arXiv subjects

Li Yan

Publications and source records attributed to Li Yan.

At least 19 recordsLinked to original sources

arXiv2026

Fundamental geometric limitations on disentangling nuclear-surface properties in relativistic heavy ion collisions

The extraction of the nuclear surface diffuseness from relativistic heavy ion collisions is limited by the intertwined responses of geometry-driven observables to surface diffuseness and intrinsic nuclear deformation. We investigate this limitation using event-by-event Monte Carlo Glauber simulations, focusing on the sensitivity of multiparticle correlations to the Woods--Saxon surface diffuseness $a_0$ in intrinsically deformed nuclei. We systematically examine the local correlations between $a_0$ and quadrupole and octupole deformation parameters, $\beta_2$ and $\beta_3$, and determine how these correlations affect the ability of different observables to constrain $a_0$. We find that observables dominated by elliptic geometry exhibit a strong response to quadrupole deformation, leading to a local $a_0$--$\beta_2$ degeneracy that substantially limits their sensitivity to the nuclear surface diffuseness. Triangular correlations provide a more independent response to the nuclear surface and therefore retain additional information on $a_0$, although their sensitivity can also be modified by intrinsic deformation. Extending the analysis to simultaneous quadrupole and octupole deformation shows that the local degeneracy and least-constrained directions depend on the nuclear configuration, demonstrating that the limitation on extracting $a_0$ is not described by a single global parameter correlation. We quantify these effects using multidimensional response maps, local sensitivity and information-geometric measures, and a Bayesian analysis of the resulting parameter constraints. The results clarify the fundamental limitations imposed by intrinsic multipole deformation on the determination of nuclear surface diffuseness from relativistic heavy ion collisions and identify multiparticle correlations that provide more independent information on $a_0$.

nucl-th↗

arXiv2026

Layer-Hybridized Wigner Crystals in MoSe2/WS2 Moir\'e Superlattice

Transition metal dichalcogenide moir\'e heterobilayers with type-II band alignment provide a versatile platform for layer-polarized generalized Wigner crystals, in which strong Coulomb interactions drive charge ordering at fractional lattice fillings. With a finite interlayer band offset, an out-of-plane electric field can tune layer-resolved moir\'e bands through resonance and enable controllable interlayer hybridization. Although hybridized Mott insulators have been previously demonstrated, whether fractional charge-ordered states can survive such hybridization remains elusive. Here we drive an H-stacked MoSe2/WS2 moir\'e heterobilayer through a type-I-to-type-II band-alignment transition and realize layer-hybridized Mott insulator and generalized Wigner crystals. For fillings below one electron per moir\'e cell, tunneling delocalizes electrons and modifies Wigner crystallization. However, above one electron per cell, Coulomb repulsion overcomes tunneling and favors layer-separated occupation, stabilizing stronger charge-ordered states. These results establish electrically tunable hybridized moir\'e heterobilayers as a powerful platform for engineering correlated charge order and exploring fractional Chern phases and emergent magnetism.

cond-mat.mes-hall↗

arXiv2026

TRUST-ESD: A Risk-Calibrated and Governance-Aware AI Framework for Enterprise Strategic Decision Support Under Uncertainty

Enterprise strategic decision support requires AI systems that are not only accurate, but also uncertainty-aware, risk-calibrated, explainable, and governance-compliant. This paper proposes TRUST-ESD, a risk-calibrated and governance-aware framework for enterprise decision support under uncertainty. TRUST-ESD evaluates feasible counterfactual strategies through predictive utility estimation, conformal uncertainty calibration, CVaR-based downside-risk scoring, risk-memory retrieval, policy-as-code governance, explainability, and human oversight. Unlike prediction-only methods that select actions by maximum expected utility, TRUST-ESD recommends strategies that balance value, reliability, risk exposure, and compliance. Experimental results show that TRUST-ESD improves risk-adjusted utility by 7.95%, reduces risk exposure by 23.22%, reduces CVaR by 23.78%, lowers calibration error by 13.89%, improves explanation fidelity by 10.90%, and increases governance compliance by 9.76% compared with strong uncertainty-aware baselines, while maintaining competitive predictive accuracy. Ablation and case-study analyses further confirm that uncertainty calibration, downside-risk scoring, risk memory, explainability, and governance validation jointly improve trustworthy enterprise decision-making.

cs.AI↗

arXiv2026

Nonlinear collective flow reveals the breakdown of quadrupole--hexadecapole scaling in heavy ion collisions

