arXiv subjects

Huiwen Xu

Publications and source records attributed to Huiwen Xu.

4 recordsLinked to original sources

arXiv2026

NaCl-Assisted Growth of Ferroelectric SnSe Nanosheets with Spin Glass-like Behavior

Two-dimensional (2D) SnSe is an emerging 2D material exhibiting intriguing properties such as ferroelectricity and nonlinear optical response. Here, high-quality single-crystalline SnSe nanosheets were synthesized via NaCl-assisted chemical vapor deposition (CVD) method. The addition of NaCl was found to significantly increase the surface coverage of the nanosheets with less influence on their lateral size. The crystalline structure and composition of as-grown nanosheets were charactered by XRD, Raman spectroscopy, and XPS. Ferroelectric domains in SnSe nanosheets are directly visualized by piezoresponse force microscopy (PFM). The magnetic hysteresis loops of SnSe nanosheets are achieved at 2 K, which indicated their weak ferromagnetism. A spin glass-like behavior was observed below 115K, which is attributed to the presence of SnSe2 impurity. This work further establishes a controllable synthesis route for SnSe nanosheets, thereby paving the way for subsequent investigation of their ferroelectric properties.

cond-mat.mtrl-sci↗

arXiv2023

Fast and Accurate Transferability Measurement by Evaluating Intra-class Feature Variance

Given a set of pre-trained models, how can we quickly and accurately find the most useful pre-trained model for a downstream task? Transferability measurement is to quantify how transferable is a pre-trained model learned on a source task to a target task. It is used for quickly ranking pre-trained models for a given task and thus becomes a crucial step for transfer learning. Existing methods measure transferability as the discrimination ability of a source model for a target data before transfer learning, which cannot accurately estimate the fine-tuning performance. Some of them restrict the application of transferability measurement in selecting the best supervised pre-trained models that have classifiers. It is important to have a general method for measuring transferability that can be applied in a variety of situations, such as selecting the best self-supervised pre-trained models that do not have classifiers, and selecting the best transferring layer for a target task. In this work, we propose TMI (TRANSFERABILITY MEASUREMENT WITH INTRA-CLASS FEATURE VARIANCE), a fast and accurate algorithm to measure transferability. We view transferability as the generalization of a pre-trained model on a target task by measuring intra-class feature variance. Intra-class variance evaluates the adaptability of the model to a new task, which measures how transferable the model is. Compared to previous studies that estimate how discriminative the models are, intra-class variance is more accurate than those as it does not require an optimal feature extractor and classifier. Extensive experiments on real-world datasets show that TMI outperforms competitors for selecting the top-5 best models, and exhibits consistently better correlation in 13 out of 17 cases.

cs.LG↗

arXiv2021

Fast and Accurate Transferability Measurement for Heterogeneous Multivariate Data

Given a set of heterogeneous source datasets with their classifiers, how can we quickly find the most useful source dataset for a specific target task? We address the problem of measuring transferability between source and target datasets, where the source and the target have different feature spaces and distributions. We propose Transmeter, a fast and accurate method to estimate the transferability of two heterogeneous multivariate datasets. We address three challenges in measuring transferability between two heterogeneous multivariate datasets: reducing time, minimizing domain gap, and extracting meaningful homogeneous representations. To overcome the above issues, we utilize a pre-trained source model, an adversarial network, and an encoder-decoder architecture. Extensive experiments on heterogeneous multivariate datasets show that Transmeter gives the most accurate transferability measurement with up to 10.3 times faster performance than its competitor. We also show that selecting the best source data with Transmeter followed by a full transfer leads to the best transfer accuracy and the fastest running time.

cs.LG↗

arXiv2018

Tabletop single-shot extreme ultraviolet Fourier transform holography of an extended object

We demonstrate single and multi-shot Fourier transform holography with the use of a tabletop extreme ultraviolet laser. The reference wave was produced by a Fresnel zone plate with a central opening that allowed the incident beam to illuminate the sample directly. The high reference wave intensity allows for larger objects to be imaged compared to mask-based lensless Fourier transform holography techniques. We obtain a spatial resolution of 169 nm from a single laser pulse and a resolution of 128 nm from an accumulation of 20 laser pulses for an object ~11x11μm 2 in size. This experiment utilized a tabletop extreme ultraviolet laser that produces a highly coherent ~1.2 ns laser pulse at 46.9 nm wavelength.

physics.optics↗