arXiv subjects

Qian Guo

Publications and source records attributed to Qian Guo.

At least 19 recordsLinked to original sources

arXiv2026

McKean-Vlasov stochastic differential equations with super-linear measure arguments: well-posedness and propagation of chaos

This paper studies McKean-Vlasov stochastic differential equations (MVSDEs) whose drift coefficients grow super-linearly in both state variables and measure arguments, and whose diffusion coefficients exhibit super-linear growth in the state variables. By constructing an Euler-like sequence, we establish the strong well-posedness of such MVSDEs under a locally monotone condition. Furthermore, the propagation of chaos is studied on both finite and infinite horizons, demonstrating convergence of the interacting particle system to the corresponding non-interacting system. To illustrate the rationality of the theoretical results, we provide examples whose drifts contain the high powers and multiple integrals of distributions, with numerical simulations presented in Section 6.

math.PR↗

arXiv2026

The Geometry of Coalition Power: Majorization, Lattices, and Displacement in Multiwinner Elections

How much influence can a coordinated coalition exert in a multiwinner Top-$k$ election under a positional scoring rule? We study the maximum displacement problem: with coalition size $m$, how many of the current top-$k$ winners can be forced out? We show coalition power decomposes into two independent prefix-majorization constraints, capturing how much the coalition can (i) boost outsiders and (ii) suppress weak winners. For arbitrary scoring rules these prefix inequalities are tight, efficiently checkable necessary conditions (exact in the continuous relaxation). For common-step arithmetic-progression (AP) score ladders, including Borda, truncated Borda, $k$-approval/$k$-veto, plurality, and multi-level rules such as $3$--$2$--$1$, we prove a Majorization--Lattice Theorem: feasible aggregate score vectors are exactly the integer points satisfying the Block--HLP prefix-sum capacity constraints plus a single global congruence condition modulo the step size $g$. For Borda ($g=1$) the congruence vanishes, yielding a pure prefix-majorization test. This characterization yields an $O(k'\log k')$ exact feasibility oracle for displacing $k'$ winners, and an $O(k(\log k)^2\log(mx))$ algorithm (via dual-envelope binary search) for computing the maximum achievable displacement $k^\ast$. Experiments on Mallows profiles and PrefLib elections confirm exact cutoffs, diminishing returns, and substantial gains over baseline heuristics; for $g>1$ they also demonstrate the predicted congruence effect, where prefix-only tests produce false positives. The oracle scales to extreme instances, processing $10^9$ candidates in under 28 seconds (memory permitting).

cs.GT↗

arXiv2026

Quantum Hall Effect at 0.002T

Graphene enables precise carrier-density control via gating, making it an ideal platform for studying electronic interactions. However, sample inhomogeneities often limit access to the low-density regimes where these interactions dominate. Enhancing carrier mobility is therefore crucial for exploring fundamental properties and developing device applications. Here, we demonstrate a significant reduction in external inhomogeneity using a double-layer graphene architecture separated by an ultra-thin hexagonal boron nitride layer. Mutual screening between the layers reduces scattering from random Coulomb potentials, resulting in a quantum mobility exceeding. Shubnikov de-Haas oscillations emerge at magnetic fields below 1 mT, while integer quantum Hall features are observed at 0.002T. Furthermore, we identify a fractional quantum Hall plateau at a filling factor of at 2T. These results demonstrate the platform's suitability for investigating strongly correlated electronic phases in graphene-based heterostructures.

cond-mat.mes-hall↗

arXiv2025

Chemical vapor deposition growth of continuous monolayer antiferromagnetic CrOCl films

The discovery of two-dimensional magnetic materials has provided an ideal platform for exploring physical phenomena in the two-dimensional limit. However, intrinsic two-dimensional antiferromagnetic materials have been rarely reported, limiting systematic studies of their electronic properties. The discovery of novel intrinsic two-dimensional antiferromagnets and the development of robust synthesis strategies, therefore, remain significant challenges. Here, we report the chemical vapor deposition synthesis of CrOCl monolayer films and nanosheets that exhibit excellent air stability. The CrOCl morphology is tunable, ranging from two-dimensional nanosheets to three-dimensional flower-like structures, with lateral sizes ranging from several microns to continuous monolayer films. Structural characterization confirms the materials composition and high crystalline quality. Furthermore, magnetic measurements, supported by theoretical calculations, reveal a N\'eel temperature for CrOCl of ~14 K. This work provides a reliable route for preparing two-dimensional antiferromagnetic materials.

