arXiv subjects

Lei Wang

Publications and source records attributed to Lei Wang.

At least 307 records · Page 17Linked to original sources

arXiv2025

Ferroelectrically Controlled Chirality Switching of Weyl Quasiparticles

Weyl quasiparticles, as gapless low-energy excitations with nontrivial chirality, have garnered extensive interest in recent years. However, archieving effective and reversible control over their chirality (topological charge) remains a major challeng due to topological protection. In this work, we propose a ferroelectric mechanism to switch the chirality of Weyl phonons, where the reversal of ferroelectric polarization is intrinsically coupled to a simultaneous reversal of the chirality of Weyl points. This enables electric-field-driven control over the topological properties of phonon excitations. Through a comprehensive symmetry analysis of polar space groups, we identify 27 groups capable of hosting symmetry-protected Weyl phonons with chiral charges $C = 1$, $2$, and $3$, whose chirality can be reversed via polarization switching. The first-principles calculations are performed to screen feasible material candidates for each type of chirality, yielding a set of prototypical ferroelectric compounds that realize the proposed mechanism. As a representative example, K$_2$ZnBr$_4$ hosts the minimal configuration of two pairs of Weyl phonons. Upon polarization reversal, the chirality of all Weyl points is inverted, accompanied by a reversal of associated topological features such as Berry curvature and surface arcs. These findings provide a viable pathway for dynamic, electrical control of topological band crossings and open new avenues for chirality-based phononic applications.

cond-mat.mtrl-sci↗

arXiv2025

Dynamic Forgetting and Spatio-Temporal Periodic Interest Modeling for Local-Life Service Recommendation

In the context of the booming digital economy, recommendation systems, as a key link connecting users and numerous services, face challenges in modeling user behavior sequences on local-life service platforms, including the sparsity of long sequences and strong spatio-temporal dependence. Such challenges can be addressed by drawing an analogy to the forgetting process in human memory. This is because users' responses to recommended content follow the recency effect and the cyclicality of memory. By exploring this, this paper introduces the forgetting curve and proposes Spatio-Temporal periodic Interest Modeling (STIM) with long sequences for local-life service recommendation. STIM integrates three key components: a dynamic masking module based on the forgetting curve, which is used to extract both recent spatiotemporal features and periodic spatiotemporal features; a query-based mixture of experts (MoE) approach that can adaptively activate expert networks under different dynamic masks, enabling the collaborative modeling of time, location, and items; and a hierarchical multi-interest network unit, which captures multi-interest representations by modeling the hierarchical interactions between the shallow and deep semantics of users' recent behaviors. By introducing the STIM method, we conducted online A/B tests and achieved a 1.54\% improvement in gross transaction volume (GTV). In addition, extended offline experiments also showed improvements. STIM has been deployed in a large-scale local-life service recommendation system, serving hundreds of millions of daily active users in core application scenarios.

cs.IR↗

arXiv2025

MLLM-Driven Semantic Identifier Generation for Generative Cross-Modal Retrieval

Generative cross-modal retrieval, which treats retrieval as a generation task, has emerged as a promising direction with the rise of Multimodal Large Language Models (MLLMs). In this setting, the model responds to a text query by generating an identifier corresponding to the target image. However, existing methods typically rely on manually crafted string IDs, clustering-based labels, or atomic identifiers requiring vocabulary expansion, all of which face challenges in semantic alignment or scalability.To address these limitations, we propose a vocabulary-efficient identifier generation framework that prompts MLLMs to generate Structured Semantic Identifiers from image-caption pairs. These identifiers are composed of concept-level tokens such as objects and actions, naturally aligning with the model's generation space without modifying the tokenizer. Additionally, we introduce a Rationale-Guided Supervision Strategy, prompting the model to produce a one-sentence explanation alongside each identifier serves as an auxiliary supervision signal that improves semantic grounding and reduces hallucinations during training.

cs.IR↗

arXiv2025

Unsupervised Monocular Depth Estimation Based on Hierarchical Feature-Guided Diffusion

