5,446 research outputs found
The Competitive Dynamics of Entrepreneurial Market Entry
Research on general market entry usually focuses on large enterprises, often, however, small entrants can alter the competitive dynamic of an industry. This volume brings together the most prominent thought leaders and the best research on the asymmetric entrant-incumbent dynamics. This ideas presented offer a more nuanced perpective on how, when, where and with whar consequence small, single-product firms enter market that are dominated by large, multiproduct and multimarket incumbents.
Sholars and student in entrepreneurship, strategy, international business and related fields will find this excellent collection of key published and original material illuminating
Metallic characteristics in superlattices composed of insulators, NdMnO3/SrMnO3/LaMnO3
We report on the electronic properties of superlattices composed of three
different antiferromagnetic insulators, NdMnO3/SrMnO3/LaMnO3 grown on SrTiO3
substrates. Photoemission spectra obtained by tuning the x-ray energy at the Mn
2p -> 3d edge show a Fermi cut-off, indicating metallic behavior mainly
originating from Mn e_g electrons. Furthermore, the density of states near the
Fermi energy and the magnetization obey a similar temperature dependence,
suggesting a correlation between the spin and charge degrees of freedom at the
interfaces of these oxides
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives
Part 2 of this monograph builds on the introduction to tensor networks and
their operations presented in Part 1. It focuses on tensor network models for
super-compressed higher-order representation of data/parameters and related
cost functions, while providing an outline of their applications in machine
learning and data analytics. A particular emphasis is on the tensor train (TT)
and Hierarchical Tucker (HT) decompositions, and their physically meaningful
interpretations which reflect the scalability of the tensor network approach.
Through a graphical approach, we also elucidate how, by virtue of the
underlying low-rank tensor approximations and sophisticated contractions of
core tensors, tensor networks have the ability to perform distributed
computations on otherwise prohibitively large volumes of data/parameters,
thereby alleviating or even eliminating the curse of dimensionality. The
usefulness of this concept is illustrated over a number of applied areas,
including generalized regression and classification (support tensor machines,
canonical correlation analysis, higher order partial least squares),
generalized eigenvalue decomposition, Riemannian optimization, and in the
optimization of deep neural networks. Part 1 and Part 2 of this work can be
used either as stand-alone separate texts, or indeed as a conjoint
comprehensive review of the exciting field of low-rank tensor networks and
tensor decompositions.Comment: 232 page
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives
Part 2 of this monograph builds on the introduction to tensor networks and
their operations presented in Part 1. It focuses on tensor network models for
super-compressed higher-order representation of data/parameters and related
cost functions, while providing an outline of their applications in machine
learning and data analytics. A particular emphasis is on the tensor train (TT)
and Hierarchical Tucker (HT) decompositions, and their physically meaningful
interpretations which reflect the scalability of the tensor network approach.
Through a graphical approach, we also elucidate how, by virtue of the
underlying low-rank tensor approximations and sophisticated contractions of
core tensors, tensor networks have the ability to perform distributed
computations on otherwise prohibitively large volumes of data/parameters,
thereby alleviating or even eliminating the curse of dimensionality. The
usefulness of this concept is illustrated over a number of applied areas,
including generalized regression and classification (support tensor machines,
canonical correlation analysis, higher order partial least squares),
generalized eigenvalue decomposition, Riemannian optimization, and in the
optimization of deep neural networks. Part 1 and Part 2 of this work can be
used either as stand-alone separate texts, or indeed as a conjoint
comprehensive review of the exciting field of low-rank tensor networks and
tensor decompositions.Comment: 232 page
- …