Dirac equation exact solutions for generalized asymmetrical Hartmann potentials

In this work we solve the Dirac equation by constructing the exact bound state solutions for a mixing of vector and scalar generalized Hartmann potentials. This is done provided the vector potential is equal to or minus the scalar potential. The cases of some quasi-exactly solvable and Morse-like potentials are briefly commented.Comment: 8 pages, no figure

A Collective Variational Autoencoder for Top-NN Recommendation with Side Information

Recommender systems have been studied extensively due to their practical use in many real-world scenarios. Despite this, generating effective recommendations with sparse user ratings remains a challenge. Side information associated with items has been widely utilized to address rating sparsity. Existing recommendation models that use side information are linear and, hence, have restricted expressiveness. Deep learning has been used to capture non-linearities by learning deep item representations from side information but as side information is high-dimensional existing deep models tend to have large input dimensionality, which dominates their overall size. This makes them difficult to train, especially with small numbers of inputs. Rather than learning item representations, which is problematic with high-dimensional side information, in this paper, we propose to learn feature representation through deep learning from side information. Learning feature representations, on the other hand, ensures a sufficient number of inputs to train a deep network. To achieve this, we propose to simultaneously recover user ratings and side information, by using a Variational Autoencoder (VAE). Specifically, user ratings and side information are encoded and decoded collectively through the same inference network and generation network. This is possible as both user ratings and side information are data associated with items. To account for the heterogeneity of user rating and side information, the final layer of the generation network follows different distributions depending on the type of information. The proposed model is easy to implement and efficient to optimize and is shown to outperform state-of-the-art top-$N$ recommendation methods that use side information.Comment: 7 pages, 3 figures, DLRS workshop 201

Management of e-technology in China

"e" technology is bringing about many challenges for companies, in particular for their managers. This concerns a vast range of business processes in many sectors of the economy and in nearly every country of the world. In rapidly industrializing China, companies and other organizations are actively finding their way by adapting, developing and exploiting new e-technologies. The paper's focus is the identification of the management issues in implementing e-technology in China. The paper reports on research into difficulties of establishing and operating e-business in China. In particular, it discusses management related to e-technology sharing and application. A brief review of literature is followed by the analysis of three recent case studies: an international IT services alliance, a financial services provider and an international manufacturing joint venture. All case companies are applying e-technology in China, but the role of e-technology differs in the three cases: adding a service line to the existing business processes; developing a new business process; and increasing efficiency and effectiveness in business processes. The conclusions present the emerging management issues: cooperation is a key asset in networking; the choice of business models plays an important role; adequate management attention for details such as a training program is require