arXiv subjects

Xiaoping Wu

Publications and source records attributed to Xiaoping Wu.

11 recordsLinked to original sources

arXiv2026

Multiple Myeloma Lesion Segmentation on Whole-Body Diffusion-Weighted Imaging via Efficient Anatomical Anticipation and Multimodal Confirmation

Whole-body diffusion-weighted imaging (WB-DWI) is widely used for multiple myeloma (MM) assessment, yet automated lesion segmentation remains challenging due to limited anatomical delineation and the low specificity of marrow hyperintensity. Existing studies have introduced bone region-of-interest (ROI) information and apparent diffusion coefficient (ADC) maps to mitigate these ambiguities, but practical limitations remain. Bone ROI construction often relies on costly manual annotation, image registration, or dedicated bone models, while ADC is usually incorporated only through simple channel fusion, limiting its ability to provide complementary structural and lesion-discriminative cues. To address these limitations, we propose a two-stage framework for MM lesion segmentation on WB-DWI. In the first stage, we train a bone ROI generation model from ADC images without dedicated bone labels, providing an efficient and practical anatomical prior for lesion analysis. In the second stage, we propose Anatomy-guided Multimodal U-Net (AMU-Net), which leverages ADC in a manner consistent with clinical lesion assessment rather than treating it as a generic auxiliary modality. Extensive experiments demonstrate the effectiveness and practicality of the proposed method. It achieves the best overall performance among the evaluated methods, with a mean Dice score of 76.2%.

cs.CV↗

arXiv2026

MedGround: Bridging the Evidence Gap in Medical Vision-Language Models with Verified Grounding Data

Vision-Language Models (VLMs) can generate convincing clinical narratives, yet frequently struggle to visually ground their statements. We posit that this limitation arises from the scarcity of high-quality, large-scale clinical referring-localization pairs. To address this, we introduce MedGround, an automated pipeline that transforms segmentation resources into high-quality medical referring grounding data. Leveraging expert masks as spatial anchors, MedGround precisely derives localization targets, extracts shape and spatial cues, and guides VLMs to synthesize natural, clinically grounded queries that reflect morphology and location. To ensure data rigor, a multi-stage verification system integrates strict formatting checks, geometry- and medical-prior rules, and image-based visual judging to filter out ambiguous or visually unsupported samples. Finally, we present MedGround-35K, a novel multimodal medical dataset. Extensive experiments demonstrate that VLMs trained with MedGround-35K consistently achieve improved referring grounding performance, enhance multi-object semantic disambiguation, and exhibit strong generalization to unseen grounding settings. This work highlights MedGround as a scalable, data-driven approach to anchor medical language to verifiable visual evidence.

cs.CV↗

arXiv2025

HY-Motion 1.0: Scaling Flow Matching Models for Text-To-Motion Generation

We present HY-Motion 1.0, a series of state-of-the-art, large-scale, motion generation models capable of generating 3D human motions from textual descriptions. HY-Motion 1.0 represents the first successful attempt to scale up Diffusion Transformer (DiT)-based flow matching models to the billion-parameter scale within the motion generation domain, delivering instruction-following capabilities that significantly outperform current open-source benchmarks. Uniquely, we introduce a comprehensive, full-stage training paradigm -- including large-scale pretraining on over 3,000 hours of motion data, high-quality fine-tuning on 400 hours of curated data, and reinforcement learning from both human feedback and reward models -- to ensure precise alignment with the text instruction and high motion quality. This framework is supported by our meticulous data processing pipeline, which performs rigorous motion cleaning and captioning. Consequently, our model achieves the most extensive coverage, spanning over 200 motion categories across 6 major classes. We release HY-Motion 1.0 to the open-source community to foster future research and accelerate the transition of 3D human motion generation models towards commercial maturity.

cs.CV↗

arXiv2025

Use of electrical resistivity tomography to map the tree roots

An efficient advanced numerical model for mapping the distribution of the buried tree roots is presented. It not only simplify the complicate root branches to an easy manipulated model, but also grasp the main structure of tree roots ignoring the unnecessary minutiae, and thus provide an intuitive impression of subsurface invisible anomalies. The processing model is combined with an adaptive finite element method, which can automatically generate unstructured triangular meshes during the process of discretization, which also enable user to specifically set the resistivity along each part of tree roots.