Determining the role of intrinsic hexadecapole deformation ($\beta_4$) in nuclear structure remains a long-standing challenge. Relativistic heavy-ion collisions provide a unique opportunity to address this problem by converting the initial nuclear geometry into the collective motion of the quark--gluon plasma (QGP). Using event-by-event viscous hydrodynamic simulations of ultra-central $^{238}$U+$^{238}$U collisions at $\sqrt{s_{NN}}=193$ GeV, we investigate whether higher-order collective flow can isolate the contribution of $\beta_4$ and test the $\beta_2-\beta_4$ correlation. We demonstrate that information carried by the sign of $\beta_4$ survives the QGP evolution and is enhanced through nonlinear hydrodynamic response: the fourth-order flow harmonic acquires its topology dependence predominantly from the linear response, whereas the sensitivity of the sixth-order harmonic originates almost entirely from nonlinear mode coupling. As a consequence, the nonlinear response coefficient $\xi_{6,222}$ cleanly separates the $(\beta_2,\beta_4)$ intrinsic nuclear topologies. These results establish the sign of $\beta_4$ as an experimentally accessible signature of deviations from the quadrupole--hexadecapole correlation, demonstrating that higher-order collective flow provides a direct probe of nuclear multipole structure while revealing how nonlinear QGP dynamics encode subtle higher-order geometric information into final-state observables.

nucl-th↗

arXiv2026

Redefining Quality Criteria and Distance-Aware Score Modeling for Image Editing Assessment

Recent advances in image editing have heightened the need for reliable Image Editing Quality Assessment (IEQA). Unlike traditional methods, IEQA requires complex reasoning over multimodal inputs and multi-dimensional assessments. Existing MLLM-based approaches often rely on human heuristic prompting, leading to two key limitations: rigid metric prompting and distance-agnostic score modeling. These issues hinder alignment with implicit human criteria and fail to capture the continuous structure of score spaces. To address this, we propose Define-and-Score Image Editing Quality Assessment (DS-IEQA), a unified framework that jointly learns evaluation criteria and score representations. Specifically, we introduce Feedback-Driven Metric Prompt Optimization (FDMPO) to automatically refine metric definitions via probabilistic feedback. Furthermore, we propose Token-Decoupled Distance Regression Loss (TDRL), which decouples numerical tokens from language modeling to explicitly model score continuity through expected distance minimization. Extensive experiments show our method's superior performance; it ranks 4th in the 2026 NTIRE X-AIGC Quality Assessment Track 2 without any additional training data.

cs.CV↗

arXiv2026

NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Professional Image Quality Assessment (Track 1)

In this paper, we present an overview of the NTIRE 2026 challenge on the 3rd Restore Any Image Model in the Wild, specifically focusing on Track 1: Professional Image Quality Assessment. Conventional Image Quality Assessment (IQA) typically relies on scalar scores. By compressing complex visual characteristics into a single number, these methods fundamentally struggle to distinguish subtle differences among uniformly high-quality images. Furthermore, they fail to articulate why one image is superior, lacking the reasoning capabilities required to provide guidance for vision tasks. To bridge this gap, recent advancements in Multimodal Large Language Models (MLLMs) offer a promising paradigm. Inspired by this potential, our challenge establishes a novel benchmark exploring the ability of MLLMs to mimic human expert cognition in evaluating high-quality image pairs. Participants were tasked with overcoming critical bottlenecks in professional scenarios, centering on two primary objectives: (1) Comparative Quality Selection: reliably identifying the visually superior image within a high-quality pair; and (2) Interpretative Reasoning: generating grounded, expert-level explanations that detail the rationale behind the selection. In total, the challenge attracted nearly 200 registrations and over 2,500 submissions. The top-performing methods significantly advanced the state of the art in professional IQA. The challenge dataset is available at https://github.com/narthchin/RAIM-PIQA, and the official homepage is accessible at https://www.codabench.org/competitions/12789/.

