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1000 tulosta hakusanalla Benoit Mattei
Following Art Spiegelman's declaration that 'the future of comics is in the past,' this book considers comics memory in the contemporary North American graphic novel. Cartoonists such as Chris Ware, Seth, Charles Burns, Daniel Clowes, and others have not only produced some of the most important graphic novels, they have also turned to the history of comics as a common visual heritage to pass on to new readers. This book is a full-length study of contemporary cartoonists when they are at work as historians: it offers a detailed description of how they draw from the archives of comics history, examining the different gestures of collecting, curating, reprinting, forging, swiping, and undrawing that give shape to their engagement with the past. In recognizing these different acts of transmission, this book argues for a material and vernacular history of how comics are remembered, shared, and recirculated over time.
One of the key challenges in linguistics is to account for the link between linguistic knowledge and our use of language in a way that is both descriptively accurate and cognitively plausible. This pioneering book addresses these challenges by combining insights from Construction Grammar and Relevance Theory, two influential approaches which until now have been considered incompatible. After a clear and detailed presentation of both theories, the author demonstrates that their integration is possible, and explains why this integration is necessary, in order to understand exactly how meaning comes about. A new theoretical model is offered that provides ground-breaking insights into the semantics-pragmatic interface, and addresses a variety of topics including the nature of lexical and grammatical concepts, procedural meaning, coercion and idiom processing. This title is part of the Flip it Open Programme and may also be available Open Access. Check our website Cambridge Core for details.
One of the key challenges in linguistics is to account for the link between linguistic knowledge and our use of language in a way that is both descriptively accurate and cognitively plausible. This pioneering book addresses these challenges by combining insights from Construction Grammar and Relevance Theory, two influential approaches which until now have been considered incompatible. After a clear and detailed presentation of both theories, the author demonstrates that their integration is possible, and explains why this integration is necessary, in order to understand exactly how meaning comes about. A new theoretical model is offered that provides ground-breaking insights into the semantics-pragmatic interface, and addresses a variety of topics including the nature of lexical and grammatical concepts, procedural meaning, coercion and idiom processing. This title is part of the Flip it Open Programme and may also be available Open Access. Check our website Cambridge Core for details.
The idea that the world needs to transition to a more sustainable future is omnipresent in environmental politics and policy today. Focusing on the energy transition as a solution to the ecological crisis represents a shift in environmental political thought and action. This Element employs a political theory approach and draws on empirical developments to explore this shift by probing the temporal, affective, and technological dimensions of transition politics. Mobilising the framework of ecopolitical imaginaries, it maps five transition imaginaries and sketches a counter-hegemonic, decolonial transition that integrates decolonial approaches to knowledge and technology. Transition Imaginaries offers a nuanced exploration of the ways in which transition politics unfolds, and a novel argument on the importance of attending to the coloniality of transition politics. A transition to just sustainable futures requires the mobilisation of post-extractivist visions, knowledges, and technologies. This title is also available as Open Access on Cambridge Core.
The idea that the world needs to transition to a more sustainable future is omnipresent in environmental politics and policy today. Focusing on the energy transition as a solution to the ecological crisis represents a shift in environmental political thought and action. This Element employs a political theory approach and draws on empirical developments to explore this shift by probing the temporal, affective, and technological dimensions of transition politics. Mobilising the framework of ecopolitical imaginaries, it maps five transition imaginaries and sketches a counter-hegemonic, decolonial transition that integrates decolonial approaches to knowledge and technology. Transition Imaginaries offers a nuanced exploration of the ways in which transition politics unfolds, and a novel argument on the importance of attending to the coloniality of transition politics. A transition to just sustainable futures requires the mobilisation of post-extractivist visions, knowledges, and technologies. This title is also available as Open Access on Cambridge Core.
This Element offers a primer for the study of meaning in a Construction Grammar approach. It reviews the main principles of meaning shared across constructionist frameworks, including its ubiquity in grammatical structure, its usage-based formation, and its nature as the output of cognitive representations. It also reviews the importance given to meaning in construction-based explanations of sentence composition, innovative language use, and language change. Paradoxically, the Element shows that there is no systematic framework delineating the rich structure of constructional meaning, which has led to theoretical disagreements and inconsistencies. It therefore proposes an operational model of meaning for practitioners of Construction Grammar. It details the characteristics of a complex interface of semantic, pragmatic, and social meaning, and shows how this framework sheds light on recent theoretical issues. The Element concludes by considering ways in which this framework can be used for future descriptive and theoretical research questions.
This Element offers a primer for the study of meaning in a Construction Grammar approach. It reviews the main principles of meaning shared across constructionist frameworks, including its ubiquity in grammatical structure, its usage-based formation, and its nature as the output of cognitive representations. It also reviews the importance given to meaning in construction-based explanations of sentence composition, innovative language use, and language change. Paradoxically, the Element shows that there is no systematic framework delineating the rich structure of constructional meaning, which has led to theoretical disagreements and inconsistencies. It therefore proposes an operational model of meaning for practitioners of Construction Grammar. It details the characteristics of a complex interface of semantic, pragmatic, and social meaning, and shows how this framework sheds light on recent theoretical issues. The Element concludes by considering ways in which this framework can be used for future descriptive and theoretical research questions.
