arXiv subjects

Yuan Fang

Publications and source records attributed to Yuan Fang.

At least 55 records · Page 3Linked to original sources

arXiv2026

Uniaxial strain tuned magnetism of the altermagnet candidate h-FeS

Altermagnets are collinear magnetic materials with 'alter'nating local crystalline environments, characterized by joint spin and crystalline symmetries that enable ferromagnetic-like transport properties but with vanishing net magnetization. Hexagonal FeS (h-FeS) is a recently identified altermagnet candidate that shows a spontaneous anomalous Hall effect (AHE) accompanied by a tiny net magnetization. Here, we show that both the spontaneous AHE and magnetization can be effectively suppressed by an in-plane compressive strain. Since neutron diffraction measurements show that the applied uniaxial strain only modifies the in-plane domain population but does not affect the in-plane magnetic structure, the major effect of the applied strain is to tune the small $c$-axis ferromagnetic moment. Our results demonstrate a strong correlation between the tiny net magnetization and the spontaneous AHE in h-FeS, and show that uniaxial strain provides an effective knob to tune both properties in this altermagnet candidate for spintronic applications.

cond-mat.mtrl-sci↗

arXiv2026

UrbanGraphEmbeddings: Learning and Evaluating Spatially Grounded Multimodal Embeddings for Urban Science

Learning transferable multimodal embeddings for urban environments is challenging because urban understanding is inherently spatial, yet existing datasets and benchmarks lack explicit alignment between street-view images and urban structure. We introduce UGData, a spatially grounded dataset that anchors street-view images to structured spatial graphs and provides graph-aligned supervision via spatial reasoning paths and spatial context captions, exposing distance, directionality, connectivity, and neighborhood context beyond image content. Building on UGData, we propose UGE, a two-stage training strategy that progressively and stably aligns images, text, and spatial structures by combining instruction-guided contrastive learning with graph-based spatial encoding. We finally introduce UGBench, a comprehensive benchmark to evaluate how spatially grounded embeddings support diverse urban understanding tasks -- including geolocation ranking, image retrieval, urban perception, and spatial grounding. We develop UGE on multiple state-of-the-art VLM backbones, including Qwen2-VL, Qwen2.5-VL, Phi-3-Vision, and LLaVA1.6-Mistral, and train fixed-dimensional spatial embeddings with LoRA tuning. UGE built upon Qwen2.5-VL-7B backbone achieves up to 44% improvement in image retrieval and 30% in geolocation ranking on training cities, and over 30% and 22% gains respectively on held-out cities, demonstrating the effectiveness of explicit spatial grounding for spatially intensive urban tasks.

cs.CV↗

arXiv2026

DF-LLaVA: Unlocking MLLMs for Synthetic Image Detection via Knowledge Injection and Conflict-Driven Self-Reflection

With the increasing prevalence of synthetic images, evaluating image authenticity and locating forgeries accurately while maintaining human interpretability remains a challenging task. Existing detection models primarily focus on simple authenticity classification, ultimately providing only a forgery probability or binary judgment, which offers limited explanatory insights into image authenticity. Moreover, while MLLM-based detection methods can provide more interpretable results, they still lag behind expert models in terms of pure authenticity classification accuracy. To address this, we propose DF-LLaVA, a novel and effective framework that unlocks the intrinsic discrimination potential of MLLMs. Our approach first mines latent knowledge from the MLLM itself and then injects it into the model via fine-tuning. During inference, conflict signals arising from the model's predictions activate a self-reflection process, leading to the final refined responses. This framework allows LLaVA to achieve outstanding detection accuracy exceeding expert models while still maintaining the interpretability offered by MLLMs. Extensive experiments confirm the superiority of DF-LLaVA, achieving both high accuracy and explainability in synthetic image detection. Code is available online at: https://github.com/Eliot-Shen/DF-LLaVA.

cs.CV↗

arXiv2026

Prompt Tuning without Labeled Samples for Zero-Shot Node Classification in Text-Attributed Graphs

