arXiv subjects

Qian Li

Publications and source records attributed to Qian Li.

At least 127 records · Page 7Linked to original sources

arXiv2025

Large Language Models for Knowledge Graph Embedding: A Survey

Large language models (LLMs) have garnered significant attention for their superior performance in many knowledge-driven applications on the world wide web.These models are designed to train hundreds of millions or more parameters on large amounts of text data, enabling them to understand and generate naturallanguage effectively. As the superior performance of LLMs becomes apparent,they are increasingly being applied to knowledge graph embedding (KGE) related tasks to improve the processing results. Traditional KGE representation learning methods map entities and relations into a low-dimensional vector space, enablingthe triples in the knowledge graph to satisfy a specific scoring function in thevector space. However, based on the powerful language understanding and seman-tic modeling capabilities of LLMs, that have recently been invoked to varying degrees in different types of KGE related scenarios such as multi-modal KGE andopen KGE according to their task characteristics. In this paper, we investigate awide range of approaches for performing LLMs-related tasks in different types of KGE scenarios. To better compare the various approaches, we summarize each KGE scenario in a classification. Finally, we discuss the applications in which the methods are mainly used and suggest several forward-looking directions for the development of this new research area.

cs.CL↗

arXiv2025

Long-Sequence Recommendation Models Need Decoupled Embeddings

Lifelong user behavior sequences are crucial for capturing user interests and predicting user responses in modern recommendation systems. A two-stage paradigm is typically adopted to handle these long sequences: a subset of relevant behaviors is first searched from the original long sequences via an attention mechanism in the first stage and then aggregated with the target item to construct a discriminative representation for prediction in the second stage. In this work, we identify and characterize, for the first time, a neglected deficiency in existing long-sequence recommendation models: a single set of embeddings struggles with learning both attention and representation, leading to interference between these two processes. Initial attempts to address this issue with some common methods (e.g., linear projections -- a technique borrowed from language processing) proved ineffective, shedding light on the unique challenges of recommendation models. To overcome this, we propose the Decoupled Attention and Representation Embeddings (DARE) model, where two distinct embedding tables are initialized and learned separately to fully decouple attention and representation. Extensive experiments and analysis demonstrate that DARE provides more accurate searches of correlated behaviors and outperforms baselines with AUC gains up to 0.9% on public datasets and notable improvements on Tencent's advertising platform. Furthermore, decoupling embedding spaces allows us to reduce the attention embedding dimension and accelerate the search procedure by 50% without significant performance impact, enabling more efficient, high-performance online serving. Code in PyTorch for experiments, including model analysis, is available at https://github.com/thuml/DARE.

cs.IR↗

arXiv2025

Restoring Missing Modes of 21cm Intensity Mapping with Deep Learning: Impact on BAO Reconstruction

In 21cm intensity mapping of the large-scale structure (LSS), regions in Fourier space could be compromised by foreground contamination. In interferometric observations, this contamination, known as the foreground wedge, is exacerbated by the chromatic response of antennas, leading to substantial data loss. Meanwhile, the baryonic acoustic oscillation (BAO) reconstruction, which operates in configuration space to "linearize" the BAO signature, offers improved constraints on the sound horizon scale. However, missing modes within these contaminated regions can negatively impact the BAO reconstruction algorithm. To address this challenge, we employ the deep learning model U-Net to recover the lost modes before applying the BAO reconstruction algorithm. Despite hardware limitations, such as GPU memory, our results demonstrate that the AI-restored 21cm temperature map achieves a high correlation with the original signal, with a correlation ratio of approximately $0.9$ at $k \sim 1 h/Mpc$. Furthermore, subsequent BAO reconstruction indicates that the AI restoration has minimal impact on the performance of the `linearized' BAO signal, proving the effectiveness of the machine learning approach to mitigate the impact of foreground contamination. Interestingly, we demonstrate that the AI model trained on coarser fields can be effectively applied to finer fields, achieving even higher correlation. This success is likely attributable to the scale-invariance properties of non-linear mode coupling in large-scale structure and the hierarchical structure of the U-Net architecture.

