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Min Wang

Publications and source records attributed to Min Wang.

At least 73 records · Page 4Linked to original sources
arXiv2025

Low-loss thin-film periodically poled lithium niobate waveguides fabricated by femtosecond laser photolithography

Periodically poled lithium niobate on insulator (PPLNOI) ridge waveguides are critical photonic components for both classical and quantum information processing. However, dry etching of PPLNOI waveguides often generates rough sidewalls and variations in the etching rates of oppositely poled lithium niobate ferroelectric domains, leading a relatively high propagation losses (0.25 - 1 dB/cm), which significantly limits net conversion efficiency and hinders scalable photonic integration. In this work, a low-loss PPLNOI ridge waveguide with a length of 7 mm was fabricated using ultra-smooth sidewalls through photolithography-assisted chemo-mechanical etching (PLACE) followed by high-voltage pulse poling with low cost. The average surface roughness was measured at just 0.27 nm, resulting in record-low propagation loss of 0.106 dB/cm in PPLNOI waveguides. Highly efficient second-harmonic generation was demonstrated with a normalized efficiency of 1643%/(W*cm^2) without temperature tuning, corresponding to a conversion efficiency of 805%/W, which is closed to the best conversion efficiency (i.e., 814%/W) reported in nanophotonic PPLNOI waveguide fabricated by expensive electron-beam lithography followed by dry etching. The absolute conversion efficiency reached 15.8% at a pump level of 21.6 mW. And the normalized efficiency can be even improved to 1742%/(W*cm^2) at optimal temperature of 59°C.

physics.optics↗
arXiv2025

The Chinese pulsar timing array data release I. Polarimetry for 56 millisecond pulsars

We present polarization pulse profiles for 56 millisecond pulsars (MSPs) monitored by the Chinese Pulsar Timing Array (CPTA) collaboration using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The observations centered at 1.25 GHz with a raw bandwidth of 500 MHz. Due to the high sensitivity ($\sim$16 K/Jy) of the FAST telescope and our long integration time, the high signal-to-noise ratio polarization profiles show features hardly detected before. Among 56 pulsars, the polarization profiles of PSRs J0406$+$3039, J1327$+$3423, and J2022$+$2534 were not previously reported. 80\% of MSPs in the sample show weak components below 3\% of peak flux, 25\% of pulsars show interpulse-like structures, and most pulsars show linear polarization position angle jumps. Six pulsars seem to be emitting for full rotation phase, with another thirteen pulsars being good candidates for such a 360$^\circ$ radiator. We find that the distribution of the polarization percentage in our sample is compatible with the normal pulsar distribution. Our detailed evaluation of the MSP polarization properties suggests that the wave propagation effects in the pulsar magnetosphere are important in shaping the MSP polarization pulse profiles.

astro-ph.HE↗
arXiv2025

The flat tail in the burst energy distribution of FRB 20240114A

Fast Radio Bursts (FRBs) are enigmatic millisecond-duration radio transients of extra-galactic origin, whose underlying mechanisms and progenitors remain poorly understood. FRBs are broadly classified into two categories: repeating FRBs, which emit multiple bursts over time, and one-off FRBs, which are detected as single events. A central question in FRB research is whether these two classes share a common origin. In this study, we present observations of FRB 20240114A, a repeating FRB that entered a hyperactive phase in January 2024. We conducted a 318-hour monitoring campaign using the Kunming 40-Meter Radio Telescope (KM40M) in the S-band (2.187-2.311 GHz), during which we detected eight radio bursts. We analyzed their properties, including dispersion measure (DM), bandwidth, pulse width, flux, fluence, and energy. Additionally, we searched for counterparts in overlapping data from the Five-hundred-meter Aperture Spherical Telescope (FAST) in the L-band (1.0-1.5 GHz). While no bursts were temporally aligned between the two telescopes, we identified one FAST burst that arrived approximately 6 ms after one of the KM40M bursts. The absence of FAST counterparts for the KM40M bursts suggests that individual bursts from FRB 20240114A are likely narrow-band, with fractional bandwidths less than 10%. By comparing the cumulative event rates from KM40M and FAST observations, we found that the two measurements are compatible, indicating a possible flattening of the event rate at higher energies. This feature aligns with observations of one-off FRBs, supporting the hypothesis that repeating and one-off FRBs may share a common origin.

