H.264/AVC intra prediction method is an efficient tool to reduce spatial redundancies by using multidirectional spatial prediction modes. In this paper, a novel intra prediction method is designed to improve coding efficiency. Firstly, we propose a direction-distance oriented prediction which considers the distance between the predict value and the reference samples according to the direction of the prediction modes. Secondly, we apply the concept of image restoration by using the projections onto convex sets (POCS) to intra prediction which uses adaptively filtering based on the surrounding reconstructed pixel to predict blocks. The experimental results show that the average bit-rate reduction of 0.75% and PSNR gain improved of 0.119dB are achieved.

I den här uppsatsen diskuterar jag, med utgångspunkt i Simon Blackburns kvasirealistiska metaetik, den moraliska motivationen. Jag redogör alltså dels för kvasirealis-men som helhetlig teori, dels för den aktuella motivationsteoretiska argumentationen. De centrala frågeställningarana rör i detta fall det sätt varpå våra värderingar motiverar oss att handla. Har de över huvud taget någon normativ kraft, eller består de enbart i kognitiva till-stånd? Utifrån dessa grundvalar bedömer jag sedan å ena sidan hur Blackburns teori funge-rar på det metaetiska planet, och å den andra för hur den svarar mot våra intuitioner om mo-ralföreställningarnas betydelse för vår handlingskraft. På det hela taget är jag emellertid ne-gativt inställd till teorins praktiska användbarhet. / This paper concerns on the one hand Simon Blackburn’s enterprise of quasi-realism, and on the other the problem of moral motivation. The main question in this second case treats the way whereupon our moral propositions motivate us to act. Do they own any normative force at all, or are they only to be seen as cognitive states? From the starting point of this discussion, I have investigated how the quasi-realist theory could work on the meta-ethical level. I have also tried to decide whether or not it corresponds to our intuitions about the real meaning of our moral judgements. The conclusive position of mine is that the fundamental problems of the theory eliminate its practical applicability.

The exhibition Is There No One in the World Who Can Fly? consists of three connected bodies of works. Life On Earth is a series of photo-performances exhibited on digital screens. Some of the images are still others are animated; they all propose that the body is a transmitter that breaths in content and breaths it out as a visual shape in the form of extensions, armatures or expulsions. Mammal is a large-scale video projection of a multi-breasted female figure projected on a free-standing wall. The breasts are animated and stretch out to explore and search the surrounding space. Worknest is a series of videos about the act of working which are projected onto the floor and appear as a community of guarded openings into tunnels beneath the ground.

<p>Capturing high dimensional complex ensembles of data is becoming commonplace in a variety of application areas. Some examples include</p><p>biological studies exploring relationships between genetic mutations and diseases, atmospheric and spatial data, and internet usage and online behavioral data. These large complex data present many challenges in their modeling and statistical analysis. Motivated by high dimensional data applications, in this thesis, we focus on building scalable Bayesian nonparametric regression algorithms and on developing models for joint distributions of complex object ensembles.</p><p>We begin with a scalable method for Gaussian process regression, a commonly used tool for nonparametric regression, prediction and spatial modeling. A very common bottleneck for large data sets is the need for repeated inversions of a big covariance matrix, which is required for likelihood evaluation and inference. Such inversion can be practically infeasible and even if implemented, highly numerically unstable. We propose an algorithm utilizing random projection ideas to construct flexible, computationally efficient and easy to implement approaches for generic scenarios. We then further improve the algorithm incorporating some structure and blocking ideas in our random projections and demonstrate their applicability in other contexts requiring inversion of large covariance matrices. We show theoretical guarantees for performance as well as substantial improvements over existing methods with simulated and real data. A by product of the work is that we discover hitherto unknown equivalences between approaches in machine learning, random linear algebra and Bayesian statistics. We finally connect random projection methods for large dimensional predictors and large sample size under a unifying theoretical framework.</p><p>The other focus of this thesis is joint modeling of complex ensembles of data from different domains. This goes beyond traditional relational modeling of ensembles of one type of data and relies on probability mixing measures over tensors. These models have added flexibility over some existing product mixture model approaches in letting each component of the ensemble have its own dependent cluster structure. We further investigate the question of measuring dependence between variables of different types and propose a very general novel scaled measure based on divergences between the joint and marginal distributions of the objects. Once again, we show excellent performance in both simulated and real data scenarios.</p> / Dissertation

