arXiv subjects

Qiao Chen

Publications and source records attributed to Qiao Chen.

At least 19 recordsLinked to original sources

arXiv2026

A Symmetric Superconducting Dome Hosts Non-Fermi Liquid Behavior at Optimal Doping in MoS2

The similarities between the phase diagrams of ionic liquid-gated transition metal dichalcogenides (TMDCs) and high-temperature superconductors have garnered considerable interest due to the presence of a superconducting dome with a non-monotonic dependence of the superconducting order parameter as a function of charge carrier density. However, the lack of a complete superconducting dome and insights into the normal state in ionic liquid-gated TMDCs prevents a detailed mapping between the two systems. In this work, we obtain a symmetric superconducting dome that extends from deep underdoped regime all the way to deep overdoped regime in ionic liquid gated MoS2 with a refined gating protocol. We demonstrate that the dome is anticorrelated with the evolution of non-Fermi liquid behavior in the normal states. The scattering rate in the non-Fermi liquid regime can reach Planckian limit. The results may shed light on the emergence of superconductivity in TMDCs.

cond-mat.supr-con↗

arXiv2025

Multi-modal Knowledge Decomposition based Online Distillation for Biomarker Prediction in Breast Cancer Histopathology

Immunohistochemical (IHC) biomarker prediction benefits from multi-modal data fusion analysis. However, the simultaneous acquisition of multi-modal data, such as genomic and pathological information, is often challenging due to cost or technical limitations. To address this challenge, we propose an online distillation approach based on Multi-modal Knowledge Decomposition (MKD) to enhance IHC biomarker prediction in haematoxylin and eosin (H\&E) stained histopathology images. This method leverages paired genomic-pathology data during training while enabling inference using either pathology slides alone or both modalities. Two teacher and one student models are developed to extract modality-specific and modality-general features by minimizing the MKD loss. To maintain the internal structural relationships between samples, Similarity-preserving Knowledge Distillation (SKD) is applied. Additionally, Collaborative Learning for Online Distillation (CLOD) facilitates mutual learning between teacher and student models, encouraging diverse and complementary learning dynamics. Experiments on the TCGA-BRCA and in-house QHSU datasets demonstrate that our approach achieves superior performance in IHC biomarker prediction using uni-modal data. Our code is available at https://github.com/qiyuanzz/MICCAI2025_MKD.

cs.CV↗

arXiv2025

Frequency-Driven Inverse Kernel Prediction for Single Image Defocus Deblurring

Single image defocus deblurring aims to recover an all-in-focus image from a defocus counterpart, where accurately modeling spatially varying blur kernels remains a key challenge. Most existing methods rely on spatial features for kernel estimation, but their performance degrades in severely blurry regions where local high-frequency details are missing. To address this, we propose a Frequency-Driven Inverse Kernel Prediction network (FDIKP) that incorporates frequency-domain representations to enhance structural identifiability in kernel modeling. Given the superior discriminative capability of the frequency domain for blur modeling, we design a Dual-Branch Inverse Kernel Prediction (DIKP) strategy that improves the accuracy of kernel estimation while maintaining stability. Moreover, considering the limited number of predicted inverse kernels, we introduce a Position Adaptive Convolution (PAC) to enhance the adaptability of the deconvolution process. Finally, we propose a Dual-Domain Scale Recurrent Module (DSRM) to fuse deconvolution results and progressively improve deblurring quality from coarse to fine. Extensive experiments demonstrate that our method outperforms existing approaches. Code will be made publicly available.

cs.CV↗

arXiv2025

Dual-mode superconducting diode effect enabled by in-plane and out-of-plane magnetic field

