arXiv subjects

Qixin Liu

Publications and source records attributed to Qixin Liu.

4 recordsLinked to original sources

arXiv2025

Enhancing Stress-Strain Predictions with Seq2Seq and Cross-Attention based on Small Punch Test

This paper introduces a novel deep-learning approach to predict true stress-strain curves of high-strength steels from small punch test (SPT) load-displacement data. The proposed approach uses Gramian Angular Field (GAF) to transform load-displacement sequences into images, capturing spatial-temporal features and employs a Sequence-to-Sequence (Seq2Seq) model with an LSTM-based encoder-decoder architecture, enhanced by multi-head cross-attention to improved accuracy. Experimental results demonstrate that the proposed approach achieves superior prediction accuracy, with minimum and maximum mean absolute errors of 0.15 MPa and 5.58 MPa, respectively. The proposed method offers a promising alternative to traditional experimental techniques in materials science, enhancing the accuracy and efficiency of true stress-strain relationship predictions.

cs.LG↗

arXiv2024

Analyzing Nobel Prize Literature with Large Language Models

This study examines the capabilities of advanced Large Language Models (LLMs), particularly the o1 model, in the context of literary analysis. The outputs of these models are compared directly to those produced by graduate-level human participants. By focusing on two Nobel Prize-winning short stories, 'Nine Chapters' by Han Kang, the 2024 laureate, and 'Friendship' by Jon Fosse, the 2023 laureate, the research explores the extent to which AI can engage with complex literary elements such as thematic analysis, intertextuality, cultural and historical contexts, linguistic and structural innovations, and character development. Given the Nobel Prize's prestige and its emphasis on cultural, historical, and linguistic richness, applying LLMs to these works provides a deeper understanding of both human and AI approaches to interpretation. The study uses qualitative and quantitative evaluations of coherence, creativity, and fidelity to the text, revealing the strengths and limitations of AI in tasks typically reserved for human expertise. While LLMs demonstrate strong analytical capabilities, particularly in structured tasks, they often fall short in emotional nuance and coherence, areas where human interpretation excels. This research underscores the potential for human-AI collaboration in the humanities, opening new opportunities in literary studies and beyond.

cs.CL↗

arXiv2021

Fake News Detection through Graph Comment Advanced Learning

Disinformation has long been regarded as a severe social problem, where fake news is one of the most representative issues. What is worse, today's highly developed social media makes fake news widely spread at incredible speed, bringing in substantial harm to various aspects of human life. Yet, the popularity of social media also provides opportunities to better detect fake news. Unlike conventional means which merely focus on either content or user comments, effective collaboration of heterogeneous social media information, including content and context factors of news, users' comments and the engagement of social media with users, will hopefully give rise to better detection of fake news. Motivated by the above observations, a novel detection framework, namely graph comment-user advanced learning framework (GCAL) is proposed in this paper. User-comment information is crucial but not well studied in fake news detection. Thus, we model user-comment context through network representation learning based on heterogeneous graph neural network. We conduct experiments on two real-world datasets, which demonstrate that the proposed joint model outperforms 8 state-of-the-art baseline methods for fake news detection (at least 4% in Accuracy, 7% in Recall and 5% in F1). Moreover, the proposed method is also explainable.

cs.SI↗

arXiv2021

Realization of electron antidoping by modulating the breathing distortion in BaBiO$_3$

The recent proposal of antidoping scheme breaks new ground in conceiving conversely functional materials and devices, yet the few available examples belong to the correlated electron systems. Here we demonstrate both theoretically and experimentally that the main group oxide BaBiO$_3$ is a model system for antidoping using oxygen vacancies. The first principles calculations show that the band gap systematically increases due to the strongly enhanced BiO breathing distortions away from the vacancies and the annihilation of Bi 6s and O 2p hybridized conduction bands near the vacancies. The spectroscopic experiments confirm the band gap increasing systematically with electron doping, with a maximal gap enhancement of 75% when the film's stoichiometry is reduced to BaBiO$_{2.75}$. The Raman and diffraction experiments show the suppression of the overall breathing distortion. The study unambiguously demonstrates the remarkable antidoping effect in a material without strong electron correlations and underscores the importance of bond disproportionation in realizing such an effect.

cond-mat.mtrl-sci↗