arXiv subjects

Jian Wang

Publications and source records attributed to Jian Wang.

At least 343 records · Page 19Linked to original sources

arXiv2025

Nonlinear Magnetoelectric Edelstein Effect

The linear Edelstein effect is a cornerstone phenomenon in spintronics that describes the generation of spin magnetization in response to an applied electric field. Recent theoretical advances have reignited interest in its nonlinear counterpart, the nonlinear Edelstein effect, in which spin magnetization is induced by a second-order electric field. However, the intrinsic contribution to both effects is generally forbidden in systems preserving time-reversal symmetry ($\mathcal{T}$) or composite symmetries such as $\mathcal{T}τ_{1/2}$, where $τ_{1/2}$ denotes a half-lattice translation. In such systems, spin magnetization typically emerges either from extrinsic mechanisms but limited to metals due to their Fermi-surface property, or from dynamical electric fields with a terahertz driving frequency. Here, we propose a new mechanism for spin magnetization, arising from the interplay of magnetic and electric fields, termed the nonlinear magnetoelectric Edelstein effect. Remarkably, its intrinsic component, determined purely by the material's band structure, can appear even in $\mathcal{T}$-invariant materials, but lacking inversion symmetry ($\mathcal{P}$), including insulators. On the other hand, we illustrate that its extrinsic component can serve as a sensitive indicator of the Néel vector reversal in $\mathcal{P}\mathcal{T}$-symmetric antiferromagnetic materials, offering a novel route for antiferromagnetic order detection. To validate our theory, we perform explicit calculations using a two-band Dirac model and a tight-binding model on a honeycomb lattice, finding that both effects yield sizable spin magnetization. Our findings establish the nonlinear magnetoelectric Edelstein effect as a versatile platform for both exploring nonlinear spin physics and enabling symmetry-based detection of antiferromagnetic order.

cond-mat.mes-hall↗

arXiv2025

SceneMI: Motion In-betweening for Modeling Human-Scene Interactions

Modeling human-scene interactions (HSI) is essential for understanding and simulating everyday human behaviors. Recent approaches utilizing generative modeling have made progress in this domain; however, they are limited in controllability and flexibility for real-world applications. To address these challenges, we propose reformulating the HSI modeling problem as Scene-aware Motion In-betweening - a more tractable and practical task. We introduce SceneMI, a framework that supports several practical applications, including keyframe-guided character animation in 3D scenes and enhancing the motion quality of imperfect HSI data. SceneMI employs dual scene descriptors to comprehensively encode global and local scene context. Furthermore, our framework leverages the inherent denoising nature of diffusion models to generalize on noisy keyframes. Experimental results demonstrate SceneMI's effectiveness in scene-aware keyframe in-betweening and generalization to the real-world GIMO dataset, where motions and scenes are acquired by noisy IMU sensors and smartphones. We further showcase SceneMI's applicability in HSI reconstruction from monocular videos.

cs.CV↗

arXiv2025

Hypergraph Turán problem of the generalized triangle with bounded matching number

Let $\mathcal{H}$ be a 3-graph on $n$ vertices. The matching number $ν(\mathcal{H})$ is defined as the maximum number of disjoint edges in $\mathcal{H}$. The generalized triangle $F_5$ is a 3-graph on the vertex set $\{a,b,c,d,e\}$ with the edge set $\{abc, abd,cde\}$. In this paper, we showed that an $F_5$-free 3-graph $\mathcal{H}$ with matching number at most $s$ has at most $s\lfloor (n-s)^2/4\rfloor$ edges for $n\geq 30(s+1)$ and $s\geq 3$. For the proof, we establish a 2-colored version of Mantel's theorem, which may be of independent interests.

