arXiv subjects

Yingpeng Zhang

Publications and source records attributed to Yingpeng Zhang.

4 recordsLinked to original sources

arXiv2026

Magnetic-Free Quantum Interference and Universal Josephson Diode Effect Driven by a Supercurrent Gauge Field

The Josephson effect, a hallmark of superconducting phase coherence, drives modern quantum technologies. However, Josephson-based quantum interference has hitherto been tethered to magnetic fields, despite phase coherence being a quintessential, intrinsic trait of superconductivity. Moreover, the Josephson diode effect (JDE) is typically viewed as an anomalous phenomenon indicative of broken symmetries in exotic phases of matter. Here, in planar Josephson junctions made with $\mathrm{Bi}_2\mathrm{O}_2\mathrm{Se}$ and bilayer graphene, we demonstrate that the JDE is a missing universal property of the Josephson effect. Simultaneously, we present an all-electric technology that replaces magnetic flux for controlling and measuring supercurrent interference. Central to our approach is a supercurrent gauge field (SGF), generated and amplified through high-kinetic-inductance superconductors and novel device architectures. By establishing the physical equivalence between the SGF and a magnetic field, we eliminate the reliance on external fields in quantum interference and reveal a universal, field-free JDE mechanism with broad implications for detecting broken-symmetry states. Finally, we show that the SGF offers capabilities beyond those of a conventional magnetic field by experimentally demonstrating a magnetic-free, phase-sensitive technique to construct and characterize finite-momentum superconductivity, opening new frontiers for exploring novel phases of matter and superconducting quantum architectures.

cond-mat.supr-con↗

arXiv2025

Asymmetric quantum Hall effect and diminished $ν=0$ longitudinal resistance in graphene/InSe heterostructures

We investigate quantum transport in graphene/InSe heterostructures and find major asymmetries in the longitudinal resistance ($R_{xx}$) and vanishing $R_{xx}$ peaks at high magnetic fields, particularly at the charge-neutrality point. Our Landauer-Buttiker analysis and numerical simulations show that a monotonically varying density gradient combined with a full equilibration mechanism can explain these phenomena. Our results also suggest the presence of trivial long-range chiral edge current and offer a broadly applicable way to engineer transport properties in quantum Hall systems.

cond-mat.mes-hall↗

arXiv2024

GenesisTex2: Stable, Consistent and High-Quality Text-to-Texture Generation

Large-scale text-guided image diffusion models have shown astonishing results in text-to-image (T2I) generation. However, applying these models to synthesize textures for 3D geometries remains challenging due to the domain gap between 2D images and textures on a 3D surface. Early works that used a projecting-and-inpainting approach managed to preserve generation diversity but often resulted in noticeable artifacts and style inconsistencies. While recent methods have attempted to address these inconsistencies, they often introduce other issues, such as blurring, over-saturation, or over-smoothing. To overcome these challenges, we propose a novel text-to-texture synthesis framework that leverages pretrained diffusion models. We first introduce a local attention reweighing mechanism in the self-attention layers to guide the model in concentrating on spatial-correlated patches across different views, thereby enhancing local details while preserving cross-view consistency. Additionally, we propose a novel latent space merge pipeline, which further ensures consistency across different viewpoints without sacrificing too much diversity. Our method significantly outperforms existing state-of-the-art techniques regarding texture consistency and visual quality, while delivering results much faster than distillation-based methods. Importantly, our framework does not require additional training or fine-tuning, making it highly adaptable to a wide range of models available on public platforms.

cs.CV↗

arXiv2024

GenesisTex: Adapting Image Denoising Diffusion to Texture Space

We present GenesisTex, a novel method for synthesizing textures for 3D geometries from text descriptions. GenesisTex adapts the pretrained image diffusion model to texture space by texture space sampling. Specifically, we maintain a latent texture map for each viewpoint, which is updated with predicted noise on the rendering of the corresponding viewpoint. The sampled latent texture maps are then decoded into a final texture map. During the sampling process, we focus on both global and local consistency across multiple viewpoints: global consistency is achieved through the integration of style consistency mechanisms within the noise prediction network, and low-level consistency is achieved by dynamically aligning latent textures. Finally, we apply reference-based inpainting and img2img on denser views for texture refinement. Our approach overcomes the limitations of slow optimization in distillation-based methods and instability in inpainting-based methods. Experiments on meshes from various sources demonstrate that our method surpasses the baseline methods quantitatively and qualitatively.

cs.CV↗