arXiv subjects

Bin Luo

Publications and source records attributed to Bin Luo.

At least 91 records · Page 5Linked to original sources

arXiv2025

Intervening nuclear obscuration changing the X-ray look of the $z\approx6$ QSO CFHQS J164121+375520

X-ray observations of the optically selected $z=6.025$ QSO CFHQS J164121+375520 (hereafter J1641) revealed that its flux dropped by a factor $\gtrsim7$ from 2018, when it was a bright and soft X-ray source, to 2021. Such a strong variability amplitude has not been observed before among $z>6$ QSOs, and the underlying physical mechanism was unclear. We carried out a new X-ray and rest-frame UV monitoring campaign of J1641 over 2022-2024. We detected J1641 with Chandra in the 2-7 keV band, while no significant emission is detected at softer X-ray energies, making J1641 an X-ray changing look QSO at $z>6$. Comparing with the 2018 epoch, the 0.5-2 keV flux dropped dramatically by a factor $>20$. We ascribe this behaviour to intervening, and still ongoing, obscuration by Compton-thick gas intercepting our line of sight between 2018 and 2021. The screening material could be an inner disk or a failed nuclear wind that increased their thickness. Another possibility is that we have witnessed an occultation event due to dust-free clouds located at sub-pc/pc scales, similar to those recently invoked to explain the remarkable X-ray weakness of AGN discovered by JWST. These interpretations are also consistent with the lack of strong variations of the QSO rest-frame UV lightcurve over the same period. Future monitoring of J1641 and the possible discovery of other X-ray changing look QSOs at $z>6$ will provide us with precious information about the physics of rapid supermassive black-hole growth at high redshift.

astro-ph.GA↗

arXiv2025

Nighttime Person Re-Identification via Collaborative Enhancement Network with Multi-domain Learning

Prevalent nighttime person re-identification (ReID) methods typically combine image relighting and ReID networks in a sequential manner. However, their performance (recognition accuracy) is limited by the quality of relighting images and insufficient collaboration between image relighting and ReID tasks. To handle these problems, we propose a novel Collaborative Enhancement Network called CENet, which performs the multilevel feature interactions in a parallel framework, for nighttime person ReID. In particular, the designed parallel structure of CENet can not only avoid the impact of the quality of relighting images on ReID performance, but also allow us to mine the collaborative relations between image relighting and person ReID tasks. To this end, we integrate the multilevel feature interactions in CENet, where we first share the Transformer encoder to build the low-level feature interaction, and then perform the feature distillation that transfers the high-level features from image relighting to ReID, thereby alleviating the severe image degradation issue caused by the nighttime scenario while avoiding the impact of relighting images. In addition, the sizes of existing real-world nighttime person ReID datasets are limited, and large-scale synthetic ones exhibit substantial domain gaps with real-world data. To leverage both small-scale real-world and large-scale synthetic training data, we develop a multi-domain learning algorithm, which alternately utilizes both kinds of data to reduce the inter-domain difference in training procedure. Extensive experiments on two real nighttime datasets, \textit{Night600} and \textit{RGBNT201$_{rgb}$}, and a synthetic nighttime ReID dataset are conducted to validate the effectiveness of CENet. We release the code and synthetic dataset at: \hyperlink{https://github.com/Alexadlu/CENet}{\color{red} https://github.com/Alexadlu/CENet}.

cs.CV↗

arXiv2024

Modality-missing RGBT Tracking: Invertible Prompt Learning and High-quality Benchmarks

Current RGBT tracking research relies on the complete multi-modal input, but modal information might miss due to some factors such as thermal sensor self-calibration and data transmission error, called modality-missing challenge in this work. To address this challenge, we propose a novel invertible prompt learning approach, which integrates the content-preserving prompts into a well-trained tracking model to adapt to various modality-missing scenarios, for robust RGBT tracking. Given one modality-missing scenario, we propose to utilize the available modality to generate the prompt of the missing modality to adapt to RGBT tracking model. However, the cross-modality gap between available and missing modalities usually causes semantic distortion and information loss in prompt generation. To handle this issue, we design the invertible prompter by incorporating the full reconstruction of the input available modality from the generated prompt. To provide a comprehensive evaluation platform, we construct several high-quality benchmark datasets, in which various modality-missing scenarios are considered to simulate real-world challenges. Extensive experiments on three modality-missing benchmark datasets show that our method achieves significant performance improvements compared with state-of-the-art methods. We have released the code and simulation datasets at: \href{https://github.com/Alexadlu/Modality-missing-RGBT-Tracking.git}{https://github.com/Alexadlu/Modality-missing-RGBT-Tracking.git}.

