arXiv subjects

Ting Li

Publications and source records attributed to Ting Li.

At least 37 records · Page 2Linked to original sources

arXiv2026

YASA: Scalable Multi-Language Taint Analysis on the Unified AST at Ant Group

Modern enterprises increasingly adopt diverse technology stacks with various programming languages, posing significant challenges for static application security testing (SAST). Existing taint analysis tools are predominantly designed for single languages, requiring substantial engineering effort that scales with language diversity. While multi-language tools like CodeQL, Joern, and WALA attempt to address these challenges, they face limitations in intermediate representation design, analysis precision, and extensibility, which make them difficult to scale effectively for large-scale industrial applications at Ant Group. To bridge this gap, we present YASA (Yet Another Static Analyzer), a unified multi-language static taint analysis framework designed for industrial-scale deployment. Specifically, YASA introduces the Unified Abstract Syntax Tree (UAST) that provides a unified abstraction for compatibility across diverse programming languages. Building on the UAST, YASA performs point-to analysis and taint propagation, leveraging a unified semantic model to manage language-agnostic constructs, while incorporating language-specific semantic models to handle other unique language features. When compared to 6 single- and 2 multi-language static analyzers on an industry-standard benchmark, YASA consistently outperformed all baselines across Java, JavaScript, Python, and Go. In real-world deployment within Ant Group, YASA analyzed over 100 million lines of code across 7.3K internal applications. It identified 314 previously unknown taint paths, with 92 of them confirmed as 0-day vulnerabilities. All vulnerabilities were responsibly reported, with 76 already patched by internal development teams, demonstrating YASA's practical effectiveness for securing large-scale industrial software systems.

cs.SE↗

arXiv2026

Riemannian Motion Generation: A Unified Framework for Human Motion Representation and Generation via Riemannian Flow Matching

Human motion generation is often learned in Euclidean spaces, although valid motions follow structured non-Euclidean geometry. We present Riemannian Motion Generation (RMG), a unified framework that represents motion on a product manifold and learns dynamics via Riemannian flow matching. RMG factorizes motion into several manifold factors, yielding a scale-free representation with intrinsic normalization, and uses geodesic interpolation, tangent-space supervision, and manifold-preserving ODE integration for training and sampling. On HumanML3D, RMG achieves state-of-the-art FID in the HumanML3D format (0.043) and ranks first on all reported metrics under the MotionStreamer format. On MotionMillion, it also surpasses strong baselines (FID 5.6, R@1 0.86). Ablations show that the compact $\mathscr{T}+\mathscr{R}$ (translation + rotations) representation is the most stable and effective, highlighting geometry-aware modeling as a practical and scalable route to high-fidelity motion generation.

cs.CV↗

arXiv2026

A filtered two-step variational integrator for charged-particle dynamics in a moderate or strong magnetic field

This article is concerned with a new filtered two-step variational integrator for solving the charged-particle dynamics in a mildly non-uniform moderate or strong magnetic field with a dimensionless parameter $\varepsilon$ inversely proportional to the strength of the magnetic field. In the case of a moderate magnetic field ($\varepsilon=1$), second-order error bounds and long-time near-conservation of energy and momentum are obtained. Moreover, the proof of the long-term analysis is accomplished by the backward error analysis. For $0<\varepsilon \ll 1$, the proposed integrator achieves uniform second-order accuracy in the position and the parallel velocity for large step sizes, while attaining first-order accuracy with respect to the small parameter $\varepsilon$ for smaller step sizes. The error bounds are derived from a comparison of the modulated Fourier expansions of the exact and numerical solutions. Moreover, long-time near-conservation of the energy and the magnetic moment is established using modulated Fourier expansion and backward error analysis. All the theoretical results of the error behavior and long-time near-conservation are numerically demonstrated by four numerical experiments.

