arXiv subjects

Xiaohong Yang

Publications and source records attributed to Xiaohong Yang.

10 recordsLinked to original sources

arXiv2026

Unveiling the nature of G6096: a likely hierarchical triple system

G6096 (Gaia DR3 609651611028044544) was recently reported as a wide ($P\sim 450$ days) and eccentric ($e\sim0.18$) binary possibly hosting a massive white dwarf or neutron star. In this work, through analyses of the projected rotational velocity between the blue and red bands, spectral disentangling, joint radial velocity and astrometric fitting, and X-ray emission, we suggest that the system contains additional visible component(s) rather than a compact object. We develop a new approach to reveal the nature of G6096 by jointly modeling the spectral energy distribution, rotational velocity, and astrometric measurements. Finally, we speculate that G6096 is a hierarchical triple main-sequence star system, comprising a primary with a mass of $\sim 0.75\,M_\odot$ orbited by an inner binary consisting of two dwarfs with masses of $\sim 0.62\,M_\odot$ and $\sim 0.40\,M_\odot$, respectively. This method may help reveal a population of triple systems when applied to {\it Gaia} astrometric data, particularly the upcoming DR4.

astro-ph.SR↗

arXiv2026

Characterizing Orbital Parameters of Hot Subdwarf Binaries with Multiple Spectroscopic Surveys

Hot subdwarfs (HSDs) provide critical insights into the physical mechanisms governing binary evolution. In this work, we conduct a systematic analysis of 157 HSDs, selected from Gaia EDR3 and characterized using multi-survey spectroscopic data. Atmospheric parameters of these HSDs are derived via a convolutional neural network (CNN) method and template-matching method. Based on the atmospheric parameters from CNN method, these HSDs exhibit a median mass of $0.45^{+0.19}_{-0.17} M_{\odot}$ and radius of $0.18^{+0.04}_{-0.05} R_{\odot}$, consistent with earlier work. Orbital parameters of 23 systems are determined through the fitting of radial velocity data and light curves, with 11 of them being new solutions. We find that reflection-dominated binaries typically have periods longer than 0.1 d and host low-mass main-sequence companions ($\sim$ 0.2 $M_{\odot}$) with rotation-inflated radii. In contrast, binaries including an HSD and a white dwarf show very short periods ($P < 0.2$ d), with the closest systems hosting more massive white dwarfs. Most of these systems share a similar mass--period distribution with that of post-common-envelope binaries, supporting a common-envelope origin.

astro-ph.SR↗

arXiv2026

HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning

The rapid growth of AI-enabled Internet of Vehicles (IoV) calls for efficient Machine Learning (ML) solutions that can handle high vehicular mobility and decentralized data. This has motivated the emergence of Hierarchical Federated Learning over vehicle-edge-cloud architectures (VEC-HFL). Nevertheless, one aspect which is underexplored in the literature on VEC-HFL is that vehicles often need to execute multiple ML tasks simultaneously, where this multi-model training environment introduces crucial challenges. First, improper aggregation rules can lead to model obsolescence and prolonged training times. Second, vehicular mobility may result in inefficient data utilization by preventing the vehicles from returning their models to the network edge. Third, achieving a balanced resource allocation across diverse tasks becomes of paramount importance as it majorly affects the effectiveness of collaborative training. We take one of the first steps towards addressing these challenges via proposing a framework for multi-model training in dynamic VEC-HFL with the goal of minimizing global training latency while ensuring balanced training across various tasks, a problem that turns out to be NP-hard. To facilitate timely model training, we introduce a hybrid synchronous-asynchronous aggregation rule. Building on this, we present a novel method called Hybrid Evolutionary And gReedy allocaTion (HEART). The framework operates in two stages: first, it achieves balanced task scheduling through a hybrid heuristic approach that combines improved Particle Swarm Optimization (PSO) and Genetic Algorithms (GA); second, it employs a low-complexity greedy algorithm to determine the training priority of assigned tasks on vehicles. Experiments on real-world datasets demonstrate the superiority of HEART over existing methods.

