arXiv subjects

Hongyu Wang

Publications and source records attributed to Hongyu Wang.

At least 19 recordsLinked to original sources

arXiv2026

On the $\mathcal{D}^+_J$ operator on higher-dimensional almost Kähler manifolds

In this paper, we introduce $\mathcal{D}^+_J$, a generalization of $\partial\bar{\partial}$ operator on higher dimensional almost Kähler manifolds. Using the $\mathcal{D}^+_J$ operator, we investigate the $\bar{\partial}$-problem in almost Kähler geometry and explore the generalized Monge-Ampère equation on almost Kähler manifolds. We establish uniqueness up to an additive constant. We also prove a local existence theorem for this equation on almost Kähler four-manifold. At last, we find an elliptical system for $\mathcal{D}^+_J$ operator. As an application, we reorganize the result of Tosatti-Weinkove-Yau in \cite{TWY}.

math.DG↗

arXiv2026

Maxima of the Q-index: Forbidden an odd wheel

In 2013, Freitas, Nikiforov and Patuzzi (EJLA, 2013) proposed the problem of determining the maximum signless Laplacian spectral radius of an $n$-vertex $F$-free graph. Recently, Yuan, Zhai and Zhang (LAA, 2026) determined the maximum signless Laplacian spectral radius of graphs without wheels of sufficiently large order. In this paper, we consider the problem of forbidding a single wheel $W_{2k+1}=K_1\vee C_{2k}$. We show that, for every fixed integer $k\ge 3$ and sufficiently large $n$, the unique extremal graph maximizing the signless Laplacian spectral radius among all $n$-vertex $W_{2k+1}$-free graphs is $K_k\vee(K_2\cup(n-k-2)K_1)$.

math.CO↗

arXiv2026

Global existence and uniqueness of Lions' weak solutions for the 2D inhomogeneous Navier-Stokes equations in bounded domains

The global well-posedness for Lions' weak solutions to the inhomogeneous incompressible Navier-Stokes equations in two-dimensional bounded domains is concerned. When the initial density $0\leq ρ_0\in L^\infty(\Om)$ (allowing for vacuum) and initial velocity $u_0\in L^2_σ(Ω)$, the global existence, regularity, and exponential decay-in-time rates of weak solutions are established via a priori estimates, which rely crucially on the duality of BMO and Hardy space for the pressure term, and the classical Sobolev extension theorem. In particular, for the non-vacuous case where $0< ρ_0\in L^\infty(\Om)$ is bounded away from zero, we further prove the uniqueness of weak solutions in the sense of approximation by strong solutions, based on the key estimate $t^{1/2}\nabla u\in L^2(0,T;L^\infty(Ω))$, which essentially follows from a delicate localized point-wise bound for $\nabla u$ near the boundary. The latter bound is derived by the Green functions estimate for the nonhomogeneous Dirichlet Stokes problem, and the uniform $C^1$ boundary estimate on the remaining homogeneous Stokes problem, through a Stokes decomposition on suitably constructed local $C^2$ domains.

math.AP↗

arXiv2026

Epitaxial Magnon Junctions with Y3Fe5O12/Gd3Ga5O12/Pt-nanoparticles/Y3Fe5O12

The excitation, propagation, manipulation, and detection of magnons lie at the core of magnonics. Among various device architectures, high-quality magnon junctions with a ferromagnetic insulator (FI)/antiferromagnetic insulator (AFI)/ferromagnetic insulator (FI) configuration are predicted to exhibit a near-100% magnon junction ratio (MJR), making them promising candidates for high-performance non-volatile memory applications. Yttrium iron garnet (Y3Fe5O12, YIG) is widely employed as the FI material in such structures for its exceptionally low magnetic damping. However, in previously reported magnon junctions, the epitaxial relationship between the two YIG layers is often disrupted by lattice-mismatched AFI spacers. Although this leads to distinct coercivities, it simultaneously degrades the crystalline quality of the top YIG layer, thereby limiting the magnontransmission efficiency. Here, an epitaxial magnon junction is demonstrated incorporating a hybrid Gd3Ga5O12 (GGG)/Pt-nanoparticles spacer that maintains coherent epitaxy while enabling an antiparallel magnetic configuration. Compared with nickel oxide-based magnon junctions, the coercivity-tuned top YIG layer in our structure exhibits superior crystalline quality and much lower magnetic damping. A clear magnon junction effect is observed with a 43% MJR achieved. The interlayer coupling between the two ferrimagnetic insulators is found to enhance the MJR. These results offer valuable insights for the development of advanced magnon-based spintronic devices.