Node classification is a fundamental problem in information retrieval with many real-world applications, such as community detection in social networks, grouping articles published online and product categorization in e-commerce. Zero-shot node classification in text-attributed graphs (TAGs) presents a significant challenge, particularly due to the absence of labeled data. In this paper, we propose a novel Zero-shot Prompt Tuning (ZPT) framework to address this problem by leveraging a Universal Bimodal Conditional Generator (UBCG). Our approach begins with pre-training a graph-language model to capture both the graph structure and the associated textual descriptions of each node. Following this, a conditional generative model is trained to learn the joint distribution of nodes in both graph and text modalities, enabling the generation of synthetic samples for each class based solely on the class name. These synthetic node and text embeddings are subsequently used to perform continuous prompt tuning, facilitating effective node classification in a zero-shot setting. Furthermore, we conduct extensive experiments on multiple benchmark datasets, demonstrating that our framework performs better than existing state-of-the-art baselines. We also provide ablation studies to validate the contribution of the bimodal generator. The code is provided at: https://github.com/Sethup123/ZPT.

cs.LG↗

arXiv2025

Time-Aware Adaptive Side Information Fusion for Sequential Recommendation

Incorporating item-side information, such as category and brand, into sequential recommendation is a well-established and effective approach for improving performance. However, despite significant advancements, current models are generally limited by three key challenges: they often overlook the fine-grained temporal dynamics inherent in timestamps, exhibit vulnerability to noise in user interaction sequences, and rely on computationally expensive fusion architectures. To systematically address these challenges, we propose the Time-Aware Adaptive Side Information Fusion (TASIF) framework. TASIF integrates three synergistic components: (1) a simple, plug-and-play time span partitioning mechanism to capture global temporal patterns; (2) an adaptive frequency filter that leverages a learnable gate to denoise feature sequences adaptively, thereby providing higher-quality inputs for subsequent fusion modules; and (3) an efficient adaptive side information fusion layer, this layer employs a "guide-not-mix" architecture, where attributes guide the attention mechanism without being mixed into the content-representing item embeddings, ensuring deep interaction while ensuring computational efficiency. Extensive experiments on four public datasets demonstrate that TASIF significantly outperforms state-of-the-art baselines while maintaining excellent efficiency in training. Our source code is available at https://github.com/jluo00/TASIF.

cs.IR↗

arXiv2025

A Surrogate-Augmented Symbolic CFD-Driven Training Framework for Accelerating Multi-objective Physical Model Development

Computational Fluid Dynamics (CFD)-driven training combines machine learning (ML) with CFD solvers to develop physically consistent closure models with improved predictive accuracy. In the original framework, each ML-generated candidate model is embedded in a CFD solver and evaluated against reference data, requiring hundreds to thousands of high-fidelity simulations and resulting in prohibitive computational cost for complex flows. To overcome this limitation, we propose an extended framework that integrates surrogate modeling into symbolic CFD-driven training in real time to reduce training cost. The surrogate model learns to approximate the errors of ML-generated models based on previous CFD evaluations and is continuously refined during training. Newly generated models are first assessed using the surrogate, and only those predicted to yield small errors or high uncertainty are subsequently evaluated with full CFD simulations. Discrete expressions generated by symbolic regression are mapped into a continuous space using averaged input-symbol values as inputs to a probabilistic surrogate model. To support multi-objective model training, particularly when fixed weighting of competing quantities is challenging, the surrogate is extended to a multi-output formulation by generalizing the kernel to a matrix form, providing one mean and variance prediction per training objective. Selection metrics based on these probabilistic outputs are used to identify an optimal training setup. The proposed surrogate-augmented CFD-driven training framework is demonstrated across a range of statistically one- and two-dimensional flows, including both single- and multi-expression model optimization. In all cases, the framework substantially reduces training cost while maintaining predictive accuracy comparable to that of the original CFD-driven approach.

