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Researcher profile

Xing Shen

Xing Shen contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer Visioncond-mat.quant-gasmath.NANumerical Analysisphysics.atom-ph

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Published work

3 published item(s)

preprint2026arXiv

MSR:Hybrid Field Modeling for CT-MRI Rigid-Deformable Registration of the Cervical Spine with an Annotated Dataset

Accurate CT-MRI registration of the cervical spine is essential for preoperative planning because this region is anatomically complex,highly variable,and vulnerable to injury of the vertebral arteries and spinal cord. However,cervical CT-MRI registration remains underexplored,particularly for rigid-deformable hybrid modeling,and the lack of high-quality annotated multimodal data further limits progress. To address these challenges, we construct and release a comprehensively annotated CT-MRI dataset, R-D-Reg, and propose MSR, a rigid-deformable hybrid registration framework for complex joint structures. Specifically, MSR includes a rigid registration module for independent local rigid alignment of individual vertebrae and a deformable registration module with an MSL block that combines Mamba-based global modeling and Swin Transformer-based local modeling through adaptive gating. The rigid and deformable deformation fields are then fused to generate a hybrid field that better preserves local anatomical consistency. The code and dataset are publicly available at https://github.com/ssc1230609-spec/MSR-registration.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Deep Euler method: solving ODEs by approximating the local truncation error of the Euler method

In this paper, we propose a deep learning-based method, deep Euler method (DEM) to solve ordinary differential equations. DEM significantly improves the accuracy of the Euler method by approximating the local truncation error with deep neural networks which could obtain a high precision solution with a large step size. The deep neural network in DEM is mesh-free during training and shows good generalization in unmeasured regions. DEM could be easily combined with other schemes of numerical methods, such as Runge-Kutta method to obtain better solutions. Furthermore, the error bound and stability of DEM is discussed.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

Expansion dynamics of a spherical Bose-Einstein condensate

We experimentally and theoretically observe the expansion behaviors of a spherical Bose-Einstein condensate. A rubidium condensate is produced in an isotropic optical dipole trap with an asphericity of 0.037. We measure the variation of the condensate size during the expansion process. The free expansion of the condensate is isotropic, which is different from that of the condensate usually produced in the anisotropic trap. The expansion in the short time is speeding and then after a long time the expansion velocity asymptotically approaches a constant value. We derive an analytic solution of the expansion behavior based on the spherical symmetry, allowing a quantitative comparison with the experimental measurement. The interaction energy of the condensate is gradually converted into the kinetic energy at the beginning of the expansion and the kinetic energy dominates after a long-time expansion. We obtain the interaction energy of the condensate in the trap by probing the expansion velocity, which is consistent with the theoretical prediction.

0 citations0 reviewsNo code linkedOpen access