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

9,9-Dimethyl-12-phenyl-8,9-dihydro-12H-benzo[a]xanthen-11(10H)-one

The title compound, C25H22O2, was synthesized via the three-component coupling of benzaldehyde, 2-naphthol and 5,5-dimethyl­cyclo­hexane-1,3-dione. In the crystal structure, centrosymmetrically related mol­ecules are linked into dimers by pairs of inter­molecular C—H⋯O hydrogen bonds. The dimers are further connected into a three-dimensional network by π–π aromatic stacking inter­actions involving the naphthalene ring system, with centroid–centroid separations of 3.695 (7) Å

The Interaction between Tides and Storm Surges for the Taiwan Coast-A Modeling Investigation

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive

Triply charmed baryons mass decomposition from lattice QCD

We present the first lattice QCD calculation about the mass decomposition of triply charmed baryons with $J^{P}$ as $\frac{3}{2}^{+}$ and $\frac{3}{2}^{-}$. The quark mass term $\langle H_{M} \rangle$ contributes about 66\% to the mass of $\frac{3}{2}^+$ state, which is slightly lower than that of the meson system with the same valence charm quark. Furthermore, based on our results, the total contribution of sea quarks, the gluons and the QCD anomaly accounts for about a quarter of the mass of these two triply charmed baryons. The mass difference of $\frac{3}{2}^+$ and $\frac{3}{2}^-$ states is mainly from the quark energy $\langle H_{E} \rangle$ of the QCD energy-momentum tensor. For comparison, the mass splitting is also calculated under the framework of the constituent quark model.Comment: 7 page, 14 figure