arXiv subjects

Yong Xu

Publications and source records attributed to Yong Xu.

At least 19 recordsLinked to original sources

arXiv2026

Weak-Signal Amplification through Stochastic Resetting

Stochastic resetting (SR) offers a distinct route to amplifying weak periodic signals in nonlinear systems. Here, we demonstrate SR-induced resonance (SRIR) in a system with double-well potential driven by a weak periodic force, where the amplitude of the mean periodic response exhibits a pronounced maximum at an optimal SR rate. The underlying mechanism exploits dynamical instability: SR repeatedly brings the system close to an unstable state, where the phase of the periodic force at each reset biases the subsequent motion toward one of the stable states. The local instability then amplifies this weak bias into a large-scale response. The SRIR arises from a competition between rare resets that limit phase-dependent state selection and frequent resets that prevent the system from fully relaxing to the selected state. We develop a general renewal formulation for the periodic response and a coarse-grained approximation that predicts the optimal resetting rate, revealing the low-frequency scaling $r_{\rm opt}(ω)\proptoω^{2/3}$, in agreement with numerical simulations. SRIR is fundamentally distinct from conventional noise-induced resonance (NIR), which relies on noise-activated interwell transitions whose rates are periodically modulated by the external driving force. In the weak-forcing and slow-driving regime, SRIR can achieve much stronger signal amplification than NIR. SRIR persists for a finite spread of resetting positions, and our framework extends beyond bistability. We further discuss how SRIR and NIR can be selectively controlled. Our work proposes a novel approach to the classical paradigm of stochastic resonance.

cond-mat.stat-mech↗

arXiv2026

At the Highest-Frequency Frontier: Thermal Gravitational Waves from Supernovae

Core-collapse supernovae can be powerful sources of high-frequency gravitational waves (GWs) as the extreme temperatures and densities in the core allow a considerable number of gravitons to be produced thermally. We present the first systematic calculation of the thermal GW spectrum of a core-collapse supernova. By evaluating the dominant emission channels---including electron and proton photoproduction ($γe^{-}\to he^{-}$, $γp\to h\,p$) and gravitational bremsstrahlung ($e^{-}p\to e^{-}p\,h$, $e^{-}e^{-}\to e^{-}e^{-}h$)---we find that a core-collapse supernova generates a total thermal GW luminosity of $1.27\times10^{26}$ W, approximately 18 orders of magnitude larger than that of the Sun. The thermal GW spectrum peaks at energies of $\mathcal{O}(0.1\,{\rm GeV})$, extending the predicted spectrum of known GW sources to an unprecedented high-frequency regime. Our results provide a benchmark thermal GW spectrum at the high-frequency frontier.

hep-ph↗

arXiv2026

Editing Images into Reusable Layers via Compositional Edit-Prior Distillation

High-quality image editing requires precise modification of target regions while preserving the content of unrelated regions. Existing editing models produce flattened outputs that limit precise local manipulation and layer reuse, while decomposition methods typically optimize layer recovery separately from instruction-driven editing. To this end, we introduce LayerEditor, a unified framework that couples instruction-guided image editing with controllable generation of reusable RGBA layers. At its core, In-Context RGB-Alpha Token Modeling jointly represents the edited canvas and an ordered sequence of RGB-Alpha layers within a single token sequence, enabling semantic edits and explicit control over individual layer content. To retain the pretrained model's editing capability during adaptation to layered outputs, we propose Compositional Edit-Prior Distillation, which transfers its editing prior to both the edited canvas and the image composed from the predicted layers. To support training and evaluation, we construct LayerEditor-100K, a large-scale paired image-editing dataset with ordered RGBA layer annotations, and introduce dedicated benchmarks for layered editing. Extensive experiments demonstrate that LayerEditor maintains strong editing fidelity while generating accurate RGBA layers, supporting both flexible layer-level manipulation and multi-layer decomposition.

cs.CV↗

arXiv2026

Big Bang Nucleosynthesis Constraint on $B$-mesogenesis

$B$-mesogenesis is a low-temperature baryogenesis mechanism, that uses $CP$-violation of the $B$ mesons in the standard model to fulfill the Sakharov conditions. It requires a late period of matter domination by a scalar field $Φ$ that decays into $b\bar b$ quarks, reheating the Universe below the QCD phase transition. We investigate the impact of this mechanism on Big Bang Nucleosynthesis, with respect to the reheating temperature $T_{rh}$, and the mass of $Φ$. Injection of hadrons skewing neutron/proton interconversions are the most important effect, followed by neutrino heating from $Φ$ decays. For $M_Φ\lesssim 100\,$GeV, $T_{rh}$ must be $\gtrsim 10\,$MeV.

