Three photon absorption in ZnO and ZnS crystals

We report a systematic investigation of both three-photon absorption (3PA)spectra and wavelength dispersions of Kerr-type nonlinear refraction in wide-gap semiconductors. The Z-scan measurements are recorded for both ZnO and ZnS with femtosecond laser pulses. While the wavelength dispersions of the Kerr nonlinearity are in agreement with a two-band model, the wavelength dependences of the 3PA are found to be given by (3Ephoton/Eg-1)5/2(3Ephoton/Eg)-9. We also evaluate higher-order nonlinear optical effects including the fifth-order instantaneous nonlinear refraction associated with virtual three-photon transitions, and effectively seventh-order nonlinear processes induced by three-photon-excited free charge carriers. These higher-order nonlinear effects are insignificant with laser excitation irradiances up to 40 GW/cm2. Both pump-probe measurements and three-photon figures of merits demonstrate that ZnO and ZnS should be a promising candidate for optical switching applications at telecommunication wavelengths.Comment: 13 pages, 7 figure

Understanding health information technology adoption: A synthesis of literature from an activity perspective

The vast body of literature on health information technology (HIT) adoption features considerably heterogeneous factors and demands for a synthesis of the knowledge in the field. This study employs text mining and network analysis techniques to identify the important concepts and their relationships in the abstracts of 979 articles of HIT adoption. Through the lens of Activity Theory, the revealed concept map of HIT adoption can be viewed as a complex activity system involving different users, technologies and tasks at both the individual level and the social level. Such a synthesis not only discloses the current knowledge domain of HIT adoption, but also provides guidance for future research on HIT adoption

LINE: Large-scale Information Network Embedding

This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes. In this paper, we propose a novel network embedding method called the "LINE," which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted. The method optimizes a carefully designed objective function that preserves both the local and global network structures. An edge-sampling algorithm is proposed that addresses the limitation of the classical stochastic gradient descent and improves both the effectiveness and the efficiency of the inference. Empirical experiments prove the effectiveness of the LINE on a variety of real-world information networks, including language networks, social networks, and citation networks. The algorithm is very efficient, which is able to learn the embedding of a network with millions of vertices and billions of edges in a few hours on a typical single machine. The source code of the LINE is available online.Comment: WWW 201