arXiv subjects

Ying Liang

Publications and source records attributed to Ying Liang.

At least 19 recordsLinked to original sources

arXiv2026

Perceptually Regularized Diffusion Model for Image Super-Resolution

Image super-resolution, which aims to reconstruct high-resolution images from their low-resolution observations, is fundamental to medical imaging, remote sensing, surveillance, microscopy, and scientific visualization. Traditional model-based methods formulate super-resolution as an inverse problem with hand-crafted regularization priors. While interpretable and theoretically grounded, they rely on fixed assumptions and require computationally intensive iterative solvers. Deep learning methods offer data-driven flexibility by learning nonlinear mappings from low- to high-resolution images, among which diffusion models have achieved particularly impressive perceptual quality. However, the standard diffusion training objective is a pixel-domain noise-prediction loss that does not explicitly enforce perceptual fidelity, which can lead to oversmoothing and loss of fine image structure. To address these limitations, we propose a perceptually regularized diffusion framework that incorporates prior knowledge through perceptual-loss-based regularization, improving training convergence and encouraging the recovery of meaningful image features. Experiments on benchmark datasets demonstrate improved perceptual quality and competitive distortion metrics, highlighting the effectiveness of regularization for diffusion-based super resolution.

eess.IV↗

arXiv2026

Polaron-mediated metal-insulator transition and proton conduction in hydrogenated nickelate perovskites

Nickel-based perovskites, owing to their spontaneous hydrogen uptake and the dramatic increase in resistivity upon hydrogenation, have emerged as promising candidates for proton-conducting fuel cell electrolytes. However, the mechanism of the hydrogen-induced metal-insulator transition (MIT) in rare-earth nickelates remains under debate, particularly regarding whether the doped electrons occupy Ni e$_g$ states or O 2p ligand hole states. Here, we reveal a comprehensive MIT mechanism using first-principles calculations on NdNiO$_3$: the electrons introduced by hydrogen doping occupy the O 2p ligand hole states of the Ni-O hybridized d$_8$L configuration, promoting electron-polaron formation. The resulting electron polarons, together with proton polarons, weaken the Ni-O hybridization and thereby drive the originally itinerant Ni e$_g$ electrons toward localization. This generates a local d8 (t$_{2g}$$^6$e$_g$$^2$) electronic configuration, leading to a Mott transition. In addition, we also find that compared with NdNiO$_3$, SmNiO$_3$ with a smaller A-site ionic radius more readily absorbs hydrogen but exhibits weaker proton diffusion capability. Hydrogenation promotes proton permeation along the [001] direction via the intraoctahedral transfer, whereas the overall proton diffusivity is reduced. These results provide guidance for experimental screening of strongly correlated oxides as electrolyte materials and offer theoretical insights for enhancing proton conductivity in rare-earth nickelates.

cond-mat.mtrl-sci↗

arXiv2026

Tuning superconducting pairing symmetry via a staggered potential in the doped honeycomb Hubbard model

The ability to control superconducting pairing symmetry is crucial for designing unconventional and topological superconductors, yet practical tuning parameters beyond chemical doping remain limited. In this study, we investigate the effect of a tunable sublattice staggered potential on the pairing symmetry in the doped honeycomb Hubbard model. Determinant quantum Monte Carlo at finite temperature and constrained-path quantum Monte Carlo at zero temperature are employed to compute spin susceptibilities and pairing correlations in different channels. We find that increasing the staggered potential suppresses antiferromagnetic fluctuations and, at low doping, induces a transition in the dominant pairing tendency from $d+id$-wave to $f_n$-wave, with consistent results from both quantum Monte Carlo methods. In contrast, at higher doping levels, the system remains dominated by $d+id$-wave pairing even under an enhanced staggered potential. Moreover, strengthening the on-site interaction $U$ enhances the dominant pairing channel, underscoring the essential role of electronic correlations. Our results establish the staggered potential as a practical band-engineering tool for selecting unconventional pairing symmetries without varying the doping concentration, providing inspiration for designing graphene-based artificial superconductors and related doped band insulators such as Li${}_x$MNCl.

cond-mat.str-el↗

arXiv2026

Role of small-radius and high-electronegativity A-Site dopants in enhancing proton transport and stability of perovskite electrolytes

