arXiv subjects

Lei Zhao

Publications and source records attributed to Lei Zhao.

At least 73 records · Page 4Linked to original sources

arXiv2025

A common first integral from three-body secular theory and Kepler billiards

We observe that a particular first integral of the partially-averaged system in the secular theory of the three-body problem appears also as an important conserved quantity of integrable Kepler billiards. In this note we illustrate their common roots with the projective dynamics of the two-center problem. We then combine these two aspects to define a class of integrable billiard systems on surfaces of constant curvature.

math.DS↗

arXiv2025

Wavelet Enhanced Adaptive Frequency Filter for Sequential Recommendation

Sequential recommendation has garnered significant attention for its ability to capture dynamic preferences by mining users' historical interaction data. Given that users' complex and intertwined periodic preferences are difficult to disentangle in the time domain, recent research is exploring frequency domain analysis to identify these hidden patterns. However, current frequency-domain-based methods suffer from two key limitations: (i) They primarily employ static filters with fixed characteristics, overlooking the personalized nature of behavioral patterns; (ii) While the global discrete Fourier transform excels at modeling long-range dependencies, it can blur non-stationary signals and short-term fluctuations. To overcome these limitations, we propose a novel method called Wavelet Enhanced Adaptive Frequency Filter for Sequential Recommendation. Specifically, it consists of two vital modules: dynamic frequency-domain filtering and wavelet feature enhancement. The former is used to dynamically adjust filtering operations based on behavioral sequences to extract personalized global information, and the latter integrates wavelet transform to reconstruct sequences, enhancing blurred non-stationary signals and short-term fluctuations. Finally, these two modules work to achieve comprehensive performance and efficiency optimization in long sequential recommendation scenarios. Extensive experiments on four widely-used benchmark datasets demonstrate the superiority of our work.

cs.IR↗

arXiv2025

MAPo : Motion-Aware Partitioning of Deformable 3D Gaussian Splatting for High-Fidelity Dynamic Scene Reconstruction

3D Gaussian Splatting, known for enabling high-quality static scene reconstruction with fast rendering, is increasingly being applied to multi-view dynamic scene reconstruction. A common strategy involves learning a deformation field to model the temporal changes of a canonical set of 3D Gaussians. However, these deformation-based methods often produce blurred renderings and lose fine motion details in highly dynamic regions due to the inherent limitations of a single, unified model in representing diverse motion patterns. To address these challenges, we introduce Motion-Aware Partitioning of Deformable 3D Gaussian Splatting (MAPo), a novel framework for high-fidelity dynamic scene reconstruction. Its core is a dynamic score-based partitioning strategy that distinguishes between high- and low-dynamic 3D Gaussians. For high-dynamic 3D Gaussians, we recursively partition them temporally and duplicate their deformation networks for each new temporal segment, enabling specialized modeling to capture intricate motion details. Concurrently, low-dynamic 3DGs are treated as static to reduce computational costs. However, this temporal partitioning strategy for high-dynamic 3DGs can introduce visual discontinuities across frames at the partition boundaries. To address this, we introduce a cross-frame consistency loss, which not only ensures visual continuity but also further enhances rendering quality. Extensive experiments demonstrate that MAPo achieves superior rendering quality compared to baselines while maintaining comparable computational costs, particularly in regions with complex or rapid motions.

cs.CV↗

arXiv2025

Forgetting by Pruning: Data Deletion in Join Cardinality Estimation

Machine unlearning in learned cardinality estimation (CE) systems presents unique challenges due to the complex distributional dependencies in multi-table relational data. Specifically, data deletion, a core component of machine unlearning, faces three critical challenges in learned CE models: attribute-level sensitivity, inter-table propagation and domain disappearance leading to severe overestimation in multi-way joins. We propose Cardinality Estimation Pruning (CEP), the first unlearning framework specifically designed for multi-table learned CE systems. CEP introduces Distribution Sensitivity Pruning, which constructs semi-join deletion results and computes sensitivity scores to guide parameter pruning, and Domain Pruning, which removes support for value domains entirely eliminated by deletion. We evaluate CEP on state-of-the-art architectures NeuroCard and FACE across IMDB and TPC-H datasets. Results demonstrate CEP consistently achieves the lowest Q-error in multi-table scenarios, particularly under high deletion ratios, often outperforming full retraining. Furthermore, CEP significantly reduces convergence iterations, incurring negligible computational overhead of 0.3%-2.5% of fine-tuning time.

