arXiv subjects

Zhuang Xiong

Publications and source records attributed to Zhuang Xiong.

18 recordsLinked to original sources

arXiv2026

Look Up and Look Back: Hidden Attention and Latent Orientation in a Frozen Foundation Model for Panoramic SLAM

Monocular panoramic SLAM benefits from substantial visual overlap under large camera rotations, yet remains prone to errors caused by camera tilt, scale drift, and false loop closures. We show that a frozen panoramic geometry foundation model provides useful internal cues beyond its explicit geometric outputs: intermediate tokens encode gravity in the camera frame, while cross-view attention provides a compatibility cue for potential revisits. Building on these cues, we present HALO-SLAM. A gravity readout enables IMU-free spherical upright canonicalization. For loop closure, we introduce a cost-aware three-stage cascade combining DBoW2 event-level retrieval, attention-based compatibility filtering, and dense geometric validation through symmetric submap augmentation. Accepted revisits yield pixel-aligned 3D--3D correspondences in both local gauges, from which robust $\mathrm{Sim}(3)$ constraints are estimated and jointly optimized with sequential constraints in a global pose graph. Across 125 sequences from five real-world panoramic benchmarks, our method achieves \textbf{100\%} sequence success (\textbf{125/125}) under the stated criterion and the lowest ATE among the evaluated methods on all five benchmarks, reducing ATE by \textbf{30--88\%} relative to the best ERP-native baseline on each benchmark.

cs.CV↗

arXiv2026

VGGT-Motion: Motion-Aware Calibration-Free Monocular SLAM for Long-Range Consistency

Despite recent progress in calibration-free monocular SLAM via 3D vision foundation models, scale drift remains severe on long sequences. Motion-agnostic partitioning breaks contextual coherence and causes zero-motion drift, while conventional geometric alignment is computationally expensive. To address these issues, we propose VGGT-Motion, a calibration-free SLAM system for efficient and robust global consistency over kilometer-scale trajectories. Specifically, we first propose a motion-aware submap construction mechanism that uses optical flow to guide adaptive partitioning, prune static redundancy, and encapsulate turns for stable local geometry. We then design an anchor-driven direct Sim(3) registration strategy. By exploiting context-balanced anchors, it achieves search-free, pixel-wise dense alignment and efficient loop closure without costly feature matching. Finally, a lightweight submap-level pose graph optimization enforces global consistency with linear complexity, enabling scalable long-range operation. Experiments show that VGGT-Motion markedly improves trajectory accuracy and efficiency, achieving state-of-the-art performance in zero-shot, long-range calibration-free monocular SLAM.

cs.CV↗

arXiv2025

Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion Models

Incoherent k-space undersampling and deep learning-based reconstruction methods have shown great success in accelerating MRI. However, the performance of most previous methods will degrade dramatically under high acceleration factors, e.g., 8$\times$ or higher. Recently, denoising diffusion models (DM) have demonstrated promising results in solving this issue; however, one major drawback of the DM methods is the long inference time due to a dramatic number of iterative reverse posterior sampling steps. In this work, a Single Step Diffusion Model-based reconstruction framework, namely SSDM-MRI, is proposed for restoring MRI images from highly undersampled k-space. The proposed method achieves one-step reconstruction by first training a conditional DM and then iteratively distilling this model four times using an iterative selective distillation algorithm, which works synergistically with a shortcut reverse sampling strategy for model inference. Comprehensive experiments were carried out on both publicly available fastMRI brain and knee images, as well as an in-house multi-echo GRE (QSM) subject. Overall, the results showed that SSDM-MRI outperformed other methods in terms of numerical metrics (e.g., PSNR and SSIM), error maps, image fine details, and latent susceptibility information hidden in MRI phase images. In addition, the reconstruction time for a 320$\times$320 brain slice of SSDM-MRI is only 0.45 second, which is only comparable to that of a simple U-net, making it a highly effective solution for MRI reconstruction tasks.

