arXiv subjects

Yujie Zhu

Publications and source records attributed to Yujie Zhu.

At least 19 recordsLinked to original sources

arXiv2026

Closed-Form Nonlocal Shrinkage for Multiplicative Image Denoising and SAR Despeckling

Multiplicative noise poses a challenge in coherent and signal-dependent imaging owing to its intensity-dependent variance and frequently non-Gaussian distribution. We propose a deterministic nonlocal estimator that combines a logarithmic Yeo--Johnson transformation, patch grouping, an adaptive singular basis, and sparse shrinkage. The orthonormal group dictionary makes the weighted Lasso separable and yields an exact coefficient-wise soft-threshold solution. This solution replaces the iterative inner solver and expresses patch reliability and atom importance through a single threshold field. Since the dictionary is estimated from the noisy group, we introduce a random-matrix correction governed by the group aspect ratio $γ=p^2/K$. The correction links patch size, group size, and shrinkage strength. Experiments cover gamma-corrupted images from three standard benchmarks and real synthetic aperture radar (SAR) imagery from five sensors. The method gives the best result in 18 of 24 PSNR/SSIM comparisons with twelve published methods and the lowest mean ratio-image deviation across six real SAR configurations. These results support geometry-calibrated nonlocal modeling for structure-preserving image restoration, with SAR despeckling serving as a demanding application. Code is available \href{https://github.com/Teriri1999/Geometry-Calibrated-Closed-Form-Shrinkage-for-SAR-Despeckling}{here}.

cs.CV↗

arXiv2026

Flexo-Strain Engineering of Phonons and Ferrons in Thin Films of Van der Waals Ferrielectrics

The influence of the flexoelectric coupling on the fluctuations of electric polarization and elastic strains can lead to the principal changes of the dispersion law of soft optical and acoustic phonons and ferrons in a bulk van der Waals ferrielectric. Since the size, gradient and strain effects determine phase diagrams and polarization behavior in thin films, it is reasonable to assume that the flexocoupling and mismatch strains should have a strong influence on the dispersion of phonons and ferrons in thin ferroelectric films. Using the Landau-Ginzburg-Devonshire approach, in this work we reveal that the dispersion of soft optical and acoustic phonons and ferrons is strongly dependent on the sign and magnitude of elastic strains, which originate from the lattice constants mismatch in thin strained films of van der Waals ferrielectric CuInP2S6. In particular, the frequency of acoustic phonons and ferrons approaches zero at nonzero wavevectors k>k_cr, where the critical value of the wavevector k_cr is determined by the mismatch strain, flexoelectric coupling strength and temperature. Zeroing of the acoustic phonon frequency, that appears with increase of tensile strains, indicates a possible emergence of a spatially modulated incommensurate polar phase induced by the flexo-strain effects. Analytical results, derived in this work, open the way for flexo-strain engineering of soft phonon and ferron dispersion in thin films of van der Waals ferrielectrics.

cond-mat.mtrl-sci↗

arXiv2026

$γ$-Bridge: A Look-Parametric Diffusion Bridge

Multiplicative Gamma noise is a signal-dependent degradation in coherent imaging; synthetic aperture radar (SAR) despeckling is its most prominent real-world instance. Existing diffusion denoisers parameterize their forward process by abstract signal-to-noise schedules rather than by the physical look number $L$, so different deployment scenarios typically require separately trained models, and transfer from synthetic Gamma training to real SAR remains challenging without clean ground truth. We introduce $γ$-Bridge, a look-parametric bridge whose schedule $L(t)$ connects the noisy observation at $L_{obs}$ to the clean limit through exact multiplicative Gamma marginals. Its closed-form Gamma--Lévy reverse posterior admits both stochastic and deterministic processes, while observation conditioning and a two-step consistency loss stabilize multi-step inference in the low-SNR single-look regime. Because bridge time directly represents $L$, one conditioned network can smart-start from any admissible input look and stop at a target look number. These two orthogonal controls enable zero-shot restoration over the full admissible grid after training only at $L_{obs} = 1$ on natural images with synthetic Gamma corruption. Combined with a homogeneous-patch look estimator, $γ$-Bridge processes data from six spaceborne and airborne SAR sensors without sensor-specific fine-tuning, achieving leading results on standard synthetic benchmarks while providing physically interpretable input and output controls absent from prior denoisers. Codes are released \href{https://github.com/Teriri1999/GammaBridge}{here}.