astro-ph.HE↗
arXiv2025

Optimizing for the Shortest Path in Denoising Diffusion Model

In this research, we propose a novel denoising diffusion model based on shortest-path modeling that optimizes residual propagation to enhance both denoising efficiency and quality. Drawing on Denoising Diffusion Implicit Models (DDIM) and insights from graph theory, our model, termed the Shortest Path Diffusion Model (ShortDF), treats the denoising process as a shortest-path problem aimed at minimizing reconstruction error. By optimizing the initial residuals, we improve the efficiency of the reverse diffusion process and the quality of the generated samples. Extensive experiments on multiple standard benchmarks demonstrate that ShortDF significantly reduces diffusion time (or steps) while enhancing the visual fidelity of generated samples compared to prior arts. This work, we suppose, paves the way for interactive diffusion-based applications and establishes a foundation for rapid data generation. Code is available at https://github.com/UnicomAI/ShortDF.

cs.CV↗
arXiv2025

PINN-MG: A physics-informed neural network for mesh generation

In numerical simulation, structured mesh generation often requires a lot of time and manpower investment. The general scheme for structured quad mesh generation is to find a mapping between the computational domain and the physical domain. This mapping can be obtained by solving partial differential equations. However, existing structured mesh generation methods are difficult to ensure both efficiency and mesh quality. In this paper, we propose a structured mesh generation method based on physics-informed neural network, PINN-MG. It takes boundary curves as input and then utilizes an attention network to capture the potential mapping between computational and physical domains, generating structured meshes for the input physical domain. PINN-MG introduces the Navier-Lamé equation in linear elastic as a partial differential equation term in the loss function, ensuring that the neural network conforms to the law of elastic body deformation when optimizing the loss value. The training process of PINN-MG is completely unsupervised and does not require any prior knowledge or datasets, which greatly reduces the previous workload of producing structured mesh datasets. Experimental results show that PINN-MG can generate higher quality structured quad meshes than other methods, and has the advantages of traditional algebraic methods and differential methods.

cs.CE↗
arXiv2025

Compact Turnkey Soliton Microcombs at Microwave Rates via Wafer-Scale Fabrication

Soliton microcombs generated in nonlinear microresonators facilitate the photonic integration of timing, frequency synthesis, and astronomical calibration functionalities. For these applications, low-repetition-rate soliton microcombs are essential as they establish a coherent link between optical and microwave signals. However, the required pump power typically scales with the inverse of the repetition rate, and the device footprint scales with the inverse of square of the repetition rate, rendering low-repetition-rate soliton microcombs challenging to integrate within photonic circuits. This study designs and fabricates silicon nitride microresonators on 4-inch wafers with highly compact form factors. The resonator geometries are engineered from ring to finger and spiral shapes to enhance integration density while attaining quality factors over 10^7. Driven directly by an integrated laser, soliton microcombs with repetition rates below 10 GHz are demonstrated via turnkey initiation. The phase noise performance of the synthesized microwave signals reaches -130 dBc/Hz at 100 kHz offset frequency for 10 GHz carrier frequencies. This work enables the high-density integration of soliton microcombs for chip-based microwave photonics and spectroscopy applications.

physics.optics↗
arXiv2025

GaussNav: Gaussian Splatting for Visual Navigation

In embodied vision, Instance ImageGoal Navigation (IIN) requires an agent to locate a specific object depicted in a goal image within an unexplored environment. The primary challenge of IIN arises from the need to recognize the target object across varying viewpoints while ignoring potential distractors. Existing map-based navigation methods typically use Bird's Eye View (BEV) maps, which lack detailed texture representation of a scene. Consequently, while BEV maps are effective for semantic-level visual navigation, they are struggling for instance-level tasks. To this end, we propose a new framework for IIN, Gaussian Splatting for Visual Navigation (GaussNav), which constructs a novel map representation based on 3D Gaussian Splatting (3DGS). The GaussNav framework enables the agent to memorize both the geometry and semantic information of the scene, as well as retain the textural features of objects. By matching renderings of similar objects with the target, the agent can accurately identify, ground, and navigate to the specified object. Our GaussNav framework demonstrates a significant performance improvement, with Success weighted by Path Length (SPL) increasing from 0.347 to 0.578 on the challenging Habitat-Matterport 3D (HM3D) dataset. The source code is publicly available at the link: https://github.com/XiaohanLei/GaussNav.

cs.CV↗
arXiv2024

Bright-NeRF:Brightening Neural Radiance Field with Color Restoration from Low-light Raw Images

Neural Radiance Fields (NeRFs) have demonstrated prominent performance in novel view synthesis. However, their input heavily relies on image acquisition under normal light conditions, making it challenging to learn accurate scene representation in low-light environments where images typically exhibit significant noise and severe color distortion. To address these challenges, we propose a novel approach, Bright-NeRF, which learns enhanced and high-quality radiance fields from multi-view low-light raw images in an unsupervised manner. Our method simultaneously achieves color restoration, denoising, and enhanced novel view synthesis. Specifically, we leverage a physically-inspired model of the sensor's response to illumination and introduce a chromatic adaptation loss to constrain the learning of response, enabling consistent color perception of objects regardless of lighting conditions. We further utilize the raw data's properties to expose the scene's intensity automatically. Additionally, we have collected a multi-view low-light raw image dataset to advance research in this field. Experimental results demonstrate that our proposed method significantly outperforms existing 2D and 3D approaches. Our code and dataset will be made publicly available.

