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Xue Li

Publications and source records attributed to Xue Li.

At least 73 records · Page 4Linked to original sources
arXiv2025

Quantifying and Narrowing the Unknown: Interactive Text-to-Video Retrieval via Uncertainty Minimization

Despite recent advances, Text-to-video retrieval (TVR) is still hindered by multiple inherent uncertainties, such as ambiguous textual queries, indistinct text-video mappings, and low-quality video frames. Although interactive systems have emerged to address these challenges by refining user intent through clarifying questions, current methods typically rely on heuristic or ad-hoc strategies without explicitly quantifying these uncertainties, limiting their effectiveness. Motivated by this gap, we propose UMIVR, an Uncertainty-Minimizing Interactive Text-to-Video Retrieval framework that explicitly quantifies three critical uncertainties-text ambiguity, mapping uncertainty, and frame uncertainty-via principled, training-free metrics: semantic entropy-based Text Ambiguity Score (TAS), Jensen-Shannon divergence-based Mapping Uncertainty Score (MUS), and a Temporal Quality-based Frame Sampler (TQFS). By adaptively generating targeted clarifying questions guided by these uncertainty measures, UMIVR iteratively refines user queries, significantly reducing retrieval ambiguity. Extensive experiments on multiple benchmarks validate UMIVR's effectiveness, achieving notable gains in Recall@1 (69.2\% after 10 interactive rounds) on the MSR-VTT-1k dataset, thereby establishing an uncertainty-minimizing foundation for interactive TVR.

cs.CV↗
arXiv2025

SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignment

While Vision-Language Models (VLMs) have shown promising progress in general multimodal tasks, they often struggle in industrial anomaly detection and reasoning, particularly in delivering interpretable explanations and generalizing to unseen categories. This limitation stems from the inherently domain-specific nature of anomaly detection, which hinders the applicability of existing VLMs in industrial scenarios that require precise, structured, and context-aware analysis. To address these challenges, we propose SAGE, a VLM-based framework that enhances anomaly reasoning through Self-Guided Fact Enhancement (SFE) and Entropy-aware Direct Preference Optimization (E-DPO). SFE integrates domain-specific knowledge into visual reasoning via fact extraction and fusion, while E-DPO aligns model outputs with expert preferences using entropy-aware optimization. Additionally, we introduce AD-PL, a preference-optimized dataset tailored for industrial anomaly reasoning, consisting of 28,415 question-answering instances with expert-ranked responses. To evaluate anomaly reasoning models, we develop Multiscale Logical Evaluation (MLE), a quantitative framework analyzing model logic and consistency. SAGE demonstrates superior performance on industrial anomaly datasets under zero-shot and one-shot settings. The code, model and dataset are available at https://github.com/amoreZgx1n/SAGE.

cs.CL↗
arXiv2025

Tri-Learn Graph Fusion Network for Attributed Graph Clustering

In recent years, models based on Graph Convolutional Networks (GCN) have made significant strides in the field of graph data analysis. However, challenges such as over-smoothing and over-compression remain when handling large-scale and complex graph datasets, leading to a decline in clustering quality. Although the Graph Transformer architecture has mitigated some of these issues, its performance is still limited when processing heterogeneous graph data. To address these challenges, this study proposes a novel deep clustering framework that comprising GCN, Autoencoder (AE), and Graph Transformer, termed the Tri-Learn Graph Fusion Network (Tri-GFN). This framework enhances the differentiation and consistency of global and local information through a unique tri-learning mechanism and feature fusion enhancement strategy. The framework integrates GCN, AE, and Graph Transformer modules. These components are meticulously fused by a triple-channel enhancement module, which maximizes the use of both node attributes and topological structures, ensuring robust clustering representation. The tri-learning mechanism allows mutual learning among these modules, while the feature fusion strategy enables the model to capture complex relationships, yielding highly discriminative representations for graph clustering. It surpasses many state-of-the-art methods, achieving an accuracy improvement of approximately 0.87% on the ACM dataset, 14.14 % on the Reuters dataset, and 7.58 % on the USPS dataset. Due to its outstanding performance on the Reuters dataset, Tri-GFN can be applied to automatic news classification, topic retrieval, and related fields.

