arXiv subjects

Li Yan

Publications and source records attributed to Li Yan.

At least 37 records · Page 2Linked to original sources

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

LDPM: Towards undersampled MRI reconstruction with MR-VAE and Latent Diffusion Prior

Diffusion models, as powerful generative models, have found a wide range of applications and shown great potential in solving image reconstruction problems. Some works attempted to solve MRI reconstruction with diffusion models, but these methods operate directly in pixel space, leading to higher computational costs for optimization and inference. Latent diffusion models, pre-trained on natural images with rich visual priors, are expected to solve the high computational cost problem in MRI reconstruction by operating in a lower-dimensional latent space. However, direct application to MRI reconstruction faces three key challenges: (1) absence of explicit control mechanisms for medical fidelity, (2) domain gap between natural images and MR physics, and (3) undefined data consistency in latent space. To address these challenges, a novel Latent Diffusion Prior-based undersampled MRI reconstruction (LDPM) method is proposed. Our LDPM framework addresses these challenges by: (1) a sketch-guided pipeline with a two-step reconstruction strategy, which balances perceptual quality and anatomical fidelity, (2) an MRI-optimized VAE (MR-VAE), which achieves an improvement of approximately 3.92 dB in PSNR for undersampled MRI reconstruction compared to that with SD-VAE \cite{sd}, and (3) Dual-Stage Sampler, a modified version of spaced DDPM sampler, which enforces high-fidelity reconstruction in the latent space. Experiments on the fastMRI dataset\cite{fastmri} demonstrate the state-of-the-art performance of the proposed method and its robustness across various scenarios. The effectiveness of each module is also verified through ablation experiments.

eess.IV↗

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

Volume effect on the extraction of sound velocity in high-energy nucleus-nucleus collisions

The determination of the speed of sound in quark-gluon plasma is a crucial aspect of understanding the properties of strongly interacting matter created in relativistic heavy-ion collisions. In this study, we investigate the impact of initial-state fluctuations on the extraction of the speed of sound in a quark-gluon plasma in the ultra-central collisions. By employing the TRENTo model for simulating initial conditions, we demonstrate that these fluctuations lead to sizable volume effect, which in turn corrects the measured values of the sound velocity. With respect to realistic conditions of high-energy heavy-ion experiments, we provide quantitative estimate of these corrections.

nucl-th↗

arXiv2024

Federated Deep Reinforcement Learning-Based Intelligent Channel Access in Dense Wi-Fi Deployments

The IEEE 802.11 MAC layer utilizes the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism for channel contention, but dense Wi-Fi deployments often cause high collision rates. To address this, this paper proposes an intelligent channel contention access mechanism that combines Federated Learning (FL) and Deep Deterministic Policy Gradient (DDPG) algorithms. We introduce a training pruning strategy and a weight aggregation algorithm to enhance model efficiency and reduce MAC delay. Using the NS3-AI framework, simulations show our method reduces average MAC delay by 25.24\% in static scenarios and outperforms A-FRL and DRL by 25.72\% and 45.9\% in dynamic environments, respectively.

cs.NI↗

arXiv2024

Exploring the small-scale magnetic fields of the solar analog KIC 8006161 using asteroseismology

The magnetic field is a significant and universal physical phenomenon in modern astrophysics. Small-scale magnetic fields are very important in the stellar atmosphere. They are ubiquitous, and strongly couple with the acoustic waves. Therefore, their presence affects the properties of acoustic waves in the stellar outer layer. In the present work, under the assumption that the small-scale magnetic features are the cause of the asteroseismic surface term (the frequency-dependent frequency offset between stars and their models), we explore the strength of such fields in the solar analog KIC 8006161. By considering the effect of small-scale magnetic fields in the stellar photosphere, we use the observed oscillation frequencies to constrain the inner structures and surface small-scale magnetic fields of solar-like star KIC 8006161. To agree with the existing observations, such as oscillation frequencies, and their frequency separation ratios, the theoretical model requires a small-scale magnetic field to form a magnetic-arch splicing layer in the stellar outer atmosphere. The small-scale magnetic field strengths for KIC 8006161 obtained from best-fit model with $Y_{\rm init} = 0.249+1.33Z_{\rm init}$ and $Y_{\rm init}$ as a free parameter, are approximately 96 G and 89 G, respectively. The corresponding locations of the magnetic-arch splicing layer are about $522$ km and 510 km, respectively.

