arXiv subjects

Hui Yang

Publications and source records attributed to Hui Yang.

At least 73 records · Page 4Linked to original sources

arXiv2025

Enhanced nonlinear Hall effect by Cooper pairs near superconductor criticality

Unlike the linear Hall effect that requires broken time-reversal symmetry, the nonlinear Hall effect may occur in time-reversal symmetric systems as long as there exists a non-zero Berry curvature dipole in the absence of inversion symmetry. Interestingly, the presence of time-reversal symmetry is consistent with and thus allows a direct transition into a superconducting phase. Indeed, superconductivity has been established in various nonlinear Hall materials, such as WTe$_2$ and MoTe$_2$, at sufficiently low temperatures. We find that the nonlinear Hall response should be significantly enhanced near the superconducting criticality, dominated by the Aslamazov-Larkin (AL) contributions augmented by superconducting fluctuations, which we attribute to the Berry curvature dipole and a divergent lifetime $τ\sim (T-T_c)^{-1}$ of the Cooper pairs, instead of the single electrons. Such a controlled enhancement brings the nonlinear Hall effect into various simple experimental observations and practical applicational potentials.

cond-mat.mes-hall↗

arXiv2025

MonoDiff9D: Monocular Category-Level 9D Object Pose Estimation via Diffusion Model

Object pose estimation is a core means for robots to understand and interact with their environment. For this task, monocular category-level methods are attractive as they require only a single RGB camera. However, current methods rely on shape priors or CAD models of the intra-class known objects. We propose a diffusion-based monocular category-level 9D object pose generation method, MonoDiff9D. Our motivation is to leverage the probabilistic nature of diffusion models to alleviate the need for shape priors, CAD models, or depth sensors for intra-class unknown object pose estimation. We first estimate coarse depth via DINOv2 from the monocular image in a zero-shot manner and convert it into a point cloud. We then fuse the global features of the point cloud with the input image and use the fused features along with the encoded time step to condition MonoDiff9D. Finally, we design a transformer-based denoiser to recover the object pose from Gaussian noise. Extensive experiments on two popular benchmark datasets show that MonoDiff9D achieves state-of-the-art monocular category-level 9D object pose estimation accuracy without the need for shape priors or CAD models at any stage. Our code will be made public at https://github.com/CNJianLiu/MonoDiff9D.

cs.CV↗

arXiv2025

Power Spectra of Velocity Fluctuations in Granular Heap Flow

This study used Speckle Visibility Spectroscopy to examine velocity fluctuations in a three-dimensional granular heap flow, where the mean velocity profile consists of a fast-flow surface layer and a creep layer beneath. The velocity spectra follow power-law scalings, $E(f) \propto f^α$, with $α\approx -0.85$ in the surface flow layer -- matching the Self-Organized Criticality (SOC) model with open boundaries ($α\approx -0.95$). In the creep layer, $α$ decreases with depth, reaching $α\approx -1.5$ at approximately 55 mean particle diameters, consistent with the SOC model with closed boundaries ($α\approx -1.58$). Analysis of the fluctuation velocity distributions offers additional insights into the microscopic origins of the spectrum's characteristics. These findings help resolve the long-standing puzzle of flow localization in gravity-driven granular flows despite a constant shear stress-to-pressure ratio throughout the material.

cond-mat.soft↗

arXiv2025

On isolated singularities of the conformal Gaussian curvature equation and $Q$-curvature equation

In this paper, we study the isolated singularities of the conformal Gaussian curvature equation \[ -Δu = K(x) e^{u} \quad ~ in ~ B_{1} \setminus \{ 0 \}, \] where $B_1 \setminus \{ 0 \} \subset \mathbb{R}^2$ is the punctured unit disc. Under the assumption that the Gaussian curvature $K \in L^\infty(B_1)$ is nonnegative, we establish the asymptotic behavior of solutions near the singularity. When $K \equiv 1$, a similar result has been obtained by Chou and Wan (Pacific J. Math. 1994) using the method of complex analysis. Our proof is entirely based on the PDE method and applies to the general Gaussian curvature $K(x)$. Furthermore, our approach is also available for characterizing isolated singularities of the conformal $Q$-curvature equation $(-Δ)^{\frac{n}{2}} u = K(x) e^{u}$ in any dimension $n\geq 3$. This equation arises from the prescribing $Q$-curvature problem.