math.CO↗

arXiv2025

PRGB Benchmark: A Robust Placeholder-Assisted Algorithm for Benchmarking Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge, where the LLM's ability to generate responses based on the combination of a given query and retrieved documents is crucial. However, most benchmarks focus on overall RAG system performance, rarely assessing LLM-specific capabilities. Current benchmarks emphasize broad aspects such as noise robustness, but lack a systematic and granular evaluation framework on document utilization. To this end, we introduce \textit{Placeholder-RAG-Benchmark}, a multi-level fine-grained benchmark, emphasizing the following progressive dimensions: (1) multi-level filtering abilities, (2) combination abilities, and (3) reference reasoning. To provide a more nuanced understanding of LLMs' roles in RAG systems, we formulate an innovative placeholder-based approach to decouple the contributions of the LLM's parametric knowledge and the external knowledge. Experiments demonstrate the limitations of representative LLMs in the RAG system's generation capabilities, particularly in error resilience and context faithfulness. Our benchmark provides a reproducible framework for developing more reliable and efficient RAG systems. Our code is available in https://github.com/Alipay-Med/PRGB.

cs.CL↗

arXiv2025

Dance Like a Chicken: Low-Rank Stylization for Human Motion Diffusion

Text-to-motion generative models span a wide range of 3D human actions but struggle with nuanced stylistic attributes such as a "Chicken" style. Due to the scarcity of style-specific data, existing approaches pull the generative prior towards a reference style, which often results in out-of-distribution low quality generations. In this work, we introduce LoRA-MDM, a lightweight framework for motion stylization that generalizes to complex actions while maintaining editability. Our key insight is that adapting the generative prior to include the style, while preserving its overall distribution, is more effective than modifying each individual motion during generation. Building on this idea, LoRA-MDM learns to adapt the prior to include the reference style using only a few samples. The style can then be used in the context of different textual prompts for generation. The low-rank adaptation shifts the motion manifold in a semantically meaningful way, enabling realistic style infusion even for actions not present in the reference samples. Moreover, preserving the distribution structure enables advanced operations such as style blending and motion editing. We compare LoRA-MDM to state-of-the-art stylized motion generation methods and demonstrate a favorable balance between text fidelity and style consistency.

cs.CV↗

arXiv2025

The Anisotropy of Thermal Activation Energy of 2H-NbS$_2$

We investigate the anisotropic flux dynamics in 2H-NbS$_2$ single crystals through temperature-dependent resistance measurements under in-plane ($H \parallel ab$ plane) and out-of-plane ($H \perp ab$ plane) magnetic fields. Analysis of thermally activated flux flow (TAFF) resistance in the superconducting mixed state reveals stark contrasts in thermal activation energy ($U_0$) scaling and field dependence. For $H \perp ab$, $U_0(0.1 \mathrm{T}) = (1228.76 \pm 53.64)$ K follows the Arrhenius relation with a power-law field decay ($U_0\propto H^{-α}$). In contrast, under $H \parallel ab$, $U_0(0.5 \mathrm{T}) = (7205.58 \pm 619.65)$ K aligns with the modified TAFF theory, where the scaling exponent $q = 2$ potentially reflects two-dimensional vortex behavior in the TAFF region, and the field dependence of $U_0$ follows parabolic relation $H^γ\left[1 - \frac{H}{H^{*}}\right]^2$. These results establish 2H-NbS$_2$ as a model system for probing the anisotropy of flux dynamics in layered superconductors.

cond-mat.supr-con↗

arXiv2025

An Improved Boris Algorithm for Charge Particle Orbit in Tokamak Plasmas

An improved Boris algorithm for simulating the motion of charged particles in electromagnetic fields has been developed. This enhancement addresses the issue of inaccurate fast-scale cyclotron phase calculations present in the original Boris algorithm, while preserving its advantage in simulating slow-scale guiding center motion. As a result, it strikes a balance between low and high-frequency dynamics, overcoming the limitations of traditional second-order volume-preserving algorithms (VPAs) which are constrained to a single characteristic frequency. Test particle simulations indicate that, in most cases, the improved Boris algorithm achieves significantly higher accuracy than conventional VPAs when simulating cases involving various frequencies of electric field within a typical Tokamak magnetic field, highlighting its superior efficacy in handling problems across a wide range of characteristic frequencies.