cs.LG↗

arXiv2025

CORE: Contrastive Masked Feature Reconstruction on Graphs

In the rapidly evolving field of self-supervised learning on graphs, generative and contrastive methodologies have emerged as two dominant approaches. Our study focuses on masked feature reconstruction (MFR), a generative technique where a model learns to restore the raw features of masked nodes in a self-supervised manner. We observe that both MFR and graph contrastive learning (GCL) aim to maximize agreement between similar elements. Building on this observation, we reveal a novel theoretical insight: under specific conditions, the objectives of MFR and node-level GCL converge, despite their distinct operational mechanisms. This theoretical connection suggests these approaches are complementary rather than fundamentally different, prompting us to explore their integration to enhance self-supervised learning on graphs. Our research presents Contrastive Masked Feature Reconstruction (CORE), a novel graph self-supervised learning framework that integrates contrastive learning into MFR. Specifically, we form positive pairs exclusively between the original and reconstructed features of masked nodes, encouraging the encoder to prioritize contextual information over the node's own features. Additionally, we leverage the masked nodes themselves as negative samples, combining MFR's reconstructive power with GCL's discriminative ability to better capture intrinsic graph structures. Empirically, our proposed framework CORE significantly outperforms MFR across node and graph classification tasks, demonstrating state-of-the-art results. In particular, CORE surpasses GraphMAE and GraphMAE2 by up to 2.80% and 3.72% on node classification tasks, and by up to 3.82% and 3.76% on graph classification tasks.

cs.LG↗

arXiv2025

Bayesian Probability Fusion for Multi-AP Collaborative Sensing in Mobile Networks

Integrated sensing and communication is widely acknowledged as a foundational technology for next-generation mobile networks. Compared with monostatic sensing, multi-access point (AP) collaborative sensing endows mobile networks with broader, more accurate, and resilient sensing capabilities, which are critical for diverse location-based sectors. This paper focuses on collaborative sensing in multi-AP networks and proposes a Bayesian probability fusion framework for target parameter estimation using orthogonal frequency-division multiplexing waveform. The framework models multi-AP received signals as probability distributions to capture stochastic observations from channel noise and scattering coefficients. Prior information is then incorporated into the joint probability density function to cast the problem as a constrained maximum a posteriori estimation. To address the high-dimensional optimization, we develop a prior-constrained gradient ascent (PCGA) algorithm that decouples correlated parameters and performs efficient gradient updates guided by the target prior. Theoretical analysis covers optimal fusion weights for global signal-to-noise ratio maximization, PCGA convergence, and the Cramer-Rao lower bound of the estimator, with insights applicable to broader fusion schemes. Extensive numerical simulations and real-world experiments with commercial devices show the framework reduces transmission overhead by 90% versus signal fusion and lowers estimation error by 41% relative to parameter fusion. Notably, field tests achieve submeter accuracy with 50% probability in typical coverage of mmWave APs. These improvements highlight a favorable balance between communication efficiency and estimation accuracy for practical multi-AP sensing deployment. The dataset is released for research purposes and is publicly available at: http://pmldatanet.com.cn/dataapp/multimodal

eess.SP↗

arXiv2025

A Probabilistic Framework for Temporal Distribution Generalization in Industry-Scale Recommender Systems

Temporal distribution shift (TDS) erodes the long-term accuracy of recommender systems, yet industrial practice still relies on periodic incremental training, which struggles to capture both stable and transient patterns. Existing approaches such as invariant learning and self-supervised learning offer partial solutions but often suffer from unstable temporal generalization, representation collapse, or inefficient data utilization. To address these limitations, we propose ELBO$_\text{TDS}$, a probabilistic framework that integrates seamlessly into industry-scale incremental learning pipelines. First, we identify key shifting factors through statistical analysis of real-world production data and design a simple yet effective data augmentation strategy that resamples these time-varying factors to extend the training support. Second, to harness the benefits of this extended distribution while preventing representation collapse, we model the temporal recommendation scenario using a causal graph and derive a self-supervised variational objective, ELBO$_\text{TDS}$, grounded in the causal structure. Extensive experiments supported by both theoretical and empirical analysis demonstrate that our method achieves superior temporal generalization, yielding a 2.33\% uplift in GMV per user and has been successfully deployed in Shopee Product Search. Code is available at https://github.com/FuCongResearchSquad/ELBO4TDS.