math.AP↗

arXiv2025

TransBox: EL++-closed Ontology Embedding

OWL (Web Ontology Language) ontologies, which are able to represent both relational and type facts as standard knowledge graphs and complex domain knowledge in Description Logic (DL) axioms, are widely adopted in domains such as healthcare and bioinformatics. Inspired by the success of knowledge graph embeddings, embedding OWL ontologies has gained significant attention in recent years. Current methods primarily focus on learning embeddings for atomic concepts and roles, enabling the evaluation based on normalized axioms through specially designed score functions. However, they often neglect the embedding of complex concepts, making it difficult to infer with more intricate axioms. This limitation reduces their effectiveness in advanced reasoning tasks, such as Ontology Learning and ontology-mediated Query Answering. In this paper, we propose EL++-closed ontology embeddings which are able to represent any logical expressions in DL via composition. Furthermore, we develop TransBox, an effective EL++-closed ontology embedding method that can handle many-to-one, one-to-many and many-to-many relations. Our extensive experiments demonstrate that TransBox often achieves state-of-the-art performance across various real-world datasets for predicting complex axioms.

cs.AI↗

arXiv2025

Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://github.com/CNJianLiu/Diff9D.

cs.CV↗

arXiv2025

An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman Problem

Recent advances in neural models have shown considerable promise in solving Traveling Salesman Problems (TSPs) without relying on much hand-crafted engineering. However, while non-autoregressive (NAR) approaches benefit from faster inference through parallelism, they typically deliver solutions of inferior quality compared to autoregressive ones. To enhance the solution quality while maintaining fast inference, we propose DEITSP, a diffusion model with efficient iterations tailored for TSP that operates in a NAR manner. Firstly, we introduce a one-step diffusion model that integrates the controlled discrete noise addition process with self-consistency enhancement, enabling optimal solution prediction through simultaneous denoising of multiple solutions. Secondly, we design a dual-modality graph transformer to bolster the extraction and fusion of features from node and edge modalities, while further accelerating the inference with fewer layers. Thirdly, we develop an efficient iterative strategy that alternates between adding and removing noise to improve exploration compared to previous diffusion methods. Additionally, we devise a scheduling framework to progressively refine the solution space by adjusting noise levels, facilitating a smooth search for optimal solutions. Extensive experiments on real-world and large-scale TSP instances demonstrate that DEITSP performs favorably against existing neural approaches in terms of solution quality, inference latency, and generalization ability. Our code is available at $\href{https://github.com/DEITSP/DEITSP}{https://github.com/DEITSP/DEITSP}$.

cs.LG↗

arXiv2025

On the Converse of Prékopa's Theorem and Berndtsson's Theorem

Given a continuous function $ϕ$ defined on a domain $Ω\subset\mathbb{R}^m\times\mathbb{R}^n$, we show that if a Prékopa-type result holds for $ϕ+ψ$ for any non-negative convex function $ψ$ on $Ω$, then $ϕ$ must be a convex function. Additionally, if the projection of $Ω$ onto $\mathbb{R}^m$ is convex, then $\overlineΩ$ is also convex. This provides a converse of Prékopa's theorem from convex analysis. We also establish analogous results for Berndtsson's theorem on the plurisubharmonic variation of Bergman kernels, showing that the plurisubharmonicity of weight functions and the pseudoconvexity of domains are necessary conditions in some sense.

math.CV↗

arXiv2024

StableMoFusion: Towards Robust and Efficient Diffusion-based Motion Generation Framework

Thanks to the powerful generative capacity of diffusion models, recent years have witnessed rapid progress in human motion generation. Existing diffusion-based methods employ disparate network architectures and training strategies. The effect of the design of each component is still unclear. In addition, the iterative denoising process consumes considerable computational overhead, which is prohibitive for real-time scenarios such as virtual characters and humanoid robots. For this reason, we first conduct a comprehensive investigation into network architectures, training strategies, and inference processs. Based on the profound analysis, we tailor each component for efficient high-quality human motion generation. Despite the promising performance, the tailored model still suffers from foot skating which is an ubiquitous issue in diffusion-based solutions. To eliminate footskate, we identify foot-ground contact and correct foot motions along the denoising process. By organically combining these well-designed components together, we present StableMoFusion, a robust and efficient framework for human motion generation. Extensive experimental results show that our StableMoFusion performs favorably against current state-of-the-art methods. Project page: https://h-y1heng.github.io/StableMoFusion-page/

cs.CV↗

arXiv2024

OOD-HOI: Text-Driven 3D Whole-Body Human-Object Interactions Generation Beyond Training Domains

