arXiv subjects

Qihui Lin

Publications and source records attributed to Qihui Lin.

4 recordsLinked to original sources

arXiv2026

Kinetically Controlled Condensation Boundary Governing Indium Incorporation in InGaN Metal Organic Vapor Phase Epitaxy

We combine in situ synchrotron X-ray crystal truncation rod measurements with a binary Burton-Cabrera-Frank model to quantify indium incorporation during InGaN growth by metal-organic vapor phase epitaxy (MOVPE) on GaN(0001). By distinguishing In adatoms from condensed droplets and incorporating coupled Ga-In incorporation kinetics, the model captures the intrinsically nonlinear dependence of indium composition on precursor flux and growth temperature. The critical In coverage corresponding to the maximum attainable In composition at a given temperature is determined by a kinetic balance between In adatom supply and incorporation capacity, defining a kinetically controlled condensation boundary that shifts with temperature and Ga flux. The model quantitatively predicts this boundary, in agreement with independent measurements, and provides a predictive framework for optimizing high-In-content InGaN growth while avoiding droplet formation.

cond-mat.mtrl-sci↗

arXiv2026

Step- and terrace-resolved crystal truncation rod scattering from vicinal surfaces under coherent heteroepitaxy

We develop a general theory of crystal truncation rod (CTR) scattering from vicinal surfaces with a coherently strained heteroepitaxial film. The formalism incorporates film-induced interference fringes, full elastic lattice distortion, terrace ordering, surface reconstruction, and real-time growth evolution within a unified description. Comparison between Nagai model and elasticity-based model shows that the lattice tilt is nearly identical in the two approaches, whereas the elasticitybased model predicts an additional triclinic deformation arising from shear strain. This deformation has little effect on specular CTRs but strongly modifies non-specular rods, making them a sensitive probe of the full elastic state of coherent epitaxial films. We further show that the characteristic sensitivity of vicinal CTRs to terrace ordering, surface reconstruction, and terrace-resolved compositional modification remains robust in the presence of a coherent film. Representative calculations for InGaN/GaN demonstrate that the framework enables quantitative interpretation of both static and real-time CTR measurements and provides access to step- and terrace-resolved structural and kinetic information during heteroepitaxial growth.

cond-mat.mtrl-sci↗

arXiv2025

Generative Data Augmentation in Graph Contrastive Learning for Recommendation

Recommendation systems have become indispensable in various online platforms, from e-commerce to streaming services. A fundamental challenge in this domain is learning effective embeddings from sparse user-item interactions. While contrastive learning has recently emerged as a promising solution to this issue, generating augmented views for contrastive learning through most existing random data augmentation methods often leads to the alteration of original semantic information. In this paper, we propose a novel framework, GDA4Rec (Generative Data Augmentation in graph contrastive learning for Recommendation) to generate high-quality augmented views and provide robust self-supervised signals. Specifically, we employ a noise generation module that leverages deep generative models to approximate the distribution of original data for data augmentation. Additionally, GDA4Rec further extracts an item complement matrix to characterize the latent correlations between items and provide additional self-supervised signals. Lastly, a joint objective that integrates recommendation, data augmentation and contrastive learning is used to enforce the model to learn more effective and informative embeddings. Extensive experiments are conducted on three public datasets to demonstrate the superiority of the model. The code is available at: https://github.com/MrYansong/GDA4Rec.

cs.IR↗

arXiv2020

EM-RBR: a reinforced framework for knowledge graph completion from reasoning perspective

Knowledge graph completion aims to predict the new links in given entities among the knowledge graph (KG). Most mainstream embedding methods focus on fact triplets contained in the given KG, however, ignoring the rich background information provided by logic rules driven from knowledge base implicitly. To solve this problem, in this paper, we propose a general framework, named EM-RBR(embedding and rule-based reasoning), capable of combining the advantages of reasoning based on rules and the state-of-the-art models of embedding. EM-RBR aims to utilize relational background knowledge contained in rules to conduct multi-relation reasoning link prediction rather than superficial vector triangle linkage in embedding models. By this way, we can explore relation between two entities in deeper context to achieve higher accuracy. In experiments, we demonstrate that EM-RBR achieves better performance compared with previous models on FB15k, WN18 and our new dataset FB15k-R, especially the new dataset where our model perform futher better than those state-of-the-arts. We make the implementation of EM-RBR available at https://github.com/1173710224/link-prediction-with-rule-based-reasoning.

cs.AI↗