cond-mat.mtrl-sci↗

arXiv2026

SCDM-HTD: Spectral-Wise Conditional Diffusion Model for Hyperspectral Target Detection

Hyperspectral target detection (HTD) aims to locate and recognize objects at a fine-grained scale, playing a pivotal role in remote sensing. Despite significant progress, current HTD methods primarily focus on feature comparison while neglecting distribution learning for background-target separation, which can limit detection robustness in practical applications. This paper addresses this issue by introducing a spectral-wise conditional diffusion model that reformulates HTD as a task of generating a detection map. Specifically, we treat background and target confidence scores as training samples and use the corresponding pixel spectra as guiding conditions to capture the distributional discrepancy between background and target through diffusion and denoising. To enhance practical applicability, we propose a lightweight conditional denoising network based on multilayer perceptron to map noisy confidence scores to their ideal counterparts and adopt a deterministic reverse non-Markov chain to skip sampling steps, thereby accelerating the generation process. Furthermore, we introduce a composite loss to improve background-target separation. This loss integrates constrained energy minimization loss to enhance target saliency and suppress the background by maximizing the target response while minimizing average output energy, alongside binary cross-entropy loss to classify background and target using pixel-wise labels. Experimental results on four benchmark datasets demonstrate the effectiveness of the proposed method, with improvements in both quantitative metrics and qualitative visual effects.

cs.CV↗

arXiv2026

Characterizing Oblivious Common-Reward Multi-Player Bandits: Universal Stationarity, Coordination Traps, and Certified Global Rates

This paper presents a theoretical analysis of the oblivious common-reward multi-player multi-armed bandit (OC-MPMAB), a minimal multi-player bandit model in which multiple players select arms simultaneously and observe only a shared reward. Crucially, the players are fully oblivious: they do not observe the actions of other players and do not even know whether any other players exist. The contributions of this work are threefold. First, we prove that the independent gradient-bandit updates applied by the players jointly form an unbiased stochastic gradient estimator of a hidden product-policy objective. This property then yields a universal finite-time first-order stationarity guarantee for every instance. Second, we show that stationarity cannot, in general, be strengthened to global optimality. To illustrate this, we identify a product-policy trap obstruction that rules out the guarantee of global optimality. Third, we show that global optimality can be recovered under structural assumptions on the reward table. To make global optimality achievable, we introduce finite-table certificates that separately characterize product-gradient dominance and self-bounding stochastic noise. We then establish certificate-conditioned bounds on the product-policy gap and pseudo-regret. Consequently, the standard $O(1/t)$ product-policy gap and $O(\log T)$ pseudo-regret rates hold when the certificates are uniform.

cs.LG↗

arXiv2026

Optimal Linear Dependence on Boundary Type for Local Gromov Hyperbolicity of the Kobayashi Metric

The Gromov hyperbolicity constant of a metric space $(X,d)$ is the infimum of all $δ\ge0$ such that $(X,d)$ is $δ$-hyperbolic. For a Kobayashi hyperbolic domain $Ω\subset\C^n$ and a boundary point $p\in\partialΩ$, let $δ_{\mathrm{loc}}(Ω,p)$ denote the local Gromov hyperbolicity constant obtained by restricting the points to arbitrarily small Euclidean neighborhoods of $p$, while distances are still measured by the ambient Kobayashi distance $K_Ω$. For each even integer $M\ge2$, let $\mathfrak H(M)$ be the supremum of these constants over all complex dimensions $n\ge2$ and all domains whose boundary is smooth and convex near the distinguished point and has D'Angelo type at most $M$ at that point. We prove \[ \frac{\log 2}{2} M \le \mathfrak H(M) < 36M. \] Thus the optimal universal dependence of the local Gromov hyperbolicity constant on boundary type is linear. We also show that no analogous bound holds for the global Gromov hyperbolicity constant, even among smooth bounded strongly convex domains.

