arXiv subjects

Bin Guo

Publications and source records attributed to Bin Guo.

At least 55 records · Page 3Linked to original sources

arXiv2026

Asymptotic behavior of the solution with positive temperature in nonlinear 3D thermoelasticity

In this paper, we study a hyperbolic-parabolic coupled system arising in nonlinear three-dimensional thermoelasticity. We establish the global well-posedness and asymptotic behavior of solutions. Our main result shows that, a thermoelastic body asymptotically converges to an equilibrium state with a uniform temperature distribution for every initial data, determined by energy conservation. The proof of the global well-posedness is divided into some steps. To begin with, we introduce an approximate problem and derive its solvability. Next, we establish a time-independent upper bound for the temperature via Moser iteration technique. Together with an estimate of gradient of entropy, we use a functional involving the Fisher information of the temperature, which enables us to handle a delicate Gronwall-type inequality, to obtain required estimates of the higher-order derivatives. Further, we prove the strict positivity of temperature by applying Moser iteration again on the negative part of the logarithm of the temperature, followed by a uniqueness argument for the weak solution. Finally, we define a dynamical system on a proper functional phase space and analyze the $ω$-limit set for every initial data. This work provides a complete proof of the global well-posedness and the long-time behavior in the nonlinear three-dimensional thermoelasticity system.

math.AP↗

arXiv2026

Detecting Fake Reviewer Groups in Dynamic Networks: An Adaptive Graph Learning Method

The proliferation of fake reviews, often produced by organized groups, undermines consumer trust and fair competition on online platforms. These groups employ sophisticated strategies that evade traditional detection methods, particularly in cold-start scenarios involving newly launched products with sparse data. To address this, we propose the \underline{D}iversity- and \underline{S}imilarity-aware \underline{D}ynamic \underline{G}raph \underline{A}ttention-enhanced \underline{G}raph \underline{C}onvolutional \underline{N}etwork (DS-DGA-GCN), a new graph learning model for detecting fake reviewer groups. DS-DGA-GCN achieves robust detection since it focuses on the joint relationships among products, reviews, and reviewers by modeling product-review-reviewer networks. DS-DGA-GCN also achieves adaptive detection by integrating a Network Feature Scoring (NFS) system and a new dynamic graph attention mechanism. The NFS system quantifies network attributes, including neighbor diversity, network self-similarity, as a unified feature score. The dynamic graph attention mechanism improves the adaptability and computational efficiency by captures features related to temporal information, node importance, and global network structure. Extensive experiments conducted on two real-world datasets derived from Amazon and Xiaohongshu demonstrate that DS-DGA-GCN significantly outperforms state-of-the-art baselines, achieving accuracies of up to \textbf{89.8\% and 88.3\%}, respectively.

cs.SI↗

arXiv2026

MeCo: Enhancing LLM-Empowered Multi-Robot Collaboration via Similar Task Memoization

Multi-robot systems have been widely deployed in real-world applications, providing significant improvements in efficiency and reductions in labor costs. However, most existing multi-robot collaboration methods rely on extensive task-specific training, which limits their adaptability to new or diverse scenarios. Recent research leverages the language understanding and reasoning capabilities of large language models (LLMs) to enable more flexible collaboration without specialized training. Yet, current LLM-empowered approaches remain inefficient: when confronted with identical or similar tasks, they must replan from scratch because they omit task-level similarities. To address this limitation, we propose MeCo, a similarity-aware multi-robot collaboration framework that applies the principle of ``cache and reuse'' (a.k.a., memoization) to reduce redundant computation. Unlike simple task repetition, identifying and reusing solutions for similar but not identical tasks is far more challenging, particularly in multi-robot settings. To this end, MeCo introduces a new similarity testing method that retrieves previously solved tasks with high relevance, enabling effective plan reuse without re-invoking LLMs. Furthermore, we present MeCoBench, the first benchmark designed to evaluate performance on similar-task collaboration scenarios. Experimental results show that MeCo substantially reduces planning costs and improves success rates compared with state-of-the-art approaches.

