arXiv subjects

Bin Luo

Publications and source records attributed to Bin Luo.

At least 217 records · Page 12Linked to original sources

arXiv2020

Supermassive black holes with high accretion rates in active galactic nuclei. XI. Accretion disk reverberation mapping of Mrk 142

We performed an intensive accretion disk reverberation mapping campaign on the high accretion rate active galactic nucleus Mrk 142 in early 2019. Mrk 142 was monitored with the Neil Gehrels Swift Observatory for 4 months in X-rays and 6 UV/optical filters. Ground-based photometric monitoring was obtained from the Las Cumbres Observatory, Liverpool Telescope and Dan Zowada Memorial Observatory in ugriz filters and the Yunnan Astronomical Observatory in V. Mrk 142 was highly variable throughout, displaying correlated variability across all wavelengths. We measure significant time lags between the different wavelength light curves, finding that through the UV and optical the wavelength-dependent lags, $τ(λ)$, generally follow the relation $τ(λ) \propto λ^{4/3}$, as expected for the $T\propto R^{-3/4}$ profile of a steady-state optically-thick, geometrically-thin accretion disk, though can also be fit by $τ(λ) \propto λ^{2}$, as expected for a slim disk. The exceptions are the u and U band, where an excess lag is observed, as has been observed in other AGN and attributed to continuum emission arising in the broad-line region. Furthermore, we perform a flux-flux analysis to separate the constant and variable components of the spectral energy distribution, finding that the flux-dependence of the variable component is consistent with the $f_ν\proptoν^{1/3}$ spectrum expected for a geometrically-thin accretion disk. Moreover, the X-ray to UV lag is significantly offset from an extrapolation of the UV/optical trend, with the X-rays showing a poorer correlation with the UV than the UV does with the optical. The magnitude of the UV/optical lags is consistent with a highly super-Eddington accretion rate.

astro-ph.HE↗

arXiv2020

On the relation between hard X-ray photon index versus accretion rate for super-Eddington accreting quasars

We investigate whether the hard X-ray photon index ($Γ$) versus accretion rate correlation for super-Eddington accreting quasars is different from that for sub-Eddington accreting quasars. We construct a sample of 113 bright quasars from the Sloan Digital Sky Survey Data Release 14 quasar catalog, including 38 quasars as the super-Eddington subsample and 75 quasars as the sub-Eddington subsample. We derive black-hole masses using a simple-epoch virial mass formula based on the ${\rm Hβ}$ lines, and we use the standard thin disk model to derive the dimensionless accretion rates ($\dot{\mathscr{M}}$) for our sample. The X-ray data for these quasars are collected from the Chandra and XMM-Newton archives. We fit the hard X-ray spectra using a single power-law model to obtain $Γ$ values. We find a statistically significant ($R_{\rm S}=0.43$, $p=7.75\times{10}^{-3}$) correlation between $Γ$ and $\dot{\mathscr{M}}$ for the super-Eddington subsample. The $Γ$-$\dot{\mathscr{M}}$ correlation for the sub-Eddington subsample is also significant, but weaker ($R_{\rm S}=0.30$, $p=9.98\times{10}^{-3}$). Linear regression analysis shows that ${\rm Γ}=(0.34\pm0.11){\rm log}{\dot{\mathscr{M}}}+(1.71\pm0.17)$ and ${\rm Γ}=(0.09\pm0.04){\rm log}{\dot{\mathscr{M}}}+(1.93\pm0.04)$ for the super- and sub-Eddington subsamples, respectively. The $Γ$-$\dot{\mathscr{M}}$ correlations of the two subsamples are different, suggesting different disk-corona connections in these two types of systems. We propose one qualitative explanation of the steeper $Γ$-$\dot{\mathscr{M}}$ correlation in the super-Eddington regime that involves larger seed photon fluxes received by the compact coronae from the thick disks in super-Eddington accreting quasars.