cs.CV↗

arXiv2026

Foundation Model-Driven Semantic Change Detection in Remote Sensing Imagery

Remote sensing (RS) change detection is essential for interpreting surface dynamics. Semantic change detection (SCD) further enables pixel-level understanding of multi-class transitions, yet remains sensitive to pseudo-changes induced by imaging conditions. Recent RS foundation models extract semantically consistent features across temporal and environmental variations, which is critical for mitigating pseudo-changes. However, existing SCD methods are often rigid and backbone-specific, lacking the flexibility to integrate diverse multi-scale features from emerging foundation models. To this end, we introduce a modular Cascaded Gated Decoder (CG-Decoder) that bridges various backbones and SCD tasks, processing multi-scale features in a coarse-to-fine manner while enabling adaptive change extraction. Building upon the RS foundation model PerA, we present PerASCD, a unified SCD framework. We further propose a Soft Semantic Consistency Loss (SSCLoss) to mitigate numerical instability in mixed-precision training. Extensive experiments on SECOND and LandsatSCD show that PerASCD achieves new state-of-the-art Sek scores (26.11% and 65.21%), surpassing the previous best by 0.61% and 4.95%, respectively. It also demonstrates exceptional data efficiency (outperforming the full-data baseline with 50% data), seamless cross-backbone generalization, and enhanced interpretability. Our approach maintains robust semantic consistency under radiometric variations, providing a reliable SCD solution. Code: https://github.com/SathShen/PerASCD.git.

cs.CV↗

arXiv2025

Clustering-based Transfer Learning for Dynamic Multimodal MultiObjective Evolutionary Algorithm

Dynamic multimodal multiobjective optimization presents the dual challenge of simultaneously tracking multiple equivalent pareto optimal sets and maintaining population diversity in time-varying environments. However, existing dynamic multiobjective evolutionary algorithms often neglect solution modality, whereas static multimodal multiobjective evolutionary algorithms lack adaptability to dynamic changes. To address above challenge, this paper makes two primary contributions. First, we introduce a new benchmark suite of dynamic multimodal multiobjective test functions constructed by fusing the properties of both dynamic and multimodal optimization to establish a rigorous evaluation platform. Second, we propose a novel algorithm centered on a Clustering-based Autoencoder prediction dynamic response mechanism, which utilizes an autoencoder model to process matched clusters to generate a highly diverse initial population. Furthermore, to balance the algorithm's convergence and diversity, we integrate an adaptive niching strategy into the static optimizer. Empirical analysis on 12 instances of dynamic multimodal multiobjective test functions reveals that, compared with several state-of-the-art dynamic multiobjective evolutionary algorithms and multimodal multiobjective evolutionary algorithms, our algorithm not only preserves population diversity more effectively in the decision space but also achieves superior convergence in the objective space.

cs.AI↗

arXiv2025

Hydrodynamic Short-Range Correlations from Boltzmann-Langevin Equation

We investigate hydrodynamic contributions to short-range two-particle correlations in relativistic heavy-ion collisions using the Boltzmann-Langevin equation. We derive and solve the transport equation for equal-time two-point correlations, obtaining both local and non-local contributions that scale with transport coefficients. The non-local correlations emerging from 2-to-2 scattering dynamics provide a hydrodynamic signature in short-range correlation measurements.

hep-ph↗

arXiv2025

An End-to-End Generative Diffusion Model for Heavy-Ion Collisions

Heavy-ion collision physics has entered the high precision era, demanding theoretical models capable of generating huge statistics to compare with experimental data. However, traditional hybrid models, which combine hydrodynamics and hadronic transport, are computationally intensive, creating a significant bottleneck. In this work, we introduce DiffHIC, an end-to-end generative diffusion model, to emulate ultra-relativistic heavy-ion collisions. The model takes initial entropy density profiles and transport coefficients as input and directly generates two-dimensional final-state particle spectra. Our results demonstrate that DiffHIC achieves a computational speedup of approximately $10^5$ against traditional simulations, while accurately reproducing a wide range of physical observables, including integrated and differential anisotropic flow, multi-particle correlations, and momentum fluctuations. This framework provides a powerful and efficient tool for phenomenological studies in the high-precision era of heavy-ion physics.

nucl-th↗

arXiv2025

LO-SDA: Latent Optimization for Score-based Atmospheric Data Assimilation

Data assimilation (DA) plays a pivotal role in numerical weather prediction by systematically integrating sparse observations with model forecasts to estimate optimal atmospheric initial condition for forthcoming forecasts. Traditional Bayesian DA methods adopt a Gaussian background prior as a practical compromise for the curse of dimensionality in atmospheric systems, that simplifies the nonlinear nature of atmospheric dynamics and can result in biased estimates. To address this limitation, we propose a novel generative DA method, LO-SDA. First, a variational autoencoder is trained to learn compact latent representations that disentangle complex atmospheric correlations. Within this latent space, a background-conditioned diffusion model is employed to directly learn the conditional distribution from data, thereby generalizing and removing assumptions in the Gaussian prior in traditional DA methods. Most importantly, we introduce latent optimization during the reverse process of the diffusion model to ensure strict consistency between the generated states and sparse observations. Idealized experiments demonstrate that LO-SDA not only outperforms score-based DA methods based on diffusion posterior sampling but also surpasses traditional DA approaches. To our knowledge, this is the first time that a diffusion-based DA method demonstrates the potential to outperform traditional approaches on high-dimensional global atmospheric systems. These findings suggest that long-standing reliance on Gaussian priors-a foundational assumption in operational atmospheric DA-may no longer be necessary in light of advances in generative modeling.

