arXiv subjects

Zijun Ding

Publications and source records attributed to Zijun Ding.

3 recordsLinked to original sources

arXiv2026

Persistent incommensurate amorphous/crystalline meta-interfaces enable engineering-grade superlubricity

Friction dissipates a substantial portion of global energy, motivating the pursuit of superlubricity, a state of near-zero friction, in real-world systems. Conventional approaches rely on crystalline lattice mismatch to suppress periodic energy barriers, but real interfaces invariably contain defects, edges and grain boundaries that restore high-friction states. Here we introduce a materials-agnostic strategy based on amorphous/crystalline heterointerfaces to achieve robust superlubricity under engineering-relevant conditions. Using diamond-like carbon (DLC) and crystalline MoS2 as a model system, we show through experiments and atomistic simulations that their interface remains incommensurate at all orientations and exhibits vanishing energy barriers during friction. In contrast, twisted MoS2 bilayers readily reorient into commensurate, high-friction states. We scale this effect by fabricating laser-patterned arrays of DLC/MoS2 meta-contacts reinforced with Ti3C2Tx MXene, forming hierarchical interfaces that sustain a friction coefficient of ~0.008 over 100000 cycles under combined extreme conditions: millimetre-scale contact size, 12.7 GPa contact pressure and RH 40% air. This unprecedented performance arises from four synergistic factors: intrinsic incommensurability at amorphous/crystalline interface, the rigidity of DLC support, MXene-based mechanical reinforcement and normalized load distribution by geometric patterning. These findings establish a general design paradigm that extends structural superlubricity from nanoscale model systems to practical technologies for sustainable engineering.

cond-mat.mtrl-sci↗

arXiv2025

Discretization-free Multicalibration through Loss Minimization over Tree Ensembles

In recent years, multicalibration has emerged as a desirable learning objective for ensuring that a predictor is calibrated across a rich collection of overlapping subpopulations. Existing approaches typically achieve multicalibration by discretizing the predictor's output space and iteratively adjusting its output values. However, this discretization approach departs from the standard empirical risk minimization (ERM) pipeline, introduces rounding error and additional sensitive hyperparameter, and may distort the predictor's outputs in ways that hinder downstream decision-making. In this work, we propose a discretization-free multicalibration method that directly optimizes an empirical risk objective over an ensemble of depth-two decision trees. Our ERM approach can be implemented using off-the-shelf tree ensemble learning methods such as LightGBM. Our algorithm provably achieves multicalibration, provided that the data distribution satisfies a technical condition we term as loss saturation. Across multiple datasets, our empirical evaluation shows that this condition is always met in practice. Our discretization-free algorithm consistently matches or outperforms existing multicalibration approaches--even when evaluated using a discretization-based multicalibration metric that shares its discretization granularity with the baselines.

cs.LG↗

arXiv2025

STSA: Spatial-Temporal Semantic Alignment for Visual Dubbing

Existing audio-driven visual dubbing methods have achieved great success. Despite this, we observe that the semantic ambiguity between spatial and temporal domains significantly degrades the synthesis stability for the dynamic faces. We argue that aligning the semantic features from spatial and temporal domains is a promising approach to stabilizing facial motion. To achieve this, we propose a Spatial-Temporal Semantic Alignment (STSA) method, which introduces a dual-path alignment mechanism and a differentiable semantic representation. The former leverages a Consistent Information Learning (CIL) module to maximize the mutual information at multiple scales, thereby reducing the manifold differences between spatial and temporal domains. The latter utilizes probabilistic heatmap as ambiguity-tolerant guidance to avoid the abnormal dynamics of the synthesized faces caused by slight semantic jittering. Extensive experimental results demonstrate the superiority of the proposed STSA, especially in terms of image quality and synthesis stability. Pre-trained weights and inference code are available at https://github.com/SCAILab-USTC/STSA.

cs.CV↗