cs.LG↗

arXiv2025

Spin Excitations and Flat Electronic Bands in a Cr-based Kagome Superconductor

In the quest for topology- and correlation-driven quantum states, kagome lattice materials have garnered significant interest for their band structures, featuring flat bands (FBs) from the quantum destructive interference of the electronic wavefunction. Tuning an FB to the chemical potential could induce electronic instabilities and emergent orders. Despite extensive studies, direct evidence of FBs tuned to the chemical potential and their role in emergent orders in bulk materials remains lacking. Using angle-resolved photoemission spectroscopy, resonant inelastic X-ray scattering, and density functional theory, we show that the low-energy structure of the Cr-based kagome metal superconductor {\Cr} is dominated by FBs at the Fermi level. We also observe low-energy magnetic excitations evolving across the low-temperature transition, largely consistent with the FB shift. Our results suggest that the low-temperature order contains a magnetic origin and that the kagome FBs may play a role in the emergence of this order.

cond-mat.str-el↗

arXiv2025

SN 2024aecx: A double-peaked rapidly evolving Type IIb supernova at 11 Mpc

We present the results of low-resolution spectroscopic and densely sampled multi-band photometric follow-up of supernova (SN) 2024aecx. The SN was discovered in the spiral galaxy NGC 3521 (distance $\sim$11 Mpc) within a day after the explosion. The early spectra of SN 2024aecx show a weak signature of hydrogen lines, which disappeared in $\sim$30 days after the explosion. Light curves in all bands show a distinct feature of two peaks, and the first peak is likely due to the shock cooling emission. The early phase light curve evolution of SN 2024aecx has similarity with the typical Type IIb events, but the decay rate in different bands (e.g., $\rm Δm_{15}$ = 1.60 $\pm$ 0.05 mag, $g$-band) is significantly faster in the post-peak phase. It attained the secondary maximum in $\sim$19 days ($g$-band) with a peak absolute magnitude of M$_{g}$ = -17.94 $\pm$ 0.10 mag. SN 2024aecx colors trend redder in early epochs ($<$8 days), followed by a duration in which it grows bluer, then later gets redder again $>$20 days after explosion. The analytical model fitting to the light curves reveals an envelope mass and progenitor radii in the range of $\sim$0.03 - 0.24 $M_\odot$ and $\sim$169 - 200 $R_\odot$, respectively. Modeling of the pseudo-bolometric light curve suggests that synthesized $^{56}$Ni in the explosion was $\sim$0.15 M$_{\odot}$ with ejecta mass and kinetic energy of $\sim$0.7 M$_{\odot}$ and $\sim$0.16 $\times$ 10$^{51}$ erg, respectively. The observational properties and modeling indicate that the SN~2024aecx progenitor belongs to the extended progenitor category.

astro-ph.HE↗

arXiv2025

Diversified and Adaptive Negative Sampling on Knowledge Graphs

In knowledge graph embedding, aside from positive triplets (ie: facts in the knowledge graph), the negative triplets used for training also have a direct influence on the model performance. In reality, since knowledge graphs are sparse and incomplete, negative triplets often lack explicit labels, and thus they are often obtained from various sampling strategies (eg: randomly replacing an entity in a positive triplet). An ideal sampled negative triplet should be informative enough to help the model train better. However, existing methods often ignore diversity and adaptiveness in their sampling process, which harms the informativeness of negative triplets. As such, we propose a generative adversarial approach called Diversified and Adaptive Negative Sampling DANS on knowledge graphs. DANS is equipped with a two-way generator that generates more diverse negative triplets through two pathways, and an adaptive mechanism that produces more fine-grained examples by localizing the global generator for different entities and relations. On the one hand, the two-way generator increase the overall informativeness with more diverse negative examples; on the other hand, the adaptive mechanism increases the individual sample-wise informativeness with more fine-grained sampling. Finally, we evaluate the performance of DANS on three benchmark knowledge graphs to demonstrate its effectiveness through quantitative and qualitative experiments.

