arXiv subjects

Deming Li

Publications and source records attributed to Deming Li.

10 recordsLinked to original sources

arXiv2026

Robust Betatron-Tune Measurement from Schottky Spectra: Complementary Classical and Deep-Learning Paradigms

Schottky spectra provide key beam diagnostics, with betatron sidebands encoding the fractional tune. Reliable tune measurement is particularly important for third-order resonance slow extraction in compact medical proton synchrotrons, where low signal-to-noise ratios and limited frequency resolution can compromise conventional peak-detection and curve-fitting methods. This work develops two complementary tune estimators with a shared spectral front-end but different temporal representations. The classical estimator coherently pools motion-compensated spectra, detects the sideband using a multi-width matched-filter bank, and performs sub-bin estimation through local argmax and an adaptive MAD-gated centroid. The deep-learning estimator converts each spectrum into a tune-likelihood map using a convolutional neural network with FFT-based global convolutions, then propagates the posterior with a discrete two-dimensional (q,v) Bayesian tracker under a Gaussian motion model while also reporting posterior uncertainty. On a synthetic dynamic-tune benchmark, the deep-learning estimator outperforms published baselines across the operating range, while the classical estimator exceeds the latency-compensated baseline and requires neither training data nor GPU acceleration. On near-stationary SAPT beam data, both methods operate end-to-end, with the deep-learning model requiring no retraining. Median per-frame latency remains below 1 ms on commodity hardware, supporting real-time-capable tune measurement in compact medical synchrotrons.

physics.acc-ph↗

arXiv2026

SyncFix: Fixing 3D Reconstructions via Multi-View Synchronization

We present SyncFix, a framework that enforces cross-view consistency during the diffusion-based refinement of reconstructed scenes. SyncFix formulates refinement as a joint latent bridge matching problem, synchronizing distorted and clean representations across multiple views to fix the semantic and geometric inconsistencies. This means SyncFix learns a joint conditional over multiple views to enforce consistency throughout the denoising trajectory. Our training is done only on image pairs, but it generalizes naturally to an arbitrary number of views during inference. Moreover, reconstruction quality improves with additional views, with diminishing returns at higher view counts. Qualitative and quantitative results demonstrate that SyncFix consistently generates high-quality reconstructions and surpasses current state-of-the-art baselines, even in the absence of clean reference images. SyncFix achieves even higher fidelity when sparse references are available.

cs.CV↗

arXiv2025

Real-Time-Capable Betatron Tune Measurement from Schottky Spectra Using Deep Learning and Uncertainty-Aware Kalman Filtering

Betatron tune measurement is essential for beam control in compact proton-therapy synchrotrons, yet conventional peak-detection techniques are not robust under the low signal-to-noise ratio (SNR) conditions typical of these machines. This work presents a lightweight convolutional neural network that performs real-time tune extraction from Schottky spectra with sub-millisecond inference latency and calibrated uncertainty estimates. The model uses attention-based pooling for reliable peak localization and a dual-branch architecture that jointly predicts the tune and its associated uncertainty. Trained with a Laplace negative log-likelihood loss, it produces uncertainty estimates whose magnitude tracks the instantaneous prediction error, which enables uncertainty-aware Kalman filtering for temporal smoothing. Experiments on a large synthetic dataset spanning SNR levels from 0 to $-20$\,dB demonstrate substantial performance gains over traditional peak-detection baselines, while the Kalman filter further suppresses transient outliers in time-series operation. Preliminary validation on operational beam data confirms stable tune tracking without retraining. With only about $2.0\times 10^{4}$ trainable parameters and real-time inference on commodity GPU hardware, the proposed diagnostic offers a practical solution for rapid and accurate betatron tune monitoring in compact medical synchrotrons and similar accelerators.

