arXiv subjects

Shuyue Feng

Publications and source records attributed to Shuyue Feng.

5 recordsLinked to original sources

arXiv2026

Poincar\'e Sphere Representation of Spin-Driven Polarization Encoding in Two-Dimensional Perovskite Light Sources

Nonlinear optical light sources enable the generation of photons with polarization states that are intrinsically determined by underlying material dynamics, rather than imposed through external modulation. Here, we investigate the fundamental quantum communication performance achievable using four-wave-mixing signal fields emitted by a representative two-dimensional perovskite system. The experimentally reconstructed signal field is represented by Stokes-vector trajectories on the Poincar\'e sphere to establish a practical framework for visualizing spin-driven polarization encoding. An empirical nonlinear response model further connects the properties of the signal field to microscopic exciton and biexciton electronic structure, revealing that interference between resonantly enhanced optical transitions governs the accessible polarization states. The model additionally predicts that modest stabilization of the lowest-energy biexciton could substantially improve the polarization-encoding performance and provide a route toward materials optimization. More broadly, these results motivate closer integration of nonlinear spectroscopy, semiconductor materials, and quantum information science in the development of novel light sources for quantum communication.

quant-ph↗

arXiv2026

TopoStyle: Supporting Iterative Design with Generative AI for 2.5D Topology Optimization

Topology optimization(TO) is widely used in engineering because of its ability to save material and optimize structural performance. Although prior work has explored 2D human-centered design tool for TO, the results are often limited in variety and offer weak customizability. Meanwhile, due to the high computational and time costs of TO, researchers have attempted to address these issues using generative AI; however, such methods often provide limited interactivity. In addition, topology optimization in many cases needs to balance structural performance and aesthetic qualities through iterative design, a perspective that has rarely been emphasized in traditional TO. We present TopoStyle, an iterative design tool for 2.5D topology optimization using a 2D diffusion model. We explore two interaction methods. The first exports 3D parts to a graphical interface for hand-drawn interaction. The second enables direct interaction within 3D modeling software using points. Our tool also supports the use of masks to apply topology optimization to specific regions, allowing users to address customized design needs. We compare and evaluate both performance and interaction methods, and investigate how TopoStyle can balance performance and aesthetics while improving design efficiency through customization and iterative design. Finally, we demonstrate the application scenarios of TopoStyle through several design cases.

cs.HC↗

arXiv2026

Sketch2Topo: Using Hand-Drawn Inputs for Diffusion-Based Topology Optimization

Topology optimization (TO) is employed in engineering to optimize structural performance while maximizing material efficiency. However, traditional TO methods incur significant computational and time costs. Although research has leveraged generative AI to predict TO outcomes and validated feasibility and accuracy, existing approaches still suffer from limited customizability and impose a high cognitive load on users. Furthermore, balancing structural performance with aesthetic attributes remains a persistent challenge. We developed Sketch2Topo, which augments a diffusion-based TO model with image-to-image generation and image editing capabilities. With Sketch2Topo, users can use sketching to customize geometries and specify physical constraints. The tool also supports mask input, enabling users to perform TO on selected regions only, thereby supporting higher levels of customization. We summarize the workflow and details of the tool and conduct a brief quantitative evaluation. Finally, we explore application scenarios and discuss how hand-drawn input improves usability while balancing functionality and aesthetics.

cs.HC↗

arXiv2025

Nonlinear Optical Quantum Communication with a Two-Dimensional Perovskite Light Source

Two-dimensional organic-inorganic hybrid perovskite (2D-OIHP) quantum wells are emerging as promising light sources for quantum communication technologies, owing to their ability to generate polarization-encoded optical signals. In this work, we explore how nonlinear optical phenomena can be exploited for quantum information applications, demonstrating the versatility that arises from resonant coupling among excited states. By tracking changes in the ellipticities of signal photons on femtosecond timescales in four-wave-mixing experiments, we first establish a method for information encoding based on exciton spin dynamics and biexciton correlations. Using single-photon detection, we then implement a proof-of-principle quantum communication protocol by mapping these polarization states onto binary sequences. While the polarizations of weak coherent pulses are typically manipulated with optical elements in traditional quantum key distribution approaches, the intrinsic electronic structure and spin relaxation processes within the 2D-OIHP system determine the characteristics of the signal photons in our method. As a demonstration, an ASCII message consisting of 56 bits is transmitted through the polarization states of photons emitted by 2D-OIHP quantum wells. These results show that the information transmission efficiency depends strongly on contributions from biexciton states, highlighting the potential of spin-dependent nonlinear optical processes for quantum communication.

quant-ph↗

arXiv2025

TactStyle: Generating Tactile Textures with Generative AI for Digital Fabrication

Recent work in Generative AI enables the stylization of 3D models based on image prompts. However, these methods do not incorporate tactile information, leading to designs that lack the expected tactile properties. We present TactStyle, a system that allows creators to stylize 3D models with images while incorporating the expected tactile properties. TactStyle accomplishes this using a modified image-generation model fine-tuned to generate heightfields for given surface textures. By optimizing 3D model surfaces to embody a generated texture, TactStyle creates models that match the desired style and replicate the tactile experience. We utilize a large-scale dataset of textures to train our texture generation model. In a psychophysical experiment, we evaluate the tactile qualities of a set of 3D-printed original textures and TactStyle's generated textures. Our results show that TactStyle successfully generates a wide range of tactile features from a single image input, enabling a novel approach to haptic design.

cs.HC↗