The practical application of BaCeO$_3$-based electrolytes is limited by their poor chemical stability in proton-conducting solid oxide fuel cells. Commonly employed B-site doping strategies typically improve proton transport with limited improvement in stability. Recent experiments show that A-site Ca doping can simultaneously enhance both properties. Here, through first-principles calculations and mechanistic analysis of Ca-doped BaCeO$_3$, we identify the synergistic roles of small-radius, high-electronegativity A-site dopants in governing proton transport and chemical stability in perovskite electrolytes. We show that the higher electronegativity of A-site dopant weakens the A-O ionic bonding, facilitating oxygen-vacancy formation and enhancing proton uptake by increasing the basicity. This weakened A-O interaction also suppresses the formation of impurity phases and reduces the adsorption strength of acidic gases such as CO$_2$ and SO$_2$. The lattice contraction induced by the smaller ionic radius improves thermal stability and can enhance proton diffusion in systems where proton transfer is the rate-limiting step. Furthermore, we find that Ca surface segregation can mitigate grain-boundary resistance effects. Our results demonstrate the advantages of A-site Ca doping in Ba-based electrolytes, clarify the mechanisms by which small-radius, high-electronegativity dopants influence proton transport and chemical stability, and provide guidance for the design of high-performance proton-conducting electrolytes.

cond-mat.mtrl-sci↗

arXiv2026

Seedance 2.0: Advancing Video Generation for World Complexity

Seedance 2.0 is a new native multi-modal audio-video generation model, officially released in China in early February 2026. Compared with its predecessors, Seedance 1.0 and 1.5 Pro, Seedance 2.0 adopts a unified, highly efficient, and large-scale architecture for multi-modal audio-video joint generation. This allows it to support four input modalities: text, image, audio, and video, by integrating one of the most comprehensive suites of multi-modal content reference and editing capabilities available in the industry to date. It delivers substantial, well-rounded improvements across all key sub-dimensions of video and audio generation. In both expert evaluations and public user tests, the model has demonstrated performance on par with the leading levels in the field. Seedance 2.0 supports direct generation of audio-video content with durations ranging from 4 to 15 seconds, with native output resolutions of 480p and 720p. For multi-modal inputs as reference, its current open platform supports up to 3 video clips, 9 images, and 3 audio clips. In addition, we provide Seedance 2.0 Fast version, an accelerated variant of Seedance 2.0 designed to boost generation speed for low-latency scenarios. Seedance 2.0 has delivered significant improvements to its foundational generation capabilities and multi-modal generation performance, bringing an enhanced creative experience for end users.

cs.CV↗

arXiv2026

Spinon Singlet Pairing: Microscopic nature of plaquettes in stripy LDOS

Scanning tunneling microscopy (STM) is a powerful tool for visualizing the local density of states (LDOS) of individual stripes in cuprates. However, the microscopic nature of the observed exotic LDOS patterns and their connection to high-$T_c$ superconductivity remain open questions. Within the framework of the quantum colored string model, we reveal that the ubiquitous $4a_0\times4a_0$ plaquettes originate from either the breaking of local spinon singlet pairs through hole insertion, or the formation of an unpaired spinon upon electron addition in a stripe. Moreover, by comparing our data with LDOS of cuprates, we identify an effect of particle-hole symmetry breaking (PHSB): a $2a_0$ shift, which is predicted and confirmed in a longer stripe ($L=18$). At last, we establish and verify a general relation between hole density and plaquette size across multiple fillings. Our work offers a fresh wavefunction-based perspective on interpreting STM signals in cuprate experiments and demonstrates that their origin may arise from spinon singlet pairing in the ground state of fluctuating stripes, the same mechanism underlying the $d$-wave sign structure [Phys. Rev. Lett. \textbf{137}, 086702 (2026)].

cond-mat.str-el↗

arXiv2026

From Frequency Bias to Spectral Balance: Operator-Aware Preconditioners for PINNs

When neural networks (NNs) are used as a type of nonlinear parametric representation to solve partial differential equations (PDEs), they often display frequency-dependent learning dynamics that can differ from those seen in direct function approximation tasks, resulting from a balance between the frequency bias of the NN representation and that of the underlying differential operator. Although many commonly used NNs exhibit a bias towards low-frequency modes in representation, the presence of differential operators in the loss function, which amplifies high-frequency components, can lead to high frequency bias. In this work, using second order elliptic PDEs as an example, we show how these two factors compete and lead to an overall frequency bias in different situations. Once the balance is determined, it is important to design computational strategies to counter the resulting bias to improve training efficiency. We propose a simple operator-aware preconditioning strategy that rebalances the optimization landscape and the learning dynamics by applying an auxiliary integral operator to the residual. The integral kernel can be the Green's function of a reference elliptic operator or an approximation, and integrates easily with common NN solvers for PDEs. Extensive experiments, including multiscale and variable-coefficient problems, show that the approach restores more balanced learning dynamics across modes and substantially improves both convergency and accuracy.

