arXiv subjects

Bing Zhang

Publications and source records attributed to Bing Zhang.

At least 109 records · Page 6Linked to original sources

arXiv2025

Quasi-periodic oscillations of GHz-band polarization in a black hole

Relativistic jets from accreting black holes (BHs) radiate non-thermal emission which is highly variable in different time scales. Magnetic fields anchored to a rotating BH or accretion disc accelerate and collimate jets of the BH systems. Previous studies on black holes of different mass scales, including supermassive and stellar-mass black holes, only report flux quasi-periodic oscillations in radio, optical, X-ray and gamma-ray bands. No quasi-periodic variations in polarization have yet been detected in any black hole systems. Here, we report the first detection of GHz radio polarization oscillations in GRS 1915+105, which harbors a spinning stellar-mass BH with a relativistic jet. Our observations show that during the increasing phase of radio emission, linear polarization and flux exhibit similar oscillation periods of $\sim 17$ and $33$ seconds, and their variation patterns anti-correlate with each other. These rare, short-period oscillations in both polarization and flux would be important to understand instabilities and special dynamics in magnetized jets.

astro-ph.HE↗

arXiv2025

EP240801a/XRF 240801B: An X-ray Flash Detected by the Einstein Probe and Implications of its Multiband Afterglow

We present multiband observations and analysis of EP240801a, a low-energy, extremely soft gamma-ray burst (GRB) discovered on August 1, 2024 by the Einstein Probe (EP) satellite, with a weak contemporaneous signal also detected by Fermi/GBM. Optical spectroscopy of the afterglow, obtained by GTC and Keck, identified the redshift of $z = 1.6734$. EP240801a exhibits a burst duration of 148 s in X-rays and 22.3 s in gamma-rays, with X-rays leading by 80.61 s. Spectral lag analysis indicates the gamma-ray signal arrived 8.3 s earlier than the X-rays. Joint spectral fitting of EP/WXT and Fermi/GBM data yields an isotropic energy $E_{γ,\rm{iso}} = (5.57^{+0.54}_{-0.50})\times 10^{51}\,\rm{erg}$, a peak energy $E_{\rm{peak}} = 14.90^{+7.08}_{-4.71}\,\rm{keV}$, a fluence ratio $\rm S(25-50\,\rm{keV})/S(50-100\,\rm{keV}) = 1.67^{+0.74}_{-0.46}$, classifying EP240801a as an X-ray flash (XRF). The host-galaxy continuum spectrum, inferred using Prospector, was used to correct its contribution for the observed outburst optical data. Unusual early $R$-band behavior and EP/FXT observations suggest multiple components in the afterglow. Three models are considered: two-component jet model, forward-reverse shock model and forward-shock model with energy injection. Both three provide reasonable explanations. The two-component jet model and the energy injection model imply a relatively small initial energy and velocity of the jet in the line of sight, while the forward-reverse shock model remains typical. Under the two-component jet model, EP240801a may resemble GRB 221009A (BOAT) if the bright narrow beam is viewed on-axis. Therefore, EP240801a can be interpreted as an off-beam (narrow) jet or an intrinsically weak GRB jet. Our findings provide crucial clues for uncovering the origin of XRFs.

astro-ph.HE↗

arXiv2025

PSRGS:Progressive Spectral Residual of 3D Gaussian for High-Frequency Recovery

3D Gaussian Splatting (3D GS) achieves impressive results in novel view synthesis for small, single-object scenes through Gaussian ellipsoid initialization and adaptive density control. However, when applied to large-scale remote sensing scenes, 3D GS faces challenges: the point clouds generated by Structure-from-Motion (SfM) are often sparse, and the inherent smoothing behavior of 3D GS leads to over-reconstruction in high-frequency regions, where have detailed textures and color variations. This results in the generation of large, opaque Gaussian ellipsoids that cause gradient artifacts. Moreover, the simultaneous optimization of both geometry and texture may lead to densification of Gaussian ellipsoids at incorrect geometric locations, resulting in artifacts in other views. To address these issues, we propose PSRGS, a progressive optimization scheme based on spectral residual maps. Specifically, we create a spectral residual significance map to separate low-frequency and high-frequency regions. In the low-frequency region, we apply depth-aware and depth-smooth losses to initialize the scene geometry with low threshold. For the high-frequency region, we use gradient features with higher threshold to split and clone ellipsoids, refining the scene. The sampling rate is determined by feature responses and gradient loss. Finally, we introduce a pre-trained network that jointly computes perceptual loss from multiple views, ensuring accurate restoration of high-frequency details in both Gaussian ellipsoids geometry and color. We conduct experiments on multiple datasets to assess the effectiveness of our method, which demonstrates competitive rendering quality, especially in recovering texture details in high-frequency regions.