cs.LG↗
arXiv2025

LLaPa: A Vision-Language Model Framework for Counterfactual-Aware Procedural Planning

While large language models (LLMs) have advanced procedural planning for embodied AI systems through strong reasoning abilities, the integration of multimodal inputs and counterfactual reasoning remains underexplored. To tackle these challenges, we introduce LLaPa, a vision-language model framework designed for multimodal procedural planning. LLaPa generates executable action sequences from textual task descriptions and visual environmental images using vision-language models (VLMs). Furthermore, we enhance LLaPa with two auxiliary modules to improve procedural planning. The first module, the Task-Environment Reranker (TER), leverages task-oriented segmentation to create a task-sensitive feature space, aligning textual descriptions with visual environments and emphasizing critical regions for procedural execution. The second module, the Counterfactual Activities Retriever (CAR), identifies and emphasizes potential counterfactual conditions, enhancing the model's reasoning capability in counterfactual scenarios. Extensive experiments on ActPlan-1K and ALFRED benchmarks demonstrate that LLaPa generates higher-quality plans with superior LCS and correctness, outperforming advanced models. The code and models are available https://github.com/sunshibo1234/LLaPa.

cs.CL↗
arXiv2025

Four ages of rotating stars in the rotation--activity relationship and gyrochronology

Gyrochronology and the rotation--activity relationship are standard techniques used to determine the evolution phase.The mismatch in the definition of the evolutionary phases has so far raised many issues in physics and mathematics and hampered the understanding of how the internal dynamo processes affect the observable properties.To address this problem, we seek a unified scheme that shows a one-to-one mapping from gyrochronology to the rotation--activity relationship.We obtained the chromospheric activity $R'_{\rm HK}$ of 6846 stars and their rotation periods to investigate the rotation--activity relationship.We apply a three-interval model to fit the relationship and find that it is best fitted by three intervals in the range of Ro$<0.7$.We associate those intervals to the convective, gap and interface phases of gyrochronology.Furthermore,we suggest an additional epoch at late times of the I phase.We further use the three-interval models to fit the period--activity relationship in temperature bins and determine the duration of the transition phase as a function of effective temperature.By comparing the critical temperature and period of the g-to-I transition with the slowly rotating sequence of 10 young open clusters whose ages range from 1 Myr to 2.5 Gyr, we conclude that our new model finds the pure I sequence without fast rotating outliers, which defines the zero-age I sequence (ZAIS).We propose that there is an ambiguous consensus on when the I sequence starts to work.This ambiguity is from the visually convergent sequence of the color--period diagrams in open clusters.This visually convergent sequence is younger than the ZAIS and is actually the pre-I sequence that can be associated with the stall of the spin-down. Our results unify the rotation--activity relationship and gyrochonology for the stellar evolution of low-mass stars, for which we coined the ``CgIW" scenario.

astro-ph.SR↗
arXiv2025

Representation Learning on Out of Distribution in Tabular Data

The open-world assumption in model development suggests that a model might lack sufficient information to adequately handle data that is entirely distinct or out of distribution (OOD). While deep learning methods have shown promising results in handling OOD data through generalization techniques, they often require specialized hardware that may not be accessible to all users. We present TCL, a lightweight yet effective solution that operates efficiently on standard CPU hardware. Our approach adapts contrastive learning principles specifically for tabular data structures, incorporating full matrix augmentation and simplified loss calculation. Through comprehensive experiments across 10 diverse datasets, we demonstrate that TCL outperforms existing models, including FT-Transformer and ResNet, particularly in classification tasks, while maintaining competitive performance in regression problems. TCL achieves these results with significantly reduced computational requirements, making it accessible to users with limited hardware capabilities. This study also provides practical guidance for detecting and evaluating OOD data through straightforward experiments and visualizations. Our findings show that TCL offers a promising balance between performance and efficiency in handling OOD prediction tasks, which is particularly beneficial for general machine learning practitioners working with computational constraints.