astro-ph.SR↗

arXiv2024

Quantum thermalization of Quark-Gluon Plasma

The thermalization of quark gluon plasma created in relativistic heavy-ion collisions is a crucial theoretical question in understanding the onset of hydrodynamics, and in a broad sense, a key step to the exploration of thermalization in isolated quantum systems. Addressing this problem theoretically, in a first principle manner, requires a real-time, non-perturbative method. To this end, we carry out a fully quantum simulation on a classical hardware, of a massive Schwinger model, which well mimics QCD as it shares the important properties such as confinement and chiral symmetry breaking. We focus on the real-time evolution of the Wigner function, namely, the two-point correlation function, which approximates quark momentum distribution. In the context of the eigenstate thermalization hypothesis and the evolution of entropy, our solution reveals the emergence of quantum thermalization in quark-gluon plasma with a strong coupling constant, while thermalization fails progressively as a consequence of the gradually increased significance of quantum many-body scar states in a more weakly coupled system. More importantly, we observe the non-trivial role of the topological vacuum in thermalization, as the thermalization properties differ dramatically in the parity-even and parity-odd components of the Wigner function.

hep-ph↗

arXiv2024

Micro-Structures Graph-Based Point Cloud Registration for Balancing Efficiency and Accuracy

Point Cloud Registration (PCR) is a fundamental and significant issue in photogrammetry and remote sensing, aiming to seek the optimal rigid transformation between sets of points. Achieving efficient and precise PCR poses a considerable challenge. We propose a novel micro-structures graph-based global point cloud registration method. The overall method is comprised of two stages. 1) Coarse registration (CR): We develop a graph incorporating micro-structures, employing an efficient graph-based hierarchical strategy to remove outliers for obtaining the maximal consensus set. We propose a robust GNC-Welsch estimator for optimization derived from a robust estimator to the outlier process in the Lie algebra space, achieving fast and robust alignment. 2) Fine registration (FR): To refine local alignment further, we use the octree approach to adaptive search plane features in the micro-structures. By minimizing the distance from the point-to-plane, we can obtain a more precise local alignment, and the process will also be addressed effectively by being treated as a planar adjustment algorithm combined with Anderson accelerated optimization (PA-AA). After extensive experiments on real data, our proposed method performs well on the 3DMatch and ETH datasets compared to the most advanced methods, achieving higher accuracy metrics and reducing the time cost by at least one-third.

cs.CV↗

arXiv2024

Anomalously Enhanced Diffusivity of Moiré Excitons via Manipulating the Interplay with Correlated Electrons

Semiconducting transitional metal dichalcogenides (TMDCs) moiré superlattice provides an exciting platform for manipulating excitons. The in-situ control of moiré potential confined exciton would usher in unprecedented functions of excitonic devices but remains challenging. Meanwhile, as a dipolar composite boson, interlayer exciton in the type-II aligned TMDC moiré superlattice strongly interacts with fermionic charge carriers. Here, we demonstrate active manipulation of the exciton diffusivity by tuning their interplay with correlated carriers in moiré potentials. At fractional fillings where carriers are known to form generalized Wigner crystals, we observed suppressed diffusivity of exciton. In contrast, in Fermi liquid states where carriers dynamically populate all moiré traps, the repulsive carrier-exciton interaction can effectively reduce the moiré potential confinement seen by the exciton, leading to enhanced diffusivity with the increase of the carrier density. Notably, the exciton diffusivity is enhanced by orders of magnitude near the Mott insulator state, and the enhancement is much more pronounced for the 0-degree than the 60-degree aligned WS2/WSe2 heterobilayer due to the more localized nature of interlayer excitons. Our study inspires further engineering and controlling exotic excitonic states in TMDC moiré superlattices for fascinating quantum phenomena and novel excitonic devices.