cs.CV↗

arXiv2024

RGBT Tracking via All-layer Multimodal Interactions with Progressive Fusion Mamba

Existing RGBT tracking methods often design various interaction models to perform cross-modal fusion of each layer, but can not execute the feature interactions among all layers, which plays a critical role in robust multimodal representation, due to large computational burden. To address this issue, this paper presents a novel All-layer multimodal Interaction Network, named AINet, which performs efficient and effective feature interactions of all modalities and layers in a progressive fusion Mamba, for robust RGBT tracking. Even though modality features in different layers are known to contain different cues, it is always challenging to build multimodal interactions in each layer due to struggling in balancing interaction capabilities and efficiency. Meanwhile, considering that the feature discrepancy between RGB and thermal modalities reflects their complementary information to some extent, we design a Difference-based Fusion Mamba (DFM) to achieve enhanced fusion of different modalities with linear complexity. When interacting with features from all layers, a huge number of token sequences (3840 tokens in this work) are involved and the computational burden is thus large. To handle this problem, we design an Order-dynamic Fusion Mamba (OFM) to execute efficient and effective feature interactions of all layers by dynamically adjusting the scan order of different layers in Mamba. Extensive experiments on four public RGBT tracking datasets show that AINet achieves leading performance against existing state-of-the-art methods.

cs.CV↗

arXiv2024

Inline-Amplification-Free Time Transfer Utilizing Waveform-Resolved Single-Photon Detection

High-precision time transfer over a long haul of fiber plays a significant role in many fields. The core method, namely cascading relay nodes for the compensation of signal attenuation and dispersion, is however insufficient to deal with crucial point-to-point transfer scenarios, such as harsh environments with extremely deficient infrastructure and emergency conditions. In long-distance signal transmission without any inline amplifiers, the high loss of the optical fiber link becomes the primary limiting factor, and direct use of traditional photodetectors at the receiving end will bring about a significant drop in the stability of detected signals. Here we propose a waveform-resolved single photon detection technique and experimentally perform tomography on the weak transferred signal with an average photon number of just 0.617 per pulse. By adopting this technique, we achieve the time deviation of 95.68 ps and 192.58 ps at 200 km and 300 km respectively at an averaging time of 1 s, overcoming the technical lower bound induced by traditional photodetectors. This work lays the foundation for through-type time transfer with high precision in those significant inline-amplification-free scenarios.

physics.optics↗

arXiv2024

Alignment-Free RGB-T Salient Object Detection: A Large-scale Dataset and Progressive Correlation Network

Alignment-free RGB-Thermal (RGB-T) salient object detection (SOD) aims to achieve robust performance in complex scenes by directly leveraging the complementary information from unaligned visible-thermal image pairs, without requiring manual alignment. However, the labor-intensive process of collecting and annotating image pairs limits the scale of existing benchmarks, hindering the advancement of alignment-free RGB-T SOD. In this paper, we construct a large-scale and high-diversity unaligned RGB-T SOD dataset named UVT20K, comprising 20,000 image pairs, 407 scenes, and 1256 object categories. All samples are collected from real-world scenarios with various challenges, such as low illumination, image clutter, complex salient objects, and so on. To support the exploration for further research, each sample in UVT20K is annotated with a comprehensive set of ground truths, including saliency masks, scribbles, boundaries, and challenge attributes. In addition, we propose a Progressive Correlation Network (PCNet), which models inter- and intra-modal correlations on the basis of explicit alignment to achieve accurate predictions in unaligned image pairs. Extensive experiments conducted on unaligned and aligned datasets demonstrate the effectiveness of our method.Code and dataset are available at https://github.com/Angknpng/PCNet.

cs.CV↗

arXiv2024

Photometric Selection of type 1 Quasars in the XMM-LSS Field with Machine Learning and the Disk-Corona Connection

