Wenjun Lu

Ying Song, Xuan Hu, Jingrui Xu et al.

An ice shelf is a floating extension of a land-based ice sheet into the ocean. It plays a crucial role in slowing down the flow of land ice into the sea, thus stabilizing the ice sheet. However, this stabilizing effect can be weakened by ice calving, a process in which large fragments of ice detach from the ice shelf. Although ice calving is widely acknowledged as a major contributor to ice mass loss, and its frequency and magnitude are highly sensitive to the environmental forcing, the underlying physics-based mechanisms remain poorly understood, particularly under ocean wave actions. In this context, we developed a nonlocal peridynamics (PD) framework to model the ice calving process subjected to wave-induced frontal corrosion. The proposed physics-based PD framework enables investigation of the coupled effects of self-weight bending, buoyancy-induced foot loosening, and ice calving process. To authors' best knowledge, this work represents the first attempt to employ a physics-based peridynamics framework for simulating ice calving processes. Compared with conventional finite element methods (FEM), the PD framework naturally captures crack initiation, interaction, and propagation without the need for special numerical treatments, thereby providing a robust tool for simulating fracture phenomena under large deformations and long-term environmental loading. To quantitatively resolve fracture processes, we implemented a static first Piola Kirchhoff virial stress formulation within the PD framework, allowing direct evaluation of stress concentration and energy release at evolving crack tips. Subsequently, the model is rigorously validated through one-to-one comparisons with finite-element stress fields, analytical beam-theory solutions, and recent field observations of wave-driven ice-shelf failure reported by Sartore et al. (2025).

Chuancheng Shi, Shangze Li, Wenjun Lu et al.

Despite their capabilities, large foundation models (LFMs) remain susceptible to adversarial manipulation. Current defenses predominantly rely on the "locality hypothesis", suppressing isolated neurons or features. However, harmful semantics act as distributed, cross-layer circuits, rendering such localized interventions brittle and detrimental to utility. To bridge this gap, we propose \textbf{TraceRouter}, a path-level framework that traces and disconnects the causal propagation circuits of illicit semantics. TraceRouter operates in three stages: (1) it pinpoints a sensitive onset layer by analyzing attention divergence; (2) it leverages sparse autoencoders (SAEs) and differential activation analysis to disentangle and isolate malicious features; and (3) it maps these features to downstream causal pathways via feature influence scores (FIS) derived from zero-out interventions. By selectively suppressing these causal chains, TraceRouter physically severs the flow of harmful information while leaving orthogonal computation routes intact. Extensive experiments demonstrate that TraceRouter significantly outperforms state-of-the-art baselines, achieving a superior trade-off between adversarial robustness and general utility. Our code will be publicly released. WARNING: This paper contains unsafe model responses.

Guoxi Huang, Haoran Wang, Zipeng Qi et al.

Underwater image degradation poses significant challenges for 3D reconstruction, where simplified physical models often fail in complex scenes. We propose \textbf{R-Splatting}, a unified framework that bridges underwater image restoration (UIR) with 3D Gaussian Splatting (3DGS) to improve both rendering quality and geometric fidelity. Our method integrates multiple enhanced views produced by diverse UIR models into a single reconstruction pipeline. During inference, a lightweight illumination generator samples latent codes to support diverse yet coherent renderings, while a contrastive loss ensures disentangled and stable illumination representations. Furthermore, we propose \textit{Uncertainty-Aware Opacity Optimization (UAOO)}, which models opacity as a stochastic function to regularize training. This suppresses abrupt gradient responses triggered by illumination variation and mitigates overfitting to noisy or view-specific artifacts. Experiments on Seathru-NeRF and our new BlueCoral3D dataset demonstrate that R-Splatting outperforms strong baselines in both rendering quality and geometric accuracy.

Wenjun Lu, Haodong Chen, Anqi Yi et al.

Novel view synthesis is a fundamental task in 3D computer vision that aims to reconstruct realistic images from a set of posed input views. However, reconstruction quality degrades significantly under sparse-view conditions due to limited geometric cues. Existing methods, such as Neural Radiance Fields (NeRF) and the more recent 3D Gaussian Splatting (3DGS), often suffer from blurred details and structural artifacts when trained with insufficient views. Recent works have identified the quality of rendered depth as a key factor in mitigating these artifacts, as it directly affects geometric accuracy and view consistency. In this paper, we address these challenges by introducing Hierarchical Depth-Guided Splatting (HDGS), a depth supervision framework that progressively refines geometry from coarse to fine levels. Central to HDGS is a novel Cascade Pearson Correlation Loss (CPCL), which aligns rendered and estimated monocular depths across multiple spatial scales. By enforcing multi-scale depth consistency, our method substantially improves structural fidelity in sparse-view scenarios. Extensive experiments on the LLFF and DTU benchmarks demonstrate that HDGS achieves state-of-the-art performance under sparse-view settings while maintaining efficient and high-quality rendering