eess.IV↗

arXiv2025

Adaptive Gate-Aware Mamba Networks for Magnetic Resonance Fingerprinting

Magnetic Resonance Fingerprinting (MRF) enables fast quantitative imaging by matching signal evolutions to a predefined dictionary. However, conventional dictionary matching suffers from exponential growth in computational cost and memory usage as the number of parameters increases, limiting its scalability to multi-parametric mapping. To address this, recent work has explored deep learning-based approaches as alternatives to DM. We propose GAST-Mamba, an end-to-end framework that combines a dual Mamba-based encoder with a Gate-Aware Spatial-Temporal (GAST) processor. Built on structured state-space models, our architecture efficiently captures long-range spatial dependencies with linear complexity. On 5 times accelerated simulated MRF data (200 frames), GAST-Mamba achieved a T1 PSNR of 33.12~dB, outperforming SCQ (31.69~dB). For T2 mapping, it reached a PSNR of 30.62~dB and SSIM of 0.9124. In vivo experiments further demonstrated improved anatomical detail and reduced artifacts. Ablation studies confirmed that each component contributes to performance, with the GAST module being particularly important under strong undersampling. These results demonstrate the effectiveness of GAST-Mamba for accurate and robust reconstruction from highly undersampled MRF acquisitions, offering a scalable alternative to traditional DM-based methods.

eess.IV↗

arXiv2024

Five-body $D\to V$ Semileptonic Decays

Our main objective is to derive the decay rate for the semileptonic decays $D\to V\ell^+ν_{\ell}\,(\ell=e,μ)$, where $V$ represents a vector particle. In these decays, the vector particle $V$ decays into three pseudo-scalar particles. To accomplish this, we evaluate the phase-space factor for the five-body decay with a set of eight independent variables which uniquely define a point in the phase space. We further conduct a detailed investigation of the $D\to ω\ell^+ν_{\ell}$, where $ω$ subsequently decays into $π^+π^-π^0$, within the Standard Model and in a general effective field theory description of the weak interactions at low energies. The outcomes of this study have potential applications in the measurement of $D\to ω$ form factors. These measurements can be performed using data obtained from BESIII.

hep-ph↗

arXiv2024

IR2QSM: Quantitative Susceptibility Mapping via Deep Neural Networks with Iterative Reverse Concatenations and Recurrent Modules

Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing technique to extract the distribution of tissue susceptibilities, demonstrating significant potential in studying neurological diseases. However, the ill-conditioned nature of dipole inversion makes QSM reconstruction from the tissue field prone to noise and artifacts. In this work, we propose a novel deep learning-based IR2QSM method for QSM reconstruction. It is designed by iterating four times of a reverse concatenations and middle recurrent modules enhanced U-net, which could dramatically improve the efficiency of latent feature utilization. Simulated and in vivo experiments were conducted to compare IR2QSM with several traditional algorithms (MEDI and iLSQR) and state-of-the-art deep learning methods (U-net, xQSM, and LPCNN). The results indicated that IR2QSM was able to obtain QSM images with significantly increased accuracy and mitigated artifacts over other methods. Particularly, IR2QSM demonstrated on average the best NRMSE (27.59%) in simulated experiments, which is 15.48%, 7.86%, 17.24%, 9.26%, and 29.13% lower than iLSQR, MEDI, U-net, xQSM, LPCNN, respectively, and led to improved QSM results with fewer artifacts for the in vivo data.

eess.IV↗

arXiv2024

Fast Controllable Diffusion Models for Undersampled MRI Reconstruction

Supervised deep learning methods have shown promise in undersampled Magnetic Resonance Imaging (MRI) reconstruction, but their requirement for paired data limits their generalizability to the diverse MRI acquisition parameters. Recently, unsupervised controllable generative diffusion models have been applied to undersampled MRI reconstruction, without paired data or model retraining for different MRI acquisitions. However, diffusion models are generally slow in sampling and state-of-the-art acceleration techniques can lead to sub-optimal results when directly applied to the controllable generation process. This study introduces a new algorithm called Predictor-Projector-Noisor (PPN), which enhances and accelerates controllable generation of diffusion models for undersampled MRI reconstruction. Our results demonstrate that PPN produces high-fidelity MR images that conform to undersampled k-space measurements with significantly shorter reconstruction time than other controllable sampling methods. In addition, the unsupervised PPN accelerated diffusion models are adaptable to different MRI acquisition parameters, making them more practical for clinical use than supervised learning techniques.

eess.IV↗

arXiv2024

Plug-and-Play Latent Feature Editing for Orientation-Adaptive Quantitative Susceptibility Mapping Neural Networks

Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) algorithms hold great potential for solving the ill-posed QSM reconstruction problem. However, a significant challenge facing current DL-QSM approaches is their limited adaptability to magnetic dipole field orientation variations during training and testing. In this work, we propose a novel Orientation-Adaptive Latent Feature Editing (OA-LFE) module to learn the encoding of acquisition orientation vectors and seamlessly integrate them into the latent features of deep networks. Importantly, it can be directly Plug-and-Play (PnP) into various existing DL-QSM architectures, enabling reconstructions of QSM from arbitrary magnetic dipole orientations. Its effectiveness is demonstrated by combining the OA-LFE module into our previously proposed phase-to-susceptibility single-step instant QSM (iQSM) network, which was initially tailored for pure-axial acquisitions. The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a self-supervised manner on a specially-designed simulation brain dataset. Comprehensive experiments are conducted on simulated and in vivo human brain datasets, encompassing subjects ranging from healthy individuals to those with pathological conditions. These experiments involve various MRI platforms (3T and 7T) and aim to compare our proposed iQSM+ against several established QSM reconstruction frameworks, including the original iQSM. The iQSM+ yields QSM images with significantly improved accuracies and mitigates artifacts, surpassing other state-of-the-art DL-QSM algorithms.

eess.IV↗

arXiv2024

QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping

Quantitative Susceptibility Mapping (QSM) dipole inversion is an ill-posed inverse problem for quantifying magnetic susceptibility distributions from MRI tissue phases. While supervised deep learning methods have shown success in specific QSM tasks, their generalizability across different acquisition scenarios remains constrained. Recent developments in diffusion models have demonstrated potential for solving 2D medical imaging inverse problems. However, their application to 3D modalities, such as QSM, remains challenging due to high computational demands. In this work, we developed a 3D image patch-based diffusion model, namely QSMDiff, for robust QSM reconstruction across different scan parameters, alongside simultaneous super-resolution and image-denoising tasks. QSMDiff adopts unsupervised 3D image patch training and full-size measurement guidance during inference for controlled image generation. Evaluation on simulated and in-vivo human brains, using gradient-echo and echo-planar imaging sequences across different acquisition parameters, demonstrates superior performance. The method proposed in QSMDiff also holds promise for impacting other 3D medical imaging applications beyond QSM.

eess.IV↗

arXiv2023

Investigating $Z_{cs}(3985)$ and $Z_{cs}(4000)$ exotic states in $Λ_b\to Z^-_{cs}p$ decays

We study the $Z_{cs}(3985)$ and $Z_{cs}(4000)$ exotic states in the decays of $Λ_b$ baryons through a molecular scenario. In the final state interaction, the $Λ_b\to Λ_c^+ D_s^{(*)-}$ decays are followed by the $Λ_c^+ D_s^{(*)-}$ to $Z^-_{cs}p$ rescatterings via exchange of a $D^{(*)}$ meson. We predict a branching fraction of $(3.1^{+1.4}_{-2.6})\times 10^{-4}$ for $Λ_b\to Z^-_{cs}p$, which can be measured in the $Λ_b\to J/ψK^{(*)-}p$ decay. This study proposes a new approach to test the molecular model, and guides future experimental searches for the $Z_{cs}(3985)$ and $Z_{cs}(4000)$.

hep-ph↗

arXiv2023

Extremal results for $\mathcal{K}^-_{r + 1}$-free signed graphs

This paper gives tight upper bounds on the number of edges and the index for $\mathcal{K}^-_{r + 1}$-free unbalanced signed graphs, where $\mathcal{K}^-_{r + 1}$ is the set of $r+1$-vertices unbalanced signed complete graphs. \indent We first prove that if $Γ$ is an $n$-vertices $\mathcal{K}^-_{r + 1}$-free unbalanced signed graph, then the number of edges of $Γ$ is $$e(Γ) \leq \frac{n(n-1)}{2} - (n - r ).$$ \indent Let $Γ_{1,r-2}$ be a signed graph obtained by adding one negative edge and $r - 2$ positive edges between a vertex and an all positive signed complete graph $K_{n - 1}$. Secondly, we show that if $Γ$ is an $n$-vertices $\mathcal{K}^-_{r + 1}$-free unbalanced signed graph, then the index of $Γ$ is $$λ_{1}(Γ) \leq λ_{1}(Γ_{1,r-2}), $$ with equality holding if and only if $Γ$ is switching equivalent to $Γ_{1,r-2}$. \indent It is shown that these results are significant in extremal graph theory. Because they can be regarded as extensions of Tur{á}n's Theorem [Math. Fiz. Lapok 48 (1941) 436--452] and spectral Tur{á}n problem [Linear Algebra Appl. 428 (2008) 1492--1498] on signed graphs, respectively. Furthermore, the second result partly resolves a recent open problem raised by Wang [arXiv preprint arXiv:2309.15434 (2023)].