astro-ph.CO↗

arXiv2025

Recovering Cosmic Structure with a Simple Physical Constraint

Radio observation of the large-scale structure (LSS) of our Universe faces major challenges from foreground contamination, which is many orders of magnitude stronger than the cosmic signal. While other foreground removal techniques struggle with complex systematics, methods like foreground avoidance emerge as effective alternatives. However, this approach inevitably results in the loss of Fourier modes and a reduction in cosmological constraints. We present a novel method that, by enforcing the non-negativity of the observed field in real space, allows us to recover some of the lost information, particularly phase angles. We demonstrate that the effectiveness of this straightforward yet powerful technique arises from the mode mixing from the non-linear evolution of LSS. Since the non-negativity is ensured by mass conservation, one of the key principles of the cosmic dynamics, we can restore the lost modes without explicitly expressing the exact form of the mode mixing. Unlike previous methods, our approach utilizes information from highly non-linear scales, and has the potential to revolutionize the analysis of radio observational data in cosmology. Crucially, we demonstrate that in long-baseline interferometric observations, such as those from the Square Kilometre Array (SKA), it is still possible to recover the baryonic acoustic oscillation (BAO) signature despite not directly covering the relevant scales. This opens up potential future survey designs for cosmological detection.

astro-ph.CO↗

arXiv2025

S2CFormer: Revisiting the RD-Latency Trade-off in Transformer-based Learned Image Compression

Transformer-based Learned Image Compression (LIC) suffers from a suboptimal trade-off between decoding latency and rate-distortion (R-D) performance. Moreover, the critical role of the FeedForward Network (FFN)-based channel aggregation module has been largely overlooked. Our research reveals that efficient channel aggregation-rather than complex and time-consuming spatial operations-is the key to achieving competitive LIC models. Based on this insight, we initiate the ``S2CFormer'' paradigm, a general architecture that simplifies spatial operations and enhances channel operations to overcome the previous trade-off. We present two instances of the S2CFormer: S2C-Conv, and S2C-Attention. Both models demonstrate state-of-the-art (SOTA) R-D performance and significantly faster decoding speed. Furthermore, we introduce S2C-Hybrid, an enhanced variant that maximizes the strengths of different S2CFormer instances to achieve a better performance-latency trade-off. This model outperforms all the existing methods on the Kodak, Tecnick, and CLIC Professional Validation datasets, setting a new benchmark for efficient and high-performance LIC. The code is at \href{https://github.com/YunuoChen/S2CFormer}{https://github.com/YunuoChen/S2CFormer}.

cs.CV↗

arXiv2025

When GNNs meet symmetry in ILPs: an orbit-based feature augmentation approach

A common characteristic in integer linear programs (ILPs) is symmetry, allowing variables to be permuted without altering the underlying problem structure. Recently, GNNs have emerged as a promising approach for solving ILPs. However, a significant challenge arises when applying GNNs to ILPs with symmetry: classic GNN architectures struggle to differentiate between symmetric variables, which limits their predictive accuracy. In this work, we investigate the properties of permutation equivariance and invariance in GNNs, particularly in relation to the inherent symmetry of ILP formulations. We reveal that the interaction between these two factors contributes to the difficulty of distinguishing between symmetric variables. To address this challenge, we explore the potential of feature augmentation and propose several guiding principles for constructing augmented features. Building on these principles, we develop an orbit-based augmentation scheme that first groups symmetric variables and then samples augmented features for each group from a discrete uniform distribution. Empirical results demonstrate that our proposed approach significantly enhances both training efficiency and predictive performance.

cs.LG↗

arXiv2025

Instrument-Splatting: Controllable Photorealistic Reconstruction of Surgical Instruments Using Gaussian Splatting

