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