physics.ao-ph↗

arXiv2025

Strong Correlation Driven Quadrupolar to Dipolar Exciton Transitions in a Trilayer Moir\'e Superlattice

The additional layer degree of freedom in trilayer moir\'e superlattices of transition metal dichalcogenides enables the emergence of novel excitonic species, such as quadrupolar excitons, which exhibit unique excitonic interactions and hold promise for realizing intriguing excitonic phases and their quantum phase transitions. Concurrently, the presence of strong electronic correlations in moir\'e superlattices, as exemplified by the observations of Mott insulators and generalized Wigner crystals, offers a direct route to manipulate these new excitonic states and resulting collective excitonic phases. Here, we demonstrate that strong exciton-exciton and electron-exciton interactions, both stemming from robust electron correlations, can be harnessed to controllably drive transitions between quadrupolar and dipolar excitons. This is achieved by tuning either the exciton density or electrostatic doping in a trilayer semiconducting moir\'e superlattice. Our findings not only advance the fundamental understanding of quadrupolar excitons but also usher in new avenues for exploring and engineering many-body quantum phenomena through novel correlated excitons in semiconducting moir\'e systems.

cond-mat.mes-hall↗

arXiv2025

SenseCrypt: Sensitivity-guided Selective Homomorphic Encryption for Joint Federated Learning in Cross-Device Scenarios

Homomorphic Encryption (HE) prevails in securing Federated Learning (FL), but suffers from high overhead and adaptation cost. Selective HE methods, which partially encrypt model parameters by a global mask, are expected to protect privacy with reduced overhead and easy adaptation. However, in cross-device scenarios with heterogeneous data and system capabilities, traditional Selective HE methods deteriorate client straggling, and suffer from degraded HE overhead reduction performance. Accordingly, we propose SenseCrypt, a Sensitivity-guided selective Homomorphic EnCryption framework, to adaptively balance security and HE overhead per cross-device FL client. Given the observation that model parameter sensitivity is effective for measuring clients' data distribution similarity, we first design a privacy-preserving method to respectively cluster the clients with similar data distributions. Then, we develop a scoring mechanism to deduce the straggler-free ratio of model parameters that can be encrypted by each client per cluster. Finally, for each client, we formulate and solve a multi-objective model parameter selection optimization problem, which minimizes HE overhead while maximizing model security without causing straggling. Experiments demonstrate that SenseCrypt ensures security against the state-of-the-art inversion attacks, while achieving normal model accuracy as on IID data, and reducing training time by 58.4%-88.7% as compared to traditional HE methods.

cs.CR↗

arXiv2025

SelectiveShield: Lightweight Hybrid Defense Against Gradient Leakage in Federated Learning

Federated Learning (FL) enables collaborative model training on decentralized data but remains vulnerable to gradient leakage attacks that can reconstruct sensitive user information. Existing defense mechanisms, such as differential privacy (DP) and homomorphic encryption (HE), often introduce a trade-off between privacy, model utility, and system overhead, a challenge that is exacerbated in heterogeneous environments with non-IID data and varying client capabilities. To address these limitations, we propose SelectiveShield, a lightweight hybrid defense framework that adaptively integrates selective homomorphic encryption and differential privacy. SelectiveShield leverages Fisher information to quantify parameter sensitivity, allowing clients to identify critical parameters locally. Through a collaborative negotiation protocol, clients agree on a shared set of the most sensitive parameters for protection via homomorphic encryption. Parameters that are uniquely important to individual clients are retained locally, fostering personalization, while non-critical parameters are protected with adaptive differential privacy noise. Extensive experiments demonstrate that SelectiveShield maintains strong model utility while significantly mitigating gradient leakage risks, offering a practical and scalable defense mechanism for real-world federated learning deployments.

