arXiv subjects

Juan Li

Publications and source records attributed to Juan Li.

At least 37 records · Page 2Linked to original sources

arXiv2026

The Reynolds-Averaged Vortex Force Map Method

Vortex-force mapping (VFM) links vortical flow structures to aerodynamic forces through compact-domain integrals weighted by geometry-only Laplace potentials, but existing formulations are tied to simple geometries and laminar flows. In this study, we derive a Reynolds-averaged vortex force map (RA-VFM) directly from the incompressible Reynolds-averaged Navier-Stokes (RANS) equations, augmenting the classical vortex-pressure (VP) term with a Reynolds-stress (RS) contribution based on the Laplace-potential-weighted divergence of the modelled Reynolds stress (Boussinesq eddy-viscosity form). The resulting framework reconstructs mean lift and drag from RANS mean fields while retaining spatial attribution of force production to specific regions and coherent structures within a compact control volume. We apply RA-VFM to unsteady RANS ($k$-$ω$ SST) simulations of a realistic gliding goshawk with strong three-dimensionality and a matched GOE803 aerofoil section. For the aerofoil, the VP term alone reproduces the CFD force curves over the pre- and near-stall range, with RS contributions becoming appreciable only in deep stall. For the bird, by contrast, the VP term underpredicts both $C_L$ and $C_D$, whereas including the RS term reduces the mean absolute error relative to CFD from $6\%$ to $2\%$ in lift and from $5\%$ to $1\%$ in drag over an angle of attack range of $0^\circ$-$20^\circ$. RA-VFM thus extends vortex-force mapping to turbulent, 3-D RANS flows and enables quantitative attribution of mean lift and drag to specific coherent structures within compact domains.

physics.flu-dyn↗

arXiv2026

X-ray Quasi-Periodic Oscillations in Active Galactic Nuclei and Their Implications for the Changing Look Phenomenon

X-ray timing of active galactic nuclei (AGN) provides a unique probe of gas accretion onto supermassive black holes (SMBHs). Quasi-periodic oscillations (QPOs), which trace gas dynamics in the strongly curved spacetime around SMBHs, are rare in AGN. These signals often are analogs of high-frequency QPOs occasionally seen in some black-hole X-ray binaries, and their scarcity in AGN can partly be attributed to the low frequencies expected for typical SMBH masses. Intriguingly, robust X-ray QPO detections in SMBH systems have so far been reported only in narrow-line Seyfert 1 galaxies (NLS1s) and tidal disruption events (TDEs). Here we report the discovery of a QPO candidate during the 2018 outburst of the changing-look AGN (CL-AGN) NGC 1566. Numerical simulations indicate that the disk epicyclic oscillations responsible for high-frequency QPOs are damped by magnetohydrodynamic turbulence unless the accretion flow is misaligned and/or eccentric. In TDEs, the stellar debris stream is naturally misaligned with the SMBH spin, while NLS1s may host misaligned disks due to their youth. Motivated by the QPO candidate in NGC 1566, we propose that CL-AGN accretion is also misaligned -- potentially fueled by captured, free-falling broad-line region clouds. This model naturally explains why CL-AGN transition timescales are much shorter than the standard disk viscous timescale. This picture can be tested by searching for QPOs or quasi-periodic eruptions in other CL-AGN.

astro-ph.HE↗

arXiv2026

Bridging the Urban Divide: Adaptive Cross-City Learning for Disaster Sentiment Understanding

Social media platforms provide a real-time lens into public sentiment during natural disasters; however, models built solely on textual data often reinforce urban-centric biases and overlook underrepresented communities. This paper introduces an adaptive cross-city learning framework that enhances disaster sentiment understanding by integrating mobility-informed behavioral signals and city similarity-based data augmentation. Focusing on the January 2025 Southern California wildfires, our model achieves state-of-the-art performance and reveals geographically diverse sentiment patterns, particularly in areas experiencing overlapping fire exposure or delayed emergency responses. We further identify positive correlations between emotional expressions and real-world mobility shifts, underscoring the value of combining behavioral and textual features. Through extensive experiments, we demonstrate that multimodal fusion and city-aware training significantly improve both accuracy and fairness. Collectively, these findings highlight the importance of context-sensitive sentiment modeling and provide actionable insights toward developing more inclusive and equitable disaster response systems.

