arXiv subjects

Wenjin Zhang

Publications and source records attributed to Wenjin Zhang.

11 recordsLinked to original sources

arXiv2026

Normal-Yang-Mills and Tangent-Yang-Mills submanifolds

This paper investigates the variational problems associated with the $L^2$-norms of the normal and tangent curvature tensors for submanifolds immersed in a unit sphere. We define the critical points of these functionals under normal variations as Normal-Yang-Mills and Tangent-Yang-Mills submanifolds, for which we explicitly establish the Euler-Lagrange equations in terms of the second fundamental form. Furthermore, by investigating the focal submanifolds of OT-FKM isoparametric hypersurfaces, we construct infinitely many non-trivial examples of both Normal-Yang-Mills and Tangent-Yang-Mills submanifolds. Notably, the curvature tensors of these examples generally do not satisfy the classical Yang-Mills equations.

math.DG↗

arXiv2026

AugLift: Depth-Aware Input Reparameterization Improves Domain Generalization in 2D-to-3D Pose Lifting

Lifting-based 3D human pose estimation infers 3D joints from 2D keypoints but generalizes poorly because $(x,y)$ coordinates alone are an ill-posed, sparse representation that discards geometric information modern foundation models can recover. We propose \emph{AugLift}, which changes the representation format of lifting from 2D coordinates to a 6D geometric descriptor via two modules: (1) an \emph{Uncertainty-Aware Depth Descriptor} (UADD) -- a compact tuple $(c, d, d_{\min}, d_{\max})$ extracted from a confidence-scaled neighborhood of an off-the-shelf monocular depth map -- and (2) a scale normalization component that handles train/test distance shifts. AugLift requires no new sensors, no new data collection, and no architectural changes beyond widening the input layer; because it operates at the representation level, it is composable with any lifting architecture or domain generalization technique. In the detection setting, AugLift reduces cross-dataset MPJPE by $10.1$% on average across four datasets and four lifting architectures while improving in-distribution accuracy by $4.0$%; post-hoc analysis shows gains concentrate on novel poses and occluded joints. In the ground-truth 2D setting, combining AugLift with PoseAug's differentiable domain generalization achieves state-of-the-art cross-dataset performance ($62.4$\,mm on 3DHP, $92.6$\,mm on 3DPW; $14.5$% and $22.2$% over PoseAug), demonstrating that foundation-model depth provides genuine geometric signal complementary to explicit 3D augmentation. Code will be made publicly available.

cs.CV↗

arXiv2026

Discrete Causal Representation Learning

Causal representation learning seeks to uncover causal relationships among high-level latent variables from low-level, entangled, and noisy observations. Existing approaches often either rely on deep neural networks, which lack interpretability and formal guarantees, or impose restrictive assumptions like linearity, continuous-only observations, and strong structural priors. These limitations particularly challenge applications with a large number of discrete latent variables and mixed-type observations. To address these challenges, we propose discrete causal representation learning (DCRL), a generative framework that models a directed acyclic graph among discrete latent variables, along with a sparse bipartite graph linking latent and observed layers. This design accommodates continuous, count, and binary responses through flexible measurement models while maintaining interpretability. Under mild conditions, we prove that both the bipartite measurement graph and the latent causal graph are identifiable from the observed data distribution alone. We further propose a three-stage estimate-resample-discovery pipeline: penalized estimation of the generative model parameters, resampling of latent configurations from the fitted model, and score-based causal discovery on the resampled latents. We establish the consistency of this procedure, ensuring reliable recovery of the latent causal structure. Empirical studies on educational assessment and synthetic image data demonstrate that DCRL recovers sparse and interpretable latent causal structures.

stat.ME↗

arXiv2026

Principal Distribution Isomorphisms and Almost Hermitian geometry on Isoparametric Hypersurfaces

This paper investigates the isomorphisms between principal distributions $\mathcal{D}_k$ $(k=1,\dots 4)$ on OT--FKM type isoparametric hypersurfaces in spheres. We recover the isomorphism $\mathcal{D}_1 \cong \mathcal{D}_3$ established by Qian--Tang--Yan \cite{Q-T-Y 2}, and further construct the isomorphism $\mathcal{D}_{2}\cong\mathcal{D}_{4}$ in specific cases. More significantly, we provide an explicit construction of a global vector bundle isomorphism $\mathcal{D}_1 \oplus \mathcal{D}_2 \cong \mathcal{D}_3 \oplus \mathcal{D}_4$ for all odd multiplicities $m$. As applications, we employ these isomorphisms to induce nearly Kähler structures on certain OT--FKM hypersurfaces. Finally, we prove that the $*$-Ricci curvature vanishes for any OT--FKM hypersurface admitting an almost Hermitian structure that interchanges principal distributions in pairs.