physics.plasm-ph↗

arXiv2025

4KAgent: Agentic Any Image to 4K Super-Resolution

We present 4KAgent, a unified agentic super-resolution generalist system designed to universally upscale any image to 4K resolution (and even higher, if applied iteratively). Our system can transform images from extremely low resolutions with severe degradations, for example, highly distorted inputs at 256x256, into crystal-clear, photorealistic 4K outputs. 4KAgent comprises three core components: (1) Profiling, a module that customizes the 4KAgent pipeline based on bespoke use cases; (2) A Perception Agent, which leverages vision-language models alongside image quality assessment experts to analyze the input image and make a tailored restoration plan; and (3) A Restoration Agent, which executes the plan, following a recursive execution-reflection paradigm, guided by a quality-driven mixture-of-expert policy to select the optimal output for each step. Additionally, 4KAgent embeds a specialized face restoration pipeline, significantly enhancing facial details in portrait and selfie photos. We rigorously evaluate our 4KAgent across 11 distinct task categories encompassing a total of 26 diverse benchmarks, setting new state-of-the-art on a broad spectrum of imaging domains. Our evaluations cover natural images, portrait photos, AI-generated content, satellite imagery, fluorescence microscopy, and medical imaging like fundoscopy, ultrasound, and X-ray, demonstrating superior performance in terms of both perceptual (e.g., NIQE, MUSIQ) and fidelity (e.g., PSNR) metrics. By establishing a novel agentic paradigm for low-level vision tasks, we aim to catalyze broader interest and innovation within vision-centric autonomous agents across diverse research communities. We will release all the code, models, and results at: https://4kagent.github.io.

cs.CV↗

arXiv2025

Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasound

Accurate fetal birth weight (FBW) estimation is essential for optimizing delivery decisions and reducing perinatal mortality. However, clinical methods for FBW estimation are inefficient, operator-dependent, and challenging to apply in cases of complex fetal anatomy. Existing deep learning methods are based on 2D standard ultrasound (US) images or videos that lack spatial information, limiting their prediction accuracy. In this study, we propose the first method for directly estimating FBW from 3D fetal US volumes. Our approach integrates a multi-scale feature fusion network (MFFN) and a synthetic sample-based learning framework (SSLF). The MFFN effectively extracts and fuses multi-scale features under sparse supervision by incorporating channel attention, spatial attention, and a ranking-based loss function. SSLF generates synthetic samples by simply combining fetal head and abdomen data from different fetuses, utilizing semi-supervised learning to improve prediction performance. Experimental results demonstrate that our method achieves superior performance, with a mean absolute error of $166.4\pm155.9$ $g$ and a mean absolute percentage error of $5.1\pm4.6$%, outperforming existing methods and approaching the accuracy of a senior doctor. Code is available at: https://github.com/Qioy-i/EFW.

eess.IV↗

arXiv2025

Hierarchical Corpus-View-Category Refinement for Carotid Plaque Risk Grading in Ultrasound

Accurate carotid plaque grading (CPG) is vital to assess the risk of cardiovascular and cerebrovascular diseases. Due to the small size and high intra-class variability of plaque, CPG is commonly evaluated using a combination of transverse and longitudinal ultrasound views in clinical practice. However, most existing deep learning-based multi-view classification methods focus on feature fusion across different views, neglecting the importance of representation learning and the difference in class features. To address these issues, we propose a novel Corpus-View-Category Refinement Framework (CVC-RF) that processes information from Corpus-, View-, and Category-levels, enhancing model performance. Our contribution is four-fold. First, to the best of our knowledge, we are the foremost deep learning-based method for CPG according to the latest Carotid Plaque-RADS guidelines. Second, we propose a novel center-memory contrastive loss, which enhances the network's global modeling capability by comparing with representative cluster centers and diverse negative samples at the Corpus level. Third, we design a cascaded down-sampling attention module to fuse multi-scale information and achieve implicit feature interaction at the View level. Finally, a parameter-free mixture-of-experts weighting strategy is introduced to leverage class clustering knowledge to weight different experts, enabling feature decoupling at the Category level. Experimental results indicate that CVC-RF effectively models global features via multi-level refinement, achieving state-of-the-art performance in the challenging CPG task.