We present photometric selection of type 1 quasars in the $\approx5.3~{\rm deg}^{2}$ XMM-Large Scale Structure (XMM-LSS) survey field with machine learning. We constructed our training and \hbox{blind-test} samples using spectroscopically identified SDSS quasars, galaxies, and stars. We utilized the XGBoost machine learning method to select a total of 1\,591 quasars. We assessed the classification performance based on the blind-test sample, and the outcome was favorable, demonstrating high reliability ($\approx99.9\%$) and good completeness ($\approx87.5\%$). We used XGBoost to estimate photometric redshifts of our selected quasars. The estimated photometric redshifts span a range from 0.41 to 3.75. The outlier fraction of these photometric redshift estimates is $\approx17\%$ and the normalized median absolute deviation ($σ_{\rm NMAD}$) is $\approx0.07$. To study the quasar disk-corona connection, we constructed a subsample of 1\,016 quasars with HSC $i<22.5$ after excluding radio-loud and potentially X-ray-absorbed quasars. The relation between the optical-to-X-ray power-law slope parameter ($α_{\rm OX}$) and the 2500 Angstrom monochromatic luminosity ($L_{2500}$) for this subsample is $α_{\rm OX}=(-0.156\pm0.007)~{\rm log}~{L_{\rm 2500}}+(3.175\pm0.211)$ with a dispersion of 0.159. We found this correlation in good agreement with the correlations in previous studies. We explored several factors which may bias the $α_{\rm OX}$-$L_{\rm 2500}$ relation and found that their effects are not significant. We discussed possible evolution of the $α_{\rm OX}$-$L_{\rm 2500}$ relation with respect to $L_{\rm 2500}$ or redshift.

astro-ph.GA↗

arXiv2024

Adapting Segment Anything Model to Multi-modal Salient Object Detection with Semantic Feature Fusion Guidance

Although most existing multi-modal salient object detection (SOD) methods demonstrate effectiveness through training models from scratch, the limited multi-modal data hinders these methods from reaching optimality. In this paper, we propose a novel framework to explore and exploit the powerful feature representation and zero-shot generalization ability of the pre-trained Segment Anything Model (SAM) for multi-modal SOD. Despite serving as a recent vision fundamental model, driving the class-agnostic SAM to comprehend and detect salient objects accurately is non-trivial, especially in challenging scenes. To this end, we develop \underline{SAM} with se\underline{m}antic f\underline{e}ature fu\underline{s}ion guidanc\underline{e} (Sammese), which incorporates multi-modal saliency-specific knowledge into SAM to adapt SAM to multi-modal SOD tasks. However, it is difficult for SAM trained on single-modal data to directly mine the complementary benefits of multi-modal inputs and comprehensively utilize them to achieve accurate saliency prediction. To address these issues, we first design a multi-modal complementary fusion module to extract robust multi-modal semantic features by integrating information from visible and thermal or depth image pairs. Then, we feed the extracted multi-modal semantic features into both the SAM image encoder and mask decoder for fine-tuning and prompting, respectively. Specifically, in the image encoder, a multi-modal adapter is proposed to adapt the single-modal SAM to multi-modal information. In the mask decoder, a semantic-geometric prompt generation strategy is proposed to produce corresponding embeddings with various saliency cues. Extensive experiments on both RGB-D and RGB-T SOD benchmarks show the effectiveness of the proposed framework. The code will be available at \url{https://github.com/Angknpng/Sammese}.

cs.CV↗

arXiv2024

Reliable and Compact Graph Fine-tuning via GraphSparse Prompting

Recently, graph prompt learning has garnered increasing attention in adapting pre-trained GNN models for downstream graph learning tasks. However, existing works generally conduct prompting over all graph elements (e.g., nodes, edges, node attributes, etc.), which is suboptimal and obviously redundant. To address this issue, we propose exploiting sparse representation theory for graph prompting and present Graph Sparse Prompting (GSP). GSP aims to adaptively and sparsely select the optimal elements (e.g., certain node attributes) to achieve compact prompting for downstream tasks. Specifically, we propose two kinds of GSP models, termed Graph Sparse Feature Prompting (GSFP) and Graph Sparse multi-Feature Prompting (GSmFP). Both GSFP and GSmFP provide a general scheme for tuning any specific pre-trained GNNs that can achieve attribute selection and compact prompt learning simultaneously. A simple yet effective algorithm has been designed for solving GSFP and GSmFP models. Experiments on 16 widely-used benchmark datasets validate the effectiveness and advantages of the proposed GSFPs.

