arXiv subjects

Yuxin Wan

Publications and source records attributed to Yuxin Wan.

2 recordsLinked to original sources

arXiv2026

Quasiparticle properties below coherence onset in YbAl3 nanostructures

Mesoscopic transport measurements are underexplored as probes of quasiparticles and their properties in correlated metals. The mixed valence compound YbAl$_3$ exhibits a single-ion Kondo temperature of 670 K, while thermodynamic and transport properties (probed with specific heat, magnetic susceptibility, Hall effect, and resistivity) imply the onset of coherence of heavy fermion quasiparticles at T$* \approx$ 37 K. To characterize these quasiparticles, we utilize mesoscopic techniques familiar from weakly correlated conductors. In lithographically-defined nanowires etched from epitaxial films, we observe weak antilocalization magnetoresistance and universal conductance fluctuations, consistent with electronic coherence lengths of tens of nanometers. Additionally, analysis of Johnson-Nyquist noise measurements as a function of bias current reveal, within the context of a range of accepted models, a significant electron-phonon energy loss that increases with decreasing temperature, a finding that we contextualize within the broader properties of YbAl$_3$.

cond-mat.str-el↗

arXiv2025

SAMBLE: Shape-Specific Point Cloud Sampling for an Optimal Trade-Off Between Local Detail and Global Uniformity

Driven by the increasing demand for accurate and efficient representation of 3D data in various domains, point cloud sampling has emerged as a pivotal research topic in 3D computer vision. Recently, learning-to-sample methods have garnered growing interest from the community, particularly for their ability to be jointly trained with downstream tasks. However, previous learning-based sampling methods either lead to unrecognizable sampling patterns by generating a new point cloud or biased sampled results by focusing excessively on sharp edge details. Moreover, they all overlook the natural variations in point distribution across different shapes, applying a similar sampling strategy to all point clouds. In this paper, we propose a Sparse Attention Map and Bin-based Learning method (termed SAMBLE) to learn shape-specific sampling strategies for point cloud shapes. SAMBLE effectively achieves an improved balance between sampling edge points for local details and preserving uniformity in the global shape, resulting in superior performance across multiple common point cloud downstream tasks, even in scenarios with few-point sampling.

cs.CV↗