hep-ph↗

arXiv2026

High Energy Neutrinos, Gravitational Waves, and Dark Matter from a Cosmological First Order Phase Transition

We investigate high energy neutrinos (HE$ν$s) from the decay of superheavy dark matter (DM) produced during a cosmological first order phase transition. Adopting the filtered DM production mechanism, we revisit the relic abundance calculation by incorporating recent progress in determining the bubble wall velocity from hydrodynamics. We show that DM with mass $m_{\text{DM}}\simeq 4.4\times10^{8}~\text{GeV}$ can reproduce the observed relic abundance and accommodate ultra high energy neutrino events such as KM3-230213A, while the same phase transition simultaneously generates a stochastic gravitational wave (GW) background. Although the GW signal (which peaks at $10^{4}\,\text{Hz}$) is not yet observable in this example, at lower DM masses $m_{\text{DM}} \lesssim10^{6}~\text{GeV}$, the neutrino spectrum shifts into the energy range relevant for IceCube observations, while the associated GW signal moves to lower frequencies and can reach the projected sensitivities of Cosmic Explorer, BBO, and DECIGO. This gives a multimessenger connection between HE$ν$s and GWs, with both signals connected to DM production in a first order phase transition.

hep-ph↗

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

Deep Learning GW Quasiparticle Hamiltonians for Many-Body Excited-State Electronic Structure at Scale

Accurate quasiparticle electronic structures are the foundation for understanding excited-state properties of materials and explaining optoelectronic, quantum, and transport phenomena. First-principles GW calculations nevertheless remain computationally intensive for large or configurationally complex systems. Here we introduce DeepH-GW, a deep-learning framework that predicts an effective GW quasiparticle Hamiltonian directly from atomic structure. Building on the local, equivariant message-passing architecture of DeepH, DeepH-GW is trained on high-fidelity plane-wave GW calculations through a real-space Hamiltonian-reconstruction interface. This approach combines the systematic accuracy and broad chemical applicability of plane-wave methods with linear-scaling neural-network inference. Across the systems examined, DeepH-GW reproduces quasiparticle band structures with errors on the order of a few meV. Moreover, we show that, despite the intrinsic nonlocality of many-body interactions, DeepH-GW exhibits strong cross-scale transferability from relatively small training structures to substantially larger supercells. We demonstrate that the framework can accurately capture GW-level electron-phonon band-gap renormalization through predictions in supercells with thermal displacements, illustrating the power of the approach. DeepH-GW therefore provides a practical route toward large-scale many-body simulations and foundation models for excited-state electronic structure.

cond-mat.mes-hall↗

arXiv2026

One Prompt Does Not Fit All: Self-Meta-Evolve for Personalized Information Extraction

Large language models (LLMs) are increasingly deployed for enterprise information extraction (IE), where the same document must be reorganized differently for each user. Existing prompt optimization methods, however, rely on a single prompt optimized against a global objective, which is misaligned with the inherent user heterogeneity of real workplaces. We formulate enterprise IE as per-user prompt adaptation under interaction feedback and propose Self-Meta-Evolve, a hierarchical framework that maintains a dedicated prompt for each user and continuously refines it through a dual-loop process: an inner loop that edits structured prompts based on persona-conditioned feedback, and an outer loop that evolves the meta-prompt itself by distilling successful editing patterns. To enable scalable training and evaluation, we release a persona-driven IE benchmark of 292 simulated enterprise users, paired with a reproducible persona-generation pipeline grounded in O*NET occupational taxonomies. On this benchmark, Self-Meta-Evolve achieves a 74.58% success rate, outperforming the strongest prompt-optimization baseline by 13.56 absolute points, and reaches 52.54\% within only two iterations. A double-blind human study with twenty real professionals further confirms that prompts adapted by our framework win against static baselines in 71% of pairwise comparisons.

cs.AI↗

arXiv2026

Time-Efficient Iterative Learning Planning for Safety-Critical Dynamic Obstacle Avoidance