The discovery of the superconducting diode effect (SDE) has been cherished as a milestone in developing superconducting electronics. Tremendous efforts are being dedicated to realizing SDE in a wide variety of material platforms. Despite the diversity in the hosting materials and device designs, SDE is usually operated in a single mode which is enabled by either out-of-plane or in-plane magnetic field/magnetization. In this work, we report the realization of a dual-mode SDE in 2H-$\mathrm{NbS_2}$/2H-$\mathrm{NbSe_2}$ heterostructures where both the out-of-plane magnetic field $B_{\perp}$ and in-plane magnetic field $B_{||}$ can independently generate and manipulate SDE. The two modes share similar diode efficiency but differ in two aspects: 1. $B_{\perp}$-induced SDE is activated by a field on the order of 1 mT while $B_{||}$-induced SDE requires a field on the order of 100 mT; 2. $η$ of $B_{\perp}$-induced SDE exhibits a square-root like temperature dependence while $η$ of $B_{||}$-induced SDE takes a more linear-like one. We demonstrate that the dual-mode SDE is most likely a result of mirror symmetry breaking along multiple orientations. Thanks to the two orders difference in the operational field for the two modes, we propose a dual-functionality device scheme to showcase the potential of the dual-mode SDE in realizing advanced superconducting architecture, where fast polarity-switching functionality is implemented with $B_{\perp}$-induced SDE and high-fidelity functionality is enabled with $B_{\perp}$-induced SDE.

cond-mat.supr-con↗

arXiv2025

Evolution of quantum criticality in underdoped cuprates

Quantum criticality, with both static and dynamic information of the system intrinsically encoded in characteristic length scales, serves as one of the most sensitive and universal probes to monitor quantum phase transition. Qualitatively different quantum criticality behaviours have been widely observed even in the same condensed matter system. The discrepancy is attributed to sample specificity but has not been systemically addressed. Here we report a single-parameter driven three-stage evolution of quantum criticality unveiled in superconductor-insulator transition in underdoped Bi2Sr2CaCu2O8+δ flakes. The evolution starts with a single quantum critical point emerging at the boundary between the superconducting and antiferromagnetic phases, then evolving into anomalous quantum Griffiths singularity at the medium doping levels and eventually being replaced by quantum Griffiths singularity in the deep superconducting regime. A puddle model that incorporates the developments of antiferromagnetic correlation can capture the evolution. The results offer a new aspect to examine previous seemingly sample-specific quantum critical behavior and lay the foundation for further exploring complex quantum criticality in strongly correlated systems; meanwhile they shed light on the detailed interaction between superconductivity and antiferromagnetism in cuprates.

cond-mat.supr-con↗

arXiv2025

Enhanced Backgate Tunability on Interfacial Carrier Concentration in Ionic Liquid-Gated MoS2 Devices

The periodic spatial modulation potential arising from the zig-zag distribution of ions at large gate voltage in an ionic liquid gated device may enable functionalities in a similar way as nanopatterning and moiré engineering. However, the inherent coupling between periodic modulation potential and carrier concentration in ionic liquid devices has hindered further exploration. Here, we demonstrate the feasibility of decoupling manipulation on periodic modulation potential and carrier density in an ionic liquid device by using a conventional backgate. The backgate is found to have a tunability on carrier concentration comparable to that of ionic gating, especially at large ionic liquid gate voltage, by activating the bulk channels mediated back tunneling between the trapped bands and interfacial channel.

cond-mat.mes-hall↗

arXiv2025

Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis

We introduce a new method to jointly reduce the dimension of the input and output space of a function between high-dimensional spaces. Choosing a reduced input subspace influences which output subspace is relevant and vice versa. Conventional methods focus on reducing either the input or output space, even though both are often reduced simultaneously in practice. Our coupled approach naturally supports goal-oriented dimension reduction, where either an input or output quantity of interest is prescribed. We consider, in particular, goal-oriented sensor placement and goal-oriented sensitivity analysis, which can be viewed as dimension reduction where the most important output or, respectively, input components are chosen. Both applications present difficult combinatorial optimization problems with expensive objectives such as the expected information gain and Sobol' indices. By optimizing gradient-based bounds, we can determine the most informative sensors and most influential parameters as the largest diagonal entries of some diagnostic matrices, thus bypassing the combinatorial optimization and objective evaluation.

stat.ML↗

arXiv2024

Ba6RE2Ti4O17 (RE= Nd, Sm,Gd, Dy-Yb): A family of Rare-earth based layered triangular lattice magnets