cs.CV↗

arXiv2026

CLIP-Guided Label-Free Discriminative Region Scoring for Fine-Grained Classification

Recent vision models such as CLIP and SAM enable training-free segmentation and semantic encoding for fine-grained classification. A common approach is to compare the representations of segmented image regions with the text prompt embeddings of the corresponding labels. However, it remains unclear how different local regions and CLIP-based scoring strategies affect the selection of discriminative evidence, especially when ground-truth labels are unavailable. In this paper, we propose a unified CLIP-guided label-free region scoring framework for fine-grained classification. The framework evaluates cosine similarity-based, margin-based, and entropy-based scoring strategies using both SAM-generated masks and random crops, and introduces two label-free pseudo-label variants based on global image embeddings and local region embeddings. We conduct experiments on five fine-grained classification datasets to systematically compare different region generation methods and scoring strategies. The results show that Soft Negative Margin scoring achieves the strongest performance, and pseudo-label scoring closely approximates true-label performance. Although SAM produces semantically meaningful masks, random-crop-based pseudo-label scoring consistently outperforms SAM-based scoring across all datasets, suggesting that random crops preserve surrounding information and provide more stable semantic context when pseudo-labels are noisy. In addition, SAM masks benefit from aggregating embeddings from all regions, whereas random crops tend to perform better with a smaller top-k subset. These findings provide new insights for fine-grained classification.

cs.CV↗

arXiv2026

Anomalous Piezoelectricity from Polarization-Dependent Electrostriction in Wurtzites

The piezoelectric coefficient is a third-rank tensor connecting the strain or stress with the electric field or polarization, whereas the electrostriction coefficient is a fourth-rank tensor relating the strain to the square of electric polarization. The electrostriction tensor components in the current literature are often treated as constants independent of polarization, resulting in piezoelectric tensor components that are linearly proportional to polarization and the dielectric susceptibility tensor. Here, we study the electrostriction and piezoelectricity in strongly polar wurtzites, including AlN, Al$_{1-x}$Sc$_x$N, Al$_{1-x}$B$_x$N, GaN, and ZnO. We discover that electrostriction and the elastic modulus in wurtzites are both strongly polarization-dependent, and the piezoelectric coefficient is highly nonlinear with respect to polarization, including the anomalous possibility that decreasing polarization increases the electromechanical strain response. These unusual dependencies of electrostriction and piezoelectric effects on polarization arise from the evolution of a layered reference nonpolar structure toward a tetrahedrally coordinated wurtzite network structure as the polarization increases. The findings have important implications in understanding the thermodynamics of the general class of wurtzite ferroelectrics and in manipulating their piezoelectric and ferroelectric behaviors.

cond-mat.mtrl-sci↗

arXiv2026

Terahertz oscillation of $180^{\circ}$ domain walls in ferroelectric membranes

A fundamentally intriguing yet not well understood topic in the field of ferroelectrics is the collective excitation of domain walls (DWs), with potential applications to DW-based nanoelectronic and optoelectronic devices. Here we use dynamical phase-field simulations to identify the collective modes of an Ising-type $180^{\circ}$ DW in a uniaxially strained BaTiO3 membrane. The membrane, which concurrently functions as a cavity for polarization and acoustic waves, permits cavity-enhanced resonant excitation of polarization waves. The simulation reveals an unconventional DW sliding mode that exhibits a bulk-polarization-charge-driven nonzero resonant frequency in the sub-terahertz (THz) regime with a dynamically changing internal structure during sliding. These features differ from the previously reported zero-frequency DW sliding mode or the surface-polarization-charge-driven DW sliding mode. The effect of strain on the frequency of this unconventional DW sliding mode and other previously known THz DW eigenmodes is investigated by dynamical phase-field simulations and interpreted by eigenmode analysis or analytical calculation in a simplified one-dimensional (1D) system. These results provide new insights into the high-frequency dynamics of ferroelectric DWs and suggest opportunities for realizing strain control of phonon-DW resonance, and more broadly, for discovering and controlling unconventional DW modes in conventional domain patterns with applications to reconfigurable THz and optical devices.

