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Hongsheng Zhang

Publications and source records attributed to Hongsheng Zhang.

At least 19 recordsLinked to original sources

A no-go theorem for closed timelike curves in globally null-foliated spacetimes

The study of closed timelike curves has proceeded one spacetime at a time, and the criteria available form three families, the time function criteria, the energy condition criteria, and the topological criteria. We initiate a fourth family of criteria, elementary in form and resting on a single scalar equation rather than any energy condition. We prove that a spacetime admitting a globally defined function whose gradient is everywhere null cannot contain a closed timelike curve, and that the hypothesis is logically independent of stable causality rather than weaker than it. The proof turns on one algebraic fact, that a null covector is never orthogonal to a timelike vector, so the function grows strictly along every timelike curve and cannot close. Because the null eikonal equation alone decides the criterion, we can use it where the classical time function theorem is silent. We establish for the first time that no Kundt spacetime and no Robinson--Trautman spacetime can contain a closed timelike curve on any region where the null coordinate is globally single valued, whatever the cosmological constant and the rotation one form, which settles a question left open for gyratonic waves. We further show that the Misner, Ori and Ahmed constructions reduce to the statement that a compact null direction has turned timelike, and that the cosmological constant, the expansion and the shear are spectators in all of them.

gr-qc↗

Building age estimation based on widely available multi-source1 geospatial data

Building age is fundamental for urban infrastructure safety, operation, and planning, as it directly affects structural safety, and sustainable development. Existing methods mostly rely on detailed building information such as street view, function, or materials, which are harder to obtain than geospatial data like satellite imagery and Digital Surface Models (DSMs). However, existing methods based on those geospatial data mainly rely on optical imagery and local texture features, limiting their ability to capture structural information and distinguish buildings from different periods. Moreover, the inherent temporal ordering among building age categories is often overlooked. To address these limitations, we propose the first deep-learning framework for multi-building age estimation using widely available multi-source geospatial data, including remote sensing data and their derived products, as well as building footprint information that estimates age at both pixel and instance levels within a unified pipeline. Specifically, a large pretrained backbone with fine-tuning adapters extracts representative features, and raster-derived and property-derived fusion modules support pixel-wise and instance-wise predictions. Pixel-level estimation employs a pyramid decoder with Cosine-guided Mixture-of-Experts (CMoE) to fuse multi-source raster data across scales, capturing fine-grained building age differences, while instance-level prediction uses attention pooling and Property-Conditional Adaptive Instance Normalization (PCAdaIN) to extract instance-level representations and integrate property attributes. A Temporal Ordinal Constraint Loss (TOCL) enforces the sequential ordering of predicted ages. We also construct a building age benchmark for Hong Kong to enable comprehensive evaluation. The source code and datasets are available at https://github.com/littlebeen/RS-buildingAge.git.

cs.CV↗

The pseudospectrum for gravitational perturbations in Kerr spacetimes

We study the pseudospectrum of the Kerr black hole for gravitational perturbations with spin weight $s=2$, the case of greatest interest in gravitational-wave astronomy. In the hyperboloidal framework with two-dimensional Chebyshev collocation, we formulate the quasinormal mode problem as a non-self-adjoint eigenvalue problem. For $s\neq0$ the fields $Ψ^{(s)}$ and $Ψ^{(-s)}$ are independent, so the conserved current method yields no single-field energy norm. We overcome this with the Teukolsky--Starobinsky identities, which relate $Ψ^{(-2)}$ to a fourth-order operator on $Ψ^{(2)}$ and yield an $H^2$ energy norm after symmetrization. With the $H^1$ norm known for $s=0$, this suggests the pattern $H^{|s|}$, verified for $s=0$ and $s=2$. All results use an $ω$-independent two-field hierarchy. We find that the pseudospectrum tightens by $1.1$ to $3.5$ decades at each step of $L^2\to H^1\to H^2$, yet stays large in every norm. The $s=+2$ and $s=-2$ operators are isospectral but not isopseudospectral: at twelve of fourteen probes the $ψ_0$ resolvent exceeds the $ψ_4$ one by $0.19$ to $0.82$ decades in the $H^2$ norm, while at the two probes furthest along the QNM line the ordering reverses by $0.18$ to $0.19$ decades. The gravitational pseudospectrum exceeds the scalar one by $0.5$ to $5.2$ decades in the window minimum and by $5.9$ decades in $L^2$ at $ωM=0.47-0.17\,i$, while along the damping direction the $s=2$ resolvent is non-monotonic and the $s=0$ resolvent smooth. QNM spectral instability is therefore a universal feature of Kerr, and is stronger for gravitational perturbations.