astro-ph.HE↗

arXiv2020

M$^5$L: Multi-Modal Multi-Margin Metric Learning for RGBT Tracking

Classifying the confusing samples in the course of RGBT tracking is a quite challenging problem, which hasn't got satisfied solution. Existing methods only focus on enlarging the boundary between positive and negative samples, however, the structured information of samples might be harmed, e.g., confusing positive samples are closer to the anchor than normal positive samples.To handle this problem, we propose a novel Multi-Modal Multi-Margin Metric Learning framework, named M$^5$L for RGBT tracking in this paper. In particular, we design a multi-margin structured loss to distinguish the confusing samples which play a most critical role in tracking performance boosting. To alleviate this problem, we additionally enlarge the boundaries between confusing positive samples and normal ones, between confusing negative samples and normal ones with predefined margins, by exploiting the structured information of all samples in each modality.Moreover, a cross-modality constraint is employed to reduce the difference between modalities and push positive samples closer to the anchor than negative ones from two modalities.In addition, to achieve quality-aware RGB and thermal feature fusion, we introduce the modality attentions and learn them using a feature fusion module in our network. Extensive experiments on large-scale datasets testify that our framework clearly improves the tracking performance and outperforms the state-of-the-art RGBT trackers.

cs.CV↗

arXiv2020

Ghost Imaging with the Optimal Binary Sampling

To extract the maximum information about the object from a series of binary samples in ghost imaging applications, we propose and demonstrate a framework for optimizing the performance of ghost imaging with binary sampling to approach the results without binarization. The method is based on maximizing the information content of the signal arm detection, by formulating and solving the appropriate parameter estimation problem - finding the binarization threshold that would yield the reconstructed image with optimal Fisher information properties. Applying the 1-bit quantized Poisson statistics to a ghost-imaging model with pseudo-thermal light, we derive the fundamental limit, i.e., the Cramer-Rao lower bound, as the benchmark for the evaluation of the accuracy of the estimator. Our theoertical model and experimental results suggest that, with the optimal binarization threshold, coincident with the statistical mean of all bucket samples, and large number of measurements, the performance of binary sampling GI can approach that of the ordinary one without binarization.

physics.data-an↗

arXiv2020

DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation

Most SLAM algorithms are based on the assumption that the scene is static. However, in practice, most scenes are dynamic which usually contains moving objects, these methods are not suitable. In this paper, we introduce DymSLAM, a dynamic stereo visual SLAM system being capable of reconstructing a 4D (3D + time) dynamic scene with rigid moving objects. The only input of DymSLAM is stereo video, and its output includes a dense map of the static environment, 3D model of the moving objects and the trajectories of the camera and the moving objects. We at first detect and match the interesting points between successive frames by using traditional SLAM methods. Then the interesting points belonging to different motion models (including ego-motion and motion models of rigid moving objects) are segmented by a multi-model fitting approach. Based on the interesting points belonging to the ego-motion, we are able to estimate the trajectory of the camera and reconstruct the static background. The interesting points belonging to the motion models of rigid moving objects are then used to estimate their relative motion models to the camera and reconstruct the 3D models of the objects. We then transform the relative motion to the trajectories of the moving objects in the global reference frame. Finally, we then fuse the 3D models of the moving objects into the 3D map of the environment by considering their motion trajectories to obtain a 4D (3D+time) sequence. DymSLAM obtains information about the dynamic objects instead of ignoring them and is suitable for unknown rigid objects. Hence, the proposed system allows the robot to be employed for high-level tasks, such as obstacle avoidance for dynamic objects. We conducted experiments in a real-world environment where both the camera and the objects were moving in a wide range.

cs.CV↗

arXiv2019

Semi-supervised Learning with Adaptive Neighborhood Graph Propagation Network

Graph Convolutional Networks (GCNs) have been widely studied for compact data representation and semi-supervised learning tasks. However, existing GCNs usually use a fixed neighborhood graph which is not guaranteed to be optimal for semi-supervised learning tasks. In this paper, we first re-interpret graph convolution operation in GCNs as a composition of feature propagation and (non-linear) transformation. Based on this observation, we then propose a unified adaptive neighborhood feature propagation model and derive a novel Adaptive Neighborhood Graph Propagation Network (ANGPN) for data representation and semi-supervised learning. The aim of ANGPN is to conduct both graph construction and graph convolution simultaneously and cooperatively in a unified formulation and thus can learn an optimal neighborhood graph that best serves graph convolution for data representation and semi-supervised learning. One main benefit of ANGPN is that the learned (convolutional) representation can provide useful weakly supervised information for constructing a better neighborhood graph which meanwhile facilitates data representation and learning. Experimental results on four benchmark datasets demonstrate the effectiveness and benefit of the proposed ANGPN.

