arXiv subjects

Xiaomin Chen

Publications and source records attributed to Xiaomin Chen.

At least 19 recordsLinked to original sources

arXiv2026

The Role of Preceding CMEs and SIRs in Enhancing Shock Acceleration of Electrons

The role of large-scale pre-existing interplanetary structures, including preceding coronal mass ejections (CMEs) and stream interaction regions (SIRs), in shaping the shock acceleration environment for energetic electrons remains not fully understood. In this study, we investigate nine interplanetary shocks observed by the Solar Terrestrial Relations Observatory (STEREO) that are associated with significant MeV electron enhancements, as such enhancements are rarely observed at interplanetary shocks. We combine remote-sensing observations, drag-based modeling, and in-situ measurements to analyze the shock propagation through pre-existing interplanetary structures. Eight of the nine events are associated with a preceding slow or intermediate-speed CME, while six shocks are in-situ observed propagating within preceding ICMEs, indicating that large-scale upstream trapping structures are a common feature of these events. Further analysis identifies three distinct scenarios associated with enhanced electron acceleration: shocks propagating through preceding ICMEs, shock-SIR interactions, and direct injection of flare-accelerated electrons into SIRs. As a representative shock-in-ICME event, the 2012 January 29 low-$β$, quasi-perpendicular shock ($θ_{Bn}\sim87^\circ$) is further investigated using observations together with one-dimensional Monte Carlo test-particle simulations of a shock propagating into a large-scale upstream magnetic loop. The simulation suggests that the upstream loop prolongs electron residence near the shock and substantially enhances acceleration efficiency. These results demonstrate that large-scale interplanetary structures can precondition the upstream magnetic environment, providing favorable conditions for prolonged electron residence and efficient shock acceleration.

astro-ph.SR↗

arXiv2026

From Traditional Automation to Embodied Wireless Intelligence: Vision-Language-Action Empowered Physics-Aware Communication Networks

Wireless network automation has progressed from rule-based self-organising networks (SON) to data-driven optimisation, yet existing systems remain fundamentally disembodied. They act on performance indicators without perceiving the physical environment that governs radio propagation. We propose the embodied intelligent empowered base station (eBS), a paradigm that adopts a Vision-Language-Action (VLA) pipeline to transform base stations into autonomous AI agents capable of situated perception, causal physical reasoning, and physics-aware action generation. The eBS employs a two-tier asynchronous architecture: a Semantic Planner powered by a frontier Vision-Language Model (VLM) generates structured action directives on human timescales, whilst a Tactical Controller executes real-time adaptation. Case studies demonstrate that a single VLA pipeline, without task-specific training, can perform zero-shot material reasoning, generalise across viewpoints, and predict dynamic events before signal degradation occurs. These results illustrate a paradigm shift from traditional rule-following network automation to embodied-intelligence-empowered future wireless networks.

cs.NI↗

arXiv2026

BioLLMAgent: A Hybrid Framework with Enhanced Structural Interpretability for Simulating Human Decision-Making in Computational Psychiatry

Computational psychiatry faces a fundamental trade-off: traditional reinforcement learning (RL) models offer interpretability but lack behavioral realism, while large language model (LLM) agents generate realistic behaviors but lack structural interpretability. We introduce BioLLMAgent, a novel hybrid framework that combines validated cognitive models with the generative capabilities of LLMs. The framework comprises three core components: (i) an Internal RL Engine for experience-driven value learning; (ii) an External LLM Shell for high-level cognitive strategies and therapeutic interventions; and (iii) a Decision Fusion Mechanism for integrating components via weighted utility. Comprehensive experiments on the Iowa Gambling Task (IGT) across six clinical and healthy datasets demonstrate that BioLLMAgent accurately reproduces human behavioral patterns while maintaining excellent parameter identifiability (correlations $>0.67$). Furthermore, the framework successfully simulates cognitive behavioral therapy (CBT) principles and reveals, through multi-agent dynamics, that community-wide educational interventions may outperform individual treatments. Validated across reward-punishment learning and temporal discounting tasks, BioLLMAgent provides a structurally interpretable "computational sandbox" for testing mechanistic hypotheses and intervention strategies in psychiatric research.