cond-mat.mes-hall↗

arXiv2025

Anomalous Meets Topological Hall Effect in Cr2Ge2Te6 Heterostructures

Introducing topologically protected skyrmions in graphene holds significant importance for developing high-speed, low-energy spintronic devices. Here, we present a centrosymmetric ferromagnetic graphene/trilayer Cr2Ge2Te6/graphene heterostructure, demonstrating the anomalous and topological Hall effect due to the magnetic proximity effect. Through gate voltage control, we effectively tune the emergence and size of skyrmions. Micromagnetic simulations reveal the formation of skyrmions and antiskyrmions, which respond differently to external magnetic fields, leading to oscillations in the topological Hall signal. Our findings provide a novel pathway for the formation and manipulation of skyrmions in centrosymmetric two-dimensional magnetic systems, offering significant insights for developing topological spintronics.

cond-mat.mes-hall↗

arXiv2025

$\mathbf{\Phi}$-GAN: Physics-Inspired GAN for Generating SAR Images Under Limited Data

Approaches for improving generative adversarial networks (GANs) training under a few samples have been explored for natural images. However, these methods have limited effectiveness for synthetic aperture radar (SAR) images, as they do not account for the unique electromagnetic scattering properties of SAR. To remedy this, we propose a physics-inspired regularization method dubbed $\Phi$-GAN, which incorporates the ideal point scattering center (PSC) model of SAR with two physical consistency losses. The PSC model approximates SAR targets using physical parameters, ensuring that $\Phi$-GAN generates SAR images consistent with real physical properties while preventing discriminator overfitting by focusing on PSC-based decision cues. To embed the PSC model into GANs for end-to-end training, we introduce a physics-inspired neural module capable of estimating the physical parameters of SAR targets efficiently. This module retains the interpretability of the physical model and can be trained with limited data. We propose two physical loss functions: one for the generator, guiding it to produce SAR images with physical parameters consistent with real ones, and one for the discriminator, enhancing its robustness by basing decisions on PSC attributes. We evaluate $\Phi$-GAN across several conditional GAN (cGAN) models, demonstrating state-of-the-art performance in data-scarce scenarios on three SAR image datasets.

cs.CV↗

arXiv2025

Discovering Dynamics with Kolmogorov Arnold Networks: Linear Multistep Method-Based Algorithms and Error Estimation

Uncovering the underlying dynamics from observed data is a critical task in various scientific fields. Recent advances have shown that combining deep learning techniques with linear multistep methods (LMMs) can be highly effective for this purpose. In this work, we propose a novel framework that integrates Kolmogorov Arnold Networks (KANs) with LMMs for the discovery and approximation of dynamical systems' vector fields. Specifically, we begin by establishing precise error bounds for two-layer B-spline KANs when approximating the governing functions of dynamical systems. Leveraging the approximation capabilities of KANs, we demonstrate that for certain families of LMMs, the total error is constrained within a specific range that accounts for both the method's step size and the network's approximation accuracy. Additionally, we analyze the difference between the numerical solution obtained from solving the ordinary differential equations with the fitted vector fields and the true solution of the dynamical system. To validate our theoretical results, we provide several numerical examples that highlight the effectiveness of our approach.

math.NA↗

arXiv2024

Construction and optimization of health behavior prediction model for the elderly in smart elderly care

With the intensification of global aging, health management of the elderly has become a focus of social attention. This study designs and implements a smart elderly care service model to address issues such as data diversity, health status complexity, long-term dependence and data loss, sudden changes in behavior, and data privacy in the prediction of health behaviors of the elderly. The model achieves accurate prediction and dynamic management of health behaviors of the elderly through modules such as multimodal data fusion, data loss processing, nonlinear prediction, emergency detection, and privacy protection. In the experimental design, based on multi-source data sets and market research results, the model demonstrates excellent performance in health behavior prediction, emergency detection, and personalized services. The experimental results show that the model can effectively improve the accuracy and robustness of health behavior prediction and meet the actual application needs in the field of smart elderly care. In the future, with the integration of more data and further optimization of technology, the model will provide more powerful technical support for smart elderly care services.

