arXiv subjects

Jingdong Zhang

Publications and source records attributed to Jingdong Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2025

The orbital period of the long-period and colliding-wind binary WR 146 from radio interferometry of the shock cone

We report the first measurement of the orbital period of a long-period colliding-wind binary (CWB) system WR 146, derived by tracing the rotational morphology of its wind-colliding region (WCR) and the relative orientation of the two binary components. This result is based on our imaging observations using the Very Long Baseline Array (VLBA) and the European Very Long Baseline Interferometry (VLBI) Network (EVN), combined with archival data from VLBA, EVN, the Very Large Array (VLA), the enhanced Multi-Element Radio-Linked Interferometer Network (eMERLIN) arrays, and optical images from the Hubble Space Telescope (HST). We evaluated two methods for determining the binary's orbital period based on the images of the WCR: (I) fitting the shock cone of the WCR and (II) stacking images using the cross-correlation function. Using these techniques, we find orbital period estimates of 810+120-90 years from method I and 1120+540-270 years from method II, both of which support a long orbital period of approximately 1,000 years. Furthermore, we analyzed archival spectral data of WR 146 to estimate the stellar wind velocities of the binary components, finding no significant orbital phase lag between the binary orientation and the WCR rotation. We also estimate the range of the binary's mass using the currently measured parameters.

astro-ph.SR↗

arXiv2024

SolidGS: Consolidating Gaussian Surfel Splatting for Sparse-View Surface Reconstruction

Gaussian splatting has achieved impressive improvements for both novel-view synthesis and surface reconstruction from multi-view images. However, current methods still struggle to reconstruct high-quality surfaces from only sparse view input images using Gaussian splatting. In this paper, we propose a novel method called SolidGS to address this problem. We observed that the reconstructed geometry can be severely inconsistent across multi-views, due to the property of Gaussian function in geometry rendering. This motivates us to consolidate all Gaussians by adopting a more solid kernel function, which effectively improves the surface reconstruction quality. With the additional help of geometrical regularization and monocular normal estimation, our method achieves superior performance on the sparse view surface reconstruction than all the Gaussian splatting methods and neural field methods on the widely used DTU, Tanks-and-Temples, and LLFF datasets.

cs.CV↗

arXiv2024

BridgeNet: Comprehensive and Effective Feature Interactions via Bridge Feature for Multi-task Dense Predictions

Multi-task dense prediction aims at handling multiple pixel-wise prediction tasks within a unified network simultaneously for visual scene understanding. However, cross-task feature interactions of current methods are still suffering from incomplete levels of representations, less discriminative semantics in feature participants, and inefficient pair-wise task interaction processes. To tackle these under-explored issues, we propose a novel BridgeNet framework, which extracts comprehensive and discriminative intermediate Bridge Features, and conducts interactions based on them. Specifically, a Task Pattern Propagation (TPP) module is firstly applied to ensure highly semantic task-specific feature participants are prepared for subsequent interactions, and a Bridge Feature Extractor (BFE) is specially designed to selectively integrate both high-level and low-level representations to generate the comprehensive bridge features. Then, instead of conducting heavy pair-wise cross-task interactions, a Task-Feature Refiner (TFR) is developed to efficiently take guidance from bridge features and form final task predictions. To the best of our knowledge, this is the first work considering the completeness and quality of feature participants in cross-task interactions. Extensive experiments are conducted on NYUD-v2, Cityscapes and PASCAL Context benchmarks, and the superior performance shows the proposed architecture is effective and powerful in promoting different dense prediction tasks simultaneously.