cond-mat.mes-hall↗

arXiv2024

Relativistic spin hydrodynamics revisited with general rotation by entropy-current analysis

We revisit the canonical formulation of spin hydrodynamics for Dirac fermions with a general thermal vorticity. The orders of the general thermal vorticity and the corresponding spin variables are considered independently from those of the conventional hydrodynamic variables and their perturbative gradients. Assuming a totally antisymmetric spin current of Dirac fermions, the entropy-current analysis with a general spin potential indicates that the constitutive relations of the stress-energy tensor have to involve spin variables, particularly those linked to boost symmetry, to adhere to the entropy principle. In the presence of the degree of freedom associated with boost symmetry, we choose the constitutive relations of the canonical formulation to be connected to those of the phenomenological formulation through pseudogauge transformation. Subsequently, a linear-mode analysis is conducted using the resulting spin hydrodynamic equations. It is observed that the spin and hydrodynamic modes in this canonical formulation display different characteristics compared to those in the phenomenological formulation up to the second order of gradient.

nucl-th↗

arXiv2024

INK: Inheritable Natural Backdoor Attack Against Model Distillation

Deep learning models are vulnerable to backdoor attacks, where attackers inject malicious behavior through data poisoning and later exploit triggers to manipulate deployed models. To improve the stealth and effectiveness of backdoors, prior studies have introduced various imperceptible attack methods targeting both defense mechanisms and manual inspection. However, all poisoning-based attacks still rely on privileged access to the training dataset. Consequently, model distillation using a trusted dataset has emerged as an effective defense against these attacks. To bridge this gap, we introduce INK, an inheritable natural backdoor attack that targets model distillation. The key insight behind INK is the use of naturally occurring statistical features in all datasets, allowing attackers to leverage them as backdoor triggers without direct access to the training data. Specifically, INK employs image variance as a backdoor trigger and enables both clean-image and clean-label attacks by manipulating the labels and image variance in an unauthenticated dataset. Once the backdoor is embedded, it transfers from the teacher model to the student model, even when defenders use a trusted dataset for distillation. Theoretical analysis and experimental results demonstrate the robustness of INK against transformation-based, search-based, and distillation-based defenses. For instance, INK maintains an attack success rate of over 98\% post-distillation, compared to an average success rate of 1.4\% for existing methods.

cs.CR↗

arXiv2024

Late-time asymptotic solutions, attractor, and focusing behavior of spin hydrodynamics

We have investigated the late-time asymptotic solutions, attractor, and focusing behavior of minimal causal spin hydrodynamics in Bjorken expansion. Using the method of dominant balance, we derive the late-time asymptotic solutions of the evolution equation for spin density and identify the specific conditions necessary for the spin density to exhibit a power-law decay. We then analyze both the late-time and early-time attractors for the decay rate of spin density. Additionally, we report the focusing behavior in spin hydrodynamics, which has not been found in conventional relativistic hydrodynamics in Bjorken expansion. Our findings suggest that spin density can be treated as a conventional hydrodynamic variable at late times under certain conditions.

hep-ph↗

arXiv2024

The weak magnetic field effect on dilepton polarization in heavy-ion collisions

The measurement of the magnetic field created in high-energy heavy-ion collisions is challenging, due the the fact that the magnetic field decays so drastically that in a thermalized quark-gluon plasma the field strength becomes rather weak. By incorporating the weak magnetic effect into the medium, and especially into the production formalism of dileptons from the quark-gluon plasma, the effect of dilepton polarization is studied through the dilepton angular distribution. We find that the anisotropic coefficients in the dilepton spectrum are quite sensitive to the orientation and strength of the weak field. Accordingly, these coefficients provide ideal probes for the magnetic field in realistic experiments.

nucl-th↗

arXiv2024

Estimate magnetic field strength in heavy-ion collisions via the direct photon elliptic flow