cs.DB↗

arXiv2025

GContextFormer: A global context-aware hybrid multi-head attention approach with scaled additive aggregation for multimodal trajectory prediction

Multimodal trajectory prediction generates multiple plausible future trajectories to address vehicle motion uncertainty from intention ambiguity and execution variability. However, HD map-dependent models suffer from costly data acquisition, delayed updates, and vulnerability to corrupted inputs, causing prediction failures. Map-free approaches lack global context, with pairwise attention over-amplifying straight patterns while suppressing transitional patterns, resulting in motion-intention misalignment. This paper proposes GContextFormer, a plug-and-play encoder-decoder architecture with global context-aware hybrid attention and scaled additive aggregation achieving intention-aligned multimodal prediction without map reliance. The Motion-Aware Encoder builds scene-level intention prior via bounded scaled additive aggregation over mode-embedded trajectory tokens and refines per-mode representations under shared global context, mitigating inter-mode suppression and promoting intention alignment. The Hierarchical Interaction Decoder decomposes social reasoning into dual-pathway cross-attention: a standard pathway ensures uniform geometric coverage over agent-mode pairs while a neighbor-context-enhanced pathway emphasizes salient interactions, with gating module mediating their contributions to maintain coverage-focus balance. Experiments on eight highway-ramp scenarios from TOD-VT dataset show GContextFormer outperforms state-of-the-art baselines. Compared to existing transformer models, GContextFormer achieves greater robustness and concentrated improvements in high-curvature and transition zones via spatial distributions. Interpretability is achieved through motion mode distinctions and neighbor context modulation exposing reasoning attribution. The modular architecture supports extensibility toward cross-domain multimodal reasoning tasks. Source: https://fenghy-chen.github.io/sources/.

cs.AI↗

arXiv2025

Projective deduction of the non-trivial first integral to the Euler problem: an explicit computation

The validity of Kepler Laws for the {\it spherical Kepler problem} -- namely, the problem of the motion of a particle on the unit sphere {in $\mathbb R^3$} undergoing an attraction by another particle in the sphere, tangent to the geodesic line between the two and inversely proportional to its squared length -- prompted geometers to try to interpret such system as a '' projection'' of the familiar Kepler problem in the plane, with the hosting plane given by some affine plane in $\mathbb R^3$. At this respect, the most convenient mutual sphere-plane position has been object of a long debate, an account of which can be found in \cite{Albouy2013}. This fascinating topic, resumed %subject, firstly by A. Albouy in the aforementioned paper, has been expanded from the theoretical side in \cite{Albouy2015}. Further investigations recently appeared in \cite{AlbouyZhao2019, Zhao1, TakeuchiZhao1, TakeuchiZhao2}. As remarked in \cite{Albouy2013, Albouy2015}, extensions of the procedure to more dynamical systems would open to the possibility of finding first integrals to a given dynamical system simply looking at the energy of the mirror problem. In this note, we focus on the case of the problem of two fixed centers, already mentioned in \cite{Albouy2013}. We provide a{n explicit} geometrical construction allowing to interpret the first integral of the problem as the energy of its projection on an ellipsoid. Compared to previous papers on the same subject, ours -- besides being based on a somehow different construction -- includes complete explicit computations. {A byproduct of our construction is the existence of two integrable mirror problems (equivalently, three quadratic integrals, including the energy) for the Kepler problem, which is an aspect of its super-integrability.

math-ph↗

arXiv2025

NL-DPE: An Analog In-memory Non-Linear Dot Product Engine for Efficient CNN and LLM Inference

Resistive Random Access Memory (RRAM) based in-memory computing (IMC) accelerators offer significant performance and energy advantages for deep neural networks (DNNs), but face three major limitations: (1) they support only \textit{static} dot-product operations and cannot accelerate arbitrary non-linear functions or data-dependent multiplications essential to modern LLMs; (2) they demand large, power-hungry analog-to-digital converter (ADC) circuits; and (3) mapping model weights to device conductance introduces errors from cell nonidealities. These challenges hinder scalable and accurate IMC acceleration as models grow. We propose NL-DPE, a Non-Linear Dot Product Engine that overcomes these barriers. NL-DPE augments crosspoint arrays with RRAM-based Analog Content Addressable Memory (ACAM) to execute arbitrary non-linear functions and data-dependent matrix multiplications in the analog domain by transforming them into decision trees, fully eliminating ADCs. To address device noise, NL-DPE uses software-based Noise Aware Fine-tuning (NAF), requiring no in-device calibration. Experiments show that NL-DPE delivers 28X energy efficiency and 249X speedup over a GPU baseline, and 22X energy efficiency and 245X speedup over existing IMC accelerators, while maintaining high accuracy.

