arXiv subjects

Yong Xu

Publications and source records attributed to Yong Xu.

At least 127 records · Page 7Linked to original sources

arXiv2025

Graviton- and Inflaton-mediated Dark Matter Production after Large Field Polynomial Inflation

Polynomial inflation is a simple cosmological scenario, which fits the cosmic microwave background data well. It provides testable predictions for the tensor-to-scalar ratio and the running of the spectral index. In this work, we investigate the production of Dirac dark matter (DM) within the framework of large-field polynomial inflation. We study all relevant production channels including $i$) non-thermal production through inflaton decays and scatterings, and $ii$) thermal production from scattering of standard model particles mediated by inflatons and gravitons. In contrast to small-field polynomial inflation, where inflaton decay dominates DM production, we find that graviton-mediated processes can be dominant in the large-field scenario. For DM lighter than the inflaton, we demonstrate that the interplay between graviton- and inflaton-mediated production channels give rise to non-trivial relations between the DM mass and the reheating temperature required to account for the DM relic abundance.

hep-ph↗

arXiv2025

Combining Knowledge Graph and LLMs for Enhanced Zero-shot Visual Question Answering

Zero-shot visual question answering (ZS-VQA), an emerged critical research area, intends to answer visual questions without providing training samples. Existing research in ZS-VQA has proposed to leverage knowledge graphs or large language models (LLMs), respectively, as external information sources to help VQA model comprehend images and questions. However, LLMs often struggle in accurately interpreting specific question meanings. Meanwhile, although knowledge graph has rich entity relationships, it is challenging to effectively connect entities to individual image content for visual question answers. In this paper, we propose a novel design to combine knowledge graph and LLMs for zero-shot visual question answer. Our approach uses LLMs' powerful understanding capabilities to accurately interpret image content through a strategic question search mechanism. Meanwhile, the knowledge graph is used to expand and connect users' queries to the image content for better visual question answering. An optimization algorithm is further used to determine the optimal weights for the loss functions derived from different information sources, towards a globally optimal set of candidate answers. Experimental results on two benchmark datasets demonstrate that our model achieves state-of-the-art (SOTA) performance. Both source code and benchmark data will be released for public access.

cs.CV↗

arXiv2025

Transfer learning electronic structure: millielectron volt accuracy for sub-million-atom moiré semiconductor

The integration of density functional theory (DFT) with machine learning enables efficient \textit{ab initio} electronic structure calculations for ultra-large systems. In this work, we develop a transfer learning framework tailored for long-wavelength moiré systems. To balance efficiency and accuracy, we adopt a two-step transfer learning strategy: (1) the model is pre-trained on a large dataset of computationally inexpensive non-twisted structures until convergence, and (2) the network is then fine-tuned using a small set of computationally expensive twisted structures. Applying this method to twisted MoTe$_2$, the neural network model generates the resulting Hamiltonian for a 1000-atom system in 200 seconds, achieving a mean absolute error below 0.1 meV. To demonstrate $O(N)$ scalability, we model nanoribbon systems with up to 0.25 million atoms ($\sim9$ million orbitals), accurately capturing edge states consistent with predicted Chern numbers. This approach addresses the challenges of accuracy, efficiency, and scalability, offering a viable alternative to conventional DFT and enabling the exploration of electronic topology in large scale moiré systems towards simulating realistic device architectures.