Unsupervised monocular depth estimation has received widespread attention because of its capability to train without ground truth. In real-world scenarios, the images may be blurry or noisy due to the influence of weather conditions and inherent limitations of the camera. Therefore, it is particularly important to develop a robust depth estimation model. Benefiting from the training strategies of generative networks, generative-based methods often exhibit enhanced robustness. In light of this, we employ a well-converging diffusion model among generative networks for unsupervised monocular depth estimation. Additionally, we propose a hierarchical feature-guided denoising module. This model significantly enriches the model's capacity for learning and interpreting depth distribution by fully leveraging image features to guide the denoising process. Furthermore, we explore the implicit depth within reprojection and design an implicit depth consistency loss. This loss function serves to enhance the performance of the model and ensure the scale consistency of depth within a video sequence. We conduct experiments on the KITTI, Make3D, and our self-collected SIMIT datasets. The results indicate that our approach stands out among generative-based models, while also showcasing remarkable robustness.

cs.CV↗

arXiv2025

Pixel-level modelling of group-scale strong lens CASSOWARY 19

We present the first high-precision model for the group-scale strong lensing system CASSOWARY 19 (CSWA19), utilising images from the Hubble Space Telescope (HST). Sixteen member galaxies identified via the red-sequence method, and the main halo, all modelled as the dual Pseudo Isothermal Elliptical profile (dPIE), are incorporated into a parametric lens model alongside an external shear field. To model the system, we adopt the PyAutoLens software package, employing a progressive search chain strategy for realizing the transition of source model from multiple Sérsic profiles to a brightness-adaptive pixelization, which uses 1000 pixels in the source plane to reconstruct the background source corresponding to 177,144 image pixels in the image plane. Our results indicate that the total mass within the Einstein radius is $M_{θ_\mathrm{E}}$ $\approx 1.41\times10^{13}$M$_{\odot}$ and the average slope of the total mass density $ρ(r)\propto r^{-γ}$ is $\tildeγ=1.33$ within the effective radius. This slope is shallower than those measured in galaxies and groups but is closer to those of galaxy clusters. In addition, our approach successfully resolves the two merging galaxies in the background source and yields a total magnification of $μ=103.18^{+0.23}_{-0.19}$, which is significantly higher than the outcomes from previous studies of CSWA19. In summary, our research demonstrates the effectiveness of the brightness-adaptive pixelization source reconstruction technique for modelling group-scale strong lensing systems. It can serve as a technical reference for future investigations into pixel-level modelling of the group- and cluster-scale strong lensing systems.

astro-ph.GA↗

arXiv2025

MPCache: MPC-Friendly KV Cache Eviction for Efficient Private LLM Inference

Private large language model (LLM) inference based on secure multi-party computation (MPC) achieves formal data privacy protection but suffers from significant latency overhead, especially for long input sequences. While key-value (KV) cache eviction and sparse attention algorithms have been proposed for efficient LLM inference in plaintext, they are not designed for MPC and cannot benefit private LLM inference directly. In this paper, we propose an accurate and MPC-friendly KV cache eviction framework, dubbed MPCache, building on the observation that historical tokens in a long sequence may have different effects on the downstream decoding. Hence, MPCache combines a look-once static eviction algorithm to discard unimportant KV cache and a query-aware dynamic selection algorithm to activate only a small subset of KV cache for attention computation. MPCache further incorporates a series of optimizations for efficient dynamic KV cache selection, including MPC-friendly similarity approximation, hierarchical KV cache clustering, and cross-layer index-sharing strategy. Extensive experiments demonstrate that MPCache consistently outperforms prior-art KV cache eviction baselines across different generation tasks and achieves 1.8 ~ 2.01x and 3.39 ~ 8.37x decoding latency and communication reduction on different sequence lengths, respectively.

cs.CR↗

arXiv2025

MTmixAtt: Integrating Mixture-of-Experts with Multi-Mix Attention for Large-Scale Recommendation