cs.AI↗

arXiv2024

In-plane staging in lithium-ion intercalation of bilayer graphene

The ongoing efforts to optimize Li-ion batteries led to the interest in intercalation of nanoscale layered compounds, including bilayer graphene. Its lithium intercalation has been demonstrated recently but the mechanisms underpinning the storage capacity remain poorly understood. Here, using magnetotransport measurements, we report in-operando intercalation dynamics of bilayer graphene. Unexpectedly, we find four distinct intercalation stages that correspond to well-defined Li-ion densities. We refer to these stages as 'in-plane', with no in-plane analogues in bulk graphite. The fully intercalated bilayers represent a stoichiometric compound C14LiC14 with a Li density of 2.7x10^{14} cm^{-2}, notably lower than fully intercalated graphite. Combining the experimental findings and DFT calculations, we show that the critical step in bilayer intercalation is a transition from AB to AA stacking which occurs at a density of 0.9x10^{14} cm^{-2}. Our findings reveal the mechanism and limits for electrochemical intercalation of bilayer graphene and suggest possible avenues for increasing the Li storage capacity.

cond-mat.mtrl-sci↗

arXiv2024

Numerical scheme for delay-type stochastic McKean-Vlasov equations driven by fractional Brownian motion

This paper focuses on the numerical scheme for delay-type stochastic McKean-Vlasov equations (DSMVEs) driven by fractional Brownian motion with Hurst parameter $H\in (0,1/2)\cup (1/2,1)$. The existence and uniqueness of the solutions to such DSMVEs whose drift coefficients contain polynomial delay terms are proved by exploting the Banach fixed point theorem. Then the propagation of chaos between interacting particle system and non-interacting system in $\mathcal{L}^p$ sense is shown. We find that even if the delay term satisfies the polynomial growth condition, the unmodified classical Euler-Maruyama scheme still can approximate the corresponding interacting particle system without the particle corruption. The convergence rates are revealed for $H\in (0,1/2)\cup (1/2,1)$. Finally, as an example that closely fits the original equation, a stochastic opinion dynamics model with both extrinsic memory and intrinsic memory is simulated to illustrate the plausibility of the theoretical result.

math.NA↗

arXiv2023

Propagation of chaos in infinite horizon and numerical stability for stochastic McKean-Vlasov equations

This paper focuses on the numerical stability of stochastic McKean-Vlasov equations (SMVEs) via the stochastic particle method. Firstly, the long-time propagation of chaos in the mean-square sense is obtained, and the almost sure propagation in infinite horizon is also proved. Next, when the coefficients satisfy linear growth conditions, the mean-square and almost sure exponential stabilities of the Euler-Maruyama (EM) scheme associated with the corresponding interacting particle system are shown through an ingenious manipulation of empirical measure. Then, for the case that the state variables in drift and diffusion are both superlinear, the mean-square exponential stability of the backward EM scheme for the interacting system is achieved without the particle corruption, which is a novel conclusion. Moreover, under the linear growth condition on the diffusion coefficient, the almost sure stability of the backward EM scheme is studied. Combining these assertions enables the numerical solutions to reproduce the stabilities of the original SMVEs. The examples, including a feedback control problem and a stochastic opinion dynamics model, are provided to demonstrate the importance of theoretical analysis of numerical stability.

math.NA↗

arXiv2023

Convergence analysis of an explicit method and its random batch approximation for the McKean-Vlasov equations with non-globally Lipschitz conditions

In this paper, we present a numerical approach to solve the McKean-Vlasov equations, which are distribution-dependent stochastic differential equations, under some non-globally Lipschitz conditions for both the drift and diffusion coefficients. We establish a propagation of chaos result, based on which the McKean-Vlasov equation is approximated by an interacting particle system. A truncated Euler scheme is then proposed for the interacting particle system allowing for a Khasminskii-type condition on the coefficients. To reduce the computational cost, the random batch approximation proposed in [Jin et al., J. Comput. Phys., 400(1), 2020] is extended to the interacting particle system where the interaction could take place in the diffusion term. An almost half order of convergence is proved in $L^p$ sense. Numerical tests are performed to verify the theoretical results.

math.NA↗

arXiv2023

Convergence rate in $\mathcal{L}^p$ sense of tamed EM scheme for highly nonlinear neutral multiple-delay stochastic McKean-Vlasov equations

This paper focuses on the numerical scheme of highly nonlinear neutral multiple-delay stohchastic McKean-Vlasov equation (NMSMVE) by virtue of the stochastic particle method. First, under general assumptions, the results about propagation of chaos in $\mathcal{L}^p$ sense are shown. Then the tamed Euler-Maruyama scheme to the corresponding particle system is established and the convergence rate in $\mathcal{L}^p$ sense is obtained. Furthermore, combining these two results gives the convergence error between the objective NMSMVE and numerical approximation, which is related to the particle number and step size. Finally, two numerical examples are provided to support the finding.

