arXiv subjects

Wenxuan Qiu

Publications and source records attributed to Wenxuan Qiu.

3 recordsLinked to original sources

arXiv2026

Observation of a Reconstructed Chern Insulator in Twisted Bilayer MoTe2

Twisted bilayer MoTe2 is a prototypical moire material in which long-wavelength superlattices amplify electron correlations, enabling a wealth of emergent quantum phases. To date, experimental efforts have focused primarily on small twist angles (typically smaller than 4deg ), whereas the larger-angle regime-where moire bands become more dispersive and correlations are reduced-has remained largely unexplored. Here we chart the topological phase space of tMoTe2 at a relatively large twist angle of approximately 4.54deg, accessing a moderately correlated regime with enhanced bandwidth. In contrast to small-angle devices that predominantly host fractional quantum anomalous Hall or spin Hall responses, we uncover multiple Chern-insulating states with C = 1 at moire fillings v = -1, -0.53 and -1/2. Strikingly, at v = -2/3 a magnetic field induces a fractional Chern insulator accompanied by an insulator-metal transition. Our results broaden the topological phase diagram of tMoTe2 and establish large-angle moire superlattices as a versatile platform for engineering robust topological states beyond the strong-correlation limit.

cond-mat.mes-hall↗

arXiv2025

An improved EfficientNetV2 for garbage classification

This paper presents an enhanced waste classification framework based on EfficientNetV2 to address challenges in data acquisition cost, generalization, and real-time performance. We propose a Channel-Efficient Attention (CE-Attention) module that mitigates feature loss during global pooling without introducing dimensional scaling, effectively enhancing critical feature extraction. Additionally, a lightweight multi-scale spatial feature extraction module (SAFM) is developed by integrating depthwise separable convolutions, significantly reducing model complexity. Comprehensive data augmentation strategies are further employed to improve generalization. Experiments on the Huawei Cloud waste classification dataset demonstrate that our method achieves a classification accuracy of 95.4\%, surpassing the baseline by 3.2\% and outperforming mainstream models. The results validate the effectiveness of our approach in balancing accuracy and efficiency for practical waste classification scenarios.

cs.CV↗

arXiv2022

Reconstruction of gene regulatory network via sparse optimization

In this paper, we tested several sparse optimization algorithms based on the public dataset of the DREAM5 Gene Regulatory Network Inference Challenge. And we find that introducing 20% of the regulatory network as a priori known data can provide a basis for parameter selection of inference algorithms, thus improving prediction efficiency and accuracy. In addition to testing common sparse optimization methods, we also developed voting algorithms by bagging them. Experiments on the DREAM5 dataset show that the sparse optimization-based inference of the moderation relation works well, achieving better results than the official DREAM5 results on three datasets. However, the performance of traditional independent algorithms varies greatly in the face of different datasets, while our voting algorithm achieves the best results on three of the four datasets.

q-bio.MN↗