arXiv subjects

Qianqian Xue

Publications and source records attributed to Qianqian Xue.

5 recordsLinked to original sources

arXiv2026

Error Analysis of Krylov Subspace approximation Based on IDR($s$) Method for Matrix Function Bilinear Forms

The matrix function bilinear form, namely $\mathbf{u}^\top f(A)\mathbf{v}$, appears in many scientific computing problems, where $\mathbf{u}, \mathbf{v} \in \mathbb{R}^n$, $A \in \mathbb{R}^{n\times n}$, and $f(z)$ is a given analytic function. The Induced Dimension Reduction (IDR($s$)) method was originally proposed to solve a large system of linear equations, and effectively reduces the complexity and storage requirement by dimensionality reduction while maintaining the numerical stability of the algorithm. In fact, the IDR($s$) method can generate an interesting Hessenberg decomposition. We make use of this decomposition to establish the numerical algorithm and a practical error estimation framework for the matrix function bilinear form. Based on an error analysis of the IDR($s$) approximation, the corresponding error expansion is derived. Crucially, we prove that, under mild conditions, the remainder term in this expansion converges to zero as the truncation order increases, thereby validating the error expansion. The leading computable term is then used as a practical a posteriori error indicator for general analytic functions. We present numerical experiments to support our theoretical findings and illustrate the efficacy of our proposed method, along with its stopping criterion, over traditional Bi-Lanczos and Arnoldi-based algorithms.

math.NA↗

arXiv2025

MDiff-FMT: Morphology-aware Diffusion Model for Fluorescence Molecular Tomography with Small-scale Datasets

Fluorescence molecular tomography (FMT) is a sensitive optical imaging technology widely used in biomedical research. However, the ill-posedness of the inverse problem poses a huge challenge to FMT reconstruction. Although end-to-end deep learning algorithms have been widely used to address this critical issue, they still suffer from high data dependency and poor morphological restoration. In this paper, we report for the first time a morphology-aware diffusion model, MDiff-FMT, based on denoising diffusion probabilistic model (DDPM) to achieve high-fidelity morphological reconstruction for FMT. First, we use the noise addition of DDPM to simulate the process of the gradual degradation of morphological features, and achieve fine-grained reconstruction of morphological features through a stepwise probabilistic sampling mechanism, avoiding problems such as loss of structure details that may occur in end-to-end deep learning methods. Additionally, we introduce the conditional fluorescence image as structural prior information to sample a high-fidelity reconstructed image from the noisy images. Numerous numerical and real phantom experimental results show that the proposed MDiff-FMT achieves SOTA results in morphological reconstruction of FMT without relying on large-scale datasets.

eess.IV↗

arXiv2024

Nonlinear optics driven magnetism reorientation in semiconductors

Based on nonlinear optics, we develop a band theory to elucidate how light could manipulate magnetization, which is rooted by the quantum geometric structure and topological nature of electronic wavefunctions. Their existence are determined by the light polarization and specific material symmetry, based on the magnetic group theory. In general, both circularly and linearly polarized light could exert an effective magnetic field and torque effect, to reorient the magnetization. They are contributed by spin and orbital angular momenta simultaneously. Aided by group theory and first-principles calculations, we illustrate this theory using a showcase example of monolayer NiCl2, showing that light irradiation effectively generates an out-of-plane effective magnetic torque, which lifts its in-plane easy magnetization. According to magnetic dynamic simulations, the in-plane magnetization could be switched to the out-of-plane direction in a few nanoseconds under a modest light intensity, demonstrating its ultrafast nature desirable for quantum manipulation.

cond-mat.mtrl-sci↗

arXiv2023

Valley contrasting bulk photovoltaic effect in antiferromagnetic MnPSe$_3$ monolayer

Valleytronics that uses the inequivalent electronic states at the band extrema in semiconductors have been considered to play a vital role in the future information read/write technology. In the current work, we theoretically show that sizable valley contrasting bulk photovoltaic (BPV) effect could emerge, even when the total photocurrent is symmetrically forbidden. We illustrate our theory by using a prototypical two-dimensional antiferromagnetic semiconductor, MnPSe3 monolayer, that is PT-symmetric (P and T refer to spatially inversion and time-reversal operators, respectively). We show that the Neel vector well controls the magnetic point group at the $Γ$ point, and the BPV current direction. In addition, $\mathbf{k}$-dependent photocurrent generally arises due to the reduction of little group constraints at the valley. This leads to hidden valley-polarized photoconductivity which could reach a magnitude of 1350 $μ$A/V&^2&, observable experimentally. We further predict that the MnPSe$_3$ monolayer could be two-dimensional ferrotoroidic, again depending on its Neel vector direction, which can be determined via magnetoelectric response measurements. Our work provides an exemplary platform for paving the route to future opto-spintronic and opto-valleytronic devices in a single antiferromagnetic nanomaterial.

cond-mat.mtrl-sci↗

arXiv2022

Magnetic Proximity Evoked Colossal Bulk Photovoltaics in Crystalline Symmetric Layers

Bulk photovoltaic (BPV) effect, a second order nonlinear process that generates static current under light irradiation, requires centrosymmetric broken systems as its application platform. In order to realize measurable BPV photocurrent in spatially centrosymmetric materials, various schemes such as chemical doping, structural deformation, or electric bias have been developed. In the current work, we suggest that magnetic proximity effect via van der Waals interfacial interaction, a contact-free strategy, also breaks the centrosymmetry and generate large BPV photocurrents. Using the Bi2Te3 quintuple layer as an exemplary material, we show that magnetic proximity from MnBi2Te4 septuple layers yield finite and tunable shift and injection photocurrents. We apply group analysis and first-principles calculations to evaluate the layer-specific shift and injection current generations under linearly polarized light irradiation. We find that the magnetic injection photoconductivity that localized on the Bi2Te3 layer can reach over 70*108 A/(V2s), so that a 1D linear current density on the order of 0.1 mA/nm can be achieved under an intermediate intensity light. In addition to charge current, we also extend our discussions into spin BPV current, giving pure photo-generated spin current. The vertical propagation direction between the charge and spin photocurrents suggest that they can be used individually in a single material. Compared with previously reported methods, the magnetic proximity effect via van der Waals interface does not significantly alter the intrinsic feature of the centrosymmetric material (e.g., Bi2Te3), and its manipulation can be easily achieved by the proximate magnetic configurations (of MnBi2Te4), interlayer distance, and light polarization.

cond-mat.mtrl-sci↗