No description available.

Kärnkraften har så länge den funnits varit en omdebatterad energikälla. Syftet med dennauppsats är att utreda och få en bild av hur och på vilket sätt natursynen hänger ihop medåsikter om kärnkraft. Jag har redogjort för vad energi är, kärnkraftens historia i Sverige ochsom energikälla samt beskrivit för- och nackdelarna med kärnkraften. För att kunna ge en bildav kärnkraft och natursyn har jag genomfört kvalitativa intervjuer med personer som på olikasätt är insatta i problematiken kring kärnkraften. Min undersökning visar bland annat attnatursyn är ett komplext och stort begrepp som vi bör vara medvetna om hur det fungerar iarbetet för att förbättra miljön. / The nuclear power have, as long as it has been existing, been a discussed source of energy.The purpose with this report is to study and to get a picture if how and in what way the viewof nature cohere with the opinions about nuclear power. I have described what energy is, thehistory of nuclear power in Sweden and as a source of energy. I have also described thepositive and the negative aspects of nuclear power. To be able to present a picture of view ofnature and nuclear power I have done quality interviews with persons who, in different ways,are familiar with the problems of nuclear power. My investigation has shown that view ofnature is a complex and wide concept that we should be aware of in our work towards a moresustainable society.

Empirical analysis of the public accounting industry has been considerably limited due to lack of data availability. This dissertation proposal leverages a unique dataset of public accounting firms in Korea ranging from 1997 to 2011 to examine the industry's strategic groups and pricing decisions in light of considerable economic forces in a changing environment. I draw upon the theory of strategic groups (Hunt 1972; Caves and Porter 1977; Porter 1980) to distinctly identify strategic groups within the public accounting industry and how group membership explains performance differences. Further, I augment traditional audit fee models (Ferguson et al. 2003, Chaney et al. 2004, and Francis et al. 2005) by incorporating strategic group analysis to show that the relationships between audit fee determinants and audit fees are moderated by auditor strategic group membership. / Business Administration/Accounting

<p> In this thesis the problem of object recognition/pose estimation using active sensing is investigated. It is assumed that multiple cameras acquire images from different view angles of an object belonging to a set of a priori known objects. The eigenspace method is used to process the sensory observations and produce an abstract measurement vector. This step is necessary to avoid the manipulation of the original sensor data, i.e. large images, that can render the sensor modelling and matching process practically infeasible.</p> <p> The eigenspace representation is known to have shortcomings in dealing with structured noise such as occlusion. To overcome this problem, models of occlusions and sensor noise have been incorporated into the probabilistic model of sensor/object to increase robustness with respect to such uncertainties. The active recognition algorithm has also been modified to consider the possibility of occlusion, as well as variation in the occlusion levels due to camera movements.</p> <p> A recursive Bayesian state estimation problem is formulated to model the observation uncertainties through a probabilistic scheme. This enables us to identify the object and estimate its pose by fusing the information obtained from individual cameras. To this end, an extensive training step is performed, providing the system with the sensor model required for the Bayesian estimation. In order to enhance the quality of the estimates and to reduce the number of images taken, we employ active real-time viewpoint planning strategies to position cameras. For that purpose, the positions of cameras are controlled based on two different statistical performance criteria, namely the Mutual Information (MI) and Cramér-Rao Lower Bound (CRLB).</p> <p> A multi-camera active vision system has been developed in order to implement the ideas proposed in this thesis. Comparative Monte Carlo experiments conducted with the two-camera system demonstrate the effectiveness of the proposed methods in object classification/pose estimation in the presence of structured noise. Different concepts introduced in this work, i.e., the multi-camera data fusion, the occlusion modelling, and the active camera movement, all improve the recognition process significantly. Specifically, these approaches all increase the recognition rate, decrease the number of steps taken before recognition is completed, and enhance robustness with respect to partial occlusion considerably.</p> / Thesis / Master of Applied Science (MASc)