Real2Sim is becoming increasingly important with the rapid development of surgical artificial intelligence (AI) and autonomy. In this work, we propose a novel Real2Sim methodology, Instrument-Splatting, that leverages 3D Gaussian Splatting to provide fully controllable 3D reconstruction of surgical instruments from monocular surgical videos. To maintain both high visual fidelity and manipulability, we introduce a geometry pre-training to bind Gaussian point clouds on part mesh with accurate geometric priors and define a forward kinematics to control the Gaussians as flexible as real instruments. Afterward, to handle unposed videos, we design a novel instrument pose tracking method leveraging semantics-embedded Gaussians to robustly refine per-frame instrument poses and joint states in a render-and-compare manner, which allows our instrument Gaussian to accurately learn textures and reach photorealistic rendering. We validated our method on 2 publicly released surgical videos and 4 videos collected on ex vivo tissues and green screens. Quantitative and qualitative evaluations demonstrate the effectiveness and superiority of the proposed method.

cs.CV↗

arXiv2025

Federated Temporal Graph Clustering

Temporal graph clustering is a complex task that involves discovering meaningful structures in dynamic graphs where relationships and entities change over time. Existing methods typically require centralized data collection, which poses significant privacy and communication challenges. In this work, we introduce a novel Federated Temporal Graph Clustering (FTGC) framework that enables decentralized training of graph neural networks (GNNs) across multiple clients, ensuring data privacy throughout the process. Our approach incorporates a temporal aggregation mechanism to effectively capture the evolution of graph structures over time and a federated optimization strategy to collaboratively learn high-quality clustering representations. By preserving data privacy and reducing communication overhead, our framework achieves competitive performance on temporal graph datasets, making it a promising solution for privacy-sensitive, real-world applications involving dynamic data.

cs.LG↗

arXiv2025

Global existence of martingale solutions to stochastic keller-segel system with degenerate diffusion

In this paper, we study the stochastic degenerate Keller-Segel system perturbed by linear multiplicative noise in a bounded domain $\mathcal{O}$. We establish the global existence of martingale solutions for this model with any nonnegative initial data in $H_{2}^{-1}(\mathcal{O})$. The main challenge in proving the existence of solutions arises from the degeneracy of the porous media diffusion and the lack of coercivity in the nonlinear chemotactic term. To overcome these difficulties, we construct a solution operator and apply the Schauder fixed point theorem within the variational framework.

math.AP↗

arXiv2025

ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment

Entity alignment (EA) aims to identify entities across different knowledge graphs that represent the same real-world objects. Recent embedding-based EA methods have achieved state-of-the-art performance in EA yet faced interpretability challenges as they purely rely on the embedding distance and neglect the logic rules behind a pair of aligned entities. In this paper, we propose the Align-Subgraph Entity Alignment (ASGEA) framework to exploit logic rules from Align-Subgraphs. ASGEA uses anchor links as bridges to construct Align-Subgraphs and spreads along the paths across KGs, which distinguishes it from the embedding-based methods. Furthermore, we design an interpretable Path-based Graph Neural Network, ASGNN, to effectively identify and integrate the logic rules across KGs. We also introduce a node-level multi-modal attention mechanism coupled with multi-modal enriched anchors to augment the Align-Subgraph. Our experimental results demonstrate the superior performance of ASGEA over the existing embedding-based methods in both EA and Multi-Modal EA (MMEA) tasks.

cs.CL↗

arXiv2025

Spin frustration and unconventional spin twisting state in van der Waals ferromagnet/antiferromagnet heterostructures

Atomically flat surfaces of van der Waals (vdW) materials pave an avenue for addressing a long-standing fundamental issue of how a perfectly compensated antiferromagnet (AFM) surface frustrates a ferromagnetic (FM) overlayer in FM/AFM heterostructures. By revealing the AFM and FM spin structures separately in vdW Fe5GeTe2/NiPS3 heterostructures, we find that C-type in-plane AFM NiPS3 develops three equivalent AFM domains which are robust against external magnetic field and magnetic coupling with Fe5GeTe2. Consequently, spin frustration at the Fe5GeTe2/NiPS3 interface was shown to develop a perpendicular Fe5GeTe2 magnetization in the interfacial region that switches separately from the bulk of the Fe5GeTe2 magnetizations. In particular, we discover an unconventional spin twisting state that the Fe5GeTe2 spins twist from perpendicular direction near the interface to in-plane direction away from the interface in Fe5GeTe2/NiPS3. Our finding of the twisting spin texture is a unique property of spin frustration in van der Waals magnetic heterostructures.