cs.CV↗

arXiv2019

GmCN: Graph Mask Convolutional Network

Graph Convolutional Networks (GCNs) have shown very powerful for graph data representation and learning tasks. Existing GCNs usually conduct feature aggregation on a fixed neighborhood graph in which each node computes its representation by aggregating the feature representations of all its neighbors which is biased by its own representation. However, this fixed aggregation strategy is not guaranteed to be optimal for GCN based graph learning and also can be affected by some graph structure noises, such as incorrect or undesired edge connections. To address these issues, we propose a novel Graph mask Convolutional Network (GmCN) in which nodes can adaptively select the optimal neighbors in their feature aggregation to better serve GCN learning. GmCN can be theoretically interpreted by a regularization framework, based on which we derive a simple update algorithm to determine the optimal mask adaptively in GmCN training process. Experiments on several datasets validate the effectiveness of GmCN.

cs.CV↗

arXiv2019

GLMNet: Graph Learning-Matching Networks for Feature Matching

Recently, graph convolutional networks (GCNs) have shown great potential for the task of graph matching. It can integrate graph node feature embedding, node-wise affinity learning and matching optimization together in a unified end-to-end model. One important aspect of graph matching is the construction of two matching graphs. However, the matching graphs we feed to existing graph convolutional matching networks are generally fixed and independent of graph matching, which thus are not guaranteed to be optimal for the graph matching task. Also, existing GCN matching method employs several general smoothing-based graph convolutional layers to generate graph node embeddings, in which extensive smoothing convolution operation may dilute the desired discriminatory information of graph nodes. To overcome these issues, we propose a novel Graph Learning-Matching Network (GLMNet) for graph matching problem. GLMNet has three main aspects. (1) It integrates graph learning into graph matching which thus adaptively learn a pair of optimal graphs that best serve graph matching task. (2) It further employs a Laplacian sharpening convolutional module to generate more discriminative node embeddings for graph matching. (3) A new constraint regularized loss is designed for GLMNet training which can encode the desired one-to-one matching constraints in matching optimization. Experiments on two benchmarks demonstrate the effectiveness of GLMNet and advantages of its main modules.

cs.CV↗

arXiv2019

Multiple reconstruction compression framework based on PNG image

It is shown that neural networks (NNs) achieve excellent performances in image compression and reconstruction. However, there are still many shortcomings in the practical application, which eventually lead to the loss of neural network image processing ability. Based on this, this paper proposes a joint framework based on neural network and zoom compression. The framework first encodes the incoming PNG or JPEG image information, and then the image is converted into binary input decoder to reconstruct the intermediate state image, next we import the intermediate state image into the zooming compressor and re-pressurize it, and reconstruct the final image. From the experimental results, this method can better process the digital image and suppress the reverse expansion problem, and the compression effect can be improved by 4 to 10 times as much as that of using RNN alone, showing better ability in application. In this paper, the method is transmitted over a digital image, the effect is far better than the existing compression method alone, the Human visual system can not feel the change of the effect.

cs.MM↗

arXiv2019

Single-shot Precise Ranging using Twisted Light

Over the past decade, optical orbital angular momentum (OAM) modes were shown to offer advantages in optical information acquisition. Here, we introduce a new scheme for optical ranging in which depth is estimated through the angular rotation of petal-like patterns produced by superposition of OAM modes. Uncertainty of depth estimation in our strategy depends on how fast the petal-like pattern rotates and how precisely the rotation angle can be estimated. The impact of these two factors on ranging accuracy are analyzed in presence of noise. We show that focusing the probe beam provides a quadratic enhancement on ranging accuracy because rotation speed of the beam is inversely proportional to the square of beam radius. Uncertainty of depth estimation is also proportional to uncertainty of rotation estimation, which can be optimized by picking proper OAM superposition. Finally, we unveil the possibility of optical ranging for scattering surface with uncertainties of few micrometers under noise. Unlike existing methods which rely on continuous detection for a period of time to achieve such ranging accuracy, our scheme needs only single-shot measurement.