Polymer matrices doped with a dispersion of noble metal nanoparticles combine the strong plasmon resonance-based optical signatures of the latter with the flexibility and processability of the former. We have developed a nonlinear lithographic technique to generate large populations of epoxide waveguides containing a uniform dispersion of Au nanoparticles. The method is based on the self-trapping of multiple beams of white light propagating through a catonic polymerizable matrix doped with a gold salt, initiating the polymerization of epoxide moieties and simultaneously the in situ synthesis of elemental Au nanoparticles. Each white light filament inscribes a cylindrical waveguide, leading to an array of metallodielectric waveguides. Field of view (FOV) measurements indicate that the metallodielectric waveguide array has a nearly 59 % increase in FOV relative to its all-dielectric counterparts and can be tuned through the concentration of Au nanoparticles and the optical intensities employed to generate waveguides. / Thesis / Master of Science (MSc)

yes / This paper critically analyzes the fiction-view of scientific modeling, which exploits presumed analogies between literary fiction and model building in science. The basic idea is that in both fiction and scientific modeling fictional worlds are created. The paper argues that the fiction-view comes closest to certain scientific thought experiments, especially those involving demons in science and to literary movements like naturalism. But the paper concludes that the dissimilarities prevail over the similarities. The fiction-view fails to do justice to the plurality of model types used in science; it fails to realize that a function like idealization only makes sense in science because models, unlike works of fiction, can be de-idealized; it fails to distinguish sufficiently between the make-believe (fictional) worlds created in fiction and the hypothetical (as-if) worlds envisaged in models. Representation characterized in the fiction-view as a license to draw inferences does not sufficiently distinguish between inferences in fiction from inferences in scientific modeling. To highlight the contrast the paper proposes to explicate representation in terms of satisfaction of constraints

With the rapid development of photography, we can easily record the 3D world by taking photos and videos. In traditional images and videos, the viewer observes the scene from fixed viewpoints and cannot navigate the scene or edit the 2D observation afterward. Thus, visual content editing and synthesis become an essential task in computer vision. However, achieving high-quality visual synthesis often requires a complex and expensive multi-camera setup. This is not practical for daily use because most people only have one cellphone camera. But a single camera, on the contrary, could not provide enough multi-view constraints to synthesize consistent visual content. Therefore, in this thesis, I address this challenging single-camera visual synthesis problem by leveraging different regularizations. I study three consistent synthesis problems: time-consistent synthesis, view-consistent synthesis, and view-time-consistent synthesis. I show how we can take cellphone-captured monocular images and videos as input to model the scene and consistently synthesize new content for an immersive viewing experience. / Doctor of Philosophy / With the rapid development of photography, we can easily record the 3D world by taking photos and videos. More recently, we have incredible cameras on cell phones, which enable us to take pro-level photos and videos. Those powerful cellphones even have advanced computational photography features build-in. However, these features focus on faithfully recording the world during capturing. We can only watch the photo and video as it is, but not navigate the scene, edit the 2D observation, or synthesize content afterward. Thus, visual content editing and synthesis become an essential task in computer vision. We know that achieving high-quality visual synthesis often requires a complex and expensive multi-camera setup. This is not practical for daily use because most people only have one cellphone camera. But a single camera, on the contrary, is not enough to synthesize consistent visual content. Therefore, in this thesis, I address this challenging single-camera visual synthesis problem by leveraging different regularizations. I study three consistent synthesis problems: time-consistent synthesis, view-consistent synthesis, and view-time-consistent synthesis. I show how we can take cellphone-captured monocular images and videos as input to model the scene and consistently synthesize new content for an immersive viewing experience.

In December 2015, the United States' Secretary of Defense, Ash Carter, issued a directive that each branch of military avail every position to women (Pellerin, 2015). Given this and the dearth of literature on women's leader development in military environments, it was imperative to research if and how these environments shape and influence leadership development among college aged women. Specifically, this study sought to reveal women's view of self as leader in the context of a military training environment at a senior military college. The Leadership Identity Development (LID) model developed by Komives, Owen, Longerbeam, Mainella, and Osteen (2005) served as a framework for this study that utilized constructivist grounded theory methods for data collection and analysis as described by Charmaz (2014). The participants in this study were 21 college students who identified as women participating in a 24-hour military training program between the ages of 19 to 23 and agreed to participate in individual face-to-face interviews. Through interviews and analysis of the data, eight themes emerged from the women's experiences that revealed how they developed as leaders in the environment, and conditions that both promoted and inhibited their leader development. These themes are leadership defined, internal dialogue, strategies for managing influences, practicing leadership, context for learning leadership, external influences, internal influences, and experiences. These findings have implications for future research and practice. / Doctor of Philosophy / It is important to understand how college aged women develop a leadership identity in a military training environment that has historically been male-dominated. This study sought to better understand the experiences of 21 women who learned leadership in a military training environment that was a 24-hour live in experience on a campus of higher education. The women agreed to meet for a face-to-face interview that lasted approximately one hour to share their experiences. Constructivist grounded theory methods for data collection and analysis (Charmaz, 2014) were utilized in this study, and the Leadership Identity Development (LID) model developed by Komives, Owen, Longerbeam, Mainella, and Osteen (2005) served as a framework. I share the findings of this study and implications for future research and practice.