cs.CV↗

arXiv2025

Isolated Black Holes as Potential PeVatrons and Ultrahigh-energy Gamma-ray Sources

The origin of PeV cosmic rays is a long-standing mystery, and ultrahigh-energy gamma-ray observations would play a crucial role in identifying it. Recently, LHAASO reported the discovery of ``dark'' gamma-ray sources that were detected above 100 TeV without any GeV--TeV gamma-ray counterparts. The origins of these dark gamma-ray sources are unknown. We propose isolated black holes (IBHs) wandering in molecular clouds as the origins of PeV cosmic rays and LHAASO dark sources. An IBH accretes surrounding dense gas, which forms a magnetically arrested disk (MAD) around the IBH. Magnetic reconnection in the MAD can accelerate cosmic-ray protons up to PeV energies. Cosmic-ray protons of GeV-TeV energies fall to the IBH, whereas cosmic-ray protons at sub-PeV energies can escape from the MAD, providing PeV CRs into the interstellar medium. The sub-PeV cosmic-ray protons interact with the surrounding molecular clouds, producing TeV-PeV gamma rays without emitting GeV-TeV gamma rays. This scenario can explain the dark sources detected by LHAASO. Taking into account the IBH and molecular cloud distributions in our Galaxy, we demonstrate that IBHs can provide a significant contribution to the PeV cosmic rays observed on Earth. Future gamma-ray detectors in the southern sky and neutrino detectors would provide a concrete test to our scenario.

astro-ph.HE↗

arXiv2025

The Host Galaxy of the Hyperactive Repeating FRB 20240114A: Behind a Galaxy Cluster

We report on the optical spectroscopic observations of the host galaxy of the hyperactive repeating fast radio burst, FRB 20240114A. The host galaxy is a dwarf galaxy at a redshift of $z=0.1306\pm0.0002$. With a rest-frame coverage of 4300-7900 Å, we have detected H$\rmα$, H$\rmβ$, [O III]$λλ$4959,5007, [N II]$λλ$6548,6583, and [S II]$λ$6716 emission lines. The emission line ratios suggest that the ionization in the host galaxy is dominated by star formation. The star formation rate (SFR) derived from the H$\rmα$ emission line is $(0.06 \pm 0.01) \ \rm{M_{\odot} \ yr^{-1}}$, and the SED fitting suggests the lower limit of the SFR(UV) is $0.09 \ \rm{M_{\odot} \ yr^{-1}}$. The stellar mass is $(\rm 4.0 \pm 1.8) \times 10^8 \ M_{\odot}$, making the specific star formation rate $\rm log \ sSFR(H\rm α) = -9.8 \pm 0.2 \ yr^{-1}$. The line ratios indicate an upper limit of a metallicity of $\rm 12+log_{10} ([O/H]) \sim 8.5$. As the nearest dwarf host galaxy with a repeating FRB, the activity of FRB 20240114A and the properties of this host galaxy closely resemble those of FRB 20121102A and FRB 20190520B. The H$\rmα$-traced dispersion measure (DM) provided by the ionized gas of the host galaxy has a moderate contribution of $\sim 200 \rm \ pc \ cm^{-3}$, assuming a warm ionized gas. We found that the distributions of the stellar mass versus SFR are significantly different between repeating and one-off FRBs, as determined by the MANOVA test with $p=0.0116$.

astro-ph.GA↗

arXiv2025

UrbanSAM: Learning Invariance-Inspired Adapters for Segment Anything Models in Urban Construction