Rare-earth-based triangular-lattice magnets provide the fertile ground to explore the exotic quantum magnetic state. Herein, we report a new family of RE-based triangular-lattice magnets Ba6RE2Ti4O17(RE= rare earth ions) crystallized into the hexagonal structure with space group of P63 mmc, where magnetic rare earth ions form an ideal triangular lattice within the ab-plane and stack in an AA -type fashion along the c-axis. The low-temperature magnetic susceptibility results reveal all the serial compounds have the dominant antiferromagnetic interactions and an absence of magnetic ordering down to 1.8 K. The magnetization and electron spin resonance results indicate distinct magnetic anisotropy for the compounds with different RE ions. Moreover, Ba6Nd2Ti4O17 single crystal is successfully grown and it exhibits strong Ising like anisotropy with magnetic easy-axis perpendicular to the triangle-lattice plane, being a candidate to explore quantum spin liquid state with dominant Ising-type interaction.

cond-mat.mtrl-sci↗

arXiv2023

Robust Discontinuity Indicators for High-Order Reconstruction of Piecewise Smooth Functions

In many applications, piecewise continuous functions are commonly interpolated over meshes. However, accurate high-order manipulations of such functions can be challenging due to potential spurious oscillations known as the Gibbs phenomena. To address this challenge, we propose a novel approach, Robust Discontinuity Indicators (RDI), which can efficiently and reliably detect both C^{0} and C^{1} discontinuities for node-based and cell-averaged values. We present a detailed analysis focusing on its derivation and the dual-thresholding strategy. A key advantage of RDI is its ability to handle potential inaccuracies associated with detecting discontinuities on non-uniform meshes, thanks to its innovative discontinuity indicators. We also extend the applicability of RDI to handle general surfaces with boundaries, features, and ridge points, thereby enhancing its versatility and usefulness in various scenarios. To demonstrate the robustness of RDI, we conduct a series of experiments on non-uniform meshes and general surfaces, and compare its performance with some alternative methods. By addressing the challenges posed by the Gibbs phenomena and providing reliable detection of discontinuities, RDI opens up possibilities for improved approximation and analysis of piecewise continuous functions, such as in data remap.

math.NA↗

arXiv2023

Exploring the interfacial coupling between graphene and the antiferromagnetic insulator MnPSe$_3$

Interfacial coupling between graphene and other 2D materials can give rise to intriguing physical phenomena. In particular, several theoretical studies predict that the interplay between graphene and an antiferromagnetic insulator could lead to the emergence of quantum anomalous Hall phases. However, such phases have not been observed experimentally yet, and further experimental studies are needed to reveal the interaction between graphene and antiferromagnetic insulators. Here, we report the study in heterostructures composed of graphene and the antiferromagnetic insulator MnPSe$_3$. It is found that the MnPSe$_3$ has little impact on the quantum Hall phases apart from doping graphene via interfacial charge transfer. However, the magnetic order can contribute indirectly via process like Kondo effect, as evidenced by the observed minimum in the temperature-resistance curve between 20-40 K, far below the Néel temperature (70 K).

cond-mat.mtrl-sci↗

arXiv2022

Vortex-driven periodic and aperiodic magnetoresistance oscillations in cuprates

The study of the interaction between superconductivity and charge ordering is helpful to resolve the pairing mechanism in high-temperature superconductors. Recently, several resistance oscillations studies trigger the speculation that a long-range charge ordering, with an enormous mesh size of several tens of nanometer, can possibly emerge in underdoped high Tc superconductor. However, spectroscopy studies have not traced this kind of long-range charge ordering. Here, we clarify the disagreement between the transport and spectroscopy studies on the mysterious long-range charge ordering by investigating the magneto-oscillations in underdoped Bi2Sr2CaCu2O8+δ flakes. Inspired by the observation that the oscillations evolve from a periodic to an aperiodic one with decreasing doping level, we conclude that the magneto-oscillations can be generated by the interaction between vortices and superconducting loops that enclose randomly distributed underdoped puddles while an assumption of long-range charge ordering is not necessary.

