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This book directly links the notion of the commons with different design praxes, and explores their social, cultural, and ecological ramifications. It draws out material conditions in four areas of design interest: social design, commons and culture, ecology and transdisciplinary design. As a collection of positions, the diversity of arguments advances the understanding of the commons as both concepts and modes of thinking, and their material translation when contextualised in the domain of design questions. In other words, it moves abstract social science concepts towards concrete design debates. This text appeals to students, researchers and practitioners working on design in architecture, architecture theory, urbanism, and ecology.
Available Open Access under CC-BY-NC licence. Reporting on the innovative, transdisciplinary research on sustainable urbanisation undertaken by Mistra Urban Futures, a highly influential research centre based in Sweden (2010-19), this book builds on the Policy Press title Rethinking Sustainable Cities to make a significant contribution to evolving theory about comparative urban research. Highlighting important methodological experiences from across a variety of diverse contexts in Africa and Europe, this book surveys key experiences and summarises lessons learned from the Mistra Urban Futures' global research platforms. It demonstrates best practice for developing and deploying different forms of transdisciplinary co-production, covering topics including neighbourhood transformation and housing justice, sustainable urban and transport development, urban food security and cultural heritage.
The COVID-19 pandemic caused the largest systemic disruption in history. The pandemic was a complex phenomenon that impacted economic, political, and education systems. The pandemic had widespread business impacts, having forced many businesses to close, and the world is still impacted by the effects of supply chain disruptions. The pandemic also impacted political systems with disputes over mask mandates, lockdowns, and vaccine distribution. The COVID-19 pandemic further caused the most extensive education system disruption in history. The pandemic has highlighted the world’s complex interdependent structures, and it will require a multidisciplinary systems thinking approach for post-pandemic recovery and future pandemic prevention. Reimagining Systems Thinking in a Post-Pandemic World examines the role of systems thinking in a post-pandemic world. It identifies effective models of systems thinking and destems design and generates continuous knowledge building on systems thinking by addressing a multitude of industries and service communities. This book provides value in understanding the complexities of an interconnected world and in the exploration of effective approaches to systems thinking and design. Covering topics such as blended learning, local governments, and systems thinking, this premier reference source is an excellent resource for practitioners, policymakers, healthcare providers, business leaders and managers, educators of both K-12 and higher education, pre-service teachers, administrators and faculty, teacher educators, sociologists, librarians, researchers, and academicians.
Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve. Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering Learn how transformers can be used for cross-lingual transfer learning Apply transformers in real-world scenarios where labeled data is scarce Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments
From one of the most respected names in business and leadership, a rare look at the specifics of how great leaders achieve "common purpose" and success within their organizations. What is common purpose? It is that rare, almost-palpable experience that happens when a leader coalesces a group, team or community into a creative, dynamic, brave and nearly invincible we. It happens the moment the organization's values, tools, objectives and hopes are internalized in a way that enables people to work tirelessly toward a goal. Common purpose is rarely achieved. But Kurtzman has observed that when a leader is able to bring it about, the results are outsized, measurable and inspiring. Based on Kurtzman's all-new interviews with more than 50 leaders, including Ron Sargent, Ilene Lang, Micky Arison, Simon Cooper, Joel Klein, Janet Field, Steve Wynn, Shivan Subramaniam, Michael Dell, Richard Boyatzis, Tom Kelley, Michael Milken, and Warren Bennis Contains research on leadership Kurtzman has conducted during his years at The New York Times, the Harvard Business Review, Booz & Company, as well as with PricewaterhouseCoopers, Mercer, and Korn/Ferry Based on all new interviews with some of the most dynamic, successful, and enduring leaders, Common Purpose sheds new light on the meaning of leadership, the crucial qualities of leaders, and most importantly, how to lead.
This book provides an overview of the emerging field of in situ visualization, i.e. visualizing simulation data as it is generated. In situ visualization is a processing paradigm in response to recent trends in the development of high-performance computers. It has great promise in its ability to access increased temporal resolution and leverage extensive computational power. However, the paradigm also is widely viewed as limiting when it comes to exploration-oriented use cases. Furthermore, it will require visualization systems to become increasingly complex and constrained in usage. As research efforts on in situ visualization are growing, the state of the art and best practices are rapidly maturing. Specifically, this book contains chapters that reflect state-of-the-art research results and best practices in the area of in situ visualization. Our target audience are researchers and practitioners from the areas of mathematics computational science, high-performance computing, and computer science that work on or with in situ techniques, or desire to do so in future.
As tech products become more prevalent today, the demand for machine learning professionals continues to grow. But the responsibilities and skill sets required of ML professionals still vary drastically from company to company, making the interview process difficult to predict. In this guide, data science leader Susan Shu Chang shows you how to tackle the ML hiring process. Having served as principal data scientist in several companies, Chang has considerable experience as both ML interviewer and interviewee. She'll take you through the highly selective recruitment process by sharing hard-won lessons she learned along the way. You'll quickly understand how to successfully navigate your way through typical ML interviews. This guide shows you how to: Explore various machine learning roles, including ML engineer, applied scientist, data scientist, and other positions Assess your interests and skills before deciding which ML role(s) to pursue Evaluate your current skills and close any gaps that may prevent you from succeeding in the interview process Acquire the skill set necessary for each machine learning role Ace ML interview topics, including coding assessments, statistics and machine learning theory, and behavioral questions Prepare for interviews in statistics and machine learning theory by studying common interview questions
This book constitutes the refereed proceedings of the 18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017, held in Guilin, China, in October/November 2017. The 65 full papers presented were carefully reviewed and selected from 110 submissions. These papers provided a sample of latest research outcomes in data engineering and automated learning, from methodologies, frameworks and techniques to applications. In addition to various topics such as evolutionary algorithms, deep learning neural networks, probabilistic modelling, particle swarm intelligence, big data analytics, and applications in image recognition, regression, classification, clustering, medical and biological modelling and prediction, text processing and social media analysis.
Upscaling, Training, Commoning features a lively account of the 10-year exploration by STEALTH.unlimited into what it means to engage with the glimpses of a world that has become conceivable in the cracks of the Global Financial Crisis (2008). Ultimately, this book is about devising methods for imagining and enacting other worlds. The perspective for this is situated between spatial practice and emerging commons, between an already plundered future and the politics of possibility, between reflection and future fiction. The book traces the moves STEALTH.unlimited (Ana Dzokić and Marc Neelen) have taken to reframe practice, to rethink economy and create urban commons. Its six-part sequence moves from critique to commitment, but along the way reveals an unsettling systemic rejection - by current politics and economy - of the urgency to upscale and 'train' for a world beyond the capitalist urban condition. The book leads us thus to a crucial question: how to construct our alternatives on the ruins of the broken system? By including personal accounts from emerging alternative voices from literature, economy and urban politics from across Europe, it expands this quest beyond the field of architecture and art.
The two-volume set LNCS 10132 and 10133 constitutes the thoroughly refereed proceedings of the 23rd International Conference on Multimedia Modeling, MMM 2017, held in Reykjavik, Iceland, in January 2017. Of the 149 full papers submitted, 36 were selected for oral presentation and 33 for poster presentation; of the 34 special session papers submitted, 24 were selected for oral presentation and 2 for poster presentation; in addition, 5 demonstrations were accepted from 8 submissions, and all 7 submissions to VBS 2017. All papers presented were carefully reviewed and selected from 198 submissions. MMM is a leading international conference for researchers and industry practitioners for sharing new ideas, original research results and practical development experiences from all MMM related areas, broadly falling into three categories: multimedia content analysis; multimedia signal processing and communications; and multimedia applications and services.