arXiv subjects

Juan Li

Publications and source records attributed to Juan Li.

At least 19 recordsLinked to original sources

arXiv2026

Metallic-Phase-Fe$_3$GaTe$_2$ Enabled Interface Engineering for Self-Powered and High-Gain WS$_2$ Photodetectors

Two-dimensional transition-metal dichalcogenides offer strong light-matter interaction but suffer from inefficient carrier separation and contact-related losses in photodetectors. Here, we demonstrate a high-gain WS$_2$/Fe$_3$GaTe$_2$ van der Waals heterostructure photodetector, where metallic Fe$_3$GaTe$_2$ serves as an active interfacial contact. The work-function mismatch, together with interfacial charge redistribution and asymmetric contact geometry, contributes to a built-in field that supports self-powered photodetection at zero bias. Under 450 nm illumination, the device delivers a zero-bias responsivity of 23.5 A/W and an apparent external quantum efficiency of 6.4 x 10$^3$%. At -1 V biasing, the heterostructure exhibits photoresponse at 450, 520 and 633 nm, achieving a responsivity of 9.7 x 10$^3$ A/W and a noise derived specific detectivity of 2.3 x 10$^13$ Jones at 100 Hz under 450 nm illumination. The high photoresponse is attributed to interfacial carrier separation, efficient extraction, and a likely contribution from trap-assisted photogating in multilayer WS$_2$. These results establish Fe$_3$GaTe$_2$-enabled interface engineering as an effective route for self-powered, highly sensitive 2D photodetectors.

cond-mat.mtrl-sci↗

arXiv2026

Rigidity of Ricci-Pinched Complete Self-Shrinkers in Arbitrary Codimension

Let $M$ be an $n$-dimensional complete connected self-shrinker in $\mathbb{R}^{n+p}$. Denote by $\operatorname{Ric}$ and $\mathbf{H}$ the Ricci curvature tensor and the mean curvature vector of $M$, respectively. We prove that if the self-shrinker $M$ satisfies $\operatorname{Ric}\ge(\frac{n-2}{n^2}+\varepsilon_n)|\mathbf{H}|^2g$, then $M$ is either a linear subspace or a round shrinking sphere, where $\varepsilon_n$ is an explicit positive constant equal to $\frac{1}{3n^2}+O\left(\frac{1}{n^5}\right)$. Moreover, we obtain a rigidity theorem that is sharp in codimension two for the self-shrinker satisfying $\operatorname{Ric}\ge\frac{n-2}{n^2}|\mathbf{H}|^2g$.

math.DG↗

arXiv2026

VATO: A Vortex-Force-Aware Transformer Operator for Unsteady Separated Aerofoil Flows

Accurate prediction of unsteady separated flows is challenging because the aerodynamic loads depend on nonlinear separation and vortex-shedding dynamics. Although high-fidelity CFD resolves these mechanisms, its cost limits repeated use in design and control. Standard field-level surrogate training, however, does not distinguish the flow regions that contribute most strongly to the aerodynamic loads. We introduce VATO (Vortex-Force-Aware Transformer Operator), which couples the Vortex Force Map (VFM) method to a geometry-aware neural operator through two complementary mechanisms. VATO-S adds training-only supervision of the local VFM force-contribution field, with no increase in model size or inference cost. VATO-A uses VFM contribution and sensitivity fields to prioritise force-relevant source locations for residual cross attention. The methods are evaluated on unsteady CFD data for double-edged-plate aerofoils over 54 trajectories from nine geometries. Over lead times of 1-20~ms, VATO-S reduces velocity, pressure, and vorticity errors by 10.4\%, 1.0\%, and 15.6\%, respectively, while VATO-A achieves reductions of 15.8\%, 7.5\%, and 31.2\%. VATO-S gives the lowest VFM-derived drag error, whereas VATO-A gives the lowest pressure-derived lift and drag errors. Over lead times extending 50\% beyond the training range, VATO-A retains a 26.9\% reduction in vorticity error and larger improvements in all four force readouts, despite reduced gains in velocity and pressure. These results show that force-aware operator learning can improve both flow-field prediction and aerodynamic functional accuracy in unsteady separated flows.

