arXiv subjects

Le Yin

Publications and source records attributed to Le Yin.

10 recordsLinked to original sources

arXiv2026

Navier-Stokes-driven analysis of mean and fluctuating wall shear stress in turbulent channel flow

We propose a Navier-Stokes-driven analysis of the mean and fluctuating wall shear stress (WSS) applied to turbulent channel flow data from direct numerical simulations at friction Reynolds numbers up to $Re_\tau\approx 2000$. Starting from the streamwise momentum equation, we derive exact integral equations that relate the square plane-average and the square fluctuating WSS to wall-normal integrals of terms combining shear with acceleration, shear with pressure-radient, and shear with viscous diffusion. The square plane-average WSS can be well approximated by the product of plane-average shear and plane-average acceleration integrated over the buffer layer with corrections from the mean pressure gradient which diminish as the reciprocal of the Reynolds number. The square fluctuating WSS is similarly well approximated by the shear-acceleration and shear-pressure-gradient covariances integrated over the buffer layer, but the latter increases in magnitude with Reynolds number and is therefore not negligible. The acceleration fluctuations around the plane-average acceleration consist of a local Eulerian fluctuating acceleration, an advective acceleration and a term which gives rise a turbulence production contribution to the shear-acceleration covariance. By Taylor's frozen turbulence hypothesis the Eulerian acceleration and the streamwise mean advection part of the advective acceleration cancel each other. The shear-acceleration covariance is characterised by a near-wall peak which results from turbulence production and, more specifically, sweeps.

physics.flu-dyn↗

arXiv2025

DREAM-B3P: Dual-Stream Transformer Network Enhanced by Feedback Diffusion Model for Blood-Brain Barrier Penetrating Peptide Prediction

Introduction: The blood-brain barrier (BBB) protects the central nervous system but prevents most neurotherapeutics from reaching effective concentrations in the brain. BBB-penetrating peptides (BBBPs) offer a promising strategy for brain drug delivery; however, the scarcity of positive samples and severe class imbalance hinder the reliable identification of BBBPs. Objectives: Our goal is to alleviate class imbalance in BBBP prediction and to develop an accurate, interpretable classifier for BBBP prediction. Methods: We propose DREAM-B3P, which couples a feedback diffusion model (FB-Diffusion) for data augmentation with a dual-stream Transformer for classification. FB-Diffusion learns the BBBP distribution via iterative denoising and uses an external analyzer to provide feedback, generating high-quality pseudo-BBBPs. The classifier contains a sequence stream that extracts structural features from peptide sequences and a physicochemical stream that captures physicochemical features such as hydrophobic surface area, molecular charge, number of rotatable bonds, and polarizability. Combining the two features leads to superior BBBP predictive performance. Results: On a benchmark test set containing equal numbers of BBBPs and non-BBBPs, DREAM-B3P surpasses baseline methods (Deep-B3P, B3Pred, BBPpredict and Augur), improving AUC/ACC/MCC by 4.3\%/17.8\%/14.9\%, respectively, over the second-best method. Conclusion: By integrating feedback diffusion with a dual-stream Transformer classifier, DREAM-B3P effectively mitigates data scarcity and imbalance and achieves state-of-the-art performance.

q-bio.QM↗

arXiv2025

Don't Trust Your Upstream: Exploiting LLM Multi-Agent System via Topology-Guided Adversarial Propagation

The digital world is witnessing the rapid rise of LLM-based multi-agent systems (MASs) and their powerful applications. However, their security remains insufficiently understood, as existing evaluations are largely limited to narrow attack settings and may substantially underestimate the real risks of MAS deployments. Inspired by the MAS inter-agent dependencies, where upstream outputs are reinterpreted and executed by downstream agents, we propose a topology-aware attack scheme that propagates adversarial contamination from exposed edge agents to high-privilege agents to induce malicious behaviors. By combining topology reconnaissance, contamination propagation modeling, and hierarchical payload encapsulation, our approach overcomes the key challenges of black-box attacks and makes such multi-hop compromise practical. Experiments show that our approach achieves success rates of 40\%--78\% on three widely-used MAS frameworks under five topologies, and 85\% on two real-world MAS applications across 20 representative scenarios. The results reveal fundamental vulnerabilities in MASs that have been overlooked by prior studies. Based on these findings, we propose a topology-trust mitigation that blocks 94.8\% of such composite attacks.

cs.CR↗

arXiv2025

PATCHEVAL: A New Benchmark for Evaluating LLMs on Patching Real-World Vulnerabilities

Software vulnerabilities are increasing at an alarming rate. However, manual patching is both time-consuming and resource-intensive, while existing automated vulnerability repair (AVR) techniques remain limited in effectiveness. Recent advances in large language models (LLMs) have opened a new paradigm for AVR, demonstrating remarkable progress. To examine the capability of LLMs in AVR, several vulnerability benchmarks have been proposed recently. However, they still suffer from key limitations of outdated vulnerabilities, limited language coverage, unreliable patch validation, and insufficient reproducibility. To overcome these challenges, we introduce PATCHEVAL, a multilingual benchmark for Go, JavaScript, and Python, languages for which existing benchmarks remain unexplored. PATCHEVAL curates a dataset of 1,000 vulnerabilities drawn from CVEs reported between 2015 and 2025, covering 65 distinct CWEs. A subset of 230 CVEs is further equipped with runtime sandbox environments, enabling patch verification through both security tests and functionality tests. To provide a systematic comparison of LLM-based vulnerability repair, we evaluate a series of state-of-the-art LLMs and agents, presenting an in-depth analysis that empirically yields key insights to guide future research in AVR.

