arXiv subjects

Guojun Huang

Publications and source records attributed to Guojun Huang.

13 recordsLinked to original sources

arXiv2026

Modeling Epidemic Dynamics of Mutant Strains with Evolutionary Game-based Vaccination Behavior

The outbreak of mutant strains and vaccination behaviors have been the focus of recent epidemiological research, but most existing epidemic models failed to simultaneously capture viral mutation and consider the complexity and behavioral dynamics of vaccination. To address this gap, we develop an extended SIRS model that distinguishes infections with the original strain and a mutant strain, and explicitly introduces a vaccinated compartment state. At the behavioral level, we employ evolutionary game theory to model individual vaccination decisions, where strategies are determined by both neighbors' choices and the current epidemiological situation. This process corresponds to the time-varying vaccination rate of susceptible individuals transitioning to vaccinated individuals at the epidemic spreading level. We then couple the epidemic and vaccination behavioral processes through the microscopic Markov chain approach (MMCA) and finally investigate the evolutionary dynamics via numerical simulations. The results show that our framework can effectively mitigate outbreaks across different disease scenarios. Sensitivity analysis further reveals that vaccination uptake is most strongly influenced by vaccine cost, efficacy, and perceived risk of side effects. Overall, this behavior-aware modeling framework captures the co-evolution of viral mutation and vaccination behavior, providing quantitative and theoretical support for designing effective public health vaccination policies.

q-bio.PE↗

arXiv2025

A Review of Machine Learning for Cavitation Intensity Recognition in Complex Industrial Systems

Cavitation intensity recognition (CIR) is a critical technology for detecting and evaluating cavitation phenomena in hydraulic machinery, with significant implications for operational safety, performance optimization, and maintenance cost reduction in complex industrial systems. Despite substantial research progress, a comprehensive review that systematically traces the development trajectory and provides explicit guidance for future research is still lacking. To bridge this gap, this paper presents a thorough review and analysis of hundreds of publications on intelligent CIR across various types of mechanical equipment from 2002 to 2025, summarizing its technological evolution and offering insights for future development. The early stages are dominated by traditional machine learning approaches that relied on manually engineered features under the guidance of domain expert knowledge. The advent of deep learning has driven the development of end-to-end models capable of automatically extracting features from multi-source signals, thereby significantly improving recognition performance and robustness. Recently, physical informed diagnostic models have been proposed to embed domain knowledge into deep learning models, which can enhance interpretability and cross-condition generalization. In the future, transfer learning, multi-modal fusion, lightweight network architectures, and the deployment of industrial agents are expected to propel CIR technology into a new stage, addressing challenges in multi-source data acquisition, standardized evaluation, and industrial implementation. The paper aims to systematically outline the evolution of CIR technology and highlight the emerging trend of integrating deep learning with physical knowledge. This provides a significant reference for researchers and practitioners in the field of intelligent cavitation diagnosis in complex industrial systems.

eess.SP↗

arXiv2025

The neural networks with tensor weights and emergent fermionic Wick rules in the large-width limit

In this paper, we study complex-valued neural network (CVNNs) with tensor-valued hidden-to-output weights within the framework of neural-network quantum field theory (NN-QFT). For standard CVNNs with scalar weights, we derive the generating functional and identify the exact Gaussian process that arises in the infinite-width limit, together with its associated effective quantum state. When the last-layer weights are promoted to Clifford-algebra-valued tensors, the network output becomes complex matrix-valued, and a fermion-like sign structure in the large-width correlation functions of the network output is induced. We show that, in the infinite-width limit, correlators with equal numbers of $f^{\dag}$ and $f$ obey fermionic Wick rules and can be written as determinants built from a scalar Euclidean kernel $S(x,y)=\langle f^{\dag}(x)f(y)\rangle$. This provides a sign-structured extension of NN-QFT at the level of Euclidean correlators and Feynman rules, even though a microscopic Grassmann path integral representation for the network parameters has not yet been constructed. Our analysis thus pushes the NN-QFT correspondence beyond purely bosonic Gaussian fields and suggests a possible route to encoding fermion-like symmetries in neural architectures for QFT correspondence.