cs.LG↗

arXiv2024

Dust-Obscured Galaxies in the XMM-SERVS Fields: Selection, Multiwavelength Characterization, and Physical Nature

Dust-obscured galaxies (DOGs) are enshrouded by dust, and many are believed to host accreting supermassive black holes (SMBHs), which makes them unique objects for probing the coevolution of galaxies and SMBHs. We select and characterize DOGs in the $13\,deg^2$ XMM-Spitzer Extragalactic Representative Volume Survey (XMM-SERVS), leveraging the superb multiwavelength data from X-rays to radio. We select 3738 DOGs at $z\approx1.6-2.1$ in XMM-SERVS, while maintaining good data quality without introducing significant bias. This represents the largest DOG sample with thorough multiwavelength source characterization. Spectral energy distribution (SED) modeling shows DOGs are a heterogeneous population consisting of both normal galaxies and active galactic nuclei (AGNs). Our DOGs are massive ($\log M_\bigstar/M_\odot\approx10.7-11.3$), 174 are detected in X-rays, and they are generally radio-quiet systems. X-ray detected DOGs are luminous and are moderately to heavily obscured in X-rays. Stacking analyses for the X-ray undetected DOGs show highly significant average detections. Critically, we compare DOGs with matched galaxy populations. DOGs have similar AGN fractions compared with typical galaxy populations. X-ray detected DOGs have higher $M_\bigstar$ and higher X-ray obscuration, but they are not more star-forming than typical X-ray AGNs. The results potentially challenge the relevance of the merger-driven galaxy-SMBH coevolution framework for X-ray detected DOGs.

astro-ph.GA↗

arXiv2024

Breaking Modality Gap in RGBT Tracking: Coupled Knowledge Distillation

Modality gap between RGB and thermal infrared (TIR) images is a crucial issue but often overlooked in existing RGBT tracking methods. It can be observed that modality gap mainly lies in the image style difference. In this work, we propose a novel Coupled Knowledge Distillation framework called CKD, which pursues common styles of different modalities to break modality gap, for high performance RGBT tracking. In particular, we introduce two student networks and employ the style distillation loss to make their style features consistent as much as possible. Through alleviating the style difference of two student networks, we can break modality gap of different modalities well. However, the distillation of style features might harm to the content representations of two modalities in student networks. To handle this issue, we take original RGB and TIR networks as the teachers, and distill their content knowledge into two student networks respectively by the style-content orthogonal feature decoupling scheme. We couple the above two distillation processes in an online optimization framework to form new feature representations of RGB and thermal modalities without modality gap. In addition, we design a masked modeling strategy and a multi-modal candidate token elimination strategy into CKD to improve tracking robustness and efficiency respectively. Extensive experiments on five standard RGBT tracking datasets validate the effectiveness of the proposed method against state-of-the-art methods while achieving the fastest tracking speed of 96.4 FPS. Code available at https://github.com/Multi-Modality-Tracking/CKD.

cs.CV↗

arXiv2024

Bi-temporal Gaussian Feature Dependency Guided Change Detection in Remote Sensing Images

Change Detection (CD) enables the identification of alterations between images of the same area captured at different times. However, existing CD methods still struggle to address pseudo changes resulting from domain information differences in multi-temporal images and instances of detail errors caused by the loss and contamination of detail features during the upsampling process in the network. To address this, we propose a bi-temporal Gaussian distribution feature-dependent network (BGFD). Specifically, we first introduce the Gaussian noise domain disturbance (GNDD) module, which approximates distribution using image statistical features to characterize domain information, samples noise to perturb the network for learning redundant domain information, addressing domain information differences from a more fundamental perspective. Additionally, within the feature dependency facilitation (FDF) module, we integrate a novel mutual information difference loss ($L_{MI}$) and more sophisticated attention mechanisms to enhance the capabilities of the network, ensuring the acquisition of essential domain information. Subsequently, we have designed a novel detail feature compensation (DFC) module, which compensates for detail feature loss and contamination introduced during the upsampling process from the perspectives of enhancing local features and refining global features. The BGFD has effectively reduced pseudo changes and enhanced the detection capability of detail information. It has also achieved state-of-the-art performance on four publicly available datasets - DSIFN-CD, SYSU-CD, LEVIR-CD, and S2Looking, surpassing baseline models by +8.58%, +1.28%, +0.31%, and +3.76% respectively, in terms of the F1-Score metric.