cs.LG↗

arXiv2026

Deforming Vortex Force Mapping for Undulating Swimmers

Undulatory propulsion is commonly interpreted through reactive added-mass loading associated with body kinematics and vortex-induced loading, but their relative contributions to the streamwise force have not been systematically quantified across swimming kinematics. Moreover, existing vortex-based descriptions do not resolve the signed spatial contribution of vortical flow to the streamwise force. We extend vortex force map (VFM) attribution to deforming bodies, demonstrated for prescribed two-dimensional undulating swimmers at $Re=5000$ across a range of Strouhal numbers and dimensionless wavelengths. The deforming-body VFM organises the streamwise force as a positive reactive added-mass scale with a signed vortex-pressure modification. Across the 80 cases, vortex pressure opposes the reactive contribution in 76 cases. Reference-configuration maps show that the near-body vortex-pressure contribution is consistently adverse, whereas the wake contribution changes sign across regimes and becomes favourable at high thrust. Net vortex-pressure assistance occurs only for the high-thrust anguilliform swimmer. A matched high-thrust pair further shows that the case with the higher peak downstream velocity has the smaller signed wake contribution, demonstrating that wake intensity alone does not determine its force effect.

physics.flu-dyn↗

arXiv2026

STCO: Conditional Neural Operators for Time-Dependent PDEs

Neural operators have emerged as efficient surrogates for time-dependent physical systems governed by partial differential equations (PDEs), but their future-state predictions are often conditioned only on observed states and static problem descriptors. For control or optimization, however, body motion, inflow, or forcing are prescribed for the query without being determined solely by the observed state. We introduce the Spatiotemporal Conditional Operator (STCO) for prescribed-condition operator learning (PCOL), a common interface that supplies prescribed target-time condition fields to heterogeneous backbone architectures while retaining their architecture-specific core computation and context pathways. Its condition interface combines Flow-Aware Graph Leaf (FAGL) with Dual-Site Feature-wise Linear Modulation (DSFiLM). Non-learned FAGL uses vorticity from the final observed frame to construct a fixed-cardinality adaptive partition, then co-locates the observed history and target-time condition fields at its regional coordinates. DSFiLM injects separate motion, inflow, and force routes before and after operator computation through current-feature-driven slot- and channel-wise gates. We evaluate twelve matched backbone architectures with different existing physical and temporal inputs. The immersed-boundary computational fluid dynamics (CFD) benchmark spans prescribed motion, inflow disturbances, body-force actuation, and morphology. Across twelve matched backbones, three regimes, and two lead ranges, STCO yields mean paired reductions of 31.1% in relative-L2 field error and 24.7% in normalized pressure-derived load error. It also lowers longer-lead field error for 11 backbones, while interventions on individual condition groups produce measurable prediction changes for every group evaluated.

cs.AI↗

arXiv2026

Neuro-Symbolic Participation Governance for Verifiable AI Agents in Open Digital Twin Ecosystems

Autonomous AI agents, increasingly empowered by large language models, are becoming important components of human-machine systems for high-stakes decision support in digital twin ecosystems. However, existing multi-agent systems often lack robust verification for identity, capability, and policy compliance, especially in decentralized environments spanning multiple institutions. This paper proposes a neuro-symbolic decentralized governance framework for verifiable agents in collaborative digital twin environments. By representing agents through multi-layer semantic profiles, the framework bridges probabilistic neural reasoning with deterministic institutional governance, thereby supporting trustworthy human-AI collaboration and meaningful human oversight. Capabilities are grounded in formal domain ontologies to enable machine-interpretable, policy-aware, and context-sensitive participation. These credentials, issued by organizational authorities, are validated via blockchain-based smart contracts, ensuring auditable participation without exposing sensitive data. We demonstrate the framework using a decision-support prototype with clinic, digital twin, and wearable provider agents effectively prevents unauthorized interaction and enforces institutional policies with manageable overhead. Our findings suggest that neuro-symbolic decentralized governance provides a scalable and trustworthy pathway for safe human-machine collaboration across institutional boundaries.

