are consciously known to the subject. The similarity of dreams to day-dreams is explored, with the conclusion that the content of dreams is better explained by an account of metaphors we use when awake and by our daily concerns, than by reference to the physiology of REM sleep. It is emphasised that dreams can be meaningful even if they do not have a function.

Hare’s psychopathy construct as defined by the Psychopathy Checklist- Revised has been utilized internationally as a risk assessment instrument for quite some time. Despite this, since its inception it has and continues to raise criticism from the academic community. There is ongoing debate over what the construct entails and how it should be used. Most recent developments in the construct revolve around it being defined as a neurological manifestation. To explore the psychopathy construct’s connection with neuroscience, this thesis focusses on one foundational experiment by the most prominent team of researchers in the field. The exploration borrows from Science and Technology Studies, more specifically Actor-Network Theory and the semiotic of scientific texts. The goal of this analysis is not to criticize nor defend the psychopathy construct, but rather explore the facticity of psychopathy as a neuroscientific fact. Considering the widespread use of the construct across criminal justice systems and mental health practices, understanding the facticity of psychopathy is imperative. Our contention is that psychopathy as defined by neuroscience was not merely a pre-discovered fact of nature, but rather it is a fact that is hybrid; it is both built by researchers and a part of our natural world, social and real. Our findings reveal that the facticity of psychopathy as a neuroscience construct is reliant on it being a Boundary Object: a scientific object that is able to intersect multiple social worlds through its adaptability (Star & Griesemer, 1989). We show how the construct is a boundary object by detailing the many translations it undergoes while it connects with a variety of heterogeneous actors. For each translation, the construct is rendered qualitatively different, yet it proves to be robust enough to maintain the identity of psychopathy and transform it into a neuroscientific fact.

Cette recherche traite du phénomène immersif dans les mondes virtuels accessibles « grâce à » internet. Second Life sert de terrain d'analyse car il permet de discuter trois régimes immersifs : le perceptif, le narratif et le lien social. Au traversd'une approche formelle (Simmel) et de la théorie de l'acteur-réseau (ANT, Latour), nous analysons diverses médiations conditionnant ces régimes : les villes modélisées dans Second Life sont étudiées de façon approfondie comme le lieu duvivre ensemble et de l'hétérogénéité des publics et des usages.Notre thèse qui s'appuie sur la sociologie a également recours aux sciences de l'information et de la communication, à la philosophie, à la géographie de l'espace social et aux digital humanities. Il s'agit d'une approche transdisciplinaire sollicitant des matériaux variés : observations participantes, entretiens, questionnaires. Nous développons une techniqueautomatisée de « tracking » pour générer des « Big Data » permettant l'analyse des pratiques spatiales des avatars dans l'univers digital.Nous montrons que l'immersion dépend des choix de l'utilisateur (point de vue visuel, choix des activités, etc.) et des qualités et possibles de Second Life (architecture technique, carte du monde, forme du cadre bâti, etc.). Des phénomènes d'appropriation et d'attachement entre des acteurs et des lieux sont décrits et permettent de constater la formalisation d'un« chez-soi ». Les diverses étapes de ce travail contribuent à une théorie de l'habiter dans les mondes virtuels / This research deals with the phenomenon of immersion in virtual worlds which are accessible "thanks to" the internet. Second Life is used as fieldwork because it allows to discuss three immersion regimes: perceptive, narrative andsocial ones. Through a formal approach (Simmel) and actor-network theory (ANT, Latour), we analyze various mediations which condition these immersion regimes: cities modeled in Second Life are studied in depth as the place to live togetherand as heterogeneous audiences and uses.Our thesis which is based on sociology also uses information and communication sciences, philosophy, geography of social space, and digital humanities. It is a transdisciplinary approach using various methods: participant observations,interviews, questionnaires. We develop an automated tracker to generate "Big Data" for the analysis of spatial practices of avatars in the digital world.We show that the immersion depends on the choice of the user (visual point of view, choice of activities, etc.) and on qualities and potentialities of Second Life (technical architecture, world map, shape of the built environment, etc.).Appropriation and attachment phenomena between actors and locations are described and allow us to observe the formalization of a "home". The various steps of this work contribute to a theory of habitat in virtual worlds