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Where Chaos Pauses: Sliding-Window Frequency Ratios Reveal Transient Resonances in Relativistic Orbits

A long-time Poincaré section flattens a chaotic orbit into one static blur, erasing the order in which the orbit visits its own local structures. We introduce the sliding-window frequency ratio (SWFR), built from nothing but radial and polar turning events, to recover that order along individual relativistic orbits. Each window spans a fixed number of radial cycles and simply counts the polar events inside it; because event times are kept, any candidate interval can be sliced out and replotted as its own Poincaré section. No spectrum, no reference center, no basis functions. Integrable Kerr benchmarks recover the known frequency ratios, and for regular motion with uniformly bounded count deviations the counting-error bound falls off as $O(W^{-1})$. The same counts then pay off in chaos. In magnetized Kerr spacetime a single charged orbit dwells near $3/5$ and later near $4/7$, and exactly those intervals open into fivefold and sevenfold section structures; a second orbit dwells near $1/2$ with two lobes. In Schwarzschild--Melvin spacetime a photon holds $4/5$ across a fivefold pattern. In every case the section covers exactly the interval SWFR selected: integer counts and phase-space geometry agree that a globally chaotic orbit is paying a temporary visit to a resonance.

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Second-order multipole response of two nearby incoherent sources in wave-optical imaging of a Schwarzschild black hole

We study wave-optical imaging of two nearby, mutually incoherent point sources by a Schwarzschild black hole. Using the spherical-harmonic addition theorem, we construct partial-wave fields for arbitrary source directions and form images through a finite-aperture Fourier transform. To isolate the binary structure, we compare the binary image with that of a single source of equal total brightness placed at the brightness centroid. Expanding about the centroid removes the first-order term exactly, so the leading structural correction is controlled by the second central moment, $μ_2=4a^2q/(1+q)^2$. Numerical calculations confirm the expected dependence of the residual on source half-separation and brightness ratio. We then study its wavelength dependence. At long wavelengths, the imaging kernel varies little across the source separation, making the binary nearly indistinguishable from the centroid reference. As the wavelength decreases, finer fringes enhance the residual, which grows by about a factor of three over $3\leq Mω\leq12$ ($2.09\geqλ/M\geq0.52$), even though the pair remains unresolved in the Rayleigh sense. Meanwhile, the second-order approximation gradually breaks down: its relative error first reaches unity for $17<Mω<18$ ($0.349<λ/M<0.370$), indicating the need for higher-order source moments. These results demonstrate that the distinguishability of a nearby binary is intrinsically chromatic and identify the wavelength range in which the binary is both detectable through its residual and accurately described at second order.

