arXiv subjects

Hexiang Wang

Publications and source records attributed to Hexiang Wang.

10 recordsLinked to original sources

arXiv2026

Wasserstein Stability, Couplings Across Volumes, and the $1+1/d$ Moment Thresholds in the Edwards--Anderson Model

We study the quenched pressure of the nearest-neighbor Edwards--Anderson Ising model with free and periodic boundary conditions. First, we prove that the infinite-volume pressure is $\beta d$-Lipschitz in the coupling law for the $1$-Wasserstein distance, yielding quantitative thermodynamic limits for spatially inhomogeneous disorder. Second, for periodic volumes, we prove that almost-sure convergence under every joint coupling of the finite-volume disorder arrays is equivalent to complete convergence of the one-volume pressure laws. Third, we prove that $\mathbb{E}\vert{}J\vert{}^{1+1/d}<\infty$ guarantees this universal-coupling conclusion. We further show that this exponent is optimal among uniform power-moment assumptions: for every $1\leq q<1+1/d$, there is a centered symmetric law with finite $q$-th moment for which canonical nested volumes converge almost surely, whereas independently resampled volumes with the same fixed-volume marginals converge in probability but not almost surely. Finally, in dimension one, we prove that the first-moment condition is also necessary for a finite limiting pressure.

math-ph↗

arXiv2026

Boundary Free Energies, Quenched Mixing, and Gibbs-State Selection in Disordered Ising Models

We study boundary free energies and half-space responses in nearest-neighbor disordered Ising models. First, we prove the existence of fixed-depth tangential pressure and identify its derivative almost everywhere with the limiting boundary response. Second, under quenched exponential boundary-response mixing, we prove Gibbs-state selection independence and exponential convergence of finite-depth pressures. We further show that uniform one-spin mixing yields DLR uniqueness and Gaussian normal localization, and verify the required mixing conditions whenever $(2d-1)\mathbb E\tanh(\beta\vert{}J\vert{})<1$. Finally, we establish an all-face surface limit in the bounded Dobrushin regime and provide counterexamples that delimit general surface and stiffness claims.

math-ph↗

arXiv2026

Subextensive Random Boundary Perturbations in the Short-Range Edwards--Anderson Model

We consider the nearest-neighbor Edwards--Anderson Ising model on cubic boxes with random perturbations supported at the boundary. We prove that any perturbation admitting an energy envelope that is negligible compared with the volume, both in expectation and almost surely, leaves the limiting quenched specific free energy unchanged. For independent finite-variance bulk disorder in dimension at least two, an Efron--Stein estimate also yields almost-sure self-averaging along the full sequence of boxes. The hypotheses are verified for i.i.d. scalar surface fields, fixed random exterior spins, and periodic wrap-around bonds, with an $ O(L^{-1}) $ comparison of quenched means. The result concerns the specific free energy and does not assert convergence of finite-volume Gibbs measures.

math-ph↗

arXiv2026

Boundary Free Energies in Disordered Ising Models

The boundary correction to the free energy of a disordered Ising model depends on the boundary condition, the normalization, and the finite-volume sequence. We give a one-dimensional i.i.d. counterexample showing that the surface free-energy density need not be independent of the van Hove sequence and that the corresponding random correction need not converge in probability. For bounded couplings in the Dobrushin uniqueness regime on cubic boxes in dimensions $d\geq2$, we prove convergence of the normalized free-to-fixed boundary free-energy difference in expectation, almost surely, and in every $L^p$, $1\leq p<\infty$. For Gaussian boundary couplings, we derive an exact finite-volume interpolation identity and explain why it does not by itself imply a low-temperature surface limit. For a seam-flip free-energy difference $D_L$ across a set $S_L$ of independent symmetric bonds of variance $v$, we prove $\Var(D_L)\leq4v\abs{S_L}$; one-dimensional examples show that symmetry and finite moments alone do not determine a stiffness exponent, and the low-temperature Gaussian Edwards--Anderson problem remains open.

math-ph↗

arXiv2025

Demographic Inference from Social Media Data with Multimodal Foundation Models: Strategies, Evaluation, and Benchmarking

Demographic inference plays a crucial role in understanding the representativeness and equity of social media-based research. However, existing methods typically rely on a single modality, such as text, image, or network, and are limited to predicting one or two demographic attributes, constraining their generalizability and robustness across populations. This study leverages GPT-5, a state-of-the-art multimodal foundation model, to infer age, gender, and race from social media profiles. Using a dataset of 263 publicly available X (formerly Twitter) users, we design a progressive multimodal framework that incrementally incorporates usernames, profile descriptions, tweets, and profile images to examine how each information source contributes to inference accuracy. Results show a consistent improvement across all conditions, with the inclusion of textual and visual cues substantially enhancing performance. GPT-5 achieves an overall accuracy of 0.90 for age, 0.98 for gender, and 0.85 for race, outperforming existing models under equivalent inputs. These findings demonstrate the potential of large multimodal foundation models to capture complex, cross-modal demographic cues with minimal task-specific training. The study further highlights a transparent, interpretable approach to multimodal reasoning that advances the accuracy, fairness, and scalability of demographic inference in social data analytics.

cs.SI↗

arXiv2024

Emphasizing Semantic Consistency of Salient Posture for Speech-Driven Gesture Generation

