The first major survey on the graceful and colorful paintings of American artist Paul Resika. This new monograph is the most comprehensive book on the work of Paul Resika (b. 1928) to date, highlighting his landscapes, portraits, and still lifes from the 1940s to the present. Resika's most important teacher was Hans Hofmann, with whom he studied on Cape Cod and in New York City in the mid-forties. Resika's subjects are drawn from nature and reflect his surroundings, which change with the seasons: in winter, he lives in New York; in summer, Cape Cod; in spring he spends time painting in the south of France and in Italy. Province-town piers, fishing boats in the harbor, figures on the beach, and French farmhouses in the countryside emanate a dreamlike serenity and make up the rich visual vocabulary for which Resika is best known. Produced in a large format with more than 220 color illustrations, this book reflects over eight decades of Resika's output, with scholarly essays that reveal his ongoing dialogue with Hofmann's sophisticated ideas about color and pictorial structure.

A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.

Look around and select a subject that you can see painted. That will paint itself. Do the obvious thing before you do the superhuman thing. It may have been accidental, but you knew enough to let this alone. The good painter is always making use of accidents. Never try to repeat a success. Swing a bigger brush — you don’t know what fun you are missing. For 31 years, Charles Hawthorne spoke in this manner to students of his famous Cape Cod School of Art. The essence of that instruction has been collected from students’ notes and captured in this book, retaining the personal feeling and the sense of on-the-spot inspiration of the original classroom. Even though Hawthorne is addressing himself to specific problems in specific paintings, his comments are so revealing that they will be found applicable a hundred times to your own work. The book is divided into sections on the outdoor model, still life, landscape, the indoor model, and watercolor. Each section begins with a concise essay and continues with comments on basic elements: general character, color, form, seeing, posture, etc. It is in the matter of color that students will especially feel themselves in the presence of a master guide and critic. Hawthorne’s ability to see color and, more important, to make the student see color, is a lesson that will aid student painters and anyone else interested in any phase of art. Although it does not pretend to be a comprehensive or closely ordered course, this book does have much to offer. It also represents the artistic insight of one of the finest painter-teachers of the twentieth century. "An excellent introduction for laymen and students alike." — Time "To read these notes and comments … is in itself an education. One cannot help but gain great help." — School Arts

Now in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tibshirani's outstanding 1990 research monograph on GAMs is largely responsible for this, there has been a long-standing need for an accessible introductory treatment of the subject that also emphasizes recent penalized regression spline approaches to GAMs and the mixed model extensions of these models. Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these very flexible tools. The author bases his approach on a framework of penalized regression splines, and builds a well-grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of the freely available R software helps explain the theory and illustrates the practicalities of linear, generalized linear, and generalized additive models, as well as their mixed effect extensions. The treatment is rich with practical examples, and it includes an entire chapter on the analysis of real data sets using R and the author's add-on package mgcv. Each chapter includes exercises, for which complete solutions are provided in an appendix. Concise, comprehensive, and essentially self-contained, Generalized Additive Models: An Introduction with R prepares readers with the practical skills and the theoretical background needed to use and understand GAMs and to move on to other GAM-related methods and models, such as SS-ANOVA, P-splines, backfitting and Bayesian approaches to smoothing and additive modelling.

As media environments and communication practices evolve over time, so do theoretical concepts. This book analyzes some of the most well-known and fiercely discussed concepts of the digital age from a historical perspective, showing how many of them have pre-digital roots and how they have changed and still are constantly changing in the digital era. Written by leading authors in media and communication studies, the chapters historicize 16 concepts that have become central in the digital media literature, focusing on three main areas. The first part, Technologies and Connections, historicises concepts like network, media convergence, multimedia, interactivity and artificial intelligence. The second one is related to Agency and Politics and explores global governance, datafication, fake news, echo chambers, digital media activism. The last one, Users and Practices, is finally devoted to telepresence, digital loneliness, amateurism, user generated content, fandom and authenticity. The book aims to shed light on how concepts emerge and are co-shaped, circulated, used and reappropriated in different contexts. It argues for the need for a conceptual media and communication history that will reveal new developments without concealing continuities and it demonstrates how the analogue/digital dichotomy is often a misleading one.

The science behind global warming, and its history: how scientists learned to understand the atmosphere, to measure it, to trace its past, and to model its future. Global warming skeptics often fall back on the argument that the scientific case for global warming is all model predictions, nothing but simulation; they warn us that we need to wait for real data, “sound science.” In A Vast Machine Paul Edwards has news for these skeptics: without models, there are no data. Today, no collection of signals or observations—even from satellites, which can “see” the whole planet with a single instrument—becomes global in time and space without passing through a series of data models. Everything we know about the world's climate we know through models. Edwards offers an engaging and innovative history of how scientists learned to understand the atmosphere—to measure it, trace its past, and model its future.

Artist secrets revealed, step by step instructions Libby Fellerhoff, North Light Magazine. Mar. 2001.