math.DG↗

arXiv2025

A Coarse-to-Fine Human Pose Estimation Method based on Two-stage Distillation and Progressive Graph Neural Network

Human pose estimation has been widely applied in the human-centric understanding and generation, but most existing state-of-the-art human pose estimation methods require heavy computational resources for accurate predictions. In order to obtain an accurate, robust yet lightweight human pose estimator, one feasible way is to transfer pose knowledge from a powerful teacher model to a less-parameterized student model by knowledge distillation. However, the traditional knowledge distillation framework does not fully explore the contextual information among human joints. Thus, in this paper, we propose a novel coarse-to-fine two-stage knowledge distillation framework for human pose estimation. In the first-stage distillation, we introduce the human joints structure loss to mine the structural information among human joints so as to transfer high-level semantic knowledge from the teacher model to the student model. In the second-stage distillation, we utilize an Image-Guided Progressive Graph Convolutional Network (IGP-GCN) to refine the initial human pose obtained from the first-stage distillation and supervise the training of the IGP-GCN in the progressive way by the final output pose of teacher model. The extensive experiments on the benchmark dataset: COCO keypoint and CrowdPose datasets, show that our proposed method performs favorably against lots of the existing state-of-the-art human pose estimation methods, especially for the more complex CrowdPose dataset, the performance improvement of our model is more significant.

cs.CV↗

arXiv2025

Nano-Raman Spectroscopy Analysis of Nanoprotuberances in MoSe2

Contaminations in the formation of two-dimensional heterostructures can hinder or generate desired properties. Recent advancements have highlighted the potential of tip-enhanced Raman spectroscopy (TERS) for studying materials in the 2D semiconductor class. In this work, we investigate the influence of 50-200nm sized nanoprotuberances within a monolayer of MoSe$_2$ deposited on hBN using nano-Raman spectroscopy, establishing correlations between the presence of localized contaminations and the observed hyperspectral variations. A figure of merit is established for the identification of surface impurities, based on MoSe$_2$ peaks ratio. Notably, new spectral peaks were identified, which are associated with the presence of nanoprotuberances and may indicate contamination and oxidation.

physics.optics↗

arXiv2024

Uniform exponential convergence of SAA with AMIS and asymptotics of its optimal value

We discuss in this paper uniform exponential convergence of sample average approximation (SAA) with adaptive multiple importance sampling (AMIS) and asymptotics of its optimal value. Using a concentration inequality for bounded martingale differences, we obtain a new exponential convergence rate. To study the asymptotics, we first derive an important functional central limit theorem (CLT) for martingale difference sequences. Subsequently, exploiting this result with the Delta theorem, we prove the asymptotics of optimal values for SAA with AMIS.

math.OC↗

arXiv2024

DMRIntTk: integrating different DMR sets based on density peak clustering

\textbf{Background}: Identifying differentially methylated regions (DMRs) is a basic task in DNA methylation analysis. However, due to the different strategies adopted, different DMR sets will be predicted on the same dataset, which poses a challenge in selecting a reliable and comprehensive DMR set for downstream analysis. \textbf{Results}: Here, we develop DMRIntTk, a toolkit for integrating DMR sets predicted by different methods on a same dataset. In DMRIntTk, the genome is segmented into bins and the reliability of each DMR set at different methylation thresholds is evaluated. Then, the bins are weighted based on the covered DMR sets and integrated into DMRs by using a density peak clustering algorithm. To demonstrate the practicality of DMRIntTk, DMRIntTk was applied to different scenarios, including different tissues with relatively large methylation differences, cancer tissues versus normal tissues with medium methylation differences, and disease tissues versus normal tissues with subtle methylation differences. The results show that DMRIntTk can effectively trim the regions with small methylation differences in the original DMR sets and therefore it can enhance the proportion of DMRs with higher methylation differences. In addition, the overlap analysis suggests that the integrated DMR sets are quite comprehensive, and the functional analysis indicates the integrated disease-related DMR sets are significantly enriched in biological pathways, which are associated with the pathological mechanisms of the diseases. \textbf{Conclusions}: Conclusively, DMRIntTk can help researchers obtaining a reliable and comprehensive DMR set from many prediction methods. \textbf{Keywords}:{Differentially methylated regions, Methylation array, Cancer-related differentially methylated regions, Tissue-specific differentially methylated regions, Density peak clustering.}

q-bio.QM↗

arXiv2024

MaskMatch: Boosting Semi-Supervised Learning Through Mask Autoencoder-Driven Feature Learning