cs.AI↗

arXiv2026

Agentic AI Empowered Intent-Based Networking for 6G

The transition towards sixth-generation (6G) wireless networks necessitates autonomous orchestration mechanisms capable of translating high-level operational intents into executable network configurations. Existing approaches to Intent-Based Networking (IBN) rely upon either rule-based systems that struggle with linguistic variation or end-to-end neural models that lack interpretability and fail to enforce operational constraints. This paper presents a hierarchical multi-agent framework where Large Language Model (LLM) based agents autonomously decompose natural language intents, consult domain-specific specialists, and synthesise technically feasible network slice configurations through iterative reasoning-action (ReAct) cycles. The proposed architecture employs an orchestrator agent coordinating two specialist agents, i.e., Radio Access Network (RAN) and Core Network agents, via ReAct-style reasoning, grounded in structured network state representations. Experimental evaluation across diverse benchmark scenarios shows that the proposed system outperforms rule-based systems and direct LLM prompting, with architectural principles applicable to Open RAN (O-RAN) deployments. The results also demonstrate that whilst contemporary LLMs possess general telecommunications knowledge, network automation requires careful prompt engineering to encode context-dependent decision thresholds, advancing autonomous orchestration capabilities for next-generation wireless systems.

cs.AI↗

arXiv2025

Local Particle Acceleration in an ICME-in-Sheath Structure Observed by Solar Orbiter

Local particle acceleration in the shock sheath region formed during the interaction between multiple coronal mass ejections (CMEs) is a complicated process that is still under investigation. On March 23, 2024, the successive eruption of two magnetic flux ropes (MFRs) from the solar active region 3614 produced twin CMEs, as identified in coronagraph images. By analyzing in-situ data from Solar Orbiter and Wind, it is found that the primary ICME-driven shock overtook the preceding ICME, trapping it in the sheath between the shock and the primary ICME, forming the ICME-in-sheath (IIS) structure. Using Solar Orbiter observations, we show that both electrons and ions are accelerated within the IIS. A clear enhancement of suprathermal electrons was observed at the IIS boundary, where strong flow shear and large magnetic field variation suggest possible local electron acceleration. Electrons (>38 keV) exhibit a long-lasting enhancement in the IIS with a spectral index of ~2.2, similar to that in the shock sheath and the primary ICME, indicating a similar solar origin. Inside both the sheath and IIS, spectra of proton and 4He are generally consistent with the prediction of the diffusive shock acceleration, whereas Fe and O present a double power-law shape. Additionally, the Fe/O ratio in the IIS is higher than that in the sheath, and more close to the abundance of the flare-related particles, suggesting the remnant particles of flare confined in the IIS.

astro-ph.SR↗

arXiv2024

Act Like a Radiologist: Radiology Report Generation across Anatomical Regions

Automating radiology report generation can ease the reporting workload for radiologists. However, existing works focus mainly on the chest area due to the limited availability of public datasets for other regions. Besides, they often rely on naive data-driven approaches, e.g., a basic encoder-decoder framework with captioning loss, which limits their ability to recognise complex patterns across diverse anatomical regions. To address these issues, we propose X-RGen, a radiologist-minded report generation framework across six anatomical regions. In X-RGen, we seek to mimic the behaviour of human radiologists, breaking them down into four principal phases: 1) initial observation, 2) cross-region analysis, 3) medical interpretation, and 4) report formation. Firstly, we adopt an image encoder for feature extraction, akin to a radiologist's preliminary review. Secondly, we enhance the recognition capacity of the image encoder by analysing images and reports across various regions, mimicking how radiologists gain their experience and improve their professional ability from past cases. Thirdly, just as radiologists apply their expertise to interpret radiology images, we introduce radiological knowledge of multiple anatomical regions to further analyse the features from a clinical perspective. Lastly, we generate reports based on the medical-aware features using a typical auto-regressive text decoder. Both natural language generation (NLG) and clinical efficacy metrics show the effectiveness of X-RGen on six X-ray datasets. Our code and checkpoints are available at: https://github.com/YtongXie/X-RGen.

cs.CV↗

arXiv2024

Three-stage Acceleration of Solar Energetic Particles Detected by Parker Solar Probe

Coronal mass ejections (CMEs) drive powerful shocks and thereby accelerate solar energetic particles (SEPs) as they propagate from the corona into interplanetary space. Here we present the processes of three-stage particle acceleration by a CME-driven shock detected by the in situ spacecraft--Parker Solar Probe (PSP) on 2022 August 27. The onset of SEPs is produced by a fast CME with a speed of 1284 km/s when it propagates to $\sim$2.85 Rs. The second stage of particle acceleration occurs when the fast CME catches up and interacts with a preceding slow one in interplanetary space at $\sim$40 Rs ($\sim$0.19 au). The CME interaction is accompanied by an intense interplanetary type II radio enhancement. Such direct measurement of particle acceleration during interplanetary CME interaction/radio enhancement is rarely recorded in previous studies. The third stage of energetic storm particles is associated with the CME-driven shock passage of the PSP at $\sim$0.38 au. Obviously, harder particle spectra are found in the latter two stages than the first one, which can arise from a stronger shock produced by the CME interaction and the enriched seed particles inside the preceding CME.