cs.CR↗

arXiv2025

A Robust Fault Detection Filter for Linear Time-Varying System with Non-Gaussian Noise

This paper addresses the problem of robust fault detection filtering for linear time-varying (LTV) systems with non-Gaussian noise and additive faults. The conventional generalized likelihood ratio (GLR) method utilizes the Kalman filter, which may exhibit inadequate performance under non-Gaussian noise conditions. To mitigate this issue, a fault detection method employing the $H_{\infty}$ filter is proposed. The $H_{\infty}$ filter is first derived as the solution to a regularized least-squares (RLS) optimization problem, and the effect of faults on the output prediction error is then analyzed. The proposed approach using the $H_{\infty}$ filter demonstrates robustness in non-Gaussian noise environments and significantly improves fault detection performance compared to the original GLR method that employs the Kalman filter. The effectiveness of the proposed approach is illustrated using numerical examples.

math.OC↗

arXiv2020

Nash Equilibrium Seeking for Games in Second-order Systems without Velocity Measurement

The design of Nash equilibrium seeking strategies for games in which the involved players are of second-order integrator-type dynamics is investigated in this paper. Noticing that velocity signals are usually noisy or not available for feedback control in practical engineering systems, this paper supposes that the velocity signals are not accessible for the players. To deal with the absence of velocity measurements, two estimators are designed, based on which Nash equilibrium seeking strategies are constructed. The first strategy is established by employing an observer, which has the same order as the players' dynamics, to estimate the unavailable system states (e.g., the players' velocities). The second strategy is designed based on a high-pass filter and is motivated by the incentive to reduce the order of the closed-loop system which in turn reduces the computation costs of the seeking algorithm. Extensions to Nash equilibrium seeking for networked games are provided. Taking the advantages of leader-following consensus protocols, it turns out that both the observer-based method and the filter-based method can be adapted to deal with games in distributed systems, which shows the extensibility of the developed strategies. Through Lyapunov stability analysis, it is analytically proven that the players' actions can be regulated to the Nash equilibrium point and their velocities can be regulated to zero by utilizing the proposed velocity-free Nash equilibrium seeking strategies. A numerical example is provided for the verifications of the proposed algorithms.

math.OC↗

arXiv2015

Shape of Alexandrov spaces with positive Ricci curvature

Under the definition of Ricci curvature bounded below for Alexandrov spaces introduced by Zhang-Zhu, we generalize a result by Colding that an n dimentional manifold with Ricci curvature greater or equal to n minus 1 and volume close to that of the unit n sphere is close (in the Gromov-Hausdorff distance) to the sphere, from the case of Riemannian manifolds to the case of Alexandrov spaces, with an additional assumption, roughly speaking, that the rough volume of the set of short cut points is small, following the basic idea in the Riemannian case with necessary modifications because of the only almost everywhere second differentiability of distance functions.

math.MG↗

arXiv2011

Isometric embedding of negatively curved complete surfaces in Lorentz-Minkowski space

Hilbert-Efimov theorem states that any complete surface with curvature bounded above by a negative constant can not be isometrically imbedded in $\mathbb{R}^3.$ We demonstrate that any simply-connected smooth complete surface with curvature bounded above by a negative constant admits a smooth isometric embedding into the Lorentz-Minkowski space $\mathbb{R}^{2,1}$.

math.DG↗

arXiv2007

Uniqueness and Pseudolocality Theorems of the Mean Curvature Flow

Mean curvature flow evolves isometrically immersed base manifolds $M$ in the direction of their mean curvatures in an ambient manifold $\bar{M}$. If the base manifold $M$ is compact, the short time existence and uniqueness of the mean curvature flow are well-known. For complete isometrically immersed submanifolds of arbitrary codimensions, the existence and uniqueness are still unsettled even in the Euclidean space. In this paper, we solve the uniqueness problem affirmatively for the mean curvature flow of general codimensions and general ambient manifolds. In the second part of the paper, inspired by the Ricci flow, we prove a pseudolocality theorem of mean curvature flow. As a consequence, we obtain a strong uniqueness theorem, which removes the assumption on the boundedness of the second fundamental form of the solution.

math.DG↗

arXiv2003

Sharp Dimension Estimates of Holomorphic Functions and Rigidity

Let $M^n$ be a complete noncompact K$\ddot{a}$hler manifold of complex dimension $n$ with nonnegative holomorphic bisectional curvature. Denote by $\mathcal{O}$$_d(M^n)$ the space of holomorphic functions of polynomial growth of degree at most $d$ on $M^n$. In this paper we prove that $$dim_{\mathbb{C}}{\mathcal{O}}_d(M^n)\leq dim_{\mathbb{C}}{\mathcal{O}}_{[d]}(\mathbb{C}^n),$$ for all $d>0$, with equality for some positive integer $d$ if and only if $M^n$ is holomorphically isometric to $\mathbb{C}^n$. We also obtain sharp improved dimension estimates when its volume growth is not maximal or its Ricci curvature is positive somewhere.

math.DG↗