In Theater as Data, Miguel Escobar Varela explores the use of computational methods and digital data in theater research. He considers the implications of these new approaches, and explains the roles that statistics and visualizations play. Reflecting on recent debates in the humanities, the author suggests that there are two ways of using data, both of which have a place in theater research. Data-driven methods are closer to the pursuit of verifiable results common in the sciences; and data-assisted methods are closer to the interpretive traditions of the humanities. The book surveys four major areas within theater scholarship: texts (not only playscripts but also theater reviews and program booklets); relationships (both the links between fictional characters and the collaborative networks of artists and producers); motion (the movement of performers and objects on stage); and locations (the coordinates of performance events, venues, and touring circuits). Theater as Data examines important contributions to theater studies from similar computational research, including in classical French drama, collaboration networks in Australian theater, contemporary Portuguese choreography, and global productions of Ibsen. This overview is complemented by short descriptions of the author’s own work in the computational analysis of theater practices in Singapore and Indonesia. The author ends by considering the future of computational theater research, underlining the importance of open data and digital sustainability practices, and encouraging readers to consider the benefits of learning to code. A web companion offers illustrative data, programming tutorials, and videos.

A comprehensive guide to Michigan’s wild-growing seed plants

DIVThe first major book to argue in favor of affirmative action in higher education since Bowen and Bok's The Shape of the River /div

This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown notebooks. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings. Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analytics address specific knowledge gaps, resolve educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications. The book’s fourteen chapters start with an introduction and progressively build foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition of the book includes additional learning-based strategies utilizing generative adversarial networks, transfer learning, and synthetic data generation, as well as eight complementary electronic appendices. This textbook is suitable for formal didactic instructor-guided course education, as well as for individual or team-supported self-learning. The material is presented at the upper-division and graduate-level college courses and covers applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide range of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory, funding, and policy agencies. The supporting book website provides many examples, datasets, functional scripts, complete electronic notebooks, extensive appendices, and additional materials.

Driving is a privilege and not a right. Drivers must drive responsibly and safely, obey traffic laws, and never drink and drive. Finally, make sure that you and your passengers are properly buckled up - it's the law! Today's vehicles are loaded with technology that was unheard of even a decade ago. Systems that warn when you are drifting from your lane, assist you in parallel parking, automatically brake in emergency situations and provide 360 degrees of vision around the vehicle via a camera are becoming standard, even on moderately priced vehicles. As remarkable as these leaps in automotive technology are, the truth is that the most important safety feature in any vehicle remains you as the driver. Therefore, it is to your benefit to continue improving and expanding your knowledge of traffic laws and safe driving practices. Driving is a privilege. Once you have been issued a driver's license, you have the responsibility to continually demonstrate the skill and knowledge to drive safely. Whether you have been behind the wheel for decades or are just starting to venture out, driving is a discipline that requires judgment, knowledge, physical and mental self-awareness, and practice. "What Every Driver Must Know" is an excellent resource for assisting you on this lifelong journey.

COVID-19 is the most significant global crisis of any of our lifetimes. The numbers have been stupefying, whether of infection and mortality, the scale of public health measures, or the economic consequences of shutdown. Coronavirus Politics identifies key threads in the global comparative discussion that continue to shed light on COVID-19 and shape debates about what it means for scholarship in health and comparative politics. Editors Scott L. Greer, Elizabeth J. King, Elize Massard da Fonseca, and André Peralta-Santos bring together over 30 authors versed in politics and the health issues in order to understand the health policy decisions, the public health interventions, the social policy decisions, their interactions, and the reasons. The book’s coverage is global, with a wide range of key and exemplary countries, and contains a mixture of comparative, thematic, and templated country studies. All go beyond reporting and monitoring to develop explanations that draw on the authors' expertise while engaging in structured conversations across the book.