cs.SI↗

arXiv2026

CH3CCH as a thermometer in warm molecular gas

Kinetic temperature is a fundamental parameter in molecular clouds. Symmetric top molecules, such as NH$_3$ and CH$_3$CCH, are often used as thermometers. However, at high temperatures, NH$_3$(2,2) can be collisionally excited to NH$_3$(2,1) and rapidly decay to NH$_3$(1,1), which can lead to an underestimation of the kinetic temperature when using rotation temperatures derived from NH$_3$(1,1) and NH$_3$(2,2). In contrast, CH$_3$CCH is a symmetric top molecule with lower critical densities of its rotational levels than those of NH$_3$, which can be thermalized close to the kinetic temperature at relatively low densities of about 10$^{4}$ cm$^{-3}$. To compare the rotation temperatures derived from NH$_3$(1,1)$\&$(2,2) and CH$_3$CCH rotational levels in warm molecular gas, we used observational data toward 55 massive star-forming regions obtained with Yebes 40m and TMRT 65m. Our results show that rotation temperatures derived from NH$_3$(1,1)$\&$(2,2) are systematically lower than those from CH$_3$CCH 5-4. This suggests that CH$_3$CCH rotational lines with the same $J$+1$\rightarrow$$J$ quantum number may be a more reliable thermometer than NH$_3$(1,1)$\&$(2,2) in warm molecular gas located in the surroundings of massive young stellar objects or, more generally, in massive star-forming regions.

astro-ph.GA↗

arXiv2026

Value existence for zero-sum ergodic stochastic differential games

In this paper we investigate two-player zero-sum stochastic differential games with an ergodic payoff, in which the diffusion coefficient does not need to be non-degenerate. We first establish the existence of a viscosity solution to the associated ergodic Hamilton-Jacobi-Bellman-Isaacs equation under a dissipativity condition. With the help of this viscosity solution, we then derive estimates for the upper and the lower ergodic value functions by constructing a series of non-degenerate approximating processes combined with the sup- and inf-convolution techniques. Finally, we prove the existence of a value for the game under the Isaacs condition and provide its representation formulae. As an application, we study the pollution accumulation problem with a long-run average social welfare to illustrate our theoretical results.

math.OC↗

arXiv2026

Thermally Configurable Multi-Order Polar Skyrmions in Multiferroic Oxide Superlattices

Polar topological textures in low-dimensional ferroelectrics have emerged as a versatile platform for high-density information storage and neuromorphic computing. While low-order topological states, such as vortices and skyrmions, have been extensively studied, high-order polar topological families remain largely unexplored due to their higher energy requirements and limited stabilization methods. Here, using a BiFeO3 (BFO)-based multiferroic superlattice as a model system, we demonstrate a thermal-modulation strategy that stabilizes multi-order polar skyrmions and enables reversible tuning of their topological order through phase-field simulations. It was found that temperature modulation drives the system from polar solitons through 1π-, 2π-, 3π-, and 4π-skyrmion states, with closed heating-cooling path analyses revealing the widest thermal stability window for 2π-skyrmions (up to 600 K). Leveraging this robustness, 2% Sm doping in BFO lowers the transition temperatures, enabling room-temperature stabilization of 2π-skyrmions. These findings enrich the fundamental understanding of multi-order polar topologies and establish a tunable strategy for realizing variable-order topological configurations in practical memory devices.

cond-mat.mtrl-sci↗

arXiv2025

A Specialized Large Language Model for Clinical Reasoning and Diagnosis in Rare Diseases