cond-mat.mtrl-sci↗

arXiv2026

Strong coupling between coherent ferrons and cavity acoustic phonons

Coherent ferrons, the quanta of polarization waves, can potentially be hybridized with many other quasiparticles for achieving novel control modalities in quantum communication, computing, and sensing. Here, we theoretically demonstrate a new hybridized state resulting from the strong coupling between fundamental-mode (wavenumber is zero) coherent ferrons and cavity bulk acoustic phonons. Using a van der Waals ferroelectric CuInP2S6 membrane as an example, we predict an ultra-strong ferron-phonon coupling at room temperature, where the coupling strength g_c reaches over 10% of the resonant frequency ω_0. We also predict an in-situ bistable electric-field control of mode-specific ferron-phonon hybridization via ferroelectric switching. We further show that CuInP2S6 allows for reaching the fundamentally intriguing but challenging deep strong coupling regime (i.e., g_c/ω_0>1) near the ferroelectric-to-paraelectric phase transition. Our findings establish the theoretical basis for exploiting coherent ferron as a new contender for hybrid quantum system with strong and highly tunable coherent coupling

cond-mat.mtrl-sci↗

arXiv2026

Multimode magnon-phonon cavity driven by symmetry-locked strain fields

Hybrid magnon-phonon cavities with precise control knobs are highly sought after for coherent energy and signal transduction in solid-state platforms. While strain offers a powerful means to tune magnonic characteristics, extending strain engineering into magnon-phonon hybridization has remained elusive. Moreover, implementing controllable strain at the meso- or nanoscale poses a formidable challenge, as spatial inhomogeneity of strain fields often leads to enhanced damping and reduced coherence, thereby hindering device integration and scalability. Here, we present an epitaxial La0.7Sr0.3MnO3/SrTiO3 (LSMO/STO) heterostructure that exhibits strong coupling between the Kittel magnon and acoustic phonon. Leveraging the emergence of structural domains when STO undergoes a cubic-to-tetragonal phase transition, we create anisotropic local strains at the interface. Remarkably, the anisotropic local strain of less than 0.1 % drives the pronounced splitting of the magnon into three branches. Each branch independently hybridizes with acoustic phonons, forming a matrix of magnon-phonon avoided crossings that underpins multimode transduction and programmable networks in frequency and magnetic field space. An analytical model reveals that the split magnon branches are deterministically locked to the three main crystalline axes, enabling robust, orientation-selective control of the hybridized magnon-phonon spectrum against spatial inhomogeneity. Our results establish designed local strain as an exceptionally sensitive trigger for multimode magnon-phonon hybridization in magnetoelastic oxide heterostructures, and highlight local strain engineering as a viable strategy for designing tunable hybrid magnonic and phononic devices.

cond-mat.mtrl-sci↗

arXiv2026

Personalized Cross-Modal Emotional Correlation Learning for Speech-Preserving Facial Expression Manipulation

Speech-preserving facial expression manipulation (SPFEM) aims to enhance human expressiveness without altering mouth movements tied to the original speech. A primary challenge in this domain is the scarcity of paired data, namely aligned frames of the same individual with identical speech but different expressions, which impedes direct supervision for emotional manipulation. While current Visual-Language Models (VLMs) can extract aligned visual and semantic features, making them a promising source of supervision, their direct application is limited. To this end, we propose a Personalized Cross-Modal Emotional Correlation Learning (PCMECL) algorithm that refines VLM-based supervision through two major improvements. First, standard VLMs rely on a single generic prompt for each emotion, failing to capture expressive variations among individuals. PCMECL addresses this limitation by conditioning on individual visual information to learn personalized prompts, thereby establishing more fine-grained visual-semantic correlations. Second, even with personalization, inherent discrepancies persist between the visual and semantic feature distributions. To bridge this modality gap, PCMECL employs feature differencing to correlate the modalities, providing more precisely aligned supervision by matching the change in visual features to the change in semantic features. As a plug-and-play module, PCMECL can be seamlessly integrated into existing SPFEM models. Extensive experiments across various datasets demonstrate the superior efficacy of our algorithm.

cs.CV↗

arXiv2026

HPC-Driven Modeling with ML-Based Surrogates for Magnon-Photon Dynamics in Hybrid Quantum Systems

Simulating hybrid magnonic quantum systems remains a challenge due to the large disparity between the timescales of the two systems. We present a massively parallel GPU-based simulation framework that enables fully coupled, large-scale modeling of on-chip magnon-photon circuits. Our approach resolves the dynamic interaction between ferromagnetic and electromagnetic fields with high spatiotemporal fidelity. To accelerate design workflows, we develop a physics-informed machine learning surrogate trained on the simulation data, reducing computational cost while maintaining accuracy. This combined approach reveals real-time energy exchange dynamics and reproduces key phenomena such as anti-crossing behavior and the suppression of ferromagnetic resonance under strong electromagnetic fields. By addressing the multiscale and multiphysics challenges in magnon-photon modeling, our framework enables scalable simulation and rapid prototyping of next-generation quantum and spintronic devices.