hep-th↗

arXiv2025

Stacked Intelligent Metasurfaces for Multi-Modal Semantic Communications

Semantic communication (SemCom) powered by generative artificial intelligence enables highly efficient and reliable information transmission. However, it still necessitates the transmission of substantial amounts of data when dealing with complex scene information. In contrast, the stacked intelligent metasurface (SIM), leveraging wave-domain computing, provides a cost-effective solution for directly imaging complex scenes. Building on this concept, we propose an innovative SIM-aided multi-modal SemCom system. Specifically, an SIM is positioned in front of the transmit antenna for transmitting visual semantic information of complex scenes via imaging on the uniform planar array at the receiver. Furthermore, the simple scene description that contains textual semantic information is transmitted via amplitude-phase modulation over electromagnetic waves. To simultaneously transmit multi-modal information, we optimize the amplitude and phase of meta-atoms in the SIM using a customized gradient descent algorithm. The optimization aims to gradually minimize the mean squared error between the normalized energy distribution on the receiver array and the desired pattern corresponding to the visual semantic information. By combining the textual and visual semantic information, a conditional generative adversarial network is used to recover the complex scene accurately. Extensive numerical results verify the effectiveness of the proposed multi-modal SemCom system in reducing bandwidth overhead as well as the capability of the SIM for imaging the complex scene.

cs.IT↗

arXiv2024

A Novel End-To-End Event Geolocation Method Leveraging Hyperbolic Space and Toponym Hierarchies

Timely detection and geolocation of events based on social data can provide critical information for applications such as crisis response and resource allocation. However, most existing methods are greatly affected by event detection errors, leading to insufficient geolocation accuracy. To this end, this paper proposes a novel end-to-end event geolocation method (GTOP) leveraging Hyperbolic space and toponym hierarchies. Specifically, the proposed method contains one event detection module and one geolocation module. The event detection module constructs a heterogeneous information networks based on social data, and then constructs a homogeneous message graph and combines it with the text and time feature of the message to learning initial features of nodes. Node features are updated in Hyperbolic space and then fed into a classifier for event detection. To reduce the geolocation error, this paper proposes a noise toponym filtering algorithm (HIST) based on the hierarchical structure of toponyms. HIST analyzes the hierarchical structure of toponyms mentioned in the event cluster, taking the highly frequent city-level locations as the coarse-grained locations for events. By comparing the hierarchical structure of the toponyms within the cluster against those of the coarse-grained locations of events, HIST filters out noisy toponyms. To further improve the geolocation accuracy, we propose a fine-grained pseudo toponyms generation algorithm (FIT) based on the output of HIST, and combine generated pseudo toponyms with filtered toponyms to locate events based on the geographic center points of the combined toponyms. Extensive experiments are conducted on the Chinese dataset constructed in this paper and another public English dataset. The experimental results show that the proposed method is superior to the state-of-the-art baselines.

cs.CL↗

arXiv2024

Stacked Intelligent Metasurfaces for Task-Oriented Semantic Communications

Semantic communication (SemCom) leveraging advanced deep learning (DL) technologies enhances the efficiency and reliability of information transmission. Emerging stacked intelligent metasurface (SIM) with an electromagnetic neural network (EMNN) architecture enables complex computations at the speed of light. In this letter, we introduce an innovative SIM-aided SemCom system for image recognition tasks, where a SIM is positioned in front of the transmitting antenna. In contrast to conventional communication systems that transmit modulated signals carrying the image information or compressed semantic information, the carrier EM wave is directly transmitted from the source. The input layer of the SIM performs source encoding, while the remaining multi-layer architecture constitutes an EMNN for semantic encoding, transforming signals into a unique beam towards a receiving antenna corresponding to the image class. Remarkably, both the source and semantic encoding occur naturally as the EM waves propagate through the SIM. At the receiver, the image is recognized by probing the received signal magnitude across the receiving array. To this end, we utilize an efficient mini-batch gradient descent algorithm to train the transmission coefficients of SIM's meta-atoms to learn the semantic representation of the image. Extensive numerical results verify the effectiveness of utilizing the SIM-based EMNN for image recognition task-oriented SemComs, achieving more than 90\% recognition accuracy.