cs.CV↗

arXiv2024

Governing equation discovery of a complex system from snapshots

Complex systems in physics, chemistry, and biology that evolve over time with inherent randomness are typically described by stochastic differential equations (SDEs). A fundamental challenge in science and engineering is to determine the governing equations of a complex system from snapshot data. Traditional equation discovery methods often rely on stringent assumptions, such as the availability of the trajectory information or time-series data, and the presumption that the underlying system is deterministic. In this work, we introduce a data-driven, simulation-free framework, called Sparse Identification of Differential Equations from Snapshots (SpIDES), that discovers the governing equations of a complex system from snapshots by utilizing the advanced machine learning techniques to perform three essential steps: probability flow reconstruction, probability density estimation, and Bayesian sparse identification. We validate the effectiveness and robustness of SpIDES by successfully identifying the governing equation of an over-damped Langevin system confined within two potential wells. By extracting interpretable drift and diffusion terms from the SDEs, our framework provides deeper insights into system dynamics, enhances predictive accuracy, and facilitates more effective strategies for managing and simulating stochastic systems.

cs.LG↗

arXiv2024

Learning Hamiltonian neural Koopman operator and simultaneously sustaining and discovering conservation law

Accurately finding and predicting dynamics based on the observational data with noise perturbations is of paramount significance but still a major challenge presently. Here, for the Hamiltonian mechanics, we propose the Hamiltonian Neural Koopman Operator (HNKO), integrating the knowledge of mathematical physics in learning the Koopman operator, and making it automatically sustain and even discover the conservation laws. We demonstrate the outperformance of the HNKO and its extension using a number of representative physical systems even with hundreds or thousands of freedoms. Our results suggest that feeding the prior knowledge of the underlying system and the mathematical theory appropriately to the learning framework can reinforce the capability of machine learning in solving physical problems.

math-ph↗

arXiv2024

From Fourier to Neural ODEs: Flow Matching for Modeling Complex Systems

Modeling complex systems using standard neural ordinary differential equations (NODEs) often faces some essential challenges, including high computational costs and susceptibility to local optima. To address these challenges, we propose a simulation-free framework, called Fourier NODEs (FNODEs), that effectively trains NODEs by directly matching the target vector field based on Fourier analysis. Specifically, we employ the Fourier analysis to estimate temporal and potential high-order spatial gradients from noisy observational data. We then incorporate the estimated spatial gradients as additional inputs to a neural network. Furthermore, we utilize the estimated temporal gradient as the optimization objective for the output of the neural network. Later, the trained neural network generates more data points through an ODE solver without participating in the computational graph, facilitating more accurate estimations of gradients based on Fourier analysis. These two steps form a positive feedback loop, enabling accurate dynamics modeling in our framework. Consequently, our approach outperforms state-of-the-art methods in terms of training time, dynamics prediction, and robustness. Finally, we demonstrate the superior performance of our framework using a number of representative complex systems.

cs.LG↗

arXiv2024

FESSNC: Fast Exponentially Stable and Safe Neural Controller

In order to stabilize nonlinear systems modeled by stochastic differential equations, we design a Fast Exponentially Stable and Safe Neural Controller (FESSNC) for fast learning controllers. Our framework is parameterized by neural networks, and realizing both rigorous exponential stability and safety guarantees. Concretely, we design heuristic methods to learn the exponentially stable and the safe controllers, respectively, in light of the classic stochastic exponential stability theory and our established theorem on guaranteeing the almost-sure safety for stochastic dynamics. More significantly, to rigorously ensure the stability and the safety guarantees for the learned controllers, we develop a projection operator, projecting to the space of exponentially-stable and safe controllers. To reduce the high computation cost of solving the projection operation, approximate projection operators are delicately proposed with closed forms that map the learned controllers to the target controller space. Furthermore, we employ Hutchinson's trace estimator for a scalable unbiased estimate of the Hessian matrix that is used in the projection operator, which thus allows for computation cost reduction and therefore can accelerate the training and testing processes. More importantly, our approximate projection operations can be applied to the nonparametric control methods to improve their stability and safety performance. We empirically demonstrate the superiority of the FESSNC over the existing methods.

math.OC↗

arXiv2024

VLBI Astrometry of Radio Stars to Link Radio and Optical Celestial Reference Frames: Observing Strategies