cond-mat.mtrl-sci↗

arXiv2025

Fractional Quantum Hall Effect Based on Weyl Orbits

The fractional quantum Hall effect is a well-known demonstration of strongly correlated topological phases in two dimensions. However, the extension of this phenomenon into a three-dimensional context has yet to be achieved. Recently, the three-dimensional integer quantum Hall effect based on Weyl orbits has been experimentally observed in a topological semimetallic material under a magnetic field. This motivates us to ask whether the Weyl orbits can give rise to the fractional quantum Hall effect when their Landau level is partially filled in the presence of interactions. Here we theoretically demonstrate that the fractional quantum Hall states based on Weyl orbits can emerge in a Weyl semimetal when a Landau level is one-third filled. Using concrete models for Weyl semimetals in magnetic fields, we project the Coulomb interaction onto a single Landau level from the Weyl orbit and find that the ground state of the many-body Hamiltoian is triply degenerate. We further show that the ground states exhibit the many-body Chern number of $1/3$ and the uniform occupation of electrons in both momentum and real space, implying that they are the fractional quantum Hall states. In contrast to the two-dimensional case, the states are spatially localized on two surfaces hosting Fermi arcs. Additionally, our findings suggest that the excitation properties of these states resemble those of the Laughlin state in two dimensions, as inferred from the particle entanglement spectrum of the ground states.

cond-mat.str-el↗

arXiv2025

HDMoLE: Mixture of LoRA Experts with Hierarchical Routing and Dynamic Thresholds for Fine-Tuning LLM-based ASR Models

Recent advancements in integrating Large Language Models (LLM) with automatic speech recognition (ASR) have performed remarkably in general domains. While supervised fine-tuning (SFT) of all model parameters is often employed to adapt pre-trained LLM-based ASR models to specific domains, it imposes high computational costs and notably reduces their performance in general domains. In this paper, we propose a novel parameter-efficient multi-domain fine-tuning method for adapting pre-trained LLM-based ASR models to multi-accent domains without catastrophic forgetting named \textit{HDMoLE}, which leverages hierarchical routing and dynamic thresholds based on combining low-rank adaptation (LoRA) with the mixer of experts (MoE) and can be generalized to any linear layer. Hierarchical routing establishes a clear correspondence between LoRA experts and accent domains, improving cross-domain collaboration among the LoRA experts. Unlike the static Top-K strategy for activating LoRA experts, dynamic thresholds can adaptively activate varying numbers of LoRA experts at each MoE layer. Experiments on the multi-accent and standard Mandarin datasets demonstrate the efficacy of HDMoLE. Applying HDMoLE to an LLM-based ASR model projector module achieves similar performance to full fine-tuning in the target multi-accent domains while using only 9.6% of the trainable parameters required for full fine-tuning and minimal degradation in the source general domain.

cs.SD↗

arXiv2024

Thermal Gravitational Waves During Reheating

In this work, we revisit the generation of stochastic gravitational waves (GWs) from interactions in the thermal plasma. We extend the existing literature by incorporating the reheating phase into the thermal history. Our results show that the amplitude of the GW spectrum can be significantly enhanced because the temperature during reheating can be much higher than the reheating temperature. Furthermore, since the temperature evolution during reheating differs from that of free radiation, the peak frequency of the spectrum can also shift. Additionally, the morphology of the spectrum can present characteristic features. We also compute the contribution of the integrated GW spectrum to the effective number of neutrino species, $ΔN_\text{eff}$, which can be substantially larger.

hep-ph↗

arXiv2024

MixRec: Heterogeneous Graph Collaborative Filtering

For modern recommender systems, the use of low-dimensional latent representations to embed users and items based on their observed interactions has become commonplace. However, many existing recommendation models are primarily designed for coarse-grained and homogeneous interactions, which limits their effectiveness in two critical dimensions. Firstly, these models fail to leverage the relational dependencies that exist across different types of user behaviors, such as page views, collects, comments, and purchases. Secondly, they struggle to capture the fine-grained latent factors that drive user interaction patterns. To address these limitations, we present a heterogeneous graph collaborative filtering model MixRec that excels at disentangling users' multi-behavior interaction patterns and uncovering the latent intent factors behind each behavior. Our model achieves this by incorporating intent disentanglement and multi-behavior modeling, facilitated by a parameterized heterogeneous hypergraph architecture. Furthermore, we introduce a novel contrastive learning paradigm that adaptively explores the advantages of self-supervised data augmentation, thereby enhancing the model's resilience against data sparsity and expressiveness with relation heterogeneity. To validate the efficacy of MixRec, we conducted extensive experiments on three public datasets. The results clearly demonstrate its superior performance, significantly outperforming various state-of-the-art baselines. Our model is open-sourced and available at: https://github.com/HKUDS/MixRec.