cs.CR↗

arXiv2026

Stochastic Control of Addiction with State-Dependent Jump Relapse

We study a continuous-time rational addiction model where addiction capital follows a piecewise deterministic Markov process with state-dependent jumps capturing relapse and recovery. The instantaneous utility combines consumption and addiction capital via a power specification, leading to Hamilton-Jacobi-Bellman (HJB) equations with nonlocal jump terms. In the capped, bounded-control case we obtain a unique bounded viscosity solution and prove that optimal policies are bang-bang, switching between minimal and maximal consumption, while in the uncapped case we derive explicit linear feedback controls and closed-form value functions in several parameter regimes.

math.OC↗

arXiv2026

Stackelberg Games with a Robust Leader

In this paper we study a Stackelberg game with one leader and multiple followers. Given the leader's control, the followers solve a Nash game with possibly multiple equilibria. We consider a robust leader who considers the worst scenario, namely the followers would select the equilibrium worst for the leader. By using the weak formulation, the problem induces a zero sum game problem with open loop controls, which is time inconsistent. We shall characterize the last problem through an HJB equation on the Wasserstein space of probability measures. The principal-agent problem with one principal and multiple agents can be viewed as a special case of our problem, but with certain constraints.

math.OC↗

arXiv2026

Molecule-dependent Abundance Behavior of Oxygen-bearing Complex Organics in High-Mass Star-Forming Regions: A Uniform 50-source Survey

We present a uniform IRAM-30\,m survey analysis of four oxygen-bearing complex organic molecules (COMs), methanol (CH$_3$OH), acetaldehyde (CH$_3$CHO), methyl formate (CH$_3$OCHO), and dimethyl ether (CH$_3$OCH$_3$), toward 50 high-mass star-forming regions (HMSFRs) associated with 6.7\,GHz methanol masers. Column densities were derived through a homogeneous rotation-diagram approach, with CH$_3$CN used as a proxy excitation-temperature reference when needed. In CH$_3$OH-normalized abundance-ratio space, CH$_3$OCHO/CH$_3$OH and CH$_3$OCH$_3$/CH$_3$OH show the strongest pairwise correlation, whereas the correlations involving CH$_3$CHO are weaker. No clear monotonic trends are found with Galactocentric distance or beam-averaged H$_2$ column density. Comparison with previous observations places the CH$_3$OCHO--CH$_3$OCH$_3$ behavior within the range of earlier abundance-ratio measurements, while CH$_3$CHO shows larger inter-study variation. A representative warm-up chemical model is used only for qualitative comparison with the observed abundance ranges, which are most closely matched during the decline from the post-desorption abundance peaks in the model. These results provide homogeneous beam-averaged abundance-ratio constraints for common O-bearing COMs in high-mass star-forming regions and show that their source-to-source behavior is molecule-dependent rather than fully described by a single common abundance pattern.

astro-ph.GA↗

arXiv2026

Forensic Schema for Psychological Manipulation in Cyber Fraud: LLM-Driven Victim Reports Analysis

Existing cybercrime classification schemas capture contact metadata and financial transactions but omit the psychological manipulation techniques perpetrators employ. We present a forensic schema (four categories, 35 questions) adding 11 manipulation indicators and cryptocurrency evidence fields to established forensic foundations. Applied to 10,994 victim reports via large language model (LLM)-driven annotation and validated against two human annotators (mean LLM-human $\kappa = 0.69$, matching inter-annotator $\kappa = 0.68$), the schema revealed a statistically distinct manipulation profile for each major fraud type (Cramer's $V$ up to $0.790$). A rationale-based evidence audit nonetheless exposed a forensic detail gap: detection of manipulation techniques was reliable, but victim narratives varied widely in the actionable detail supporting each Yes answer, and blockchain-specific identifiers were nearly absent. These findings point to AI-assisted victim intake with schema-informed follow-up questions as the most direct way to close the gap. The tiered annotation strategy also provides a reusable template for LLM-based extraction from other forensic text domains.