cs.RO↗

arXiv2026

Frequently hypercyclic meromorphic curves with slow growth

We construct entire curves in projective spaces that exhibit frequent hypercyclicity under translations along countably many prescribed directions while maintaining optimal slow growth rates. Furthermore, we establish a fundamental dichotomy by proving the impossibility of such curves simultaneously preserving frequent hypercyclicity for uncountably many directions under equivalent growth constraints. This result reveals a striking contrast with classical hypercyclicity phenomena, where entire functions can achieve hypercyclicity over some uncountable direction set without growth rate compromise. Our methodology is rooted in Nevanlinna theory and guided by the Oka principle, offering new insights into the relationship between dynamical properties and growth rates of entire curves in projective spaces.

math.CV↗

arXiv2026

Interference Propagation Analysis for Large-Scale Multi-RIS-Empowered Wireless Communications:An Epidemiological Perspective

Reconfigurable intelligent surfaces (RISs) have gained significant attention in recent years due to their ability to control the reflection of radio-frequency signals and reshape the wireless propagation environment. Unlike traditional studies that primarily focus on the advantages of RISs, this paper examines the negative impacts of RISs by investigating interference propagation caused by user mobility in downlink wireless systems. We employ a stochastic geometric model to simulate the locations of base stations and RISs using the Matérn hard core point process, while user locations are modeled with the homogeneous Poisson point process. We derive novel closed-form expressions for the power distributions of the received signal at the users and the interfering signal. Additionally, we present a novel expression for coverage probability and introduce the concept of interference propagation intensity. To characterize the dynamics of interference caused by user mobility, we adopt an epidemiological approach using the susceptible-infected-susceptible model. Finally, crucial factors influencing the propagation of interference are analyzed. Numerical results validate our theoretical analysis and provide suggestions for managing interference propagation in large-scale multi-RIS wireless communication networks.

cs.NI↗

arXiv2026

Trajectory-Aware Multi-RIS Activation and Configuration: A Riemannian Diffusion Method

Reconfigurable intelligent surfaces (RISs) offer a low-cost, energy-efficient means for enhancing wireless coverage. Yet, their inherently programmable reflections may unintentionally amplify interference, particularly in large-scale, multi-RIS-enabled mobile communication scenarios where dense user mobility and frequent line-of-sight overlaps can severely degrade the signal-to-interference-plus-noise ratio (SINR). To address this challenge, this paper presents a novel generative multi-RIS control framework that jointly optimizes the ON/OFF activation patterns of multiple RISs in the smart wireless environment and the phase configurations of the activated RISs based on predictions of multi-user trajectories and interference patterns. We specially design a long short-term memory (LSTM) artificial neural network, enriched with speed and heading features, to forecast multi-user trajectories, thereby enabling reconstruction of future channel state information. To overcome the highly nonconvex nature of the multi-RIS control problem, we develop a Riemannian diffusion model on the torus to generate geometry-consistent phase-configuration, where the reverse diffusion process is dynamically guided by reinforcement learning. We then rigorously derive the optimal ON/OFF states of the metasurfaces by comparing predicted achievable rates under RIS activation and deactivation conditions. Extensive simulations demonstrate that the proposed framework achieves up to 30\% SINR improvement over learning-based control and up to 44\% gain compared with the RIS always-on scheme, while consistently outperforming state-of-the-art baselines across different transmit powers, RIS configurations, and interference densities.

cs.NI↗

arXiv2026

Fundamental groups of compact Kahler varieties with nef anti canonical bundle

It is proved by M. Paun (1997, 2017) that the fundamental group of a compact Kahler manifold X is almost Abelian if the anti-canonical bundle -KX is nef. In this paper, we apply the recent geometric analytic theory of Kahler spaces developed by Guo-Phong-Song-Sturm to study fundamental groups of mildly singular compact Kahler varieties. We first extend Paun's result to log canonical pairs (X,Delta) with smooth X and nef -(KX+Delta) as well as to compact Kahler manifolds X with pseudo-effective -KX under a suitable assumption on the singularities of c1(-KX). We further prove that, for a 3-dimensional log canonical pair (X,Δ) with X being klt, pi 1(X) is almost Abelian if -(KX+Δ) is nef. Moreover, as one of the main ingredients for the proof of these results, we establish the surjectivity of the Albanese maps of compact normal complex varieties X in Fujiki class C that admits an effective R-divisor Δsuch that the pair (X,Δ) is log canonical with nef anti-log canonical divisor -(KX+Δ).This generalizes the corresponding theorems for projective varieties (Zhang, 2005), for klt pairs (Matsumura-Wang-Wu-Zhang, 2025) and for log smooth case (Fu-Han-Zou, 2025)