cs.CV↗

arXiv2024

Rare Occasions: Tidal Disruption Events Rarely Power the AGNs Observed in Dwarf Galaxies

Tidal disruption events (TDEs) could be an important growth channel for massive black holes in dwarf galaxies. Theoretical work suggests that the observed active galactic nuclei (AGNs) in dwarf galaxies are predominantly TDE-powered. To assess this claim, we perform variability analyses on the dwarf-hosted AGNs detected in the $7$ Ms Chandra Deep Field-South (CDF-S) survey, with observations spanning $\approx 16$ years. Based on the spectral energy distribution (SED) modeling with X-CIGALE, we select AGNs hosted by dwarf galaxies (stellar mass below $10^{10}\ M_\odot$). We focus on X-ray sources with full-band detections, leading to a sample of $78$ AGNs (0.122 $\leq$ $z$ $\leq$ 3.515). We fit the X-ray light curves with a canonical TDE model of $t^{-5/3}$ and a constant model. If the former outperforms the latter in fitting quality for a source, we consider the source as a potential TDE. We identify five potential TDEs, constituting a small fraction of our sample. Using true- and false-positive rates obtained from fitting models to simulated light curves, we perform Bayesian analysis to obtain the posterior of the TDE fraction for our sample. The posterior peaks close to zero ($2.56\%$), and we obtain a $2$-$σ$ upper limit of $9.80\%$. Therefore, our result indicates that the observed AGNs in dwarf galaxies are not predominantly powered by TDEs.

astro-ph.GA↗

arXiv2024

The Cosmic Evolution of the Supermassive Black Hole Population: A Hybrid Observed Accretion and Simulated Mergers Approach

Supermassive black holes (SMBHs) can grow through both accretion and mergers. It is still unclear how SMBHs evolve under these two channels from high redshifts to the SMBH population we observe in the local universe. Observations can directly constrain the accretion channel but cannot effectively constrain mergers yet, while cosmological simulations provide galaxy merger information but can hardly return accretion properties consistent with observations. In this work, we combine the observed accretion channel and the simulated merger channel, taking advantage of both observations and cosmological simulations, to depict a realistic evolution pattern of the SMBH population. With this methodology, we can derive the scaling relation between the black-hole mass ($M_\mathrm{BH}$) and host-galaxy stellar mass ($M_\star$) and the local black-hole mass function (BHMF). Our scaling relation is lower than those based on dynamically measured $M_\mathrm{BH}$, supporting the claim that dynamically measured SMBH samples may be biased. We show that the scaling relation has little redshift evolution. The BHMF steadily increases from $z=4$ to $z=1$ and remains largely unchanged from $z=1$ to $z=0$. The overall SMBH growth is generally dominated by the accretion channel, with possible exceptions at high mass ($M_\mathrm{BH}\gtrsim10^{8}~M_\odot$ or $M_\star\gtrsim10^{11}~M_\odot$) and low redshift ($z\lesssim1$). We also predict that around 25% of the total SMBH mass budget in the local universe may be locked within long-lived, wandering SMBHs, and the wandering mass fraction and wandering SMBH counts increase with $M_\star$.

astro-ph.GA↗

arXiv2024

Dual-comb-enhanced microwave clock synchronization over commercial fiber

The large-scale clock network is the key ingredient to obtain high precision in many scenarios, from fundamental research to cutting-edge applications. The advantage of the time synchronization among microwave clocks is their cost, size, and accessibility. Here, we demonstrate a femtosecond-level time synchronization of microwave clocks through a commercial link of 205.86 km via dual-comb-enhanced optical two-way time transfer, which achieves a 6.23-fs residual time deviation between synchronized timescales at 1 s and an instability below 6E-18 at 10,000 s. Further, the high-precision time synchronization of microwave clocks significantly enhances the probe ability of subtle reciprocity changes of fiber to the sub-picosecond level. This work provides a path toward secure fiber time-frequency networks to support future microwave-clock-based precise timing and sensing systems.