The Gaia celestial reference frame (Gaia-CRF) will benefit from a close assessment with independent methods, such as Very Long Baseline Interferometry (VLBI) measurements of radio stars at bright magnitudes. However, obtaining full astrometric parameters for each radio star through VLBI measurements demands a significant amount of observation time. This study proposes an efficient observing strategy that acquires double-epoch VLBI positions to measure the positions and proper motions of radio stars at a reduced cost. The solution for CRF link compatible with individual VLBI position measurements is introduced, and the optimized observing epoch scheduling is discussed. Applying this solution to observational data yields results sensitive to sample increase or decrease, yet they remain consistently in line with the literature at the 1-sigma level. This suggests the potential for improvement with a larger sample size. Simulations for adding observations demonstrate the double-epoch strategy reduces CRF link parameter uncertainties by over 30% compared to the five-parameter strategy.

astro-ph.IM↗

arXiv2023

Rethinking Cross-Domain Pedestrian Detection: A Background-Focused Distribution Alignment Framework for Instance-Free One-Stage Detectors

Cross-domain pedestrian detection aims to generalize pedestrian detectors from one label-rich domain to another label-scarce domain, which is crucial for various real-world applications. Most recent works focus on domain alignment to train domain-adaptive detectors either at the instance level or image level. From a practical point of view, one-stage detectors are faster. Therefore, we concentrate on designing a cross-domain algorithm for rapid one-stage detectors that lacks instance-level proposals and can only perform image-level feature alignment. However, pure image-level feature alignment causes the foreground-background misalignment issue to arise, i.e., the foreground features in the source domain image are falsely aligned with background features in the target domain image. To address this issue, we systematically analyze the importance of foreground and background in image-level cross-domain alignment, and learn that background plays a more critical role in image-level cross-domain alignment. Therefore, we focus on cross-domain background feature alignment while minimizing the influence of foreground features on the cross-domain alignment stage. This paper proposes a novel framework, namely, background-focused distribution alignment (BFDA), to train domain adaptive onestage pedestrian detectors. Specifically, BFDA first decouples the background features from the whole image feature maps and then aligns them via a novel long-short-range discriminator.

cs.CV↗

arXiv2023

VLBI Astrometry of Radio Stars to Link Radio and Optical Celestial Reference Frames. I. HD 199178 $\&$ AR Lacertae

To accurately link the radio and optical Celestial Reference Frames (CRFs) at optical bright end, i.e., with Gaia G band magnitude < 13, increasing number and improving sky distribution of radio stars with accurate astrometric parameters from both Very Long Baseline Interferometry (VLBI) and Gaia measurements are mandatory. We selected two radio stars HD 199178 and AR Lacertae as the target for a pilot program for the frame link, using the Very Long Baseline Array (VLBA) at 15 GHz at six epochs spanning about 1 year, to measure their astrometric parameters. The measured parallax of HD 199178 is $8.949 \pm 0.059$ mas and the proper motion is $μ_αcos δ= 26.393 \pm 0.093$, $μ_δ= -0.950 \pm 0.083~mas~yr^{-1}$, while the parallax of AR Lac is $23.459 \pm 0.094$ mas and the proper motion is $μ_αcos δ= -51.906 \pm 0.138$, $μ_δ= 46.732 \pm 0.131~mas~yr^{-1}$. Our VLBI measured astrometric parameters have accuracies about 4-5 times better than the corresponding historic VLBI measurements and comparable accuracies with those from Gaia, validating the feasibility of frame link using radio stars. With the updated astrometric parameters for these two stars, there is a 25% reduction of the uncertainties on the Y axis for both orientation and spin parameters.

astro-ph.SR↗

arXiv2023

The parallax and 3D kinematics of water masers in the massive star-forming region G034.43+0.24

