1,238 research outputs found
Emoticon-based Ambivalent Expression: A Hidden Indicator for Unusual Behaviors in Weibo
Recent decades have witnessed online social media being a big-data window for
quantificationally testifying conventional social theories and exploring much
detailed human behavioral patterns. In this paper, by tracing the emoticon use
in Weibo, a group of hidden "ambivalent users" are disclosed for frequently
posting ambivalent tweets containing both positive and negative emotions.
Further investigation reveals that this ambivalent expression could be a novel
indicator of many unusual social behaviors. For instance, ambivalent users with
the female as the majority like to make a sound in midnights or at weekends.
They mention their close friends frequently in ambivalent tweets, which attract
more replies and thus serve as a more private communication way. Ambivalent
users also respond differently to public affairs from others and demonstrate
more interests in entertainment and sports events. Moreover, the sentiment
shift of words adopted in ambivalent tweets is more evident than usual and
exhibits a clear "negative to positive" pattern. The above observations, though
being promiscuous seemingly, actually point to the self regulation of negative
mood in Weibo, which could find its base from the emotion management theories
in sociology but makes an interesting extension to the online environment.
Finally, as an interesting corollary, ambivalent users are found connected with
compulsive buyers and turn out to be perfect targets for online marketing.Comment: Data sets can be downloaded freely from www.datatang.com/data/47207
or http://pan.baidu.com/s/1mg67cbm. Any issues feel free to contact
[email protected]
Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
In this paper, we present a hierarchical path planning framework called SG-RL
(subgoal graphs-reinforcement learning), to plan rational paths for agents
maneuvering in continuous and uncertain environments. By "rational", we mean
(1) efficient path planning to eliminate first-move lags; (2) collision-free
and smooth for agents with kinematic constraints satisfied. SG-RL works in a
two-level manner. At the first level, SG-RL uses a geometric path-planning
method, i.e., Simple Subgoal Graphs (SSG), to efficiently find optimal abstract
paths, also called subgoal sequences. At the second level, SG-RL uses an RL
method, i.e., Least-Squares Policy Iteration (LSPI), to learn near-optimal
motion-planning policies which can generate kinematically feasible and
collision-free trajectories between adjacent subgoals. The first advantage of
the proposed method is that SSG can solve the limitations of sparse reward and
local minima trap for RL agents; thus, LSPI can be used to generate paths in
complex environments. The second advantage is that, when the environment
changes slightly (i.e., unexpected obstacles appearing), SG-RL does not need to
reconstruct subgoal graphs and replan subgoal sequences using SSG, since LSPI
can deal with uncertainties by exploiting its generalization ability to handle
changes in environments. Simulation experiments in representative scenarios
demonstrate that, compared with existing methods, SG-RL can work well on
large-scale maps with relatively low action-switching frequencies and shorter
path lengths, and SG-RL can deal with small changes in environments. We further
demonstrate that the design of reward functions and the types of training
environments are important factors for learning feasible policies.Comment: 20 page
Structural Embedding of Syntactic Trees for Machine Comprehension
Deep neural networks for machine comprehension typically utilizes only word
or character embeddings without explicitly taking advantage of structured
linguistic information such as constituency trees and dependency trees. In this
paper, we propose structural embedding of syntactic trees (SEST), an algorithm
framework to utilize structured information and encode them into vector
representations that can boost the performance of algorithms for the machine
comprehension. We evaluate our approach using a state-of-the-art neural
attention model on the SQuAD dataset. Experimental results demonstrate that our
model can accurately identify the syntactic boundaries of the sentences and
extract answers that are syntactically coherent over the baseline methods
Homotypic fusion of endoplasmic reticulum membranes in plant cells
The endoplasmic reticulum (ER) is a membrane-bounded organelle whose membrane comprises a network of tubules and sheets. The formation of these characteristic shapes and maintenance of their continuity through homotypic membrane fusion appears to be critical for the proper functioning of the ER. The atlastins (ATLs), a family of ER-localized dynamin-like GTPases, have been identified as fusogens of the ER membranes in metazoans. Mutations of the ATL proteins in mammalian cells cause morphological defects in the ER, and purified Drosophila ATL mediates membrane fusion in vitro. Plant cells do not possess ATL, but a family of similar GTPases, named root hair defective 3 (RHD3), are likely the functional orthologs of ATLs. In this review, we summarize recent advances in our understanding of how RHD3 proteins play a role in homotypic ER fusion. We also discuss the possible physiological significance of forming a tubular ER network in plant cells
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