cs.AR↗

arXiv2025

Prediction of bypass transition in hypersonic blunt-plate boundary layers subject to noisy conditions

In hypersonic boundary-layer flows over blunt bodies, laminar-turbulent transition exhibits two distinct regimes: for small nose radii, increased bluntness delays transition; beyond a critical radius, further increasing bluntness reverses this trend. The latter regime corresponds to a bypass transition route, whose onset remains challenging to predict. The primary difficulty lies in capturing the excitation of non-modal streaks in the nose region, which is strongly affected by the bow shock and entropy layer effects. Recently, Zhao & Dong (J. Fluid Mech., 2025, 1013: A44) develops a high-efficient, high-accuracy shock-fitting harmonic linearised Navier-Stokes (SF-HLNS) approach to quantify the excitation of linear non-modal perturbations. In this paper, we present a predictive framework for bypass transition by integrating the SF-HLNS approach with the nonlinear parabolised stability equations (NPSE) and the bi-global stability analysis (BSA). The NPSE is employed to track the nonlinear evolution of the streaky perturbations up to nonlinear saturation, while the BSA approach is used to capture the high-growth secondary instabilities. By integrating the growth rates of these secondary instability from their neutral positions, an amplitude amplification factor is obtained, enabling the prediction of transition onset. Under slow-acoustic forcing conditions drawn from wind-tunnel experiments, the present hybrid framework successfully reproduces the transition-reversal phenomenon at large nose radii, and yields quantitative agreement with measured transition locations, thereby validating its predictive capability.

physics.flu-dyn↗

arXiv2025

LGM: Enhancing Large Language Models with Conceptual Meta-Relations and Iterative Retrieval

Large language models (LLMs) exhibit strong semantic understanding, yet struggle when user instructions involve ambiguous or conceptually misaligned terms. We propose the Language Graph Model (LGM) to enhance conceptual clarity by extracting meta-relations-inheritance, alias, and composition-from natural language. The model further employs a reflection mechanism to validate these meta-relations. Leveraging a Concept Iterative Retrieval Algorithm, these relations and related descriptions are dynamically supplied to the LLM, improving its ability to interpret concepts and generate accurate responses. Unlike conventional Retrieval-Augmented Generation (RAG) approaches that rely on extended context windows, our method enables large language models to process texts of any length without the need for truncation. Experiments on standard benchmarks demonstrate that the LGM consistently outperforms existing RAG baselines.

cs.CL↗

arXiv2025

Integrability of Kepler Billiards at Zero-Energy

We consider a Kepler billiard with zero-energy in the plane defined inside a smooth closed connected simple curve which intersects all focused parabola at at most two points. {We show that} if has an invariant curve consisting of $2$-periodic orbits and there exists a $C^{1}$-first integral with non-vanishing gradient in the region between the invariant curve and the boundary curve, then the system is defined actually inside an ellipse with the Kepler center occupying one of the foci. This statement is obtained as a simple ``translation'' of the theorem of Bialy-Mironov with Levi-Civita transformation.

math.DS↗

arXiv2025

Zero-Shot Document-Level Biomedical Relation Extraction via Scenario-based Prompt Design in Two-Stage with LLM

With the advent of artificial intelligence (AI), many researchers are attempting to extract structured information from document-level biomedical literature by fine-tuning large language models (LLMs). However, they face significant challenges such as the need for expensive hardware, like high-performance GPUs and the high labor costs associated with annotating training datasets, especially in biomedical realm. Recent research on LLMs, such as GPT-4 and Llama3, has shown promising performance in zero-shot settings, inspiring us to explore a novel approach to achieve the same results from unannotated full documents using general LLMs with lower hardware and labor costs. Our approach combines two major stages: named entity recognition (NER) and relation extraction (RE). NER identifies chemical, disease and gene entities from the document with synonym and hypernym extraction using an LLM with a crafted prompt. RE extracts relations between entities based on predefined relation schemas and prompts. To enhance the effectiveness of prompt, we propose a five-part template structure and a scenario-based prompt design principles, along with evaluation method to systematically assess the prompts. Finally, we evaluated our approach against fine-tuning and pre-trained models on two biomedical datasets: ChemDisGene and CDR. The experimental results indicate that our proposed method can achieve comparable accuracy levels to fine-tuning and pre-trained models but with reduced human and hardware expenses.