cond-mat.supr-con↗

arXiv2022

Josephson Effect in NbS$_{2}$ van der Waals Junctions

Van der Waals (vdW) Josephson junctions can possibly accelerate the development of advanced superconducting device that utilizes the unique properties of two-dimensional (2D) transition metal dichalcogenide (TMD) superconductors such as spin-orbit coupling, spin-valley locking. Here, we fabricate vertically stacked NbS$_{2}$/NbS$_{2}$ Josephson junctions using a modified all-dry transfer technique and characterize the device performance via systematic low-temperature transport measurements. The experimental results show that the superconducting transition temperature of the NbS$_{2}$/NbS$_{2}$ Josephson junction is 5.84 K, and the critical current density reaches 3975 A/cm$^{2}$ at 2K. Moreover, we extract a superconducting energy gap $Δ=0.58$ meV, which is considerably smaller than that expected from the single band s-wave Bardeen-Cooper-Schrieffer (BCS) model ($Δ=0.89$ meV).

cond-mat.supr-con↗

arXiv2022

End-to-End Multi-View Structure-from-Motion with Hypercorrelation Volumes

Image-based 3D reconstruction is one of the most important tasks in Computer Vision with many solutions proposed over the last few decades. The objective is to extract metric information i.e. the geometry of scene objects directly from images. These can then be used in a wide range of applications such as film, games, virtual reality, etc. Recently, deep learning techniques have been proposed to tackle this problem. They rely on training on vast amounts of data to learn to associate features between images through deep convolutional neural networks and have been shown to outperform traditional procedural techniques. In this paper, we improve on the state-of-the-art two-view structure-from-motion(SfM) approach of [11] by incorporating 4D correlation volume for more accurate feature matching and reconstruction. Furthermore, we extend it to the general multi-view case and evaluate it on the complex benchmark dataset DTU [4]. Quantitative evaluations and comparisons with state-of-the-art multi-view 3D reconstruction methods demonstrate its superiority in terms of the accuracy of reconstructions.

cs.CV↗

arXiv2022

Motion Estimation for Large Displacements and Deformations

Large displacement optical flow is an integral part of many computer vision tasks. Variational optical flow techniques based on a coarse-to-fine scheme interpolate sparse matches and locally optimize an energy model conditioned on colour, gradient and smoothness, making them sensitive to noise in the sparse matches, deformations, and arbitrarily large displacements. This paper addresses this problem and presents HybridFlow, a variational motion estimation framework for large displacements and deformations. A multi-scale hybrid matching approach is performed on the image pairs. Coarse-scale clusters formed by classifying pixels according to their feature descriptors are matched using the clusters' context descriptors. We apply a multi-scale graph matching on the finer-scale superpixels contained within each matched pair of coarse-scale clusters. Small clusters that cannot be further subdivided are matched using localized feature matching. Together, these initial matches form the flow, which is propagated by an edge-preserving interpolation and variational refinement. Our approach does not require training and is robust to substantial displacements and rigid and non-rigid transformations due to motion in the scene, making it ideal for large-scale imagery such as Wide-Area Motion Imagery (WAMI). More notably, HybridFlow works on directed graphs of arbitrary topology representing perceptual groups, which improves motion estimation in the presence of significant deformations. We demonstrate HybridFlow's superior performance to state-of-the-art variational techniques on two benchmark datasets and report comparable results with state-of-the-art deep-learning-based techniques.

cs.CV↗

arXiv2021

Approximate Generalized Inverses with Iterative Refinement for $ε$-Accurate Preconditioning of Singular Systems

We introduce a new class of preconditioners to enable flexible GMRES to find a least-squares solution, and potentially the pseudoinverse solution, of large-scale sparse, asymmetric, singular, and potentially inconsistent systems. We develop the preconditioners based on a new observation that generalized inverses (i.e., $\boldsymbol{A}^{g}\in\{\boldsymbol{G}\mid\boldsymbol{A}\boldsymbol{G}\boldsymbol{A}=\boldsymbol{A}\}$) enable the preconditioned Krylov subspaces to converge in a single step. We then compute an approximate generalized inverse (AGI) efficiently using a hybrid incomplete factorization (HIF), which combines multilevel incomplete LU with rank-revealing QR on its final Schur complement. We define the criteria of $ε$-accuracy and stability of AGI to guarantee the convergence of preconditioned GMRES for consistent systems. For inconsistent systems, we fortify HIF with iterative refinement to obtain HIFIR, which allows accurate computations of the null-space vectors. By combining the two techniques, we then obtain a new solver, called PIPIT, for obtaining the pseudoinverse solutions for systems with low-dimensional null spaces. We demonstrate the robustness of HIF and HIFIR and show that they improve both accuracy and efficiency of the prior state of the art by orders of magnitude for systems with up to a million unknowns.