Rare diseases affect hundreds of millions worldwide, yet diagnosis often spans years. Convectional pipelines decouple noisy evidence extraction from downstream inferential diagnosis, and general/medical large language models (LLMs) face scarce real world electronic health records (EHRs), stale domain knowledge, and hallucinations. We assemble a large, domain specialized clinical corpus and a clinician validated reasoning set, and develop RareSeek R1 via staged instruction tuning, chain of thought learning, and graph grounded retrieval. Across multicenter EHR narratives and public benchmarks, RareSeek R1 attains state of the art accuracy, robust generalization, and stability under noisy or overlapping phenotypes. Augmented retrieval yields the largest gains when narratives pair with prioritized variants by resolving ambiguity and aligning candidates to mechanisms. Human studies show performance on par with experienced physicians and consistent gains in assistive use. Notably, transparent reasoning highlights decisive non phenotypic evidence (median 23.1%, such as imaging, interventions, functional tests) underpinning many correct diagnoses. This work advances a narrative first, knowledge integrated reasoning paradigm that shortens the diagnostic odyssey and enables auditable, clinically translatable decision support.

cs.CL↗

arXiv2025

Self-Correction Distillation for Structured Data Question Answering

Structured data question answering (QA), including table QA, Knowledge Graph (KG) QA, and temporal KG QA, is a pivotal research area. Advances in large language models (LLMs) have driven significant progress in unified structural QA frameworks like TrustUQA. However, these frameworks face challenges when applied to small-scale LLMs since small-scale LLMs are prone to errors in generating structured queries. To improve the structured data QA ability of small-scale LLMs, we propose a self-correction distillation (SCD) method. In SCD, an error prompt mechanism (EPM) is designed to detect errors and provide customized error messages during inference, and a two-stage distillation strategy is designed to transfer large-scale LLMs' query-generation and error-correction capabilities to small-scale LLM. Experiments across 5 benchmarks with 3 structured data types demonstrate that our SCD achieves the best performance and superior generalization on small-scale LLM (8B) compared to other distillation methods, and closely approaches the performance of GPT4 on some datasets. Furthermore, large-scale LLMs equipped with EPM surpass the state-of-the-art results on most datasets.

cs.CL↗

arXiv2025

A Relaxed Control Problem With $L^\infty$ Cost and Jump Dynamics Motivated by Cyber Risks Insurance

This paper has a double aim. One the one hand, we introduce a uni-nodal network model for cyber risks with firewalled edges and SIR intra-edge spreading. In connection to this, we formulate an insurance problem in which one seeks the running maximal reputation index against all control strategies of the companies represented by edges. On the other hand, we seek to characterize the value function with $L^\infty$ cost through linear programming techniques and more standard Hamilton-Jacobi integro-differential inequalities.

math.OC↗

arXiv2025

BoRe-Depth: Self-supervised Monocular Depth Estimation with Boundary Refinement for Embedded Systems

Depth estimation is one of the key technologies for realizing 3D perception in unmanned systems. Monocular depth estimation has been widely researched because of its low-cost advantage, but the existing methods face the challenges of poor depth estimation performance and blurred object boundaries on embedded systems. In this paper, we propose a novel monocular depth estimation model, BoRe-Depth, which contains only 8.7M parameters. It can accurately estimate depth maps on embedded systems and significantly improves boundary quality. Firstly, we design an Enhanced Feature Adaptive Fusion Module (EFAF) which adaptively fuses depth features to enhance boundary detail representation. Secondly, we integrate semantic knowledge into the encoder to improve the object recognition and boundary perception capabilities. Finally, BoRe-Depth is deployed on NVIDIA Jetson Orin, and runs efficiently at 50.7 FPS. We demonstrate that the proposed model significantly outperforms previous lightweight models on multiple challenging datasets, and we provide detailed ablation studies for the proposed methods. The code is available at https://github.com/liangxiansheng093/BoRe-Depth.

cs.CV↗

arXiv2025

Widespread Hot Molecular Gas Heated by Shear-induced Turbulence in the Galactic Center