cs.IR↗

arXiv2024

Ultra-high Frequency Gravitational Waves from Scattering, Bremsstrahlung and Decay during Reheating

We investigate ultra-high frequency gravitational waves (GWs) from gravitons generated during inflationary reheating. Specifically, we study inflaton scattering with its decay product, where the couplings involved in this $2 \to 2$ scattering are the same as those in the $1 \to 3$ graviton Bremsstrahlung process. We compute the graviton production rate via such $2 \to 2$ scattering. Additionally, we compare the resulting GW spectrum with that from Bremsstrahlung as well as that from pure $2 \to 2$ inflaton scatterings. For completeness, the GW spectrum from graviton pair production through one-loop induced $1 \to 2$ inflaton decay is also analyzed. With a systematic comparison among the four sources of GWs, we find that $2 \to 2$ inflaton scattering with its decay product can dominate over Bremsstrahlung if the reheating temperature is larger than the inflaton mass. Pure inflaton $2 \to 2$ scattering is typically subdominant compared to Bremsstrahlung except in the high-frequency tail. The contribution from one-loop induced $1 \to 2$ inflaton decay is shown to be suppressed compared to Bremsstrahlung and pure inflaton $2 \to 2$ scattering.

hep-ph↗

arXiv2024

Experimental discovery of Sarma state in atomically thick superconducting FeSe films under high magnetic fields

Many-body ground states of imbalanced Fermi gas have been studied both theoretically and experimentally for several decades because of their fundamental significance in condensed matter physics, cold atom physics and nuclear physics. The Sarma state, a gapless spin-polarized superfluid, is one of those long sought-after exotic ground states of spin imbalanced Fermi gas. Yet, an unambiguous experimental evidence of Sarma superfluid state has not been found. Here, we report the experimental discovery of the Sarma state in atomically thick FeSe films by a dilution-refrigerator scanning tunneling microscope under high magnetic fields. In the bilayer or trilayer FeSe films, we directly observe the key evidence of the entrance of the Sarma state: the inner Zeeman splitting coherence peaks cross the Fermi level under high in-plane magnetic fields. The angle dependent critical in-plane magnetic field of coherence peak crossing shows a two-fold symmetry due to the anisotropy of the in-plane g-factor of FeSe films. Moreover, in a superconducting FeSe monolayer of a lateral size of several hundred nanometers, the Sarma state can also be induced by strong out-of-plane magnetic fields. Our findings pave the way to explore the unusual physical properties and potential applications in superconducting spintronics of the spin-polarized Sarma superfluid state.

cond-mat.supr-con↗

arXiv2024

Averaging principle for semilinear slow-fast rough partial differential equations

In this paper, we investigate the averaging principle for a class of semilinear slow-fast partial differential equations driven by finite-dimensional rough multiplicative noise. Specifically, the slow component is driven by a general random $γ$-Hölder rough path for some $γ\in (1/3,1/2)$, while the fast component is driven by a Brownian rough path. Using controlled rough path theory and the classical Khasminskii's time discretization scheme, we demonstrate that the slow component converges strongly to the solution of the corresponding averaged equation under the Hölder topology.

math.PR↗

arXiv2024

Correlated topological flat bands in rhombohedral graphite

Flat bands and nontrivial topological physics are two important topics of condensed matter physics. With a unique stacking configuration analogous to the Su-Schrieffer-Heeger (SSH) model, rhombohedral graphite (RG) is a potential candidate for realizing both flat bands and nontrivial topological physics. Here we report experimental evidence of topological flat bands (TFBs) on the surface of bulk RG, which are topologically protected by bulk helical Dirac nodal lines via the bulk-boundary correspondence. Moreover, upon {\it in situ} electron doping, the surface TFBs show a splitting with exotic doping evolution, with an order-of-magnitude increase in the bandwidth of the lower split band, and pinning of the upper band near the Fermi level. These experimental observations together with Hartree-Fock calculations suggest that correlation effects are important in this system. Our results demonstrate RG as a new platform for investigating the rich interplay between nontrivial band topology, correlation effects, and interaction-driven symmetry-broken states.