cs.CV↗
arXiv2024

Analysis of Age of Information for A Discrete-Time hybrid Dual-Queue System

Using multiple sensors to update the status process of interest is promising in improving the information freshness. The unordered arrival of status updates at the monitor end poses a significant challenge in analyzing the timeliness performance of parallel updating systems. This work investigates the age of information (AoI) of a discrete-time dual-sensor status updating system. Specifically, the status update is generated following the zero-waiting policy. The two sensors are modeled as a geometrically distributed service time queue and a deterministic service time queue in parallel. We derive the analytical expressions for the average AoI and peak AoI using the graphical analysis method. Moreover, the connection of average AoI between discrete-time and continuous-time systems is also explored. It is shown that the AoI result of the continuous-time system is a limit case of that of the corresponding discrete-time system. Hence, the AoI result of the discrete-time system is more general than the continuous one. Numerical results validate the effectiveness of our analysis and further show that randomness of service time contributes more AoI reduction than determinacy of service time in dual-queue systems in most cases, which is different from what is known about the single-queue system.

cs.IT↗
arXiv2024

Second harmonic generation with 48% conversion efficiency from cavity polygon modes in a monocrystalline lithium niobate microdisk resonator

Thin-film lithium niobate (TFLN) based optical microresonators offer large nonlinear coefficient d_33 and high light-wave confinement, allowing highly efficient second-order optical nonlinear frequency conversion. Here, we achieved ultra-efficiency second harmonic generation (SHG) from high-Q polygon modes by maximizing the utilization of the highest nonlinear coefficient d_33 in a monocrystalline X-cut TFLN microdisk resonator for the first time. The polygon modes are designed and formed with two parallel sides perpendicular to the optical axis of the lithium niobate crystal by introducing weak perturbations into the microdisk of a tapered fiber, which maximizes the utilization of d_33. The polygon modes exhibit ultrahigh intrinsic Q factors of ~3.86X10(7), due to the fact that polygon modes are located far from the relatively rough sidewall of the microdisk. Moreover, the pump and second harmonic polygon modes share high modal overlap factor of ~80%. Consequently, SHG from cavity polygon modes with absolute conversion efficiency as high as 48.08% was realized at an on-chip pump level of only 4.599 mW without fine domain structures, surpassing the best results (23% and 30%) reported in other two domain-inversion-free phase matching schemes and even approaching the record (52%) in PPLN microresonators.

physics.optics↗
arXiv2024

A new view of the Spiral Structure of the Northern Outer Milky Way in Carbon Monoxide

Based on 32162 molecular clouds from the Milky Way Imaging Scroll Painting project, we obtain new face-on molecular gas maps of the northern outer Galaxy. The total molecular gas surface density map reveals three segments of spirals, extending 16-43 kiloparsecs in length. The Perseus and Outer arms stand out prominently, appearing as quasi-continuous structures along most of their length. At the Galactic outskirts, about 1306 clouds connect the two segments of the new spiral arm discovered by Dame & Thaddeus (2011) in the first quadrant and Sun et al. (2015) in the second quadrant, possibly extending the arm into the outer third quadrant. Logarithmic spirals can be fitted to the CO arm segments with pitch angles ranging from 4 to 12 degree. These CO arms extend beyond previous CO studies and the optical radius, reaching a galactic radius of about 22 kiloparsecs, comparable to the HI radial range.

astro-ph.GA↗
arXiv2024

Superpixel-informed Implicit Neural Representation for Multi-Dimensional Data

Recently, implicit neural representations (INRs) have attracted increasing attention for multi-dimensional data recovery. However, INRs simply map coordinates via a multi-layer perception (MLP) to corresponding values, ignoring the inherent semantic information of the data. To leverage semantic priors from the data, we propose a novel Superpixel-informed INR (S-INR). Specifically, we suggest utilizing generalized superpixel instead of pixel as an alternative basic unit of INR for multi-dimensional data (e.g., images and weather data). The coordinates of generalized superpixels are first fed into exclusive attention-based MLPs, and then the intermediate results interact with a shared dictionary matrix. The elaborately designed modules in S-INR allow us to ingenuously exploit the semantic information within and across generalized superpixels. Extensive experiments on various applications validate the effectiveness and efficacy of our S-INR compared to state-of-the-art INR methods.

cs.CV↗
arXiv2024

Mapped Hermite Functions and their applications to two-dimensional weakly singular Fredholm-Hammerstein integral equations

