4,189 research outputs found
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
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