Constraining AGN Torus Sizes with Optical and Mid-Infrared Ensemble Structure Functions

We propose a new method to constrain the size of the dusty torus in broad-line active galactic nuclei (AGNs) using optical and mid-infrared (MIR) ensemble structure functions (SFs). Because of the geometric dilution of the torus, the MIR response to optical continuum variations has suppressed variability with respect to the optical that depends on the geometry (e.g., size, orientation, opening angle) of the torus. More extended tori have steeper MIR SFs with respect to the optical SFs. We demonstrate the feasibility of this SF approach using simulated AGN light curves and a geometric torus model. While it is difficult to use SFs to constrain the orientation and opening angle due to insensitivity of the SF on these parameters, the size of the torus can be well determined. Applying this method to the ensemble SFs measured for 587 SDSS quasars, we measure a torus $R-L$ relation of ${\rm log}\,R_{\rm eff}\, {(\rm pc)} = 0.51_{-0.04}^{+0.04} \times {\rm log}\,(L_{\rm bol}/10^{46}\,\rm erg\,s^{-1}) -0.38_{-0.01}^{+0.01}$ in the WISE $W1$ band, and sizes $\sim1.4$ times larger in the $W2$ band, which are in good agreement with dust reverberation mapping measurements. Compared with the reverberation mapping technique, the SF method is much less demanding in data quality and can be applied to any optical+MIR light curves for which a lag measurement may not be possible, as long as the variability process and torus structure are stationary. While this SF method does not extract all information contained in the light curves (i.e., the transfer function), it provides an intuitive interpretation for the observed trends of AGN MIR SFs compared with optical SFs.Comment: 16 pages, 10 figures, accepted for publication in Ap

AU-Supervised Convolutional Vision Transformers for Synthetic Facial Expression Recognition

The paper describes our proposed methodology for the six basic expression classification track of Affective Behavior Analysis in-the-wild (ABAW) Competition 2022. In Learing from Synthetic Data(LSD) task, facial expression recognition (FER) methods aim to learn the representation of expression from the artificially generated data and generalise to real data. Because of the ambiguous of the synthetic data and the objectivity of the facial Action Unit (AU), we resort to the AU information for performance boosting, and make contributions as follows. First, to adapt the model to synthetic scenarios, we use the knowledge from pre-trained large-scale face recognition data. Second, we propose a conceptually-new framework, termed as AU-Supervised Convolutional Vision Transformers (AU-CVT), which clearly improves the performance of FER by jointly training auxiliary datasets with AU or pseudo AU labels. Our AU-CVT achieved F1 score as $0.6863$, accuracy as $0.7433$ on the validation set. The source code of our work is publicly available online: https://github.com/msy1412/ABAW

Improving Neural Radiance Fields with Depth-aware Optimization for Novel View Synthesis

With dense inputs, Neural Radiance Fields (NeRF) is able to render photo-realistic novel views under static conditions. Although the synthesis quality is excellent, existing NeRF-based methods fail to obtain moderate three-dimensional (3D) structures. The novel view synthesis quality drops dramatically given sparse input due to the implicitly reconstructed inaccurate 3D-scene structure. We propose SfMNeRF, a method to better synthesize novel views as well as reconstruct the 3D-scene geometry. SfMNeRF leverages the knowledge from the self-supervised depth estimation methods to constrain the 3D-scene geometry during view synthesis training. Specifically, SfMNeRF employs the epipolar, photometric consistency, depth smoothness, and position-of-matches constraints to explicitly reconstruct the 3D-scene structure. Through these explicit constraints and the implicit constraint from NeRF, our method improves the view synthesis as well as the 3D-scene geometry performance of NeRF at the same time. In addition, SfMNeRF synthesizes novel sub-pixels in which the ground truth is obtained by image interpolation. This strategy enables SfMNeRF to include more samples to improve generalization performance. Experiments on two public datasets demonstrate that SfMNeRF surpasses state-of-the-art approaches. Code is available at https://github.com/XTU-PR-LAB/SfMNeR

Response of terrestrial net primary productivity (NPPT) in the Wujiang catchment (China) to the construction of cascade hydropower stations

The damming of rivers results in hydrological modifications that not only affect the aquatic ecosystem but also adjoining terrestrial systems. Thirteen dams commissioned along the Wujiang River have induced ecological problems, including decreased water turbidity and loss of biodiversity, which potentially influence ecosystem net primary production (NPP) and hence the sequestration, transformation, and storage of carbon. We used terrestrial NPP (NPPT) as a bioindicator to assess the impact of dams on carbon storage in the Wujiang catchment. MODIS satellite and meteorological data were used as inputs to the CASA model to calculate annual NPPT from 2000 to 2014. NPPT was calculated at the catchment and landscape scale to quantify the impact of dams on surrounding terrestrial ecosystems. Mean NPPT was calculated for concentric buffer zones covering a range of spatial extents (0–10 km) from the reservoir shoreline. We found a negligible impact from construction of a single dam on NPPT at the catchment scale. By contrast, the impact of dam construction was scale-dependent, with a stronger landscape-scale effect observed at short distances (i.e., 0–1 km) from the reservoir. Decreases in NPPT were mainly ascribed to the loss of vegetated land resulting from dam impoundment and subsequent urbanization of the surrounding area