math.NA↗

arXiv2026

Precompression engineering of metal-insulator transition and magnetism in designed breathing kagome systems

Kagome materials featuring dispersive Dirac cones and topological flat bands exhibit unique electronic and magnetic properties. However, kagome compounds with tunable electrical conductivity remain scarce, which severely impedes their device applications. Here, based on density functional theory (DFT) and Boltzmann transport theory, we introduce the breathing effect into kagome materials $\mathrm{Nb_3XCl_7}$ (X = F, Cl, Br, I) via chemical precompression, thereby inducing a metal-insulator transition and magnetic variation. We determine that the band structures, optical absorption spectra and magnetic ground states agree well with experimental results at the effective correlation strength $U_{\text{eff}} = 2$ eV. The calculated conductivity and magnetic properties reveal that the monolayer $\mathrm{Nb_3Cl_8}$ and $\mathrm{Nb_3XCl_7}$ undergoes transitions from paramagnetic metals to Mott insulators at $U_{\text{eff}} = 1$ eV and $t_{\text{out}}/t_{\text{in}} = 0.6674$, respectively. Our detailed analysis establishes that the stronger breathing effect corresponds to enhanced chemical precompression, which reduces the region of free electron gas between intercell Nb atoms and facilitates the metal-insulator transition. Finally, we propose several viable synthesis routes for $\mathrm{Nb_3FCl_7}$, $\mathrm{Nb_3BrCl_7}$, and $\mathrm{Nb_3ICl_7}$, providing predictive guidance for experimental studies. Our study establishes a practical framework for investigating the breathing effect in correlated kagome systems and yields valuable insights into the mechanisms underlying metal-insulator transition and magnetic properties in real breathing kagome materials.

cond-mat.str-el↗

arXiv2025

Seedance 1.5 pro: A Native Audio-Visual Joint Generation Foundation Model

Recent strides in video generation have paved the way for unified audio-visual generation. In this work, we present Seedance 1.5 pro, a foundational model engineered specifically for native, joint audio-video generation. Leveraging a dual-branch Diffusion Transformer architecture, the model integrates a cross-modal joint module with a specialized multi-stage data pipeline, achieving exceptional audio-visual synchronization and superior generation quality. To ensure practical utility, we implement meticulous post-training optimizations, including Supervised Fine-Tuning (SFT) on high-quality datasets and Reinforcement Learning from Human Feedback (RLHF) with multi-dimensional reward models. Furthermore, we introduce an acceleration framework that boosts inference speed by over 10X. Seedance 1.5 pro distinguishes itself through precise multilingual and dialect lip-syncing, dynamic cinematic camera control, and enhanced narrative coherence, positioning it as a robust engine for professional-grade content creation. Seedance 1.5 pro is now accessible on Volcano Engine at https://console.volcengine.com/ark/region:ark+cn-beijing/experience/vision?type=GenVideo.

cs.CV↗

arXiv2025

Quantum phase transitions of the anisotropic Dicke-Ising model in driven Rydberg arrays

We study the properties of a generalized Dicke-Ising model realized with an array of Rydberg atoms, driven by microwave electric fields and coupled to an optical cavity. As this platform allows for a precisely tunable anisotropy parameter, the model exhibits a rich landscape of phase transitions and critical phenomena, induced by the interplay of rotating-wave, counter-rotating-wave, and Ising interactions. We develop an improved quantum Monte Carlo algorithm based on the stochastic series expansion that explicitly tracks the Fock state of the quantum cavity. In the superradiant (SR) phase, this allows us to determine, through data collapse, the scaling laws of the photon number. We also demonstrate the vanishing of parity symmetry in finite-size simulations and show that the Rydberg blockade leads to a significant suppression of cavity occupation. Notably, stronger quantum fluctuations induced by the counter-rotating wave terms slightly favor the superradiant solid (SRS) phase over the Solid-1/2 state. Finally, we confirm that the SR phase transition and the transition from the Solid-1/2 to the SRS are second-order. In contrast, the transitions from the Solid-1/2 or SRS to the SR phase are both first-order for any value of the normalized anisotropy parameter.

