arXiv subjects

Bin Luo

Publications and source records attributed to Bin Luo.

At least 235 records · Page 13Linked to original sources

arXiv2019

Segmenting Objects in Day and Night:Edge-Conditioned CNN for Thermal Image Semantic Segmentation

Despite much research progress in image semantic segmentation, it remains challenging under adverse environmental conditions caused by imaging limitations of visible spectrum. While thermal infrared cameras have several advantages over cameras for the visible spectrum, such as operating in total darkness, insensitive to illumination variations, robust to shadow effects and strong ability to penetrate haze and smog. These advantages of thermal infrared cameras make the segmentation of semantic objects in day and night. In this paper, we propose a novel network architecture, called edge-conditioned convolutional neural network (EC-CNN), for thermal image semantic segmentation. Particularly, we elaborately design a gated feature-wise transform layer in EC-CNN to adaptively incorporate edge prior knowledge. The whole EC-CNN is end-to-end trained, and can generate high-quality segmentation results with the edge guidance. Meanwhile, we also introduce a new benchmark dataset named "Segment Objects in Day And night"(SODA) for comprehensive evaluations in thermal image semantic segmentation. SODA contains over 7,168 manually annotated and synthetically generated thermal images with 20 semantic region labels and from a broad range of viewpoints and scene complexities. Extensive experiments on SODA demonstrate the effectiveness of the proposed EC-CNN against the state-of-the-art methods.

cs.CV↗

arXiv2019

Dense Feature Aggregation and Pruning for RGBT Tracking

How to perform effective information fusion of different modalities is a core factor in boosting the performance of RGBT tracking. This paper presents a novel deep fusion algorithm based on the representations from an end-to-end trained convolutional neural network. To deploy the complementarity of features of all layers, we propose a recursive strategy to densely aggregate these features that yield robust representations of target objects in each modality. In different modalities, we propose to prune the densely aggregated features of all modalities in a collaborative way. In a specific, we employ the operations of global average pooling and weighted random selection to perform channel scoring and selection, which could remove redundant and noisy features to achieve more robust feature representation. Experimental results on two RGBT tracking benchmark datasets suggest that our tracker achieves clear state-of-the-art against other RGB and RGBT tracking methods.

cs.CV↗

arXiv2019

PH-GCN: Person Re-identification with Part-based Hierarchical Graph Convolutional Network

The person re-identification (Re-ID) task requires to robustly extract feature representations for person images. Recently, part-based representation models have been widely studied for extracting the more compact and robust feature representations for person images to improve person Re-ID results. However, existing part-based representation models mostly extract the features of different parts independently which ignore the relationship information between different parts. To overcome this limitation, in this paper we propose a novel deep learning framework, named Part-based Hierarchical Graph Convolutional Network (PH-GCN) for person Re-ID problem. Given a person image, PH-GCN first constructs a hierarchical graph to represent the pairwise relationships among different parts. Then, both local and global feature learning are performed by the messages passing in PH-GCN, which takes other nodes information into account for part feature representation. Finally, a perceptron layer is adopted for the final person part label prediction and re-identification. The proposed framework provides a general solution that integrates local, global and structural feature learning simultaneously in a unified end-to-end network. Extensive experiments on several benchmark datasets demonstrate the effectiveness of the proposed PH-GCN based Re-ID approach.

cs.CV↗

arXiv2019

Discovery of the first heavily obscured QSO candidate at $z>6$ in a close galaxy pair

While theoretical arguments predict that most of the early growth of supermassive black holes (SMBHs) happened during heavily obscured phases of accretion, current methods used for selecting $z>6$ quasars (QSOs) are strongly biased against obscured QSOs, thus considerably limiting our understanding of accreting SMBHs during the first Gyr of the Universe from an observational point of view. We report the $Chandra$ discovery of the first heavily obscured QSO candidate in the early universe, hosted by a close ($\approx5$ kpc) galaxy pair at $z=6.515$. One of the members is an optically classified type 1 QSO, PSO167-13. The companion galaxy was first detected as a [C II] emitter by ALMA. An X-ray source is significantly ($P=0.9996$) detected by $Chandra$ in the 2-5 keV band, with $<1.14$ net counts in the 0.5-2 keV band, although the current positional uncertainty does not allow a conclusive association with either PSO167-13 or its companion galaxy. From X-ray photometry and hardness-ratio arguments, we estimated an obscuring column density of $N_H>2\times10^{24}\,\mathrm{cm^{-2}}$ and $N_H>6\times10^{23}\,\mathrm{cm^{-2}}$ at $68\%$ and $90\%$ confidence levels, respectively. Thus, regardless of which of the two galaxies is associated with the X-ray emission, this source is the first heavily obscured QSO candidate at $z>6$.

