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This thesis discusses the Vaiśeṣika philosopher Praśastapāda's conception of self in his text, the Padārthadharmasaṃgraha, and its key commentaries. It examines the arguments these texts propose for the existence of a self that is non-physical and yet non-Cartesian, based in a paradigm I term ‘differential naturalism'. I examine Praśastapāda's four arguments for the existence of a self from: the structures of agency and action in human cognition; in intentional mental and bodily acts; in the homeostatic regulation of the human body; and in the biological life of the human organism. The core thesis, I argue, is that the rational structures of agency and action we find in these activities require a conscious owner. This must be a self. The dualist argument for the self's non-physical nature emerges from a dichotomous ontology of mental and physical properties and causal powers, which entails that only a non-physical substance can be a bearer of the normative and intentional structures that agency and mental causation demand. The minded self is, however, necessarily embodied. Praśastapāda, I suggest, postulates a naturalistic conception of self. Such a self enables the integration of the mental, moral and physical realms as aspects of natural order, for self is the bearer not only of psychological, vital and normative powers in the natural world, but of natural law qua moral law. This integrative, yet differential, naturalism provides an innovative alternative to Western and classical Indian physicalist and dualist perspectives.

Pulsed electron-electron double resonance (PELDOR) is an emerging technique for nanometre distance measurements in nano-sized assemblies and between specific sites of molecules. Most commonly nitroxide radicals are used as probes for EPR distance measurements because they are easy to introduce in biological systems such as soluble and membrane proteins or nucleic acids. PELDOR distance measurements currently rely on data processing software which has been proven to accurately extract inter-spin distances from the dipolar coupling between two paramagnetic centres. However, when the dipolar coupling is affected by contributions from other close-by unpaired electrons inaccuracies as broadening effects and artefacts are introduced in the distance distributions derived. This challenge, commonly referred as multi-spin effects, has been affecting the extraction of accurate distance information from PELDOR measurements in chemical and biological systems with multiple spin labels. The aim of this project is to approach, identify and suppress inaccuracies introduced in PELDOR-based distance distributions by multi-spin effects. This is achieved through the synthesis of multiply labelled model systems which would allow for assessment of the impact of multi-spin effects on distance measurements of simple geometries whose behaviour can be easily predicted and modelled. In this work existing methods for suppression of multi-spin effects are tested, together with their efficiency and limitations. The results are used to devise better sets of parameters including alternative settings for extraction of accurate distances from multi-spin systems and to explore their efficiency and limitations. Additional effects influencing distance measurements by pulsed EPR are also examined; in particular the effects of orientation selection and their interplay with multi-spin effects is studied in depth. Studies on rigid symmetric and asymmetric chemical model systems together with heptameric channel membrane proteins allow for outlining of recommendations for PELDOR distance measurements settings on systems presenting similar structural features, including symmetries and inter-spin distances.

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Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Since 1997, the University of Saskatchewan Library has used “subject pages” to highlight key library resources. When Springshare announced it was launching LibGuides v2, a project team was assembled to transition a mixture of locally produced guides and guides created with the original LibGuides v1 software. This article synthesizes best practices for LibGuides found in the literature, outlines our best intentions in the migration process, and shares what actually transpired after considering factors such as technical challenges and institutional culture. We hope other academic libraries can learn from our experience and make decisions that suit their institution best. / Pre-print article

In this paper, a Kinect-based distributed and real-time motion capture system is developed. A trigonometric method is applied to calculate the relative positions of Kinect v2 sensors with a calibration wand and register the sensors’ positions automatically. By combining results from multiple sensors with a nonlinear least square method, the accuracy of motion capture is optimized. Moreover, to exclude inaccurate results from sensors, a computational geometry is applied in the occlusion approach to discover occluded joint data. The synchronization approach is based on the NTP protocol, which synchronizes the time between the clocks of a server and of clients dynamically, leading to the proposed system being real time. Experiments to validate the proposed system are conducted from the perspective of calibration, occlusion, and accuracy. More specifically, the mean absolute error of the calibration results is 0.73 cm, the proposed occlusion method is tested on upper and lower limbs, and the synchronization component guarantees the clock synchronization and real-time performance for more than 99% of the measurement process. Furthermore, to demonstrate the practical performance of our system, a comparison with previously developed motion capture systems (the linear trilateration approach [52] and the geometric trilateration approach [51]) with the benchmark Opti Track system is performed for the tracked joints of the head, shoulder, elbow, and wrist, therein showing that the accuracy of our proposed system is 38.3% and 24.1% better than the aforementioned two trilateration systems. Quantitative analysis is also conducted on our proposed system with the commercial inertial motion capture system Delsys smart sensor system by comparing the measurements of lower limbs (i.e., hips, knees, and ankles), and the standard deviation of our proposed system’s measurement results is 4.92 cm.