Industrial recommender systems critically depend on high-quality ranking models. However, traditional pipelines still rely on manual feature engineering and scenario-specific architectures, which hinder cross-scenario transfer and large-scale deployment. To address these challenges, we propose \textbf{MTmixAtt}, a unified Mixture-of-Experts (MoE) architecture with Multi-Mix Attention, designed for large-scale recommendation tasks. MTmixAtt integrates two key components. The \textbf{AutoToken} module automatically clusters heterogeneous features into semantically coherent tokens, removing the need for human-defined feature groups. The \textbf{MTmixAttBlock} module enables efficient token interaction via a learnable mixing matrix, shared dense experts, and scenario-aware sparse experts, capturing both global patterns and scenario-specific behaviors within a single framework. Extensive experiments on the industrial TRec dataset from Meituan demonstrate that MTmixAtt consistently outperforms state-of-the-art baselines including Transformer-based models, WuKong, HiFormer, MLP-Mixer, and RankMixer. At comparable parameter scales, MTmixAtt achieves superior CTR and CTCVR metrics; scaling to MTmixAtt-1B yields further monotonic gains. Large-scale online A/B tests validate the real-world impact: in the \textit{Homepage} scenario, MTmixAtt increases Payment PV by \textbf{+3.62\%} and Actual Payment GTV by \textbf{+2.54\%}. Overall, MTmixAtt provides a unified and scalable solution for modeling arbitrary heterogeneous features across scenarios, significantly improving both user experience and commercial outcomes.

cs.IR↗

arXiv2025

Multi-level Reliable Guidance for Unpaired Multi-view Clustering

In this thesis, we address the challenging problem of unpaired multi-view clustering (UMC), which aims to achieve effective joint clustering using unpaired samples observed across multiple views. Traditional incomplete multi-view clustering (IMC) methods typically rely on paired samples to capture complementary information between views. However, such strategies become impractical in the UMC due to the absence of paired samples. Although some researchers have attempted to address this issue by preserving consistent cluster structures across views, effectively mining such consistency remains challenging when the cluster structures {with low confidence}. Therefore, we propose a novel method, Multi-level Reliable Guidance for UMC (MRG-UMC), which integrates multi-level clustering and reliable view guidance to learn consistent and confident cluster structures from three perspectives. Specifically, inner-view multi-level clustering exploits high-confidence sample pairs across different levels to reduce the impact of boundary samples, resulting in more confident cluster structures. Synthesized-view alignment leverages a synthesized-view to mitigate cross-view discrepancies and promote consistency. Cross-view guidance employs a reliable view guidance strategy to enhance the clustering confidence of poorly clustered views. These three modules are jointly optimized across multiple levels to achieve consistent and confident cluster structures. Furthermore, theoretical analyses verify the effectiveness of MRG-UMC in enhancing clustering confidence. Extensive experimental results show that MRG-UMC outperforms state-of-the-art UMC methods, achieving an average NMI improvement of 12.95\% on multi-view datasets. {The source code is available at: https://anonymous.4open.science/r/MRG-UMC-5E20.

cs.CV↗

arXiv2025

Thermocapillary migration of an odd viscous droplet on a uniformly heated surface: A lattice Boltzmann study

In this study, the thermocapillary actuation behavior of an odd viscous droplet on a uniformly heated surface is numerically investigated using a phase-field-based lattice Boltzmann method. The numerical results reveal that unlike a conventional viscous droplet that remains stationary on a uniformly heated surface, the presence of odd viscosity converts tangential Marangoni stresses into asymmetric normal stresses along the interface, thereby inducing spontaneous droplet motion. Specifically, when the odd viscosity coefficient is positive (negative), the droplet migrates toward the right (left). Additionally, due to the enhanced interfacial temperature gradient, the droplet migration velocity consistently increases with the contact angle. Further, it is observed that the droplet's migration velocity decreases with an increasing viscosity ratio between the surrounding fluid and the droplet. Finally, as the droplet is placed on an inclined surface, its migration direction and velocity are governed by the interaction between gravity and the odd viscosity-induced force, and in certain cases, the droplet can even climb upward against gravity.