cs.AI↗

arXiv2025

Unified Molecule Pre-training with Flexible 2D and 3D Modalities: Single and Paired Modality Integration

Molecular representation learning plays a crucial role in advancing applications such as drug discovery and material design. Existing work leverages 2D and 3D modalities of molecular information for pre-training, aiming to capture comprehensive structural and geometric insights. However, these methods require paired 2D and 3D molecular data to train the model effectively and prevent it from collapsing into a single modality, posing limitations in scenarios where a certain modality is unavailable or computationally expensive to generate. To overcome this limitation, we propose FlexMol, a flexible molecule pre-training framework that learns unified molecular representations while supporting single-modality input. Specifically, inspired by the unified structure in vision-language models, our approach employs separate models for 2D and 3D molecular data, leverages parameter sharing to improve computational efficiency, and utilizes a decoder to generate features for the missing modality. This enables a multistage continuous learning process where both modalities contribute collaboratively during training, while ensuring robustness when only one modality is available during inference. Extensive experiments demonstrate that FlexMol achieves superior performance across a wide range of molecular property prediction tasks, and we also empirically demonstrate its effectiveness with incomplete data. Our code and data are available at https://github.com/tewiSong/FlexMol.

cs.LG↗

arXiv2025

FusionAdapter for Few-Shot Relation Learning in Multimodal Knowledge Graphs

Multimodal Knowledge Graphs (MMKGs) incorporate various modalities, including text and images, to enhance entity and relation representations. Notably, different modalities for the same entity often present complementary and diverse information. However, existing MMKG methods primarily align modalities into a shared space, which tends to overlook the distinct contributions of specific modalities, limiting their performance particularly in low-resource settings. To address this challenge, we propose FusionAdapter for the learning of few-shot relationships (FSRL) in MMKG. FusionAdapter introduces (1) an adapter module that enables efficient adaptation of each modality to unseen relations and (2) a fusion strategy that integrates multimodal entity representations while preserving diverse modality-specific characteristics. By effectively adapting and fusing information from diverse modalities, FusionAdapter improves generalization to novel relations with minimal supervision. Extensive experiments on two benchmark MMKG datasets demonstrate that FusionAdapter achieves superior performance over state-of-the-art methods.

cs.AI↗

arXiv2025

Integrated photonic ultrawideband real-time spectrum sensing for 6G wireless networks

The sixth generation (6G) wireless networks require dynamic spectrum management to optimize the utilization of scarce spectral resources and support emerging integrated sensing and communication (ISAC) applications. This necessitates real-time spectrum sensing (RT-SS) capability with ultrawide measurement range, compact size, and low latency. Conventional electronic RT-SS solutions face critical challenges in operating across the millimeter-wave and sub-terahertz bands, which are essential spectra for 6G wireless. While photonic RT-SS has the potential to surpass this limitation, the current implementations feature limited bandwidths below 50 GHz and mostly rely on bulky dispersive fibers with high latency. Here, we address these challenges by developing an integrated photonic RT-SS system capable of ultrabroadband measurement from microwave to sub-terahertz bands, covering the full spectrum for 6G wireless. The photonic RT-SS circuit integrates a broadband electro-optic (EO) modulator for unknown signal loading, an EO tunable microring filter bank for high-speed parallel frequency-to-time mapping, as well as an EO comb for precise channel frequency referencing, all realized on a single thin-film lithium niobate chip. We achieve an unprecedented spectral measurement range beyond 120 GHz at a low latency of less than 100 ns. To validate the effectiveness of our photonic RT-SS system in future 6G scenes, we further propose a heuristic spectro-temporal resource allocation algorithm and conduct a proof-of-concept ISAC demonstration, where a radar adaptively access RT-SS-informed spectrally underutilized regions for high-quality target sensing under dynamic communication interferences. Our work presents a compact and cost-effective solution for efficient spectrum sharing and dynamic management in future 6G ISAC networks.

physics.optics↗

arXiv2025

Using Deep Learning to Identify Artificial Satellite Trails in Multi-band Photometric Astronomical Images