cs.LG↗

arXiv2026

Nature of HD 251108: an RS CVn binary with a long-term evolving spot

Recently, the Lobster Eye Imager for Astronomy (LEIA) detected the longest-lasting and most energetic stellar X-ray flare event from HD 251108. In this work, we re-determined the atmospheric parameters of HD 251108 using three spectroscopic observations obtained with the 2.4 m Lijiang Telescope. Combined with the stellar radius derived from spectral energy distribution fitting, we found that HD 251108 contains a K-type giant with a mass of approximately 1.3 $M_{\odot}$. Long-term photometric monitoring over 12 years reveals a modulation suggestive of a stellar activity cycle, but inconclusive given the limited time span to date. Light curve fitting indicates that the variations in both amplitude and shape are primarily driven by the evolution of a large spot. The fitting further indicates that the spot migrated from low latitudes toward the pole between 2014 and 2020, and began to recede from the pole after 2022. Using spot parameters from light curve fitting, we found that the observed radial velocity variations arise from both the spot-induced distortions and the Keplerian orbital motion of the giant star. Additionally, we detect a possible M-dwarf companion with a mass of approximately 0.25 $M_{\odot}$. Our finding suggests a notable effect on the radial velocity caused by stellar magnetic activity.

astro-ph.SR↗

arXiv2026

RE-MCDF: Closed-Loop Multi-Expert LLM Reasoning for Knowledge-Grounded Clinical Diagnosis

Electronic medical records (EMRs), particularly in neurology, are inherently heterogeneous, sparse, and noisy, which poses significant challenges for large language models (LLMs) in clinical diagnosis. In such settings, single-agent systems are vulnerable to self-reinforcing errors, as their predictions lack independent validation and can drift toward spurious conclusions. Although recent multi-agent frameworks attempt to mitigate this issue through collaborative reasoning, their interactions are often shallow and loosely structured, failing to reflect the rigorous, evidence-driven processes used by clinical experts. More fundamentally, existing approaches largely ignore the rich logical dependencies among diseases, such as mutual exclusivity, pathological compatibility, and diagnostic confusion. This limitation prevents them from ruling out clinically implausible hypotheses, even when sufficient evidence is available. To overcome these, we propose RE-MCDF, a relation-enhanced multi-expert clinical diagnosis framework. RE-MCDF introduces a generation--verification--revision closed-loop architecture that integrates three complementary components: (i) a primary expert that generates candidate diagnoses and supporting evidence, (ii) a laboratory expert that dynamically prioritizes heterogeneous clinical indicators, and (iii) a multi-relation awareness and evaluation expert group that explicitly enforces inter-disease logical constraints. Guided by a medical knowledge graph (MKG), the first two experts adaptively reweight EMR evidence, while the expert group validates and corrects candidate diagnoses to ensure logical consistency. Extensive experiments on the neurology subset of CMEMR (NEEMRs) and on our curated dataset (XMEMRs) demonstrate that RE-MCDF consistently outperforms state-of-the-art baselines in complex diagnostic scenarios (https://github.com/shenshaowei/RE-MCDF).

cs.AI↗

arXiv2026

Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT

Hierarchical Federated Learning (HFL) extends conventional Federated Learning (FL) by introducing intermediate aggregation layers, enabling distributed learning in geographically dispersed environments, particularly relevant for smart IoT systems, such as remote monitoring and battlefield operations, where cellular connectivity is limited. In these scenarios, UAVs serve as mobile aggregators, dynamically connecting terrestrial IoT devices. This paper investigates an HFL architecture with energy-constrained, dynamically deployed UAVs prone to communication disruptions. We propose a novel approach to minimize global training costs by formulating a joint optimization problem that integrates learning configuration, bandwidth allocation, and device-to-UAV association, ensuring timely global aggregation before UAV disconnections and redeployments. The problem accounts for dynamic IoT devices and intermittent UAV connectivity and is NP-hard. To tackle this, we decompose it into three subproblems: \textit{(i)} optimizing learning configuration and bandwidth allocation via an augmented Lagrangian to reduce training costs; \textit{(ii)} introducing a device fitness score based on data heterogeneity (via Kullback-Leibler divergence), device-to-UAV proximity, and computational resources, using a TD3-based algorithm for adaptive device-to-UAV assignment; \textit{(iii)} developing a low-complexity two-stage greedy strategy for UAV redeployment and global aggregator selection, ensuring efficient aggregation despite UAV disconnections. Experiments on diverse real-world datasets validate the approach, demonstrating cost reduction and robust performance under communication disruptions.

cs.LG↗

arXiv2026

ELSA: Efficient LLM-Centric Split Aggregation for Privacy-Aware Hierarchical Federated Learning over the Network Edge