math.AG↗

arXiv2026

Quantum Avalanche Stability of Many-Body Localization with Power-Law Interactions

We investigate the stability of the many-body localized phase against quantum avalanche instabilities in a one-dimensional Heisenberg spin chain with long-range power-law interactions ($V\propto r^{-α}$). By combining exact diagonalization of static properties with Lindblad master equation simulations of open-system dynamics, we systematically map the interplay between interaction range and disorder strength. Our finite-size scaling analysis of entanglement entropy identifies a critical interaction exponent $α_c \approx 2$, which separates a fragile regime, characterized by an exponentially diverging critical disorder, from a robust short-range regime. To rigorously test the system's resistance to avalanches, we couple the boundary to an infinite-temperature bath and track the propagation of the thermalization front into the localized bulk. We find that the characteristic thermalization time follows a unified scaling law, $T_{r_{\text{th}}} \sim \exp[κ(α) LW]$ (herein, $L$ is the system size, and $W$ is the disorder intensity), which diverges exponentially with the product of system size and disorder strength. This suppression enables the derivation of a quantitative stability criterion, $W_{\text{stab}}(α)$, representing the minimum critical disorder strength required to maintain avalanche stability. Our results confirm that the MBL phase remains asymptotically stable in the thermodynamic limit when disorder exceeds an interaction-dependent threshold, bridging theoretical debates on long-range MBL and providing a roadmap for observing these dynamics in experimental platforms such as Rydberg atom arrays.

cond-mat.dis-nn↗

arXiv2026

Qualitative Behavior of Solutions to a Forced Nonlocal Thin-Film Equation

We study a one-dimensional nonlocal degenerate fourth-order parabolic equation with inhomogeneous forces relevant to hydraulic fracture modeling. Employing a regularization scheme, modified energy/entropy methods, and novel differential inequality techniques, we establish global existence and long-time behavior results for weak solutions under both time-and space-dependent and time-and space-independent inhomogeneous forces. Specifically, for the time-and space-dependent force $S(t, x)$, we prove that the solution converges to $\bar{u}_0+\frac{1}{|Ω|}\int_0^\infty \int_ΩS(r, x)\, dxdr $, where $\bar{u}_0=\frac{1}{|Ω|}\int_Ωu_{0}(x)\,dx$ is the spatial average of the initial data, and we provide bilateral estimates for the convergence rate. For the time-and space-independent force $S_0$, we show that the solution approaches the linear function $\bar{u}_0 + tS_0$ at an exponential rate.

math.AP↗

arXiv2026

Adaptive Multi-Stage Patent Claim Generation with Unified Quality Assessment

Current patent claim generation systems face three fundamental limitations: poor cross-jurisdictional generalization, inadequate semantic relationship modeling between claims and prior art, and unreliable quality assessment. We introduce a novel three-stage framework that addresses these challenges through relationship-aware similarity analysis, domain-adaptive claim generation, and unified quality assessment. Our approach employs multi-head attention with eight specialized heads for explicit relationship modeling, integrates curriculum learning with dynamic LoRA adapter selection across five patent domains, and implements cross-attention mechanisms between evaluation aspects for comprehensive quality assessment. Extensive experiments on USPTO HUPD dataset, EPO patent collections, and Patent-CE benchmark demonstrate substantial improvements: 7.6-point ROUGE-L gain over GPT-4o, 8.3\% BERTScore enhancement over Llama-3.1-8B, and 0.847 correlation with human experts compared to 0.623 for separate evaluation models. Our method maintains 89.4\% cross-jurisdictional performance retention versus 76.2\% for baselines, establishing a comprehensive solution for automated patent prosecution workflows.