cond-mat.str-el↗

arXiv2024

Stable magic angle in twisted Kane-Mele materials

We propose that flat bands and van Hove singularities near the magic angle can be stabilized against angle disorder in the twisted Kane-Mele model. With continuum model and maximally localized Wannier function approaches, we identify a quadratic dispersion relationship between the bandwidth, interaction parameters versus the twist angle, in contrast to twisted bilayer graphene (TBG). Introducing Kane-Mele spin-orbit coupling to TBG greatly reduces the fractional Chern insulator indicator and enhances the stability of fractional Chern states near the magic angle, as confirmed by exact diagonalization calculations. Moreover, in twisted bilayer Pt$_2$HgSe$_3$ with intrinsic Kane-Mele spin-orbit coupling, we identify a topological flat band at a large twist angle around 4 degrees.

cond-mat.str-el↗

arXiv2024

Multi-path Exploration and Feedback Adjustment for Text-to-Image Person Retrieval

Text-based person retrieval aims to identify the specific persons using textual descriptions as queries. Existing ad vanced methods typically depend on vision-language pre trained (VLP) models to facilitate effective cross-modal alignment. However, the inherent constraints of VLP mod-els, which include the global alignment biases and insuffi-cient self-feedback regulation, impede optimal retrieval per formance. In this paper, we propose MeFa, a Multi-Pathway Exploration, Feedback, and Adjustment framework, which deeply explores intrinsic feedback of intra and inter-modal to make targeted adjustment, thereby achieving more precise person-text associations. Specifically, we first design an intra modal reasoning pathway that generates hard negative sam ples for cross-modal data, leveraging feedback from these samples to refine intra-modal reasoning, thereby enhancing sensitivity to subtle discrepancies. Subsequently, we intro duce a cross-modal refinement pathway that utilizes both global information and intermodal feedback to refine local in formation, thus enhancing its global semantic representation. Finally, the discriminative clue correction pathway incorpo rates fine-grained features of secondary similarity as discrim inative clues to further mitigate retrieval failures caused by disparities in these features. Experimental results on three public benchmarks demonstrate that MeFa achieves superior person retrieval performance without necessitating additional data or complex structures.

cs.CV↗

arXiv2024

Large deviation principle for slow-fast system with mixed fractional Brownian motion

This work focuses on a slow-fast system perturbed by mixed fractional Brownian motion with Hurst parameter $H\in(1/2,1)$. The integral with respect to fractional Brownian motion is the generalized Riemann-Stieltjes integral and the integral with respect to Brownian motion is the standard Itô integral. Our approach is based on the variational framework and the weak convergence criteria for mixed fractional Brownian motion. By combining the weak convergence method and Khasminskii's averaging principle, we show a large deviation principle for the slow component.

math.PR↗

arXiv2024

MambaSCI: Efficient Mamba-UNet for Quad-Bayer Patterned Video Snapshot Compressive Imaging

Color video snapshot compressive imaging (SCI) employs computational imaging techniques to capture multiple sequential video frames in a single Bayer-patterned measurement. With the increasing popularity of quad-Bayer pattern in mainstream smartphone cameras for capturing high-resolution videos, mobile photography has become more accessible to a wider audience. However, existing color video SCI reconstruction algorithms are designed based on the traditional Bayer pattern. When applied to videos captured by quad-Bayer cameras, these algorithms often result in color distortion and ineffective demosaicing, rendering them impractical for primary equipment. To address this challenge, we propose the MambaSCI method, which leverages the Mamba and UNet architectures for efficient reconstruction of quad-Bayer patterned color video SCI. To the best of our knowledge, our work presents the first algorithm for quad-Bayer patterned SCI reconstruction, and also the initial application of the Mamba model to this task. Specifically, we customize Residual-Mamba-Blocks, which residually connect the Spatial-Temporal Mamba (STMamba), Edge-Detail-Reconstruction (EDR) module, and Channel Attention (CA) module. Respectively, STMamba is used to model long-range spatial-temporal dependencies with linear complexity, EDR is for better edge-detail reconstruction, and CA is used to compensate for the missing channel information interaction in Mamba model. Experiments demonstrate that MambaSCI surpasses state-of-the-art methods with lower computational and memory costs. PyTorch style pseudo-code for the core modules is provided in the supplementary materials.