cs.NE↗

arXiv2025

TKHist: Cardinality Estimation for Join Queries via Histograms with Dominant Attribute Correlation Finding

Cardinality estimation has long been crucial for cost-based database optimizers in identifying optimal query execution plans, attracting significant attention over the past decades. While recent advancements have significantly improved the accuracy of multi-table join query estimations, these methods introduce challenges such as higher space overhead, increased latency, and greater complexity, especially when integrated with the binary join framework. In this paper, we introduce a novel cardinality estimation method named TKHist, which addresses these challenges by relaxing the uniformity assumption in histograms. TKHist captures bin-wise non-uniformity information, enabling accurate cardinality estimation for join queries without filter predicates. Furthermore, we explore the attribute independent assumption, which can lead to significant over-estimation rather than under-estimation in multi-table join queries. To address this issue, we propose the dominating join path correlation discovery algorithm to highlight and manage correlations between join keys and filter predicates. Our extensive experiments on popular benchmarks demonstrate that TKHist reduces error variance by 2-3 orders of magnitude compared to SOTA methods, while maintaining comparable or lower memory usage.

cs.DB↗

arXiv2025

BAAF: A benchmark attention adaptive framework for medical ultrasound image segmentation tasks

The AI-based assisted diagnosis programs have been widely investigated on medical ultrasound images. Complex scenario of ultrasound image, in which the coupled interference of internal and external factors is severe, brings a unique challenge for localize the object region automatically and precisely in ultrasound images. In this study, we seek to propose a more general and robust Benchmark Attention Adaptive Framework (BAAF) to assist doctors segment or diagnose lesions and tissues in ultrasound images more quickly and accurately. Different from existing attention schemes, the BAAF consists of a parallel hybrid attention module (PHAM) and an adaptive calibration mechanism (ACM). Specifically, BAAF first coarsely calibrates the input features from the channel and spatial dimensions, and then adaptively selects more robust lesion or tissue characterizations from the coarse-calibrated feature maps. The design of BAAF further optimizes the "what" and "where" focus and selection problems in CNNs and seeks to improve the segmentation accuracy of lesions or tissues in medical ultrasound images. The method is evaluated on four medical ultrasound segmentation tasks, and the adequate experimental results demonstrate the remarkable performance improvement over existing state-of-the-art methods. In addition, the comparison with existing attention mechanisms also demonstrates the superiority of BAAF. This work provides the possibility for automated medical ultrasound assisted diagnosis and reduces reliance on human accuracy and precision.

eess.IV↗

arXiv2025

New Classes of Non-monotone Variational Inequality Problems Solvable via Proximal Gradient on Smooth Gap Functions

In this paper, we study the local linear convergence behavior of proximal-gradient (PG) descent algorithm on a parameterized gap-function reformulation of a smooth but non-monotone variational inequality problem (VIP). The aim is to solve the non-monotone VI problem without assuming the existence of a Minty-type solution. We first introduce and study various error bound conditions for the gap functions in relation to the VI model. In particular, we show that uniform type error bounds imply level-set type error bounds for composite optimization, revealing a key hierarchical structure there. As a result, local linear convergence is established under some easy-verifiable conditions induced by level-set error bounds, the gradient Lipschitz condition and a suitable initialization condition. Furthermore, for non-monotone affine VIs we present a homotopy continuation scheme that achieves global convergence by dynamically tracing a solution path. Our numerical experiments show the efficacy of the proposed approach, leading to the solutions of a broad class of non-monotone VI problems resulting from the need to compute Nash equilibria, traffic controls, and the GAN models.

math.OC↗

arXiv2025

MagicTryOn: Harnessing Diffusion Transformer for Garment-Preserving Video Virtual Try-on

Video Virtual Try-On (VVT) aims to synthesize garments that appear natural across consecutive video frames, capturing both their dynamics and interactions with human motion. Despite recent progress, existing VVT methods still suffer from inadequate garment fidelity and limited spatiotemporal consistency. The reasons are: (1) under-exploitation of garment information, with limited garment cues being injected, resulting in weaker fine-detail fidelity; and (2) a lack of spatiotemporal modeling, which hampers cross-frame identity consistency and causes temporal jitter and appearance drift. In this paper, we present MagicTryOn, a diffusion-transformer based framework for garment-preserving video virtual try-on. To preserve fine-grained garment details, we propose a fine-grained garment-preservation strategy that disentangles garment cues and injects these decomposed priors into the denoising process. To improve temporal garment consistency and suppress jitter, we introduce a garment-aware spatiotemporal rotary positional embedding (RoPE) that extends RoPE within full self-attention, using spatiotemporal relative positions to modulate garment tokens. We further impose a mask-aware loss during training to enhance fidelity within garment regions. Moreover, we adopt distribution-matching distillation to compress the sampling trajectory to four steps, enabling real-time inference without degrading garment fidelity. Extensive quantitative and qualitative experiments demonstrate that MagicTryOn outperforms existing methods, delivering superior garment-detail fidelity and temporal stability in unconstrained settings.

