arXiv subjects

Haohao Xu

Publications and source records attributed to Haohao Xu.

2 recordsLinked to original sources

arXiv2026

Realization of Air-Stable Two-Dimensional Superconductor Nb2Pd3Te5 With Quasi-One-Dimensional Pair Density Modulation

Two-dimensional (2D) superconductors provide a fertile platform for exploring reduced-dimensional superconductivity and emergent quantum phenomena. Incorporating quasi-one-dimensional (quasi-1D) structural motifs into 2D superconductors offers a powerful route to engineer strong electronic anisotropy, enabling unconventional superconducting states and anisotropic superconducting transport functionalities. However, such systems remain rarely realized. Here we report the realization of a 2D superconductor Nb2Pd3Te5, exhibiting an intrinsic quasi-1D pair density modulation. Monolayer and bilayer Nb2Pd3Te5 is synthesized via van-der-Waals epitaxy. Using ultralow-temperature scanning tunneling microscopy/spectroscopy, we observe the quasi-1D crystal structure and superconductivity below ~0.6 K with a pronounced quasi-1D pair density modulation. Remarkably, both monolayer and bilayer Nb2Pd3Te5 show strong air stability. Our findings establish atomically 2D Nb2Pd3Te5 as a robust and promising platform for exploring novel low-dimensional quantum phenomena and anisotropy-enabled superconducting devices.

cond-mat.mtrl-sci↗

arXiv2026

Delta-K: Boosting Multi-Instance Generation via Cross-Attention Augmentation

While Diffusion Models excel in text-to-image synthesis, they often suffer from concept omission when synthesizing complex multi-instance scenes. Existing training-free methods attempt to resolve this by rescaling attention maps, which merely exacerbates unstructured noise without establishing coherent semantic representations. To address this, we propose Delta-K, a backbone-agnostic and plug-and-play inference framework that tackles omission by operating directly in the shared cross-attention Key space. Specifically, with Vision-language model, we extract a differential key $ΔK$ that encodes the semantic signature of missing concepts. This signal is then injected during the early semantic planning stage of the diffusion process. Governed by a dynamically optimized scheduling mechanism, Delta-K grounds diffuse noise into stable structural anchors while preserving existing concepts. Extensive experiments demonstrate the generality of our approach: Delta-K consistently improves compositional alignment across both modern DiT models and classical U-Net architectures, without requiring spatial masks, additional training, or architectural modifications.

cs.CV↗