cs.CV↗

arXiv2025

Geometric quantification of photonic 4D spin-orbit states

High-dimensional photonic states have significantly advanced the fundamentals and applications of light. However, it remains huge challenges to quantify arbitrary states in high-dimensional Hilbert spaces with spin and orbital angular momentum bases. Here we introduce a geometric method to quantify arbitrary states in a 4D Hilbert space by interferometrically mapping them to unified centroid ellipses. Specifically, nine Stokes parameters can be deduced from three ellipses to quantify the 4D spin-orbit states described by SU(4) Poincaré hypersphere. We verify its feasibility by detecting these spin-orbit states gotten by both free-space wave plates and few-mode fibers. For the first time, we completely quantify and reconstruct higher-order modal group evolution of a weakly guiding few-mode fiber under twist perturbation. This geometric quantification, beyond the classical Stokes polarimetry, may pave the way to multi-dimensional optical metrology, sensing, and high-dimensional classical or quantum communications.

physics.optics↗

arXiv2025

A Glimpse of Satellite Galaxies in the Milky Way with the 2.5-meter Wide Field Survey Telescope (WFST): Bootes III and Draco

We carry out deep imaging of the Milky Way satellite galaxies, Bootes III and Draco, with WFST as one pilot observing program to demonstrate the capability of WFST. Combining catalogs with PS1 DR2 and Gaia DR3, we derive proper motions for candidate member stars in these two satellite galaxies over a 12-year time baseline, yielding uncertainties of ~1.8 mas/yr at 21 mag and ~3.0 mas/yr at 22 mag in the r band. The proper motions derived from bright and faint stars are consistent, indicating no significant variation in proper motion across stellar luminosity as these galaxies undergo tidal interactions with the MW. Meanwhile, we suggest that Bootes III represents the bound remnant of the progenitor galaxy that gave rise to the Styx stream, as evidenced by its elongated density profile and overdensity in both spatial and kinematic space. This is the first paper to use WFST to measure the proper motions of faint stars in Milky Way satellite galaxies. More detailed analyses will be presented in forthcoming papers from the wide field survey (WFS) program.

astro-ph.GA↗

arXiv2025

Topology of $3$-manifolds with uniformly positive scalar curvature

In this article, we classify (non-compact) $3$-manifolds with uniformly positive scalar curvature. Precisely, we show that an oriented $3$-manifold has a complete metric with uniformly positive scalar curvature if and only if it is homeomorphic to an (possibly) infinite connected sum of spherical $3$-manifolds and some copies of $\mathbb{S}^1\times \mathbb{S}^2$. Further, we study an oriented $3$-manifold with mean convex boundary and with uniformly positive scalar curvature. If the boundary is a disjoint union of closed surfaces, then the manifold is an (possibly) infinite conned sum of spherical $3$-manifolds, some handlebodies and some copies of $\mathbb{S}^1\times \mathbb{S}^2$.

math.DG↗

arXiv2025

Averaging principles for time-inhomogeneous multi-scale SDEs with partially dissipative coefficients

In this paper, we study averaging principles for a class of time-inhomogeneous stochastic differential equations (SDEs) with slow and fast time-scales, where the drift term in the fast component is time-dependent and only partially dissipative. Under asymptotic assumptions on the coefficients, we prove that the slow component $(X^{\varepsilon}_t)_{t\geq 0}$ converges strongly to the unique solution $(\bar{X}_t)_{t\geq 0}$ to an averaged SDE, when the diffusion coefficient in the slow component is independent of the fast component; on the other hand, we establish the weak convergence of $(X_t^{\varepsilon})_{t\ge0}$ in the space $C([0,T];\mathbb{R}^n)$ and identify the limiting process by the martingale problem approach, when the diffusion coefficient of the slow component depends on the fast component. The proofs of strong and weak averaging principles are partly based on the study of the existence and uniqueness of an evolution system of measures for time-inhomogeneous SDEs with partially dissipative drift.