physics.flu-dyn↗

arXiv2025

A Review of Longitudinal Radiology Report Generation: Dataset Composition, Methods, and Performance Evaluation

Chest Xray imaging is a widely used diagnostic tool in modern medicine, and its high utilization creates substantial workloads for radiologists. To alleviate this burden, vision language models are increasingly applied to automate Chest Xray radiology report generation (CXRRRG), aiming for clinically accurate descriptions while reducing manual effort. Conventional approaches, however, typically rely on single images, failing to capture the longitudinal context necessary for producing clinically faithful comparison statements. Recently, growing attention has been directed toward incorporating longitudinal data into CXR RRG, enabling models to leverage historical studies in ways that mirror radiologists diagnostic workflows. Nevertheless, existing surveys primarily address single image CXRRRG and offer limited guidance for longitudinal settings, leaving researchers without a systematic framework for model design. To address this gap, this survey provides the first comprehensive review of longitudinal radiology report generation (LRRG). Specifically, we examine dataset construction strategies, report generation architectures alongside longitudinally tailored designs, and evaluation protocols encompassing both longitudinal specific measures and widely used benchmarks. We further summarize LRRG methods performance, alongside analyses of different ablation studies, which collectively highlight the critical role of longitudinal information and architectural design choices in improving model performance. Finally, we summarize five major limitations of current research and outline promising directions for future development, aiming to lay a foundation for advancing this emerging field.

cs.CV↗

arXiv2025

A 95 GeV Higgs boson and spontaneous CP-violation at the finite temperature

The ATLAS and CMS collaborations reported a diphoton excess in the invariant mass distribution around the 95.4 GeV with a local significance of $3.1σ$. Moreover, there is another $2.3σ$ local excess in the $b\bar{b}$ final state at LEP in the same mass region. A plausible solution is that the Higgs sector is extended to include an additional Higgs boson with a mass of $95.4$ GeV. We study a complex singlet scalar extension of the two-Higgs-doublet model in which the 95.4 GeV Higgs is from the mixing of three CP-even Higgs fields. In addition, the extended Higgs potential can achieve spontaneous CP-violation at the finite temperature and restore CP symmetry at the present temperature of the Universe. We find that the model can simultaneously explain the baryon asymmetry of the Universe, the diphoton and $b\bar{b}$ excesses around the 95.4 GeV while satisfying various relevant constraints including the experiments of collider and electric dipole moment.

hep-ph↗

arXiv2025

95 GeV Higgs boson and nano-Hertz gravitational waves from domain walls in the N2HDM

We explore the diphoton and $b\bar{b}$ excesses at 95.4 GeV, as well as nano-Hertz gravitational waves originating from domain walls, within the framework of the next-to-two-Higgs-doublet model (N2HDM), which extends the two-Higgs-doublet model by introducing a real singlet scalar subject to a discrete $Z_2$ symmetry. The $Z_2$ symmetry is spontaneously broken by the non-zero vacuum expectation value of the singlet scalar, $v_s$, which leads to the formation of domain walls. We discuss two different scenarios: in scenario A, the 95.4 GeV Higgs boson predominantly originates from the singlet field, while in scenario B, it arises mainly from the CP-even components of the Higgs doublets. Taking into account relevant theoretical and experimental constraints, we find that scenario A can fully account for both the diphoton and $b\bar{b}$ excesses at 95.4 GeV within the $1σ$ range. Moreover, the peak amplitude of the gravitational wave spectrum at a peak frequency of $10^{-9}$ Hz can reach $2 \times 10^{-12}$ for $v_s = 100$ TeV. Scenario B only marginally accounts for the diphoton and $b\bar{b}$ excesses at the $1σ$ level, but the peak amplitude of the gravitational wave spectrum at the peak frequency of $10^{-9}$ Hz can reach $6\times 10^{-8}$ for $v_s=100$ TeV. The nano-Hertz gravitational wave signals predicted in both scenarios can be tested by the current and future pulsar timing array projects.