cond-mat.quant-gas↗

arXiv2025

Lattice-Distortion-Mediated Proton Pairing and Trapping in Solid State Oxides

Experiments have evidenced proton pairing in Y-doped BaZrO3. However, the nature of proton pairing and its impact on conduction remain insufficiently understood theoretically. Here, through quantitative computational analysis of proton-proton interactions in Y-doped BaZrO3, we identify lattice-distortion-mediated elastic interaction as the key factor determining whether two protons form a stable pair or exhibit net repulsion. When a proton resides at an inward-bending distortion site induced by another proton, the resulting net repulsive interaction leads to an unstable configuration. In contrast, the proton tends to be trapped at a nearby outward-bending site that favors the formation of a stable proton pair. Moreover, the site where the two protons form the lowest-energy configuration also corresponds to a proton trapping site. By calculating the long-range diffusion pathways accessible to protons under different local environments in both single- and two-proton cases, we find that the range of rate-limiting barriers is 0.24-0.45 eV for two-proton conduction and 0.19-0.39 eV for single-proton conduction. The higher and more experimentally consistent barriers in the two-proton pathways indicate that the proton trapping effect induced by pairing hinders proton conduction. Our study elucidates the multi-proton diffusion mechanism, providing a theoretical foundation for the experimental design of electrolytes with enhanced proton conductivity.

cond-mat.mtrl-sci↗

arXiv2025

What Can One Expect When Solving PDEs Using Shallow Neural Networks?

We use elliptic partial differential equations (PDEs) as examples to show various properties and behaviors when shallow neural networks (SNNs) are used to represent the solutions. In particular, we study the numerical ill-conditioning, frequency bias, and the balance between the differential operator and the shallow network representation for different formulations of the PDEs and with various activation functions. Our study shows that the performance of Physics-Informed Neural Networks (PINNs) or Deep Ritz Method (DRM) using linear SNNs with power ReLU activation is dominated by their inherent ill-conditioning and spectral bias against high frequencies. Although this can be alleviated by using non-homogeneous activation functions with proper scaling, achieving such adaptivity for nonlinear SNNs remains costly due to ill-conditioning.

math.NA↗

arXiv2025

Interplay of magnetic and thermodynamic responses in the kagome-triangular system

Inspired by the recent experimental progress in pyrochlore derivative RE$_3$Sb$_3$A$_2$O$_{14}$ (A = Mg, Zn), we investigate the Hubbard model on the kagome lattice with an additional hopping $t'/t$, which enables continuous interpolation between the kagome and triangular lattices by using determinant quantum Monte Carlo simulations. We find that increasing $t'/t$ suppresses the nearest-neighbor antiferromagnetic correlations. Concurrently, the next-nearest-neighbor antiferromagnetic correlations are enhanced and closely associated with the emergence of a pronounced low-temperature peak in the specific heat. Increasing on-site interaction $U$ enhances magnetic correlations and shifts the associated $t'/t$ crossover points to larger values. We also discuss the sign problem to clarify which parameter region of our numerical simulations is accessible and reliable. Our results uncover the competition between frustration and correlations and the interplay of magnetic and thermodynamic responses in the kagome lattice, providing insights into correlated states in frustrated materials.

cond-mat.str-el↗

arXiv2025

In-depth Investigation of Conduction Mechanism on Defect-induced Proton-conducting Electrolytes BaHfO$_3$

This study utilizes first-principles computational methods to comprehensively analyze the impact of A-site doping on the proton conduction properties of BaHfO$_3$. The goal is to offer theoretical support for the advancement of electrolyte materials for solid oxide fuel cells. Our research has uncovered that BaHfO$_3$ demonstrates promising potential for proton conduction, with a low proton migration barrier of $0.28$ eV, suggesting efficient proton conduction can be achieved at lower temperatures. Through A-site doping, particularly with low-valence-state ions and the introduction of Ba vacancies, we can effectively decrease the formation energy of oxygen vacancies ($ E_{\text{vac}} $), leading to an increase in proton concentration. Additionally, our study reveals that the primary mechanism for proton migration in BaHfO$_3$ is the Grotthuss mechanism rather than the vehicle mechanism. Examination of the changes in lattice parameters during proton migration indicates that while doping or vacancy control strategies do not alter the mode of H$^+$ migration, they do influence the migration pathway and barrier. These findings provide valuable insights into optimizing the proton conduction properties of BaHfO$_3$ through A-site doping and lay a solid theoretical foundation for the development of novel, highly efficient solid oxide fuel cell electrolyte materials.