Conventional methods in semi-supervised learning (SSL) often face challenges related to limited data utilization, mainly due to their reliance on threshold-based techniques for selecting high-confidence unlabeled data during training. Various efforts (e.g., FreeMatch) have been made to enhance data utilization by tweaking the thresholds, yet none have managed to use 100% of the available data. To overcome this limitation and improve SSL performance, we introduce \algo, a novel algorithm that fully utilizes unlabeled data to boost semi-supervised learning. \algo integrates a self-supervised learning strategy, i.e., Masked Autoencoder (MAE), that uses all available data to enforce the visual representation learning. This enables the SSL algorithm to leverage all available data, including samples typically filtered out by traditional methods. In addition, we propose a synthetic data training approach to further increase data utilization and improve generalization. These innovations lead \algo to achieve state-of-the-art results on challenging datasets. For instance, on CIFAR-100 with 2 labels per class, STL-10 with 4 labels per class, and Euro-SAT with 2 labels per class, \algo achieves low error rates of 18.71%, 9.47%, and 3.07%, respectively. The code will be made publicly available.

cs.CV↗

arXiv2022

Electromagnetic Environment Analysis of High-Power Wireless Charging Device

Objective Aiming at the problems of many interference factors in the electromagnetic radiation simulation of electric vehicles, a field measurement scheme for charging devices is designed. Through the monitoring of wireless charging equipment, the radiation level and distribution of the electric field value and magnetic field value around the charging equipment is explored, to analyze the influence law of the electromagnetic environment. Method This paper introduces the principle and development status of electric vehicle charging, and analyzes the classification and methods of wireless charging. In this paper, the electric field and magnetic field of cars and minibuses are monitored, at positions such as: around the body, attenuation section, inside the car and inside the minibus. Result The range of electric field strength is 0.9 V/m to 48.1 V/m for cars, and 0.8 V/m to 74.7 V/m for minibuses. The electric field strength decays rapidly with the increase of the distance from the vehicle body, and the law is obvious. The range of magnetic induction intensity is 0.12μT-12.70μT for cars, and 0.15μT-27.06μT for minibuses. The magnetic induction intensity decays rapidly with the increase of the distance from the vehicle body, and the law is obvious. Conclusion This paper explores the radiation level and distribution of the electromagnetic field around this type of charging equipment. It is recommended that manufacturers of wireless charging devices for electric vehicles strengthen research on electromagnetic radiation shielding and take corresponding measures to control the level of electromagnetic radiation in areas accessible to the public.

eess.SY↗

arXiv2020

Topological Lattice Metamaterials -- A Platform For Novel Electromagnetic Material Design Based On An Artificial Topological "Atom"

In nature, most materials are composed of atoms with periodic structures. Hence, it's impossible to introduce topological structures into their lattice compose, because the atoms as basic blocks cannot be modulated. However, the lattice compose of metamaterials can be designed conveniently. In our work, we propose to introduce topological non-trivial structures, Mobius unknots, as the basic block (the artificial chiral "atoms") to design metamaterials. A 5.95 GHz intrinsic peak, in addition to the electrical resonance peak near 11 GHz on the transmission coefficient spectrum was confirmed by theoretical calculations, finite-difference time-domain (FDTD) simulations and experiments when electromagnetic waves transfer to a chiral Mobius unknot. Theoretical analysis indicates that this intrinsic peak originates from the phase transition caused by the electromagnetic waves propagate along the Mobius unknot non-trivial structure. It is similar to the state of spin-splitting of electron levels. Take the artificial chiral "atoms" - Mobius unknots as the basic block, we can construct two-dimensional and even three-dimensional ordered metamaterials. The simulation and experimental results showed that the response to electromagnetic wave in the GHz band can be modulated by the coupling between the periodic potential and the spin-like of energy levels.

cond-mat.mes-hall↗