Object extraction and segmentation from remote sensing (RS) images is a critical yet challenging task in urban environment monitoring. Urban morphology is inherently complex, with irregular objects of diverse shapes and varying scales. These challenges are amplified by heterogeneity and scale disparities across RS data sources, including sensors, platforms, and modalities, making accurate object segmentation particularly demanding. While the Segment Anything Model (SAM) has shown significant potential in segmenting complex scenes, its performance in handling form-varying objects remains limited due to manual-interactive prompting. To this end, we propose UrbanSAM, a customized version of SAM specifically designed to analyze complex urban environments while tackling scaling effects from remotely sensed observations. Inspired by multi-resolution analysis (MRA) theory, UrbanSAM incorporates a novel learnable prompter equipped with a Uscaling-Adapter that adheres to the invariance criterion, enabling the model to capture multiscale contextual information of objects and adapt to arbitrary scale variations with theoretical guarantees. Furthermore, features from the Uscaling-Adapter and the trunk encoder are aligned through a masked cross-attention operation, allowing the trunk encoder to inherit the adapter's multiscale aggregation capability. This synergy enhances the segmentation performance, resulting in more powerful and accurate outputs, supported by the learned adapter. Extensive experimental results demonstrate the flexibility and superior segmentation performance of the proposed UrbanSAM on a global-scale dataset, encompassing scale-varying urban objects such as buildings, roads, and water.

cs.CV↗

arXiv2025

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis; improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and ultimately enabling AI-empowered virtual cells.

q-bio.OT↗

arXiv2025

Magnetic Interactions in White Dwarf Binaries as Mechanism for Long-Period Radio Transients

A growing population of long-period radio transients has been discovered and their physical origin is still up to debate. Recently, a new such source named ILT J1101 + 5521 was discovered, which is in a white dwarf (WD) -- red dwarf (RD) binary system, with the observed 125.5 min period in radio emission being identified as the orbital period and the radio emission occurs at the inferior conjunction of the WD. We suggest that the radio emission properties of the system can be well explained within the framework of the unipolar inductor magnetic interaction model between the magnetized WD and the RD with low magnetization, with a relativistic version of electron cyclotron maser emission being the most likely radiation mechanism. We suggest that this mechanism can interpret at least some long-period radio transients, especially the ultra-long period sub-population. Within this model, high energy emission in X-rays via relativistically boosted cyclotron radiation and $γ$-rays via inverse Compton scattering off stellar light are expected, but the predicted luminosities are relatively low. This model likely applies to the ultra-long period population of LPRTs. The short-period population of LPRTs is likely powered by other engines such as slow magnetars.

astro-ph.HE↗

arXiv2025

A flaring radio counterpart to a fast radio burst reveals a newborn magnetized engine

Fast Radio Bursts (FRBs) are energetic millisecond radio bursts at cosmological distances, whose underlying engine is not identified. Among a sub-population that emit repeated radio bursts, a handful were associated with a persistent radio source (PRS) whose origin is unknown. Here we report the discovery of a compact flaring radio source (FRS) associated with a newly-active repeating FRB within one month after the first radio burst was detected. Its temporal and spectral characteristics differ from those of the PRSs but are similar to those of engine-powered supernovae and low-luminosity active galactic nuclei. We detected a spectral peak around $1.6\pm0.2$ GHz that is consistent with synchrotron self-absorption. Assuming equipartition, the magnetic field strength in the FRS is larger than the line-of-sight component constrained from the FRB Faraday rotation, suggesting a highly magnetized engine. The radius of the FRS is constrained to be $\sim0.03$ pc and the minimum total energy is $\sim~6.2\times{10}^{47}~{\rm ergs~}$. This FRS reveals the birth of a highly magnetized FRB engine, and hints that PRSs associated with other active FRBs may be in the later stage of evolution.

astro-ph.HE↗

arXiv2025

Deploying Privacy Guardrails for LLMs: A Comparative Analysis of Real-World Applications