astro-ph.GA↗

arXiv2019

Reversible Data Hiding in JPEG Images with Multi-objective Optimization

Among various methods of reversible data hiding (RDH) in JPEG images, the consideration in designing is only the image quality, but the image quality and the file size expansion are equally important in JPEG images. Based on this situation, we propose a RDH scheme in JPEG images considering both the image quality and the file size expansion while designing the algorithm. The multi-objective optimization strategy is utilized to realize the balance of the two objectives. Specifically, the cover is divided into several non-overlapping signals firstly, and after that, the embedding costs of signals are calculated using the knowledge of the JPEG compression. Next, the optimized combination of signals for embedding data is gained by the multi-objective optimization. Experimental results show the better performance of our proposed RDH compared with state-of-the-art RDH in JPEG images.

cs.MM↗

arXiv2019

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.

astro-ph.GA↗

arXiv2019

Multifunctional photonic integrated circuit for diverse microwave signal generation, transmission and processing

Microwave photonics (MWP) studies the interaction between microwave and optical waves for the generation, transmission and processing of microwave signals (i.e., three key domains), taking advantages of broad bandwidth and low loss offered by modern photonics. Integrated MWP using photonic integrated circuits (PICs) can reach a compact, reliable and green implementation. Most PICs, however, are recently developed to perform one or more functions restricted inside a single domain. In this paper, as highly desired, a multifunctional PIC is proposed to cover the three key domains. The PIC is fabricated on InP platform by monolithically integrating four laser diodes and two modulators. Using the multifunctional PIC, seven fundamental functions across microwave signal generation, transmission and processing are demonstrated experimentally. Outdoor field trials for electromagnetic environment surveillance along an in-service high-speed railway are also performed. The success to such a PIC marks a key step forward for practical and massive MWP implementations.

physics.app-ph↗

arXiv2019

Electromagnetic Window into the Dawn of Black Holes

Massive 10^6-10^10 Msun black holes (BHs) are ubiquitous in local galactic nuclei. They were common by the time the Universe is several Gyr old, and many of them were in place within the first 1~Gyr after the Big Bang. Their quick assembly has been attributed to mechanisms such as the rapid collapse of gas into the nuclei of early protogalaxies, accretion and mergers of stellar-mass BHs accompanying structure formation at early times, and the runaway collapse of early, ultra-dense stellar clusters. The origin of the early massive BHs remains an intriguing and long-standing unsolved puzzle in astrophysics. Here we discuss strategies for discerning between BH seeding models using electromagnetic observations. We argue that the most direct answers will be obtained through detection of BHs with masses M<10^5 Msun at redshifts z>10, where we expect them to first form. Reaching out to these redshifts and down to these masses is crucial, because BHs are expected to lose the memory of their initial assembly by the time they grow well above 10^5 Msun and are incorporated into higher-mass galaxies. The best way to detect 10^4-10^5 Msun BHs at high redshifts is by a sensitive X-ray survey. Critical constraining power is augmented by establishing the properties and the environments of their host galaxies in deep optical/IR imaging surveys. Required OIR data can be obtained with the JWST and WFIRST missions. The required X-ray flux limits (down to 10^{-19} erg/s/cm^2) are accessible only with a next-generation X-ray observatory which has both high (sub-1") angular resolution and high throughput. A combination of deep X-ray and OIR surveys will be capable of probing several generic markers of the BH seed scenarios, and resolving the long-stanging puzzle of their origin. These electromagnetic observations are also highly synergistic with the information from LISA on high-z BH mergers.