math.CV↗

arXiv2026

TacDyn-WAM: Learning Implicit Tactile Dynamics in a Heterogeneous Visuo-Tactile World Action Model

World action models improve robotic manipulation by conditioning actions on predicted futures, yet existing tactile variants largely inherit video-generation pipelines that reconstruct future tactile observations through iterative denoising. Such prediction can become unreliable under deployment drift: small changes in contact position or force may substantially alter tactile pixels even when the underlying contact evolution remains predictable. We introduce TacDyn-WAM, a heterogeneous visuo-tactile world action model that predicts implicit tactile dynamics rather than reconstructing future tactile observations. It learns TacRep, a dynamics-aware tactile target space trained through masked spatio-temporal prediction on tactile clips and regularized by relational structure distillation. A visual expert and an Implicit Tactile Dynamics Expert predict future visual and tactile representations in separate target spaces while interacting through joint attention; the tactile expert predicts future representations and their changes at multiple horizons in a single forward pass, and a read-only tactile memory supplies the current tactile state. On UniVTAC, TacDyn-WAM achieves an average success rate of 81.5% using only the provided demonstrations, reaching state-of-the-art-level performance and remaining competitive with models pretrained on large-scale visuo-tactile trajectories. Ablations confirm the benefits of both tactile pathways and TacRep over pixel-reconstruction and static alternatives. On five real-robot tasks, TacDyn-WAM reaches 71.0% average success, and modest-scale pretraining raises it to 85.0%, further validating our method.

cs.RO↗

arXiv2026

AI-driven ionic liquid discovery with unified chemical intelligence

The rational discovery of functional solvents remains challenging because macroscopic solvent behaviors emerge from complex many-body interactions across vast chemical spaces. Ionic Liquids (ILs) constitute one of the most chemically diverse solvent families, yet their exceptional structural tunability remains only partially explored owing to the limitations of existing experimental and computational approaches. Here we introduce AIonopedia, a large-language-model-orchestrated agentic framework for IL research. Built around a domain-specific multimodal foundation model, it integrates information retrieval, chemical reasoning and autonomous tool execution within a unified architecture. Pretrained on large-scale unlabeled corpora and fine-tuned using over 80,000 curated measurements, AIonopedia learns expressive joint representations of multicomponent ionic systems, enabling accurate prediction across diverse physicochemical properties. Through a series of literature-grounded evaluations, AIonopedia exhibits robust out-of-distribution generalization, while many-body-expansion-based analysis provides physically grounded explanations of structure-property relationships. By coupling molecular screening and optimization with wet-lab validation in a closed-loop workflow, AIonopedia identifies high-performance ILs and reveals transferable design principles for the capture of various volatile organic compounds. This work establishes an end-to-end AI-driven paradigm for understanding and exploring complex ionic systems, demonstrating the promise of chemically specialized foundation models as a generalizable strategy for scientific discovery.

cs.AI↗

arXiv2026

Global Well-Posedness for Planar Compressible Magnetohydrodynamics with Degenerate Viscosity on Unbounded Domains

The one-dimensional planar compressible magnetohydrodynamic system is studied on unbounded domains. The longitudinal viscosity obeys a power law of the specific volume, which permits it to degenerate as the density approaches vacuum, and the heat conductivity follows a power law of the temperature. For any fixed non-negative exponent in the heat conductivity term, the existence and uniqueness of global strong solutions are established for arbitrarily large initial data, provided that the positive exponent for viscosity is sufficiently small. The specific volume and temperature possess time-independent upper and lower bounds. Moreover, for each admissible viscosity exponent, the solution converges uniformly in space to the equilibrium state as time tends to infinity.

math.AP↗

arXiv2026

The Outer Pressure Problem for the Compressible Navier--Stokes System with Temperature-Dependent Transport Coefficients

