Zeyao Zhang | Searcharxiv

arXiv2026

High-Density Horizontal Arrays of Single-Chirality Carbon Nanotubes

Highly ordered high-density arrays of single-chirality single-walled carbon nanotubes (SWCNTs) are greatly desired for exploring the intrinsic anisotropic properties and collective performance of such 1-dimensional (1D) nanomaterials. Here we present a Marangoni flow-induced self-assembly (MISA) strategy to fabricate monolayered SWCNT arrays achieving a packing density of ~200 ${μm}^{-1}$ and a 2-dimensional order parameter ($S_{2\mathrm{D}}$) of ~0.95. Relying on its general compatibility with both organic and aqueous dispersions, we prepare single-chirality and enantiomer-pure SWCNT arrays from organic and aqueous dispersions resulting from the sorting processes. The anisotropic optical and electrical properties of the arrays are demonstrated by the polarization-dependent Rabi splitting as well as polarized near-infrared light emission and detection. With the great tolerance to solutions, substrates, and materials, as well as the feasibility and controllability, MISA shows great potential in the assembly of 1D nanomaterials.

cond-mat.mtrl-sci↗

arXiv2022

CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation

Acquiring the most representative examples via active learning (AL) can benefit many data-dependent computer vision tasks by minimizing efforts of image-level or pixel-wise annotations. In this paper, we propose a novel Collaborative Panoptic-Regional Active Learning framework (CPRAL) to address the semantic segmentation task. For a small batch of images initially sampled with pixel-wise annotations, we employ panoptic information to initially select unlabeled samples. Considering the class imbalance in the segmentation dataset, we import a Regional Gaussian Attention module (RGA) to achieve semantics-biased selection. The subset is highlighted by vote entropy and then attended by Gaussian kernels to maximize the biased regions. We also propose a Contextual Labels Extension (CLE) to boost regional annotations with contextual attention guidance. With the collaboration of semantics-agnostic panoptic matching and regionbiased selection and extension, our CPRAL can strike a balance between labeling efforts and performance and compromise the semantics distribution. We perform extensive experiments on Cityscapes and BDD10K datasets and show that CPRAL outperforms the cutting-edge methods with impressive results and less labeling proportion.

cs.CV↗