physics.geo-ph↗

arXiv2024

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction

Diffusion Probabilistic Models (DPMs) have emerged as the de facto approach for high-fidelity image synthesis, operating diffusion processes on continuous VAE latent, which significantly differ from the text generation methods employed by Large Language Models (LLMs). In this paper, we introduce a novel generative framework, the Recurrent Diffusion Probabilistic Model (RDPM), which enhances the diffusion process through a recurrent token prediction mechanism, thereby pioneering the field of Discrete Diffusion. By progressively introducing Gaussian noise into the latent representations of images and encoding them into vector-quantized tokens in a recurrent manner, RDPM facilitates a unique diffusion process on discrete-value domains. This process iteratively predicts the token codes for subsequent timesteps, transforming the initial standard Gaussian noise into the source data distribution, aligning with GPT-style models in terms of the loss function. RDPM demonstrates superior performance while benefiting from the speed advantage of requiring only a few inference steps. This model not only leverages the diffusion process to ensure high-quality generation but also converts continuous signals into a series of high-fidelity discrete tokens, thereby maintaining a unified optimization strategy with other discrete tokens, such as text. We anticipate that this work will contribute to the development of a unified model for multimodal generation, specifically by integrating continuous signal domains such as images, videos, and audio with text. We will release the code and model weights to the open-source community.

cs.CV↗

arXiv2024

Ferroelectricity-Driven Metallicity and Magnetic Skyrmions in van der Waals Cr2Ge2Te6/Hf2Ge2Te6 Multiferroic Heterostructure

Two-dimensional (2D) multiferroic heterostructures present a promising platform for advanced spin devices by leveraging the coexisting ferromagnetic (FM) and ferroelectric (FE) orders. Through first-principles calculations and micromagnetic simulations, we reveal non-volatile control of metallicity and topological spin textures in the Cr2Ge2Te6/Hf2Ge2Te6(CGT/HGT) heterostructure. Notably, manipulating ferroelectric polarization in HGT significantly modulates the magnetic anisotropy energy (MAE) and Dzyaloshinskii-Moriya interaction (DMI) of CGT/HGT, reversing the easy magnetization axis from in-plane to out-of-plane. By analyzing the atomic-resolved SOC energy (\Delta Esoc), it is found that the cause of the change comes from the Fert-Levy mechanism. Additionally, this polarization control enables the creation and annihilation of bimerons and skyrmions, with interlayer sliding further altering magnetic ordering. Our findings offer valuable insights into magnetoelectric coupling and spin texture manipulation in 2D magnets, highlighting their potential for next-generation spintronic and memory devices.

cond-mat.mtrl-sci↗

arXiv2024

Kangaroo: A Powerful Video-Language Model Supporting Long-context Video Input

Rapid advancements have been made in extending Large Language Models (LLMs) to Large Multi-modal Models (LMMs). However, extending input modality of LLMs to video data remains a challenging endeavor, especially for long videos. Due to insufficient access to large-scale high-quality video data and the excessive compression of visual features, current methods exhibit limitations in effectively processing long videos. In this paper, we introduce Kangaroo, a powerful Video LMM aimed at addressing these challenges. Confronted with issue of inadequate training data, we develop a data curation system to build a large-scale dataset with high-quality annotations for vision-language pre-training and instruction tuning. In addition, we design a curriculum training pipeline with gradually increasing resolution and number of input frames to accommodate long videos. Evaluation results demonstrate that, with 8B parameters, Kangaroo achieves state-of-the-art performance across a variety of video understanding benchmarks while exhibiting competitive results on others. Particularly, on benchmarks specialized for long videos, Kangaroo excels some larger models with over 10B parameters and proprietary models.

cs.CV↗

arXiv2024

High-topological-number skyrmions and phase transition in two-dimensional frustrated $J_1$-$J_2$ magnets

With the rapidly expanded field of two-dimensional(2D) magnetic materials, the frustrated magnetic skyrmions are attracting growing interest recently. Here, based on hexagonal close-packed (HCP) lattice of $J_1$-$J_2$ Heisenberg spins model, we systematically investigate the frustrated skyrmions and phase transition by micromagnetic simulations and first-principles calculations. The results show that four spin phases of antiferromagnetic, labyrinth domain, skyrmion and ferromagnetic textures are determined by the identified ranges of $J_1$-$J_2$. Importantly, skyrmion phase with an increasing topological number ($Q$) covers a wider $J_1$-$J_2$ area. Then, the diameter of skyrmions can be tuned by the frustration strength ($|J_2/J_1|$) or external magnetic field. Besides, a phase transition from N$\acute{e}$el to Bloch type skyrmion is observed due to the change of the helicity with the variation of $|J_2/J_1|$. Furthermore, as increasing magnetic field, the skyrmions with high $Q$ ($\ge 3$) tend to split into the ones with $Q=1$, thereby achieving a lower systematic energy. Additionally, we find that the CoCl$_2$ monolayer satisfies the requirement of the frustrated $J_1$-$J_2$ magnet, and the related magnetic behaviors agree with the above conclusions. The frustration-induced skyrmions are stable without the manipulation of temperature and magnetic field. Our results may open a possible way toward spintronic applications based on High-topological-number and nanoscale topological spin textures of skyrmions.

