As entrepreneurship education grows across disciplines and permeates through various areas of university programs, this timely book offers an interdisciplinary, comparative and global perspective on best practices and new insights for the field. Through the theoretical lens of collaborative partnerships, it examines innovative practices of entrepreneurship education and advances understanding of the discipline. Exploring and showcasing how global collaboration can foster entrepreneurship education, international contributors share their experiences as educators, scholars and thought-leaders involved in the Babson Collaborative. Chapters illustrate the challenges faced by educators and creative methods for tackling them, offering useful insights from a range of disciplinary perspectives. Highlighting the significance of the field to higher education environments, this book encourages active participation in entrepreneurial practice and collaboration between stakeholders and disciplines to ensure high-quality education in a variety of settings. This insightful book is a rousing and inspiring view of entrepreneurship education for scholars and academic entrepreneurs who are working to build robust education ecosystems in the field.

International talent management has become a critically important topic for scholarly discussion, in policy debates, and among the business community. Despite this, however, research into talent management tends to lack theoretical underpinnings, especially from an international, multidisciplinary and comparative perspective. This Research Handbook fills this gap, bringing together a range of leading researchers, scholars, and thinkers to debate and advance the conceptualization and understanding of this multifaceted subject. With chapters covering key topics within multiple domains of management and organization studies, the Research Handbook of International Talent Management explores the topic in innovative entrepreneurial enterprises to international businesses. It also examines how talent management relates to sustainability and public management, providing in depth coverage of the field for an interdisciplinary approach to what is one of the grand contemporary challenges facing the global economy today. This Research Handbook will be a vital resource for students of human resources management, business studies and public management policy, as well as for researchers with an interest in talent management, international management, and entrepreneurship and innovation.

As entrepreneurship education grows across disciplines and permeates through various areas of university programs, this timely book offers an interdisciplinary, comparative and global perspective on best practices and new insights for the field. Through the theoretical lens of collaborative partnerships, it examines innovative practices of entrepreneurship education and advances understanding of the discipline. Exploring and showcasing how global collaboration can foster entrepreneurship education, international contributors share their experiences as educators, scholars and thought-leaders involved in the Babson Collaborative. Chapters illustrate the challenges faced by educators and creative methods for tackling them, offering useful insights from a range of disciplinary perspectives. Highlighting the significance of the field to higher education environments, this book encourages active participation in entrepreneurial practice and collaboration between stakeholders and disciplines to ensure high-quality education in a variety of settings. This insightful book is a rousing and inspiring view of entrepreneurship education for scholars and academic entrepreneurs who are working to build robust education ecosystems in the field.

Talent has become the most important resource for organizations across a wide range of sectors throughout the world including business, non-profit, and government. These organizations are now engaged in an increasingly fierce competition to acquire the best talent as they seek to gain the upper hand in today's fast changing environment. By combining the body of knowledge on entrepreneurship and talent management from a global perspective, this book provides a synthesized understanding of entrepreneurial mobility and talent management in the entrepreneurship and innovation ecosystem.The expert contributors combine empirical evidence and case studies to provide a nuanced understanding of global talent management from an international comparative perspective. The topics discussed include China's return migration and its impact on Chinese development, local engagement and transformation of Chinese communities in England, and reverse migration from the US to China. Furthermore, from a comparative perspective, contributors examine global talent and entrepreneurial mobility in the contexts of Silicon Valley, European university spin-off practices and entrepreneurial ecosystems in France, Italy, and South Korea, respectively. Scholars and students in entrepreneurship and talent management will find the scope for future research useful in their work. Entrepreneurs, managers, and policymakers will benefit from the examination of global perspectives and different national contexts. Contributors include: D. Assimakopoulos, D. Baglieri, Z. Chen, M.C. Cinici, L. Daily, T. Duan, T. Fang, W. Hou, A. Isaak, R. Isaak, K.W. Jensen, K-.C. Kim, X. Liu, D. Mavridis, M.L. Miao, A. Moore, Y.M. Myint, T. Petrin, F. Qin, S. Rezaei, T. Schott, F. Sussan, M. Tsouri, H. Wang, B. Wu, W. Zhang, J. Zybura

International talent management has become a critically important topic for scholarly discussion, in policy debates, and among the business community. Despite this, however, research into talent management tends to lack theoretical underpinnings, especially from an international, multidisciplinary and comparative perspective. This Research Handbook fills this gap, bringing together a range of leading researchers, scholars, and thinkers to debate and advance the conceptualization and understanding of this multifaceted subject. With chapters covering key topics within multiple domains of management and organization studies, the Research Handbook of International Talent Management explores the topic in innovative entrepreneurial enterprises to international businesses. It also examines how talent management relates to sustainability and public management, providing in depth coverage of the field for an interdisciplinary approach to what is one of the grand contemporary challenges facing the global economy today. This Research Handbook will be a vital resource for students of human resources management, business studies and public management policy, as well as for researchers with an interest in talent management, international management, and entrepreneurship and innovation.

