arXiv subjects

Luying Li

Publications and source records attributed to Luying Li.

4 recordsLinked to original sources

arXiv2025

From Disorder to Function: Entropy-Engineered Broadband Photonics with Ion-Transport-Stabilized Spectral Fidelity

The high-entropy halide-perovskite field has expanded rapidly, yet a key gap remains: configurational entropy is not yet a reliable, designable lever to co-deliver expanded photonic functionality and operational robustness with a composition-transferable mechanistic basis. Here we develop entropy-engineered rare-earth halide double-perovskite single crystals, Cs2Na(Sb, RE)Cl6 (RE3+ = Sc3+, Er3+, Yb3+, Tm3+), that simultaneously expand near-infrared (NIR) functionality and establish a mechanistic stability rule. Near-equiatomic B(III)-site alloying yields a single-phase high-entropy solid solution (Delta_Sconfig about 1.6R). Sb3+ serves as a sensitizer that unifies excitation and cooperatively activates multiple lanthanide channels, transforming the parent single-mode response into a broadband NIR output (~850-1600 nm) with three spectrally orthogonal fingerprint bands at 996, 1220, and 1540 nm. This tri-peak, self-referenced output enables redundancy-based ratiometric solvent identification and quantitative mixture sensing with reduced susceptibility to intensity drift. Accelerated aging under humidity and oxygen shows improved phase and emission stability versus single-component analogues. DFT and molecular dynamics attribute the robustness to strongly suppressed RE$^{3+}$/Cl$^-$ self-diffusion despite comparable H$_2$O/O$_2$ adsorption, kinetically impeding ion-migration-assisted reconstruction and degradation. Integration into a phosphor-converted LED delivers spectrally stable, broadband NIR illumination, establishing entropy engineering as a practical handle to couple expanded photonic functionality with mechanistically accountable durability in metal-halide photonics.

physics.optics↗

arXiv2025

A$^\text{T}$A: Adaptive Transformation Agent for Text-Guided Subject-Position Variable Background Inpainting

Image inpainting aims to fill the missing region of an image. Recently, there has been a surge of interest in foreground-conditioned background inpainting, a sub-task that fills the background of an image while the foreground subject and associated text prompt are provided. Existing background inpainting methods typically strictly preserve the subject's original position from the source image, resulting in inconsistencies between the subject and the generated background. To address this challenge, we propose a new task, the "Text-Guided Subject-Position Variable Background Inpainting", which aims to dynamically adjust the subject position to achieve a harmonious relationship between the subject and the inpainted background, and propose the Adaptive Transformation Agent (A$^\text{T}$A) for this task. Firstly, we design a PosAgent Block that adaptively predicts an appropriate displacement based on given features to achieve variable subject-position. Secondly, we design the Reverse Displacement Transform (RDT) module, which arranges multiple PosAgent blocks in a reverse structure, to transform hierarchical feature maps from deep to shallow based on semantic information. Thirdly, we equip A$^\text{T}$A with a Position Switch Embedding to control whether the subject's position in the generated image is adaptively predicted or fixed. Extensive comparative experiments validate the effectiveness of our A$^\text{T}$A approach, which not only demonstrates superior inpainting capabilities in subject-position variable inpainting, but also ensures good performance on subject-position fixed inpainting.

cs.CV↗

arXiv2022

CtlGAN: Few-shot Artistic Portraits Generation with Contrastive Transfer Learning

Generating artistic portraits is a challenging problem in computer vision. Existing portrait stylization models that generate good quality results are based on Image-to-Image Translation and require abundant data from both source and target domains. However, without enough data, these methods would result in overfitting. In this work, we propose CtlGAN, a new few-shot artistic portraits generation model with a novel contrastive transfer learning strategy. We adapt a pretrained StyleGAN in the source domain to a target artistic domain with no more than 10 artistic faces. To reduce overfitting to the few training examples, we introduce a novel Cross-Domain Triplet loss which explicitly encourages the target instances generated from different latent codes to be distinguishable. We propose a new encoder which embeds real faces into Z+ space and proposes a dual-path training strategy to better cope with the adapted decoder and eliminate the artifacts. Extensive qualitative, quantitative comparisons and a user study show our method significantly outperforms state-of-the-arts under 10-shot and 1-shot settings and generates high quality artistic portraits. The code will be made publicly available.

cs.CV↗

arXiv2021

Constant Tamagawa numbers of special elliptic curves

For the elliptic curves $E_{σ2D} : y^2 = x^3 + σ2Dx$ , which has 2-isogeny curve $E'_{σ2D} : y^2 = x^3 -σ8Dx$, $σ= \pm 1,\ D = p_1^{e_1}p_2^{e_2}\cdots p_n^{e_n}$, where $p_i$ are different odd prime numbers and $e_i = 1 \text{ or } 3$, we demonstrate that Tamagawa numbers of these elliptic curves are always one or zero by the use of matrix in finite field $\mathbb F_2$. The specific number depends on the value of $σ$. By our proofs of these results, we find a method to quickly sieve a part of the elliptic curves with Mordell-Weil rank zero or rank one in this form as an application.

math.NT↗