You awake from a coma to find the world strangely altered yet everyone pretending that nothing has changed. Do you pretend with the rest and try to fit in, or do you stand for reality and face social ostracism? (Also available in the short story collection "The Wrong Sort of Stories".)

A collection of stories about young girls who, as Cuban immigrants to the United States, grow in confidence and spirit as they confront painful challenges, meeting them head-on.

If you're like most people, your life is so hectic that it's hard to imagine squeezing in time for daily exercise. The good news is that you can get fit without an expensive gym membership or rigorous workout schedule. New research proves that you can "sneak up" on fitness by grabbing a little time here and there throughout the day so you total at least thirty minutes of moderate activity on most days. The American Heart Association's Fitting in Fitness guide will show you how to work spurts of activity into the way you live right now. Those few minutes can add up to huge rewards, including a stronger heart and bones, higher energy levels, better weight control, and more. You'll find hundreds of tips for fitting in fitness in this easy-to-use, inspiring guide. You'll even learn how to bring your kids into the act and have a lifestyle program that works for all of you.

Real life phenomena in engineering, natural, or medical sciences are often described by a mathematical model with the goal to analyze numerically the behaviour of the system. Advantages of mathematical models are their cheap availability, the possibility of studying extreme situations that cannot be handled by experiments, or of simulating real systems during the design phase before constructing a first prototype. Moreover, they serve to verify decisions, to avoid expensive and time consuming experimental tests, to analyze, understand, and explain the behaviour of systems, or to optimize design and production. As soon as a mathematical model contains differential dependencies from an additional parameter, typically the time, we call it a dynamical model. There are two key questions always arising in a practical environment: 1 Is the mathematical model correct? 2 How can I quantify model parameters that cannot be measured directly? In principle, both questions are easily answered as soon as some experimental data are available. The idea is to compare measured data with predicted model function values and to minimize the differences over the whole parameter space. We have to reject a model if we are unable to find a reasonably accurate fit. To summarize, parameter estimation or data fitting, respectively, is extremely important in all practical situations, where a mathematical model and corresponding experimental data are available to describe the behaviour of a dynamical system.

If you think, live, create, or deal with problems differently from those around you, here are practical solutions to resolve that conflict-but perhaps not the type of solutions you might expect. Rather than trying to fit in to a group, you'll learn how to be successful and effective because of those differences-and how make them your greatest gifts to the world. Book jacket.

Because circular objects are projected to ellipses in images, ellipse fitting is a first step for 3-D analysis of circular objects in computer vision applications. For this reason, the study of ellipse fitting began as soon as computers came into use for image analysis in the 1970s, but it is only recently that optimal computation techniques based on the statistical properties of noise were established. These include renormalization (1993), which was then improved as FNS (2000) and HEIV (2000). Later, further improvements, called hyperaccurate correction (2006), HyperLS (2009), and hyper-renormalization (2012), were presented. Today, these are regarded as the most accurate fitting methods among all known techniques. This book describes these algorithms as well implementation details and applications to 3-D scene analysis. We also present general mathematical theories of statistical optimization underlying all ellipse fitting algorithms, including rigorous covariance and bias analyses and the theoretical accuracy limit. The results can be directly applied to other computer vision tasks including computing fundamental matrices and homographies between images. This book can serve not simply as a reference of ellipse fitting algorithms for researchers, but also as learning material for beginners who want to start computer vision research. The sample program codes are downloadable from the website: https://sites.google.com/a/morganclaypool.com/ellipse-fitting-for-computer-vision-implementation-and-applications.

Fitting into Place adopts a multi-dimensional interdisciplinary approach to explore shifting geographies and temporalities that re-constitute 'city publics' - and the place of the 'public sociologist'. Class, race and gender (dis)advantages are situated in relation to urban-rural contrasts, where 'future selves' are reconfigured in and through 'local' and 'global' sites: people inhabit shifting times and places, from industrial landscapes of the 'past', to a current present and (imagined) 'cosmopolitan' 'regenerated' future. The rhetorics and vocabularies of place, as affective and material, suggest a more complex 'fit' than the language of masculine 'crisis' for past-times, or 'feminised' fit into new-futures, suggests. Across the generations, women's labour is still effaced as maps of loyalty hold up families as reference points of belonging and 'fitting in'; such architecture of place complicates reified 'geographies of choice' which centre a middle-class mobile subject. Based upon funded empirical research, this book will be of interest to sociologists and geographers.