Speech-driven gesture generation aims at synthesizing a gesture sequence synchronized with the input speech signal. Previous methods leverage neural networks to directly map a compact audio representation to the gesture sequence, ignoring the semantic association of different modalities and failing to deal with salient gestures. In this paper, we propose a novel speech-driven gesture generation method by emphasizing the semantic consistency of salient posture. Specifically, we first learn a joint manifold space for the individual representation of audio and body pose to exploit the inherent semantic association between two modalities, and propose to enforce semantic consistency via a consistency loss. Furthermore, we emphasize the semantic consistency of salient postures by introducing a weakly-supervised detector to identify salient postures, and reweighting the consistency loss to focus more on learning the correspondence between salient postures and the high-level semantics of speech content. In addition, we propose to extract audio features dedicated to facial expression and body gesture separately, and design separate branches for face and body gesture synthesis. Extensive experimental results demonstrate the superiority of our method over the state-of-the-art approaches.

cs.CV↗

arXiv2024

Solve paint color effect prediction problem in trajectory optimization of spray painting robot using artificial neural network inspired by the Kubelka Munk model

Currently, the spray-painting robot trajectory planning technology aiming at spray painting quality mainly applies to single-color spraying. Conventional methods of optimizing the spray gun trajectory based on simulated thickness can only qualitatively reflect the color distribution, and can not simulate the color effect of spray painting at the pixel level. Therefore, it is not possible to accurately control the area covered by the color and the gradation of the edges of the area, and it is also difficult to deal with the situation where multiple colors of paint are sprayed in combination. To solve the above problems, this paper is inspired by the Kubelka-Munk model and combines the 3D machine vision method and artificial neural network to propose a spray painting color effect prediction method. The method is enabled to predict the execution effect of the spray gun trajectory with pixel-level accuracy from the dimension of the surface color of the workpiece after spray painting. On this basis, the method can be used to replace the traditional thickness simulation method to establish the objective function of the spray gun trajectory optimization problem, and thus solve the difficult problem of spray gun trajectory optimization for multi-color paint combination spraying. In this paper, the mathematical model of the spray painting color effect prediction problem is first determined through the analysis of the Kubelka-Munk paint film color rendering model, and at the same time, the spray painting color effect dataset is established with the help of the depth camera and point cloud processing algorithm. After that, the multilayer perceptron model was improved with the help of gating and residual structure and was used for the color prediction task. To verify ...

cs.RO↗

arXiv2024

Continuous Piecewise-Affine Based Motion Model for Image Animation

Image animation aims to bring static images to life according to driving videos and create engaging visual content that can be used for various purposes such as animation, entertainment, and education. Recent unsupervised methods utilize affine and thin-plate spline transformations based on keypoints to transfer the motion in driving frames to the source image. However, limited by the expressive power of the transformations used, these methods always produce poor results when the gap between the motion in the driving frame and the source image is large. To address this issue, we propose to model motion from the source image to the driving frame in highly-expressive diffeomorphism spaces. Firstly, we introduce Continuous Piecewise-Affine based (CPAB) transformation to model the motion and present a well-designed inference algorithm to generate CPAB transformation from control keypoints. Secondly, we propose a SAM-guided keypoint semantic loss to further constrain the keypoint extraction process and improve the semantic consistency between the corresponding keypoints on the source and driving images. Finally, we design a structure alignment loss to align the structure-related features extracted from driving and generated images, thus helping the generator generate results that are more consistent with the driving action. Extensive experiments on four datasets demonstrate the effectiveness of our method against state-of-the-art competitors quantitatively and qualitatively. Code will be publicly available at: https://github.com/DevilPG/AAAI2024-CPABMM.

cs.CV↗

arXiv2022

A Modified Walk-on-sphere Method for High Dimensional Fractional Poisson Equation

We develop walk-on-sphere for fractional Poisson equations with Dirichilet boundary conditions in high dimensions. The walk-on-sphere method is based on probabilistic represen tation of the fractional Poisson equation. We propose effcient quadrature rules to evaluate integral representation in the ball and apply rejection sampling method to drawing from the computed probabilities in general domains. Moreover, we provide an estimate of the number of walks in the mean value for the method when the domain is a ball. We show that the number of walks is increasing in the fractional order and the distance of the starting point to the origin. We also give the relationship between the Green function of fractional Laplace equation and that of the classical Laplace equation. Numerical results for problems in 2-10 dimensions verify our theory and the effciency of the modified walk-on-sphere method.

math.NA↗

arXiv2020

Time-Continuous Energy-Conservation Neural Network for Structural Dynamics Analysis

Fast and accurate structural dynamics analysis is important for structural design and damage assessment. Structural dynamics analysis leveraging machine learning techniques has become a popular research focus in recent years. Although the basic neural network provides an alternative approach for structural dynamics analysis, the lack of physics law inside the neural network limits the model accuracy and fidelity. In this paper, a new family of the energy-conservation neural network is introduced, which respects the physical laws. The neural network is explored from a fundamental single-degree-of-freedom system to a complicated multiple-degrees-of-freedom system. The damping force and external forces are also considered step by step. To improve the parallelization of the algorithm, the derivatives of the structural states are parameterized with the novel energy-conservation neural network instead of specifying the discrete sequence of structural states. The proposed model uses the system energy as the last layer of the neural network and leverages the underlying automatic differentiation graph to incorporate the system energy naturally, which ultimately improves the accuracy and long-term stability of structures dynamics response calculation under an earthquake impact. The trade-off between computation accuracy and speed is discussed. As a case study, a 3-story building earthquake simulation is conducted with realistic earthquake records.

physics.geo-ph↗