Autonomous mobile robots require timeefficient planning and safety-critical dynamic obstacle avoidance under constrained onboard computation. While Iterative Learning Planning (ILP) offers lightweight and efficient traversal planning, it lacks explicit mechanisms for dynamic obstacle perception and avoidance. This article extends ILP to safety-critical navigation in dynamic environments by integrating an anticipatory risk-blended control barrier function (ARB-CBF). The extended ILP learns traversal-speed and steering-bias profiles via a fractionalpower update based on local obstacle risk, generating nominal control commands that ARB-CBF modifies at runtime for real-time safety guarantees. Algorithmic analysis demonstrates that the ILP replanning stage scales at O(kN) for k iterations and N waypoints, while ARB-CBF executes with linear complexity. Comprehensive simulations and real-world experiments validate the framework, demonstrating superior temporal efficiency and safety with lower computational overhead compared to optimizationbased baselines, making it highly suitable for resourceconstrained platforms.

cs.RO↗

arXiv2026

Fractional Chern Insulators in Twisted Bilayer Optical Lattices

Twisted bilayer materials provide a versatile platform for realizing correlated topological states. Motivated by the recent experimental realization of atomic Bose-Einstein condensates in twisted bilayer optical lattices, we investigate the emergence of fractional topological phases in such systems. For experimentally realistic parameters, the single-particle spectrum hosts a pair of quasi-degenerate nearly flat moiré bands. We show using self-consistent Hartree-Fock calculations that atomic interactions spontaneously break time-reversal symmetry, lift the quasi-degeneracy, and generate an isolated nearly flat band with a nonzero Chern number. At fractional filling, exact diagonalization of the Hamiltonian projected to this band reveals a threefold degenerate ground-state manifold and a characteristic particle entanglement spectrum, providing strong evidence for a fractional Chern insulator. The resulting many-body gap is of order $10^{-3}E_R$, corresponding to a nanokelvin energy scale accessible to state-of-the-art cold-atom experiments. Our scheme requires neither spin-orbit coupling, as in transition metal dichalcogenides, nor a finite magnetic field, as in existing twisted-bilayer-graphene experiments, providing a highly tunable route to strongly correlated topological phases in twisted bilayer optical lattices.

cond-mat.quant-gas↗

arXiv2026

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation

In the era of information explosion, efficiently leveraging large-scale unlabeled data while minimizing the reliance on high-quality pixel-level annotations remains a critical challenge in the field of medical imaging. Semi-supervised learning (SSL) enhances the utilization of unlabeled data by facilitating knowledge transfer, significantly improving the performance of fully supervised models and emerging as a highly promising research direction in medical image analysis. Inspired by the ability of Vision Foundation Models (e.g., SAM-2) to provide rich prior knowledge, we propose SSS (Semi-Supervised SAM-2), a novel approach that leverages SAM-2's robust feature extraction capabilities to uncover latent knowledge in unlabeled medical images, thus effectively enhancing feature support for fully supervised medical image segmentation. Specifically, building upon the single-stream "weak-to-strong" consistency regularization framework, this paper introduces a Discriminative Feature Enhancement (DFE) mechanism to further explore the feature discrepancies introduced by various data augmentation strategies across multiple views. By leveraging feature similarity and dissimilarity across multi-scale augmentation techniques, the method reconstructs and models the features, thereby effectively optimizing the salient regions. Furthermore, a prompt generator is developed that integrates Physical Constraints with a Sliding Window (PCSW) mechanism to generate input prompts for unlabeled data, fulfilling SAM-2's requirement for additional prompts. Extensive experiments demonstrate the superiority of the proposed method for semi-supervised medical image segmentation on two multi-label datasets, i.e., ACDC and BHSD. Notably, SSS achieves an average Dice score of 53.15 on BHSD, surpassing the previous state-of-the-art method by +3.65 Dice. Code will be available at https://github.com/AIGeeksGroup/SSS.