This research addresses the growing challenge of artificial satellite trail interference in ground-based astronomical observations by developing an efficient deep learning identification method. With the proliferation of satellite constellations in low Earth orbit, accurate detection of satellite trails has become crucial for preserving astronomical data quality. Using multi-band photometric survey observational data from the $Multi-channel$ $Photometric$ $Survey$ $Telescope$ (Mephisto) of Yunnan University , we constructed a specialized dataset of astronomical images containing satellite trails. We propose a novel ASA-U-Net model that integrates atrous spatial pyramid pooling with channel attention mechanisms into the U-Net architecture to effectively capture sparse satellite trail features that traditional semantic segmentation models often miss during downsampling. The model was implemented and validated on actual telescope data, demonstrating superior performance in end-to-end detection and marking of satellite trails compared to traditional methods. This approach significantly improves data processing precision without requiring manual parameter adjustments, making it suitable for processing massive nightly survey data and enhancing the quality of astronomical data products.

astro-ph.IM↗

arXiv2025

CubeDN: Real-time Drone Detection in 3D Space from Dual mmWave Radar Cubes

As drone use has become more widespread, there is a critical need to ensure safety and security. A key element of this is robust and accurate drone detection and localization. While cameras and other optical sensors like LiDAR are commonly used for object detection, their performance degrades under adverse lighting and environmental conditions. Therefore, this has generated interest in finding more reliable alternatives, such as millimeter-wave (mmWave) radar. Recent research on mmWave radar object detection has predominantly focused on 2D detection of road users. Although these systems demonstrate excellent performance for 2D problems, they lack the sensing capability to measure elevation, which is essential for 3D drone detection. To address this gap, we propose CubeDN, a single-stage end-to-end radar object detection network specifically designed for flying drones. CubeDN overcomes challenges such as poor elevation resolution by utilizing a dual radar configuration and a novel deep learning pipeline. It simultaneously detects, localizes, and classifies drones of two sizes, achieving decimeter-level tracking accuracy at closer ranges with overall $95\%$ average precision (AP) and $85\%$ average recall (AR). Furthermore, CubeDN completes data processing and inference at 10Hz, making it highly suitable for practical applications.

cs.RO↗

arXiv2025

GRB 240825A: Early Reverse Shock and Its Physical Implications

Early multiwavelength observations offer crucial insights into the nature of the relativistic jets responsible for gamma-ray bursts and their interaction with the surrounding medium.We present data of GRB 240825A from 17 space- and ground-based telescopes/instruments, covering wavelengths from NIR/optical to X-ray and GeV, and spanning from the prompt emission to the afterglow phase triggered by Swift and Fermi. The early afterglow observations were carried out by SVOM/C-GFT, and spectroscopic observations of the afterglow by GTC, VLT, and TNG determined the redshift of the burst ($z = 0.659$) later.A comprehensive analysis of the prompt emission spectrum observed by Swift-BAT and Fermi-GBM/LAT reveals a rare and significant high-energy cutoff at ~76 MeV. Assuming this cutoff is due to $γγ$ absorption allows us to place an upper limit on the initial Lorentz factor, $Γ_0 < 245$. The optical/NIR and GeV afterglow light curves be described by the standard external shock model, with early-time emission dominated by a reverse shock (RS) and a subsequent transition to forward shock (FS) emission. Our afterglow modelling yields a consistent estimate of the initial Lorentz factor ($Γ_{\rm 0} \sim 234$). Furthermore, the RS-to-FS magnetic field ratio ($\mathcal{R}_B \sim 302$) indicates that the reverse shock region is significantly more magnetized than the FS region. An isotropic-equivalent kinetic energy of $E_{\text{k,iso}} = 5.25 \times 10^{54}$ erg is derived, and the corresponding $γ$-ray radiation efficiency is estimated to be $η_γ$ = 3.1%. On the other hand, the standard afterglow model can not reproduce the X-ray light curve of GRB 240825A, calling for improved models to characterize all multiwavelength data.

astro-ph.HE↗