Examines and provides comments on language trends while tracing the origins of timely words and phrases that discuss such topics as technology, entertainment, and everyday life.

"Engaged and insightful, this collection explores the dynamics of gender, class, and race in today's United States. Sophisticated theory, passionate concern, and vivid examples make this a landmark work of social criticism." --Renato Rosaldo

Teach to the Common Core, differentiate instruction, and keep students engaged—all at the same time! With new Common Core–aligned tools and strategies, this second edition of a bestseller is an all-in-one math classroom management resource. Covering everything from lesson design to math-specific learning styles, the book's 60+ tools will enable you to: Work in smarter, more efficient ways with all of your students, no matter the class size or make up Create standards-based lesson plans, tests, and formative assessments Reach every learner regardless of understanding level or learning style Integrate technology into class time for more engaging math lessons

This book reports on the latest advances in adaptive critic control with robust stabilization for uncertain nonlinear systems. Covering the core theory, novel methods, and a number of typical industrial applications related to the robust adaptive critic control field, it develops a comprehensive framework of robust adaptive strategies, including theoretical analysis, algorithm design, simulation verification, and experimental results. As such, it is of interest to university researchers, graduate students, and engineers in the fields of automation, computer science, and electrical engineering wishing to learn about the fundamental principles, methods, algorithms, and applications in the field of robust adaptive critic control. In addition, it promotes the development of robust adaptive critic control approaches, and the construction of higher-level intelligent systems.

Scholars from a range of disciplines interrogate terms relevant to critical studies of big data, from abuse and aggregate to visualization and vulnerability. This pathbreaking work offers an interdisciplinary perspective on big data, interrogating key terms. Scholars from a range of disciplines interrogate concepts relevant to critical studies of big data--arranged glossary style, from from abuse and aggregate to visualization and vulnerability--both challenging conventional usage of such often-used terms as prediction and objectivity and introducing such unfamiliar ones as overfitting and copynorm. The contributors include both leading researchers, including N. Katherine Hayles, Johanna Drucker and Lisa Gitelman, and such emerging agenda-setting scholars as Safiya Noble, Sarah T. Roberts and Nicole Starosielski.