math.NA↗

arXiv2026

Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learning

A/B testing has become a gold standard for modern technological companies to conduct policy evaluation. Yet, its application to time series experiments, where policies are sequentially assigned over time, remains challenging. Existing designs suffer from two limitations: (i) they do not fully leverage the entire history for treatment allocation; (ii) they rely on strong assumptions to approximate the objective function (e.g., the mean squared error of the estimated treatment effect) for optimizing the design. We first establish an impossibility theorem showing that failure to condition on the full history leads to suboptimal designs, due to the dynamic dependencies in time series experiments. To address both limitations simultaneously, we next propose a transformer reinforcement learning (RL) approach which leverages transformers to condition allocation on the entire history and employs RL to directly optimize the MSE without relying on restrictive assumptions. Empirical evaluations on synthetic data, a publicly available dispatch simulator, and a real-world ridesharing dataset demonstrate that our proposal consistently outperforms existing designs.

cs.LG↗

arXiv2026

Operational Solar Flare Forecasting System Using an Explainable Large Language Model

This study focuses on forecasting major (>=M-class) solar flares that can severely impact the near-Earth environment. We construct two types of datasets using the Space Weather HMI Active Region Patches (SHARP), and develop a flare prediction network based on large language model (LLMFlareNet). We apply SHapley Additive exPlanations (SHAP) to explain the model predictions. We develop an operational forecasting system based on the LLMFlareNet model. We adopt a daily mode for performance comparison across various operational forecasting systems under identical active region (AR) number and prediction date, using daily operational observational data. The main results are as follows. (1) Through ablation experiments and comparison with baseline models, LLMFlareNet achieves the best TSS scores of 0.720 +/- 0.040 on the ten cross-validation (CV) dataset with mixed ARs. (2) By both global and local SHAP analyses, we identify that R_VALUE is the most influential physical feature for the prediction of LLMFlareNet, aligning with flare magnetic reconnection theory. (3) In daily mode, LLMFlareNet achieves TSS scores of 0.680/0.571 (0.689/0.661, respectively) on the dataset with single/mixed ARs, markedly outperforming NASA/CCMC (SolarFlareNet, respectively). This work introduces the first application of a large language model as a universal computation engine with explainability method in this domain, and presents the first comparison between operational flare forecasting systems in daily mode. The proposed LLMFlareNet-based system demonstrates substantial improvements over existing systems.

astro-ph.SR↗

arXiv2026

Causal Inference in Biomedical Imaging via Functional Linear Structural Equation Models

Understanding the causal effects of organ-specific features from medical imaging on clinical outcomes is essential for biomedical research and patient care. We propose a novel Functional Linear Structural Equation Model (FLSEM) to capture the relationships among clinical outcomes, functional imaging exposures, and scalar covariates like genetics, sex, and age. Traditional methods struggle with the infinite-dimensional nature of exposures and complex covariates. Our FLSEM overcomes these challenges by establishing identifiable conditions using scalar instrumental variables. We develop the Functional Group Support Detection and Root Finding (FGS-DAR) algorithm for efficient variable selection, supported by rigorous theoretical guarantees, including selection consistency and accurate parameter estimation. We further propose a test statistic to test the nullity of the functional coefficient, establishing its null limit distribution. Our approach is validated through extensive simulations and applied to UK Biobank data, demonstrating robust performance in detecting causal relationships from medical imaging.

stat.ME↗

arXiv2026

Optical Continuum Light Curves and Bolometric Energy Estimates of Solar White-light Flares

Solar white-light flares (WLFs) are solar flares exhibiting enhanced emission in the optical continuum. They are critical for understanding energy release and transport mechanisms in solar flares and for conducting comparative studies with stellar WLFs. However, the scarcity of accurately and reliably measured optical continuum light curves for solar WLFs significantly hampers related studies. Based on the optimized solar WLF identification method, we construct a dataset of optical continuum light curves for 70 solar WLFs using 6173 Å continuum intensity images from the Solar Dynamics Observatory. Moreover, for each solar WLF event, we also provide the location of the white-light emission enhancement signals and key parameters including bolometric energies and durations derived from both the traditional fixed-temperature blackbody model and the refined variable-temperature blackbody model. This dataset will serve as a valuable resource for future statistical investigations of solar WLFs and for comparative studies between solar and stellar flares.

astro-ph.SR↗

arXiv2026

Beyond Uniform SVD:Dual-Level Optimization across Columns and Modules for LLM Compression