quant-ph↗

arXiv2025

Breaking the Latency Barrier: Synergistic Perception and Control for High-Frequency 3D Ultrasound Servoing

Real-time tracking of dynamic targets amidst large-scale, high-frequency disturbances remains a critical unsolved challenge in Robotic Ultrasound Systems (RUSS), primarily due to the end-to-end latency of existing systems. This paper argues that breaking this latency barrier requires a fundamental shift towards the synergistic co-design of perception and control. We realize it in a novel framework with two tightly-coupled contributions: (1) a Decoupled Dual-Stream Perception Network that robustly estimates 3D translational state from 2D images at high frequency, and (2) a Single-Step Flow Policy that generates entire action sequences in one inference pass, bypassing the iterative bottleneck of conventional policies. This synergy enables a closed-loop control frequency exceeding 60Hz. On a dynamic phantom, our system not only tracks complex 3D trajectories with a mean error below 6.5mm but also demonstrates robust re-acquisition from over 170mm displacement. Furthermore, it can track targets at speeds of 102mm/s, achieving a terminal error below 1.7mm. Moreover, in-vivo experiments on a human volunteer validate the framework's effectiveness and robustness in a realistic clinical setting. Our work presents a RUSS holistically architected to unify high-bandwidth tracking with large-scale repositioning, a critical step towards robust autonomy in dynamic clinical environments.

cs.RO↗

arXiv2025

Uncertainty-Based Smooth Policy Regularisation for Reinforcement Learning with Few Demonstrations

In reinforcement learning with sparse rewards, demonstrations can accelerate learning, but determining when to imitate them remains challenging. We propose Smooth Policy Regularisation from Demonstrations (SPReD), a framework that addresses the fundamental question: when should an agent imitate a demonstration versus follow its own policy? SPReD uses ensemble methods to explicitly model Q-value distributions for both demonstration and policy actions, quantifying uncertainty for comparisons. We develop two complementary uncertainty-aware methods: a probabilistic approach estimating the likelihood of demonstration superiority, and an advantage-based approach scaling imitation by statistical significance. Unlike prevailing methods (e.g. Q-filter) that make binary imitation decisions, SPReD applies continuous, uncertainty-proportional regularisation weights, reducing gradient variance during training. Despite its computational simplicity, SPReD achieves remarkable gains in experiments across eight robotics tasks, outperforming existing approaches by up to a factor of 14 in complex tasks while maintaining robustness to demonstration quality and quantity. Our code is available at https://github.com/YujieZhu7/SPReD.

cs.LG↗

arXiv2025

Forward Convolutive Prediction for Frame Online Monaural Speech Dereverberation Based on Kronecker Product Decomposition

Dereverberation has long been a crucial research topic in speech processing, aiming to alleviate the adverse effects of reverberation in voice communication and speech interaction systems. Among existing approaches, forward convolutional prediction (FCP) has recently attracted attention. It typically employs a deep neural network to predict the direct-path signal and subsequently estimates a linear prediction filter to suppress residual reverberation. However, a major drawback of this approach is that the required linear prediction filter is often excessively long, leading to considerable computational complexity. To address this, our work proposes a novel FCP method based on Kronecker product (KP) decomposition, in which the long prediction filter is modeled as the KP of two much shorter filters. This decomposition significantly reduces the computational cost. An adaptive algorithm is then provided to iteratively update these shorter filters online. Experimental results show that, compared to conventional methods, our approach achieves competitive dereverberation performance while substantially reducing computational cost.

eess.AS↗

arXiv2025

Magneto-optical spectroscopy based on pump-probe strobe light

We demonstrate a pump-probe strobe light spectroscopy for sensitive detection of magneto-optical dynamics in the context of hybrid magnonics. The technique uses a combinatorial microwave-optical pump-probe scheme, leveraging both the high-energy resolution of microwaves and the high-efficiency detection using optical photons. In contrast to conventional stroboscopy using a continuous-wave light, we apply microwave and optical pulses with varying pulse widths, and demonstrate magnetooptical detection of magnetization dynamics in Y3Fe5O12 films. The detected magneto-optical signals strongly depend on the characteristics of both the microwave and the optical pulses as well as their relative time delays. We show that good magneto-optical sensitivity and coherent stroboscopic character are maintained even at a microwave pump pulse of 1.5 ns and an optical probe pulse of 80 ps, under a 7 megahertz clock rate, corresponding to a pump-probe footprint of ~1% in one detection cycle. Our results show that time-dependent strobe light measurement of magnetization dynamics can be achieved in the gigahertz frequency range under a pump-probe detection scheme.

