28,094 research outputs found
SciTech News Volume 70, No. 4 (2016)
Columns and Reports
From the Editor 3
Division News
Science-Technology Division 4
SLA Annual Meeting 2016 Report (S. Kirk Cabeen Travel Stipend Award recipient) 6
Reflections on SLA Annual Meeting (Diane K. Foster International Student Travel Award recipient) 8
SLA Annual Meeting Report (Bonnie Hilditch International Librarian Award recipient)10
Chemistry Division 12
Engineering Division 15
Reflections from the 2016 SLA Conference (SPIE Digital Library Student Travel Stipend recipient)15
Fundamentals of Knowledge Management and Knowledge Services (IEEE Continuing Education Stipend recipient) 17
Makerspaces in Libraries: The Big Table, the Art Studio or Something Else? (by Jeremy Cusker) 19
Aerospace Section of the Engineering Division 21
Reviews
Sci-Tech Book News Reviews 22
Advertisements
IEEE 17
WeBuyBooks.net 2
Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters
Conversations on Twitter create networks with identifiable contours as people reply to and mention one another in their tweets. These conversational structures differ, depending on the subject and the people driving the conversation. Six structures are regularly observed: divided, unified, fragmented, clustered, and inward and outward hub and spoke structures. These are created as individuals choose whom to reply to or mention in their Twitter messages and the structures tell a story about the nature of the conversatio
A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
In this study, a novel Distributed Representation of News (DRNews) model is
developed and applied in deep learning-based stock market predictions. With the
merit of integrating contextual information and cross-documental knowledge, the
DRNews model creates news vectors that describe both the semantic information
and potential linkages among news events through an attributed news network.
Two stock market prediction tasks, namely the short-term stock movement
prediction and stock crises early warning, are implemented in the framework of
the attention-based Long Short Term-Memory (LSTM) network. It is suggested that
DRNews substantially enhances the results of both tasks comparing with five
baselines of news embedding models. Further, the attention mechanism suggests
that short-term stock trend and stock market crises both receive influences
from daily news with the former demonstrates more critical responses on the
information related to the stock market {\em per se}, whilst the latter draws
more concerns on the banking sector and economic policies.Comment: 25 page
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