astro-ph.SR↗

arXiv2022

Compact gradient Einstein-type manifolds with boundary and constant scalar curvature

Inspired by the study of $V$-static manifold about classification, in this article, we apply the recent results obtained by Freitas and Gomes (Compact gradient Einstein-type manifolds with boundary, 2022) to prove the rigidity results for compact gradient Einstein-type manifolds with nonempty boundary and constant scalar curvature under some suitable pinching conditions. As a special case of gradient Einstein-type manifold, we also give a rigidity result of $(m,ρ)$-quasi-Einstein manifold with boundary.

math.DG↗

arXiv2021

Geometry-Based Stochastic Line-of-Sight Probability Model for A2G Channels under Urban Scenarios

Line-of-sight (LoS) path is essential for the reliability of air-to-ground (A2G) communications, but the existence of LoS path is difficult to predict due to random obstacles on the ground. Based on the statistical geographic information and Fresnel clearance zone, a general stochastic LoS probability model for three-dimensional (3D) A2G channels under urban scenarios is developed. By considering the factors, i.e., building height distribution, building width, building space, carrier frequency, and transceiver's heights, the proposed model is suitable for different frequencies and altitudes. Moreover, in order to get a closed-form expression and reduce the computational complexity, an approximate parametric model is also built with the machine-learning (ML) method to estimate model parameters. The simulation results show that the proposed model has good consistency with existing models at the low altitude. When the altitude increases, it has better performance by comparing with that of the ray-tracing Monte-Carlo simulation data. The analytical results of proposed model are helpful for the channel modeling and performance analysis such as cell coverage, outage probability, and bit error rate in A2G communications.

eess.SP↗

arXiv2021

Map-based Channel Modeling and Generation for U2V mmWave Communication

Unmanned aerial vehicle (UAV) aided millimeter wave (mmWave) technologies have a promising prospect in the future communication networks. By considering the factors of three-dimensional (3D) scattering space, 3D trajectory, and 3D antenna array, a non-stationary channel model for UAV-to-vehicle (U2V) mmWave communications is proposed. The computation and generation methods of channel parameters including interpath and intra-path are analyzed in detail. The inter-path parameters are calculated in a deterministic way, while the parameters of intra-path rays are generated in a stochastic way. The statistical properties are obtained by using a Gaussian mixture model (GMM) on the massive ray tracing (RT) data. Then, a modified method of equal areas (MMEA) is developed to generate the random intra-path variables. Meanwhile, to reduce the complexity of RT method, the 3D propagation space is reconstructed based on the user-defined digital map. The simulated and analyzed results show that the proposed model and generation method can reproduce non-stationary U2V channels in accord with U2V scenarios. The generated statistical properties are consistent with the theoretical and measured ones as well.

eess.SP↗

arXiv2021

Private and Utility Enhanced Recommendations with Local Differential Privacy and Gaussian Mixture Model

Recommendation systems rely heavily on users behavioural and preferential data (e.g. ratings, likes) to produce accurate recommendations. However, users experience privacy concerns due to unethical data aggregation and analytical practices carried out by the Service Providers (SP). Local differential privacy (LDP) based perturbation mechanisms add noise to users data at user side before sending it to the SP. The SP then uses the perturbed data to perform recommendations. Although LDP protects the privacy of users from SP, it causes a substantial decline in predictive accuracy. To address this issue, we propose an LDP-based Matrix Factorization (MF) with a Gaussian Mixture Model (MoG). The LDP perturbation mechanism, Bounded Laplace (BLP), regulates the effect of noise by confining the perturbed ratings to a predetermined domain. We derive a sufficient condition of the scale parameter for BLP to satisfy $ε$ LDP. At the SP, The MoG model estimates the noise added to perturbed ratings and the MF algorithm predicts missing ratings. Our proposed LDP based recommendation system improves the recommendation accuracy without violating LDP principles. The empirical evaluations carried out on three real world datasets, i.e., Movielens, Libimseti and Jester, demonstrate that our method offers a substantial increase in predictive accuracy under strong privacy guarantee.

cs.LG↗

arXiv2020

Effects of Digital Map on the RT-based Channel Model for UAV mmWave Communications

Based on the geometry and ray tracing (RT) theory, a millimeter wave (mmWave) channel model and parameter computation method for unmanned aerial vehicle (UAV) assisted air-to-ground (A2G) communications are proposed in this paper. In order to speed up the parameter calculation, a reconstruction process of scene database on the original digital map is developed. Moreover, the effects of reconstruction accuracy on the channel parameter and characteristic are analyzed by extensive simulations at 28 GHz under the campus scene. The simulation and analysis results show that the simplified database can save up to 50% time consumption. However, the difference of statistical properties is slight in the campus scenario.