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Poisson spot and wave scattering of scalar fields by conformal anomaly black holes: probing the near-horizon geometry

We investigate the scattering and diffraction of scalar plane waves by static conformal anomaly black holes. We determine the truncation requirements for the partial-wave series (PWS) method at finite distances and compute the full waveforms, which display a clear diffraction pattern: on the negative $z$-axis the field reduces to the incident plane wave, while on the positive $z$-axis a distorted plane wave coexists with a scattered spherical wave, and a bright Poisson spot appears at $θ=0$ surrounded by concentric diffraction rings. We obtain the on-axis intensity of the Poisson spot as a function of the radial coordinate. The intensity is most sensitive to the near-horizon geometry: close to the horizon it differs markedly from that of the RN black hole, whereas in the far field the two curves converge. We trace this to the metric functions, where different values of $f'(r_+)$ produce different phase increments that compress and shift the interference fringes, while the conformal anomaly correction falls off as $O(\tildeαM^2/r^4)$. By analyzing the potential barrier we study the absorption cross section, and use the series reduction method to accelerate the PWS convergence for the differential scattering cross section. The low- and high-frequency absorption cross sections are strongly correlated with the $\ell=0$ and $\ell$-dependent parts of the potential barrier, respectively. The differential cross section and glory scattering arise mainly from scattering in a finite region excluding a small neighborhood of the outer horizon. Charge and $\tildeα$ have opposite effects on the width of the glory peak but the same effect on its height.

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Turning-Point Count Discrepancy as a Diagnostic of Relativistic Orbital Chaos

We propose the turning-point count-discrepancy indicator (TPCD) for diagnosing orbital chaos from a single trajectory in relativistic Hamiltonian systems with two oscillatory degrees of freedom. TPCD measures the largest cumulative departure of one turning-event count from its mean rate per reference cycle, requiring neither a neighboring orbit nor a phase-space partition and applying to both massive particles and photons. We establish its long-time behavior under explicit event--phase assumptions. Rigid phases with an exact event--phase correspondence obey a strict discrepancy bound of unity, and linearizable regular tori with bounded degree-one phase deformations obey a finite, orbit-dependent bound; both imply that the normalized indicator decays to zero as the record grows. A diffusive fluctuation mechanism instead yields a Brownian-bridge scaling and a finite statistical scale. Integrable Kerr motion validates the construction, recovering prescribed frequency ratios from event counts to within $2.6\times10^{-5}$ for six targets, including an irrational ratio. In charged-particle scans around a Kerr black hole in an external test magnetic field, TPCD and the fast Lyapunov indicator agree for all 80 sampled trajectories. In the Schwarzschild--Melvin photon model, a trajectory with elevated finite-time TPCD but low fast-Lyapunov values is identified as regular once its indicator trends downward over an extended integration, showing that finite-time values must be read together with their long-time trend.

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Accretion of a Plasma Vlasov Gas onto a Reissner-Nordström Black Hole

We develop a steady-state, spherically symmetric accretion model for a two-component plasma Vlasov gas in the background of a Reissner--Nordström black hole. Analytical expressions in parametric form for the critical angular momentum and impact parameter are derived for each species, leading to a rigorous partition of the absorption and scattering domains in phase space. For both monoenergetic and Jüttner distributions, electromagnetic attraction enhances, while repulsion suppresses, the particle number density, energy density, pressure and accretion rates of individual charged components relative to the neutral case. After proper normalization, the total physical quantities of the plasma (under non-extreme conditions) can be expressed exactly, or to high accuracy, as weighted averages of the two species. When the two components share identical distribution functions, accretion drives the Reissner--Nordström black hole naturally toward the Schwarzschild limit---a charge evolution that is absent in neutral-gas models. Numerical results further reveal that near the horizon the radial pressure is significantly smaller than the tangential pressure, sharply distinguishing the Vlasov gas from a perfect fluid.

