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Qi Zhou

Publications and source records attributed to Qi Zhou.

At least 73 records · Page 4Linked to original sources
arXiv2026

Graph-Augmented Reasoning with Large Language Models for Tobacco Pest and Disease Management

This paper proposes a graph-augmented reasoning framework for tobacco pest and disease management that integrates structured domain knowledge into large language models. Building on GraphRAG, we construct a domain-specific knowledge graph and retrieve query-relevant subgraphs to provide relational evidence during answer generation. The framework adopts ChatGLM as the Transformer backbone with LoRA-based parameter-efficient fine-tuning, and employs a graph neural network to learn node representations that capture symptom-disease-treatment dependencies. By explicitly modeling diseases, symptoms, pesticides, and control measures as linked entities, the system supports evidence-aware retrieval beyond surface-level text similarity. Retrieved graph evidence is incorporated into the LLM input to guide generation toward domain-consistent recommendations and to mitigate hallucinated or inappropriate treatments. Experimental results show consistent improvements over text-only baselines, with the largest gains observed on multi-hop and comparative reasoning questions that require chaining multiple relations.

cs.CL↗
arXiv2026

On a two-season faecal-oral model with impulsive intervention

Rainfall is associated with the outbreak of certain waterborne faecal-oral diseases, driving the implementation of various human interventions for their control and prevention. Taking into account human intervention and temporal variation in rainfall, this paper develops a two-season switching faecal-oral model with impulsive intervention and free boundaries. In this model, the infection fronts are represented by fixed boundaries during the dry season and by moving boundaries during the wet season, with impulsive intervention occurring at the end of each wet season. The simultaneous introduction of impulsive intervention and seasonal switching creates new difficulties for mathematical analysis. We overcome these challenges through novel analytical techniques, resulting in a spreading-vanishing dichotomy and a sharp criteria governing this dichotomy. Finally, numerical simulations are presented to validate the theoretical results and to visually illustrate the influence of seasonal switching and impulsive intervention. Our results mathematically explain that two factors, the duration of the dry season and the intensity of impulsive intervention are both positively correlated with effective disease control.

math.AP↗
arXiv2026

Spectral Transitions and Singular Continuous Spectrum in A New Family of Quasi-periodic Quantum Walks

This paper introduces and rigorously analyzes a new class of one-dimensional discrete-time quantum walks whose dynamics are governed by a parametrized family of extended CMV matrices. The model generalizes the unitary almost Mathieu operator (UAMO) and exhibits a richer spectral phase diagram, closely resembling the extended Harper's model. It provides the first example of a solvable quasi-periodic quantum walk that exhibits a stable region of purely singular continuous spectrum.

quant-ph↗
arXiv2026

An $O(\log N)$ Monte Carlo method for periodic Coulomb systems

Efficient Monte Carlo (MC) sampling of many-body systems with long-range electrostatics is often limited by the cost of per-move energy-difference evaluation under periodic boundary conditions. We present DMK-MC, an accelerated MC method that adapts the dual-space multilevel kernel-splitting (DMK) framework to single-particle Metropolis updates. DMK-MC computes the energy change and, upon acceptance, updates the stored incoming plane-wave fields with $O(1)$ work per tree level, yielding an overall $O(\log N)$ expected work per trial move for fixed accuracy. The method decomposes the Coulomb kernel into three components: a global, periodized smooth part; a multilevel sequence of smooth difference kernels whose interactions are restricted to same-level colleague boxes; and a singular residual kernel whose short-range interactions are evaluated directly. Benchmarks on uniform, highly nonuniform, and implicit-solvent electrolyte and colloidal configurations show that DMK-MC consistently outperforms a recent FMM-based $O(\log N)$ Monte Carlo method, delivering several-fold speedups at comparable tolerances.

physics.comp-ph↗
arXiv2025

Examining Student Interactions with a Pedagogical AI-Assistant for Essay Writing and their Impact on Students Writing Quality

The dynamic nature of interactions between students and GenAI, as well as their relationship to writing quality, remains underexplored. While most research has examined how general-purpose GenAI can support writing, fewer studies have investigated how students interact with pedagogically designed systems across different phases of the writing process. To address this gap, we evaluated a GenAI-driven essay-writing assistant (EWA) designed to support higher education students in argumentative writing. Drawing on 1,282 interaction logs from 32 undergraduates during a two-hour writing session, Sequential Pattern Mining and K-Means clustering were used to identify behavioral patterns. Two clusters emerged: Cluster 1 emphasized outline planning and essay structure, while Cluster 2 focused on content development. A Mann-Whitney U test revealed a moderate effect size (r = 0.36) in the essay Organization dimension, with Cluster 1 showing higher scores. Qualitative analysis indicated that students with better performance actively wrote and shared essay sections with EWA for feedback, rather than interacted passively by asking questions. These findings suggest implications for teaching and system design. Teachers can encourage active engagement, while future EWAs may integrate automatic labeling and monitoring to prompt students to move from questioning to writing, enabling fuller benefits from GenAI-supported learning.

