Michael Kane – författare

Working memory--the ability to keep important information in mind while comprehending, thinking, and acting--varies considerably from person to person and changes dramatically during each person''s life. Understanding such individual and developmental differences is crucial because working memory is a major contributor to general intellectual functioning. This volume offers a state-of-the-art, integrative, and comprehensive approach to understanding variation in working memory by presenting explicit, detailed comparisons of the leading theories. It incorporates views from the different research groups that operate on each side of the Atlantic, and covers working-memory research on a wide variety of populations, including healthy adults, children with and without learning difficulties, older adults, and adults and children with neurological disorders. A particular strength of this volume is that each research group explicitly addresses the same set of theoretical questions, from the perspective of both their own theoretical and experimental work and from the perspective of relevant alternative approaches. Through these questions, each research group considers their overarching theory of working memory, specifies the critical sources of working memory variation according to their theory, reflects on the compatibility of their approach with other approaches, and assesses their contribution to general working memory theory. This shared focus across chapters unifies the volume and highlights the similarities and differences among the various theories. Each chapter includes both a summary of research positions and a detailed discussion of each position. Variation in Working Memory achieves coherence across its chapters, while presenting the entire range of current theoretical and experimental approaches to variation in working memory.

Working memory - the ability to keep important information in mind while comprehending, thinking, and acting - varies considerably from person to person and changes dramatically during each person's life. Understanding such individual and developmental differences is crucial because working memory is a major contributor to general intellectual functioning. This volume offers a state-of-the-art, integrative, and comprehensive approach to understanding variation in working memory by presenting explicit, detailed comparisons of the leading theories. It incorporates views from the different research groups that operate on each side of the Atlantic, and covers working-memory research on a wide variety of populations, including healthy adults, children with and without learning difficulties, older adults, and adults and children with neurological disorders. A particular strength of this volume is that each research group explicitly addresses the same set of theoretical questions, from the perspective of both their own theoretical and experimental work and from the perspective of relevant alternative approaches. Through these questions, each research group considers their overarching theory of working memory, specifies the critical sources of working memory variation according to their theory, reflects on the compatibility of their approach with other approaches, and assesses their contribution to general working memory theory. This shared focus across chapters unifies the volume and highlights the similarities and differences among the various theories. Each chapter includes both a summary of research positions and a detailed discussion of each position. Variation in Working Memory achieves coherence across its chapters, while presenting the entire range of current theoretical and experimental approaches to variation in working memory.

Working memory--the ability to keep important information in mind while comprehending, thinking, and acting--varies considerably from person to person and changes dramatically during each person''s life. Understanding such individual and developmental differences is crucial because working memory is a major contributor to general intellectual functioning. This volume offers a state-of-the-art, integrative, and comprehensive approach to understanding variation in working memory by presenting explicit, detailed comparisons of the leading theories. It incorporates views from the different research groups that operate on each side of the Atlantic, and covers working-memory research on a wide variety of populations, including healthy adults, children with and without learning difficulties, older adults, and adults and children with neurological disorders. A particular strength of this volume is that each research group explicitly addresses the same set of theoretical questions, from the perspective of both their own theoretical and experimental work and from the perspective of relevant alternative approaches. Through these questions, each research group considers their overarching theory of working memory, specifies the critical sources of working memory variation according to their theory, reflects on the compatibility of their approach with other approaches, and assesses their contribution to general working memory theory. This shared focus across chapters unifies the volume and highlights the similarities and differences among the various theories. Each chapter includes both a summary of research positions and a detailed discussion of each position. Variation in Working Memory achieves coherence across its chapters, while presenting the entire range of current theoretical and experimental approaches to variation in working memory.

An examination of some of the canonical works of modern literature in English and German with regard to masculinity, relations between men, national identity and patriarchy. These were major preoccupations of male writers as they came to terms with or reacted against the decline of patriarchal authority. The book identifies five leitmotifs which serve to characterize the period between 1880 and 1930: the "double", the "other" (narcissus and Salome), the nationalization of Narcissus, Kampf or male bondage, and after patriarchy. Again and again one sees how men attempted to define themselves against what they imagined as "femininity", not merely outside but also within their selves, and further how men sought to overcome or find a socially acceptable expression for their narcissistic, homosexual and even sadomasochist libido.

