Representing Belief

Representing Belief provides a detailed discussion and analysis of the forms and meanings in religious art of nineteenth-century France. This genre, usually assigned minimal importance by writers on the period, turns out to occupy a central place in the cultural history of the era, touching the core of the century's conflict between tradition and modernity, science and faith, ultramontanism and naturalism. Although it was generally assumed that this kind of art was of little importance in the evolution of modern painting, Driskel demonstrates that in reality it played a crucial role. Many of the artists discussed are firmly installed in the present canon (Delacroix, Ingres, Manet, Gauguin), while others (Flandrin, Orsel, Gleyre, Cazin) were major figures in their own time, though largely forgotten today. Writing from an interdisciplinary perspective and employing concepts derived from structuralist and poststructuralist theory, Driskel moves beyond simple formalism to restore a category of once-important works to a meaningful context, thereby offering others a model by which to discuss and interpret these paintings. Carefully charting the genealogies of hieraticism and naturalism, he demonstrates that a dramatic shift occurred in the 1860s and 1870s as naturalism gained acceptance among ultramontanes and the hieratic mode began to attract the interest of adherents to the belief system of modernism. Representing Belief is the first book to situate this art in its social and historical contexts and to approach it from this point of view.

In this paper, we examine several issues for ordering or partially ordering elements of hyperpowertsets involved in the recent theory of plausible, uncertain and paradoxical reasoning (DSmT) developed by the authors. We will show the benefit of some of these issues to obtain a nice and useful matrix representation of belief functions.

Fictions of Fact and Value looks at logical positivism's major influence on the development of postwar American fiction, charting a literary and philosophical genealogy that has been absent from criticism on the American novel since 1945.

Although numerous studies have examined biblical and theological rationales for using the visual arts in worship, this book by Lisa J. DeBoer fills in a piece of the picture missing so far — the social dimensions of both our churches and the various art worlds represented in our congregations. The first part of the book looks at Orthodoxy, Catholicism, and Protestantism in turn — including case studies of specific congregations — showing how each tradition’s use of the visual arts reveals an underlying ecclesiology. DeBoer then focuses on six themes that emerge when Orthodox, Catholic, and Protestant uses of the visual arts are examined together — the arts as expressions of the church’s local and universal character, the meanings attributed to particular styles of art for the church, the role of the arts in enculturating the gospel, and more. DeBoer’s Visual Arts in the Worshiping Church will focus and deepen the thinking of pastors, worship leaders, artists, students, and laypeople regarding what the arts might do in the midst of their congregations.

In recent years rough set theory has attracted the attention of many researchers and practitioners all over the world, who have contributed essentially to its development and applications. Weareobservingagrowingresearchinterestinthefoundationsofroughsets, including the various logical, mathematical and philosophical aspects of rough sets. Some relationships have already been established between rough sets and other approaches, and also with a wide range of hybrid systems. As a result, rough sets are linked with decision system modeling and analysis of complex systems, fuzzy sets, neural networks, evolutionary computing, data mining and knowledge discovery, pattern recognition, machine learning, and approximate reasoning. In particular, rough sets are used in probabilistic reasoning, granular computing (including information granule calculi based on rough mereology), intelligent control, intelligent agent modeling, identi?cation of autonomous s- tems, and process speci?cation. Methods based on rough set theory alone or in combination with other - proacheshavebeendiscoveredwith awide rangeofapplicationsinsuchareasas: acoustics, bioinformatics, business and ?nance, chemistry, computer engineering (e.g., data compression, digital image processing, digital signal processing, p- allel and distributed computer systems, sensor fusion, fractal engineering), de- sion analysis and systems, economics, electrical engineering (e.g., control, signal analysis, power systems), environmental studies, informatics, medicine, mole- lar biology, musicology, neurology, robotics, social science, software engineering, spatial visualization, Web engineering, and Web mining.

Within traditional decision theory, common decision principles -- e.g. the principle to maximize utility -- generally invoke idealization; they govern ideal agents in ideal circumstances. In Realistic Decision Theory, Paul Weirch adds practicality to decision theory by formulating principles applying to nonideal agents in nonideal circumstances, such as real people coping with complex decisions. Bridging the gap between normative demands and psychological resources, Realistic Decision Theory is essential reading for theorists seeking precise normative decision principles that acknowledge the limits and difficulties of human decision-making.