Generating realistic 3D human-object interactions (HOIs) from text descriptions is a active research topic with potential applications in virtual and augmented reality, robotics, and animation. However, creating high-quality 3D HOIs remains challenging due to the lack of large-scale interaction data and the difficulty of ensuring physical plausibility, especially in out-of-domain (OOD) scenarios. Current methods tend to focus either on the body or the hands, which limits their ability to produce cohesive and realistic interactions. In this paper, we propose OOD-HOI, a text-driven framework for generating whole-body human-object interactions that generalize well to new objects and actions. Our approach integrates a dual-branch reciprocal diffusion model to synthesize initial interaction poses, a contact-guided interaction refiner to improve physical accuracy based on predicted contact areas, and a dynamic adaptation mechanism which includes semantic adjustment and geometry deformation to improve robustness. Experimental results demonstrate that our OOD-HOI could generate more realistic and physically plausible 3D interaction pose in OOD scenarios compared to existing methods.

cs.CV↗

arXiv2024

Topological incommensurate Fulde-Ferrell-Larkin-Ovchinnikov superconductor and Bogoliubov Fermi surface in rhombohedral tetra-layer graphene

We performed a random phase approximation (RPA) calculation for a spin-valley polarized model of the rhombohedral tetra-layer graphene to study the possibility of chiral superconductor from the Kohn-Luttinger mechanism. We included the realistic band structure and form factor in our calculation and solved the self-consistent equation numerically by sampling 20,000 points in the momentum space at a given temperature. Around the Van-Hove singularity (VHS), we find p-ip pairing with Chern number switching from $C=-1$ to $C=0$ through a gap closing at $\mathbf k=(0,0)$ (defined relative to $\mathbf K$). Although the superconductor is generically fully gapped at low temperature, we find Bogoliubov Fermi surface at temperature just below mean field $T_c$. Besides, through calculation of the free energy, we conclude that the optimal Cooper pair momentum $\mathbf Q$ is generically finite and can be as large as $0.1 k_F$. We dub the $\mathbf Q\neq 0$ phase as an incommensurate Fulde-Ferrell-Larkin-Ovchinnikov(FFLO) superconductor to distinguish it from the $\mathbf Q=0$ phase. Compared to the $\mathbf Q=0$ phase, our incommensurate $\mathbf Q$ phase is a nematic superconductor if it is in the Fulde-Ferrell(FF) phase or exhibts charge density wave (CDW) if it is in the Larkin-Ovchinnikov (LO) phase. Our work demonstrates the rhombohedral tetra-layer graphene as a wonderful platform to explore Majorana zero-mode, FFLO physics and Bogoliubov fermi surface within one single platform.

cond-mat.supr-con↗

arXiv2024

The direct moving sphere for fractional Laplace equation

This paper works on the direct method of moving spheres and establishes a Liouville-type theorem for the fractional elliptic equation \[ (-Δ)^{α/2} u =f(u) ~~~~~~ \text{in } \mathbb{R}^{n} \] with general non-linearity. One of the key improvement over the previous work is that we do not require the usual Lipschitz condition. In fact, we only assume the structural condition that $f(t) t^{- \frac{n+α}{n-α}}$ is monotonically decreasing. This differs from the usual approach such as Chen-Li-Li (Adv. Math. 2017), which needed the Lipschitz condition on $f$, or Chen-Li-Zhang (J. Funct. Anal. 2017), which relied on both the structural condition and the monotonicity of $f$. We also use the direct moving spheres method to give an alternative proof for the Liouville-type theorem of the fractional Lane-Emden equation in a half space. Similarly, our proof does not depend on the integral representation of solutions compared to existing ones. The methods developed here should also apply to problems involving more general non-local operators, especially if no equivalent integral equations exist.