Low-rank decomposition, particularly Singular Value Decomposition (SVD), is a pivotal technique for mitigating the storage and computational demands of Large Language Models (LLMs). However, prevalent SVD-based approaches overlook the critical phenomenon that decomposition errors exhibit significant disparity across different components of the parameter matrix, often leading to suboptimal approximation. Furthermore, existing methods lack a direct metric to evaluate the importance of individual weight matrices. To address these limitations, we propose Duo-SVD (Dual-level Optimization SVD), a novel training-free framework that synergizes optimization at both the column and the module levels. First, Duo-SVD incorporates a Column-Preserving Strategy that explicitly retains columns exhibiting high decomposition errors, while applying low-rank approximation solely to those with lower errors. Second, at the module level, we employ a Module-Adaptive Allocation Strategy that formulates ratio allocation as a global constrained optimization problem based on perturbation-induced model deviation. Extensive experiments demonstrate that Duo-SVD consistently outperforms state-of-the-art SVD-based baselines and structured pruning methods, establishing it as a superior paradigm for efficient LLM compression.

cs.LG↗

arXiv2025

Research on the descent direction of prediction correction algorithms for pseudo-convex/convex optimization problems

Prediction-correction algorithms are a highly effective class of methods for solving pseudo-convex optimization problems. The descent direction of these algorithms can be viewed as an adjustment to the gradient direction based on the prediction step. This paper investigates the adjustment coefficients of these descent directions and offers explanations from the perspective of differential equations. Unlike existing algorithms where the adjustment coefficient is always set to 1, we establish that the range of the adjustment coefficient lies within (1/2,1] for pseudo-convex optimization problems, and [0,1] for convex optimization problems. We also provide rigorous convergence proofs for these proposed algorithms. Numerical experiment results show that the algorithms perform best when the value of the adjustment coefficient makes the algorithm approach or equal to those in differential equations with higher-order global discrete error.

math.OC↗

arXiv2025

Systematic validation of time-resolved diffuse optical simulators via non-contact SPAD-based measurements

Objective: Time-domain diffuse optical imaging (DOI) requires accurate forward models for photon propagation in scattering media. However, existing simulators lack comprehensive experimental validation, especially for non-contact configurations with oblique illumination. This study rigorously evaluates three widely used open-source simulators, including MMC, NIRFASTer, and Toast++, using time-resolved experimental data. Approach: All simulations employed a unified mesh and point-source illumination. Virtual source correction was applied to FEM solvers for oblique incidence. A time-resolved DOI system with a 32 $\times$ 32 single-photon avalanche diode (SPAD) array acquired transmission-mode data from 16 standardized phantoms with varying absorption coefficient $μ_a$ and reduced scattering coefficient $μ_s'$. The simulation results were quantified across five metrics: spatial-domain (SD) precision, time-domain (TD) precision, oblique beam accuracy, computational speed, and mesh-density independence. Results: Among three simulators, MMC achieves superior accuracy in SD and TD metrics, and shows robustness across all optical properties. NIRFASTer and Toast++ demonstrate comparable overall performance. In general, MMC is optimal for accuracy-critical TD-DOI applications, while NIRFASTer and Toast++ suit scenarios prioritizing speed with sufficiently large $μ_s'$. Besides, virtual source correction is essential for non-contact FEM modeling, which reduced average errors by > 34% in large-angle scenarios. Significance: This work provides benchmarked guidelines for simulator selection during the development phase of next-generation TD-DOI systems. Our work represents the first study to systematically validate TD simulators against SPAD array-based data under clinically relevant non-contact conditions, bridging a critical gap in biomedical optical simulation standards.

physics.optics↗

arXiv2025

Knowledge-Augmented Question Error Correction for Chinese Question Answer System with QuestionRAG

Input errors in question-answering (QA) systems often lead to incorrect responses. Large language models (LLMs) struggle with this task, frequently failing to interpret user intent (misinterpretation) or unnecessarily altering the original question's structure (over-correction). We propose QuestionRAG, a framework that tackles these problems. To address misinterpretation, it enriches the input with external knowledge (e.g., search results, related entities). To prevent over-correction, it uses reinforcement learning (RL) to align the model's objective with precise correction, not just paraphrasing. Our results demonstrate that knowledge augmentation is critical for understanding faulty questions. Furthermore, RL-based alignment proves significantly more effective than traditional supervised fine-tuning (SFT), boosting the model's ability to follow instructions and generalize. By integrating these two strategies, QuestionRAG unlocks the full potential of LLMs for the question correction task.