math.PR↗

arXiv2025

DuetGen: Music Driven Two-Person Dance Generation via Hierarchical Masked Modeling

We present DuetGen, a novel framework for generating interactive two-person dances from music. The key challenge of this task lies in the inherent complexities of two-person dance interactions, where the partners need to synchronize both with each other and with the music. Inspired by the recent advances in motion synthesis, we propose a two-stage solution: encoding two-person motions into discrete tokens and then generating these tokens from music. To effectively capture intricate interactions, we represent both dancers' motions as a unified whole to learn the necessary motion tokens, and adopt a coarse-to-fine learning strategy in both the stages. Our first stage utilizes a VQ-VAE that hierarchically separates high-level semantic features at a coarse temporal resolution from low-level details at a finer resolution, producing two discrete token sequences at different abstraction levels. Subsequently, in the second stage, two generative masked transformers learn to map music signals to these dance tokens: the first producing high-level semantic tokens, and the second, conditioned on music and these semantic tokens, producing the low-level tokens. We train both transformers to learn to predict randomly masked tokens within the sequence, enabling them to iteratively generate motion tokens by filling an empty token sequence during inference. Through the hierarchical masked modeling and dedicated interaction representation, DuetGen achieves the generation of synchronized and interactive two-person dances across various genres. Extensive experiments and user studies on a benchmark duet dance dataset demonstrate state-of-the-art performance of DuetGen in motion realism, music-dance alignment, and partner coordination.

cs.GR↗

arXiv2025

The noncommutative residue, divergence theorems and the spectral geometry functional

In this paper, a simple proof of the divergence theorem is given by using the Dirac operator and noncommutative residues. Then we extend the divergence theorem to compact manifolds with boundary by the noncommutative residue of the B-algebra. Furthermore, we present some spectral geometric functionals which we call spectral divergence functionals, and we calculate the spectral divergence functionals of manifolds with (or without) boundary.

math-ph↗

arXiv2025

Region analysis of $H\to γγ$ via a bottom quark loop

The $H\to γγ$ decay is an ideal process to study the structure of next-to-leading power logarithms induced by quarks due to its simple initial and final states. We perform a region analysis of this process up to two-loop level to inspect the origins of the logarithms. To deal with the endpoint singularities that are prevalent for the next-to-leading power logarithms, we have adopted two different kinds of regulators to exhibit the advantages and disadvantages of each regulator. In the analytic regulator we have chosen, the power of the propagator is changed by $η$. And the endpoint singularities are regulated in the form of $1/η$. These poles cancel between the collinear and anti-collinear sectors since there is no soft mode in such a regulator. In the $Δ$ regulator, the soft sector is important. The leading and next-to-leading logarithms can be inferred from only this sector. Moreover, the symmetry between the collinear and anti-collinear sectors is preserved. After imposing a cut on the bottom quark transverse momentum, the leading order result is finite in each sector. We also discuss the next-to-next-to-leading power contributions and find that the potential factorization formulae involve two-dimensional endpoint singularities. Our region analysis could help to develop sophisticated factorization and resummation schemes beyond leading power.

hep-ph↗

arXiv2025

Next-to-next-to-leading order threshold soft function for $tW$ production

We compute the two-loop soft function for the associated production of a top quark and a $W$ boson near the threshold, where the invariant mass of the $tW$ system approaches the collider energy. We employ the reverse unitarity technique and integration-by-parts identities to reduce soft loop integrals to a minimal basis of master integrals. These master integrals are analytically evaluated using the method of differential equations, yielding results expressed in terms of multiple polylogarithms. Additionally, we analyze the asymptotic behavior of the soft function in the low and high energy limits. Our results provide a vital component for threshold resummation at the next-to-next-to-next-to-leading logarithmic level in this process.

hep-ph↗