arXiv subjects

Yuzhong Wang

Publications and source records attributed to Yuzhong Wang.

7 recordsLinked to original sources

arXiv2026

EdMCGS: Event-Driven Markov Chain Gaussian Splatting for Extreme-Low-Frame-Rate Dynamic Scene Reconstruction

We present EdMCGS (Event-driven Markov chain Gaussian Splatting), an end-to-end method for reconstructing dynamic 3D scenes from extreme-low-frame-rate RGB together with an event stream, which can then be rendered at any intermediate timestamp. Methods relying solely on RGB images generate numerous artifacts due to the lack of evidence from between consecutive frames. To supply this missing evidence, we model the scene motion as an event-driven Markov chain, in which the sparse RGB frames anchor the state at their own timestamps while the events recorded within an interval drive the transition across it. Since the transition reads the events of the current interval, it remains active at inference and produces the in-between motion of the 3D Gaussians directly from the events rather than by interpolation, which sets our method apart from prior work that uses events only as training-time supervision. The state is carried by a compact set of control points, each driven by the events sampled in the neighborhood of its own image projection, and a temporal local isometry term keeps the propagated motion locally rigid. Experiments on synthetic and real-world scenes show that EdMCGS outperforms both RGB-based and event-based baselines, while rendering in real time with far fewer Gaussians than the strongest event-based baseline. We release our source code and a new dataset at https://github.com/joseclipse/EdMCGS.

cs.CV↗

arXiv2026

PointCSP: Cross-Sample Semantic Propagation and Stability Preservation in Self-Supervised Point Cloud Learning

Scene-level point cloud self-supervised learning (PC-SSL) has demonstrated potential in enhancing the generalization capability of 3D vision models. Despite the advances in the field through existing methods, the sample-independent modeling paradigm still poses significant limitations in terms of maintaining consistent semantic representations across scenes. This challenge hinders the construction of a unified and transferable semantic space. To address this issue, we propose a PC-SSL framework based on cross-sample semantic propagation (CSP), in which samples within a batch are serialized into continuous input and processed by a state-space model to enable semantic state propagation. This mechanism explicitly models the dynamic dependencies across samples in the state space, allowing the network to establish cross-sample semantic consistency in the latent space and achieve global semantic alignment. Since serialization-based pretraining requires batch-level input organization, we further introduce an asymmetric semantic preservation distillation (SPD) during finetuning to achieve structural alignment of semantic transfer and eliminate inconsistencies caused by batch dependency. The proposed SPD ensures stable transfer of pretrained semantics through a heterogeneous input mechanism and a semantic feature alignment constraint. This enables the model to maintain structured semantic consistency and robustness under single-scene testing conditions. Extensive experiments on multiple benchmark datasets demonstrate that our method consistently outperforms state-of-the-art methods in both performance and semantic consistency.

cs.CV↗

arXiv2026

Scalable Training of Mixture-of-Experts Models with Megatron Core

Scaling Mixture-of-Experts (MoE) training introduces systems challenges absent in dense models. Because each token activates only a subset of experts, this sparsity allows total parameters to grow much faster than per-token computation, creating coupled constraints across memory, communication, and computation. Optimizing one dimension often shifts pressure to another, demanding co-design across the full system stack. We address these challenges for MoE training through integrated optimizations spanning memory (fine-grained recomputation, offloading, etc.), communication (optimized dispatchers, overlapping, etc.), and computation (Grouped GEMM, fusions, CUDA Graphs, etc.). The framework also provides Parallel Folding for flexible multi-dimensional parallelism, low-precision training support for FP8 and NVFP4, and efficient long-context training. On NVIDIA GB300 and GB200, it achieves 1,233/1,048 TFLOPS/GPU for DeepSeek-V3-685B and 974/919 TFLOPS/GPU for Qwen3-235B. As a performant, scalable, and production-ready open-source solution, it has been used across academia and industry for training MoE models ranging from billions to trillions of parameters on clusters scaling up to thousands of GPUs. This report explains how these techniques work, their trade-offs, and their interactions at the systems level, providing practical guidance for scaling MoE models with Megatron Core.

cs.DC↗

arXiv2025

Dispersion-Engineered Compact Twisted Metasurfaces Enabling 3D Frequency-Reconfigurable Holography