Visualization plays an important role in epidemic time series analysis and forecasting. Viewing time series data plotted on a graph can help researchers identify anomalies and unexpected trends that could be overlooked if the data were reviewed in tabular form. However,there are challenges in reviewing data sets from multiple data sources (data can be aggregated in different ways and measure different criteria which can make a direct comparison between time series difficult. In the face of an emerging epidemic, the ability to visualize time series from various sources and organizations and to reconcile these datasets based on different criteria could be key in developing accurate forecasts and identifying effective interventions. Many tools have been developed for visualizing temporal data; however, none yet supports all the functionality needed for easy collaborative visualization and analysis of epidemic data. In this thesis, we develop EpiViewer, a time series exploration dashboard where users can upload epidemiological time series data from a variety of sources and compare, organize, and track how data evolves as an epidemic progresses. EpiViewer provides an easy-to-use web interface for visualizing temporal datasets either as line charts or bar charts. The application provides enhanced features for visual analysis, such as hierarchical categorization, zooming, and filtering, to enable detailed inspection and comparison of multiple time series on a single canvas. Finally, EpiViewer provides a built-in statistical Epi-features module to help users interpret the epidemiological curves. / Master of Science / We present EpiViewer, a time series exploration dashboard where users can upload epidemiological time series data from a variety of sources and compare, organize, and track how data evolves as an epidemic progresses. EpiViewer is a single page web application that provides a framework for exploring, comparing, and organizing temporal datasets. It offers a variety of features for convenient filtering and analysis of epicurves based on meta-attribute tagging. EpiViewer also provides a platform for sharing data between groups for better comparison and analysis.

The ability to understand occlusion and disocclusion is critical in analyzing motion and forecasting changes. For example, when we see a car gradually blocks our view of a human figure, we know that either the car or the human is moving. We also know that the human behind the car will be visible again if we move to other positions. As many vision-based intelligent systems need to handle and react to visual data with potentially intensive motions, it is therefore beneficial to incorporate the occlusion reasoning into such systems. In this thesis, we study how we can improve the performance of vision-based deep learning models by harnessing the power of occlusion handling. We first visit the problem of optical flow estimation for motion analysis. We present a deep learning module that builds upon occlusion handling methods in classic Computer Vision literature. Our results show performance improvement in occluded regions on standard benchmarks, as well as real-world applications. We then examine the problem of view synthesis for 3D photography. We propose an inpainting method that leverages local color and depth context for novel view synthesis. We validate the proposed inpainting approach with a series of quantitative and qualitative experiments, and demonstrate promising results in predicting plausible content in occluded regions. / Master of Science / Human has the ability to understand occlusion, and make use of such knowledge to make predictions about motions and occluded contents. For example, when we see a car gradually blocks our view of a human figure, we know that either the car or the human is moving. We also know that the human behind the car will be visible again if we move to other positions. In this thesis, we study how we can replicate such an ability to artificial intelligence systems. We first investigate the effect of occlusion reasoning in the task of predicting motion. Our experimental results show that a system equipped with our occlusion reasoning module can better capture the motions happening in image sequences. Next, we examine the problem of hallucinating visual contents that are blocked in an image. We develop a model that can produce plausible content in occluded regions. In our experiments, we show that given one single RGB image with an estimated depth map, our model can produce a corresponding 3D photo by hallucinating the structures that are not visible in the image.