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Scalar absorption beyond geometric optics in Klein-Gordon-separable Johannsen black hole spacetimes

Johannsen metric is a natural and significant generalization of the Kerr metric, representing the most general stationary, axisymmetric spacetime that preserves the Carter constant of motion. The theoretical status furnishes a powerful, systematic framework for strong-field tests of the no-hair theorem and for investigations of deviations from Kerr black-hole geometries. We formulate massless scalar plane-wave absorption in a Klein-Gordon-separable subclass of Johannsen spacetimes. In the asymptotically flat Johannsen metric, we impose Klein-Gordon separability, derive the separated angular and radial equations, and build a partial wave framework for the leading deformation sectors $A_1(r)$, $A_2(r)$, and $A_5(r)$. The resulting description separates deformations that change the radial size function $X(r)$ from those that enter only the radial kinetic term. The former modify the low-frequency area law, the high-frequency null-capture cross section, and the finite-frequency absorption spectra, whereas a pure $A_5$ deformation leaves the leading null-capture observable unchanged while remaining detectable in wave propagation. We further examine off-axis incidence, co-/counter-rotating contributions, and superradiant modes, where changes in $X(r_+)$ shift the horizon angular velocity and hence the superradiant threshold. Our results identify finite-frequency absorption as a wave-optics diagnostic that can probe radial propagation sectors inaccessible to both the area law and null geodesic capture observables, offering a new tool for strong-field tests of black hole geometry.

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Time-reversed Shannon entropy as a chaos indicator for non-integrable systems

We propose a novel chaos indicator -- time-reversed Shannon entropy (TRSE) -- that leverages the interplay between time-reversal symmetry breaking and information entropy in curved spacetimes. By quantifying statistical discrepancies between forward and backward temporal evolution of particle orbits, TRSE robustly distinguishes chaotic from regular dynamics in non-integrable systems. In contrast, integrable systems exhibit stable, symmetric probability distributions preserved by conserved quantities such as the Carter constant. We validate the method through high-precision numerical simulations in both Kerr and Schwarzschild-Melvin black hole geometries, evolving trajectories forward and backward in time. Furthermore, we refine our previously introduced particle-pair mutual information (MIPP) and perform comprehensive parameter-space scans, revealing a strong quantitative agreement between MIPP and TRSE. The two indicators emerge as complementary probes of chaos: TRSE captures symmetry breaking in orbital evolution, while MIPP measures statistical correlations. Together, they establish a unified framework for diagnosing chaos in general relativistic systems, paving a new path to understand the fundamental nature of chaos in non-integrable systems.

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Absorption and scattering of massless scalar waves by Frolov black holes

We study the absorption and scattering of massless scalar waves by Frolov black holes, a regular deformation of the Reissner--Nordström geometry. A null-geodesic analysis provides the photon-sphere radius and the critical impact parameters governing capture and glory scattering. Using a partial-wave approach, we compute total/partial absorption cross sections and differential scattering cross sections over a broad frequency range. When the absorption spectrum is rescaled by photon-sphere scales, the high-frequency oscillations exhibit a pronounced data collapse in the variables $\hatσ=σ_{\rm abs}/σ_{\rm geo}$ and $x=ω/Ω_c$, highlighting photon-sphere control of the absorption fine structure. We clarify the parameter dependence under the horizon-radius normalization and relate the apparent trends to the variation of the dimensionless mass across parameter space. Finally, comparing Frolov, Reissner--Nordström, and Hayward black holes with matched critical or glory impact parameters, we find that their absorption and scattering patterns can become remarkably close, indicating that in the intermediate-to-high frequency regime the dominant signatures are largely governed by the unstable photon orbit, while core effects enter as subleading corrections.

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Observability of gravitational waves excited by binary stars orbiting around a supermassive black hole by space-based gravitational wave observatory

We produce the gravitational waveforms for the extreme mass ratio inspiral systems (EMRIs) of binary stars moving around central supermassive black hole (SBH), or called B-EMRIs. We calculate the external orbits of the binary stars via the commonly used Hamilton-Jacobi (HJ) approach, and calculate the internal orbits of the binary stars via Lagrangian approach. To improve accuracy we adopt the quadrupole-octupole expression of gravitational wave (GW) and study the contribution of radiation reaction. Compared to the waveforms of EMRIs, there are higher frequency oscillations superposed on the waveforms of B-EMRIs. We perform frequency spectrum analysis of the GW waveforms, and find that higher frequency signals give their prominency in the waveforms of B-EMRIs. To obtain high precise result for future observation of GWs from space-based detector, we take into account gravito-electromagnetic (GEM) force, and compare the waveforms of B-EMRIs with GEM effects against those of B-EMRIs without GEM effects and against those of EMRIs. The result of mismatch shows that the waveforms of B-EMRIs are credibly distinguishable by the space-based GW detectors when GEM force is considered.