Tensors for Data Processing: Theory, Methods and Applications presents both classical and state-of-the-art methods on tensor computation for data processing, covering computation theories, processing methods, computing and engineering applications, with an emphasis on techniques for data processing. This reference is ideal for students, researchers and industry developers who want to understand and use tensor-based data processing theories and methods. As a higher-order generalization of a matrix, tensor-based processing can avoid multi-linear data structure loss that occurs in classical matrix-based data processing methods. This move from matrix to tensors is beneficial for many diverse application areas, including signal processing, computer science, acoustics, neuroscience, communication, medical engineering, seismology, psychometric, chemometrics, biometric, quantum physics and quantum chemistry.

Regression analysis is a key area of interest in the field of data analysis and machine learning which is devoted to exploring the dependencies between variables, often using vectors. The emergence of high dimensional data in technologies such as neuroimaging, computer vision, climatology and social networks, has brought challenges to traditional data representation methods. Tensors, as high dimensional extensions of vectors, are considered as natural representations of high dimensional data. In this book, the authors provide a systematic study and analysis of tensor-based regression models and their applications in recent years. It groups and illustrates the existing tensor-based regression methods and covers the basics, core ideas, and theoretical characteristics of most tensor-based regression methods. In addition, readers can learn how to use existing tensor-based regression methods to solve specific regression tasks with multiway data, what datasets can be selected, and what software packages are available to start related work as soon as possible. Tensor Regression is the first thorough overview of the fundamentals, motivations, popular algorithms, strategies for efficient implementation, related applications, available datasets, and software resources for tensor-based regression analysis. It is essential reading for all students, researchers and practitioners of working on high dimensional data.

Tensor is a natural representation for multi-dimensional data, and tensor computation can avoid possible multi-linear data structure loss in classical matrix computation-based data analysis. This book is intended to provide non-specialists an overall understanding of tensor computation and its applications in data analysis, and benefits researchers, engineers, and students with theoretical, computational, technical and experimental details. It presents a systematic and up-to-date overview of tensor decompositions from the engineer's point of view, and comprehensive coverage of tensor computation based data analysis techniques. In addition, some practical examples in machine learning, signal processing, data mining, computer vision, remote sensing, and biomedical engineering are also presented for easy understanding and implementation. These data analysis techniques may be further applied in other applications on neuroscience, communication, psychometrics, chemometrics, biometrics, quantum physics, quantum chemistry, etc. The discussion begins with basic coverage of notations, preliminary operations in tensor computations, main tensor decompositions and their properties. Based on them, a series of tensor-based data analysis techniques are presented as the tensor extensions of their classical matrix counterparts, including tensor dictionary learning, low rank tensor recovery, tensor completion, coupled tensor analysis, robust principal tensor component analysis, tensor regression, logistical tensor regression, support tensor machine, multilinear discriminate analysis, tensor subspace clustering, tensor-based deep learning, tensor graphical model and tensor sketch. The discussion also includes a number of typical applications with experimental results, such as image reconstruction, image enhancement, data fusion, signal recovery, recommendation system, knowledge graph acquisition, traffic flow prediction, link prediction, environmental prediction, weather forecasting, background extraction, human pose estimation, cognitive state classification from fMRI, infrared small target detection, heterogeneous information networks clustering, multi-view image clustering, and deep neural network compression.

Tensor is a natural representation for multi-dimensional data, and tensor computation can avoid possible multi-linear data structure loss in classical matrix computation-based data analysis. This book is intended to provide non-specialists an overall understanding of tensor computation and its applications in data analysis, and benefits researchers, engineers, and students with theoretical, computational, technical and experimental details. It presents a systematic and up-to-date overview of tensor decompositions from the engineer's point of view, and comprehensive coverage of tensor computation based data analysis techniques. In addition, some practical examples in machine learning, signal processing, data mining, computer vision, remote sensing, and biomedical engineering are also presented for easy understanding and implementation. These data analysis techniques may be further applied in other applications on neuroscience, communication, psychometrics, chemometrics, biometrics, quantum physics, quantum chemistry, etc. The discussion begins with basic coverage of notations, preliminary operations in tensor computations, main tensor decompositions and their properties. Based on them, a series of tensor-based data analysis techniques are presented as the tensor extensions of their classical matrix counterparts, including tensor dictionary learning, low rank tensor recovery, tensor completion, coupled tensor analysis, robust principal tensor component analysis, tensor regression, logistical tensor regression, support tensor machine, multilinear discriminate analysis, tensor subspace clustering, tensor-based deep learning, tensor graphical model and tensor sketch. The discussion also includes a number of typical applications with experimental results, such as image reconstruction, image enhancement, data fusion, signal recovery, recommendation system, knowledge graph acquisition, traffic flow prediction, link prediction, environmental prediction, weather forecasting, background extraction, human pose estimation, cognitive state classification from fMRI, infrared small target detection, heterogeneous information networks clustering, multi-view image clustering, and deep neural network compression.