Indiana University-Purdue University Indianapolis (IUPUI) / Machine learning models are rising every day. Most of the Computer Vision oriented machine learning models arise from Convolutional Neural Network’s(CNN) basic structure. Machine learning developers use CNNs extensively in Image classification, Object Recognition, and Image segmentation. Although CNN produces highly compatible models with superior accuracy, they have their disadvantages. Estimating pose and transformation for computer vision applications is a difficult task for CNN. The CNN’s functions are capable of learning only shift-invariant features of an image. These limitations give machine learning developers motivation towards generating more complex algorithms. Search for new machine learning models led to Capsule Networks. This Capsule Network was able to estimate objects’ pose in an image and recognize transformations to these objects. Handwritten digit classification is the task for which capsule networks are to solve at the initial stages. Capsule Networks outperforms all models for the MNIST dataset for handwritten digits, but to use Capsule networks for image classification is not a straightforward multiplication of parameters. By replacing the Capsule Network’s initial layer, a simple Convolutional Layer, with complex architectures in CNNs, authors of Residual Capsule Network achieved a tremendous change in capsule network applications without a high number of parameters. This thesis focuses on improving this recent Residual Capsule Network (RCN) to an extent where accuracy and model size is optimal for the Image classification task with a benchmark of the CIFAR-10 dataset. Our search for an exemplary capsule network led to the invention of RCN2: Residual Capsule Network 2 and RCNX: Residual Capsule NeXt. RCNX, as the next generation of RCN. They outperform existing architectures in the domain of Capsule networks, focusing on image classification such as 3-level RCN, DCNet, DC Net++, Capsule Network, and even outperforms compact CNNs like MobileNet V3. RCN2 achieved an accuracy of 85.12% with 1.95 Million parameters, and RCNX achieved 89.31% accuracy with 1.58 Million parameters on the CIFAR-10 benchmark.

Une des innovations technologiques majeures de ces dernières années a été le lancement des caméras de profondeur qui peuvent être utilisées dans un large spectre d’applications, notamment pour la robotique, la vision par ordinateur, l’automatisation, etc. Ces dispositifs ont ouvert de nouvelles opportunités pour la recherche scientifique appliquée au domaine médical. Dans le cadre de cette thèse, nous évaluerons l’apport potentiel de l’utilisation du capteur de profondeur grand public « Kinect V2 » dans l’optique de répondre à des problématiques cliniques actuelles en radiothérapie ainsi qu’en réanimation. Le traitement par radiothérapie étant administré sur plusieurs séances, l'un des objectifs clés de ce traitement est le positionnement quotidien du patient dont la précision est impactée par les mouvements respiratoires. D’autre part, les mouvements de la machine ainsi que les éventuels mouvements du patient peuvent entraîner des collisions machine/machine ou machine/patient. Nous proposons un système de détection surfacique pour la gestion des mouvements inter- et intrafractions en radiothérapie externe. Celui-ci est basé sur un algorithme rigide de recalage surfacique pour estimer la position de traitement et un système de détection de collisions en temps réel pour satisfaire les conditions de sécurité durant le traitement. Les résultats obtenus sont encourageants et montrent un bon accord avec les systèmes cliniques. Coté réanimation médicale, la recherche de nouveaux dispositifs non invasifs et sans contact tend à optimiser la prise en charge des patients. La surveillance non invasive de la respiration des patients sous ventilation spontanée est capitale pour les patients instables mais aucun système de suivi à distance n’existe à ce jour. Dans ce contexte, nous proposons un système de mesure sans contact capable de calculer les paramètres ventilatoires en observant les changements morphologiques de la zone thoracique des patients. La méthode développée donne une précision de mesures cliniquement acceptable. / In recent years, one of the major technological innovations has been the introduction of depth cameras that can be used in a wide range of applications, including robotics, computer vision, automation, etc. These devices have opened up new opportunities for scientific research applied to the medical field. In this thesis, we will evaluate the potential use of the "Kinect V2" depth camera in order to respond to current clinical issues in radiotherapy and resuscitation in intensive care unit.Given that radiotherapy treatment is administered over several sessions, one of the key task is to daily reposition the patient in the same way as during the planning session.The precision of such repositioning is impacted by the respiratory motion. On the other hand, the movements of the machine as well as the possible movements of the patient can lead to machine / machine or machine /patient collisions. We propose a surface detection system for the management of inter and intra-fraction motion in external radiotherapy. This system is based on a rigid surface registration algorithm to estimate the treatment position and a real-time collision detection system to ensure patient safety during the treatment.Obtained results are encouraging and show a good agreement with available clinical systems.Concerning medical resuscitation, there is a need for new non-invasive and non-contact devices in order to optimize patient care. Non-invasive monitoring of spontaneous breathing for unstable patients is crucial in the intensive care unit. In this context, we propose a non-contact measurement system capable of calculating the parameters of patient's ventilation by observing thoracic morphological movements. The developed method gives a clinically acceptable precision. Such system is the first to solve previously described issue.

Nesta tese estudamos o fluxo dirigido, a primeira componente na expansão de Fourier da distribuição azimutal das partículas emitidas. Diferente do fluxo elíptico, que é muito bem estudado e descrito na literatura, a física que gera este observável ainda não é muito conhecida. Mostramos que este observável é altamente sensível a condições iniciais, comparando vários resultados numéricos para diferentes condições iniciais, entre analíticas e numéricas. Propomos que o fluxo dirigido também é sensível à aceleração longitudinal e formulamos um modelo analítico baseado nesta hipótese. Este modelo sugere quais ingredientes são relevantes para o fluxo dirigido. Ele é confrontado com sucesso com cálculos numéricos, resultados experimentais e cálculos que não incluem a aceleração longitudinal. / In this thesis we study the directed flow, the first component in the Fourier\'s expansion of the azimuthal distribution of emitted particles. Unlike the elliptic flow, which is well studied and described in the literature, the physics that generates this observable is not yet well described. We show that this observable is highly sensible to the initial conditions, comparing several numeric results with different initial conditions, between analytic and numeric ones. We propose that the directed flow is also sensitive to the longitudinal acceleration and we formulate an analytic model based in this hypothesis. This model suggests which ingredients are relevant to the directed flow. It is confronted with success against numeric calculus, experimental results, and calculus that do not include the longitudinal acceleration.