Hyunjee Song | Searcharxiv

arXiv2026

Strain-tuned orbital-dependent electronic correlations in FeTe thin films

Iron chalcogenides exhibit rich phenomena which are governed by orbital-dependent electronic interactions and strong electronic correlation. In particular, many studies have explored orbital selectivity in FeTe through Se doping. Here, applying tensile strain to thin films allows us to precisely control the system without other impurities that may arise from chemical doping to investigate the emergent behaviors in FeTe. Using angle-resolved photoemission spectroscopy, we observe a spectral weight transfer between $d_{\rm xy}$ and $d_{\rm z^{2}}$ orbitals, evidence of an orbital-selective Mott phase (OSMP). Beyond OSMP, we reveal hitherto unobserved strain-induced effects, distinct from chemical doping. The evolution of $d_{\rm xz}$ orbital demonstrates how electron hopping mechanism plays an important role in defining the electronic properties of the system. Our findings highlight a direct correlation between epitaxial strain and the evolution of electronic structures in FeTe.

cond-mat.str-el↗

arXiv2025

SynRES: Towards Referring Expression Segmentation in the Wild via Synthetic Data

Despite the advances in Referring Expression Segmentation (RES) benchmarks, their evaluation protocols remain constrained, primarily focusing on either single targets with short queries (containing minimal attributes) or multiple targets from distinctly different queries on a single domain. This limitation significantly hinders the assessment of more complex reasoning capabilities in RES models. We introduce WildRES, a novel benchmark that incorporates long queries with diverse attributes and non-distinctive queries for multiple targets. This benchmark spans diverse application domains, including autonomous driving environments and robotic manipulation scenarios, thus enabling more rigorous evaluation of complex reasoning capabilities in real-world settings. Our analysis reveals that current RES models demonstrate substantial performance deterioration when evaluated on WildRES. To address this challenge, we introduce SynRES, an automated pipeline generating densely paired compositional synthetic training data through three innovations: (1) a dense caption-driven synthesis for attribute-rich image-mask-expression triplets, (2) reliable semantic alignment mechanisms rectifying caption-pseudo mask inconsistencies via Image-Text Aligned Grouping, and (3) domain-aware augmentations incorporating mosaic composition and superclass replacement to emphasize generalization ability and distinguishing attributes over object categories. Experimental results demonstrate that models trained with SynRES achieve state-of-the-art performance, improving gIoU by 2.0% on WildRES-ID and 3.8% on WildRES-DS. Code and datasets are available at https://github.com/UTLLab/SynRES.

cs.LG↗