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Consistency-Regularized GAN for Few-Shot SAR Target Recognition

Few-shot recognition in synthetic aperture radar (SAR) imagery remains a critical bottleneck for real-world applications due to extreme data scarcity. A promising strategy involves synthesizing a large dataset with a generative adversarial network (GAN), pre-training a model via self-supervised learning (SSL), and then fine-tuning on the few labeled samples. However, this approach faces a fundamental paradox: conventional GANs themselves require abundant data for stable training, contradicting the premise of few-shot learning. To resolve this, we propose the consistency-regularized generative adversarial network (Cr-GAN), a novel framework designed to synthesize diverse, high-fidelity samples even when trained under these severe data limitations. Cr-GAN introduces a dual-branch discriminator that decouples adversarial training from representation learning. This architecture enables a channel-wise feature interpolation strategy to create novel latent features, complemented by a dual-domain cycle consistency mechanism that ensures semantic integrity. Our Cr-GAN framework is adaptable to various GAN architectures, and its synthesized data effectively boosts multiple SSL algorithms. Extensive experiments on the MSTAR and SRSDD datasets validate our approach, with Cr-GAN achieving a highly competitive accuracy of 71.21% and 51.64%, respectively, in the 8-shot setting, significantly outperforming leading baselines, while requiring only ~5 of the parameters of state-of-the-art diffusion models. Code is available at: https://github.com/yikuizhai/Cr-GAN.

cs.CV↗

Democratizing planetary-scale analysis: An ultra-lightweight Earth embedding database for accurate and flexible global land monitoring

The rapid evolution of satellite-borne Earth Observation (EO) systems has revolutionized terrestrial monitoring, yielding petabyte-scale archives. However, the immense computational and storage requirements for global-scale analysis often preclude widespread use, hindering planetary-scale studies. To address these barriers, we present Embedded Seamless Data (ESD), an ultra-lightweight, 30-m global Earth embedding database spanning the 25-year period from 2000 to 2024. By transforming high-dimensional, multi-sensor observations from the Landsat series (5, 7, 8, and 9) and MODIS Terra into information-dense, quantized latent vectors, ESD distills essential geophysical and semantic features into a unified latent space. Utilizing the ESDNet architecture and Finite Scalar Quantization (FSQ), the dataset achieves a transformative ~340-fold reduction in data volume compared to raw archives. This compression allows the entire global land surface for a single year to be encapsulated within approximately 2.4 TB, enabling decadal-scale global analysis on standard local workstations. Rigorous validation demonstrates high reconstructive fidelity (MAE: 0.0130; RMSE: 0.0179; CC: 0.8543). By condensing the annual phenological cycle into 12 temporal steps, the embeddings provide inherent denoising and a semantically organized space that outperforms raw reflectance in land-cover classification, achieving 79.74% accuracy (vs. 76.92% for raw fusion). With robust few-shot learning capabilities and longitudinal consistency, ESD provides a versatile foundation for democratizing planetary-scale research and advancing next-generation geospatial artificial intelligence.

cs.CV↗

Wetland mapping from sparse annotations with satellite image time series and temporal-aware segment anything model