math.AP↗

arXiv2024

Attentive-based Multi-level Feature Fusion for Voice Disorder Diagnosis

Voice disorders negatively impact the quality of daily life in various ways. However, accurately recognizing the category of pathological features from raw audio remains a considerable challenge due to the limited dataset. A promising method to handle this issue is extracting multi-level pathological information from speech in a comprehensive manner by fusing features in the latent space. In this paper, a novel framework is designed to explore the way of high-quality feature fusion for effective and generalized detection performance. Specifically, the proposed model follows a two-stage training paradigm: (1) ECAPA-TDNN and Wav2vec 2.0 which have shown remarkable effectiveness in various domains are employed to learn the universal pathological information from raw audio; (2) An attentive fusion module is dedicatedly designed to establish the interaction between pathological features projected by EcapTdnn and Wav2vec 2.0 respectively and guide the multi-layer fusion, the entire model is jointly fine-tuned from pre-trained features by the automatic voice pathology detection task. Finally, comprehensive experiments on the FEMH and SVD datasets demonstrate that the proposed framework outperforms the competitive baselines, and achieves the accuracy of 90.51% and 87.68%.

cs.SD↗

arXiv2024

Creation of independently controllable and long lifetime polar skyrmion textures in ferroelectric-metallic heterostructures

Topological textures like vortices, labyrinths and skyrmions formed in ferroic materials have attracted extensive interests during the past decade for their fundamental physics, intriguing topology, and technological prospects. So far, polar skyrmions remain scarce in ferroelectrics as they require a delicate balance between various dipolar interactions. Here, we report that PbTiO3 thin films in a metallic contact undergo a topological phase transition and stabilize a broad family of skyrmion-like textures (e.g., skyrmion bubbles, multiple π-twist target skyrmions, and skyrmion bags) with independent controllability, analogous to those reported in magnetic systems. Weakly-interacted skyrmion arrays with a density over 300 Gb/inch2 are successfully written, erased and read-out by local electrical and mechanical stimuli of a scanning probe. Interestingly, in contrast to the relatively short lifetime <20 hours of the skyrmion bubbles, the multiple π-twist target skyrmions and skyrmion bags show topology-enhanced stability with lifetime over two weeks. Experimental and theoretical analysis implies the heterostructures carry electric Dzyaloshinskii-Moriya interaction mediated by oxygen octahedral tiltings. Our results demonstrate ferroelectric-metallic heterostructures as fertile playground for topological states and emergent phenomena.

cond-mat.mtrl-sci↗

arXiv2024

Chandra X-ray Observatory Observations of 13 Fermi LAT Sources

In the latest data release from the Fermi $γ$-ray Space Telescope (the 4th Fermi LAT 14 yr Catalog, or 4FGL), more than 50% of the Galactic sources are yet to be identified. We observed 13 unidentified Fermi LAT sources with the Chandra X-Ray Observatory to explore their nature. We report the results of the classification of X-ray sources in the fields of these $γ$-ray sources and discuss the implications for their nature. We use multiwavelength (MW) data for a machine-learning classification, accompanied by a more detailed spectral/variability analysis for brighter sources. Eight 4FGL sources have $γ$-ray pulsars within their position error ellipses. We consider three of these pulsars (PSR J1906+0722, PSR J1105-6037, and PSR J1358-6025) to be detected in X-rays, while PSR J1203-6242 shows a hint of X-ray emission. Within the positional uncertainties of three of the 4FGL sources, we detect X-ray sources that may be yet unknown pulsars, depending on the MW association. In addition to point sources, we discovered two extended sources, one of which is likely to be a bow-shock pulsar-wind nebula associated with PSR J1358-6025. Finally, we classify other X-ray sources detected in these observations and report the most interesting classifications.

astro-ph.HE↗

arXiv2024

A Multiwavelength Machine-learning Approach to Classifying X-ray Sources in the Fields of Unidentified 4FGL-DR4 sources