cond-mat.mes-hall↗

arXiv2025

HGAdapter: Hypergraph-based Adapters in Language Models for Code Summarization and Clone Detection

Pre-trained language models (PLMs) are increasingly being applied to code-related tasks. Although PLMs have achieved good results, they do not take into account potential high-order data correlations within the code. We propose three types of high-order correlations in code tokens, i.e. abstract syntax tree family correlation, lexical correlation, and line correlation. We design a tokens and hyperedges generator to capture these high-order data correlations. We improve the architecture of hypergraph neural networks and combine it with adapter tuning to propose a novel hypergraph-based adapter (HGAdapter) to fine-tune PLMs. HGAdapter can encode high-order data correlations and is allowed to be inserted into various PLMs to enhance performance. Experiments were conducted on several public datasets, including six languages of code summarization and code clone detection tasks. Our methods improved the performance of PLMs in datasets to varying degrees. Experimental results validate the introduction of high-order data correlations that contribute to improved effectiveness.

cs.CL↗

arXiv2025

First-order State Space Model for Lightweight Image Super-resolution

State space models (SSMs), particularly Mamba, have shown promise in NLP tasks and are increasingly applied to vision tasks. However, most Mamba-based vision models focus on network architecture and scan paths, with little attention to the SSM module. In order to explore the potential of SSMs, we modified the calculation process of SSM without increasing the number of parameters to improve the performance on lightweight super-resolution tasks. In this paper, we introduce the First-order State Space Model (FSSM) to improve the original Mamba module, enhancing performance by incorporating token correlations. We apply a first-order hold condition in SSMs, derive the new discretized form, and analyzed cumulative error. Extensive experimental results demonstrate that FSSM improves the performance of MambaIR on five benchmark datasets without additionally increasing the number of parameters, and surpasses current lightweight SR methods, achieving state-of-the-art results.

cs.CV↗

arXiv2025

Tuning Magneto-Optical Zero-Reflection via Dual-Channel Hybrid Magnonics

Multi-channel coupling in hybrid systems makes an attractive testbed not only because of the distinct advantages entailed in each constituent mode, but also the opportunity to leverage interference among the various excitation pathways. Here, via combined analytical calculation and experiment, we demonstrate that the phase of the magnetization precession at the interface of a coupled yttrium iron garnet(YIG)/permalloy(Py) bilayer is collectively controlled by the microwave photon field torque and the interlayer exchange torque, manifesting a coherent, dual-channel excitation scheme that effectively tunes the magneto-optic spectrum. The different torque contributions vary with frequency, external bias field, and types of interlayer coupling between YIG and Py, which further results in destructive or constructive interferences between the two excitation channels, and hence, selective suppression or amplification of the hybridized magnon modes.

cond-mat.mtrl-sci↗

arXiv2025

Theory of terahertz pulse transmission through ferroelectric nanomembranes

An analytical model is developed to predict the temporal evolution of the lattice polarization in ferroelectric nanomembranes upon the excitation by a terahertz (THz) electromagnetic pulse of an arbitrary waveform, and the concurrent transmission of the THz pulse in both the linear and the nonlinear regimes. It involves the use of the perturbation method to solve the equation of motion for the lattice polarization in both unclamped and strained ferroelectric nanomembranes within the framework of Landau-Ginzburg-Devonshire theory. The model is applicable to perovskite oxides such as BaTiO3 and SrTiO3, wurtzite Al1-xScxN, and trigonal LiNbO3. Our analytical model provides a theoretical basis for determining the thermodynamic and kinetic parameters of ferroelectric materials through THz transmission experiment. The calculation results also suggest an approach to reversing the chirality of a circularly polarized THz pulse by harnessing the resonant polarization-photon coupling in ferroelectrics. This capability of chirality reversal, along with the high tunability from a strain applied along any arbitrarily oriented in-plane axis, provides new opportunities for THz wave modulation without relying on complex metasurface designs.

cond-mat.mtrl-sci↗