cs.CY↗
arXiv2025

When Harmful Content Gets Camouflaged: Unveiling Perception Failure of LVLMs with CamHarmTI

Large vision-language models (LVLMs) are increasingly used for tasks where detecting multimodal harmful content is crucial, such as online content moderation. However, real-world harmful content is often camouflaged, relying on nuanced text-image interplay, such as memes or images with embedded malicious text, to evade detection. This raises a key question: \textbf{can LVLMs perceive such camouflaged harmful content as sensitively as humans do?} In this paper, we introduce CamHarmTI, a benchmark for evaluating LVLM ability to perceive and interpret camouflaged harmful content within text-image compositions. CamHarmTI consists of over 4,500 samples across three types of image-text posts. Experiments on 100 human users and 12 mainstream LVLMs reveal a clear perceptual gap: humans easily recognize such content (e.g., over 95.75\% accuracy), whereas current LVLMs often fail (e.g., ChatGPT-4o achieves only 2.10\% accuracy). Moreover, fine-tuning experiments demonstrate that \bench serves as an effective resource for improving model perception, increasing accuracy by 55.94\% for Qwen2.5VL-7B. Attention analysis and layer-wise probing further reveal that fine-tuning enhances sensitivity primarily in the early layers of the vision encoder, promoting a more integrated scene understanding. These findings highlight the inherent perceptual limitations in LVLMs and offer insight into more human-aligned visual reasoning systems.

cs.MM↗
arXiv2025

Minimum Width of Deep Narrow Networks for Universal Approximation

Determining the minimum width of fully connected neural networks has become a fundamental problem in recent theoretical studies of deep neural networks. In this paper, we study the lower bounds and upper bounds of the minimum width required for fully connected neural networks in order to have universal approximation capability, which is important in network design and training. We show that $w_{min}\leq\max(2d_x+1, d_y)$ also holds true for networks with ELU, SELU activation functions, and the upper bound of this inequality is attained when $d_y=2d_x$, where $d_x$, $d_y$ denote the input and output dimensions, respectively. Besides, we show that $d_x+1\leq w_{min}\leq d_x+d_y$ for networks with LeakyReLU, ELU, CELU, SELU, Softplus activation functions, by proving that ReLU activation function can be approximated by these activation functions. In addition, in the case that the activation function is injective or can be uniformly approximated by a sequence of injective functions (e.g., ReLU), we present a new proof of the inequality $w_{min}\ge d_y+\mathbf{1}_{d_x<d_y\leq2d_x}$ by constructing a more intuitive example via a new geometric approach based on Poincaré-Miranda Theorem.

cs.LG↗
arXiv2025

Sharp Spectral Gaps, Arithmetic Localization, and Reducibility via Resonance Analys

This paper establishes several sharp spectral results for analytic quasiperiodic Schrodinger operators. Key contributions include: (1) exact exponential decay rates for spectral gaps of the almost Mathieu operator, addressing a question raised by Goldstein; (2) sharp arithmetic Anderson localization for a class of quasiperiodic operators on higher-dimensional lattices, which in particular resolves and generalizes Jitomirskaya's phase transition conjecture; and (3) stratified growth patterns for extended eigenfunctions revealing universal partial hierarchical structures for subcritical quasiperiodic Schrodinger operators. The proofs are based on novel frameworks-structured quantitative almost reducibility and sharp quantitative duality-to overcome the longstanding challenge of taming infinitely many rotation-number resonances, which enables us to obtain optimal arithmetic reducibility results for analytic SL(2,R)-cocycles, thereby solving another Jitomirskaya's conjecture. These methods enable a first comprehensive treatment of resonance-driven dynamical asymptotics.

math.DS↗
arXiv2025

Lead-Free Europium Halide Perovskite Nanoplatelets

Metal halide perovskites possess desirable optical, material, and electrical properties which have had substantial impact on next-generation optoelectronics. However, given the toxicity of lead, alternative lead-free perovskite semiconductors are needed. By fully replacing lead with rare-earth elements, one can simultaneously address toxicity concerns and achieve comparable optoelectronic performance. Here, we demonstrate the synthesis of two-dimensional europium halide perovskite nanoplatelets governed by the formula $\mathrm{L_{2}EuX_{4}}$ where L is an organic ligand and X is a halide anion. The structure, morphology, and composition of the nanoplatelets are confirmed by XRD, AFM, and XPS. Deep blue-emitting $\mathrm{PEA_{2}EuBr_{4}}$ perovskite nanoplatelets are synthesized in both the solution- and solid-states with photoluminescence emission centered at 446 nm and CIE color coordinates of (0.1515, 0.0327) and (0.1515, 0.0342), respectively. Additionally, near-ultraviolet $\mathrm{PEA_{2}EuCl_{4}}$ perovskite nanoplatelets are synthesized in both the solution- and solid-states with photoluminescence emission centered at 400 nm and 401 nm, respectively. Overall, europium halide perovskite nanoplatelets offer a lead-free alternative for deep blue, violet, and near-ultraviolet light emission $\unicode{x2013}$ charting new pathways for optoelectronics in this energy regime.