cs.LG↗
arXiv2025

Random Client Selection on Contrastive Federated Learning for Tabular Data

Vertical Federated Learning (VFL) has revolutionised collaborative machine learning by enabling privacy-preserving model training across multiple parties. However, it remains vulnerable to information leakage during intermediate computation sharing. While Contrastive Federated Learning (CFL) was introduced to mitigate these privacy concerns through representation learning, it still faces challenges from gradient-based attacks. This paper presents a comprehensive experimental analysis of gradient-based attacks in CFL environments and evaluates random client selection as a defensive strategy. Through extensive experimentation, we demonstrate that random client selection proves particularly effective in defending against gradient attacks in the CFL network. Our findings provide valuable insights for implementing robust security measures in contrastive federated learning systems, contributing to the development of more secure collaborative learning frameworks

cs.LG↗
arXiv2025

FashionDPO:Fine-tune Fashion Outfit Generation Model using Direct Preference Optimization

Personalized outfit generation aims to construct a set of compatible and personalized fashion items as an outfit. Recently, generative AI models have received widespread attention, as they can generate fashion items for users to complete an incomplete outfit or create a complete outfit. However, they have limitations in terms of lacking diversity and relying on the supervised learning paradigm. Recognizing this gap, we propose a novel framework FashionDPO, which fine-tunes the fashion outfit generation model using direct preference optimization. This framework aims to provide a general fine-tuning approach to fashion generative models, refining a pre-trained fashion outfit generation model using automatically generated feedback, without the need to design a task-specific reward function. To make sure that the feedback is comprehensive and objective, we design a multi-expert feedback generation module which covers three evaluation perspectives, \ie quality, compatibility and personalization. Experiments on two established datasets, \ie iFashion and Polyvore-U, demonstrate the effectiveness of our framework in enhancing the model's ability to align with users' personalized preferences while adhering to fashion compatibility principles. Our code and model checkpoints are available at https://github.com/Yzcreator/FashionDPO.

cs.MM↗
arXiv2025

CDER: Collaborative Evidence Retrieval for Document-level Relation Extraction

Document-level Relation Extraction (DocRE) involves identifying relations between entities across multiple sentences in a document. Evidence sentences, crucial for precise entity pair relationships identification, enhance focus on essential text segments, improving DocRE performance. However, existing evidence retrieval systems often overlook the collaborative nature among semantically similar entity pairs in the same document, hindering the effectiveness of the evidence retrieval task. To address this, we propose a novel evidence retrieval framework, namely CDER. CDER employs an attentional graph-based architecture to capture collaborative patterns and incorporates a dynamic sub-structure for additional robustness in evidence retrieval. Experimental results on the benchmark DocRE dataset show that CDER not only excels in the evidence retrieval task but also enhances overall performance of existing DocRE system.

cs.CL↗
arXiv2025

The Mini-SiTian Array: first-two-year operation

The SiTian project, designed to utilize 60 telescopes distributed across multiple sites in China, is a next-generation time-domain survey initiative. As a pathfinder for the SiTian project, the Mini-SiTian (MST) has been proposed and implemented to test the SiTian's brain and data pipeline, and to evaluate the feasibility of its technology and science cases. Mounted at the Xinglong Observatory, the MST project comprises three 30 cm telescopes and has been operated since Nov. 2022. Each telescope of the MST possesses a large field of view, covering $2.29^{\circ}$ $\times$ $1.53^{\circ}$ FOV, and is equipped with $g'$, $r'$ and $i'$ filters, respectively. Acting as the pioneer of the forthcoming SiTian project, the MST is dedicated to the discovery of variable stars, transients, and outburst events, and has already obtained some interesting scientific results. In this paper, we will summarize the first-two-year operation of the MST project.