astro-ph.HE↗

arXiv2019

Multiple Graph Adversarial Learning

Recently, Graph Convolutional Networks (GCNs) have been widely studied for graph-structured data representation and learning. However, in many real applications, data are coming with multiple graphs, and it is non-trivial to adapt GCNs to deal with data representation with multiple graph structures. One main challenge for multi-graph representation is how to exploit both structure information of each individual graph and correlation information across multiple graphs simultaneously. In this paper, we propose a novel Multiple Graph Adversarial Learning (MGAL) framework for multi-graph representation and learning. MGAL aims to learn an optimal structure-invariant and consistent representation for multiple graphs in a common subspace via a novel adversarial learning framework, which thus incorporates both structure information of intra-graph and correlation information of inter-graphs simultaneously. Based on MGAL, we then provide a unified network for semi-supervised learning task. Promising experimental results demonstrate the effectiveness of MGAL model.

cs.CV↗

arXiv2018

Accretion in Strong Field Gravity with eXTP

In this paper we describe the potential of the enhanced X-ray Timing and Polarimetry (eXTP) mission for studies related to accretion flows in the strong field gravity regime around both stellar-mass and supermassive black-holes. eXTP has the unique capability of using advanced 'spectral-timing-polarimetry' techniques to analyze the rapid variations with three orthogonal diagnostics of the flow and its geometry, yielding unprecedented insight into the inner accreting regions, the effects of strong field gravity on the material within them and the powerful outflows which are driven by the accretion process.

astro-ph.HE↗

arXiv2018

Describe and Attend to Track: Learning Natural Language guided Structural Representation and Visual Attention for Object Tracking

The tracking-by-detection framework requires a set of positive and negative training samples to learn robust tracking models for precise localization of target objects. However, existing tracking models mostly treat different samples independently while ignores the relationship information among them. In this paper, we propose a novel structure-aware deep neural network to overcome such limitations. In particular, we construct a graph to represent the pairwise relationships among training samples, and additionally take the natural language as the supervised information to learn both feature representations and classifiers robustly. To refine the states of the target and re-track the target when it is back to view from heavy occlusion and out of view, we elaborately design a novel subnetwork to learn the target-driven visual attentions from the guidance of both visual and natural language cues. Extensive experiments on five tracking benchmark datasets validated the effectiveness of our proposed method.

cs.CV↗

arXiv2018

Quality-Aware Multimodal Saliency Detection via Deep Reinforcement Learning

Incorporating various modes of information into the machine learning procedure is becoming a new trend. And data from various source can provide more information than single one no matter they are heterogeneous or homogeneous. Existing deep learning based algorithms usually directly concatenate features from each domain to represent the input data. Seldom of them take the quality of data into consideration which is a key issue in related multimodal problems. In this paper, we propose an efficient quality-aware deep neural network to model the weight of data from each domain using deep reinforcement learning (DRL). Specifically, we take the weighting of each domain as a decision-making problem and teach an agent learn to interact with the environment. The agent can tune the weight of each domain through discrete action selection and obtain a positive reward if the saliency results are improved. The target of the agent is to achieve maximum rewards after finished its sequential action selection. We validate the proposed algorithms on multimodal saliency detection in a coarse-to-fine way. The coarse saliency maps are generated from an encoder-decoder framework which is trained with content loss and adversarial loss. The final results can be obtained via adaptive weighting of maps from each domain. Experiments conducted on two kinds of salient object detection benchmarks validated the effectiveness of our proposed quality-aware deep neural network.

cs.CV↗

arXiv2018

No evidence for an Eddington-ratio dependence of X-ray weakness in BALQSOs

Several works have studied the relation between X-ray, UV, and wind properties in broad absorption line quasars (BALQSOs), generally concluding that the formation of strong winds is tightly connected with the suppression of the ionizing EUV/X-ray emission. The Eddington ratio ($λ_{Edd}$), which measures the accretion rate, is also known to be related with outflow and emission-line properties in the general quasar population. Moreover, models describing quasar accretion depend on $λ_{Edd}$, which can thus possibly affect the relative production of accelerating UV and ionizing EUV/X-ray radiation. In this work, for the first time, we investigated whether BALQSO X-ray properties are related with the Eddington ratio. We selected a sample of 30 BALQSOs with accurate measurements of black-hole mass and BAL properties from the literature, and we complemented it with 4 additional BALQSOs we observed with \xmm\, to populate the low and high Eddington-ratio regimes. We did not find evidence for a strong relation between $λ_{Edd}$ and X-ray suppression, which however shows a significant correlation with the strength of the UV absorption features. These findings are confirmed also by considering a sample of mini-BALQSOs collected from the literature.