cond-mat.mtrl-sci↗

arXiv2025

NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results

This paper presents a review for the NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement. The challenge comprises two tracks: (i) Efficient Video Quality Assessment (KVQ), and (ii) Diffusion-based Image Super-Resolution (KwaiSR). Track 1 aims to advance the development of lightweight and efficient video quality assessment (VQA) models, with an emphasis on eliminating reliance on model ensembles, redundant weights, and other computationally expensive components in the previous IQA/VQA competitions. Track 2 introduces a new short-form UGC dataset tailored for single image super-resolution, i.e., the KwaiSR dataset. It consists of 1,800 synthetically generated S-UGC image pairs and 1,900 real-world S-UGC images, which are split into training, validation, and test sets using a ratio of 8:1:1. The primary objective of the challenge is to drive research that benefits the user experience of short-form UGC platforms such as Kwai and TikTok. This challenge attracted 266 participants and received 18 valid final submissions with corresponding fact sheets, significantly contributing to the progress of short-form UGC VQA and image superresolution. The project is publicly available at https://github.com/lixinustc/KVQE- ChallengeCVPR-NTIRE2025.

eess.IV↗

arXiv2025

Enhanced Semantic Extraction and Guidance for UGC Image Super Resolution

Due to the disparity between real-world degradations in user-generated content(UGC) images and synthetic degradations, traditional super-resolution methods struggle to generalize effectively, necessitating a more robust approach to model real-world distortions. In this paper, we propose a novel approach to UGC image super-resolution by integrating semantic guidance into a diffusion framework. Our method addresses the inconsistency between degradations in wild and synthetic datasets by separately simulating the degradation processes on the LSDIR dataset and combining them with the official paired training set. Furthermore, we enhance degradation removal and detail generation by incorporating a pretrained semantic extraction model (SAM2) and fine-tuning key hyperparameters for improved perceptual fidelity. Extensive experiments demonstrate the superiority of our approach against state-of-the-art methods. Additionally, the proposed model won second place in the CVPR NTIRE 2025 Short-form UGC Image Super-Resolution Challenge, further validating its effectiveness. The code is available at https://github.c10pom/Moonsofang/NTIRE-2025-SRlab.

cs.CV↗

arXiv2025

Frustrated Rydberg Atom Arrays Meet Cavity-QED: Emergence of the Superradiant Clock Phase

Rydberg atom triangular arrays in an optical cavity serve as an ideal platform for understanding the interplay between geometric frustration and quantized photons. Using a large-scale quantum Monte Carlo method, we obtain a rich ground state phase diagram. Around half-filling, the infinite long-range light-matter interaction lifts the ground state degeneracy, resulting in a novel order-coexisted superradiant clock phase that completely destroys the fragile order-by-disorder phase observed in classical light fields. According to the Ginzburg-Landau theory, this replacement may result from the competition between threefold and sixfold clock terms. Similar to the spin supersolid, the clear first-order phase transition at the $Z_2$ symmetry line is attributed to the nonzero photon density, which couples to the threefold clock term. Finally, we discuss the low-energy physics in the dimer language and propose that cavity-mediated nonlocal ring exchange interactions may play a critical role in the rich physics induced by the attachment of cavity-QED. Our work opens a new arena of research on the emergent phenomena of quantum phase transitions in many-body quantum optics.

cond-mat.quant-gas↗

arXiv2025

Time-EAPCR-T: A Universal Deep Learning Approach for Anomaly Detection in Industrial Equipment

With the advancement of Industry 4.0, intelligent manufacturing extensively employs sensors for real-time multidimensional data collection, playing a crucial role in equipment monitoring, process optimisation, and efficiency enhancement. Industrial data exhibit characteristics such as multi-source heterogeneity, nonlinearity, strong coupling, and temporal interactions, while also being affected by noise interference. These complexities make it challenging for traditional anomaly detection methods to extract key features, impacting detection accuracy and stability. Traditional machine learning approaches often struggle with such complex data due to limitations in processing capacity and generalisation ability, making them inadequate for practical applications. While deep learning feature extraction modules have demonstrated remarkable performance in image and text processing, they remain ineffective when applied to multi-source heterogeneous industrial data lacking explicit correlations. Moreover, existing multi-source heterogeneous data processing techniques still rely on dimensionality reduction and feature selection, which can lead to information loss and difficulty in capturing high-order interactions. To address these challenges, this study applies the EAPCR and Time-EAPCR models proposed in previous research and introduces a new model, Time-EAPCR-T, where Transformer replaces the LSTM module in the time-series processing component of Time-EAPCR. This modification effectively addresses multi-source data heterogeneity, facilitates efficient multi-source feature fusion, and enhances the temporal feature extraction capabilities of multi-source industrial data.Experimental results demonstrate that the proposed method outperforms existing approaches across four industrial datasets, highlighting its broad application potential.

cs.LG↗