Cool transition region loops observed by the Interface Region Imaging Spectrograph

We report on the first Interface Region Imaging Spectrograph (IRIS) study of cool transition region loops. This class of loops has received little attention in the literature. A cluster of such loops was observed on the solar disk in active region NOAA11934, in the Si IV 1402.8 \AA\ spectral raster and 1400 \AA\ slit-jaw (SJ) images. We divide the loops into three groups and study their dynamics and interaction. The first group comprises relatively stable loops, with 382--626\,km cross-sections. Observed Doppler velocities are suggestive of siphon flows, gradually changing from -10 km/s at one end to 20 km/s at the other end of the loops. Nonthermal velocities from 15 to 25 km/s were determined. These physical properties suggest that these loops are impulsively heated by magnetic reconnection occurring at the blue-shifted footpoints where magnetic cancellation with a rate of $10^{15}$ Mx/s is found. The released magnetic energy is redistributed by the siphon flows. The second group corresponds to two footpoints rooted in mixed-magnetic-polarity regions, where magnetic cancellation occurred at a rate of $10^{15}$ Mx/s and line profiles with enhanced wings of up to 200 km/s were observed. These are suggestive of explosive-like events. The Doppler velocities combined with the SJ images suggest possible anti-parallel flows in finer loop strands. In the third group, interaction between two cool loop systems is observed. Evidence for magnetic reconnection between the two loop systems is reflected in the line profiles of explosive events, and a magnetic cancellation rate of $3\times10^{15}$ Mx/s observed in the corresponding area. The IRIS observations have thus opened a new window of opportunity for in-depth investigations of cool transition region loops. Further numerical experiments are crucial for understanding their physics and their role in the coronal heating processes.Comment: Accepted for publication in Ap

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