astro-ph.GA↗

arXiv2018

Data-Driven Exploration of Factors Affecting Federal Student Loan Repayment

Student loans occupy a significant portion of the federal budget, as well as, the largest financial burden in terms of debt for graduates. This paper explores data-driven approaches towards understanding the repayment of such loans. Using statistical and machine learning models on the College Scorecard Data, this research focuses on extracting and identifying key factors affecting the repayment of a student loan. The specific factors can be used to develop models which provide predictive capability towards repayment rate, detect irregularities/non-repayment, and help understand the intricacies of student loans.

stat.AP↗

arXiv2018

Temporal Coherent and Graph Optimized Manifold Ranking for Visual Tracking

Recently, weighted patch representation has been widely studied for alleviating the impact of background information included in bounding box to improve visual tracking results. However, existing weighted patch representation models generally exploit spatial structure information among patches in each frame separately which ignore (1) unary featureof each patch and (2) temporal correlation among patches in different frames. To address this problem, we propose a novel unified temporal coherence and graph optimized ranking model for weighted patch representation in visual tracking problem. There are three main contributions of this paper. First, we propose to employ a flexible graph ranking for patch weight computation which exploits both structure information among patches and unary feature of each patch simultaneously. Second, we propose a new more discriminative ranking model by further considering the temporal correlation among patches in different frames. Third, a neighborhood preserved, low-rank graph is learned and incorporated to build a unified optimized ranking model. Experiments on two benchmark datasets show the benefits of our model.

cs.CV↗

arXiv2018

Timing Channel in IaaS: How to Identify and Investigate

Recently, the IaaS (Infrastructure as a Service) Cloud (e.g., Amazon EC2) has been widely used by many organizations. However, some IaaS security issues create serious threats to its users. A typical issue is the timing channel. This kind of channel can be a cross-VM information channel, as proven by many researchers. Because it is covert and traceless, the traditional identification methods cannot build an accurate analysis model and obtain a compromised result. We investigated the underlying behavior of the timing channel from the perspective of the memory activity records and summarized the signature of the timing channel in the underlying memory activities. An identification method based on long-term behavior signatures was proposed. We proposed a complete set of forensics steps including evidence extraction, identification, record reserve, and evidence reports. We studied four typical timing channels, and the experiments showed that these channels can be detected and investigated, even with disturbances from normal processes.

cs.CR↗

arXiv2017

Sidebands suppression of 852 nm Cesium Faraday anomalous dispersion optical filter by argon gas

In this work, sidebands suppression brought by buffer gas argon (Ar) in cesium Faraday anomalous dispersion optical filter (FADOF) at 852 nm is investigated. FADOF performances at different Ar pressures (0 torr, 1 torr, 5 torr and 10 torr) are compared, and a single-peak transmittance spectrum with peak transmittance up to 80% is achieved at the high Ar pressure. A detailed analysis shows that, this sidebands suppression comes from the depopulation enhancement by the buffer gas. This result can be generalized to other FADOFs with similar level structures such as the D2 lines of other alkali metal atoms.

physics.atom-ph↗

arXiv2017

Atomic optical amplifier with narrow bandwidth optical filtering

Taking advantages of ultra-narrow bandwidth and high noise rejection performance of the Faraday anomalous dispersion optical filter (FADOF), simultaneously with the coherent amplification of atomic stimulated emission, a stimulated amplified Faraday anomalous dispersion optical filter (SAFADOF) at cesium 1470 nm is realized. The SAFADOF is able to significantly amplify very weak laser signals and reject noise in order to obtain clean signals in strong background. Experiment results show that, for a weak signal of 50 pW, the gain factor can be larger than 25000 (44 dB) within a bandwidth as narrow as 13 MHz. Having this ability to amplify weak signals with low background contribution, the SAFADOF finds outstanding potential applications in weak signal detections.

physics.optics↗