cs.CV↗

arXiv2026

A Self-Adaptive First-Principles Approach for Magnetic Excited States

The profound impact of excited magnetic states on the intricate interplay between electron and lattice behaviors in magnetic materials is a topic of great interest. Unfortunately, despite the significant strides that have been made in first-principles methods, accurately tracking these phenomena remains a challenging and elusive task. The crux of the challenge that lies before us is centered on the intricate task of characterizing the magnetic configuration of an excited state, utilizing a first-principle approach that is firmly rooted in the ground state of the system. We propose a versatile self-adaptive spin-constrained density functional theory formalism. By iteratively optimizing the constraining field alongside the electron wave function during energy minimization, we are able to obtain an accurate potential energy surface that captures the longitudinal and transverse variations of magnetization in itinerant ferromagnetic Fe. Moreover, this technique allows us to identify the subtle coupling between magnetic moments and other degrees of freedom by tracking energy variation, providing new insights into the intricate interplay between magnetic interactions, electronic band structure, and phonon dispersion curves in single-layered CrI$_3$. This new methodology represents a significant breakthrough in our ability to probe the complex and multifaceted properties of magnetic systems.

cond-mat.mtrl-sci↗

arXiv2026

Ab initio-based Deep-Learning Prediction of Carrier Mobility in Strongly Anharmonic Materials

Predicting charge transport in strongly anharmonic materials, particularly ultralow thermal conductors, remains a major challenge for first-principles methods. In such systems, perturbative treatments of electron-phonon interactions and the harmonic phonon picture often break down, necessitating non-perturbative approaches. The ab initio Kubo-Greenwood(aiKG) formalism provides a rigorous framework for evaluating temperature-dependent carrier transport beyond the harmonic approximation. Nevertheless, its practical application is computationally demanding because it requires large supercells, extensive statistical sampling, and extrapolation to the zero-frequency limit. In this work, we introduce an artificial-intelligence(AI)-assisted aiKG framework that incorporates the deep-learning Hamiltonian model. By predicting the Kohn-Sham Hamiltonian with sub-meV accuracy for supercells of up to 250 atoms, the model bypasses the costly iterative self-consistent field calculations while retaining first-principles reliability within the scope of effects captured by the training data. Using a strongly anharmonic thermal insulator, potassium iodide(KI) as a benchmark system, we demonstrate that the proposed approach enables efficient simulations of electronic structure and transport properties from a large supercell. The framework reproduces temperature-dependent carrier mobilities, spectral functions, and effective masses in close agreement with the underlying density functional theory while reducing computational cost to 10%. These results suggest that the AI-assisted aiKG framework can make non-perturbative transport calculations tractable for strongly anharmonic materials, opening a scalable route towards realistic simulations and accelerated discovery of new functional materials.

cond-mat.mtrl-sci↗

arXiv2026

Inverse Problems for Partial Differential Equations with Jump Discontinuities in Coefficients via Two-Stage Physics-Informed Deep Learning and Statistical Mixture Models

This work proposes a two-stage physics-informed deep learning framework that combines neural-network-based sampling with statistical inference and constrained parameter refinement. In the first stage, a dual-network physics-informed architecture is used, where a main network approximates the PDE solution and an auxiliary coefficient sub network provides a relaxed continuous surrogate of the true discontinuous coefficient field. A gradient-adaptive weighting strategy is incorporated into the physics residual to improve residual training and enhance sampling reliability near possible discontinuity regions. The sampled coefficient values are then analyzed using Bayesian learning for Gaussian mixture models and birth-death Markov chain model selection, which estimate the number of coefficient regimes and provide heuristic search intervals for coefficient values and candidate transition regions. In the second stage, the inverse problem is reformulated as a constrained physics-informed estimator, in which the coefficient is represented explicitly as a hard piecewise-constant function over the spatiotemporal domain. Numerical experiments on different PDE types with jump-discontinuous coefficients demonstrate that the proposed framework achieves accurate parameter estimation with acceptable computational costs compared to existing methods. This work provides an effective integrated workflow for inverse problems governed by PDEs with discontinuous parameter structures, particularly in nonstationary and heterogeneous systems.

stat.ML↗

arXiv2026

Identifying parameter couplings and uncertainties of mixed-noise stochastic systems via full-covariance Gaussian mixture network

