1,220 research outputs found
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 in the process
Recently, the LHCb Collaboration has measured the processes
and , where the
and invariant mass distributions show the significant
signals of two new open-flavor tetraquark states and
, as the two of the isospin triplet. In this work, we
have investigated the process by taking into
account the intermediate nucleon resonance and the tetraquark state
, which could be dynamically generated by the
interactions of the and the pseoduscalar mesons-octet
baryons, respectively. Our results show that a clear peak of the open-flavor
tetraquark may appear in the invariant mass
distribution of the process , which could be tested
by future experiments.Comment: 9 pages, 11 figures, 1 tabl
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