cs.CL↗

arXiv2026

A relativistic quantum Euler-Poisson system derived from the Klein-Gordon-Poisson equation: hyperbolic-elliptic structure

In the Klein-Gordon equation, quantum and relativistic parameters are strongly coupled, which poses significant analytical challenges in the derivation and analysis of related classical fluid models. In this paper, starting from the Klein-Gordon-Poisson system, we formally derive a relativistic quantum hydrodynamic (RQHD) system via the Madelung transformation, in which the relativistic and quantum correction terms in the Euler-Poisson framework are clearly exhibited. In particular, at a formal level, the RQHD system reduces to the relativistic hydrodynamics system in the semiclassical regime and to the quantum hydrodynamics system in the non-relativistic regime. These limiting procedures highlight the unified structure of the proposed model and clarify the role played by the coupled relativistic and quantum effects. From an analytical point of view, by reformulating the RQHD system as a coupled hyperbolic-elliptic system with a nonlocal Poisson interaction, we establish the local-in-time existence and uniqueness of classical solutions to the associated Cauchy problem. The initial density is assumed to be a small perturbation of a positive constant state, while the remaining initial data are taken to be general smooth functions. The analysis relies on energy estimates and suitable estimates for the nonlocal terms, and provides a rigorous well-posedness result in the natural energy space.

math.AP↗

arXiv2026

Asymptotic Stability and the Forcing Term: An Analysis of Non-Newtonian Thin-Film Flows

We study a class of fourth-order quasilinear degenerate parabolic equations under both time-and space-dependent and time-and space-independent forces, modeling non-Newtonian thin-film flow over a solid surface in the "complete wetting" regime. By analyzing the quantitative properties of solutions to non-autonomous differential inequalities and employing refined integral estimates, we derive two-sided convergence rate estimates for the solution. Numerical simulations are further provided to illustrate the consistency of our main results with the observed physical phenomena.

math.AP↗

arXiv2026

Irregular Diffusions and Loss of Regularity in Polyconvex Gradient Flows

We investigate diffusion-type partial differential equations that are irregular in the sense that they admit weak solutions which are nowhere smooth, even for prescribed smooth data. By reformulating these equations as first-order partial differential relations and adapting the method of convex integration, we develop a construction scheme based on new geometric structures, referred to as $\mathcal{T}_N$-configurations, together with a simplified structural hypothesis on the diffusion functions, termed Condition $O_N$. Under this condition, we show that the associated initial and boundary value problems with certain smooth initial-boundary data admit infinitely many Lipschitz weak solutions that are nowhere $C^1$. We further analyze specific $\mathcal{T}_N$-configurations and establish nondegeneracy conditions that are essential for verifying Condition $O_N$. As an application, we construct examples of strongly polyconvex energy functionals whose gradient flows generate irregular diffusion equations, thereby revealing a failure of regularity and uniqueness even within the class of polyconvex gradient flows.

math.AP↗

arXiv2025

AG-MPBS: a Mobility-Aware Prediction and Behavior-Based Scheduling Framework for Air-Ground Unmanned Systems

As unmanned systems such as Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) become increasingly important to applications like urban sensing and emergency response, efficiently recruiting these autonomous devices to perform time-sensitive tasks has become a critical challenge. This paper presents MPBS (Mobility-aware Prediction and Behavior-based Scheduling), a scalable task recruitment framework that treats each device as a recruitable "user". MPBS integrates three key modules: a behavior-aware KNN classifier, a time-varying Markov prediction model for forecasting device mobility, and a dynamic priority scheduling mechanism that considers task urgency and base station performance. By combining behavioral classification with spatiotemporal prediction, MPBS adaptively assigns tasks to the most suitable devices in real time. Experimental evaluations on the real-world GeoLife dataset show that MPBS significantly improves task completion efficiency and resource utilization. The proposed framework offers a predictive, behavior-aware solution for intelligent and collaborative scheduling in unmanned systems.