math.CO↗

arXiv2023

Maxima of the index: forbidden unbalanced cycles

This paper aims to address the problem: what is the maximum index among all $\mathcal{C}^-_r$-free unbalanced signed graphs, where $\mathcal{C}^-_r$ is the set of unbalanced cycle of length $r$. Let $Γ_1 = C_3^- \bullet K_{n-2}$ be a signed graph obtained by identifying a vertex of $K_{n-2}$ with a vertex of $C_3^-$ whose two incident edges in $C_3^-$ are all positive, where $C_3^-$ is an unbalanced triangle with one negative edge. It is shown that if $Γ$ is an unbalanced signed graph of order $n$, $r$ is an integer in $\{4, \cdots, \lfloor \frac{n}{3}\rfloor + 1 \}$, and $$λ_{1}(Γ) \geq λ_{1}(Γ_1), $$ then $Γ$ contains an unbalanced cycle of length $r$, unless $Γ\sim Γ_1$. \indent It is shown that the result are significant in spectral extremal graph problems. Because they can be regarded as a extension of the spectral Tur{á}n problem for cycles [Linear Algebra Appl. 428 (2008) 1492--1498] in the context of signed graphs. Furthermore, our result partly resolved a recent open problem raised by Lin and Wang [arXiv preprint arXiv:2309.04101 (2023)].

math.CO↗

arXiv2023

On the eigenvalues and Seidel eigenvalues of chain graphs

In this paper we consider the eigenvalues and the Seidel eigenvalues of a chain graph. An$\dbar$elić, da Fonseca, Simić, and Du \cite{andelic2020tridiagonal} conjectured that there do not exist non-isomorphic cospectral chain graphs with respect to the adjacency spectrum. Here we disprove this conjecture. Furthermore, by considering the relation between the Seidel matrix and the adjacency matrix of a graph, we solve two problems on the number of distinct Seidel eigenvalues of a chain graph, which was posed by Mandal, Mehatari, and Das \cite{mandal2022spectrum}.

math.CO↗

arXiv2023

The nullity of the net Laplacian matrix of a signed graph

Let $Γ= (G, σ)$ be a signed graph, where $G = (V(G),E(G))$ is an (unsigned) graph, called the underlying graph. The net Laplacian matrix of $Γ$ is defined as $L^{\pm}(Γ) = D^{\pm}(Γ) - A(Γ)$, where $D^{\pm}(Γ)$ and $A(Γ)$ are the diagonal matrix of net-degrees and the adjacency matrix of $Γ$, respectively. The nullity of $L^{\pm}(Γ)$, written as $ η(L^{\pm} (Γ))$, is the multiplicity of 0 as an eigenvalue of $L^{\pm}(Γ)$. In this paper, we focus our attention on the nullity of the net Laplacian matrix of a connected signed graph $Γ$ and prove that $1 \leq η(L^{\pm} (Γ)) \leq min\{ β(Γ) + 1, |V(Γ)| - 1 \}$, where $β(Γ) = |E(Γ)| - |V(Γ)| + 1$ is the cyclomatic number of $Γ$. The connected signed graphs with nullity $|V(Γ)| - 1$ are completely determined. Moreover, we characterize the signed cactus graphs with nullity $1$ or $β(Γ) + 1$

math.CO↗

arXiv2023

Quantitative Susceptibility Mapping through Model-based Deep Image Prior (MoDIP)