The adoption of Large Language Models (LLMs) has revolutionized AI applications but poses significant challenges in safeguarding user privacy. Ensuring compliance with privacy regulations such as GDPR and CCPA while addressing nuanced privacy risks requires robust and scalable frameworks. This paper presents a detailed study of OneShield Privacy Guard, a framework designed to mitigate privacy risks in user inputs and LLM outputs across enterprise and open-source settings. We analyze two real-world deployments:(1) a multilingual privacy-preserving system integrated with Data and Model Factory, focusing on enterprise-scale data governance; and (2) PR Insights, an open-source repository emphasizing automated triaging and community-driven refinements. In Deployment 1, OneShield achieved a 0.95 F1 score in detecting sensitive entities like dates, names, and phone numbers across 26 languages, outperforming state-of-the-art tool such as StarPII and Presidio by up to 12\%. Deployment 2, with an average F1 score of 0.86, reduced manual effort by over 300 hours in three months, accurately flagging 8.25\% of 1,256 pull requests for privacy risks with enhanced context sensitivity. These results demonstrate OneShield's adaptability and efficacy in diverse environments, offering actionable insights for context-aware entity recognition, automated compliance, and ethical AI adoption. This work advances privacy-preserving frameworks, supporting user trust and compliance across operational contexts.

cs.CR↗

arXiv2025

Adaptive PII Mitigation Framework for Large Language Models

Artificial Intelligence (AI) faces growing challenges from evolving data protection laws and enforcement practices worldwide. Regulations like GDPR and CCPA impose strict compliance requirements on Machine Learning (ML) models, especially concerning personal data use. These laws grant individuals rights such as data correction and deletion, complicating the training and deployment of Large Language Models (LLMs) that rely on extensive datasets. Public data availability does not guarantee its lawful use for ML, amplifying these challenges. This paper introduces an adaptive system for mitigating risk of Personally Identifiable Information (PII) and Sensitive Personal Information (SPI) in LLMs. It dynamically aligns with diverse regulatory frameworks and integrates seamlessly into Governance, Risk, and Compliance (GRC) systems. The system uses advanced NLP techniques, context-aware analysis, and policy-driven masking to ensure regulatory compliance. Benchmarks highlight the system's effectiveness, with an F1 score of 0.95 for Passport Numbers, outperforming tools like Microsoft Presidio (0.33) and Amazon Comprehend (0.54). In human evaluations, the system achieved an average user trust score of 4.6/5, with participants acknowledging its accuracy and transparency. Observations demonstrate stricter anonymization under GDPR compared to CCPA, which permits pseudonymization and user opt-outs. These results validate the system as a scalable and robust solution for enterprise privacy compliance.

cs.LG↗

arXiv2025

The origin of very-high-energy gamma-rays from GRB 221009A: implications for reverse shock proton synchrotron emission

Recently, GRB 221009A, known as the brightest of all time (BOAT), has been observed across an astounding range of $\sim 18$ orders of magnitude in energy, spanning from radio to VHE bands. Notably, the Large High Altitude Air Shower Observatory (LHAASO) recorded over $60000$ photons with energies exceeding $0.2\rm~TeV$, including the first-ever detection of photons above $10\rm~TeV$. However, explaining the observed energy flux evolution in the VHE band alongside late-time multi-wavelength data poses a significant challenge. Our approach involves a two-component structured jet model, consisting of a narrow core dominated by magnetic energy and a wide jet component dominated by matter. We show that the combination of the forward shock electron synchrotron self-Compton emission from both jets and reverse shock proton synchrotron emission from the wide jet could account for both the energy flux and spectral evolution in the VHE band, and the early TeV lightcurve may be influenced by prompt photons which could explain the initial steep rising phase. We noticed the arrival time of the highest energy photons detected by LHAASO-KM2A coincident with the peak of the reverse shock proton synchrotron emission, especially a minor flare occurring about $\sim500-800$ seconds after the trigger, coinciding with the observed spectral hardening and arrival time of the $\sim 13\rm~TeV$ photons detected by LHAASO. These findings imply that the GRB reverse shock may serve as a potential accelerator of ultra-high-energy cosmic rays, a hypothesis that could be tested through future multimessenger observations.