astro-ph.IM↗
arXiv2025

Impact of Spectral Resolution on S-index and Its Application to Spectroscopic Surveys

Utilizing the PHOENIX synthetic spectra, we investigated the impact of spectral resolution on the calculation of $S$-indices. We found that for spectra with a resolution lower than $\approx$30,000, it is crucial to calibrate $S$-indices for accurate estimations. This is especially essential for low-resolution spectral observations. We provided calibrations for several ongoing or upcoming spectroscopic surveys such as the LAMOST low-resolution survey, the SEGUE survey, the SDSS-V/BOSS survey, the DESI survey, the MSE survey, and the MUST survey. Using common targets between the HARPS and MWO observations, we established conversions from spectral $S$-indices to the well-known $S_{\rm MWO}$ values, applicable to stars with [Fe/H] values greater than $-$1. These calibrations offer a reliable approach to convert $S$-indices obtained from various spectroscopic surveys into $S_{\rm{MWO}}$ values and can be widely applied in studies on chromospheric activity.

astro-ph.SR↗
arXiv2025

Continual Contrastive Learning on Tabular Data with Out of Distribution

Out-of-distribution (OOD) prediction remains a significant challenge in machine learning, particularly for tabular data where traditional methods often fail to generalize beyond their training distribution. This paper introduces Tabular Continual Contrastive Learning (TCCL), a novel framework designed to address OOD challenges in tabular data processing. TCCL integrates contrastive learning principles with continual learning mechanisms, featuring a three-component architecture: an Encoder for data transformation, a Decoder for representation learning, and a Learner Head. We evaluate TCCL against 14 baseline models, including state-of-the-art deep learning approaches and gradient-boosted decision trees (GBDT), across eight diverse tabular datasets. Our experimental results demonstrate that TCCL consistently outperforms existing methods in both classification and regression tasks on OOD data, with particular strength in handling distribution shifts. These findings suggest that TCCL represents a significant advancement in handling OOD scenarios for tabular data.

cs.LG↗
arXiv2025

Magnetoelastic coupling in intercalated transition metal dichalcogenides

The large van der Waals gap in transition metal dichalcogenides (TMDs) offers an avenue to host external metal atoms that modify the ground state of these 2D materials. Here, we experimentally and theoretically address the charge correlations in a family of intercalated TMDs. While short-range charge fluctuations develop in Co$_{1/3}$TaS$_{2}$ and Fe$_{1/3}$TaS$_{2}$, long-range charge order switches-on in Fe$_{1/3}$NbS$_{2}$ driven by the interplay of magnetic order and lattice degrees of freedom. The magnetoelastic coupling is demonstrated in Fe$_{1/3}$NbS$_{2}$ by the enhancement of the charge modulations upon magnetic field below T$_\mathrm{N}$, although Density Functional Perturbation Theory (DFPT) calculations predict negligible electron(spin)-phonon coupling. Furthermore, we show that Co-intercalated TaS$_2$ displays a kagome-like Fermi surface, hence opening the path to engineer electronic band structures and study the entanglement of spin, charge, and spin-phonon mechanisms in the large family of intercalated TMDs.

cond-mat.str-el↗
arXiv2025

Gate-tunable band-edge in few-layer MoS$_2$

Transition metal dichalcogenides (TMDs) have garnered significant research interest due to the variation in band-edge locations within the hexagonal Brillouin zone between single-layer and bulk configurations. In monolayers, the conduction band minima are centered at the $K$-points, whereas in multilayers, they shift to the $Q$-points, midway between the $Γ$ and $K$ points. In this study, we conduct magnetotransport experiments to measure the occupation in the $Q$ and $K$ valleys in fourlayer molybdenum disulfide (MoS$_2$). We demonstrate electrostatic tunability of the conduction band edge by combining our experimental results with a hybrid $k\cdot p$ tight-binding model that accounts for interlayer screening effects in a self-consistent manner. Furthermore, we extend our model to bilayer and trilayer MoS$_2$, reconciling prior experimental results and quantifying the tunable range of band edges in atomically thin TMDs.