Accurate wetland mapping is essential for ecosystem monitoring, yet dense pixel-level annotation is prohibitively expensive and practical applications usually rely on sparse point labels, under which existing deep learning models perform poorly, while strong seasonal and inter-annual wetland dynamics further render single-date imagery inadequate and lead to significant mapping errors; although foundation models such as SAM show promising generalization from point prompts, they are inherently designed for static images and fail to model temporal information, resulting in fragmented masks in heterogeneous wetlands. To overcome these limitations, we propose WetSAM, a SAM-based framework that integrates satellite image time series for wetland mapping from sparse point supervision through a dual-branch design, where a temporally prompted branch extends SAM with hierarchical adapters and dynamic temporal aggregation to disentangle wetland characteristics from phenological variability, and a spatial branch employs a temporally constrained region-growing strategy to generate reliable dense pseudo-labels, while a bidirectional consistency regularization jointly optimizes both branches. Extensive experiments across eight global regions of approximately 5,000 km2 each demonstrate that WetSAM substantially outperforms state-of-the-art methods, achieving an average F1-score of 85.58%, and delivering accurate and structurally consistent wetland segmentation with minimal labeling effort, highlighting its strong generalization capability and potential for scalable, low-cost, high-resolution wetland mapping.

cs.CV↗

OCCDiff: Occupancy Diffusion Model for High-Fidelity 3D Building Reconstruction from Noisy Point Clouds

A major challenge in reconstructing buildings from LiDAR point clouds lies in accurately capturing building surfaces under varying point densities and noise interference. To flexibly gather high-quality 3D profiles of the building in diverse resolution, we propose OCCDiff applying latent diffusion in the occupancy function space. Our OCCDiff combines a latent diffusion process with a function autoencoder architecture to generate continuous occupancy functions evaluable at arbitrary locations. Moreover, a point encoder is proposed to provide condition features to diffusion learning, constraint the final occupancy prediction for occupancy decoder, and insert multi-modal features for latent generation to latent encoder. To further enhance the model performance, a multi-task training strategy is employed, ensuring that the point encoder learns diverse and robust feature representations. Empirical results show that our method generates physically consistent samples with high fidelity to the target distribution and exhibits robustness to noisy data.

cs.CV↗

Dynamics of photons and shadows for black holes haired with parity-odd fields

Strong self-gravitational fields enable the realization of macroscopic odd-parity quantum objects. Using ray-tracing methods, we systematically analyze the dynamics of photons and the shadow features of rotating black holes with parity-odd scalar hair and contrast them with those of Kerr black holes. Our results demonstrate measurable distinctions between scalar-haired black hole shadows and their Kerr counterparts. Notably, even for tiny scalar charge and negligible scalar hair mass contributions, these differences remain quantitatively resolvable. In particular, one of the hairy black hole reported here lies within the Event Horizon Telescope observational uncertainties, probing the scalar masses of $1.02\times10^{-20}$eV with M87*. These findings may provide related theoretical benchmarks for future observational campaigns targeting scalar-field dark matter candidates through black hole shadow imaging.

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Multimodal Feature Fusion Network with Text Difference Enhancement for Remote Sensing Change Detection

Although deep learning has advanced remote sensing change detection (RSCD), most methods rely solely on image modality, limiting feature representation, change pattern modeling, and generalization especially under illumination and noise disturbances. To address this, we propose MMChange, a multimodal RSCD method that combines image and text modalities to enhance accuracy and robustness. An Image Feature Refinement (IFR) module is introduced to highlight key regions and suppress environmental noise. To overcome the semantic limitations of image features, we employ a vision language model (VLM) to generate semantic descriptions of bitemporal images. A Textual Difference Enhancement (TDE) module then captures fine grained semantic shifts, guiding the model toward meaningful changes. To bridge the heterogeneity between modalities, we design an Image Text Feature Fusion (ITFF) module that enables deep cross modal integration. Extensive experiments on LEVIRCD, WHUCD, and SYSUCD demonstrate that MMChange consistently surpasses state of the art methods across multiple metrics, validating its effectiveness for multimodal RSCD. Code is available at: https://github.com/yikuizhai/MMChange.

cs.CV↗