The data-driven approach of supervised learning methods has limited applicability in solving dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan parameters across different objects. To address this generalization issue in supervised QSM methods, we propose a novel training-free model-based unsupervised method called MoDIP (Model-based Deep Image Prior). MoDIP comprises a small, untrained network and a Data Fidelity Optimization (DFO) module. The network converges to an interim state, acting as an implicit prior for image regularization, while the optimization process enforces the physical model of QSM dipole inversion. Experimental results demonstrate MoDIP's excellent generalizability in solving QSM dipole inversion across different scan parameters. It exhibits robustness against pathological brain QSM, achieving over 32% accuracy improvement than supervised deep learning and traditional iterative methods. It is also 33% more computationally efficient and runs 4 times faster than conventional DIP-based approaches, enabling 3D high-resolution image reconstruction in under 4.5 minutes.

eess.IV↗

arXiv2022

Affine Transformation Edited and Refined Deep Neural Network for Quantitative Susceptibility Mapping

Deep neural networks have demonstrated great potential in solving dipole inversion for Quantitative Susceptibility Mapping (QSM). However, the performances of most existing deep learning methods drastically degrade with mismatched sequence parameters such as acquisition orientation and spatial resolution. We propose an end-to-end AFfine Transformation Edited and Refined (AFTER) deep neural network for QSM, which is robust against arbitrary acquisition orientation and spatial resolution up to 0.6 mm isotropic at the finest. The AFTER-QSM neural network starts with a forward affine transformation layer, followed by an Unet for dipole inversion, then an inverse affine transformation layer, followed by a Residual Dense Network (RDN) for QSM refinement. Simulation and in-vivo experiments demonstrated that the proposed AFTER-QSM network architecture had excellent generalizability. It can successfully reconstruct susceptibility maps from highly oblique and anisotropic scans, leading to the best image quality assessments in simulation tests and suppressed streaking artifacts and noise levels for in-vivo experiments compared with other methods. Furthermore, ablation studies showed that the RDN refinement network significantly reduced image blurring and susceptibility underestimation due to affine transformations. In addition, the AFTER-QSM network substantially shortened the reconstruction time from minutes using conventional methods to only a few seconds.

physics.med-ph↗

arXiv2022

Instant tissue field and magnetic susceptibility mapping from MR raw phase using Laplacian enabled deep neural networks

Quantitative susceptibility mapping (QSM) is a valuable MRI post-processing technique that quantifies the magnetic susceptibility of body tissue from phase data. However, the traditional QSM reconstruction pipeline involves multiple non-trivial steps, including phase unwrapping, background field removal, and dipole inversion. These intermediate steps not only increase the reconstruction time but amplify noise and errors. This study develops a large-stencil Laplacian preprocessed deep learning-based neural network for near instant quantitative field and susceptibility mapping (i.e., iQFM and iQSM) from raw MR phase data. The proposed iQFM and iQSM methods were compared with established reconstruction pipelines on simulated and in vivo datasets. In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the novel neural networks. The proposed iQFM and iQSM methods yielded comparable results to multi-step methods in healthy subjects while dramatically improving reconstruction accuracies on intracranial hemorrhages with large susceptibilities. The reconstruction time was also substantially shortened from minutes using multi-step methods to only 30 milliseconds using the trained iQFM and iQSM neural networks.

eess.IV↗

arXiv2016

Extended Eckart Theorem and New Variation Method for Excited States of Atoms

We extend the Eckart theorem, from the ground state to excited statew, which introduces an energy augmentation to the variation criterion for excited states. It is shown that the energy of a very good excited state trial function can be slightly lower than the exact eigenvalue. Further, the energy calculated by the trial excited state wave function, which is the closest to the exact eigenstate through Gram-Schmidt orthonormalization to a ground state approximant, is lower than the exact eigenvalue as well. In order to avoid the variation restrictions inherent in the upper bound variation theory based on Hylleraas, Undheim, and McDonald [HUM] and Eckart Theorem, we have proposed a new variation functional Omega-n and proved that it has a local minimum at the eigenstates, which allows approaching the eigenstate unlimitedly by variation of the trial wave function. As an example, we calculated the energy and the radial expectation values of Triplet-S(even) Helium atom by the new variation functional, and by HUM and Eckart theorem, respectively, for comparison. Our preliminary numerical results reveal that the energy of the calculated excited states 3rd Triplet-S(even) and 4th Triplet-S(even) may be slightly lower than the exact eigenvalue (inaccessible by HUM theory) according to the General Eckart Theorem proved here, while the approximate wave function is better than HUM.

physics.comp-ph↗