cs.CL↗

arXiv2025

LLMAtKGE: Large Language Models as Explainable Attackers against Knowledge Graph Embeddings

Adversarial attacks on knowledge graph embeddings (KGE) aim to disrupt the model's ability of link prediction by removing or inserting triples. A recent black-box method has attempted to incorporate textual and structural information to enhance attack performance. However, it is unable to generate human-readable explanations, and exhibits poor generalizability. In the past few years, large language models (LLMs) have demonstrated powerful capabilities in text comprehension, generation, and reasoning. In this paper, we propose LLMAtKGE, a novel LLM-based framework that selects attack targets and generates human-readable explanations. To provide the LLM with sufficient factual context under limited input constraints, we design a structured prompting scheme that explicitly formulates the attack as multiple-choice questions while incorporating KG factual evidence. To address the context-window limitation and hesitation issues, we introduce semantics-based and centrality-based filters, which compress the candidate set while preserving high recall of attack-relevant information. Furthermore, to efficiently integrate both semantic and structural information into the filter, we precompute high-order adjacency and fine-tune the LLM with a triple classification task to enhance filtering performance. Experiments on two widely used knowledge graph datasets demonstrate that our attack outperforms the strongest black-box baselines and provides explanations via reasoning, and showing competitive performance compared with white-box methods. Comprehensive ablation and case studies further validate its capability to generate explanations.

cs.CL↗

arXiv2025

Variations of the vector magnetic structures in the solar polar regions observed by Hinode

Using the polar vector magnetic field data observed by Hinode from 2012 to 2021, we study the long-term variations of the magnetic flux, the flux proportion of different polarities, and the magnetic inclination with respect to the local normal in the solar polar regions above 70 degree latitude during solar cycle 24. In both polar regions after the polarity reversal, the unsigned magnetic fluxes of the dominant polarity increased to a peak of about 1.3$\times$10$^{22}$ Mx during the solar minimum, while those of the non-dominant polarity remained stable at approximately 0.5$\times$10$^{22}$ Mx. The proportions of the dominant polarity flux in the total flux in both polar regions increased to more than 70% at the solar minimum. These results reveal that the total magnetic flux and the number of open field lines in solar cycle 24 were larger than those in solar cycle 23, and also imply the existence of a local dynamo in polar regions. After the polarity reversal, the magnetic inclination of the dominant polarity fields decreased, indicating that the stronger the dominant polarity field, the more vertical the field lines. The inclination angle decreased with the increase of the threshold of radial magnetic flux density, revealing a fanning-out structure of the polar magnetic patches.

astro-ph.SR↗

arXiv2025

Error and long-term analysis of two-step symmetric methods for relativistic charged-particle dynamics

In this work, we consider the error estimates and the long-time conservation or near-conservation of geometric structures, including energy, mass shell and phase-space volume, for four two-step symmetric methods applied to relativistic charged-particle dynamics. We begin by introducing a two-step symmetric numerical method based on a splitting scheme that exactly preserves the mass shell and the phase-space volume of the relativistic system. Building on this formulation, we develop three additional two-step symmetric methods with further modifications, for which the long-time near-conservation of energy and mass shell can be rigorously established through the backward error analysis. All methods are shown to achieve second-order accuracy. The theoretical results are illustrated and complemented by numerical experiments.

math.NA↗

arXiv2025

A re-identification of six Candidate Gravitationally Lensed Gamma-Ray Bursts

The gravitational lensing effect of gamma-ray bursts (GRBs) holds significant and diverse applications in the field of astronomy. Nevertheless, the identification of millilensing events in GRBs presents substantial challenges. We re-evaluate the gravitational lensing candidacy of six previously proposed GRBs (GRB 081122A, GRB 081126A, GRB 090717A, GRB 110517B, GRB 200716C, and GRB 210812A) using a comprehensive set of temporal and spectral diagnostics. These include $χ^2$ light-curve similarity tests, photon-count-based hardness ratio ($HR_{count}$) comparisons, $T_{90}$ duration measurements, spectral lag, Norris pulse-shape fitting, and both time-resolved and time-integrated spectral analyses. We propose an evaluation framework, any single test that reveals a statistically significant inconsistency between the two pulses is sufficient to reject the lensing hypothesis for that burst.Although certain diagnostics, such as $T_{90}$ and parametric model fits, have known limitations, they are applied and interpreted in conjunction with the more robust, model-independent $χ^2$ and $HR_{count}$ tests. For all six GRBs, at least one diagnostic shows a significant discrepancy, leading us to conclude that none are consistent with a gravitational lensing interpretation.