physics.optics↗

arXiv2024

The Remarkable X-ray Spectra and Variability of the Ultraluminous Weak-Line Quasar SDSS J1521+5202

We present a focused X-ray and multiwavelength study of the ultraluminous weak-line quasar (WLQ) SDSS J1521+5202, one of the few X-ray weak WLQs that is amenable to basic X-ray spectral and variability investigations. J1521+5202 shows striking X-ray variability during 2006--2023, by up to a factor of $\approx 32$ in 0.5--2 keV flux, and our new 2023 Chandra observation caught it in its brightest X-ray flux state to date. Concurrent infrared/optical observations show only mild variability. The 2023 Chandra spectrum can be acceptably described by a power law with intrinsic X-ray absorption, and it reveals a nominal intrinsic level of X-ray emission relative to its optical/ultraviolet emission. In contrast, an earlier Chandra spectrum from 2013 shows apparent spectral complexity that is not well fit by a variety of models, including ionized-absorption or standard Compton-reflection models. Overall, the observations are consistent with the thick-disk plus outflow model previously advanced for WLQs, where a nominal level of underlying X-ray emission plus variable absorption lead to the remarkable observed X-ray variability. In the case of J1521+5202 it appears likely that the outflow, and not the thick disk itself, lies along our line-of-sight and causes the X-ray absorption.

astro-ph.HE↗

arXiv2024

Adaptive optical signal-to-noise ratio recovery for long-distance optical fiber transmission

In long-distance fiber optic transmission, the optic fiber link and erbium-doped fiber amplifiers can introduce excessive noise, which reduces the optical signal-to-noise ratio (OSNR). The narrow-band optical filters can be used to eliminate noise and thereby improve OSNR. However, there is a relative frequency drift between the signal and the narrow-band filter, which leads to filtered signal instability. This paper proposes an adaptive OSNR recovery scheme based on a Fabry-Perot (F-P) cavity with mode width of 6 MHz. Utilizing the comb filtering of F-P cavity, the noise around the carrier and sidebands of the signal is filtered out simultaneously. To avoid frequency mismatch, we propose a double-servo scheme to suppress relative frequency drift between the signal and the F-P cavity. We constructed a stable radio frequency transfer system based on passive phase compensation and compared our scheme with other OSNR recovery schemes based on optical filters. Compared to the schemes based on dense wavelength division multiplexing (DWDM) and Waveshaper, our scheme demonstrates an improvement in OSNR of carrier by at least 12 dB and sidebands by at least 23.5 dB. The short-term transfer stability (1 s) is improved by one order of magnitude compared to DWDM and half an order of magnitude compared to Waveshper. This scheme can be applied to the recovery of signals with low OSNR in long-distance fiber optic transmission, improving signal quaility and extending the transmission distance limit.

physics.optics↗

arXiv2024

A Novel Hybrid Digital and Analog Laser Synchronization System

Laser synchronization is a technique that locks the wavelength of a free-running laser to that of the reference laser, thereby enabling synchronous changes in the wavelengths of the two lasers. This technique is of crucial importance in both scientific and industrial applications. Conventional synchronization systems, whether digital or analog, have intrinsic limitations in terms of accuracy or bandwidth. The hybrid "digital + analog" system can address this shortcoming. However, all above systems face the challenge of achieving an both high locking accuracy and low structural complexity simultaneously. This paper presents a hybrid "digital + analog" laser synchronization system with low-complexity and high-performance. In the digital part, we proposed a electric intensity locking method based on a band-pass filter, which realizes the fluctuation of frequency offset between a single frequency laser (SFL) and a mode-locked laser (MLL) less than 350 kHz in 24 hours. Following the incorporation of the analog control component, frequency fluctuation is less than 2.5 Hz in 24 hours. By synchronizing two SFLs to a repetition-frequency locked MLL, we achieve indirect synchronization between SFLs with a frequency offset of 10.6 GHz and fluctuation less than 5 Hz in 24 hours, demonstrating robust long- and short-term stability. Since the MLL is employed as a reference, the system can be utilized for cross-band indirect synchronization of multiple lasers. Based on the synchronization system, we propose a photonic-assisted microwave frequency identification scheme, which has detection error of less than 0.6 MHz. The high performance of the synchronization system enables the proposed frequency identification scheme to achieve high measurement accuracy and a theoretically large frequency range.

physics.optics↗