The Fredholm-Hammerstein integral equations (FHIEs) with weakly singular kernels exhibit multi-point singularity at the endpoints or boundaries. The dense discretized matrices result in high computational complexity when employing numerical methods. To address this, we propose a novel class of mapped Hermite functions, which are constructed by applying a mapping to Hermite polynomials.We establish fundamental approximation theory for the orthogonal functions. We propose MHFs-spectral collocation method and MHFs-smoothing transformation method to solve the two-point weakly singular FHIEs, respectively. Error analysis and numerical results demonstrate that our methods, based on the new orthogonal functions, are particularly effective for handling problems with weak singularities at two endpoints, yielding exponential convergence rate. We position this work as the first to directly study the mapped spectral method for multi-point singularity problems, to the best of our knowledge.

math.NA↗
arXiv2024

Moments of Gamma type and three-parametric Mittag-Leffler function

We study a class of positive random variables having moments of Gamma type, whose density can be expressed by the three-parametric Mittag-Leffler functions. We give some necessary conditions and some sufficient conditions for their existence. As a corollary, we give some conditions for non-negativity of the three-parametric Mittag-Leffler functions. As an application, we study the infinite divisibility of the powers of half $\a$-Cauchy variable. In addition, we find that a random variable $\X$ having moment of Gamma type if and only if $\log \X$ is quasi infinitely divisible. From this perspective, we can solve many Hausdorff moment problems of sequences of factorial ratios.

math.PR↗
arXiv2024

Machine Learning Aided Modeling of Granular Materials: A Review

Artificial intelligence (AI) has become a buzz word since Google's AlphaGo beat a world champion in 2017. In the past five years, machine learning as a subset of the broader category of AI has obtained considerable attention in the research community of granular materials. This work offers a detailed review of the recent advances in machine learning-aided studies of granular materials from the particle-particle interaction at the grain level to the macroscopic simulations of granular flow. This work will start with the application of machine learning in the microscopic particle-particle interaction and associated contact models. Then, different neural networks for learning the constitutive behaviour of granular materials will be reviewed and compared. Finally, the macroscopic simulations of practical engineering or boundary value problems based on the combination of neural networks and numerical methods are discussed. We hope readers will have a clear idea of the development of machine learning-aided modelling of granular materials via this comprehensive review work.

physics.geo-ph↗
arXiv2024

Multicontinuum splitting scheme for multiscale flow problems

In this paper, we propose multicontinuum splitting schemes for multiscale problems, focusing on a parabolic equation with a high-contrast coefficient. Using the framework of multicontinuum homogenization, we introduce spatially smooth macroscopic variables and decompose the multicontinuum solution space into two components to effectively separate the dynamics at different speeds (or the effects of contrast in high-contrast cases). By treating the component containing fast dynamics (or dependent on the contrast) implicitly and the component containing slow dynamics (or independent of the contrast) explicitly, we construct partially explicit time discretization schemes, which can reduce computational cost. The derived stability conditions are contrast-independent, provided the continua are chosen appropriately. Additionally, we discuss possible methods to obtain an optimized decomposition of the solution space, which relaxes the stability conditions while enhancing computational efficiency. A Rayleigh quotient problem in tensor form is formulated, and simplifications are achieved under certain assumptions. Finally, we present numerical results for various coefficient fields and different continua to validate our proposed approach. It can be observed that the multicontinuum splitting schemes enjoy high accuracy and efficiency.

math.NA↗
arXiv2024

Investigation of individual pulse emission behaviours from pulsar J1741$-$0840

We have carried out a detailed study of individual pulse emission from the pulsar J1741$-$0840 (B1738$-$08), observed using the Parkes and Effelsberg radio telescopes at the $L$ band. The pulsar exhibits four emission components which are not well resolved by employing multi-component Gaussian fitting. The radio emission originates at a height of approximately 1000 km, with the viewing geometry characterized by inclination and impact angles roughly estimated at 81$^\circ$ and 3$^\circ$, respectively. Fluctuation spectral analysis of single pulse behaviour reveals two prominent periodicities, around 32 and 5 rotation periods. The longer periodic modulation feature is linked to nulling behaviour across the entire emission window, with an updated nulling fraction of 23$\pm$2\% is derived from pulse energy distribution via Gaussian mixture modeling. In addition to quasiperiodic nulling, the pulsar also exhibits the presence of subpulse drifting in the trailing component, with the shorter periodic feature in the fluctuation spectra related to the phenomenon of subpulse drifting, and the longitudinal separation estimated to be about 5 degrees. Both periodic modulations show significant temporal evolution with time-dependent fluctuation power. The ramifications for understanding the radio emission mechanisms are discussed.

astro-ph.HE↗