cond-mat.mtrl-sci↗
arXiv2025

Domain Adaptation-based Edge Computing for Cross-Conditions Fault Diagnosis

Fault diagnosis of mechanical equipment provides robust support for industrial production. It is worth noting that, the operation of mechanical equipment is accompanied by changes in factors such as speed and load, leading to significant differences in data distribution, which pose challenges for fault diagnosis. Additionally, in terms of application deployment, commonly used cloud-based fault diagnosis methods often encounter issues such as time delays and data security concerns, while common fault diagnosis methods cannot be directly applied to edge computing devices. Therefore, conducting fault diagnosis under cross-operating conditions based on edge computing holds significant research value. This paper proposes a domain-adaptation-based lightweight fault diagnosis framework tailored for edge computing scenarios. Incorporating the local maximum mean discrepancy into knowledge transfer aligns the feature distributions of different domains in a high-dimensional feature space, to discover a common feature space across domains. The acquired fault diagnosis expertise from the cloud-based deep neural network model is transferred to the lightweight edge-based model (edge model) using adaptation knowledge transfer methods. It aims to achieve accurate fault diagnosis under cross-working conditions while ensuring real-time diagnosis capabilities. We utilized the NVIDIA Jetson Xavier NX kit as the edge computing platform and conducted validation experiments on two devices. In terms of diagnostic performance, the proposed method significantly improved diagnostic accuracy, with average increases of 34.44% and 17.33% compared to existing methods, respectively.

cs.DC↗
arXiv2025

Aspects of holographic Langevin diffusion in the presence of anisotropic magnetic field

We study the holographic Langevin diffusion coefficients of a heavy quark, when traveling through a strongly coupled anisotropic plasma in the presence of magnetic field~$\mathcal{B}$. In particular, we clarified how motion velocities shape momentum broadening and its directional dependence. It is observed that the transverse Langevin diffusion coefficients depend more on the direction of motion rather than the directions of momentum diffusion at the ultra-fast limit, while an opposite conclusion is found when the moving speed is sufficiently low. For the Longitudinal Langevin diffusion coefficient, we find that motion perpendicular to~$\mathcal{B}$ affects the Langevin coefficients stronger at any fixed velocity. We should also emphasize that all five Langevin coefficients are becoming larger with increasing velocity. We find that the universal relation~$κ^{\parallel}>κ^{\perp}$ in the isotropic background, is broken in a different new case that a quark moving paralleled to~$\mathcal{B}$. This is one more particular example where the violation of the universal relation occurs for the anisotropic background. Further, we find the critical velocity of the violation will become larger with increasing~$\mathcal{B}$.

hep-ph↗
arXiv2025

A Set of Generalized Components to Achieve Effective Poison-only Clean-label Backdoor Attacks with Collaborative Sample Selection and Triggers

Poison-only Clean-label Backdoor Attacks aim to covertly inject attacker-desired behavior into DNNs by merely poisoning the dataset without changing the labels. To effectively implant a backdoor, multiple \textbf{triggers} are proposed for various attack requirements of Attack Success Rate (ASR) and stealthiness. Additionally, sample selection enhances clean-label backdoor attacks' ASR by meticulously selecting ``hard'' samples instead of random samples to poison. Current methods 1) usually handle the sample selection and triggers in isolation, leading to severely limited improvements on both ASR and stealthiness. Consequently, attacks exhibit unsatisfactory performance on evaluation metrics when converted to PCBAs via a mere stacking of methods. Therefore, we seek to explore the bidirectional collaborative relations between the sample selection and triggers to address the above dilemma. 2) Since the strong specificity within triggers, the simple combination of sample selection and triggers fails to substantially enhance both evaluation metrics, with generalization preserved among various attacks. Therefore, we seek to propose a set of components to significantly improve both stealthiness and ASR based on the commonalities of attacks. Specifically, Component A ascertains two critical selection factors, and then makes them an appropriate combination based on the trigger scale to select more reasonable ``hard'' samples for improving ASR. Component B is proposed to select samples with similarities to relevant trigger implanted samples to promote stealthiness. Component C reassigns trigger poisoning intensity on RGB colors through distinct sensitivity of the human visual system to RGB for higher ASR, with stealthiness ensured by sample selection, including Component B. Furthermore, all components can be strategically integrated into diverse PCBAs.

