This paper examines the concept of uploading one’s consciousness on to a computer, and its role in personal identity. I first examine the technology behind uploading, and the likely timeline for that technology to become widespread. Then taking uploading as a given, I examine our intuitions about how we will interact with these uploads on a daily basis. Then, I argue that Derek Parfit’s account of survival and identity is the one best suited for a post-upload world. After explaining the benefits of Parfit’s view in this world, I defend Parfit against criticisms by Eric Olson, and Susan Schneider. Finally, I show why animalism and four-dimensionalism are not as strong of accounts of personal identity as Parfit’s view, in a world where uploading is a reality.

Transhumanism is a philosophy and a contemporary movement dedicated to improving the human condition in various ways. This thesis explores how the transhumanist movement contributes to the contemporary conceptualization or reconceptualization of the human body and the human condition. Furthermore, this thesis discusses three transhumanist key concepts, namely Mind Uploading, Cryonics, and the Primo Posthuman, and how these ideas might affect the research participants’ understanding and enactment of the human body and the human condition. By relying on ethnographic methods, including semi-structured interviews and participant observations from a 12-week fieldwork in Arizona, the ethnographic material is then discussed with the use of Annemarie Mol’s body multiple theory. In addition to this, the material is also analyzed with literature discussing the three main themes of this thesis, namely the re-conceptualization of the body, the mind, and body dualism, and the redefinition of death and the dying body. The ethnographic material and the accounts made from the research participants illustrate that the body is enacted and understood as insufficient and fragile, cartesian, a current vehicle readable and quantifiable through numbers and graphs, and a body whose mind can be uploaded into a computational substrate by the transhumanist community. It also describes how the body can be enacted as alive, an anatomical object, and a body with agency and personhood. Furthermore, the thesis concludes that the transhumanist movement contributes to the contemporary conceptualization and reconceptualization of the human body and the human condition by enacting multiple bodies as well as discussing and imagining new possible human conditions and human bodies. Lastly, this thesis does not only address important anthropological questions regarding ontology and the enactment of the human body. It also discusses relevant questions about humans and technology, as well as questions regarding death and imagination.

A short story collection containing nine stories and a 20,000-word novella. The stories are speculative fiction and they experiment with form.

published_or_final_version / Politics and Public Administration / Master / Master of Public Administration

La collecte participative des données mobiles est un nouveau paradigme dédié aux applications de détection urbaines utilisant une foule de participants munis de téléphones intelligents. Pour mener à bien les tâches de collecte participative des données mobiles, diverses préoccupations relatives aux participants et aux organisateurs doivent être soigneusement prises en considération. Pour les participants, la principale préoccupation porte sur la consommation d'énergie, le coût des données mobiles, etc. Pour les organisateurs, la qualité des données et le budget sont les deux préoccupations essentielles. Dans cette thèse, deux mécanismes de collecte participative des données mobiles sont proposés : le téléchargement montant collaboratif des données et la collecte clairsemée des données mobiles. Pour le téléchargement montant collaboratif des données, deux procédés sont proposés 1) « effSense », qui fournit la meilleure solution permettant d’économiser la consommation d'énergie aux participants ayant un débit suffisant, et de réduire le coût des communications mobiles aux participants ayant un débit limité; 2) « ecoSense », qui permet de réduire le remboursement incitatif par les organisateurs des frais associés au coût des données mobiles des participants. Dans la collecte clairsemée des données mobiles, les corrélations spatiales et temporelles entre les données détectées sont exploitées pour réduire de manière significative le nombre de tâches allouées et, par conséquent, le budget associé aux organisateurs, tout en assurant la qualité des données. De plus, l’intimité différentielle est afin de répondre au besoin de préservation de la localisation des participants / Mobile crowdsensing is a novel paradigm for urban sensing applications using a crowd of participants' sensor-equipped smartphones. To successfully complete mobile crowdsensing tasks, various concerns of participants and organizers need to be carefully considered. For participants, primary concerns include energy consumption, mobile data cost, privacy, etc. For organizers, data quality and budget are two critical concerns. In this dissertation, to address both participants' and organizers' concerns, two mobile crowdsensing mechanisms are proposed - collaborative data uploading and sparse mobile crowdsensing. In collaborative data uploading, participants help each other through opportunistic encounters and data relays in the data uploading process of crowdsensing, in order to save energy consumption, mobile data cost, etc. Specifically, two collaborative data uploading procedures are proposed (1) effSense, which helps participants with enough data plan to save energy consumption, and participants with little data plan to save mobile data cost; (2) ecoSense, which reduces organizers' incentive refund that is paid for covering participants' mobile data cost. In sparse mobile crowdsensing, spatial and temporal correlations among sensed data are leveraged to significantly reduce the number of allocated tasks thus organizers' budget, still ensuring data quality. Specifically, a sparse crowdsensing task allocation framework, CCS-TA, is implemented with compressive sensing, active learning, and Bayesian inference techniques. Furthermore, differential privacy is introduced into sparse mobile crowdsensing to address participants' location privacy concerns