hep-ph↗

arXiv2025

LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement

Foundation models (FMs) achieve strong performance across diverse tasks with task-specific fine-tuning, yet full parameter fine-tuning is often computationally prohibitive for large models. Parameter-efficient fine-tuning (PEFT) methods like Low-Rank Adaptation (LoRA) reduce this cost by introducing low-rank matrices for tuning fewer parameters. While LoRA allows for efficient fine-tuning, it requires significant data for adaptation, making Federated Learning (FL) an appealing solution due to its privacy-preserving collaborative framework. However, combining LoRA with FL introduces two key challenges: the \textbf{Server-Side Aggregation Bias}, where server-side averaging of LoRA matrices diverges from the ideal global update, and the \textbf{Client-Side Initialization Lag}, emphasizing the need for consistent initialization across rounds. Existing approaches address these challenges individually, limiting their effectiveness. We propose LoRA-FAIR, a novel method that tackles both issues by introducing a correction term on the server, enhancing aggregation efficiency and accuracy. LoRA-FAIR maintains computational and communication efficiency, yielding superior performance over state-of-the-art methods. Experimental results on ViT and MLP-Mixer models across large-scale datasets demonstrate that LoRA-FAIR consistently achieves performance improvements in FL settings.

cs.LG↗

arXiv2025

Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning

Large Language Models (LLMs) have demonstrated impressive capabilities across various tasks, but fine-tuning them for domain-specific applications often requires substantial domain-specific data that may be distributed across multiple organizations. Federated Learning (FL) offers a privacy-preserving solution, but faces challenges with computational constraints when applied to LLMs. Low-Rank Adaptation (LoRA) has emerged as a parameter-efficient fine-tuning approach, though a single LoRA module often struggles with heterogeneous data across diverse domains. This paper addresses two critical challenges in federated LoRA fine-tuning: 1. determining the optimal number and allocation of LoRA experts across heterogeneous clients, and 2. enabling clients to selectively utilize these experts based on their specific data characteristics. We propose FedLEASE (Federated adaptive LoRA Expert Allocation and SElection), a novel framework that adaptively clusters clients based on representation similarity to allocate and train domain-specific LoRA experts. It also introduces an adaptive top-$M$ Mixture-of-Experts mechanism that allows each client to select the optimal number of utilized experts. Our extensive experiments on diverse benchmark datasets demonstrate that FedLEASE significantly outperforms existing federated fine-tuning approaches in heterogeneous client settings while maintaining communication efficiency.

cs.LG↗

arXiv2025

Hidden phonon-assisted charge density wave transition in BaFe2Al9 revealed by ultrafast optical spectroscopy

The interplay between electronic and lattice degrees of freedom is fundamental to charge density wave (CDW) formation, yet the microscopic origin often remains elusive. Here, we investigate the transient optical response of the intermetallic compound BaFe2Al9 using polarization-resolved ultrafast optical spectroscopy. We identify a discontinuous sign reversal in the transient reflectivity at Tc ~ 110 K, providing unambiguous evidence for the first-order transition. The anisotropic quasiparticle relaxation establishes the three-dimensional nature of the ordered state. Below Tc, a single coherent 1.6 THz oscillation appears abruptly and remains confined to the CDW phase. This mode exhibits weak temperature dependence with negligible softening and is absent in Raman spectra. First-principles calculations imply that it is a precursor phonon at the CDW wave vector with strong electron-phonon coupling. Our results indicate that the CDW in BaFe2Al9 arises from intertwined electronic and lattice instabilities, assisted by a displacive mechanism mediated by a hidden strongly coupled phonon, distinct from conventional amplitude-mode softening scenarios.

cond-mat.str-el↗

arXiv2025

Internal multiplicity distributions of jets from nonlinear evolution within the jet function framework