We study global strong solutions and their large-time behavior for the one-dimensional outer pressure problem of a viscous, heat-conducting ideal polytropic gas. The viscosity and heat conductivity satisfy $μ=\tildeμθ^α$ and $κ=\tildeκθ^β$. For each prescribed nonnegative pressure and each fixed $β\geq0$, we allow large $H^1$ initial data with positive specific volume and temperature, provided that $α\geq0$ is sufficiently small. When the limiting outer pressure is positive, the solution has uniform bounds and converges to a stationary state. When the limiting outer pressure is zero and the stated weighted pressure conditions hold, the normalized solution converges to an expanding state at an algebraic rate in physical time.

math.AP↗

arXiv2026

Initial and initial boundary value problems for the Navier-Stokes equations with temperature-dependent transport coefficients and large data

We consider initial and initial boundary value problems for the one-dimensional compressible Navier--Stokes equations of an ideal polytropic gas in unbounded domains. The viscosity and heat conductivity are proportional to $θ^α$ and $θ^β$, respectively. For every fixed $β\geq0$ and sufficiently small $α\geq0$, depending on the initial data, we prove the global existence and asymptotic stability of a strong solution for arbitrarily large $H^1$ initial data. Dividing the momentum equation by the viscosity allows us to estimate the specific-volume derivative without an initial $H^2$ assumption.

math.AP↗

arXiv2026

Global stability of superposition of viscous contact wave and rarefaction waves for compressible Navier-Stokes system with temperature-dependent transport coefficients and large data

We study the Cauchy problem for the one-dimensional full compressible Navier--Stokes equations with viscosity $μ(θ)=\tildeμθ^α$ and heat conductivity $κ(θ)=\tildeκθ^β$. For each fixed $β\ge0$, we prove the global stability of the combination of a viscous contact wave with rarefaction waves, provided that the viscosity exponent $α$ and the total wave strength are sufficiently small. The initial perturbation may be large in $H^1$, and the specific volume and temperature are assumed to have positive initial lower bounds. The solution remains uniformly bounded in $H^1$ relative to the wave, and converges uniformly to that wave as time tends to infinity. We show that both the specific volume and the temperature admit time-uniform lower and upper bound. Estimates for the logarithmic volume derivative and the temperature gradient then close the $H^1$ estimates without requiring a second derivative of the initial specific volume.

math.AP↗

arXiv2026

Task-Oriented Semantic Feature Transmission for Multi-Task Satellite Remote Sensing over Low-SNR Channels

Conventional satellite remote sensing transmission follows a reconstruct-then-infer paradigm that optimizes pixel-level fidelity, creating an objective mismatch with downstream tasks such as classification and detection, especially at low SNR. This paper investigates a task-oriented framework that bypasses image reconstruction and directly transmits semantic features extracted by a multitask-pretrained backbone. A lightweight channel adaptation module (CAM) compresses feature dimensionality for bandwidth reduction, and a feature restorer recovers task-relevant structure after channel corruption. With the backbone frozen, the CAM and task-specific downstream heads are jointly optimized with task and feature-level supervision under random-SNR training. Under the adopted AWGN setting, experiments on scene classification and object detection show consistent gains over reconstruction-oriented JSCC baselines across different SNR conditions, with the largest improvements in the low-SNR regime.

cs.CV↗

arXiv2026

STR-Agent: An LLM-Driven Agent for QoS-Aware Routing in LEO Satellite Networks

LEO satellite networks feature dynamic topologies, time-varying links, and diverse service requirements, which make conventional routing schemes difficult to support fine-grained quality-of-service (QoS) provisioning. Existing studies mainly optimize routing over network states with predefined objectives, but rarely address the practical challenge of translating unstructured natural-language service requests into adaptive routing decisions. To bridge this gap, we propose STR-Agent, an LLM-driven framework for QoS-aware routing in LEO satellite networks. The key innovation of STR-Agent lies in unifying intent perception, tool-based execution, experience accumulation, and reflection-based policy adaptation within a single agent architecture. Specifically, the Perception Module converts natural-language requests into structured routing semantics, while the Reflection Module dynamically adjusts the service-to-routing-policy mapping according to real-time congestion conditions and historical routing outcomes, rather than relying on a fixed routing objective. In addition, we develop a specialized perception model, and construct a domain-specific supervised fine-tuning dataset for LEO service understanding. Simulation results in a Walker-Delta constellation show that STR-Agent significantly outperforms conventional baselines: it reduces end-to-end delay by up to 60% compared with DQ-Dijkstra, improves average intent-understanding accuracy from 45.4% to 92.45% after supervised fine-tuning, and the Reflection Module further reduces the delay by 120 ms at 600 Mbps. These results demonstrate the potential of LLM-driven agent architectures to enable service-aware and adaptive QoS routing in future LEO satellite networks.