astro-ph.HE↗

arXiv2025

Conditional Stochastic Interpolation for Generative Learning

We propose a conditional stochastic interpolation (CSI) method for learning conditional distributions. CSI is based on estimating probability flow equations or stochastic differential equations that transport a reference distribution to the target conditional distribution. This is achieved by first learning the conditional drift and score functions based on CSI, which are then used to construct a deterministic process governed by an ordinary differential equation or a diffusion process for conditional sampling. In our proposed approach, we incorporate an adaptive diffusion term to address the instability issues arising in the diffusion process. We derive explicit expressions of the conditional drift and score functions in terms of conditional expectations, which naturally lead to an nonparametric regression approach to estimating these functions. Furthermore, we establish nonasymptotic error bounds for learning the target conditional distribution. We illustrate the application of CSI on image generation using a benchmark image dataset.

stat.ML↗

arXiv2025

Sun-as-a-star Analysis of the Solar Eruption Source Region Using Ha Spectroscopic Observations of CHASE

Sun-as-a-star analyses serve as a bridge for comparative studies on solar and stellar activities. To investigate the typical Sun-as-a-star Ha temporal spectral characteristics in solar eruption source regions, we analyzed five different types of solar eruptions, using spectroscopic data from the Chinese Ha Solar Explorer (CHASE). Because the spatially-integral Ha spectrum of source region is mainly contributed by emission from heated plasma in flare ribbons and absorption from cold plasma in evolving filaments, we separately analyze the sub-regions of the source region dominated by different dynamical processes. It is revealed that filament eruptions show emission near Ha line center, accompanied by blueshifted/redshifted absorption, while flare ribbons show Ha line center emission with red asymmetry and line broadening. Moreover, a special spectral signature likely associated with coronal mass ejections (CMEs) is identified: prominent blueshifted absorption without a clear deceleration phase, along with redshifted absorption, which can be used as a probe when searching stellar CMEs. Furthermore, in the X9.0 flare (SOL2024-10-03T12:18) accompanied by a violent CME, the expected blueshifted signal is not visible in the spatially-integral Ha spectra. This suggests that filament-lifting signals associated with CMEs in the source region can be obscured by the simultaneous dominant flare-ribbon emission within the integration region, which may explain the relatively small number of confirmed stellar CMEs observed in Ha. We also find that comparison between the Ha and UV spectral observations can effectively reveal the velocity evolution of erupting filaments and potential existence of associated CMEs.

astro-ph.SR↗

arXiv2025

Current Helicity in Response to Coronal Mass Ejections

Coronal mass ejections (CMEs), powerful solar eruptions with massive plasma ejected into the interplanetary space, are caused by the release of the magnetic free enengy stored in coronal electric currents. Photospheric current helicity, defined as the integral of the product of vertical electric current density and vertical magnetic field ($H_c=\int j_zB_z\ dS$), serves as a key parameter in understanding the eruptions. Using a 3D magnetohydrodynamic model, we identify a current helicity reversal pattern associated with the eruption: a pre-eruption decrease and a post-eruption increase. This helicity reversal is attributed to the redistribution of electric currents: before the eruption, currents concentrate toward the polarity inversion line (PIL); after the eruption they move away from the PIL, consistent with the flare ribbon separation, which is caused by the upward progression reconnection site. To validate this pattern, we conducted an observational analysis of 50 $\geq$M5.0 eruptive flares. The results reveal that 58\% of cases exhibited a pre-eruption decrease and 92\% showed the post-eruption increase in current helicity. Detailed analysis of two cases with this reversal suggests that they share the same current redistribution pattern, consistent with the mechanism identified in the simulations. Moreover, the pre-eruption decrease could be observed clearly even in the long-term evolution of the two cases. Current helicity can serve as an indicator of when electric currents are built up for the subsequent eruption, and it has the potential to predict CMEs to some extent.

astro-ph.SR↗