Jets selected with high internal charged-particle multiplicity exhibit markedly different substructure patterns compared to inclusive jet samples. Such correlations motivate a systematic study of jet observables as a function of the normalized multiplicity, $ν= N_{\rm ch}/\langle N_{\rm ch}\rangle$. In this work, we develop a theoretical framework for the full charged-particle multiplicity distribution of exclusive and inclusive jets, formulated within the jet-function approach. The hard production and jet function are evaluated at NLO+LL$_R$ accuracy. The internal parton dynamics governing the multiplicity distribution are described by coupled nonlinear branching equations with angular ordering, supplemented by a nonperturbative modeling term that accounts for hadron-level effects. The resulting predictions are validated against \textsc{Pythia8} simulations and compared with CMS data. We examine the effects of both nonperturbative and perturbative components in shaping the multiplicity distribution, and show that Koba--Nielsen--Olesen (KNO) scaling is notably violated in the region $ν> 2$ in the full solution, with a trend consistent with Monte Carlo results. This framework that numerically solves the nonlinear multiplicity evolution goes beyond DLA-like approximations and reproduces key features seen in event generators, providing a solid foundation for future investigations of multiplicity -- conditioned jet substructure within the jet function formalism.

hep-ph↗

arXiv2025

Unlocking Multimodal Mathematical Reasoning via Process Reward Model

Process Reward Models (PRMs) have shown promise in enhancing the mathematical reasoning capabilities of Large Language Models (LLMs) through Test-Time Scaling (TTS). However, their integration into multimodal reasoning remains largely unexplored. In this work, we take the first step toward unlocking the potential of PRMs in multimodal mathematical reasoning. We identify three key challenges: (1) the scarcity of high-quality reasoning data constrains the capabilities of foundation Multimodal Large Language Models (MLLMs), which imposes further limitations on the upper bounds of TTS and reinforcement learning (RL); (2) a lack of automated methods for process labeling within multimodal contexts persists; (3) the employment of process rewards in unimodal RL faces issues like reward hacking, which may extend to multimodal scenarios. To address these issues, we introduce URSA, a three-stage Unfolding multimodal Process-Supervision Aided training framework. We first construct MMathCoT-1M, a high-quality large-scale multimodal Chain-of-Thought (CoT) reasoning dataset, to build a stronger math reasoning foundation MLLM, URSA-8B. Subsequently, we go through an automatic process to synthesize process supervision data, which emphasizes both logical correctness and perceptual consistency. We introduce DualMath-1.1M to facilitate the training of URSA-8B-RM. Finally, we propose Process-Supervised Group-Relative-Policy-Optimization (PS-GRPO), pioneering a multimodal PRM-aided online RL method that outperforms vanilla GRPO. With PS-GRPO application, URSA-8B-PS-GRPO outperforms Gemma3-12B and GPT-4o by 8.4% and 2.7% on average across 6 benchmarks. Code, data and checkpoint can be found at https://github.com/URSA-MATH.

cs.CL↗

arXiv2025

KAIROS: Unified Training for Universal Non-Autoregressive Time Series Forecasting

In the World Wide Web, reliable time series forecasts provide the forward-looking signals that drive resource planning, cache placement, and anomaly response, enabling platforms to operate efficiently as user behavior and content distributions evolve. Compared with other domains, time series forecasting for Web applications requires much faster responsiveness to support real-time decision making. We present KAIROS, a non-autoregressive time series forecasting framework that directly models segment-level multi-peak distributions. Unlike autoregressive approaches, KAIROS avoids error accumulation and achieves just-in-time inference, while improving over existing non-autoregressive models that collapse to over-smoothed predictions. Trained on the large-scale corpus, KAIROS demonstrates strong zero-shot generalization on six widely used benchmarks, delivering forecasting performance comparable to state-of-the-art foundation models with similar scale, at a fraction of their inference cost. Beyond empirical results, KAIROS highlights the importance of non-autoregressive design as a scalable paradigm for foundation models in time series.

cs.LG↗