arXiv subjects

Xiaomeng Cui

Publications and source records attributed to Xiaomeng Cui.

7 recordsLinked to original sources

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

Visualizing a Terahertz Superfluid Plasmon in a Two-Dimensional Superconductor

The superconducting gap defines the fundamental energy scale for the emergence of dissipationless transport and collective phenomena in a superconductor. In layered high-temperature cuprate superconductors, where the Cooper pairs are confined to weakly coupled two-dimensional copper-oxygen planes, terahertz (THz) spectroscopy at sub-gap millielectronvolt energies has provided crucial insights into the collective superfluid response perpendicular to the superconducting layers. However, within the copper-oxygen planes the collective superfluid response manifests as plasmonic charge oscillations at energies far exceeding the superconducting gap, obscured by strong dissipation. Here, we present spectroscopic evidence of a below-gap, two-dimensional superfluid plasmon in few-layer Bi2Sr2CaCu2O8+x and spatially resolve its deeply sub-diffractive THz electrodynamics. By placing the superconductor in the near-field of a spintronic THz emitter, we reveal this distinct resonance-absent in bulk samples and observed only in the superconducting phase-and determine its plasmonic nature by mapping the geometric anisotropy and dispersion. Crucially, these measurements offer a direct view of the momentum- and frequency dependent superconducting transition in two dimensions. These results establish a new platform for investigating superfluid phenomena at finite momenta and THz frequencies, highlighting the potential to engineer and visualize superconducting devices operating at ultrafast THz rates.

cond-mat.supr-con↗

arXiv2023

On model selection criteria for climate change impact studies

Climate change impact studies inform policymakers on the estimated damages of future climate change on economic, health and other outcomes. In most studies, an annual outcome variable is observed, e.g. agricultural yield, along with a higher-frequency regressor, e.g. daily temperature. Applied researchers then face a problem of selecting a model to characterize the nonlinear relationship between the outcome and the high-frequency regressor to make a policy recommendation based on the model-implied damage function. We show that existing model selection criteria are only suitable for the policy objective if one of the models under consideration nests the true model. If all models are seen as imperfect approximations to the true nonlinear relationship, the model that performs well in the normal climate conditions is not guaranteed to perform well at the projected climate that is different from the historical norm. We therefore propose a new criterion, the proximity-weighted mean-squared error (PWMSE), that directly targets precision of the damage function at the projected future climate. To make this criterion feasible, we assign higher weights to prior years that can serve as weather analogs to the projected future climate when evaluating competing models using the PWMSE. We show that our approach selects the best approximate regression model that has the smallest weighted error of predicted impacts for a projected future climate. A simulation study and an application revisiting the impact of climate change on agricultural production illustrate the empirical relevance of our theoretical analysis.

stat.ME↗

arXiv2021

Emergent Interfacial Superconductivity between Twisted Cuprate Superconductors

Twisted interfaces between stacked van der Waals cuprate crystals enable tunable Josephson coupling between in-plane anisotropic superconducting order parameters. Employing a novel cryogenic assembly technique, we fabricate Josephson junctions with an atomically sharp twisted interface between Bi2Sr2CaCu2O8+x crystals. The Josephson critical current density sensitively depends on the twist angle, reaching the maximum value comparable to that of the intrinsic junctions at small twisting angles, and is suppressed by almost 2 orders of magnitude yet remains finite close to 45 degree twist angle. Through the observation of fractional Shapiro steps and the analysis of Fraunhofer patterns we show that the remaining superconducting coherence near 45 degree is due to the co-tunneling of Cooper pairs, a necessary ingredient for high-temperature topological superconductivity.

cond-mat.supr-con↗

arXiv2021

Josephson effects in twisted nodal superconductors

Motivated by the recent proposals for unconventional emergent physics in twisted bilayers of nodal superconductors, we study the peculiarities of the Josephson effect at the twisted interface between $d$-wave superconductors. We demonstrate that for clean interfaces with a twist angle $\theta_0$ in the range $0^\circ<\theta_0<45^\circ$ the critical current can exhibit nonmonotonic temperature dependence with a maximum at a nonzero temperature as well as a complex dependence on the twist angle at low temperatures. The former is shown to arise quite generically due to the contributions of the momenta around the gap nodes, which are negative for nonzero twist angles. It is demonstrated that these features reflect the geometry of the Fermi surface and are sensitive to the form of the momentum dependence of the tunneling at the twisted interface. Close to $\theta_0=45^\circ$ we find that the critical current does not vanish due to Cooper pair cotunneling, which leads to a transition to a time-reversal breaking topological superconducting $d+id$ phase. Weak interface roughness, quasiperiodicity, and inhomogeneity broaden the momentum dependence of the interlayer tunneling leading to a critical current $I_c\sim \cos(2\theta_0)$ with $\cos(6\theta_0)$ corrections. Furthermore, strong disorder at the interface is demonstrated to suppress the time-reversal breaking superconducting phase near $\theta_0=45^\circ$. Last, we provide a comprehensive theoretical analysis of experiments that can reveal the full current-phase relation for twisted superconductors close to $\theta_0=45^\circ$. In particular, we demonstrate the emergence of the Fraunhofer interference pattern near $\theta_0=45^\circ$, while accounting for realistic sample geometries, and show that its temperature dependence can yield unambiguous evidence of Cooper pair cotunneling, necessary for topological superconductivity.

cond-mat.supr-con↗

arXiv2020

On-chip terahertz modulation and emission with integrated graphene junctions

The efficient modulation and control of ultrafast signals on-chip is of central importance in terahertz (THz) communications and a promising route toward sub-diffraction limit THz spectroscopy. Two-dimensional (2D) materials may provide a platform for these endeavors. We explore this potential, integrating high-quality graphene p-n junctions within two types of planar transmission line circuits to modulate and emit picosecond pulses. In a coplanar stripline geometry, we demonstrate electrical modulation of THz signal transmission by 95%. In a Goubau waveguide geometry, we achieve complete gate-tunable control over THz emission from a photoexcited graphene junction. These studies inform the development of on-chip signal manipulation and highlight prospects for 2D materials in THz applications.

cond-mat.mes-hall↗

arXiv2019

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

cs.CV↗