physics.acc-ph↗

arXiv2025

MS-GS: Multi-Appearance Sparse-View 3D Gaussian Splatting in the Wild

In-the-wild photo collections often contain limited volumes of imagery and exhibit multiple appearances, e.g., taken at different times of day or seasons, posing significant challenges to scene reconstruction and novel view synthesis. Although recent adaptations of Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) have improved in these areas, they tend to oversmooth and are prone to overfitting. In this paper, we present MS-GS, a novel framework designed with Multi-appearance capabilities in Sparse-view scenarios using 3DGS. To address the lack of support due to sparse initializations, our approach is built on the geometric priors elicited from monocular depth estimations. The key lies in extracting and utilizing local semantic regions with a Structure-from-Motion (SfM) points anchored algorithm for reliable alignment and geometry cues. Then, to introduce multi-view constraints, we propose a series of geometry-guided supervision steps at virtual views in pixel and feature levels to encourage 3D consistency and reduce overfitting. We also introduce a dataset and an in-the-wild experiment setting to set up more realistic benchmarks. We demonstrate that MS-GS achieves photorealistic renderings under various challenging sparse-view and multi-appearance conditions, and outperforms existing approaches significantly across different datasets.

cs.CV↗

arXiv2025

Spatial-offset pump-probe imaging of nonradiative dynamics at optical resolution

Nonradiative photothermal (PT) and photoacoustic (PA) processes have found widespread applications in imaging, stimulation, and therapy. Mapping the generation and propagation of PA and PT waves with resolution is important to elucidate how these fields interact with biological systems. To this end, we introduce spatial offset pump-probe imaging (SOPPI). By spatially offsetting the pump beam and the probe beam, SOPPI can image simultaneously PA and PT wave propagation with nanosecond temporal resolution, micrometer spatial resolution, 65 MHz detection bandwidth, and a sensitivity of 9.9 Pa noise equivalent pressure. We first map the PA and PT evolution from a fiber emitter, and how the wave interacting with a mouse skull and brain slices. SOPPI imaging of PA waves from a tapered fiber with water as an absorber shows a wavelength-dependent generation, evanescent wave generated PA, and back-propagated acoustic Mach Cone. At last, a SOPPI-PACT is developed to reconstruct the pigment distribution inside a zebrafish larva with high precision and signal-to-noise ratio.

physics.optics↗

arXiv2024

High-Accuracy Schottky Diagnostics for Low-SNR Betatron Tune Measurement in Ramping Synchrotrons

This study introduces a novel real-time betatron tune measurement algorithm, utilizing Schottky signals and an FPGA-based backend architecture, specifically designed for rapidly ramping synchrotrons, with particular application to the Shanghai Advanced Proton Therapy (SAPT) facility. The developed algorithm demonstrates improved measurement accuracy under challenging operational conditions, especially in scenarios with limited sampling time and signal-to-noise ratios (SNR) as low as $-20$ dB. By applying Short-Time Fourier Transform (STFT) analysis, the algorithm effectively accommodates the rapid increase in revolution frequency from 4 MHz to 7.5 MHz over 0.35 seconds, along with tune shifts. A macro-particle simulation methodology is employed to generate Schottky signals, which are then combined with real noise collected from an analog-to-digital converter (ADC) to simulate practical conditions. The proposed betatron tune measurement algorithm integrates advanced spectral processing techniques and an enhanced peak detection algorithm specifically tailored for low SNR conditions. Experimental validation confirms the superior performance of the proposed algorithm over conventional approaches in terms of measurement accuracy, stability, and system robustness, while meeting the stringent operational requirements of proton therapy applications. This innovative approach effectively addresses critical limitations associated with Schottky diagnostics for betatron tune measurement in rapidly ramping synchrotrons operating under low SNR conditions, laying a robust foundation and providing a viable solution for advanced applications in proton therapy and related accelerator physics fields.

physics.acc-ph↗

arXiv2024

SPARS3R: Semantic Prior Alignment and Regularization for Sparse 3D Reconstruction