math.NA↗

arXiv2021

HIFIR: Hybrid Incomplete Factorization with Iterative Refinement for Preconditioning Ill-conditioned and Singular Systems

We introduce a software package called HIFIR for preconditioning sparse, unsymmetric, ill-conditioned, and potentially singular systems. HIFIR computes a hybrid incomplete factorization, which combines multilevel incomplete LU factorization with a truncated, rank-revealing QR factorization on the final Schur complement. This novel hybridization is based on the new theory of approximate generalized inverse and $ε$-accuracy. It enables near-optimal preconditioners for consistent systems and enables flexible GMRES to solve inconsistent systems when coupled with iterative refinement. In this paper, we focus on some practical algorithmic and software issues of HIFIR. In particular, we introduce a new inverse-based rook pivoting into ILU, which improves the robustness and the overall efficiency for some ill-conditioned systems by significantly reducing the size of the final Schur complement for some systems. We also describe the software design of HIFIR in terms of its efficient data structures for supporting rook pivoting in a multilevel setting, its template-based generic programming interfaces for mixed-precision real and complex values in C++, and its user-friendly high-level interfaces in MATLAB and Python. We demonstrate the effectiveness of HIFIR for ill-conditioned or singular systems arising from several applications, including the Helmholtz equation, linear elasticity, stationary incompressible Navier--Stokes equations, and time-dependent advection-diffusion equation.

math.NA↗

arXiv2021

Local evidence for collective spin excitations in the distorted kagome antiferromagnet Pr$_3$BWO$_9$

We report the local probe investigation of a frustrated antiferromagnet Pr$_3$BWO$_9$ with the distorted kagome lattice. Absence of magnetic order or spin freezing is indicated by the spectral analysis down to 0.3 K and specific heat measurements down to 0.09 K. The Knight shifts show an upturn behavior with the sample cooling down, which is further suppressed by external field. For the spin dynamics, gapped spin excitation is observed from the temperature dependence of spin-lattice relaxation rates, with the gap size proportional to the applied magnetic field intensity. Comparatively, an unexpected sharp peak is observed in the nuclear spin-spin relaxation rate data at $T^*\sim 4-5$ K. These results indicate an unconventional persistent fluctuating paramagnetic ground state with antiferromagnetic collective spin excitations in the strongly frustrated spin system.

cond-mat.str-el↗

arXiv2021

Robust and Efficient Multilevel-ILU Preconditioning of Hybrid Newton-GMRES for Incompressible Navier-Stokes Equations

We introduce a robust and efficient preconditioner for a hybrid Newton-GMRES method for solving the nonlinear systems arising from incompressible Navier-Stokes equations. When the Reynolds number is relatively high, these systems often involve millions of degrees of freedom (DOFs), and the nonlinear systems are difficult to converge, partially due to the strong asymmetry of the system and the saddle-point structure. In this work, we propose to alleviate these issues by leveraging a multilevel ILU preconditioner called HILUCSI, which is particularly effective for saddle-point problems and can enable robust and rapid convergence of the inner iterations in Newton-GMRES. We further use Picard iterations with the Oseen systems to hot-start Newton-GMRES to achieve global convergence, also preconditioned using HILUCSI. To further improve efficiency and robustness, we use the Oseen operators as physics-based sparsifiers when building preconditioners for Newton iterations and introduce adaptive refactorization and iterative refinement in HILUCSI. We refer to the resulting preconditioned hybrid Newton-GMRES as HILUNG. We demonstrate the effectiveness of HILUNG by solving the standard 2D driven-cavity problem with Re 5000 and a 3D flow-over-cylinder problem with low viscosity. We compare HILUNG with some state-of-the-art customized preconditioners for INS, including two variants of augmented Lagrangian preconditioners and two physics-based preconditioners, as well as some general-purpose approximate-factorization techniques. Our comparison shows that HILUNG is much more robust for solving high-Re problems and it is also more efficient in both memory and runtime for moderate-Re problems.

cs.CE↗