Delving into Multimodal Prompting for Fine-grained Visual Classification

Fine-grained visual classification (FGVC) involves categorizing fine subdivisions within a broader category, which poses challenges due to subtle inter-class discrepancies and large intra-class variations. However, prevailing approaches primarily focus on uni-modal visual concepts. Recent advancements in pre-trained vision-language models have demonstrated remarkable performance in various high-level vision tasks, yet the applicability of such models to FGVC tasks remains uncertain. In this paper, we aim to fully exploit the capabilities of cross-modal description to tackle FGVC tasks and propose a novel multimodal prompting solution, denoted as MP-FGVC, based on the contrastive language-image pertaining (CLIP) model. Our MP-FGVC comprises a multimodal prompts scheme and a multimodal adaptation scheme. The former includes Subcategory-specific Vision Prompt (SsVP) and Discrepancy-aware Text Prompt (DaTP), which explicitly highlights the subcategory-specific discrepancies from the perspectives of both vision and language. The latter aligns the vision and text prompting elements in a common semantic space, facilitating cross-modal collaborative reasoning through a Vision-Language Fusion Module (VLFM) for further improvement on FGVC. Moreover, we tailor a two-stage optimization strategy for MP-FGVC to fully leverage the pre-trained CLIP model and expedite efficient adaptation for FGVC. Extensive experiments conducted on four FGVC datasets demonstrate the effectiveness of our MP-FGVC.Comment: The first two authors contributed equally to this wor

A machine learning approach to assessing the presence of substructure in quasar host galaxies using the Hyper Suprime-Cam Subaru Strategic Program

The conditions under which galactic nuclear regions become active are largely unknown, although it has been hypothesized that secular processes related to galaxy morphology could play a significant role. We investigate this question using optical i-band images of 3096 SDSS quasars and galaxies at 0.3<z<0.6 from the Hyper Suprime-Cam Subaru Strategic Program, which possess a unique combination of area, depth and resolution, allowing the use of residual images, after removal of the quasar and smooth galaxy model, to investigate internal structural features. We employ a variational auto-encoder which is a generative model that acts as a form of dimensionality reduction. We analyze the lower dimensional latent space in search of features which correlate with nuclear activity. We find that the latent space does separate images based on the presence of nuclear activity which appears to be associated with more pronounced components (i.e., arcs, rings and bars) as compared to a matched control sample of inactive galaxies. These results suggest the importance of secular processes, and possibly mergers (by their remnant features) in activating or sustaining black hole growth. Our study highlights the breadth of information available in ground-based imaging taken under optimal seeing conditions and having accurate characterization of the point spread function (PSF) thus demonstrating future science to come from the Rubin Observatory

Piercing Through Highly Obscured and Compton-thick AGNs in the Chandra Deep Fields: I. X-ray Spectral and Long-term Variability Analyses

We present a detailed X-ray spectral analysis of 1152 AGNs selected in the Chandra Deep Fields (CDFs), in order to identify highly obscured AGNs ($N_{\rm H} > 10^{23}\ \rm cm^{-2}$). By fitting spectra with physical models, 436 (38%) sources with $L_{\rm X} > 10^{42}\ \rm erg\ s^{-1}$ are confirmed to be highly obscured, including 102 Compton-thick (CT) candidates. We propose a new hardness-ratio measure of the obscuration level which can be used to select highly obscured AGN candidates. The completeness and accuracy of applying this method to our AGNs are 88% and 80%, respectively. The observed logN-logS relation favors cosmic X-ray background models that predict moderate (i.e., between optimistic and pessimistic) CT number counts. 19% (6/31) of our highly obscured AGNs that have optical classifications are labeled as broad-line AGNs, suggesting that, at least for part of the AGN population, the heavy X-ray obscuration is largely a line-of-sight effect, i.e., some high-column-density clouds on various scales (but not necessarily a dust-enshrouded torus) along our sightline may obscure the compact X-ray emitter. After correcting for several observational biases, we obtain the intrinsic NH distribution and its evolution. The CT-to-highly-obscured fraction is roughly 52% and is consistent with no evident redshift evolution. We also perform long-term (~17 years in the observed frame) variability analyses for 31 sources with the largest number of counts available. Among them, 17 sources show flux variabilities: 31% (5/17) are caused by the change of NH, 53% (9/17) are caused by the intrinsic luminosity variability, 6% (1/17) are driven by both effects, and 2 are not classified due to large spectral fitting errors.Comment: 32 pages, 21 figures, 9 tables, accepted for publication in Ap

Varstrometry for Off-nucleus and Dual sub-Kpc AGN (VODKA). SDSS J1608+2716: A Sub-arcsec Quadruply Lensed Quasar at z=2.575z=2.575

We report Hubble Space Telescope (HST) Wide Field Camera 3 (WFC3) deep IR (F160W) imaging of SDSS J1608+2716. This system, located at a redshift of $z=2.575$, was recently reported as a triple quasar candidate with subarcsecond separations ($\sim0.25''$) based on selection from Gaia astrometry and follow-up Keck adaptive optics-assisted integral field unit spectroscopy. Our new HST deep IR imaging reveals the presence of a fourth point-like component located $\sim0.9''$ away from the triple system. Additionally, we detect an edge-on disk galaxy located in between the four point sources, which appears to be in the process of merging with a fainter companion galaxy. The entire system exhibits a characteristic cusp structure in the context of strong gravitational lensing, and the observed image configuration can be successfully reproduced using a lens model based on a singular isothermal ellipsoid mass profile. These findings indicate that this system is a quadruply lensed quasar. Our results highlight the challenges associated with identifying dual/multiple quasars on $\sim$kpc scales at high redshifts, and emphasize the crucial role of deep, high-resolution IR imaging in robustly confirming such systems.Comment: 9 pages, 3 figures, submitted to ApJ