math.NA↗

arXiv2022

Photoelectrochemical water splitting with ITO/WO3/BiVO4/CoPi multishell nanotubes fabricated by soft-templating in vacuum

A well-established procedure for the photoelectrochemical (PEC) splitting of water relies on using porous electrodes of WO3 sensitized with BiVO4 as a visible scavenger photoanode semiconductor. In this work, we propose an evolved photoelectrode fabricated by a soft-template approach consisting of supported multishell nanotubes (NTs). These NTs are formed by a concentric layered structure of indium tin oxide (ITO), WO3, and BiVO4, together with a final film of cobalt phosphate (CoPi) co-catalyst. Photoelectrode manufacturing is easily implemented at large scale and combines thermal evaporation of single crystalline organic nanowires (ONWs), magnetron sputtering (for ITO and WO3), solution dripping, and electrochemical deposition processes (for BiVO4 and CoPi, respectively) plus annealing under mild conditions. The obtained NT electrodes depict a large electrochemically active surface and outperform by more than one order of magnitude the efficiency of equivalent planar-layered electrodes. A thorough electrochemical analysis of the electrodes under blue and solar light illumination demonstrates the critical role of the WO3/BiVO4 Schottky barrier heterojunction in the control of the NT electrode efficiency and its dependence on the BiVO4 outer layer thickness. Oxygen evolution reaction (OER) performance was maximized with the CoPi electrocatalyst, rendering high photocurrents under one sun illumination. The reported results demonstrate the potential of the soft-template methodology for the large area fabrication of highly efficient multishell ITO/WO3/BiVO4/CoPi NT electrodes, or other alternative combinations, for the photoelectrochemical splitting of water.

physics.chem-ph↗

arXiv2022

Mean-square convergence and stability of the backward Euler method for stochastic differential delay equations with highly nonlinear growing coefficients

Over the last few decades, the numerical methods for stochastic differential delay equations (SDDEs) have been investigated and developed by many scholars. Nevertheless, there is still little work to be completed. By virtue of the novel technique, this paper focuses on the mean-square convergence and stability of the backward Euler method (BEM) for SDDEs whose drift and diffusion coefficients can both grow polynomially. The upper mean-square error bounds of BEM are obtained. Then the convergence rate, which is one-half, is revealed without using the moment boundedness of numerical solutions. Furthermore, under fairly general conditions, the novel technique is applied to prove that the BEM can inherit the exponential mean-square stability with a simple proof. At last, two numerical experiments are implemented to illustrate the reliability of the theories.

math.NA↗

arXiv2022

An explicit Euler method for McKean-Vlasov SDEs driven by fractional Brownian motion

In this paper, we establish the theory of chaos propagation and propose an Euler-Maruyama scheme for McKean-Vlasov stochastic differential equations driven by fractional Brownian motion with Hurst exponent $H \in (0,1)$. Meanwhile, upper bounds for errors in the Euler method is obtained. A numerical example is demonstrated to verify the theoretical results.

math.NA↗

arXiv2021

The truncated $θ$-Milstein method for nonautonomous and highly nonlinear stochastic differential delay equations

This paper focuses on the strong convergence of the truncated $θ$-Milstein method for a class of nonautonomous stochastic differential delay equations whose drift and diffusion coefficients can grow polynomially. The convergence rate, which is close to one, is given under the weaker assumption than the monotone condition. To verify our theoretical findings, we present a numerical example.

math.NA↗

arXiv2021

Logic could be learned from images

Logic reasoning is a significant ability of human intelligence and also an important task in artificial intelligence. The existing logic reasoning methods, quite often, need to design some reasoning patterns beforehand. This has led to an interesting question: can logic reasoning patterns be directly learned from given data? The problem is termed as a data concept logic. In this study, a learning logic task from images, called a LiLi task, first is proposed. This task is to learn and reason the logic relation from images, without presetting any reasoning patterns. As a preliminary exploration, we design six LiLi data sets (Bitwise And, Bitwise Or, Bitwise Xor, Addition, Subtraction and Multiplication), in which each image is embedded with a n-digit number. It is worth noting that a learning model beforehand does not know the meaning of the n-digit numbers embedded in images and the relation between the input images and the output image. In order to tackle the task, in this work we use many typical neural network models and produce fruitful results. However, these models have the poor performances on the difficult logic task. For furthermore addressing this task, a novel network framework called a divide and conquer model by adding some label information is designed, achieving a high testing accuracy.

cs.CV↗