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Opening up closings - the Ecuadorian way
In the conversation analytic tradition, this paper examines the procedures Ecuadorian Spanish (ES) speakers employ to close telephone conversations. Conversation analysts (cf. Schegloff, 1979) examined telephone talk in American English and that found that conversations are opened and brought to a close by the joint work of participants. Concerning closings, they observed, for example, that participants employ certain procedures to signal their desire to bring the conversation to an end and others to actually close the interaction. They also suggested that the conversational procedures they describe are of a universal character (cf. Schegloff and Sacks, 1974 [1973]). The examination of telephone closings in the present study reveals that similar procedures are employed in Ecuadorian Spanish. Nevertheless, it also highlights some of the features that appear to be characteristic of Ecuadorian Spanish only, that is, that seem to be culture-bound, and thus contests Schegloff and Sacks's unversality claims. The need for a culturally contexted conversation analysis, along the lines proposed by Moerman (1988) is supported here
Strain accommodation through facet matching in LaSrCuO/NdCeCuO ramp-edge junctions
Scanning nano-focused X-ray diffraction (nXRD) and high-angle annular
dark-field scanning transmission electron microscopy (HAADF-STEM) are used to
investigate the crystal structure of ramp-edge junctions between
superconducting electron-doped NdCeCuO
and superconducting hole-doped LaSrCuO
thin films, the latter being the top layer. On the ramp, a new growth mode of
LaSrCuO with a 3.3 degree tilt of the
c-axis is found. We explain the tilt by developing a strain accommodation model
that relies on facet matching, dictated by the ramp angle, indicating that a
coherent domain boundary is formed at the interface. The possible implications
of this growth mode for the creation of artificial domains in morphotropic
materials are discussed.Comment: 5 pages, 4 figures & 3 pages supplemental information with 2 figures.
Copyright (2015) American Institute of Physics. This article may be
downloaded for personal use only. Any other use requires prior permission of
the author and the American Institute of Physics. The following article
appeared in APL Mat. 3, 086101 (2015) and may be found at
http://dx.doi.org/10.1063/1.492779
Text segmentation with character-level text embeddings
Learning word representations has recently seen much success in computational
linguistics. However, assuming sequences of word tokens as input to linguistic
analysis is often unjustified. For many languages word segmentation is a
non-trivial task and naturally occurring text is sometimes a mixture of natural
language strings and other character data. We propose to learn text
representations directly from raw character sequences by training a Simple
recurrent Network to predict the next character in text. The network uses its
hidden layer to evolve abstract representations of the character sequences it
sees. To demonstrate the usefulness of the learned text embeddings, we use them
as features in a supervised character level text segmentation and labeling
task: recognizing spans of text containing programming language code. By using
the embeddings as features we are able to substantially improve over a baseline
which uses only surface character n-grams.Comment: Workshop on Deep Learning for Audio, Speech and Language Processing,
ICML 201
Plan of Group Insurance for Employes of the Pullman Company.
This item is part of a Brotherhood of Sleeping Car Porters Collection, 1934-1965 .https://stars.library.ucf.edu/brotherhoodofsleepingcarporters-text/1003/thumbnail.jp
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