physics.comp-ph↗

arXiv2023

Classification of skyrmionic textures and extraction of Hamiltonian parameters via machine learning

Classifying skyrmionic textures and extracting magnetic Hamiltonian parameters are fundamental and demanding endeavors within the field of two-dimensional (2D) spintronics. By using micromagnetic simulation and machine learning (ML) methods, we theoretically realize the recognition of nine skyrmionic textures and the mining of magnetic Hamiltonian parameters from massive spin texture images in 2D Heisenberg model. For textures classification, a deep neural network (DNN) trained according to transfer learning is proposed to distinguish nine different skyrmionic textures. For parameters extraction, based on the textures generated by different Heisenberg exchange stiffness (J), Dzyaloshinskii-Moriya strength (D), and anisotropy constant (K), we apply a multi-input single-output (MISO) deep learning model (handling with both images and parameters) and a support vector regression (SVR) model (dealing with Fourier features) to extract the parameters embedded in the spin textures. The models for classification and extraction both achieve great results with the accuracy of 98% (DNN),90% (MISO) and 80% (SVR). Importantly, via our ML methods, the skyrmionic textures with blurred phase boundaries can be effectively distinguished, and the concluded formation conditions of various skyrmionic textures, especially the skyrmion crystal, are consistent with previous reports. Besides, our models demonstrate the mapping relationship between spin texture images and magnetic parameters, which proves the feasibility of extracting microscopic mechanisms from experimental images and has guiding significance for the experiments of spintronics.

cond-mat.mes-hall↗

arXiv2022

Reducibility of the dispersive Camassa-Holm equation with unbounded perturbations

Considered herein is the reducibility of the quasi-periodically time dependent linear dynamical system with a diophantine frequency vector $\omega \in \mathcal{O}_0 \subset \mathbb{R}^{\nu}$. This system is derived from linearizing the dispersive Camassa-Holm equation with unbounded perturbations at a small amplitude quasi-periodic function. It is shown that there is a set $\mathcal{O}_{\infty} \subset \mathcal{O}_0$ of asymptotically full Lebesgue measure such that for any $\omega \in \mathcal{O}_{\infty}$, the system can be reduced to the one with constant coefficients by a quasi-periodic linear transformation. The strategy adopted in this paper consists of two steps: (a) A reduction based on the orders of the pseudo differential operators in the system which conjugates the linearized operator to a one with constant coefficients up to a small remainder; (b) A perturbative reducibility scheme which completely diagonalizes the remainder of the previous step. The main difficulties in the reducibility we need to tackle come from the operator $J=(1-\partial_{xx})^{-1}\partial_{x}$, which induces the symplectic structure of the dispersive Camassa-Holm equation.

math.AP↗

arXiv2020

An Intelligent Prediction System for Mobile Source Localization Using Time Delay Measurements

In this paper, we introduce an intelligent prediction system for mobile source localization in industrial Internet of things. The position and velocity of mobile source are jointly predicted by using Time Delay (TD) measurements in the intelligent system. To predict the position and velocity, the Relaxed Semi-Definite Programming (RSDP) algorithm is firstly designed by dropping the rank-one constraint. However, dropping the rank-one constraint leads to produce a suboptimal solution. To improve the performance, we further put forward a Penalty Function Semi-Definite Programming (PF-SDP) method to obtain the rank-one solution of the optimization problem by introducing the penalty terms. Then an Adaptive Penalty Function Semi-Definite Programming (APF-SDP) algorithm is also proposed to avoid the excessive penalty by adaptively choosing the penalty coefficient. We conduct experiments in both a simulation environment and a real system to demonstrate the effectiveness of the proposed method. The results have demonstrated that the proposed intelligent APF-SDP algorithm outperforms the PF-SDP in terms of the position and velocity estimation whether the noise level is large or not.

cs.NI↗