Over Time

It was the Rumor of the New Millennium for the entire hockey world. The biggest story of the 2000-2001 National Hockey League season began as a whisper in Pittsburgh, and then raged across the sports pages and web sites of North America as the holidays neared. Mario Lemieux, the super-sized star of the National Hockey League driven too young from the game at which he excelled, was contemplating a comeback. In the wake of an interminable string of ailments and injuries, Super Mario had left the game in 1997 and limped into the Hockey Hall of Fame, barely into his thirties. By age thirty-four, he was president of the Pittsburgh Penguins, once again resurrecting a foundering franchise, this time in a suit. Ironically, accepting that responsibility only heightened his desire to address some unfinished business...on the ice. Healthier than he had been at any time in the last five years of his playing career, No.66 confirmed his return to the NHL ice wars, a belated Christmas present for Pittsburgh and the hockey world. Few could imagine the impact he would have on the league. From his early days as a hockey prodigy to the brilliant rise of his career (and its unexpected fall due to injury) and finally his return in a blaze of glory, "Mario Lemieux: Over Time" is the definitive book on one of the greatest hockey players of all time.

Overtime is about the politics of time, and specifically the amount of time that we spend labouring within capitalist society. It argues that reactivating the longstanding demand for shorter working hours should be central to any progressive trajectory in the years ahead. This book explains what a shorter working week means, as well as its history and its political implications. Will Stronge and Kyle Lewis examine the idea of reducing the time we all spend labouring for other on both a theoretical and political level, and offer an analysis rooted in the radical traditions from which the idea first emerged. Throughout, the reader is introduced to key theorists of work and working time alongside the relevant research regarding our contemporary 'crisis of work', to which the authors' proposal of a shorter working week responds.

Jekatyerina Dunajeva explores how two dominant stereotypes—“bad Gypsies” and “good Roma”—took hold in formal and informal educational institutions in Russia and Hungary. She shows that over centuries “Gypsies” came to be associated with criminality, lack of education, and backwardness. The second notion, of proud, empowered, and educated “Roma,” is a more recent development. By identifying five historical phases—pre-modern, early-modern, early and “ripe” communism, and neomodern nation-building—the book captures crucial legacies that deepen social divisions and normalize the constructed group images. The analysis of the state-managed Roma identity project in the brief korenizatsija program for the integration of non-Russian nationalities into the Soviet civil service in the 1920s is particularly revealing, while the critique of contemporary endeavors is a valuable resource for policy makers and civic activists alike. The top-down view is complemented with the bottom-up attention to everyday Roma voices. Personal stories reveal how identities operate in daily life, as Dunajeva brings out hidden narratives and subaltern discourse. Her handling of fieldwork and self-reflexivity is a model of sensitive research with vulnerable groups.

Data sources such as simulations, sensor networks across many application domains generate large volumes of time-series data which exhibit characteristics that evolve over time. Visual data analysis methods can help us in exploring and understanding the underlying patterns present in time-series data but, due to their ever-increasing size, the visual data analysis process can become complex. Large data sets can be handled using data abstraction techniques by transforming the raw data into a simpler format while, at the same time, preserving significant features that are important for the user. When dealing with time-series data, abstraction techniques should also take into account the underlying temporal characteristics. This thesis focuses on different data abstraction and pattern identification methods particularly in the cases of large 1D time-series and 2D spatio-temporal time-series data which exhibit spatiotemporal discontinuity. Based on the dimensionality and characteristics of the data, this thesis proposes a variety of efficient data-adaptive and user-controlled data abstraction methods that transform the raw data into a symbol sequence. The transformation of raw time-series into a symbol sequence can act as input to different sequence analysis methods from data mining and machine learning communities to identify interesting patterns of user behavior. In the case of very long duration 1D time-series, locally adaptive and user-controlled data approximation methods were presented to simplify the data, while at the same time retaining the perceptually important features. The simplified data were converted into a symbol sequence and a sketch-based pattern identification was then used to identify patterns in the symbolic data using regular expression based pattern matching. The method was applied to financial time-series and patterns such as head-and-shoulders, double and triple-top patterns were identified using hand drawn sketches in an interactive manner. Through data smoothing, the data approximation step also enables visualization of inherent patterns in the time-series representation while at the same time retaining perceptually important points. Very long duration 2D spatio-temporal eye tracking data sets that exhibit spatio-temporal discontinuity was transformed into symbolic data using scalable clustering and hierarchical cluster merging processes, each of which can be parallelized. The raw data is transformed into a symbol sequence with each symbol representing a region of interest in the eye gaze data. The identified regions of interest can also be displayed in a Space-Time Cube (STC) that captures both the temporal and contextual information. Through interactive filtering, zooming and geometric transformation, the STC representation along with linked views enables interactive data exploration. Using different sequence analysis methods, the symbol sequences are analyzed further to identify temporal patterns in the data set. Data collected from air traffic control officers from the domain of Air traffic control were used as application examples to demonstrate the results.

International Organization in Time investigates why reformers often pledge to unify international organizations (IOs), but end up fragmenting them instead. The book reconstructs the institutional history of the World Health Organization (WHO) since its creation in 1946. It theorizes the fragmentation trap, which is both a cause and a consequence of reform failure in the WHO. A comparison between the International Labour Organization (ILO) and the United Nations Educational, Scientific and Cultural Organization (UNESCO) illustrates the relevance of path dependence and fragmentation across the United Nations (UN) system. As the UN approaches its 70th anniversary, this book helps to understand the path dependent dynamics that reformers encounter in international organizations.

When considering inequality, one goal for educators is to enhance critical engagement to allow learners an opportunity to participate in an inquiry process that advances democracy. Service?learning pedagogy offers an opportunity to advance engaged?learning opportunities within higher education. This is particularly important given the power dynamics that are endemic within conversations about education, including the conversations around the Common Core, charter schools, and the privatization of education. Critical inquiry is central to the ethos of service?learning pedagogy, a pedagogy that is built upon community partner participation and active reflection. Within higher education, service?learning offers an important opportunity to enhance practice within the community, allowing students to engage stakeholders and youth which is particularly important given the dramatic inequalities that are endemic in today’s society.

This book is about urban terror - its meaning, its ramifications, and its impact on city life. Written by a well-known expert in the field, "Cities in a Time of Terror" draws on data from more than a thousand cities across the globe and traces the evolution of urban terrorism between 1968 and 2006. It explains what kinds of cities have become prime targets, why terrorism has become increasingly lethal, and how its inspiration has changed from secular to religious. The author describes urban terrorism as an attempt to use the city's own strength against itself, forcing it to implode, and delineates three basic logics of terrorist choices for targeting cities. The book also includes a discussion of local resilience - the city's capacity to bounce back from attack - and suggests how that can be sustained. Examples from New York, London, Jerusalem, Istanbul, Moscow, Paris, and Madrid illustrate the book's central themes.

In recent years numerous films, television series, comic books, graphic novels and video games have featured time travel narratives, with characters jumping backward, forward and laterally through time. No rules govern time travel in these stories. Some characters move by machine, some by magic, others by unexplained means. Sometime travelers can alter the timeline, while others are prevented from causing temporal aberrations. The fluid forms of imagined time travel have fascinated audiences and prompted debate since at least the 19th century. What is behind our fascination with time travel? What does it mean to be out of one's own era? How do different media tell these stories and what does this reveal about the media's relationship to time? This collection of new essays--the first to address time travel across a range of media--answers these questions by locating time travel narratives within their cultural, historical and philosophical contexts. Texts discussed include Doctor Who, The Terminator, The Georgian House, Save the Date, Back to the Future, Inception, Source Code and others.