physics.optics↗

arXiv2019

Stereo-based Multi-motion Visual Odometry for Mobile Robots

With the development of computer vision, visual odometry is adopted by more and more mobile robots. However, we found that not only its own pose, but the poses of other moving objects are also crucial for the decision of the robot. In addition, the visual odometry will be greatly disturbed when a significant moving object appears. In this letter, a stereo-based multi-motion visual odometry method is proposed to acquire the poses of the robot and other moving objects. In order to obtain the poses simultaneously, a continuous motion segmentation module and a coordinate conversion module are applied to the traditional visual odometry pipeline. As a result, poses of all moving objects can be acquired and transformed into the ground coordinate system. The experimental results show that the proposed multi-motion visual odometry can effectively eliminate the influence of moving objects on the visual odometry, as well as achieve 10 cm in position and 3° in orientation RMSE (Root Mean Square Error) of each moving object.

cs.CV↗

arXiv2019

FANet: Quality-Aware Feature Aggregation Network for Robust RGB-T Tracking

This paper investigates how to perform robust visual tracking in adverse and challenging conditions using complementary visual and thermal infrared data (RGBT tracking). We propose a novel deep network architecture called qualityaware Feature Aggregation Network (FANet) for robust RGBT tracking. Unlike existing RGBT trackers, our FANet aggregates hierarchical deep features within each modality to handle the challenge of significant appearance changes caused by deformation, low illumination, background clutter and occlusion. In particular, we employ the operations of max pooling to transform these hierarchical and multi-resolution features into uniform space with the same resolution, and use 1x1 convolution operation to compress feature dimensions to achieve more effective hierarchical feature aggregation. To model the interactions between RGB and thermal modalities, we elaborately design an adaptive aggregation subnetwork to integrate features from different modalities based on their reliabilities and thus are able to alleviate noise effects introduced by low-quality sources. The whole FANet is trained in an end-to-end manner. Extensive experiments on large-scale benchmark datasets demonstrate the high-accurate performance against other state-of-the-art RGBT tracking methods.

cs.CV↗

arXiv2019

Control of Structured Light Enables Nearly Perfect Noise-filtering

The performance of laser-based active sensing has been severely limited by two types of noise: electrical noise, stemming from elements; optical noise, laser jamming from an eavesdropper and background from environment. Conventional methods to filter optical noise take advantage of the differences between signal and noise in time, wavelength, and polarization. However, they may be limited when the noise and signal share the same information on these degrees of freedoms (DoFs). In order to overcome this drawback, we experimentally demonstrate a groundbreaking noise-filtering method by controlling orbital angular momentum (OAM) to distinguish signal from noise. We provide a proof-of-principle experiment and discuss the dependence of azimuthal index of OAM and detection aperture on signal-to-noise ratio (SNR). Our results suggest that using OAM against noise is an efficient method, offering a new route to optical sensing immersed in high-level noise.

physics.optics↗

arXiv2019

Context-Aware Graph Attention Networks

Graph Neural Networks (GNNs) have been widely studied for graph data representation and learning. However, existing GNNs generally conduct context-aware learning on node feature representation only which usually ignores the learning of edge (weight) representation. In this paper, we propose a novel unified GNN model, named Context-aware Adaptive Graph Attention Network (CaGAT). CaGAT aims to learn a context-aware attention representation for each graph edge by further exploiting the context relationships among different edges. In particular, CaGAT conducts context-aware learning on both node feature representation and edge (weight) representation simultaneously and cooperatively in a unified manner which can boost their respective performance in network training. We apply CaGAT on semi-supervised learning tasks. Promising experimental results on several benchmark datasets demonstrate the effectiveness and benefits of CaGAT.

cs.LG↗

arXiv2019

The X-ray properties of $z>6$ quasars: no evident evolution of accretion physics in the first Gyr of the Universe

