Corporate Governance and Management Succession in Family Businesses

Family businesses carry the weight of economic wealth creation in most economies. In the U.S. alone, family businesses account for 80 to 90 percent of the 18-million business enterprises in the United States, and 50 percent of the employment and GNP. In many ways, the family business is synonymous with the entrepreneurial organization as many were started as a means to provide for the financial well being of the founder's family. Founders who went on to build family empires started many of today's large corporations (e.g., Anheuser-Busch, Dupont, and Seagrams). Still, we know relatively little about the issues peculiar to a family business, such as the process and impact of succession planning. Yet, no recurring event in the life of the family firm is more critical to survival than the transfer of power from the incumbent to the successor. Organizations are especially susceptible to loss of vision and purpose during periods of CEO transition, as the leaders who helped shape the vision are replaced by others who may not share the same values and abilities. This study addresses the importance of understanding business succession planning by proposing and empirically verifying a model of succession planning and firm effectiveness in the family business. It links aspects of succession planning and successor preparation to the effectiveness of transition and from performance. The model depicts multiple interactive relationships, with emphasis placed not only on the planning and process-specific but also on successor-specific factors that lead to effectiveness.corporate governance, family businesses, management succession, firm performance, successor characteristics

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

Discovery of the Youngest Molecular Outflow associated with an Intermediate-mass protostellar Core, MMS-6/OMC-3

We present sub-arcsecond resolution HCN (4-3) and CO (3-2) observations made with the Submillimeter Array (SMA), toward an extremely young intermediate-mass protostellar core, MMS 6-main, located in the Orion Molecular Cloud 3 region (OMC-3). We have successfully imaged a compact molecular outflow lobe (~1500 AU) associated with MMS6-main, which is also the smallest molecular outflow ever found in the intermediate-mass protostellar cores. The dynamical time scale of this outflow is estimated to be <100 yr. The line width dramatically increases downstream at the end of the molecular outflow ({\Delta}v~25 km s^{-1}), and clearly shows the bow-shock type velocity structure. The estimated outflow mass (~10^{-4} M_{sun}) and outflow size are approximately 2-4 orders and 1-3 orders of magnitude smaller, while the outflow force (~10^{-4} M_{sun} km s^{-1} yr^{-1}) is similar, as compared to the other molecular outflows studied in OMC-2/3. These results show that MMS 6-main is a protostellar core at the earliest evolutionary stage, most likely shortly after the 2nd core formation.Comment: Accepted to ApJ

SMA observations of the proto brown dwarf candidate SSTB213 J041757

Context. The previously identified source SSTB213 J041757 is a proto brown dwarf candidate in Taurus, which has two possible components A and B. It was found that component B is probably a class 0/I proto brown dwarf associated with an extended envelope. Aims. Studying molecular outflows from young brown dwarfs provides important insight into brown dwarf formation mechanisms, particularly brown dwarfs at the earliest stages such as class 0, I. We therefore conducted a search for molecular outflows from SSTB213 J041757. Methods. We observed SSTB213 J041757 with the Submillimeter Array to search for CO molecular outflow emission from the source. Results. Our CO maps do not show any outflow emission from the proto brown dwarf candidate. Conclusions. The non-detection implies that the molecular outflows from the source are weak; deeper observations are therefore needed to probe the outflows from the source.Comment: 7 pages, 4 figures, accepted for publication in A&

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