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In this paper we present an econometric framework aimed at obtaining projections of income growth in Europe at the regional level. We account for model uncertainty in terms of the choice of explanatory variables, as well as the nature of the spatial spillovers of output growth and human capital investment. Building on recent advances in Bayesian model averaging, we construct projected trajectories of income and human capital simultaneously, while integrating out the effects of other covariates. This approach allows us to assess the potential contribution of future educational attainment to economic growth and income convergence among European regions over the next decades. Our findings suggest that income convergence dynamics and human capital act as important drivers of income growth for the decades to come. In addition we find that the relative return of improving educational attainment levels in terms of economic growth appears to be higher in peripheral European regions. (authors' abstract) / Series: Department of Economics Working Paper Series

This project implements a parallel algorithm for Computed Tomography based on the Algebraic Reconstruction Technique (ART) algorithm. This technique for reconstructing pictures from projections is useful for applications such as Computed Tomography (CT or CAT). The algorithm requires fewer views, and hence less radiation, to produce an image of comparable or better quality. However, the approach is not widely used because of its computationally intensive nature in comparison with rival technologies. A faster ART algorithm could reduce the amount of radiation needed for CT imaging by producing a better image with fewer projections. A reconstruction from projections version of the ART algorithm for two dimensions was implemented in parallel using the Message Passing Interface (MPI) and OpenMP extensions for C. The message passing implementation did not result in faster reconstructions due to prohibitively long and variant communication latency. The shared memory implementation produced positive results, showing a clear computational advantage for multiple processors and measured efficiency ranging from 60-95%. Consistent with the literature, image quality proved to be significantly better compared to the industry standard Filtered Backprojection algorithm especially when reconstructing from fewer projection angles.

In terms of sustainability, climate change has defined the last decade, underlining the need for a rigorous assessment of the intersection between economic and social development and environmental sustainability. This project's objectives were to define "green job" and to identify existing baselines and projections on the green economy across a selection of published reports. These findings were then examined further by interviewing representatives from the associated organisations seeking a better understanding of parameters, methodologies and policy stances on the green economy. A mixed methods approach triangulated data to gain a clearer understanding of BC's green economy. Findings confirmed a lack of consensus on definition but shed light on a range of parameters and resulting complexities. Methodologies varied, producing incongruent data that made comparisons challenging but, although the sample was relatively small, organizational mandates did fall in line with their respective estimates of the magnitude of the green economy in BC.

This project implements a parallel algorithm for Computed Tomography based on the Algebraic Reconstruction Technique (ART) algorithm. This technique for reconstructing pictures from projections is useful for applications such as Computed Tomography (CT or CAT). The algorithm requires fewer views, and hence less radiation, to produce an image of comparable or better quality. However, the approach is not widely used because of its computationally intensive nature in comparison with rival technologies. A faster ART algorithm could reduce the amount of radiation needed for CT imaging by producing a better image with fewer projections. A reconstruction from projections version of the ART algorithm for two dimensions was implemented in parallel using the Message Passing Interface (MPI) and OpenMP extensions for C. The message passing implementation did not result in faster reconstructions due to prohibitively long and variant communication latency. The shared memory implementation produced positive results, showing a clear computational advantage for multiple processors and measured efficiency ranging from 60-95%. Consistent with the literature, image quality proved to be significantly better compared to the industry standard Filtered Backprojection algorithm especially when reconstructing from fewer projection angles.

Given the expansion of multimedia technology and proliferation of moving projections on the theatre stage in the 21st century, this thesis examines how a virtual or projected character can appear to be present without a physical body on the stage. This study is grounded in the theories of effet de présence (effect of presence) as elaborated by Josette Féral, but also uses other theories to look at how productions can create such an effect for virtual characters. Specifically, this thesis examines the character’s relationship with the real, framing devices and actions of the characters. The specific examples of Rwanda 94 (Groupov), La Belle et la Bête (4D Art) and Les Aveugles (UBU CC) are used as case studies in order to focus on these techniques. Partant de l’intégration des nouvelles technologies et la prolifération des projections vidéo au sein des scènes théâtrales du 21e siècle, cette thèse examine les techniques par lesquel les personnages projeté peut sembler présent en l’absence d’un corps physique visible. Cette réflexion est basée sur l’effet de présence, concept élaboré par Josette Féral, ainsi que d’autres théories et examine comment un effet de présence est constitué pour ces personnages virtuels en considérant leur relation au réel (mimesis), les dispositifs de cadrage dans lesquels ils s’inscrivent de même que leurs actions. Cette analyse sera menée à porter de avec trois étudies de cas : Rwanda 94 (Groupov), La Belle et la Bête (4D Art) et Les Aveugles (UBU CC).