We report a trigonometric parallax measurement of 22 GHz water masers in the massive star-forming region G034.43+0.24 as part of the Bar and Spiral Structure Legacy (BeSSeL) Survey using the Very Long Baseline Array. The parallax is 0.330$\pm$50.018 mas, corresponding to a distance of $3.03^{+0.17}_{-0.16}$ kpc. This locates G034.43+0.24 near the inner edge of the Sagittarius spiral arm and at one end of a linear distribution of massive young stars which cross nearly the full width of the arm. The measured 3-dimensional motion of G034.43+0.24 indicates a near-circular Galactic orbit. The water masers display arc-like distributions, possibly bow shocks, associated with winds from one or more massive young stars.

astro-ph.GA↗

arXiv2022

The Astrometric Animation of Water Masers towards the Mira Variable BX Cam

We report VLBI monitoring observations of the 22 GHz water (H$_{2}$O) masers around the Mira variable BX Cam, which were carried out as a part of the EAVN Synthesis of Stellar Maser Animations (ESTEMA) project. Data of 37 epochs in total were obtained from 2018 May to 2021 June with a time interval of 3-4 weeks, spanning approximately three stellar pulsation periods ($P= \sim$440 d). In particular, the dual-beam system equipped on the VERA stations was used to measure the kinematics and parallaxes of the H$_{2}$O maser features. The measured parallax, $π=1.79\pm 0.08$ mas, is consistent with $Gaia$ EDR3 and previously measured VLBI parallaxes within a 1-$σ$ error level. The position of the central star was estimated, based on both the $Gaia$ EDR3 data and the center position of the ring-like 43 GHz silicon-monoxide (SiO) maser distribution imaged with the KVN. The three-dimensional H$_{2}$O maser kinematics indicates that the circumstellar envelope is expanding at a velocity of $13\pm4$ km s$^{-1}$, while there are asymmetries in both the spatial and velocity distributions of the maser features. Furthermore, the H$_{2}$O maser animation achieved by our dense monitoring program manifests the propagation of shock waves in the circumstellar envelope of BX Cam.

astro-ph.SR↗

arXiv2022

Neural Stochastic Control

Control problems are always challenging since they arise from the real-world systems where stochasticity and randomness are of ubiquitous presence. This naturally and urgently calls for developing efficient neural control policies for stabilizing not only the deterministic equations but the stochastic systems as well. Here, in order to meet this paramount call, we propose two types of controllers, viz., the exponential stabilizer (ES) based on the stochastic Lyapunov theory and the asymptotic stabilizer (AS) based on the stochastic asymptotic stability theory. The ES can render the controlled systems exponentially convergent but it requires a long computational time; conversely, the AS makes the training much faster but it can only assure the asymptotic (not the exponential) attractiveness of the control targets. These two stochastic controllers thus are complementary in applications. We also investigate rigorously the linear controller and the proposed neural stochastic controllers in both convergence time and energy cost and numerically compare them in these two indexes. More significantly, we use several representative physical systems to illustrate the usefulness of the proposed controllers in stabilization of dynamical systems.

eess.SY↗

arXiv2022

A VLBA Trigonometric Parallax for RR Aql and the Mira PL Relation

We report VLBA observations of 22 GHz H$_{2}$O and 43 GHz SiO masers toward the Mira variable RR Aql. By fitting the SiO maser emission to a circular ring, we estimate the absolute stellar position of RR Aql and find agreement with Gaia astrometry to within the joint uncertainty of $\approx1$ mas. Using the maser astrometry we measure a stellar parallax of 2.44 $\pm$ 0.07 mas, corresponding to a distance of 410$^{+12}_{-11}$ pc. The maser parallax deviates significantly from the Gaia EDR3 parallax of 1.95 $\pm$ 0.11 mas, indicating a $3.8σ$ tension between radio and optical measurements. This tension is most likely caused by optical photo-center variations limiting the Gaia astrometric accuracy for this Mira variable. Combining infrared magnitudes with parallaxes for RR Aql and other Miras, we fit a period-luminosity relation using a Bayesian approach with MCMC sampling and a strong prior for the slope of -3.60 $\pm$ 0.30 from the LMC. We find a $K$-band zero-point (defined at logP(days) = 2.30) of -6.79 $\pm$ 0.15 mag using VLBI parallaxes and -7.08 $\pm$ 0.29 mag using Gaia parallaxes. The Gaia zero-point is statistically consistent with the more accurate VLBI value.