X-ray emission from QSOs has been used to assess SMBH accretion properties up to $z$~6. However, at $z>6$ only ~15 QSOs are covered by sensitive X-ray observations, preventing a statistically significant investigation of the X-ray properties of QSOs in the first Gyr of the Universe. We present new Chandra observations of 10 $z>6$ QSOs, selected to have virial black-hole mass estimates from Mg II line spectroscopy. Adding archival X-ray data for an additional 15 $z>6$ QSOs, we investigate the X-ray properties of the QSO population in the first Gyr of the Universe, focusing in particular on the $L_{UV}-L_{X}$ relation, which is traced by the $α_{ox}$ parameter, and the shape of their X-ray spectra. We performed photometric analyses to derive estimates of the X-ray luminosities, and thus the $α_{ox}$ values and bolometric corrections ($K_{bol}=L_{bol}/L_{X}$). We compared the resulting $α_{ox}$ and $K_{bol}$ distributions with the results found for QSO samples at lower redshift. Finally, we performed a basic X-ray spectral analysis of the brightest $z>6$ QSOs to derive their individual photon indices, and joint spectral analysis of the whole sample to estimate the average photon index. We confirm a lack of significant evolution of $α_{ox}$ with redshift, extending the results from previous works up to $z>6$, and the trend of an increasing bolometric correction with increasing luminosity found for QSOs at lower redshifts. The average power-law photon index of our sample ($Γ=2.20_{-0.34}^{+0.39}$ and $Γ=2.13_{-0.13}^{+0.13}$ for sources with $<30$ and $>30$ net counts, respectively) is slightly steeper than, but still consistent with, typical QSOs at $z=1-6$. All these results point toward a lack of substantial evolution of the inner accretion-disk/hot-corona structure in QSOs from low redshift to $z>6$. Our data hint at generally high Eddington ratios at $z>6$.

astro-ph.GA↗

arXiv2019

Residual Objectness for Imbalance Reduction

For a long time, object detectors have suffered from extreme imbalance between foregrounds and backgrounds. While several sampling/reweighting schemes have been explored to alleviate the imbalance, they are usually heuristic and demand laborious hyper-parameters tuning, which is hard to achieve the optimality. In this paper, we first reveal that such the imbalance could be addressed in a learning-based manner. Guided by this illuminating observation, we propose a novel Residual Objectness (ResObj) mechanism that addresses the imbalance by end-to-end optimization, while no further hand-crafted sampling/reweighting is required. Specifically, by applying multiple cascaded objectness-related modules with residual connections, we formulate an elegant consecutive refinement procedure for distinguishing the foregrounds from backgrounds, thereby progressively addressing the imbalance. Extensive experiments present the effectiveness of our method, as well as its compatibility and adaptivity for both region-based and one-stage detectors, namely, the RetinaNet-ResObj, YOLOv3-ResObj and FasterRCNN-ResObj achieve relative 3.6%, 3.9%, 3.2% Average Precision (AP) improvements compared with their vanilla models on COCO, respectively.

cs.CV↗

arXiv2019

Edge-guided Non-local Fully Convolutional Network for Salient Object Detection

Fully Convolutional Neural Network (FCN) has been widely applied to salient object detection recently by virtue of high-level semantic feature extraction, but existing FCN based methods still suffer from continuous striding and pooling operations leading to loss of spatial structure and blurred edges. To maintain the clear edge structure of salient objects, we propose a novel Edge-guided Non-local FCN (ENFNet) to perform edge guided feature learning for accurate salient object detection. In a specific, we extract hierarchical global and local information in FCN to incorporate non-local features for effective feature representations. To preserve good boundaries of salient objects, we propose a guidance block to embed edge prior knowledge into hierarchical feature maps. The guidance block not only performs feature-wise manipulation but also spatial-wise transformation for effective edge embeddings. Our model is trained on the MSRA-B dataset and tested on five popular benchmark datasets. Comparing with the state-of-the-art methods, the proposed method achieves the best performance on all datasets.

cs.CV↗

arXiv2019

Improved Hard Example Mining by Discovering Attribute-based Hard Person Identity

In this paper, we propose Hard Person Identity Mining (HPIM) that attempts to refine the hard example mining to improve the exploration efficacy in person re-identification. It is motivated by following observation: the more attributes some people share, the more difficult to separate their identities. Based on this observation, we develop HPIM via a transferred attribute describer, a deep multi-attribute classifier trained from the source noisy person attribute datasets. We encode each image into the attribute probabilistic description in the target person re-ID dataset. Afterwards in the attribute code space, we consider each person as a distribution to generate his view-specific attribute codes in different practical scenarios. Hence we estimate the person-specific statistical moments from zeroth to higher order, which are further used to calculate the central moment discrepancies between persons. Such discrepancy is a ground to choose hard identity to organize proper mini-batches, without concerning the person representation changing in metric learning. It presents as a complementary tool of hard example mining, which helps to explore the global instead of the local hard example constraint in the mini-batch built by randomly sampled identities. Extensive experiments on two person re-identification benchmarks validated the effectiveness of our proposed algorithm.

cs.CV↗