cs.CV↗

arXiv2024

Transverse Cooper-Pair Rectifier

Non-reciprocal devices are key components in modern electronics covering broad applications ranging from transistors to logic circuits thanks to the output rectified signal in the direction parallel to the input. In this work, we propose a transverse Cooper-pair rectifier in which a non-reciprocal current is perpendicular to the driving field, when inversion, time reversal, and mirror symmetries are broken simultaneously. The Blonder-Tinkham-Klapwijk formalism is developed to describe the transverse current-voltage relation in a normal-metal/superconductor tunneling junction, where symmetry constraints are achieved by an effective built-in supercurrent manifesting in an asymmetric and anisotropic Andreev reflection. The asymmetry in the Andreev reflection is induced when inversion and time reversal symmetry are broken by the supercurrent component parallel to the junction while the anisotropy occurs when the mirror symmetry with respect to the normal of the junction interface is broken by the perpendicular supercurrent component to the junction. Compared to the conventional longitudinal one, the transverse rectifier supports fully polarized diode efficiency and colossal nonreciprocal conductance rectification, completely decoupling the path of the input excitation from the output rectified signal. This work provides a formalism for realizing transverse non-reciprocity in superconducting junctions, which is expected to be achieved by modifying current experimental setups and may pave the way for future low-dissipation superconducting electronics.

cond-mat.mes-hall↗

arXiv2024

Restorative Speech Enhancement: A Progressive Approach Using SE and Codec Modules

In challenging environments with significant noise and reverberation, traditional speech enhancement (SE) methods often lead to over-suppressed speech, creating artifacts during listening and harming downstream tasks performance. To overcome these limitations, we propose a novel approach called Restorative SE (RestSE), which combines a lightweight SE module with a generative codec module to progressively enhance and restore speech quality. The SE module initially reduces noise, while the codec module subsequently performs dereverberation and restores speech using generative capabilities. We systematically explore various quantization techniques within the codec module to optimize performance. Additionally, we introduce a weighted loss function and feature fusion that merges the SE output with the original mixture, particularly at segments where the SE output is heavily distorted. Experimental results demonstrate the effectiveness of our proposed method in enhancing speech quality under adverse conditions. Audio demos are available at: https://sophie091524.github.io/RestorativeSE/.

eess.AS↗

arXiv2024

Deep Band Crossings Enhanced Nonlinear Optical Effects

Nonlinear optical (NLO) effects in materials with band crossings have attracted significant research interests due to the divergent band geometric quantities around these crossings. Most current research has focused on band crossings between the valence and conduction bands. However, such crossings are absent in insulators, which are more relevant for NLO applications. In this work, we demonstrate that NLO effects can be significantly enhanced by band crossings within the valence or conduction bands, which we designate as "deep band crossings" (DBCs). As an example, in two dimensions, we show that shift conductivity can be substantially enhanced or even divergent due to a mirror-protected "deep Dirac nodal point". In three dimensions, we propose GeTe as an ideal material where shift conductivity is enhanced by "deep Dirac nodal lines". The ubiquity of this enhancement is further confirmed by high-throughput calculations. Other types of DBCs and NLO effects are also discussed. By manipulating band crossings between arbitrary bands, our work offers a simple, practical, and universal way to greatly enhance NLO effects.

cond-mat.mes-hall↗