cond-mat.mtrl-sci↗

arXiv2025

ILETIA: An AI-enhanced method for individualized trigger-oocyte pickup interval estimation of progestin-primed ovarian stimulation protocol

In vitro fertilization-embryo transfer (IVF-ET) stands as one of the most prevalent treatments for infertility. During an IVF-ET cycle, the time interval between trigger shot and oocyte pickup (OPU) is a pivotal period for follicular maturation, which determines mature oocytes yields and impacts the success of subsequent procedures. However, accurately predicting this interval is severely hindered by the variability of clinicians'experience that often leads to suboptimal oocyte retrieval rate. To address this challenge, we propose ILETIA, the first machine learning-based method that could predict the optimal trigger-OPU interval for patients receiving progestin-primed ovarian stimulation (PPOS) protocol. Specifically, ILETIA leverages a Transformer to learn representations from clinical tabular data, and then employs gradient-boosted trees for interval prediction. For model training and evaluating, we compiled a dataset PPOS-DS of nearly ten thousand patients receiving PPOS protocol, the largest such dataset to our knowledge. Experimental results demonstrate that our method achieves strong performance (AUROC = 0.889), outperforming both clinicians and other widely used computational models. Moreover, ILETIA also supports premature ovulation risk prediction in a specific OPU time (AUROC = 0.838). Collectively, by enabling more precise and individualized decisions, ILETIA has the potential to improve clinical outcomes and lay the foundation for future IVF-ET research.

q-bio.QM↗

arXiv2025

Orthogonal Geometry of Magneto-Optical Kerr Effect Enabled by Magnetization Multipole of Berry Curvature

The Magneto-Optical Kerr Effect (MOKE) is a fundamental tool in magnetometry, pivotal for advancing research in optics, magnetism, and spintronics as a direct probe of magnetization. Traditional MOKE measurements primarily detect the magnetization components parallel to the Poynting vector, which can only access the magnitude but not the direction of the orthogonal component. In this study, we introduce an orthogonal MOKE geometry in which the Kerr signal detects both the magnitude and direction of the magnetization component perpendicular to the Poynting vector. We demonstrate the broad applicability of this orthogonal geometry through the MOKE measurements in cubic ferromagnets and van der Waals ferromagnet. We theoretically show that the orthogonal MOKE geometry is enabled by the multipolar structure of Berry curvature in the magnetization space, which generally induces a Voigt vector orthogonal to the magnetization, thereby accounting for the unique magnetization angle dependence distinct from conventional MOKE. The establishment of the orthogonal MOKE geometry not only introduces a new paradigm for magneto-optical measurements but also provides a framework for exploring the magnetization multipoles of Berry curvature across the electromagnetic spectrum.

physics.optics↗

arXiv2024

A Hidden Quantum Paraelectric Phase in SrTiO3 Induced by Terahertz Field

Coherent manipulation of lattice vibrations using ultrafast light pulses enables access to nonequilibrium 'hidden' phases with designed functionalities in quantum materials. However, expanding the understanding of nonlinear light-phonon interaction mechanisms remains crucial for developing new strategies. Here, we report re-entrant ultrafast phase transitions in SrTiO3 driven by intense terahertz excitation. As the terahertz field increases, the system transitions from the quantum paraelectric (QPE) ground state to an intermediate ferroelectric phase, and then unexpectedly reverts to a QPE state above ~500 kV/cm. The latter hidden QPE phase exhibits distinct lattice dynamics compared to the initial phases, highlighting activated antiferrodistortive phonon modes. Aided by first-principles dynamical calculations, we identify the mechanism for these complex behaviors as a superposition of multiple coherently excited eigenstates of the polar soft mode. Our results reveal a previously uncharted quantum facet of SrTiO3 and open pathways for harnessing high-order excitations to engineer quantum materials in the ultrafast regime.

