Outflow and hot dust emission in high redshift quasars

Correlations of hot dust emission with outflow properties are investigated, based on a large z~2 non-broad absorption lines quasar sample built from the Wide-field Infrared Survey and the Sloan Digital Sky Survey data releases. We use the near infrared slope and the infrared to UV luminosity ratio to indicate the hot dust emission relative to the emission from the accretion disk. In our luminous quasars, these hot dust emission indicators are almost independent of the fundamental parameters, such as luminosity, Eddington ratio and black hole mass, but moderately dependent on the blueshift and asymmetry index (BAI) and full width at half-maximum (FWHM) of CIV lines. Interestingly, the latter two correlations dramatically strengthen with increasing Eddington ratio. We suggest that, in high Eddington ratio quasars, CIV regions are dominated by outflows so the BAI and FWHM(CIV) can reliably reflect the general properties and velocity of outflows, respectively. While in low Eddington ratio quasars, CIV lines are primarily emitted by virialized gas so the BAI and FWHM(CIV) become less sensitive to outflows. Therefore, the correlations for the highest Eddington ratio quasars are more likely to represent the true dependence of hot dust emission on outflows and the correlations for the entire sample are significantly diluted by the low Eddington ratio quasars. Our results show that an outflow with a large BAI or velocity can double the hot dust emission on average. We suggest that outflows either contain hot dust in themselves or interact with the dusty interstellar medium or torus.Comment: 14 page, 4 figures, accepted for publication in ApJ

Multimodal Learning For Hateful Memes Detection

Memes are used for spreading ideas through social networks. Although most memes are created for humor, some memes become hateful under the combination of pictures and text. Automatically detecting hateful memes can help reduce their harmful social impact. Compared to the conventional multimodal tasks, where the visual and textual information is semantically aligned, hateful memes detection is a more challenging task since the image and text in memes are weakly aligned or even irrelevant. Thus, it requires the model to have a deep understanding of the content and perform reasoning over multiple modalities. This paper focuses on multimodal hateful memes detection and proposes a novel method incorporating the image captioning process into the meme\u27s detection process. We conduct extensive experiments on multimodal meme datasets and illustrate the effectiveness of our approach. Our model achieves promising results on the Hateful Memes Detection Challenge. Our code is made publicly available at GitHub

Outflow and hot dust emission in broad absorption line quasars

We have investigated a sample of 2099 broad absorption line (BAL) quasars with z=1.7-2.2 built from the Sloan Digital Sky Survey Data Release Seven and the Wide-field Infrared Survey. This sample is collected from two BAL quasar samples in the literature, and refined by our new algorithm. Correlations of outflow velocity and strength with hot dust indicator (beta_NIR) and other quasar physical parameters, such as Eddington ratio, luminosity and UV continuum slope, are explored in order to figure out which parameters drive outflows. Here beta_NIR is the near-infrared continuum slope, a good indicator of the amount of hot dust emission relative to accretion disk emission. We confirm previous findings that outflow properties moderately or weakly depends on Eddington ratio, UV slope and luminosity. For the first time, we report moderate and significant correlations of outflow strength and velocity with beta_NIR in BAL quasars. It is consistent with the behavior of blueshifted broad emission lines in non-BAL quasars. The statistical analysis and composite spectra study both reveal that outflow strength and velocity are more strongly correlated with beta_NIR than Eddington ratio, luminosity and UV slope. In particular, the composites show that the entire C IV absorption profile shifts blueward and broadens as beta_NIR increases, while Eddington ratio and UV slope only affect the high and low velocity part of outflows, respectively. We discuss several potential processes and suggest that dusty outflow scenario, i.e. dust is intrinsic to outflows and may contribute to the outflow acceleration, is most likely. The BAL quasar catalog is available from the authors upon request.Comment: 16 pages, 10 figures, 2 tables; Accepted for publication in The Astrophysical Journa

Exploiting Semantic Embedding And Visual Feature For Facial Action Unit Detection

Recent study on detecting facial action units (AU) has utilized auxiliary information (i.e., facial landmarks, relationship among AUs and expressions, web facial images, etc.), in order to improve the AU detection performance. As of now, no semantic information of AUs has yet been explored for such a task. As a matter of fact, AU semantic descriptions provide much more information than the binary AU labels alone, thus we propose to exploit the Semantic Embedding and Visual feature (SEV-Net) for AU detection. More specifically, AU semantic embeddings are obtained through both Intra-AU and Inter-AU attention modules, where the Intra-AU attention module captures the relation among words within each sentence that describes individual AU, and the Inter-AU attention module focuses on the relation among those sentences. The learned AU semantic embeddings are then used as guidance for the generation of attention maps through a cross-modality attention network. The generated cross-modality attention maps are further used as weights for the aggregated feature. Our proposed method is unique in that the semantic features are exploited as the first of this kind. The approach has been evaluated on three public AU-coded facial expression databases and has achieved a superior performance than the state-of-the-art peer methods