Training large language models (LLMs) at the network edge faces fundamental challenges arising from device resource constraints, severe data heterogeneity, and heightened privacy risks. To address these challenges, we propose ELSA (Efficient LLM-centric Split Aggregation), a novel framework that systematically integrates split learning (SL) and hierarchical federated learning (HFL) for distributed LLM fine-tuning over resource-constrained edge networks. ELSA introduces three key innovations. First, it employs a task-agnostic, behavior-aware client clustering mechanism that constructs semantic fingerprints using public probe inputs and symmetric Kullback-Leibler (KL) divergence, augmented by prediction-consistency trust scoring and latency-aware edge assignment to jointly mitigate data heterogeneity, device unreliability, and communication constraints. Second, it employs a resource-aware dynamic model splitting strategy to adaptively partition the LLM into three segments across clients and edge servers, with the cloud used only for adapter aggregation, enabling an effective balance between on-device computation cost and global convergence stability. Third, it incorporates a lightweight communication scheme based on computational sketches combined with semantic subspace orthogonal perturbation (SS-OP) to reduce communication overhead while mitigating privacy leakage during model exchanges across the network. Extensive experiments across diverse NLP tasks demonstrate that ELSA consistently outperforms state-of-the-art baselines in terms of adaptability, convergence behavior, and robustness, establishing a scalable and privacy-aware solution for edge-side LLM fine-tuning under resource constraints.

cs.LG↗

arXiv2025

Numerical simulations of the line-force-driven winds from active galactic nuclei: The special relativistic effects

Ultra-fast outflows (UFOs) with mildly relativistic velocities are frequently observed in active galactic nuclei (AGNs). The line-force-driving mechanism is often taken as a potential mechanism for driving UFOs. Due to the line-force-driven winds moving at mildly relativistic velocities, the special relativistic effects become important. There are two special relativistic effects: one is the influence of the disc rotation on the radiation field; the other is the radiation-drag effect. We wish to study the influence of the special relativistic effects on the line-force-driven winds, and we performed numerical simulations to investigate this. We find that the line-force-driven winds are significantly weakened when the special relativistic effects are considered. Compared with the case without special relativistic effects, when special relativistic effects are considered the winds are closer to the disc surface, the maximum speed of winds is reduced by $\sim$20 percent--70 percent, and the mass outflow rate and the kinetic power is significantly reduced.

astro-ph.HE↗

arXiv2015

Image tag completion by local learning

The problem of tag completion is to learn the missing tags of an image. In this paper, we propose to learn a tag scoring vector for each image by local linear learning. A local linear function is used in the neighborhood of each image to predict the tag scoring vectors of its neighboring images. We construct a unified objective function for the learning of both tag scoring vectors and local linear function parame- ters. In the objective, we impose the learned tag scoring vectors to be consistent with the known associations to the tags of each image, and also minimize the prediction error of each local linear function, while reducing the complexity of each local function. The objective function is optimized by an alternate optimization strategy and gradient descent methods in an iterative algorithm. We compare the proposed algorithm against different state-of-the-art tag completion methods, and the results show its advantages.

cs.CV↗

arXiv2013

Radiation driven outflow in active galactic nuclei: the feedback effects of scattered and reprocessed photons

We perform time-dependent, 2DHD numerical simulations to study the dynamics of a slowly rotating accretion flow from sub-pc to pc scales under the irradiation from the central AGN. Compared to previous work, we improve the calculation of the radiative force due to X-rays. More importantly, in addition to radiative pressure and radiative heating/cooling directly from the central AGN, in the momentum equation we also include the force due to the scattered and reprocessed photons. We find that the accretion flow properties change significantly due to this "re-radiation" effect. The inflow rate at the inner boundary is reduced, while the outflow rate at the outer boundary is enhanced by about one order of magnitude. This effect is more significant when the density at the outer boundary is higher. The properties of outflows such as velocity, momentum and energy fluxes, and the ratio of outflow rate and the accretion rate, are calculated. We find that the efficiency of transferring the radiation power into the kinetic power of outflow is typically $10^{-3}$, far below the value of $\sim 0.05$ which is assumed in some cosmological simulations. The effect of the temperature of the gas at the outer boundary ($T_0$) is investigated. When $T_0$ is high, the emitted luminosity of the accretion flow oscillates. This is because in this case the gas around the Bondi radius can be more easily heated to be above the virial temperature due to its high internal energy. Another question we hope to address is the so-called "sub-Eddington" puzzle. Observationally, the luminosity of almost all AGNs are sub-Eddington, while theoretically the luminosity of an accretion flow can easily be super-Eddington. We find that even when the re-radiation effect is included and outflow does become much stronger, the luminosity, while reduced, can still be super-Eddington.

astro-ph.HE↗