Wearable technologies are being adopted in increasing numbers and the market space appears poised for continued growth in virtually all areas, from medicine, to self-quantification, to sports. While the overwhelming majority of work on wearables has been done on their medical applications and their role in shaping identity, this dissertation examines the roles that wearable technologies play on the decision-making processes in athletic contexts. Using new materialism and Actor Network Theory as lenses, I attempt to break from the Cartesian model that places human subjectivity and intentionality at the center of a rhetorical situation and, rather, allow that non-human actants are agentive. I examine the interactions that age-group triathletes have with their wearable technologies and the shifting agencies that accompany those interactions. These interactions call on disparate human and non-human actors in forming a series of temporary, shifting networks that utilize a distributed agency in the decision making process.

The physical landscape is a patchwork of different physical and immaterial structures and networks built up through time, where nature and culture cooperate in processes of shaping the landscape. Since the first settlers in Sweden the grass meadow has taken a central part in the agricultural practices, but today little of this landscape remains. As the most biodiverse land type in Sweden, the loss of meadows states a threat to many endangered species. To maintain an agricultural landscape with high variation in land use, where biological values and cultural environments are maintained, the current landscape pattern and activities must be redesigned. This thesis takes it departure from the territory of Småland as a region defined by its long agricultural practises. The research aims to provide new learnings and insights from the agricultural landscape through historic and modern activities and patterns, to understand how ecological processes and human actions has influenced the landscape. Theories and methods supporting the research and design choices are Actor-Network Theory, landscape ecology and case studies from the agricultural landscape. Actor-Network Theory is used to prevent braking down a complex problem into static actors, but to instead recognise the links and connection the actors already make. In this case the main actors are agricultural tools and structures, landscape elements, landscape ecology, plants and animals. Through a set of design guides based on landscape design principles and the understanding of the rules actors follow in the landscape network, a new diverse landscape pattern can emerge, defined as a meadowscape. As architects we have the possibility to thorough understanding the rules and logic of historic and modern actors in the landscape, rethink their patterns and use them to create a new type of landscape that cater both ecological activity.

We relate properties of attributed random graph models to the performance of GNN architectures. We identify regimes where GNNs outperform feedforward neural networks and non-attributed graph clustering methods. We compare GNN performance on our synthetic benchmark to performance on popular real-world datasets. We analyze the theoretical foundations for weak recovery in GNNs for popular one- and two-layer architectures. We obtain an explicit formula for the performance of a 1-layer GNN, and we obtain useful insights on how to proceed in the 2-layer case. Finally, we improve the bound for a notable result on the GNN size generalization problem by 1.

Nanotechnology has captured the attention of governments and corporations around the globe. It has become the subject and context for numerous conferences, media articles, websites and scientific research papers. Nano enthusiasts and government officials claim that it is an area that promises new understandings of nature, and use of that understanding to build technologies that might change our lives. Despite the growing hype surrounding this new science, what appears to be lacking is scholarly literature that examines its growth and expansion from a social science perspective. This study addressed this limitation through a sociological analysis of the network of actors, events, rhetorical strategies, practices and instrumentation that went into the construction and growth of nanotechnology. Relying heavily on actor-network theory (ANT), this study focused on a small part of the total network referred to as the knowledge education production process, which involved the enrollment of high school teachers into the nanotechnology network through a series of collaborative workshops -- the Nanotechnology Curriculum Development Project (NCDP) -- with Virginia Polytechnic and State University (Virginia Tech) scientists over a period of two years. By investigating how the nanotechnology network was constructed and maintained, this case study examined the relevance of ANT as nanotechnology moved beyond the laboratory into the public domain of high school education. It looked at the intermediary role of high school science and math teachers and revealed the function of conflict, power, authority, hierarchy, interests, motivations, gender and race in the construction and expansion of scientific networks. / Ph. D.