cs.NI↗

arXiv2026

Wearable Multimodal Human-Machine Interface for Integrated Hand Intentions Decoding in Dynamic Teleoperation

Under ubiquitous teleoperation environments with optically challenging conditions, an interface for tele-operated grasping that combines wearability with precise decoding of hand intentions (hand pose, gestures, and grasping force) is essential. Yet, existing interfaces often fall short in meeting these demands, compromising either the diversity of multiple intentions decoding or wearability. To address this, we developed a novel Multiple Intentions Decoding Human-Machine Interface (MI-DHMI) that integrates high-throughput surface electromyography (sEMG) sensors with hand-mounted and forearm-mounted inertial measurement units (IMUs). The developed interface is supported by a unified framework for simultaneous multiple intentions decoding. By employing multimodal deep learning and hardware design with a low noise floor, the decoding framework selectively focuses on the sEMG components that are genuinely associated with finger movements. This effectively reduces decoding errors caused by sEMG variability during unconstrained upper-limb motions, thereby significantly enhancing robustness. Even under unconstrained wrist and forearm motion, the interface achieves a gesture recognition accuracy exceeding 97%, grasping force estimation with $R^2 = 0.95$, and hand pose decoding consistent with the actual hand pose, outperforming baseline devices and algorithms. Ablation studies further validate the effectiveness of the proposed decoding framework. Finally, two online experiments were conducted to validate the device, demonstrating its superior performance in high-stability tasks, including a pouring task and object grasping. The developed interface provides a new solution of a fully wearable, multiple intentions decoding system, offering effective support for ubiquitous teleoperation and contributing to the advancement of human-machine interaction research.

cs.RO↗

arXiv2026

Counter-rotating density and current structures in a partially magnetized $\mathbf{E}\times\mathbf{B}$ plasma

The first 3D kinetic simulation of a magnetron discharge reproduces the measured rotating spoke and reveals that density and current rotate in opposite directions: the $m=1$ spoke turns in the $\mathbf{E}\times\mathbf{B}$ direction at 90~kHz while $m\simeq16$ electron-cyclotron-drift-type filaments turn at 1.0~MHz, with no net propagation along $\mathbf{B}$. Magnetic drifts exchange far more energy than they deposit, yet their spoke-front heating sustains the ionization. Following helical paths, the anode-directed current is relayed by de-trapping turbulence at the sheath edge (50\%) and by the spoke channel in the bulk (97\%).

physics.plasm-ph↗

arXiv2026

Counting Lattice Points in Minkowski Sums of Cross Polytopes

Motivated by Postnikov's study of lattice-point enumeration in Minkowski sums of simplices, we investigate lattice points in Minkowski sums of cross polytopes and establish analogous results, together with several related consequences. In particular, we introduce the support-enumerator associated with Postnikov's notion of draconian sequences and show that it coincides with the $h^*$-polynomial of the corresponding root polytope. This provides a new interpretation of the $h^*$-polynomial and yields a simple method for computing the volume of the corresponding polytope. By exploiting the symmetry of these root polytopes, we further establish a duality property for support-enumerators, which in turn provides a proof of a conjecture by Athanasiadis and Chapoton concerning the $h$-polynomials of preorders. Consequently, we obtain a formula for the number of lattice points in Minkowski sums of cross polytopes in terms of draconian sequences and show that these polytopes are Ehrhart positive. Furthermore, this formula leads to analogous expressions for the number of lattice points on their boundaries and for their surface volumes.

math.CO↗