arXiv subjects

Zhiye Tang

Publications and source records attributed to Zhiye Tang.

4 recordsLinked to original sources

arXiv2026

Super-Arrhenius Dynamic Slowdown Revealed by Slow Variable Modulation in the Fragile Supercooled Liquid

The super-Arrhenius dynamic slowdown in fragile supercooled liquids remains one of the central unresolved questions in condensed matter physics. In this study, we analyze particle jump dynamics in a prototypical fragile glass-forming liquid, the Kob-Andersen Lennard-Jones (KALJ) model. Using the displacement of jumping particles as the reaction coordinate, we demonstrate the emergence of non-Poissonian dynamics as the temperature decreases. In the mildly supercooled regime, the outer region of the first coordination shell of a jumping particle exhibits a significant distribution shift during the jump motion. By comparing the survival probability with its slow-fluctuation limit using this distribution as a slow variable, we confirm that particles in this region modulate the jump dynamics, enhance the jump rate fluctuations, and thereby induce the dynamic slowdown as supercooling proceeds. As the temperature decreases, this behavior extends to the outer regions of the second coordination shell and beyond, intensifying the dynamic slowdown. This spatial growth of the slow variables responsible for dynamic disorder exhibits close correspondence with an increase in the static correlation length. These results provide a microscopic mechanism for the super-Arrhenius dynamic slowdown in the KALJ model.

cond-mat.soft↗

arXiv2026

ProGS: Towards Progressive Coding for 3D Gaussian Splatting

Progressive transmission of 3D Gaussian Splatting (3DGS) requires each completed transmission stage to be decodable from received data and directly renderable. This work presents ProGS, a progressive codec that organizes anchor-based 3DGS as parent-closed octree prefixes. ProGS combines parent-causal entropy coding, level-balanced anchor growth, bounded multi-prefix training, and lightweight parent-anchor refinement to improve early-prefix quality without altering the complete-model rendering path. One fixed-$\lambda$ training run yields five deployable rate--quality points from a single bitstream. Experiments on 17 scenes from three datasets evaluate rate--distortion performance, rendering speed, and transmission efficiency against progressive and single-rate baselines. On one representative scene per dataset, ProGS reaches a common quality target with 30.7 $\sim$ 60.7\% fewer bytes than HAC-Rand and 22.6 $\sim$ 53.4\% fewer bytes than HAC++-Rand. Across the three dataset averages, ProGS-LR uses 4.7 $\sim$ 6.1\% fewer bytes than HAC-high while improving SSIM by 0.002 $\sim$ 0.041 and reducing LPIPS by 4.8 $\sim$ 51.2\%. The parent-closed syntax makes every prefix causally decodable and directly renderable without future topology. ProGS-HR also yields higher endpoint SSIM and lower LPIPS than PCGS across all three dataset averages, and all five prefixes render in real time. Code is available at https://github.com/ZhiyeTang/ProGS-Official

cs.CV↗

arXiv2026

GSStream: 3D Gaussian Splatting based Volumetric Scene Streaming System

Recently, the 3D Gaussian splatting (3DGS) technique for real-time radiance field rendering has revolutionized the field of volumetric scene representation, providing users with an immersive experience. But in return, it also poses a large amount of data volume, which is extremely bandwidth-intensive. Cutting-edge researchers have tried to introduce different approaches and construct multiple variants for 3DGS to obtain a more compact scene representation, but it is still challenging for real-time distribution. In this paper, we propose GSStream, a novel volumetric scene streaming system to support 3DGS data format. Specifically, GSStream integrates a collaborative viewport prediction module to better predict users' future behaviors by learning collaborative priors and historical priors from multiple users and users' viewport sequences and a deep reinforcement learning (DRL)-based bitrate adaptation module to tackle the state and action space variability challenge of the bitrate adaptation problem, achieving efficient volumetric scene delivery. Besides, we first build a user viewport trajectory dataset for volumetric scenes to support the training and streaming simulation. Extensive experiments prove that our proposed GSStream system outperforms existing representative volumetric scene streaming systems in visual quality and network usage. Demo video: https://youtu.be/3WEe8PN8yvA.

cs.CV↗

arXiv2025

Dynamic Slowdown and Spatial Correlations in Viscous Silica Melt: Perspectives from Dynamic Disorder

The dynamic slowdown in glass-forming liquids remains a central topic in condensed matter science. Here, we report a theoretical investigation of the microscopic origin of the slowdown in amorphous silica, a prototypical strong glass former with a tetrahedral network structure. Using molecular dynamics simulations, we analyze atomic jump dynamics, the elementary structural change processes underlying relaxation. We find that the jump statistics deviate from Poisson behavior with decreasing temperature, reflecting the emergence of dynamic disorder in which slowly evolving variables modulate the jump motion. The slowdown is species-dependent: for silicon, the primary constraint arises from the fourth-nearest oxygen neighbor, while at lower temperatures, the fourth-nearest silicon also becomes relevant; for oxygen, the dominant influence comes from the second-nearest silicon neighbors. As the system is cooled, the jump dynamics become increasingly slow and intermittent, proceeding in a higher-dimensional space of multiple slow variables that reflect cooperative rearrangements of the network. Species-resolved point-to-set correlations further reveal that the spatial extent of cooperative relaxation grows differently for silicon and oxygen, directly linking their relaxation asymmetry to the extent of collective motion. Together, these results provide a microscopic framework linking dynamic disorder, species-dependent constraints, and cooperative correlations, offering deeper insight into the slowdown of strong glass-forming networks.

cond-mat.soft↗