cs.CR↗
arXiv2025

Symmetry-preserving random batch Ewald method for constant-potential simulation of electrochemical systems

Constant potential molecular dynamics simulation plays important role for applications of electrochemical systems, yet the calculation of charge fluctuation on electrodes remains a computational bottleneck. We propose a highly scalable, symmetry-preserving random batch Ewald (SRBE) algorithm to address this challenge. The SRBE algorithm deterministically computes the low-frequency components along the direction perpendicular to electrodes, while efficiently approximating the remaining components using random batch sampling. This approach simultaneously reduces charge and force fluctuations while satisfying the symmetry-preserving mean field condition in anisotropic systems with large aspect ratios. Numerical experiments on electrode/ionic liquid systems validate the high accuracy of the SRBE method in capturing dynamic charging processes and equilibrium electric double layer structures. The SRBE method achieves parallel efficiency improvements of up to two orders of magnitude compared with conventional FFT-based algorithms. These findings highlight its strong potential for enabling large-scale electrochemical simulations and its broad applicability to practical problems in the field.

physics.comp-ph↗
arXiv2025

Dipole condensates in synthetic rank-2 electric fields

Dipole condensates, formed from particle-hole pairs, represent a unique class of charge-neutral quantum fluids that evade conventional vector gauge fields, making their electrodynamic responses difficult to probe in natural materials. Here, we propose a tunable platform using strongly interacting two-component ultracold atoms to realize dipole condensates and probe their coupling to rank-2 electric fields. By applying spin-dependent forces and treating spin as a synthetic dimension, we engineer a synthetic rank-2 electric field that induces measurable electrodynamic responses. We identify the atomic analog of perfect Coulomb drag: increasing intercomponent interactions leads to equal and opposite displacements of the centers of mass of the two spin components. Furthermore, a rank-2 electric field imprints a phase twist in the dipole condensate and generates a supercurrent of dipoles that obeys the dipolar Josephson relation -- a smoking gun for dipole condensation. Our results establish a powerful platform for exploring dipolar superfluidity under tensor gauge fields.

cond-mat.quant-gas↗
arXiv2025

Log-Hausdorff multifractality of the absolutely continuous spectral measure of the almost Mathieu operator

This paper focuses on the fractal characteristics of the absolutely continuous spectral measure of the subcritical almost Mathieu operator (AMO) and Diophantine frequency. In particular, we give a complete description of the (classical) multifractal spectrum and a finer description in the logarithmic gauge. The proof combines continued$-$fraction$/$metric Diophantine techniques and refined covering arguments. These results rigorously substantiate (and quantify in a refined gauge) the physicists' intuition that the absolutely continuous component of the spectrum is dominated by energies with trivial scaling index, while also exhibiting nontrivial exceptional sets which are negligible for classical Hausdorff measure but large at the logarithmic scale.

math-ph↗
arXiv2025

Defending LVLMs Against Vision Attacks through Partial-Perception Supervision

Recent studies have raised significant concerns regarding the vulnerability of Large Vision Language Models (LVLMs) to maliciously injected or perturbed input images, which can mislead their responses. Existing defense methods show that such vision attacks are sensitive to image modifications especially cropping, using majority voting across responses of modified images as corrected responses. However, these modifications often result in partial images and distort the semantics, which reduces response quality on clean images after voting. Instead of directly using responses from partial images for voting, we investigate using them to supervise the LVLM's responses to the original images. We propose a black-box, training-free method called DPS (Defense through Partial-Perception Supervision). In this approach, the model is prompted using the responses generated by a model that perceives only a partial image. With DPS, the model can adjust its response based on partial image understanding when under attack, while confidently maintaining its original response for clean input. Our findings show that the weak model can supervise the strong model: when faced with an attacked input, the strong model becomes less confident and adjusts its response based on the weak model's partial understanding, effectively defending against the attack. With clean input, it confidently maintains its original response. Empirical experiments show our method outperforms the baseline, cutting the average attack success rate by 76.3% across six datasets on three popular models.

cs.CV↗
arXiv2025

Excitonic skin effect

We show that strong interactions combined with band-dependent imaginary vector potentials give rise to boundary localization of particle-hole pairs, which we term the excitonic skin effect. In a bilayer system with layer-specific gain/loss and an in-plane magnetic field, excitons experience a net imaginary vector potential, resulting in directional amplification of particle-hole pairs. Including nearest-neighbor interactions leads to a non-Hermitian bosonic Kitaev model, where the pairing effects grow exponentially with the size of the system, revealing a unique form of critical skin effect in interacting systems. Our framework applies to both atomic and electronic platforms and is directly testable in current experiments. These results also provide a route to explore non-Hermitian analogs of tensor gauge fields.

cond-mat.quant-gas↗