We observed NH3 metastable inversion lines from (3, 3) to (18, 18) toward G0.66-0.13 in the Galactic center with the Shanghai Tianma 65m radio telescope and Yebes 40 m telescope. Highly-excited lines of NH3 (17, 17), (18, 18) were detected in emission for the first time in the interstellar medium, with upper energy levels up to 3100 K. Mapping observations reveal widespread hot molecular gas traced by NH3 (13, 13) toward G0.66-0.13. The rotation temperatures of hot gas traced by NH3 exceed 400 K, which amounts to five percent of the total NH3 in the Galactic Center. Hot gas (>400 K) and warm gas (100-140 K) are found in distinct clumps, with the hot gas located at the interfacing regions between different warm clouds. The theory of intermittency in turbulence reproduces the complex temperature structure in the central molecular zone, especially the hot gas observed here. The results presented here demonstrate that turbulence heating dominates the heating of the molecular gas in the Central Molecular Zone, while the turbulence is induced by the shear-motion of molecular clouds under the gravitational potential of the nuclear star clusters and the supermassive black hole. Our results suggest that shear-induced turbulence heating could be a widespread factor influencing galactic evolution.

astro-ph.GA↗

arXiv2025

General mean-field BSDEs with integrable terminal values

This paper investigates $L^{1}$ solutions for mean-field backward stochastic differential equations (MFBSDEs) under different weak assumptions in both one-dimensional and multi-dimensional settings, whose generator $f(ω,t,y,z,μ)$ depends not only on the solution process $(Y,Z)$ but also on the law of $(Y,Z)$. In the one-dimensional case where $f$ depends on the law of $Y$, we show with the help of a test function method and a localization procedure that such type of equations with an integrable terminal condition admits an $L^{1}$ solution, when the generator $f(ω,t,y,z,μ)$ has a one-sided linear growth in $(y,μ)$, and an iterated-logarithmically sub-linear growth in $z$. Furthermore, by leveraging the additional extended monotonicity in $y$ and an iterated-logarithmically uniform continuity in $z$ of the generator $f(ω,t,y,z,μ)$ together with a strengthened nondecreasing condition in $μ$, we derive a comparison theorem for $L^{1}$ solutions, which immediately leads to the uniqueness of the $L^{1}$ solutions. Next, we establish the existence and the uniqueness of $L^{1}$ solutions for multi-dimensional mean-field BSDEs with integrable parameters in which the generator $f(ω,t,y,z,μ)$ depends on $μ=\mathbb{P}_{Y}$ and satisfies a one-sided Osgood condition as well as a general growth condition in $y$, a Lipschitz continuity as well as a sublinear growth condition in $z$, and a Lipschitz condition in $μ$. Finally, the solvability of $L^{1}$ solutions for general MFBSDEs is studied, where the generator $f(ω,t,y,z,μ)$ depends on both the solution process $(Y,Z)$ and its joint law $\mathbb{P}_{(Y,Z)}$.

math.PR↗

arXiv2025

RTQA : Recursive Thinking for Complex Temporal Knowledge Graph Question Answering with Large Language Models

Current temporal knowledge graph question answering (TKGQA) methods primarily focus on implicit temporal constraints, lacking the capability of handling more complex temporal queries, and struggle with limited reasoning abilities and error propagation in decomposition frameworks. We propose RTQA, a novel framework to address these challenges by enhancing reasoning over TKGs without requiring training. Following recursive thinking, RTQA recursively decomposes questions into sub-problems, solves them bottom-up using LLMs and TKG knowledge, and employs multi-path answer aggregation to improve fault tolerance. RTQA consists of three core components: the Temporal Question Decomposer, the Recursive Solver, and the Answer Aggregator. Experiments on MultiTQ and TimelineKGQA benchmarks demonstrate significant Hits@1 improvements in "Multiple" and "Complex" categories, outperforming state-of-the-art methods. Our code and data are available at https://github.com/zjukg/RTQA.

cs.CL↗

arXiv2025

Fine-grained Alignment of Large Language Models for General Medication Recommendation without Overprescription

Large language models (LLMs) holds significant promise in achieving general medication recommendation systems owing to their comprehensive interpretation of clinical notes and flexibility to medication encoding. We evaluated both general-purpose and medical-specific LLMs for medication recommendations, showing their unsatisfactory precision and severe overprescription. To address this, we introduce Language-Assisted Medication Recommendation, which tailors LLMs for medication recommendation in a medication-aware manner, improving the usage of clinical notes. Fine-tuning LLMs with this framework can outperform existing methods by more than 10% in internal validation and generalize across temporal and external validations. Furthermore, the model maintains high accuracy when encountering out-of-distribution medication.