Handbook of Big Data provides a state-of-the-art overview of the analysis of large-scale datasets. Featuring contributions from well-known experts in statistics and computer science, this handbook presents a carefully curated collection of techniques from both industry and academia. Thus, the text instills a working understanding of key statistical and computing ideas that can be readily applied in research and practice.Offering balanced coverage of methodology, theory, and applications, this handbook: Describes modern, scalable approaches for analyzing increasingly large datasetsDefines the underlying concepts of the available analytical tools and techniquesDetails intercommunity advances in computational statistics and machine learning Handbook of Big Data also identifies areas in need of further development, encouraging greater communication and collaboration between researchers in big data sub-specialties such as genomics, computational biology, and finance.

A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset.The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models.Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015.Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010.Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.

Handbook of Big Data provides a state-of-the-art overview of the analysis of large-scale datasets. Featuring contributions from well-known experts in statistics and computer science, this handbook presents a carefully curated collection of techniques from both industry and academia. Thus, the text instills a working understanding of key statistical

A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset.The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models.

A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset.The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models.

A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset.The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models.

A year inside the fierce rivalries and big business of competitive videogaming A technology-fueled spin on a classic sports tale, Game Boys profiles "cyber-athletes" who compete for dominance in the professional gaming circuit, a world populated with rivalries, big egos, corporate sponsorships, and large cash prizes. Michael Kane''s pioneering account of the lifestyle and business of gaming takes readers to the heart of "e-sports," what many consider the successor in sports entertainment to the X-games and competitive poker. Following the top teams-3D and CompLexity, a rivalry as bitter as the Yankees versus the Red Sox-Kane profiles the star players as they cheat, strategize, sign with rival teams, and get berated by sideline-pacing coaches. Are gamers really the athletes of tomorrow? They act like they are. A lively tour of the quirks and dramas of a subculture on the cusp of big things, Game Boys is a tale of sports glory and a glimpse into the lucrative business of gaming.

An examination of some of the canonical works of modern literature in English and German with regard to masculinity, relations between men, national identity and patriarchy. These were major preoccupations of male writers as they came to terms with or reacted against the decline of patriarchal authority. The book identifies five leitmotifs which serve to characterize the period between 1880 and 1930: the "double", the "other" (narcissus and Salome), the nationalization of Narcissus, Kampf or male bondage, and after patriarchy. Again and again one sees how men attempted to define themselves against what they imagined as "femininity", not merely outside but also within their selves, and further how men sought to overcome or find a socially acceptable expression for their narcissistic, homosexual and even sadomasochist libido.

Handbook of Big Data provides a state-of-the-art overview of the analysis of large-scale datasets. Featuring contributions from well-known experts in statistics and computer science, this handbook presents a carefully curated collection of techniques from both industry and academia. Thus, the text instills a working understanding of key statistical and computing ideas that can be readily applied in research and practice.Offering balanced coverage of methodology, theory, and applications, this handbook: Describes modern, scalable approaches for analyzing increasingly large datasetsDefines the underlying concepts of the available analytical tools and techniquesDetails intercommunity advances in computational statistics and machine learning Handbook of Big Data also identifies areas in need of further development, encouraging greater communication and collaboration between researchers in big data sub-specialties such as genomics, computational biology, and finance.

Handbook of Big Data provides a state-of-the-art overview of the analysis of large-scale datasets. Featuring contributions from well-known experts in statistics and computer science, this handbook presents a carefully curated collection of techniques from both industry and academia. Thus, the text instills a working understanding of key statistical

Postmodern Time and Space in Fiction and Theory seeks to place the contemporary transformation of notions of space and time, often attributed to the technologies we use, in the context of the ongoing transformations of modernity. Bringing together examples of modern and contemporary fiction (from Defoe to DeLillo, Frankenstein to Finnegans Wake) and theoretical discussions of the modern and the post-modern, the author explores the legacy of modern transformations of space and time under five headings: “The Space of Nature”; “The Space of the City”; “Postmodern or Most Modern Time”; “The Time and Space of the Work of Art in the Age of Digital Reproduction”; and “Travel: from Modernity to…?”. These five essays re-examine the meanings of modernity and its aftermath in relation to the spaces and times of the natural, the urban and the media environment.