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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.
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.
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.
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