astro-ph.HE↗

arXiv2025

Cloud Removal With PolSAR-Optical Data Fusion Using A Two-Flow Residual Network

Optical remote sensing images play a crucial role in the observation of the Earth's surface. However, obtaining complete optical remote sensing images is challenging due to cloud cover. Reconstructing cloud-free optical images has become a major task in recent years. This paper presents a two-flow Polarimetric Synthetic Aperture Radar (PolSAR)-Optical data fusion cloud removal algorithm (PODF-CR), which achieves the reconstruction of missing optical images. PODF-CR consists of an encoding module and a decoding module. The encoding module includes two parallel branches that extract PolSAR image features and optical image features. To address speckle noise in PolSAR images, we introduce dynamic filters in the PolSAR branch for image denoising. To better facilitate the fusion between multimodal optical images and PolSAR images, we propose fusion blocks based on cross-skip connections to enable interaction of multimodal data information. The obtained fusion features are refined through an attention mechanism to provide better conditions for the subsequent decoding of the fused images. In the decoding module, multi-scale convolution is introduced to obtain multi-scale information. Additionally, to better utilize comprehensive scattering information and polarization characteristics to assist in the restoration of optical images, we use a dataset for cloud restoration called OPT-BCFSAR-PFSAR, which includes backscatter coefficient feature images and polarization feature images obtained from PoLSAR data and optical images. Experimental results demonstrate that this method outperforms existing methods in both qualitative and quantitative evaluations.

cs.CV↗

arXiv2025

Science objectives of the Einstein Probe mission

The Einstein Probe (EP) is an interdisciplinary mission of time-domain and X-ray astronomy. Equipped with a wide-field lobster-eye X-ray focusing imager, EP will discover cosmic X-ray transients and monitor the X-ray variability of known sources in 0.5-4 keV, at a combination of detecting sensitivity and cadence that is not accessible to the previous and current wide-field monitoring missions. EP can perform quick characterisation of transients or outbursts with a Wolter-I X-ray telescope onboard. In this paper, the science objectives of the Einstein Probe mission are presented. EP is expected to enlarge the sample of previously known or predicted but rare types of transients with a wide range of timescales. Among them, fast extragalactic transients will be surveyed systematically in soft X-rays, which include γ-ray bursts and their variants, supernova shock breakouts, and the predicted X-ray transients associated with binary neutron star mergers. EP will detect X-ray tidal disruption events and outbursts from active galactic nuclei, possibly at an early phase of the flares for some. EP will monitor the variability and outbursts of X-rays from white dwarfs, neutron stars and black holes in our and neighbouring galaxies at flux levels fainter than those detectable by the current instruments, and is expected to discover new objects. A large sample of stellar X-ray flares will also be detected and characterised. In the era of multi-messenger astronomy, EP has the potential of detecting the possible X-ray counterparts of gravitational wave events, neutrino sources, and ultra-high energy γ-ray and cosmic ray sources. EP is expected to help advance the studies of extreme objects/phenomena and their underlying physical processes revealed in the dynamic X-ray universe, as well as studies in other areas of X-ray astronomy.

astro-ph.HE↗

arXiv2025

Multiwavelength Afterglow Analysis of GRB 221009A: Unveiling the Evolution of a Cooling Break in a Wind-like Medium

Gamma-ray bursts (GRBs) are the most energetic explosions in the universe, and their afterglow emission provides an opportunity to probe the physics of relativistic shock waves in an extreme environment. Several key pieces for completing the picture of the GRB afterglow physics are still missing, including jet properties, emission mechanism, and particle acceleration. Here we present a study of the afterglow emission of GRB 221009A, the most energetic GRB ever observed. Using optical, X-ray, and gamma-ray data up to approximately two days after the trigger, we trace the evolution of the multi-wavelength spectrum and the physical parameters behind the emission process. The broadband spectrum is consistent with the synchrotron emission emitted by relativistic electrons with its index of $p = 2.29\pm 0.02$. We identify a break energy at keV and an exponential cutoff at GeV in the observed multi-wavelength spectrum. The break energy increases in time from $16.0_{-4.9}^{+7.1}$ keV at 0.65 days to $46.8_{-15.5}^{+25.0}$ keV at 1.68 days, favoring a stellar wind-like profile of the circumburst medium with $k=2.4\pm0.1$ as in $ρ(r) \propto r^{-k}$. The high-energy attenuation at around 0.4 to 4 GeV is attributed to the maximum of the particle acceleration in the relativistic shock wave. This study confirms that the synchrotron process can explain the multi-wavelength afterglow emission and its evolution.