cond-mat.mes-hall↗
arXiv2025

Echo: Efficient Co-Scheduling of Hybrid Online-Offline Tasks for Large Language Model Serving

Large language models have been widely deployed in various applications, encompassing both interactive online tasks and batched offline tasks. Given the burstiness and latency sensitivity of online tasks, over-provisioning resources is common practice. This allows for the integration of latency-insensitive offline tasks during periods of low online load, enhancing resource utilization. However, strategically serving online and offline tasks through a preemption mechanism fails to fully leverage the flexibility of offline tasks and suffers from KV cache recomputation and irregular workloads. In this paper, we introduce Echo, a collaborative online-offline task serving system, including a scheduler, a KV cache manager, and estimation toolkits. The scheduler and KV cache manager work tightly to maximize the throughput of offline tasks, while the estimator further predicts execution time to ensure online task SLOs. The scheduler leverages the batch information of last iteration to reduce the search space for finding the optimal schedule. The KV cache manager sets the priority of the KV cache based on the type of tasks and the opportunity of prefix sharing to reduce the recomputation. Finally, the estimation toolkits predict the execution time, future memory consumption, and the throughput of offline tasks to guide the scheduler, KV cache manager, and the system deployer. Evaluation based on real-world workloads demonstrates that Echo can increase offline task throughput by up to $3.3\times$, while satisfying online task SLOs.

cs.DC↗
arXiv2025

Contrastive Federated Learning with Tabular Data Silos

Learning from vertical partitioned data silos is challenging due to the segmented nature of data, sample misalignment, and strict privacy concerns. Federated learning has been proposed as a solution. However, sample misalignment across silos often hinders optimal model performance and suggests data sharing within the model, which breaks privacy. Our proposed solution is Contrastive Federated Learning with Tabular Data Silos (CFL), which offers a solution for data silos with sample misalignment without the need for sharing original or representative data to maintain privacy. CFL begins with local acquisition of contrastive representations of the data within each silo and aggregates knowledge from other silos through the federated learning algorithm. Our experiments demonstrate that CFL solves the limitations of existing algorithms for data silos and outperforms existing tabular contrastive learning. CFL provides performance improvements without loosening privacy.

cs.LG↗
arXiv2025

DNMDR: Dynamic Networks and Multi-view Drug Representations for Safe Medication Recommendation

Medication Recommendation (MR) is a promising research topic which booms diverse applications in the healthcare and clinical domains. However, existing methods mainly rely on sequential modeling and static graphs for representation learning, which ignore the dynamic correlations in diverse medical events of a patient's temporal visits, leading to insufficient global structural exploration on nodes. Additionally, mitigating drug-drug interactions (DDIs) is another issue determining the utility of the MR systems. To address the challenges mentioned above, this paper proposes a novel MR method with the integration of dynamic networks and multi-view drug representations (DNMDR). Specifically, weighted snapshot sequences for dynamic heterogeneous networks are constructed based on discrete visits in temporal EHRs, and all the dynamic networks are jointly trained to gain both structural correlations in diverse medical events and temporal dependency in historical health conditions, for achieving comprehensive patient representations with both semantic features and structural relationships. Moreover, combining the drug co-occurrences and adverse drug-drug interactions (DDIs) in internal view of drug molecule structure and interactive view of drug pairs, the safe drug representations are available to obtain high-quality medication combination recommendation. Finally, extensive experiments on real world datasets are conducted for performance evaluation, and the experimental results demonstrate that the proposed DNMDR method outperforms the state-of-the-art baseline models with a large margin on various metrics such as PRAUC, Jaccard, DDI rates and so on.

cs.LG↗