Highway bridges are among the most vulnerable and expensive components in transportation networks. In response, the Government of Ontario has allocated $26 billion in the next 10 years to address issues pertaining to aging bridge and deteriorating highway infrastructure in the province. Although several approaches have been developed to guide their rehabilitation, most bridge rehabilitation approaches are focused on the component level (individual bridge) in a relative isolation of other bridges in the network. The current study utilizes a complex network theoretic approach to quantify the topological characteristics of the Ontario Bridge Network (OBN) and subsequently evaluate the OBN robustness and vulnerability characteristics. These measures are then integrated in the development of a Multi Scale Bridge Classification (MSBC) approach—an innovative classification approach that links the OBN component level data (i.e., Bridge Condition Index and year of construction, etc.) to the corresponding dynamic network-level measures. The novel approach calls for a paradigm shift in the strategy governing classifying and prioritizing bridge rehabilitation projects based on bridge criticality within the entire network, rather than only the individual bridge’s structural conditions. The model was also used to identify the most critical bridges in the OBN under different disruptions to facilitate rapid implementation of the study results. / Thesis / Master of Applied Science (MASc)

Ensembles of graphs arise naturally in many applications, for example, the temporal evolution of social contacts or computer communications, tissue-specific protein interaction networks, annual citation or co-authorship networks in a field, or a family of high-likelihood Bayesian networks inferred from systems biology data. Several techniques have been developed to analyze such ensembles. A canonical problem is that of computing communities that are persistent across the ensemble. This problem is usually formulated as one of computing dense subgraphs (communities) that are frequent, i.e., appear in many graphs in the ensemble. In this thesis, we seek to find "unstable communities" which are the antithesis of frequent, dense subgraphs. Informally, an unstable community is a set of nodes that induces highly-varying subgraphs in the ensemble. In other words, the graphs in the ensemble disagree about the precise pairwise connections among these nodes. The primary contribution of this dissertation is to introduce the concept of unstable communities as a novel problem in the field of graph mining. Specifically, it presents three approaches to mathematically formulate the concept of unstable communities, devises algorithms for computing such communities in a given ensemble of networks, and shows the usefulness of this concept in a variety of settings. Our first definition of unstable community relies on two parameters: the first ensures that a node set induces several different subgraphs in the ensemble and the second guarantees that each of these subgraphs occurs in a large number of graphs in the ensemble. We present two algorithms to enumerate unstable communities that match this definition. The first approach, ClustMiner, is a heuristic that transforms the problem into one of computing dense subgraphs in a single graph that summarizes the ensemble. The second approach, UCMiner, is guaranteed to enumerate all maximal unstable communities correctly. We apply both approaches to systems biology datasets to demonstrate that UCMiner is superior to ClustMiner in the sense that ClustMiner's output contains node sets that are not unstable while also missing several communities computed by UCMiner. We find several node sets that capture the uncertain connectivity of genes in relevant protein complexes, suggesting that further experiments may be required to precisely discern their interaction patterns. Our second and third definitions of unstable community rely on a novel concept of (scaled) subgraph divergence, a formulation that uses the concept of relative entropy to measure the instability of a community. We propose another algorithm, SDMiner, that can exactly enumerate all maximal unstable communities with small (scaled) subgraph divergence. We perform extensive experiments on social network datasets to show that we can discover UCs that capture the main structural variations of the given set of networks and also provide us with interesting and relevant insights about these datasets. / Ph. D.

This thesis examines development as a catalyst for the decision to migrate. Specifically, the two complementary theories of relative deprivation and social networks are examined to explore possible associations between level of household development and migrants' designation of savings or remittances towards development-related purposes and whether remittances are positively affected by migrants' participation in a hometown association (HTA). The study relied on data from the Mexican Migration Project (MMP), using the historical Mexican sending state of Jalisco. The MMP, using an ethnosurvey method, gathers data on individual migrant experiences, including border-crossing methods, jobs held, and participation in migrant hometown associations, amenities found in individual households, and available services in communities. No support was found for the first hypothesis, which predicted that relative deprivation was a catalyst of migration. Support was found for the second hypothesis, that migrant participation in HTAs, specifically in social clubs, positively influenced designation of savings or remittances for development-related purposes. This same support was not the case for migrant involvement in sport clubs. This thesis contributes to social network theory, pinpointing the positive effect that migrant participation in hometown associations has on designating money towards development. / Master of Arts