cs.CR↗

arXiv2026

The evolution of C4H and c-C3H2 in molecular cores

Linear C4H and cyclic c-C3H2, as small unsaturated hydrocarbons, are the key precursors to complex organic molecules and are critical components of the interstellar medium. We present on-the-fly mapping observations of C4H 9-8 lines, c-C3H2 2-1, H13CO+ 1-0, and H42 toward a sample of 22 massive star-forming regions using the IRAM 30m telescope. Our aim is to further explore the evolution of these carbon-chain molecules by combining observational results obtained in cold cores. We employed H13CO+ 1-0 and H42 as tracers to probe the positions of molecular cloud cores and ionised hydrogen regions (HII regions), respectively. One chemical model in particular, which includes gas, dust grain surface, and icy mantle phases for C4H and c-C3H2 molecules, was used to make comparisons with observed abundances. From mapping observations targeting 31 regions across 22 sources, C4H 9-8 (J = 19/2-17/2) and C4H 9-8 (J = 17/2-15/2) were detected in only 17 regions, while H13CO+ 1-0 and c-C3H2 2-1 were successfully detected in all 31 regions. We find that the emission of C4H 9-8 and c-C3H2 2-1 is concentrated at the edges of H42 emission regions. The C4H/H13CO+ and c-C3H2/H13CO+ relative abundance ratios range from 0.17 to 1.77 and 1.42 to 6.69, respectively, with a median C4H/c-C3H2 ratio of 0.13. By combining the observational results of cold cores, we find that C4H/H13CO+ and c-C3H2/H13CO+ ratios show a strong decreasing trend as molecular cores evolve. The decreasing trends in C4H/H13CO+ and c-C3H2/H13CO+ ratios imply that small unsaturated hydrocarbons can be consumed and converted into other organic molecules during the evolution of molecular cores. The spatial concentration of C4H and c-C3H2 emission at the edges of H42 regions further supports their role as precursors in the chemical pathways that lead to complex organic molecules in the interstellar medium.

astro-ph.GA↗

arXiv2026

MiMIC: Mitigating Visual Modality Collapse in Universal Multimodal Retrieval While Avoiding Semantic Misalignment

Universal Multimodal Retrieval (UMR) aims to map different modalities (e.g., visual and textual) into a shared embedding space for multi-modal retrieval. Existing UMR methods can be broadly divided into two categories: early-fusion approaches, such as Marvel, which projects visual features into the language model (LM) space for integrating with text modality, and late-fusion approaches, such as UniVL-DR, which encode visual and textual inputs using separate encoders and obtain fused embeddings through addition. Our pilot study reveals that Marvel exhibits visual modality collapse, which is characterized by the model's tendency to disregard visual features while depending excessively on textual cues. In contrast, although UniVL-DR is less affected by this issue, it is more susceptible to semantic misalignment, where semantically related content is positioned far apart in the embedding space. To address these challenges, we propose MiMIC, which introduces two key innovations: (1) a fusion-in-decoder architecture for effective multimodal integration, and (2) robust training through single modality mixin and random caption dropout. Experiments on the WebQA+ and EVQA+ datasets, where image in documents or queries might lack captions, indicate that MiMIC consistently outperforms both early- and late-fusion baselines.

cs.CV↗

arXiv2026

ViraHinter: a dual-modal artificial intelligence framework for predicting virus-host interactions