Recent efforts in Gaussian-Splat-based Novel View Synthesis can achieve photorealistic rendering; however, such capability is limited in sparse-view scenarios due to sparse initialization and over-fitting floaters. Recent progress in depth estimation and alignment can provide dense point cloud with few views; however, the resulting pose accuracy is suboptimal. In this work, we present SPARS3R, which combines the advantages of accurate pose estimation from Structure-from-Motion and dense point cloud from depth estimation. To this end, SPARS3R first performs a Global Fusion Alignment process that maps a prior dense point cloud to a sparse point cloud from Structure-from-Motion based on triangulated correspondences. RANSAC is applied during this process to distinguish inliers and outliers. SPARS3R then performs a second, Semantic Outlier Alignment step, which extracts semantically coherent regions around the outliers and performs local alignment in these regions. Along with several improvements in the evaluation process, we demonstrate that SPARS3R can achieve photorealistic rendering with sparse images and significantly outperforms existing approaches.

cs.CV↗

arXiv2024

BAGS: Blur Agnostic Gaussian Splatting through Multi-Scale Kernel Modeling

Recent efforts in using 3D Gaussians for scene reconstruction and novel view synthesis can achieve impressive results on curated benchmarks; however, images captured in real life are often blurry. In this work, we analyze the robustness of Gaussian-Splatting-based methods against various image blur, such as motion blur, defocus blur, downscaling blur, \etc. Under these degradations, Gaussian-Splatting-based methods tend to overfit and produce worse results than Neural-Radiance-Field-based methods. To address this issue, we propose Blur Agnostic Gaussian Splatting (BAGS). BAGS introduces additional 2D modeling capacities such that a 3D-consistent and high quality scene can be reconstructed despite image-wise blur. Specifically, we model blur by estimating per-pixel convolution kernels from a Blur Proposal Network (BPN). BPN is designed to consider spatial, color, and depth variations of the scene to maximize modeling capacity. Additionally, BPN also proposes a quality-assessing mask, which indicates regions where blur occur. Finally, we introduce a coarse-to-fine kernel optimization scheme; this optimization scheme is fast and avoids sub-optimal solutions due to a sparse point cloud initialization, which often occurs when we apply Structure-from-Motion on blurry images. We demonstrate that BAGS achieves photorealistic renderings under various challenging blur conditions and imaging geometry, while significantly improving upon existing approaches.

cs.CV↗

arXiv2021

Powerful supercontinuum vortices generated by femtosecond vortex beams with thin plates

We demonstrate numerically and experimentally the generation of powerful supercontinuum vortices from femtosecond vortex beams by using multiple thin fused silica plates. The supercontinuum vortices are shown to preserve the vortex phase profile of the initial beam for spectral components ranging from 500 nm to 1200 nm. The transfer of the vortex phase profile results from the inhibition of multiple filamentation and the preservation of vortex ring with relatively uniform intensity distribution by means of the thin-plate scheme, where the supercontinuum is mainly generated from the self-phase modulation and self-steepening effects. Our scheme works for vortex beams with different topological charges, which provides a simple and effective method to generate supercontinuum vortices with high power.

physics.optics↗

arXiv2018

On extremal cacti with respect to the edge revised Szeged index

Let $G$ be a connected graph. The edge revised Szeged index of $G$ is defined as $Sz^{\ast}_{e}(G)=\sum\limits_{e=uv\in E(G)}(m_{u}(e|G)+\frac{m_{0}(e|G)}{2})(m_{v}(e|G)+\frac{m_{0}(e|G)}{2})$, where $m_{u}(e|G)$ (resp., $m_{v}(e|G)$) is the number of edges whose distance to vertex $u$ (resp., $v$) is smaller than the distance to vertex $v$ (resp., $u$), and $m_{0}(e|G)$ is the number of edges equidistant from both ends of $e$. In this paper, we give the minimal and the second minimal edge revised Szeged index of cacti with order $n$ and $k$ cycles, and all the graphs that achieve the minimal and second minimal edge revised Szeged index are identified.

math.CO↗