Parameter identification of stochastic dynamical systems driven by mixed noises is challenging due to intractable likelihood functions. We propose PENN-GMD, a parameter estimation neural network that maps partially observed trajectories to a Gaussian mixture distribution (GMD) over the system parameters. Unlike conventional uncertainty estimates, the GMD employs full covariance matrices to explicitly reveal parameter couplings and multi-modal likelihood structures. The network is trained by minimizing the negative log-likelihood via a surjective parameterization that hard-encodes all GMD constraints, thereby approximating the true likelihood. We validate the method on five numerical examples with increasing complexity, including systems driven by fractional Gaussian and Lévy noises, oscillators with colored noise, coupled neurons under different observability, and an aeroelastic airfoil with unidentifiable stochastic disturbances. Results demonstrate that PENN-GMD accurately recovers likelihood distributions, captures parameter couplings, and naturally diagnoses non-identifiability through variance broadening or mode splitting. These capabilities establish PENN-GMD as a practical tool for uncertainty-aware parameter identification in complex stochastic systems where conventional likelihood-based methods are infeasible.

stat.ML↗

arXiv2026

StageWAM: Joint-Embedding Stage Prediction for World-Action Models in Robot Manipulation

Generalist robot policies aim to map multimodal observations and linguistic task instructions to actions across diverse tasks. However, existing methods typically represent the future as a fixed, short video-action chunk. This short-term future captures local scene evolution for action execution, but it does not explicitly describe the stage-level future that specifies how a task should progress from its current stage to the next. We therefore distinguish two complementary futures for robot manipulation: a short-term physical future to capture local scene evolution and a stage-level semantic future to represent task progress. We introduce StageWAM, which augments a Motus-based World Action Model (WAM) with Stage-JEPA, a goal-conditioned Joint-Embedding Predictive Architecture (JEPA) predictor. Given the current observation and task instruction, Stage-JEPA uses a frozen V-JEPA2 encoder to extract the current-state representation and predicts the latent target of the next inferred stage. Across 50 RoboTwin 2.0 tasks in clean and randomized environments, StageWAM achieves 90.25% overall success and reduces the mean number of execution steps in successful rollouts by 5.97% relative to the strongest baseline.

cs.RO↗

arXiv2026

Spin-Chirality-Driven Bulk Photovoltaic Effect in van der Waals Magnet CrSBr

The bulk photovoltaic effect (BPVE) can be greatly enriched in magnetic materials. Here, we establish vector spin chirality as a tunable knob for generating an unconventional time-reversal-even magnetic BPVE, comprising the chiral shift current (CSC) and chiral injection current (CIC). Using bilayer antiferromagnetic (AFM) CrSBr as a prototype, we theoretically demonstrate the emergence of CSC and CIC. Compared with conventional photovoltaic currents arising from noncentrosymmetric crystal structures or collinear magnetic orderings, CSC and CIC not only possess comparable magnitudes but also exhibit exceptional tunability. Specifically, they can be switched on and off by magnetic-field-induced spin canting, reversed in direction upon canting-direction reversal, and continuously modulated in intensity via canting-angle variation. Furthermore, we reveal an unusual optical transition channel governing both currents in CrSBr. Our work establishes an unconventional magnetic BPVE with remarkable controllability, paving the way for applications in optoelectronics and magnetic sensing in noncollinear magnets.

cond-mat.mtrl-sci↗

arXiv2026

NCGR: Noise-Conditional Gated Rectification for Camera Extrinsic Perturbations in BEV 3D Object Detection

Camera-based bird's-eye-view (BEV) 3D detection typically assumes accurate and fixed camera extrinsics. In detectors using spatial cross-attention (SCA), extrinsic perturbations displace the image-plane projections of BEV reference points, causing queries to sample features from incorrect regions and degrading detection performance. To address this failure mode, Noise-Conditional Gated Rectification (NCGR) is proposed to compensate for projection errors without explicitly estimating a full six-degree-of-freedom extrinsic correction. For each query-camera pair, a 2D rectification offset is predicted and modulated by a camera-level gate to rectify the base projection before native deformable sampling. During training, the perturbation-derived quantities used to construct the condition and gate are gradually replaced through scheduled interpolation by counterparts generated from an auxiliary scalar predicted from camera features. This transition enables blind inference without perturbation metadata. During training, a weight-shared clean-teacher/perturbed-student pair is used, and the rectification module is supervised by a BEV-consistency objective between the two branches. NCGR is evaluated on nuScenes with simulated dynamic and static extrinsic perturbations. In a five-camera dynamic stress test, NCGR achieves 39.69% NDS, compared with 28.00% for BEVFormer and 33.23% for CAPE. Under clean extrinsics, NCGR maintains performance comparable to that of BEVFormer.

cs.CV↗