cs.IR↗

arXiv2025

A Multimodal, Multilingual, and Multidimensional Pipeline for Fine-grained Crowdsourcing Earthquake Damage Evaluation

Rapid, fine-grained disaster damage assessment is essential for effective emergency response, yet remains challenging due to limited ground sensors and delays in official reporting. Social media provides a rich, real-time source of human-centric observations, but its multimodal and unstructured nature presents challenges for traditional analytical methods. In this study, we propose a structured Multimodal, Multilingual, and Multidimensional (3M) pipeline that leverages multimodal large language models (MLLMs) to assess disaster impacts. We evaluate three foundation models across two major earthquake events using both macro- and micro-level analyses. Results show that MLLMs effectively integrate image-text signals and demonstrate a strong correlation with ground-truth seismic data. However, performance varies with language, epicentral distance, and input modality. This work highlights the potential of MLLMs for disaster assessment and provides a foundation for future research in applying MLLMs to real-time crisis contexts. The code and data are released at: https://github.com/missa7481/EMNLP25_earthquake

cs.CL↗

arXiv2025

PTST: A polar topological structure toolkit and database

Ferroelectric oxide superlattices with complex topological structures such as vortices, skyrmions, and flux closure domains have garnered significant attention due to their fascinating properties and potential applications. However, progress in this field is often impeded by challenges such as limited data-sharing mechanisms, redundant data generation efforts, high barriers between simulations and experiments, and the underutilization of existing datasets. To address these challenges, we have created the Polar Topological Structure Toolbox and Database(PTST). This community driven repository compiles both standard datasets from high throughput phase field simulations and user submitted nonstandard datasets. The PTST utilizes a Global Local Transformer (GL Transformer) to classify polarization states by dividing each sample into spatial sub blocks and extracting hierarchical features, resulting in ten distinct topological categories. Through the PTST web interface, users can easily retrieve polarization data based on specific parameters or by matching experimental images. Additionally, a Binary Phase Diagram Generator allows users to create strain and electric field phase diagrams within seconds. By providing ready-to-use configurations and integrated machine-learning workflows, PTST significantly reduces computational load, streamlines reproducible research, and promotes deeper insights into ferroelectric topological transitions.

cond-mat.mtrl-sci↗

arXiv2025

Controllability concepts for mean-field dynamics with reduced-rank coefficients

In this paper we explore several novel notions of exact controllability for mean-field linear controlled stochastic differential equations (SDEs). A key feature of our study is that the noise coefficient is not required to be of full rank. We begin by demonstrating that classical exact controllability with $\mathbb{L}^2$-controls necessarily requires both rank conditions on the noise introduced in [8] and subsequent works. When these rank conditions are not satisfied, we introduce alternative rank requirements on the drift, which enable exact controllability by relaxing the regularity of the controls. In cases where both the aforementioned rank conditions fail, we propose and characterize a new notion of exact terminal controllability to normal laws (ETCNL). Additionally, we investigate a new class of Wasserstein-set-valued backward SDEs that arise naturally associated to ETCNL.

math.OC↗

arXiv2025

A stochastic maximum principle of mean-field type with monotonicity conditions

The objective of this paper is to weaken the Lipschitz condition to a monotonicity condition and to study the corresponding Pontryagin stochastic maximum principle (SMP) for a mean-field optimal control problem under monotonicity conditions.The dynamics of the controlled state process is governed by a mean-field stochastic differential equation (SDE) whose coefficients depend not only on the control, the controlled state process itself but also on its law, and in particular, these coefficients satisfy the monotonicity condition with respect to both the controlled state process and its distribution. The associated cost functional is also of mean-field type. Under the assumption of a convex control domain we derive the SMP, which provides a necessary optimality condition for control processes. Under additional convexity assumptions on the Hamiltonian, we further prove that this necessary condition is also a sufficient one. To achieve this, we first address the challenges related to the existence and the uniqueness of solutions for mean-field backward stochastic differential equations and mean-field SDEs whose coefficients satisfy monotonicity conditions with respect to both the solution as well as its distribution. On the other hand we also construct several illustrative examples demonstrating the generality of our results compared to existing literature.

math.OC↗