cs.RO↗

arXiv2025

Tri-Select: A Multi-Stage Visual Data Selection Framework for Mobile Visual Crowdsensing

Mobile visual crowdsensing enables large-scale, fine-grained environmental monitoring through the collection of images from distributed mobile devices. However, the resulting data is often redundant and heterogeneous due to overlapping acquisition perspectives, varying resolutions, and diverse user behaviors. To address these challenges, this paper proposes Tri-Select, a multi-stage visual data selection framework that efficiently filters redundant and low-quality images. Tri-Select operates in three stages: (1) metadata-based filtering to discard irrelevant samples; (2) spatial similarity-based spectral clustering to organize candidate images; and (3) a visual-feature-guided selection based on maximum independent set search to retain high-quality, representative images. Experiments on real-world and public datasets demonstrate that Tri-Select improves both selection efficiency and dataset quality, making it well-suited for scalable crowdsensing applications.

cs.RO↗

arXiv2025

Unified Bulk-Entanglement Correspondence in Non-Hermitian Systems

The non-Hermitian skin effect (NHSE) fundamentally invalidates the conventional bulk-boundary correspondence (BBC), leading topological diagnostics into a crisis. While the non-Bloch polarization $P_β$ defined on the generalized Brillouin zone restores momentum-space topology, a direct, robust real-space bulk probe has remained elusive. We resolve this by establishing a universal correspondence between $P_β$ and the entanglement polarization $χ$ of the biorthogonal ground state. Introducing a quasi-reciprocal Hamiltonian $\tilde{H}$ that removes the NHSE while preserving bulk topology, we rigorously prove the fundamental identity $P_β \equiv χ(\tilde{H})\pmod 1$ in the thermodynamic limit under the quasi-locality assumption. Crucially, we demonstrate that this equivalence transcends the locality constraints that limit traditional topological invariants. While the conventional Resta polarization fails when $\tilde{H}$ becomes non-local due to the divergence of position variance, we reveal that $χ(\tilde{H})$ remains robustly quantized, protected by the Fredholm index of Toeplitz operators. Our work thus identifies entanglement as the unique real-space diagnostic capable of capturing non-Bloch topology beyond the breakdown of locality, successfully restoring the BBC across diverse non-Hermitian systems such as line-gap, point-gap, and gapless phases, thereby unifying the geometric and entanglement paradigms in non-Hermitian physics.

quant-ph↗

arXiv2025

Geometric invariants and the Monge-Ampere equation in Kähler geometry

This is a contribution to the special issue of Surveys in Differential Geometry celebrating the 75th birthday of Shing-Tung Yau. The bulk of the paper is devoted to a survey of some new geometric inequalities and estimates for the Monge-Ampere equation, obtained by the authors in the last few years in joint work with F. Tong, J. Song, and J. Sturm. These all depend in an essential way on Yau's solution of the Calabi conjecture, which is itself nearing its own 50th birthday. The opportunity is also taken to survey briefly many current directions in complex geometry, which he more recently pioneered.

math.DG↗

arXiv2025

VIL2C: Value-of-Information Aware Low-Latency Communication for Multi-Agent Reinforcement Learning

Inter-agent communication serves as an effective mechanism for enhancing performance in collaborative multi-agent reinforcement learning(MARL) systems. However, the inherent communication latency in practical systems induces both action decision delays and outdated information sharing, impeding MARL performance gains, particularly in time-critical applications like autonomous driving. In this work, we propose a Value-of-Information aware Low-latency Communication(VIL2C) scheme that proactively adjusts the latency distribution to mitigate its effects in MARL systems. Specifically, we define a Value of Information (VOI) metric to quantify the importance of delayed message transmission based on each delayed message's importance. Moreover, we propose a progressive message reception mechanism to adaptively adjust the reception duration based on received messages. We derive the optimized VoI aware resource allocation and theoretically prove the performance advantage of the proposed VIL2C scheme. Extensive experiments demonstrate that VIL2C outperforms existing approaches under various communication conditions. These gains are attributed to the low-latency transmission of high-VoI messages via resource allocation and the elimination of unnecessary waiting periods via adaptive reception duration.

cs.MA↗