arXiv subjects

Ziqiang Guan

Publications and source records attributed to Ziqiang Guan.

6 recordsLinked to original sources

arXiv2026

Layer Architecture Shapes Electronic, Magnetic, and Lattice Interactions in Ruddlesden-Popper Nickelates

The discovery of superconductivity in Ruddlesden-Popper nickelates has raised a central question: how does layer architecture shape the electronic, magnetic, and lattice interactions relevant to pairing? Here, we report a detailed comparative study of the two polymorphs of La3Ni2O7--the alternating monolayer-trilayer (LNO-1313) and bilayer (LNO-2222) structures--and the related trilayer compound La4Ni3O10, using both Ni L3- and O K-edge RIXS. We find that LNO-1313 and La4Ni3O10 share strikingly similar electronic, magnetic, and lattice excitations, whereas bilayer LNO-2222 exhibits distinct features. Compared to LNO-2222, LNO-1313 and La4Ni3O10 have weaker orbital polarization, enhanced 3d8L character, a reduced out-of-plane magnetic-exchange scale, and stronger EPC. Within an effective local-moment framework, an entangled-dimer scenario provides a natural description of the spin excitations generated by strong antiferromagnetic interlayer coupling. Its advantage over conventional spin-wave theory is clearest in bilayer LNO-2222, where the interlayer coupling dominates the intralayer interactions. These findings provide critical experimental constraints for future theoretical models for the low-energy physics relevant to superconductivity in these layered nickelates.

cond-mat.str-el↗

arXiv2026

Electronic layer decoupling driven by density-wave order in La$_4$Ni$_3$O$_{10}$

We probe the density-wave transition of the trilayer nickelate La$_4$Ni$_3$O$_{10}$ with polarization-resolved infrared spectroscopy. The low-energy electrodynamics is strongly anisotropic, with metallic in-plane and insulating out-of-plane character. In the ordered phase, the anisotropy grows more than an order of magnitude as the out-of-plane conductivity is sharply suppressed. We interpret this enhancement as an effective electronic decoupling of the Ni-O layers, driven by a spin-density-wave-induced redistribution of Ni-$d_{z^2}$ occupation within the trilayers. This electronic response is accompanied by clear shifts and splittings of the out-of-plane phonons, compatible with a density-wave instability of electronic origin.

cond-mat.str-el↗

arXiv2025

CRAG-MM: Multi-modal Multi-turn Comprehensive RAG Benchmark

Wearable devices such as smart glasses are transforming the way people interact with their surroundings, enabling users to seek information regarding entities in their view. Multi-Modal Retrieval-Augmented Generation (MM-RAG) plays a key role in supporting such questions, yet there is still no comprehensive benchmark for this task, especially regarding wearables scenarios. To fill this gap, we present CRAG-MM -- a Comprehensive RAG benchmark for Multi-modal Multi-turn conversations. CRAG-MM contains a diverse set of 6.5K (image, question, answer) triplets and 2K visual-based multi-turn conversations across 13 domains, including 6.2K egocentric images designed to mimic captures from wearable devices. We carefully constructed the questions to reflect real-world scenarios and challenges, including five types of image-quality issues, six question types, varying entity popularity, differing information dynamism, and different conversation turns. We design three tasks: single-source augmentation, multi-source augmentation, and multi-turn conversations -- each paired with an associated retrieval corpus and APIs for both image-KG retrieval and webpage retrieval. Our evaluation shows that straightforward RAG approaches achieve only 32% and 43% truthfulness on CRAG-MM single- and multi-turn QA, respectively, whereas state-of-the-art industry solutions have similar quality (32%/45%), underscoring ample room for improvement. The benchmark has hosted KDD Cup 2025, attracting about 1K participants and 5K submissions, with winning solutions improving baseline performance by 28%, highlighting its early impact on advancing the field.

cs.CV↗

arXiv2025

Beyond-Hubbard pairing in a cuprate ladder

The Hubbard model is believed to capture the essential physics of cuprate superconductors. However, recent theoretical studies suggest that it fails to reproduce a robust and homogeneous superconducting ground state. Here, using resonant inelastic x-ray scattering and density matrix renormalization group calculations, we show that magnetic excitations in the prototypical cuprate ladder Sr$_{14}$Cu$_{24}$O$_{41}$ are inconsistent with those of a simple Hubbard model. The magnetic response of hole carriers, contributing to an emergent branch of spin excitations, is strongly suppressed. This effect is the consequence of d-wave-like pairing, enhanced by nearly an order of magnitude through a large nearest-neighbor attractive interaction. The similarity between cuprate ladders and the two-dimensional compounds suggests that such an enhanced hole pairing may be a universal feature of superconducting cuprates.

cond-mat.str-el↗

arXiv2019

Alzheimer's Disease Brain MRI Classification: Challenges and Insights

In recent years, many papers have reported state-of-the-art performance on Alzheimer's Disease classification with MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset using convolutional neural networks. However, we discover that when we split that data into training and testing sets at the subject level, we are not able to obtain similar performance, bringing the validity of many of the previous studies into question. Furthermore, we point out that previous works use different subsets of the ADNI data, making comparison across similar works tricky. In this study, we present the results of three splitting methods, discuss the motivations behind their validity, and report our results using all of the available subjects.

eess.IV↗

arXiv2019

A Comprehensive Study of Alzheimer's Disease Classification Using Convolutional Neural Networks

A plethora of deep learning models have been developed for the task of Alzheimer's disease classification from brain MRI scans. Many of these models report high performance, achieving three-class classification accuracy of up to 95%. However, it is common for these studies to draw performance comparisons between models that are trained on different subsets of a dataset or use varying imaging preprocessing techniques, making it difficult to objectively assess model performance. Furthermore, many of these works do not provide details such as hyperparameters, the specific MRI scans used, or their source code, making it difficult to replicate their experiments. To address these concerns, we present a comprehensive study of some of the deep learning methods and architectures on the full set of images available from ADNI. We find that, (1) classification using 3D models gives an improvement of 1% in our setup, at the cost of significantly longer training time and more computation power, (2) with our dataset, pre-training yields minimal ($<0.5\%$) improvement in model performance, (3) most popular convolutional neural network models yield similar performance when compared to each other. Lastly, we briefly compare the effects of two image preprocessing programs: FreeSurfer and Clinica, and find that the spatially normalized and segmented outputs from Clinica increased the accuracy of model prediction from 63% to 89% when compared to FreeSurfer images.

cs.CV↗