astro-ph.HE↗

arXiv2024

On the Duration of Gamma-Ray Bursts

Recently, a short-duration GRB with supernova association (GRB 200826A) and two long-duration GRBs with kilonova associations (GRB 211211A and GRB 230307A) have been detected, which demolished the hope for a tidy connection between GRB duration and their progenitor systems. Here I summarize various physical factors that can shape the duration of a GRB and propose that the duration of a GRB can be defined by four factors: progentor, central engine, emitter, and geometry. The progenitor-defined duration is only relevant when the central engine is powered by accretion and when the modifications by other factors are not important. The untidy situation of duration - progenitor mismatches suggests that other factors likely play important roles in defining GRB duration at least in some GRBs. In particular, a GRB may not be powered by accretion but rather by a millisecond magnetar at least for some GRBs. The complicated lightcurve of GRB 211211A suggests both progenitor- and engine-defined durations, which may require a new type of progenitor system involving a white dwarf - neutron star merger with a magnetar merger product. The single broad pulse lightcurve with well-behaved energy-dependent behavior of GRB 230307A suggests an emitter-defined long duration. The central engine timescale may be short enough to be accommodated within the framework of a standard binary neutron star merger. Its spiky lightcurve with fast variability as well as extended X-ray emission suggest the existence of mini-jets in the global dissipation region, powered by an underlying magnetar.

astro-ph.HE↗

arXiv2024

Dual-Branch Subpixel-Guided Network for Hyperspectral Image Classification

Deep learning (DL) has been widely applied into hyperspectral image (HSI) classification owing to its promising feature learning and representation capabilities. However, limited by the spatial resolution of sensors, existing DL-based classification approaches mainly focus on pixel-level spectral and spatial information extraction through complex network architecture design, while ignoring the existence of mixed pixels in actual scenarios. To tackle this difficulty, we propose a novel dual-branch subpixel-guided network for HSI classification, called DSNet, which automatically integrates subpixel information and convolutional class features by introducing a deep autoencoder unmixing architecture to enhance classification performance. DSNet is capable of fully considering physically nonlinear properties within subpixels and adaptively generating diagnostic abundances in an unsupervised manner to achieve more reliable decision boundaries for class label distributions. The subpixel fusion module is designed to ensure high-quality information fusion across pixel and subpixel features, further promoting stable joint classification. Experimental results on three benchmark datasets demonstrate the effectiveness and superiority of DSNet compared with state-of-the-art DL-based HSI classification approaches. The codes will be available at https://github.com/hanzhu97702/DSNet, contributing to the remote sensing community.

eess.IV↗

arXiv2024

Multisource Collaborative Domain Generalization for Cross-Scene Remote Sensing Image Classification

Cross-scene image classification aims to transfer prior knowledge of ground materials to annotate regions with different distributions and reduce hand-crafted cost in the field of remote sensing. However, existing approaches focus on single-source domain generalization to unseen target domains, and are easily confused by large real-world domain shifts due to the limited training information and insufficient diversity modeling capacity. To address this gap, we propose a novel multi-source collaborative domain generalization framework (MS-CDG) based on homogeneity and heterogeneity characteristics of multi-source remote sensing data, which considers data-aware adversarial augmentation and model-aware multi-level diversification simultaneously to enhance cross-scene generalization performance. The data-aware adversarial augmentation adopts an adversary neural network with semantic guide to generate MS samples by adaptively learning realistic channel and distribution changes across domains. In views of cross-domain and intra-domain modeling, the model-aware diversification transforms the shared spatial-channel features of MS data into the class-wise prototype and kernel mixture module, to address domain discrepancies and cluster different classes effectively. Finally, the joint classification of original and augmented MS samples is employed by introducing a distribution consistency alignment to increase model diversity and ensure better domain-invariant representation learning. Extensive experiments on three public MS remote sensing datasets demonstrate the superior performance of the proposed method when benchmarked with the state-of-the-art methods.

cs.CV↗