Flexible dispersion manipulation is critical for holography to achieve broadband imaging or frequency division multiplexing. Within this context, metasurface-based holography offers advanced dispersion control, yet dynamic reconfigurability remains largely unexplored. This work develops a dispersion-engineered inverse design framework that enables 3D frequency-reconfigurable holography through a twisted metasurface system. The physical implementation is based on a compact layered configuration that cascades the broadband radiation-type metasurface (RA-M) and phase-only metasurface (P-M). The RA-M provides a phase-adjustable input to excite P-M, while the rotation of P-M creates a reconfigurable response of holograms. By employing the proposed scheme, dynamic switching of frequency-space multiplexing and achromatic holograms are designed and experimentally demonstrated in the microwave region. This method advances flexible dispersion engineering for metasurface-based holography, and the compact system holds significant potential for applications in ultra-broadband imaging, high-capacity optical display, and switchable meta-devices.

physics.optics↗

arXiv2025

High Numerical Aperture Achromatic Meta-Devices through Dispersion Compensation

Dispersion engineering is a long-standing challenge in optical systems, and it is particularly important for metasurfaces, which naturally suffer from strong chromatic aberrations due to their ultralow profile. Stacks of metasurfaces have recently implemented dispersion control to address these challenges. However, these approaches still suffer from bottlenecks in terms of the available material refractive index and required aspect ratios, resulting in limited phase and group delay coverage, constraining their numerical aperture (NA), size and operating bandwidth. To address these challenges, we explore a dispersion compensation strategy combined with full-wave simulation-free inverse design to implement ultra-high NA, broadband dispersion control in metasurfaces, not requiring large refractive index materials and high aspect ratio processing technology. We experimentally demonstrate multiple meta-devices with highly customized dispersion engineering in the microwave regime, including broadband achromatic diffraction-limited meta-devices with NA=0.98 and 60% fractional bandwidth. Our proposed platform explores a paradigm for dispersion control with metasurfaces, which may facilitate advanced and scalable dispersion functionalities.

physics.app-ph↗

arXiv2025

Cat-Eye Inspired Active-Passive-Composite Aperture-Shared Sub-Terahertz Meta-Imager for Non-Interactive Concealed Object Detection

Within the feline eye, a distinctive tapetum lucidum as a mirror resides posterior to the retina, reflecting the incident rays to simulate light source emission. This secondary emission property enables felines to be highly sensitive to light, possessing remarkable visual capabilities even in dark settings. Drawing inspiration from this natural phenomenon, we propose an active-passive-composite sub-terahertz meta-imager integrated with a bifocus metasurface, a high-sensitivity radiometer, and a low-power signal hidden radiation source. Benefiting from its aperture-shared advantage, this advanced fusion imaging system, enabled to be deployed by a simplified portable hardware platform, allows for the concurrent acquisition of active and passive electromagnetic properties to extend the target detection category and realize multi-mode fusion perception. Notably, it also enables the extraction of radiation and reflection characteristics without additional calibration modules. Experiments demonstrate the multi-target fusion imaging and localized information decoupling with the tailored field of view and emission energy. This compact and multi-mode fusion imaging system may have plenty of potential for airplane navigation positioning, abnormal monitoring, and non-interactive concealed security checks.

physics.optics↗

arXiv2021

Equality before the Law: Legal Judgment Consistency Analysis for Fairness

In a legal system, judgment consistency is regarded as one of the most important manifestations of fairness. However, due to the complexity of factual elements that impact sentencing in real-world scenarios, few works have been done on quantitatively measuring judgment consistency towards real-world data. In this paper, we propose an evaluation metric for judgment inconsistency, Legal Inconsistency Coefficient (LInCo), which aims to evaluate inconsistency between data groups divided by specific features (e.g., gender, region, race). We propose to simulate judges from different groups with legal judgment prediction (LJP) models and measure the judicial inconsistency with the disagreement of the judgment results given by LJP models trained on different groups. Experimental results on the synthetic data verify the effectiveness of LInCo. We further employ LInCo to explore the inconsistency in real cases and come to the following observations: (1) Both regional and gender inconsistency exist in the legal system, but gender inconsistency is much less than regional inconsistency; (2) The level of regional inconsistency varies little across different time periods; (3) In general, judicial inconsistency is negatively correlated with the severity of the criminal charges. Besides, we use LInCo to evaluate the performance of several de-bias methods, such as adversarial learning, and find that these mechanisms can effectively help LJP models to avoid suffering from data bias.

cs.CL↗