Drawing from the social theories of Niklas Luhmann and Mary Douglas, Predicting the Past advocates a reflexive understanding of the paradoxical institutional dynamic of American literary history as a professional discipline and field of study. Contrary to most disciplinary accounts, Michael Boyden resists the utopian impulse to offer supposedly definitive solutions for the legitimation crises besetting American literature studies by "going beyond" its inherited racist, classist, and sexist underpinnings. Approaching the existence of the American literary tradition as a typically modern problem generating diverse but functionally equivalent solutions, Boyden argues how its peculiarity does not, as is often supposed, reside in its restrictive exclusivity but rather in its massive inclusivity, which drives it to constantly revert to a self-negating "beyond" perspective. Predicting the Past covers a broad range of literary histories and reference works, from Rufus Griswold's 1847 Prose Writers of America to Sacvan Bercovitch's monumental Cambridge History of American Literature. Throughout, Boyden focuses on particular themes and topics illustrating the self-induced complexity of American literary history, such as the early "Anglocentric" roots theories of American literature; the debate on contemporary authors in the age of naturalism; the plurilingual ethnocentrism of the pioneer Americanists of the mid-twentieth century; and the genealogical misrepresentation of founding figures such as Jonathan Edwards, Emily Dickinson, and Robert Lowell.

This work provides an in-depth investigation of after-the-fact predictions in ancient Near Eastern texts from roughly 1200 B.C.E.–70 C.E. It argues that the Akkadian, Aramaic, Hebrew, and Greek works discussed are all part of a developing scribal discourse of “mantic historiography” by which scribes blend their local traditions of history writing and predictive texts to produce a new mode of historiographic expression. This in turn calls into question the use and usefulness of traditional literary categories such as “apocalypse” to analyze such works.

This brochure presents abstracts from the books by Anastasia Novykh “Sensei of Shambala” (Books II, III, IV). Small but very informative, it brings new important information to those who want to know the truth.

This handbook focuses on the enormous literature applying statistical methodology and modelling to environmental and ecological processes. The 21st century statistics community has become increasingly interdisciplinary, bringing a large collection of modern tools to all areas of application in environmental processes. In addition, the environmental community has substantially increased its scope of data collection including observational data, satellite-derived data, and computer model output. The resultant impact in this latter community has been substantial; no longer are simple regression and analysis of variance methods adequate. The contribution of this handbook is to assemble a state-of-the-art view of this interface. Features: An internationally regarded editorial team. A distinguished collection of contributors. A thoroughly contemporary treatment of a substantial interdisciplinary interface. Written to engage both statisticians as well as quantitative environmental researchers. 34 chapters covering methodology, ecological processes, environmental exposure, and statistical methods in climate science.

This book offers an introduction to digital signal processing (DSP) with an emphasis on audio signals and computer music. It covers the mathematical foundations of DSP, important DSP theories including sampling, LTI systems, the z-transform, FIR/IIR filters, classic sound synthesis algorithms, various digital effects, topics in time and frequency-domain analysis/synthesis, and associated musical/sound examples. Whenever possible, pictures and graphics are included when presenting DSP concepts of various abstractions. To further facilitate understanding of ideas, a plethora of MATLAB® code examples are provided, allowing the reader tangible means to “connect dots” via mathematics, visuals, as well as aural feedback through synthesis and modulation of sound. This book is designed for both technically and musically inclined readers alike-folks with a common goal of exploring digital signal processing.

Maximize performance with better data Developing a successful workforce requires more than a gut check. Data can help guide your decisions on everything from where to seat a team to optimizing production processes to engaging with your employees in ways that ring true to them. People analytics is the study of your number one business asset—your people—and this book shows you how to collect data, analyze that data, and then apply your findings to create a happier and more engaged workforce. Start a people analytics project Work with qualitative data Collect data via communications Find the right tools and approach for analyzing data If your organization is ready to better understand why high performers leave, why one department has more personnel issues than another, and why employees violate, People Analytics For Dummies makes it easier.

Learn how to use data science and Python to solve everyday business problems. Dive into the exciting world of data science with this practical introduction. Packed with essential skills and useful examples, Dive Into Data Science will show you how to obtain, analyze, and visualize data so you can leverage its power to solve common business challenges. With only a basic understanding of Python and high school math, you’ll be able to effortlessly work through the book and start implementing data science in your day-to-day work. From improving a bike sharing company to extracting data from websites and creating recommendation systems, you’ll discover how to find and use data-driven solutions to make business decisions. Topics covered include conducting exploratory data analysis, running A/B tests, performing binary classification using logistic regression models, and using machine learning algorithms. You’ll also learn how to: Forecast consumer demand Optimize marketing campaigns Reduce customer attrition Predict website traffic Build recommendation systems With this practical guide at your fingertips, harness the power of programming, mathematical theory, and good old common sense to find data-driven solutions that make a difference. Don’t wait; dive right in!

Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that "artificial intelligence is included." We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data.