cs.IT↗

arXiv2024

Rotation induced color confinement

The rotation effect on the QCD properties is an open question. We study the dynamic gluon mass in a dense QCD matter, the rotation is introduced by taking a covariant transformation between the flat and curved spaces. The law of causality which restricts the rotation strength of the system is carefully considered in the calculation. we find that the rotation effect is not monotonous. Overall, it behaves like an anti-screening effect, reflecting in the decreasing gluon mass, but the strength changes with the rotation. For a QCD matter with low baryon density, the screening effect in the flat space can be completely canceled by the rotation, and gluons are confined in a strongly rotating matter. When the rotation is extremely high, the matter approaches to a weakly interacting gas.

hep-ph↗

arXiv2023

Resonant screening in dense and magnetized QCD matter

We calculate the Debye screening mass in thermal, dense and magnetized QCD matter in the frame of resummed perturbation theory. In the limit of zero temperature, when the Landau energy level and Fermi surface of quarks match each other $μ_q^2=2n|qB|$, where $q$, $μ_q$ and $B$ are respectively the quark electric charge, chemical potential and external magnetic field, the screening mass diverges and the system is in the state of weakly interacting parton gas, which is very different from the known result of strongly interacting quark-gluon plasma at high temperature. The divergence disappears in thermal medium, but the screening mass oscillates with clear peaks at the matched magnetic field.

hep-ph↗

arXiv2023

Quantum color screening in external magnetic field

We calculate color screening mass in a thermalized and magnetized QCD matter in the frame of loop resummation theory without restriction to the magnetic field strength. Our full calculation covers the often used approximations for weak magnetic field at high temperature and strong magnetic field at low temperature. We find that while the magnetic field created in heavy ion collisions at RHIC and LHC energies is probably the strongest one in nature, its effect on the QCD matter is still weaker in comparison with the high temperature of the fireball, and therefore can safely be treated as a perturbation.

hep-ph↗

arXiv2022

Heavy-quark potential in Gribov-Zwanziger approach around deconfinement phase transition

The interaction potential between a pair of heavy quarks is calculated with resummed perturbation method in Gribov-Zwanziger approach at finite temperature. The resummed loop correction makes the potential complex. While the real part is, as expected, screened and becomes short-ranged in hot medium, the strength of the imaginary part increases with temperature and is comparable with the real part, which is very different from the previous calculation in HTL approach. This means that, both the color screening and Landau damping play important role in the dissociation of heavy flavor hadrons in hot medium.

hep-ph↗

arXiv2021

Treating divergence in quark matter by using energy projectors

We calculate gluon self-energy using quark energy projectors in a general quark-gluon plasma. By separating the quark field into a positive- and a negative-energy mode, the quark loop constructed with the same mode is always convergent, and the divergence appears only in the mixed loop with different modes and is medium independent. After removing the divergence in vacuum, we obtain the one-loop gluon self-energy at finite temperature, chemical potential and quarks mass without approximation. With the method of quark loop resummation, we calculate non-perturbatively the gluon Debye mass and thermodynamic potential. In the limit of small gluon momentum in comparison with temperature, chemical potential and quark mass, our calculation comes back to the known HTL/HDL results in literature.

hep-ph↗

arXiv2021

Pair structure of heavy tetraquark systems

We study the pair description of heavy tetraquark systems $|QQ\bar Q \bar Q\rangle$ in the frame of a non-relativistic potential model. By taking the two heavy quark pairs $(Q\bar Q)$ as colored clusters, the four-quark Schrödinger equation is reduced to a two-pair equation, when the inner motion inside the pairs can be neglected. Taking into account all the Casimir scaling potentials between two quarks and using the lattice QCD simulated mixing angle between the two color-singlet states for the tetraquark system, we extracted a detailed pair potential between the two heavy quark pairs.

hep-ph↗

arXiv2020

Quark-quark-gluon vertex for heavy quarks up to order 1/m^5

Instead of the often used Foldy-Wouthuysen-Tani (FWT) transformation in non-relativistic quantum chromodynamics (NRQCD), we take a more general relation between the relativistic and non-relativistic on-shell spinors to recalculate the quark-quark-gluon vertex for heavy quarks. In comparison with the previous result using FWT, the recalculated coefficients in the NRQCD Lagrangian are different at order $1/m^3$ and new at order $1/m^4$ and $1/m^5$, where $m$ is the heavy quark mass.

hep-ph↗