Rarefied Broad-Line Regions in Active Galactic Nuclei: Anomalous Responses in Reverberation Mapping and Implications for Weak-Emission Line Quasars

Reverberation mapping (RM) is a widely-used method for probing the physics of broad-line regions (BLRs) in active galactic nuclei (AGNs). There are increasing preliminary evidences that the RM behaviors of broad emission lines are influenced by BLR densities, however, the influences have not been investigated systematically from theoretical perspective. In the present paper, we adopt locally optimally emitting cloud model and use CLOUDY to obtain the one-dimensional transfer functions of the prominent UV and optical emission lines for different BLR densities. We find that the influences of BLR densities to RM behaviors have mainly three aspects. First, rarefied BLRs (with low gas densities) may show anomalous responses in RM observations. Their emission-line light curves inversely response the variations of continuum light curves, which may have been observed in some UV RM campaigns. Second, the different BLR densities in AGNs may result in correlations between the time lags and equivalent widths of emission lines, and may contribute to the scatters of the radius-luminosity relationships. Third, the variations of BLR densities may explain the changes of time lags in individual objects in different years. Some weak emission-line quasars (WLQs) are probably extreme cases of rarefied BLRs. We predict that their RM observations may show the anomalous responses.Comment: 28 pages, 12 figures, accepted for publication in The Astrophysical Journa

Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph Generation

This paper investigates the problem of scene graph generation in videos with the aim of capturing semantic relations between subjects and objects in the form of $\langle$subject, predicate, object$\rangle$ triplets. Recognizing the predicate between subject and object pairs is imbalanced and multi-label in nature, ranging from ubiquitous interactions such as spatial relationships (\eg \emph{in front of}) to rare interactions such as \emph{twisting}. In widely-used benchmarks such as Action Genome and VidOR, the imbalance ratio between the most and least frequent predicates reaches 3,218 and 3,408, respectively, surpassing even benchmarks specifically designed for long-tailed recognition. Due to the long-tailed distributions and label co-occurrences, recent state-of-the-art methods predominantly focus on the most frequently occurring predicate classes, ignoring those in the long tail. In this paper, we analyze the limitations of current approaches for scene graph generation in videos and identify a one-to-one correspondence between predicate frequency and recall performance. To make the step towards unbiased scene graph generation in videos, we introduce a multi-label meta-learning framework to deal with the biased predicate distribution. Our meta-learning framework learns a meta-weight network for each training sample over all possible label losses. We evaluate our approach on the Action Genome and VidOR benchmarks by building upon two current state-of-the-art methods for each benchmark. The experiments demonstrate that the multi-label meta-weight network improves the performance for predicates in the long tail without compromising performance for head classes, resulting in better overall performance and favorable generalizability. Code: \url{https://github.com/shanshuo/ML-MWN}.Comment: ICMR 202

Continuous-Time Graph Learning for Cascade Popularity Prediction

Information propagation on social networks could be modeled as cascades, and many efforts have been made to predict the future popularity of cascades. However, most of the existing research treats a cascade as an individual sequence. Actually, the cascades might be correlated with each other due to the shared users or similar topics. Moreover, the preferences of users and semantics of a cascade are usually continuously evolving over time. In this paper, we propose a continuous-time graph learning method for cascade popularity prediction, which first connects different cascades via a universal sequence of user-cascade and user-user interactions and then chronologically learns on the sequence by maintaining the dynamic states of users and cascades. Specifically, for each interaction, we present an evolution learning module to continuously update the dynamic states of the related users and cascade based on their currently encoded messages and previous dynamic states. We also devise a cascade representation learning component to embed the temporal information and structural information carried by the cascade. Experiments on real-world datasets demonstrate the superiority and rationality of our approach.Comment: 9 pages, 5 figures, IJCAI 202

ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning

Real-Time Bidding (RTB) is an important mechanism in modern online advertising systems. Advertisers employ bidding strategies in RTB to optimize their advertising effects subject to various financial requirements, especially the return-on-investment (ROI) constraint. ROIs change non-monotonically during the sequential bidding process, and often induce a see-saw effect between constraint satisfaction and objective optimization. While some existing approaches show promising results in static or mildly changing ad markets, they fail to generalize to highly dynamic ad markets with ROI constraints, due to their inability to adaptively balance constraints and objectives amidst non-stationarity and partial observability. In this work, we specialize in ROI-Constrained Bidding in non-stationary markets. Based on a Partially Observable Constrained Markov Decision Process, our method exploits an indicator-augmented reward function free of extra trade-off parameters and develops a Curriculum-Guided Bayesian Reinforcement Learning (CBRL) framework to adaptively control the constraint-objective trade-off in non-stationary ad markets. Extensive experiments on a large-scale industrial dataset with two problem settings reveal that CBRL generalizes well in both in-distribution and out-of-distribution data regimes, and enjoys superior learning efficiency and stability.Comment: Accepted by SIGKDD 202

Spatiotemporal expression of regulatory kinases directs the transition from mitosis to cellular morphogenesis in Drosophila

Embryogenesis depends on a tightly regulated balance between mitosis, differentiation, and morphogenesis. Understanding how the embryo uses a relatively small number of proteins to transition between growth and morphogenesis is a central question of developmental biology, but the mechanisms controlling mitosis and differentiation are considered to be fundamentally distinct. Here we show the mitotic kinase Polo, which regulates all steps of mitosis in Drosophila, also directs cellular morphogenesis after cell cycle exit. In mitotic cells, the Aurora kinases activate Polo to control a cytoskeletal regulatory module that directs cytokinesis. We show that in the post-mitotic mesoderm, the control of Polo activity transitions from the Aurora kinases to the uncharacterized kinase Back Seat Driver (Bsd), where Bsd and Polo cooperate to regulate muscle morphogenesis. Polo and its effectors therefore direct mitosis and cellular morphogenesis, but the transition from growth to morphogenesis is determined by the spatiotemporal expression of upstream activating kinases

Reduced expression of Metastasis Suppressor-1 (MTSS1) accelerates progression of human bladder uroepithelium cell carcinoma

Background: Metastasis suppressor 1 (MTSS1) is a multi-functional cytoskeletal protein. Recent research showed that MTSS1 is a potential tumor suppressor in many types of cancer cells, including kidney and bladder cancer cells. However, the clinical implication of MTSS1 in human bladder uroepithelium cell carcinoma (BUCC) and its potential in suppressing BUCC tumorigenesis remains undetermined. In the present study, the expression of MTSS1 in human BUCC tissue samples, and correlations between MTSS1 and pathological grade and stage of the tumors were examined in BUCC specimens. The function of MTSS1 in BUCC progression was explored. Materials and Methods: The mRNA and protein expression of MTSS1 were examined in 68 BUCC tissue samples with matching adjacent normal bladder tissues using quantitative real-time PCR and western blotting. Furthermore, the bladder cancer cell line 5637 was used to determine the anticancer effect of MTSS1. Results: Lower MTSS1 mRNA expression was recorded in BUCC tissues compared to normal bladder tissues. A lower MTSS1 mRNA level was observed in tumors with high clinical stage and with high pathological nuclear grade. Likewise, MTSS1 protein expression in normal bladder tissue was significantly higher than that in BUCC tissue. The protein level of MTSS1 significantly negatively correlated with clinical stage and pathological nuclear grade of BUCC. Cumulative survival curves indicated that MTSS1 expression was negatively correlated with survival time: patients with a high level of MTSS1 had significantly longer survival time than those with a low level of MTSS1 (p<0.001). Overexpression of MTSS1 reduced BUCC cell proliferation, cell-cycle progression and colony formation, but had no influence on BUCC cell apoptosis. Conclusion: Overexpression of MTSS1 suppresses BUCC development, providing a novel perspective for BUCC tumorigenesis and a potential therapeutic target for BUCC