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Graphics Shaders: Theory and Practice is intended for a second course in computer graphics at the undergraduate or graduate level, introducing shader programming in general, but focusing on the GLSL shading language. While teaching how to write programmable shaders, the authors also teach and reinforce the fundamentals of computer graphics. The second edition has been updated to incorporate changes in the OpenGL API (OpenGL 4.x and GLSL 4.x0) and also has a chapter on the new tessellation shaders, including many practical examples. The book starts with a quick review of the graphics pipeline, emphasizing features that are rarely taught in introductory courses, but are immediately exposed in shader work. It then covers shader-specific theory for vertex, tessellation, geometry, and fragment shaders using the GLSL 4.x0 shading language. The text also introduces the freely available glman tool that enables you to develop, test, and tune shaders separately from the applications that will use them. The authors explore how shaders can be used to support a wide variety of applications and present examples of shaders in 3D geometry, scientific visualization, geometry morphing, algorithmic art, and more. Features of the Second Edition: Written using the most recent specification releases (OpenGL 4.x and GLSL 4.x0) including code examples brought up-to-date with the current standard of the GLSL language. More examples and more exercises A chapter on tessellation shaders An expanded Serious Fun chapter with examples that illustrate using shaders to produce fun effects A discussion of how to handle the major changes occurring in the OpenGL standard, and some C++ classes to help you manage that transition The authors thoroughly explain the concepts, use sample code to describe details of the concepts, and then challenge you to extend the examples. They provide sample source code for many of the books examples at www.cgeducation.org
Programming --- Artificial intelligence. Robotics. Simulation. Graphics --- computer animation --- robots --- animatiesoftware --- beeldverwerking --- games --- digitale vormgeving --- robots
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Landscape --- Landscape --- Biodiversity --- Biodiversity --- Wild plants --- Wild plants --- Food service industry --- Food service industry --- Plant cover --- Plant cover --- Prairies --- Prairies --- Marginal land --- Marginal land --- Waste land --- Waste land --- Domestic gardens --- Domestic gardens --- Fauna --- Fauna --- Useful insects --- Useful insects --- propagation materials --- propagation materials
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digitale vormgeving --- Computer. Automation --- grafische computerprogramma's --- software --- graphic arts --- CAD (computer aided design) --- computer-aided design --- computer-aided design [process]
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In this paper--the first in a series of two papers that use data on 21 billion friendships from Facebook to study social capital--we measure and analyze three types of social capital by ZIP code in the United States: (i) connectedness between different types of people, such as those with low vs. high socioeconomic status (SES); (ii) social cohesion, such as the extent of cliques in friendship networks; and (iii) civic engagement, such as rates of volunteering. These measures vary substantially across areas, but are not highly correlated with each other. We demonstrate the importance of distinguishing these forms of social capital by analyzing their associations with economic mobility across areas. The fraction of high-SES friends among low-SES individuals--which we term economic connectedness--is among the strongest predictors of upward income mobility identified to date, whereas other social capital measures are not strongly associated with economic mobility. If children with low-SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high-SES parents, their incomes in adulthood would increase by 20% on average. Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality. To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at www.socialcapital.org.
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Low levels of social interaction across class lines have generated widespread concern and are associated with worse outcomes, such as lower rates of upward income mobility. Here, we analyze the determinants of cross-class interaction using data from Facebook, building upon the analysis in the first paper in this series. We show that about half of the social disconnection across socioeconomic lines--measured as the difference in the share of high-socioeconomic status (SES) friends between low- and high-SES people--is explained by differences in exposure to high- SES people in groups such as schools and religious organizations. The other half is explained by friending bias--the tendency for low-SES people to befriend high-SES people at lower rates even conditional on exposure. Friending bias is shaped by the structure of the groups in which people interact. For example, friending bias is higher in larger and more diverse groups and lower in religious organizations than in schools and workplaces. Distinguishing exposure from friending bias is helpful for identifying interventions to increase cross-SES friendships (economic connectedness). Using fluctuations in the share of high-SES students across high school cohorts, we show that increases in high-SES exposure lead low-SES people to form more friendships with high-SES people in schools that exhibit low levels of friending bias. Hence, socioeconomic integration can increase economic connectedness in communities where friending bias is low. In contrast, when friending bias is high, increasing cross-SES interaction among existing members may be necessary to increase economic connectedness. To support such efforts, we release privacy-protected statistics on economic connectedness, exposure, and friending bias for each ZIP code, high school, and college in the U.S. at www.socialcapital.org.
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