Dust-Deficient Palomar-Green Quasars and the Diversity of AGN Intrinsic IR Emission

To elucidate the intrinsic broadband infrared (IR) emission properties of active galactic nuclei (AGNs), we analyze the spectral energy distributions (SEDs) of 87 z<0.5 Palomar-Green (PG) quasars. While the Elvis AGN template with a moderate far-IR correction can reasonably match the SEDs of the AGN components in ~60% of the sample (and is superior to alternatives such as that by Assef), it fails on two quasar populations: 1) hot-dust-deficient (HDD) quasars that show very weak emission thoroughly from the near-IR to the far-IR, and 2) warm-dust-deficient (WDD) quasars that have similar hot dust emission as normal quasars but are relatively faint in the mid- and far-IR. After building composite AGN templates for these dust-deficient quasars, we successfully fit the 0.3-500 {\mu}m SEDs of the PG sample with the appropriate AGN template, an infrared template of a star-forming galaxy, and a host galaxy stellar template. 20 HDD and 12 WDD quasars are identified from the SED decomposition, including seven ambiguous cases. Compared with normal quasars, the HDD quasars have AGN with relatively low Eddington ratios and the fraction of WDD quasars increases with AGN luminosity. Moreover, both the HDD and WDD quasar populations show relatively stronger mid-IR silicate emission. Virtually identical SED properties are also found in some quasars from z = 0.5 to 6. We propose a conceptual model to demonstrate that the observed dust deficiency of quasars can result from a change of structures of the circumnuclear tori that can occur at any cosmic epoch.Comment: minor corrections to match the published versio

Asynchronous, Option-Based Multi-Agent Policy Gradient: A Conditional Reasoning Approach

Multi-agent policy gradient methods have demonstrated success in games and robotics but are often limited to problems with low-level action space. However, when agents take higher-level, temporally-extended actions (i.e. options), when and how to derive a centralized control policy, its gradient as well as sampling options for all agents while not interrupting current option executions, becomes a challenge. This is mostly because agents may choose and terminate their options \textit{asynchronously}. In this work, we propose a conditional reasoning approach to address this problem, and empirically validate its effectiveness on representative option-based multi-agent cooperative tasks.Comment: Submitted to ICRA202

Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review

Sea-surface object detection is critical for navigation safety of autonomous ships. Electrooptical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of these approaches. The arti

DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection

A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of a standardized, unified, comprehensive benchmark. This issue leads to unfair performance comparisons and potentially misleading results. Specifically, there is a lack of uniformity in data processing pipelines, resulting in inconsistent data inputs for detection models. Additionally, there are noticeable differences in experimental settings, and evaluation strategies and metrics lack standardization. To fill this gap, we present the first comprehensive benchmark for deepfake detection, called DeepfakeBench, which offers three key contributions: 1) a unified data management system to ensure consistent input across all detectors, 2) an integrated framework for state-of-the-art methods implementation, and 3) standardized evaluation metrics and protocols to promote transparency and reproducibility. Featuring an extensible, modular-based codebase, DeepfakeBench contains 15 state-of-the-art detection methods, 9 deepfake datasets, a series of deepfake detection evaluation protocols and analysis tools, as well as comprehensive evaluations. Moreover, we provide new insights based on extensive analysis of these evaluations from various perspectives (e.g., data augmentations, backbones). We hope that our efforts could facilitate future research and foster innovation in this increasingly critical domain. All codes, evaluations, and analyses of our benchmark are publicly available at https://github.com/SCLBD/DeepfakeBench

Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction

Entities, as the essential elements in relation extraction tasks, exhibit certain structure. In this work, we formulate such structure as distinctive dependencies between mention pairs. We then propose SSAN, which incorporates these structural dependencies within the standard self-attention mechanism and throughout the overall encoding stage. Specifically, we design two alternative transformation modules inside each self-attention building block to produce attentive biases so as to adaptively regularize its attention flow. Our experiments demonstrate the usefulness of the proposed entity structure and the effectiveness of SSAN. It significantly outperforms competitive baselines, achieving new state-of-the-art results on three popular document-level relation extraction datasets. We further provide ablation and visualization to show how the entity structure guides the model for better relation extraction. Our code is publicly available.Comment: Accepted to AAAI 202

Theoretical study of the open-flavor tetraquark Tcsˉ(2900)T_{c\bar{s}}(2900) in the process Λb→K0D0Λ\Lambda_b\to K^0D^0\Lambda

Recently, the LHCb Collaboration has measured the processes $B^0\to\bar{D}^0D_s^+\pi^-$ and $B^+\to\bar{D}^0D_s^+\pi^+$, where the $D_s^+\pi^-$ and $D_s^+\pi^+$ invariant mass distributions show the significant signals of two new open-flavor tetraquark states $T_{c\bar{s}}(2900)^0$ and $T_{c\bar{s}}(2900)^{++}$, as the two of the isospin triplet. In this work, we have investigated the process $\Lambda_b\to K^0D^0\Lambda$ by taking into account the intermediate nucleon resonance $N^*(1535)$ and the tetraquark state $T_{c\bar{s}}(2900)^0$, which could be dynamically generated by the interactions of the $D^*K^*/D^*_s\rho$ and the pseoduscalar mesons-octet baryons, respectively. Our results show that a clear peak of the open-flavor tetraquark $T_{c\bar{s}}(2900)$ may appear in the $K^0D^0$ invariant mass distribution of the process $\Lambda_b\to K^0D^0\Lambda$, which could be tested by future experiments.Comment: 9 pages, 11 figures, 1 tabl