astro-ph.GA↗

arXiv2019

Extending the dynamic strain sensing rang of phase-OTDR with frequency modulation pulse and frequency interrogation

We propose and experimentally demonstrate a technique to extend the dynamic sensing range of phase sensitive optical time domain reflectometry system based on the frequency interrogation. Benefitting from the range Doppler coupling feature, the frequency modulation pulse is capable of measuring the frequency shift induced by the dynamic strain, thus the large dynamic strain can be recovered. The performance of the proposed method is experimentally evaluated by comparing it with phase unwrapping. The strain sensing rang can at least be increased by factor of hundreds, and fast dynamic strain with peak to peak 130 microstrain vibration frequency of 20 kHz is measured.

physics.app-ph↗

arXiv2018

Long range fading free phase-sensitive reflectometry based on multi-frequency NLFM pulse

A long range phase-sensitive optical time domain reflectometer (phi-OTDR) with a multi-frequency non-linear frequency modulation (NLFM) optical pulse is proposed in this Letter. To boost the pulse energy while suppressing the optical nonlinear effects, the distortion of the amplified pulse is rectified, and a three-tone pulse is used. Combining with the NLFM technic which provides 42.7 dB side lobe suppression ration (SLSR), these two approaches guarantee that a sensing distance of 80 km is achieved in the experiment with 2.5 m spatial resolution, 49.6 dB dynamic range, and 45 dB phase signal-to-noise ratio (SNR). To the best of our knowledge, this is the first time that a phase-demodulated phi-OTDR over such a long sensing range has been reported with un-pumped sensing fiber.

physics.ins-det↗

arXiv2018

Distributed fiber sparse-wideband vibration sensing by sub-Nyquist additive random sampling

The round trip time of the light pulse limits the maximum detectable vibration frequency response range of phase-sensitive optical time domain reflectometry (ϕ-OTDR). Unlike the uniform laser pulse interval in conventional ϕ-OTDR, we randomly modulate the pulse interval, so that an equivalent sub-Nyquist additive random sampling (sNARS) is realized for every sensing point of the long interrogation fiber. For an ϕ-OTDR system with 10 km sensing length, the sNARS method is optimized by theoretical analysis and Monte Carlo simulation, and the experimental results verify that a wide-band spars signal can be identified and reconstructed. Such a method can broaden the vibration frequency response range of ϕ-OTDR, which is of great significance in sparse-wideband-frequency vibration signal detection, such as rail track monitoring and metal defect detection.

physics.ins-det↗

arXiv2017

Optical-controlled wavelength-tunable Q-switched mode-locked fiber laser based on graphene-deposited micro-FBG

We report a wavelength-tunable Q-switched mode-locked fiber laser based on a compact optical tuning device, which is fabricated by coating single-layer graphene on the surface of micro-fiber Bragg grating (MFBG). Based on thermal-optical effect through evanescent interaction between graphene and MFBG, the center wavelength of MFBG can be accurately controlled by adjusting power of an external laser. By inserting the fabricated device into a compact fiber laser cavity mode-locked by single-wall carbon nanotubes, stable Q-switched mode-locked pulse is generated. Experimental results show that the wavelength can be precisely optical-tuned from 1550.1 nm to 1551.9 nm with a linear sensitivity of 6.3 pm/mW. Such an optical-controlled wavelength-tunable Q-switched mode-locked fiber laser may find potential applications in all-optical computing, all-optical logic gating and generation of microwave source.

physics.optics↗