5,533 research outputs found
Dependence of the Spin Transfer Torque Switching Current Density on the Exchange Stiffness Constant
We investigate the dependence of the switching current density on the
exchange stiffness constant in the spin transfer torque magnetic tunneling
junction structure with micromagnetic simulations. Since the widely accepted
analytic expression of the switching current density is based on the macro-spin
model, there is no dependence of the exchange stiffness constant. When the
switching is occurred, however, the spin configuration forms C-, S-type, or
complicated domain structures. Since the spin configuration is determined by
the shape anisotropy and the exchange stiffness constant, the switching current
density is very sensitive on their variations. It implies that there are more
rooms for the optimization of the switching current density with by controlling
the exchange stiffness constant, which is determined by composition and the
detail fabrication processes
SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment
Because word semantics can substantially change across communities and
contexts, capturing domain-specific word semantics is an important challenge.
Here, we propose SEMAXIS, a simple yet powerful framework to characterize word
semantics using many semantic axes in word- vector spaces beyond sentiment. We
demonstrate that SEMAXIS can capture nuanced semantic representations in
multiple online communities. We also show that, when the sentiment axis is
examined, SEMAXIS outperforms the state-of-the-art approaches in building
domain-specific sentiment lexicons.Comment: Accepted in ACL 2018 as a full pape
Predicting Successful Memes using Network and Community Structure
We investigate the predictability of successful memes using their early
spreading patterns in the underlying social networks. We propose and analyze a
comprehensive set of features and develop an accurate model to predict future
popularity of a meme given its early spreading patterns. Our paper provides the
first comprehensive comparison of existing predictive frameworks. We categorize
our features into three groups: influence of early adopters, community
concentration, and characteristics of adoption time series. We find that
features based on community structure are the most powerful predictors of
future success. We also find that early popularity of a meme is not a good
predictor of its future popularity, contrary to common belief. Our methods
outperform other approaches, particularly in the task of detecting very popular
or unpopular memes.Comment: 10 pages, 6 figures, 2 tables. Proceedings of 8th AAAI Intl. Conf. on
Weblogs and social media (ICWSM 2014
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