cond-mat.mtrl-sci↗

arXiv2024

Absorption and scattering of charged scalar waves by charged Horndeski black hole

We investigate the absorption and scattering of a charged massive scalar field by a charged Horndeski black hole using both the approximation or classical geometric method and the partial wave method and compare the numerical and analytical results, which are found to agree with each other very well. We observe that an increase in either the BH charge $Q$ or the field charge $q$ when $qQ>0$ leads to a smaller absorption cross section and a widening of the interference fringes in the scattering cross section, while the increase in the field mass enlarges the absorption cross section and the width of the interference fringes. Compared to the Reissner-Nordstr$\ddot{\rm{o}}$m BH with the same charge and other parameter settings, the absorption and scattering cross sections of the charged Horndeski BH are higher, and its interference fringes are narrower. We also investigate the effect of the field charge $q$ on the absorption and scattering cross sections when superradiance is triggered. It is shown that the total absorption cross section can be negative, and the scattering intensity can be significantly enhanced by superradiance.

gr-qc↗

arXiv2024

Learn to Unlearn: Meta-Learning-Based Knowledge Graph Embedding Unlearning

Knowledge graph (KG) embedding methods map entities and relations into continuous vector spaces, improving performance in tasks like link prediction and question answering. With rising privacy concerns, machine unlearning (MU) has emerged as a critical AI technology, enabling models to eliminate the influence of specific data. Existing MU approaches often rely on data obfuscation and adjustments to training loss but lack generalization across unlearning tasks. This paper introduces MetaEU, a Meta-Learning-Based Knowledge Graph Embedding Unlearning framework. MetaEU leverages meta-learning to unlearn specific embeddings, mitigating their impact while preserving model performance on remaining data. Experiments on benchmark datasets demonstrate its effectiveness in KG embedding unlearning.

cs.AI↗

arXiv2024

Lightweight Contenders: Navigating Semi-Supervised Text Mining through Peer Collaboration and Self Transcendence

The semi-supervised learning (SSL) strategy in lightweight models requires reducing annotated samples and facilitating cost-effective inference. However, the constraint on model parameters, imposed by the scarcity of training labels, limits the SSL performance. In this paper, we introduce PS-NET, a novel framework tailored for semi-supervised text mining with lightweight models. PS-NET incorporates online distillation to train lightweight student models by imitating the Teacher model. It also integrates an ensemble of student peers that collaboratively instruct each other. Additionally, PS-NET implements a constant adversarial perturbation schema to further self-augmentation by progressive generalizing. Our PS-NET, equipped with a 2-layer distilled BERT, exhibits notable performance enhancements over SOTA lightweight SSL frameworks of FLiText and DisCo in SSL text classification with extremely rare labelled data.

cs.CL↗

arXiv2024

Quantum Griffiths singularity in a three-dimensional superconductor to Anderson critical insulator transition

Disorder is ubiquitous in real materials and can have dramatic effects on quantum phase transitions. Originating from the disorder enhanced quantum fluctuation, quantum Griffiths singularity (QGS) has been revealed as a universal phenomenon in quantum criticality of low-dimensional superconductors. However, due to the weak fluctuation effect, QGS is very challenging to detect experimentally in three-dimensional (3D) superconducting systems. Here we report the discovery of QGS associated with the quantum phase transition from 3D superconductor to Anderson critical insulator in a spinel oxide MgTi2O4 (MTO). Under both perpendicular and parallel magnetic field, the dynamical critical exponent diverges when approaching the quantum critical point, demonstrating the existence of 3D QGS. Among 3D superconductors, MTO shows a relatively strong fluctuation effect featured as a wide superconducting transition region. The enhanced fluctuation, which may arise from the mobility edge of Anderson localization, finally leads to the occurrence of 3D quantum phase transition and QGS. Our findings offer a new perspective to understand quantum phase transitions in strongly disordered 3D systems.

cond-mat.supr-con↗