cs.DC↗

arXiv2025

Realization of a Kondo Insulator in a Multilayer Moire Superlattice

Kondo insulators are a paradigmatic strongly correlated electron system, arising from the hybridization between itinerary conduction electrons and localized magnetic moments, which opens a gap in the band of conduction electrons. Traditionally, the known Kondo insulators are found in materials with f-electrons. Recent developments in two-dimensional (2D) moire systems provide a new approach to generate flat bands with strong electron correlation, which host localized moments at half filling. In this work, we demonstrate the realization of a Kondo insulator phase in a moire superlattice of monolayer WS2 / bilayer WSe2 which hosts a set of moire flat bands in the WSe2 layer interfacing the WS2 layer and dispersive bands in the other WSe2 layer. When both WSe2 layers are partially doped but with a total density of two holes per moire unit cell, an insulating state appears when the density of the moire band is below one hole per moire unit cell. The insulating state disappears above a certain threshold magnetic field and the system becomes metallic, which is a telltale signature of the Kondo insulator. The physics can be well explained by a periodic Anderson lattice model that includes both the on-site Coulomb repulsion in the moire flat band and the hybridization between moire flat and non-moire dispersive bands. Our results suggest that multilayer moire structures of transition metal dichalcogenides provide a tunable platform to simulate the Kondo insulator, which holds promise to tackle many critical open questions in the Kondo insulators.

cond-mat.str-el↗

arXiv2025

Align-DA: Align Score-based Atmospheric Data Assimilation with Multiple Preferences

Data assimilation (DA) aims to estimate the full state of a dynamical system by combining partial and noisy observations with a prior model forecast, commonly referred to as the background. In atmospheric applications, this problem is fundamentally ill-posed due to the sparsity of observations relative to the high-dimensional state space. Traditional methods address this challenge by simplifying background priors to regularize the solution, which are empirical and require continual tuning for application. Inspired by alignment techniques in text-to-image diffusion models, we propose Align-DA, which formulates DA as a generative process and uses reward signals to guide background priors, replacing manual tuning with data-driven alignment. Specifically, we train a score-based model in the latent space to approximate the background-conditioned prior, and align it using three complementary reward signals for DA: (1) assimilation accuracy, (2) forecast skill initialized from the assimilated state, and (3) physical adherence of the analysis fields. Experiments with multiple reward signals demonstrate consistent improvements in analysis quality across different evaluation metrics and observation-guidance strategies. These results show that preference alignment, implemented as a soft constraint, can automatically adapt complex background priors tailored to DA, offering a promising new direction for advancing the field.

physics.ao-ph↗

arXiv2025

SHE-LoRA: Selective Homomorphic Encryption for Federated Tuning with Heterogeneous LoRA

Federated fine-tuning is critical for improving the performance of large language models (LLMs) in handling domain-specific tasks while keeping training data decentralized and private. However, prior work has shown that clients' private data can actually be recovered via gradient inversion attacks. Existing privacy preservation techniques against such attacks typically entail performance degradation and high costs, making them ill-suited for clients with heterogeneous data distributions and device capabilities. In this paper, we propose SHE-LoRA, which integrates selective homomorphic encryption (SHE) and low-rank adaptation (LoRA) to enable efficient and privacy-preserving federated tuning of LLMs in cross-device environments. Based on model parameter sensitivity assessment, heterogeneous clients adaptively negotiate and select a subset of model parameters for homomorphic encryption. To ensure accurate model aggregation, we design a column-aware secure aggregation method and customized reparameterization techniques to align the aggregation results with the heterogeneous device capabilities of clients. Extensive experiments demonstrate that SHE-LoRA maintains performance comparable to non-private baselines, achieves strong resistance to state-of-the-art attacks, and significantly reduces communication overhead by 99.71% and encryption time by 99.87%, compared to HE baselines.

cs.CR↗

arXiv2025

Extracting the Speed of Sound in Heavy-Ion Collisions: A Study of Quantum-Initiated Fluctuations and Thermalization

The thermalization of quark-gluon plasma created in heavy-ion collisions is crucial for understanding its behavior as a relativistic fluid and the thermodynamic properties of the Quantum Chromodynamics (QCD). This study investigates the role of fluctuations in the relationship between transverse momentum and particle multiplicity, with a particular focus on their impact on extracting the QCD speed of sound. In a thermalized quark-gluon plasma, these fluctuations mostly originate from quantum fluctuations in the colliding nuclei, and exhibit a Gaussian distribution as a consequence of their independence from thermodynamic response. In contrast, non-thermalized systems display non-Gaussian fluctuations, reflecting the breakdown of thermalization. By leveraging the Gaussianity condition of quantum-initiated fluctuations, the physical value of the speed of sound can be extracted statistically, even in the presence of significant event-by-event fluctuations. This framework provides a robust diagnostic tool for probing thermalization and extracting thermodynamic properties in both large and small collision systems.

nucl-th↗