This work has been selected by scholars as being culturally important and is part of the knowledge base of civilization as we know it.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. To ensure a quality reading experience, this work has been proofread and republished using a format that seamlessly blends the original graphical elements with text in an easy-to-read typeface.We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
This work has been selected by scholars as being culturally important and is part of the knowledge base of civilization as we know it.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. To ensure a quality reading experience, this work has been proofread and republished using a format that seamlessly blends the original graphical elements with text in an easy-to-read typeface.We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Atlas of Canada Project: a Preliminary Survey
Benoît 1904-1979 Brouillette
Hassell Street Press
2021
nidottu
This work has been selected by scholars as being culturally important and is part of the knowledge base of civilization as we know it.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. To ensure a quality reading experience, this work has been proofread and republished using a format that seamlessly blends the original graphical elements with text in an easy-to-read typeface.We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Mathematical Engineering of Deep Learning
Benoit Liquet; Sarat Moka; Yoni Nazarathy
TAYLOR FRANCIS LTD
2024
nidottu
Mathematical Engineering of Deep Learning provides a complete and concise overview of deep learning using the language of mathematics. The book provides a self-contained background on machine learning and optimization algorithms and progresses through the key ideas of deep learning. These ideas and architectures include deep neural networks, convolutional models, recurrent models, long/short-term memory, the attention mechanism, transformers, variational auto-encoders, diffusion models, generative adversarial networks, reinforcement learning, and graph neural networks. Concepts are presented using simple mathematical equations together with a concise description of relevant tricks of the trade. The content is the foundation for state-of-the-art artificial intelligence applications, involving images, sound, large language models, and other domains. The focus is on the basic mathematical description of algorithms and methods and does not require computer programming. The presentation is also agnostic to neuroscientific relationships, historical perspectives, and theoretical research. The benefit of such a concise approach is that a mathematically equipped reader can quickly grasp the essence of deep learning.Key Features:A perfect summary of deep learning not tied to any computer language, or computational framework.An ideal handbook of deep learning for readers that feel comfortable with mathematical notation.An up-to-date description of the most influential deep learning ideas that have made an impact on vision, sound, natural language understanding, and scientific domains.The exposition is not tied to the historical development of the field or to neuroscience, allowing the reader to quickly grasp the essentials.Deep learning is easily described through the language of mathematics at a level accessible to many professionals. Readers from fields such as engineering, statistics, physics, pure mathematics, econometrics, operations research, quantitative management, quantitative biology, applied machine learning, or applied deep learning will quickly gain insights into the key mathematical engineering components of the field.
Mathematical Engineering of Deep Learning
Benoit Liquet; Sarat Moka; Yoni Nazarathy
TAYLOR FRANCIS LTD
2024
sidottu
Mathematical Engineering of Deep Learning provides a complete and concise overview of deep learning using the language of mathematics. The book provides a self-contained background on machine learning and optimization algorithms and progresses through the key ideas of deep learning. These ideas and architectures include deep neural networks, convolutional models, recurrent models, long/short-term memory, the attention mechanism, transformers, variational auto-encoders, diffusion models, generative adversarial networks, reinforcement learning, and graph neural networks. Concepts are presented using simple mathematical equations together with a concise description of relevant tricks of the trade. The content is the foundation for state-of-the-art artificial intelligence applications, involving images, sound, large language models, and other domains. The focus is on the basic mathematical description of algorithms and methods and does not require computer programming. The presentation is also agnostic to neuroscientific relationships, historical perspectives, and theoretical research. The benefit of such a concise approach is that a mathematically equipped reader can quickly grasp the essence of deep learning.Key Features:A perfect summary of deep learning not tied to any computer language, or computational framework.An ideal handbook of deep learning for readers that feel comfortable with mathematical notation.An up-to-date description of the most influential deep learning ideas that have made an impact on vision, sound, natural language understanding, and scientific domains.The exposition is not tied to the historical development of the field or to neuroscience, allowing the reader to quickly grasp the essentials.Deep learning is easily described through the language of mathematics at a level accessible to many professionals. Readers from fields such as engineering, statistics, physics, pure mathematics, econometrics, operations research, quantitative management, quantitative biology, applied machine learning, or applied deep learning will quickly gain insights into the key mathematical engineering components of the field.
This book opens a novel perspective on comics and literature interactions. It claims that the two artistic media have always maintained a mutual emulation, for as long as they have coexisted in media culture. To demonstrate this, the present research does not focus on literary adaptations in comics form but rather on a literary corpus that remains virtually unexplored: comics-related novels. The purpose of this volume is to inventory French comics-related novels and to study them. Within the limits of the French-speaking world, this book pieces together a literary history of bande dessinée through its novels, from the nineteenth to twenty-first centuries. Although the comic strip – including the aptly named "graphic novel" – has sometimes been regarded as the disciple of an unsurpassable literary model, do these under-studied adaptations in novel form not rather indicate a mutual relationship, or even an emulation, between the two media?
This book opens a novel perspective on comics and literature interactions. It claims that the two artistic media have always maintained a mutual emulation, for as long as they have coexisted in media culture. To demonstrate this, the present research does not focus on literary adaptations in comics form but rather on a literary corpus that remains virtually unexplored: comics-related novels. The purpose of this volume is to inventory French comics-related novels and to study them. Within the limits of the French-speaking world, this book pieces together a literary history of bande dessinée through its novels, from the nineteenth to twenty-first centuries. Although the comic strip – including the aptly named "graphic novel" – has sometimes been regarded as the disciple of an unsurpassable literary model, do these under-studied adaptations in novel form not rather indicate a mutual relationship, or even an emulation, between the two media?