Protein-protein interactions (PPIs) between a virus and its host govern infection, replication, and pathogenesis. While high-throughput mapping has identified thousands of virus-host associations, much of the virus-host interactome remains uncharacterized due to the labor-intensive nature of experimental screens, the inherent difficulty in capturing transient interactions, and the limited sequence homology across divergent viral families. Here, we introduce ViraHinter, a dual-modal deep learning framework for the precise prediction of virus-host interactions and large-scale inference of interaction landscapes. ViraHinter couples a structure-generation branch with a sequence-representation branch, integrating structure-informed pair representations with ESM-derived embeddings to learn generalizable interaction rules across unseen viruses. We benchmark ViraHinter on pathogenic coronaviruses and influenza A viruses and show that it consistently outperforms RoseTTAFold2-PPI, AlphaFold 3 and RoseTTAFold2-Lite in prioritizing high-confidence candidates even under severe class imbalance and across diverse interface regimes. Notably, it successfully identifies novel functionally relevant host factors and recapitulates the structural plasticity of the complex interfaces. By intersecting predictions across multiple influenza subtypes, ViraHinter reveals 33 shared host factors, offering a roadmap for broad-spectrum antiviral discovery. ViraHinter therefore serves as a robust computational approach for studying virus-host interactions, enabling systematic screening of host factors for all known human-infecting viruses, providing new insights into the shared mechanisms of viral pathogenesis, and accelerating the discovery of novel therapeutic targets and the development of broad-spectrum antivirals.

q-bio.BM↗

arXiv2026

CyberJustice Tutor: An Agentic AI Framework for Cybersecurity Learning via Think-Plan-Act Reasoning and Pedagogical Scaffolding

The integration of Large Language Models (LLMs) into cybersecurity education for criminal justice professionals is currently hindered by the "statelessness" of reactive chatbots and the risk of hallucinations in high-stakes legal contexts. To address these limitations, we propose the CyberJustice Tutor, an educational dialogue system powered by an Agentic AI framework. Unlike reactive chatbots, our system employs a "Think-Plan-Act" cognitive cycle, enabling autonomous goal decomposition, longitudinal planning, and dynamic context maintenance. We integrate a Pedagogical Scaffolding Layer grounded in Vygotsky's Zone of Proximal Development (ZPD), which dynamically adapts instructional support based on the learner's real-time progress. Furthermore, an Adaptive Retrieval Augmented Generation (RAG) core anchors the agent's reasoning in verified curriculum materials to ensure legal and technical accuracy. A comprehensive user study with 123 participants, including students, educators, and active law enforcement officers, validated the system's efficacy. Quantitative results demonstrate high user acceptance for Response Speed (4.7/5), Ease of Use (4.4/5), and Accuracy (4.3/5). Qualitative feedback indicates that the agentic architecture is perceived as highly effective in guiding learners through personalized paths, demonstrating the feasibility and usability of agentic AI for specialized professional education.

cs.HC↗

arXiv2026

Evontree: Ontology Rule-Guided Self-Evolution of Large Language Models

Although Large Language Models (LLMs) perform exceptionally well in general domains, the problem of hallucinations poses significant risks in specialized fields such as healthcare and law, where high interpretability is essential. Existing fine-tuning methods depend heavily on large-scale professional datasets, which are often hard to obtain due to the privacy regulations. Moreover, existing self-evolution methods are primarily designed for general domains, which may struggle to adapt to knowledge-intensive domains due to the lack of knowledge constraints. In this paper, we propose an ontology rule guided method Evontree to enable self-evolution of LLMs in low-resource specialized domains. Specifically, Evontree first extracts domain ontology knowledge from raw models, then detects knowledge inconsistencies using two core ontology rules, and finally reinforces gap knowledge into model via self-distilled fine-tuning. Extensive evaluations on medical QA benchmarks using Llama3-8B-Instruct and Med42-V2 demonstrate the effectiveness of Evontree, which outperforms both the base models and strong baselines, achieving up to a 3.7\% improvement in accuracy. Detailed ablation studies further validate the robustness of our approach.

cs.CL↗

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↗