There must be electromagnetic fields created during high-energy heavy-ion collisions. As the quark-gluon plasma (QGP) starts to evolve hydrodynamically, although these fields may become weak comparing to the energy scales of the strong interaction, they are potentially important to some electromagnetic probes. In this work, we focus on the dissipative corrections in QGP due to the presence of a weak external magnetic field, and calculate accordingly the induced photon radiation in the framework of viscous hydrodynamics. By event-by-event hydrodynamical simulations, the experimentally measured direct photon elliptic flow can be well reproduced. Correspondingly, the direct photon elliptic flow implies a magnetic field strength around 0.1$m_π^2 \sim 10^{16}$ G. This is indeed a weak field in heavy-ion physics that is compatible to the theoretical predictions, however, it is still an ultra-strong magnetic field in nature.

nucl-th↗

arXiv2024

Weak magnetic effect in quark-gluon plasma and local spin polarization

We propose the weak magnetic effect, which emerges in quark-gluon plasma close to local thermal equilibrium as the dissipative correction to the quark phase space distribution function, as a novel contribution to the observed Lambda hyperon local spin polarization. With a finite field strength, which is consistent with previous estimate of the magnetic field in heavy-ion collisions, one is able to explain the experimentally observed Lambda local spin polarization through all centrality classes. Moreover, the weak magnetic effect plays an unambiguous role in the ordering between the second-order and the third-order modulations of the Lambda local spin polarization in experiments.

nucl-th↗

arXiv2024

Stark Effects of Rydberg Excitons in a Monolayer WSe2 P-N Junction

The enhanced Coulomb interaction in two-dimensional (2D) semiconductors leads to the tightly bound electron-hole pairs known as excitons. The large binding energy of excitons enables the formation of Rydberg excitons with high principal quantum numbers (n), analogous to Rydberg atoms. Rydberg excitons possess strong interactions among themselves, as well as sensitive responses to external stimuli. Here, we probe Rydberg exciton resonances through photocurrent spectroscopy in a monolayer WSe2 p-n junction formed by a split-gate geometry. We show that an external in-plane electric field not only induces a large Stark shift of Rydberg excitons up to quantum principal number n=3 but also mixes different orbitals and brightens otherwise dark states such as 3p and 3d. Our study provides an exciting platform for engineering Rydberg excitons for new quantum states and quantum sensing.

cond-mat.mes-hall↗

arXiv2023

Why are hydrodynamic theories applicable beyond the hydrodynamic regime?

We present an alternative approach to deriving second-order non-conformal hydrodynamics from the relativistic Boltzmann equation. We demonstrate how constitutive relations for shear and bulk stresses can be transformed into dynamical evolution equations, resulting in Israel-Stewart-like (ISL) hydrodynamics. To understand the far-from-equilibrium applicability of such ISL theories, we investigate the one-dimensional boost-invariant Boltzmann equation using special moments of the distribution function for a system with finite particle mass. Our analysis reveals that the mathematical structure of the ISL equations is akin to that of moment equations, enabling them to approximately replicate even the collisionless dynamics. We conclude that this particular feature is important in extending the applicability of ISL theories beyond the hydrodynamic regime.

nucl-th↗

arXiv2023

The effect of weak magnetic photon emission from quark-gluon plasma

We propose a novel effect that accounts for the photon emission from a quark-gluon plasma in the presence of a weak external magnetic field. Although the weak magnetic photon emission from quark-gluon plasma only leads to a small correction to the photon production rate, the induced photon spectrum can be highly azimuthally anisotropic, as a consequence of the coupled effect of the magnetic field and the longitudinal dynamics in the background medium. With respect to a realistic medium evolution containing a tilted fireball configuration, the direct photon elliptic flow from experiments is reproduced. In comparison to the experimental data of direct photon elliptic flow, in heavy-ion collisions the magnitude of the magnetic field before 1 fm/c can be extracted. For the top energy of RHIC collisions, right after the pre-equilibrium evolution, $|eB|$ is found no larger than a few percent of the pion mass square.

nucl-th↗