7,152 research outputs found
Least Squares Regression Estimations Based on Censored Data with Measurement Errors
[[notice]]補正完畢[[journaltype]]國內[[booktype]]紙本[[countrycodes]]TW
Setup Cost Reduction in the Lumpy Demand Production Quantity Model with Discounting
[[notice]]補正完
Strong solutions of the compressible nematic liquid crystal flow
We study strong solutions of the simplified Ericksen-Leslie system modeling
compressible nematic liquid crystal flows in a domain . We first prove the local existence of unique strong solutions provided
that the initial data are sufficiently regular and satisfy a
natural compatibility condition. The initial density function may
vanish on an open subset (i.e., an initial vacuum may exist). We then prove a
criterion for possible breakdown of such a local strong solution at finite time
in terms of blow up of the quantities and
Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks
Prediction of popularity has profound impact for social media, since it
offers opportunities to reveal individual preference and public attention from
evolutionary social systems. Previous research, although achieves promising
results, neglects one distinctive characteristic of social data, i.e.,
sequentiality. For example, the popularity of online content is generated over
time with sequential post streams of social media. To investigate the
sequential prediction of popularity, we propose a novel prediction framework
called Deep Temporal Context Networks (DTCN) by incorporating both temporal
context and temporal attention into account. Our DTCN contains three main
components, from embedding, learning to predicting. With a joint embedding
network, we obtain a unified deep representation of multi-modal user-post data
in a common embedding space. Then, based on the embedded data sequence over
time, temporal context learning attempts to recurrently learn two adaptive
temporal contexts for sequential popularity. Finally, a novel temporal
attention is designed to predict new popularity (the popularity of a new
user-post pair) with temporal coherence across multiple time-scales.
Experiments on our released image dataset with about 600K Flickr photos
demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms,
with an average of 21.51% relative performance improvement in the popularity
prediction (Spearman Ranking Correlation).Comment: accepted in IJCAI-1
Strong solutions of the compressible nematic liquid crystal flow
AbstractWe study strong solutions of the simplified Ericksen–Leslie system modeling compressible nematic liquid crystal flows in a domain Ω⊂R3. We first prove the local existence of a unique strong solution provided that the initial data ρ0,u0,d0 are sufficiently regular and satisfy a natural compatibility condition. The initial density function ρ0 may vanish on an open subset (i.e., an initial vacuum may exist). We then prove a criterion for possible breakdown of such a local strong solution at finite time in terms of blow up of the quantities ‖ρ‖Lt∞Lx∞ and ‖∇d‖Lt3Lx∞
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