eess.SP↗

arXiv2020

A Practical Non-Stationary Channel Model for Vehicle-to-Vehicle MIMO Communications

In this paper, a practical model for non-stationary Vehicle-to-Vehicle (V2V) multiple-input multiple-output (MIMO) channels is proposed. The new model considers more accurate output phase of Doppler frequency and is simplified by the Taylor series expansions. It is also suitable for generating the V2V channel coefficient with arbitrary velocities and trajectories of the mobile transmitter (MT) and mobile receiver (MR). Meanwhile, the channel parameters of path delay and power are investigated and analyzed. The closed-form expressions of statistical properties, i.e., temporal autocorrelation function (TACF) and spatial cross-correlation function (SCCF) are also derived with the angle of arrival (AoA) and angle of departure (AoD) obeying the Von Mises (VM) distribution. In addition, the good agreements between the theoretical, simulated and measured results validate the correctness and usefulness of the proposed model.

eess.SP↗

arXiv2019

A graph inequality on the common neighbourhood

In this note we prove a graph inequality based on the sizes of the common neighbourhoods. We also characterize the extremal graphs that achieve the equality. The result was first discovered as a consequence of the classical Forster's theorem in electric networks. We also present a short combinatorial proof that was inspired by a similar inequality related to the celebrated Turán's theorem.

math.CO↗

arXiv2019

Ricci solitons in almost $f$-cosymplectic manifolds

In this article we study an almost $f$-cosymplectic manifold admitting a Ricci soliton. We first prove that there do not exist Ricci solitons on an almost cosymplectic $(κ,μ)$-manifold. Further, we consider an almost $f$-cosymplectic manifold admitting a Ricci soliton whose potential vector field is the Reeb vector field and show that a three dimesional almost $f$-cosymplectic is a cosymplectic manifold. Finally we classify a three dimensional $η$-Einstein almost $f$-cosymplectic manifold admitting a Ricci soliton.

math.DG↗

arXiv2019

On weakly Einstein almost contact manifolds

In this article we study almost contact manifolds admitting weakly Einstein metrics. We first prove that if a (2n+1)-dimensional Sasakian manifold admits a weakly Einstein metric then its scalar curvature $s$ satisfies $-6\leqslant s \leqslant 6$ for $n=1$ and $-2n(2n+1)\frac{4n^2-4n+3}{4n^2-4n-1}\leqslant s \leqslant 2n(2n+1)$ for $n\geqslant2$. Secondly, for a (2n+1)-dimensional weakly Einstein contact metric $(κ,μ)$-manifold with $κ<1$, we prove that it is flat or is locally isomorphic to the Lie group $SU(2)$, $SL(2)$, or $E(1,1)$ for $n=1$ and that for $n\geqslant2$ there are no weakly Einstein metrics on contact metric $(κ,μ)$-manifolds with $0<κ<1$. For $κ<0$, we get a classification of weakly Einstein contact metric $(κ,μ)$-manifolds. Finally, it is proved that a weakly Einstein almost cosymplectic $(κ,μ)$-manifold with $κ<0$ is locally isomorphic to a solvable non-nilpotent Lie group.

math.DG↗

arXiv2019

Quasi-Einstein hypersurfaces of complex space forms

Based on a well-known fact that there are no Einstein hypersurfaces in a non-flat complex space form, in this article we study the quasi-Einstein condition, which is a generalization of an Einstein metric, on the real hyersurface of a non-flat complex space form. For the real hypersurface with quasi-Einstein metric of a complex Euclidean space, we also give a classification. Since a gradient Ricci soliton is a special quasi-Einstein metric, our results improve some conclusions of \cite{CK}.

math.DG↗

arXiv2019

Quasi-Einstein structures and almost cosymplectic manifolds

In this article, we study almost cosymplectic manifolds admitting quasi-Einstein structures $(g, V, m, λ)$. First we prove that an almost cosymplectic $(κ,μ)$-manifold is locally isomorphic to a Lie group if $(g, V, m, λ)$ is closed and on a compact almost $(κ,μ)$-cosymplectic manifold there do not exist quasi-Einstein structures $(g, V, m, λ)$, in which the potential vector field $V$ is collinear with the Reeb vector filed $ξ$. Next we consider an almost $α$-cosymplectic manifold admitting a quasi-Einstein structure and obtain some results. Finally, for a $K$-cosymplectic manifold with a closed, non-steady quasi-Einstein structure, we prove that it is $η$-Einstein. If $(g, V, m, λ)$ is non-steady and $V$ is a conformal vector field, we obtain the same conclusion.

math.DG↗