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

Siliceous foam material and its application in post-combustion carbon capture for NGCC plants: effects of aging conditions

In an effort to reduce the overall energy penalty and capital expenditure associated with carbon capture technologies, a variety of porous solid adsorbents have been developed. The limitations of solid sorbent in large-scale process are related to its CO2 uptake, physicochemical stability, lifecycle, regenerability and operation condition. In this paper, siliceous foam materials were synthesized via a modified microemulsion templating method and functionalized with branched polyethylenimine (PEI). The physical characteristics of synthesized silica adsorbents under different aging conditions were analysed via N2 sorption analysis and Scanned Electron Microscopy (SEM) morphological analysis. CO2 uptake was evaluated by thermogravimetric analyser (TGA). The results show that CO2 uptake is desirable even under low CO2 partial pressure and is predictable with multiple linear regression (MLR) model in the range of examined materials