cs.CV↗

arXiv2025

SparkUI-Parser: Enhancing GUI Perception with Robust Grounding and Parsing

The existing Multimodal Large Language Models (MLLMs) for GUI perception have made great progress. However, the following challenges still exist in prior methods: 1) They model discrete coordinates based on text autoregressive mechanism, which results in lower grounding accuracy and slower inference speed. 2) They can only locate predefined sets of elements and are not capable of parsing the entire interface, which hampers the broad application and support for downstream tasks. To address the above issues, we propose SparkUI-Parser, a novel end-to-end framework where higher localization precision and fine-grained parsing capability of the entire interface are simultaneously achieved. Specifically, instead of using probability-based discrete modeling, we perform continuous modeling of coordinates based on a pre-trained Multimodal Large Language Model (MLLM) with an additional token router and coordinate decoder. This effectively mitigates the limitations inherent in the discrete output characteristics and the token-by-token generation process of MLLMs, consequently boosting both the accuracy and the inference speed. To further enhance robustness, a rejection mechanism based on a modified Hungarian matching algorithm is introduced, which empowers the model to identify and reject non-existent elements, thereby reducing false positives. Moreover, we present ScreenParse, a rigorously constructed benchmark to systematically assess structural perception capabilities of GUI models across diverse scenarios. Extensive experiments demonstrate that our approach consistently outperforms SOTA methods on ScreenSpot, ScreenSpot-v2, CAGUI-Grounding and ScreenParse benchmarks. The resources are available at https://github.com/antgroup/SparkUI-Parser.

cs.AI↗

arXiv2025

Scene-Agnostic Traversability Labeling and Estimation via a Multimodal Self-supervised Framework

Traversability estimation is critical for enabling robots to navigate across diverse terrains and environments. While recent self-supervised learning methods achieve promising results, they often fail to capture the characteristics of non-traversable regions. Moreover, most prior works concentrate on a single modality, overlooking the complementary strengths offered by integrating heterogeneous sensory modalities for more robust traversability estimation. To address these limitations, we propose a multimodal self-supervised framework for traversability labeling and estimation. First, our annotation pipeline integrates footprint, LiDAR, and camera data as prompts for a vision foundation model, generating traversability labels that account for both semantic and geometric cues. Then, leveraging these labels, we train a dual-stream network that jointly learns from different modalities in a decoupled manner, enhancing its capacity to recognize diverse traversability patterns. In addition, we incorporate sparse LiDAR-based supervision to mitigate the noise introduced by pseudo labels. Finally, extensive experiments conducted across urban, off-road, and campus environments demonstrate the effectiveness of our approach. The proposed automatic labeling method consistently achieves around 88% IoU across diverse datasets. Compared to existing self-supervised state-of-the-art methods, our multimodal traversability estimation network yields consistently higher IoU, improving by 1.6-3.5% on all evaluated datasets.

cs.RO↗

arXiv2025

FoleySpace: Vision-Aligned Binaural Spatial Audio Generation

Recently, with the advancement of AIGC, deep learning-based video-to-audio (V2A) technology has garnered significant attention. However, existing research mostly focuses on mono audio generation that lacks spatial perception, while the exploration of binaural spatial audio generation technologies, which can provide a stronger sense of immersion, remains insufficient. To solve this problem, we propose FoleySpace, a framework for video-to-binaural audio generation that produces immersive and spatially consistent stereo sound guided by visual information. Specifically, we develop a sound source estimation method to determine the sound source 2D coordinates and depth in each video frame, and then employ a coordinate mapping mechanism to convert the 2D source positions into a 3D trajectory. This 3D trajectory, together with the monaural audio generated by a pre-trained V2A model, serves as a conditioning input for a diffusion model to generate spatially consistent binaural audio. To support the generation of dynamic sound fields, we constructed a training dataset based on recorded Head-Related Impulse Responses that includes various sound source movement scenarios. Experimental results demonstrate that the proposed method outperforms existing approaches in spatial perception consistency, effectively enhancing the immersive quality of the audio-visual experience.

cs.SD↗