A large fraction of Fermi-Large Area Telescope (LAT) sources in the fourth Fermi-LAT 14 yr catalog (4FGL) still remain unidentified (unIDed). We continued to improve our machine-learning pipeline and used it to classify 1206 X-ray sources with signal-to-noise ratios >3 located within the extent of 73 unIDed 4FGL sources with Chandra X-ray Observatory observations included in the Chandra Source Catalog 2.0. Recent improvements to our pipeline include astrometric corrections, probabilistic cross-matching to lower-frequency counterparts, and a more realistic oversampling method. X-ray sources are classified into eight broad predetermined astrophysical classes defined in the updated training data set, which we also release. We present details of the machine-learning classification, describe the pipeline improvements, and perform an additional spectral and variability analysis for brighter sources. The classifications give 103 plausible X-ray counterparts to 42 GeV sources. We identify 2 GeV sources as isolated neutron star candidates, 16 as active galactic nucleus candidates, seven as sources associated with star-forming regions, and eight as ambiguous cases. For the remaining 40 unIDed 4FGL sources, we could not identify any plausible counterpart in X-rays, or they are too close to the Galactic Center. Finally, we outline the observational strategies and further improvements in the pipeline that can lead to more accurate classifications.

astro-ph.HE↗

arXiv2024

Fractional quantum anomalous Hall effects in rhombohedral multilayer graphene in the moiréless limit and in Coulomb imprinted superlattice

The standard theoretical framework for fractional quantum anomalous Hall effect (FQAH) assumes an isolated flat Chern band in the single particle level. In this paper we challenges this paradigm for the FQAH recently observed in the pentalayer rhombohedral stacked graphene aligned with hexagon boron nitride (hBN). We show that the external moiré superlattice potential is simply a perturbation in a model with continuous translation symmetry. Through Hartree Fock calculation, we find that interaction opens a sizable remote band gap, resulting an isolated narrow $C=1$ Chern band at filling $ν=1$. From exact diagonalization (ED) we identify FQAH phases at various fillings. But they exist also in the calculations without any external moiré potential. We suggest that the QAH insulator at $ν=1$ should be viewed as an interaction driven topological Wigner crystal with QAH effect, which is then pinned by a small moiré potential. The $C=1$ QAH crystal is robust with a crystal period around $10\mathrm{nm}$ in 4-layer, 5-layer, 6-layer and 7-layer graphene systems. Our work suggests a new direction to exploring the interplay of topology and FQAH with spontaneous crystal formation in the vanishing moiré potential limit. We also propose a new system to generate and control both honeycomb and triangular moiré superlattice potential through Coulomb interaction from another control layer, which can stabilize or suppress the QAH crystal depending on the density of the control layer.

cond-mat.str-el↗

arXiv2024

Strong pairing and symmetric pseudogap metal in double Kondo lattice model: from nickelate superconductor to tetralayer optical lattice

In this work, we propose and study a double Kondo lattice model which hosts robust superconductivity. The system consists of two identical Kondo lattice model, each with Kondo coupling $J_K$ within each layer, while the localized spin moments are coupled together via an inter-layer on-site antiferromagnetic spin coupling $J_\perp$. We consider the strong $J_\perp$ limit, wherein the local moments tend to form rung singlets and are thus gapped. However, the Kondo coupling $J_K$ transmits the inter-layer entanglement between the local moments to the itinerant electrons. Consequently, the itinerant electrons experience a strong inter-layer antiferromangetic spin coupling and form strong inter-layer pairing, which is confirmed through numerical simulation in one dimensional system. Experimentally, the $J_K \rightarrow -\infty$ limits of the model describes the recently found bilayer nickelate La$_3$Ni$_2$O$_7$, while the $J_K>0$ side can be realized in tetralayer optical lattice of cold atoms. Two extreme limits, $J_K \rightarrow -\infty$ and $J_K \rightarrow +\infty$ limit are shown to be simplified to a bilayer type II t-J model and a bilayer one-orbital t-J model, respectively. Thus, our double Kondo lattice model offers a unified framework for nickelate superconductor and tetralayer optical lattice quantum simulator upon changing the sign of $J_K$. We highlight both the qualitative similarity and the quantitative difference in the two sides of $J_K$. Finally, we discuss the possibility of a symmetric Kondo breakdown transition in the model with a symmetric pseudogap metal corresponding to the usual heavy Fermi liquid.

cond-mat.str-el↗