arXiv subjects

Yan Luo

Publications and source records attributed to Yan Luo.

At least 37 records · Page 2Linked to original sources

arXiv2026

StoryLensEdu: Personalized Learning Report Generation through Narrative-Driven Multi-Agent Systems

Personalized feedback plays an important role in self-regulated learning (SRL), helping students track progress and refine their strategies. However, current common solutions, such as text-based reports or learning analytics dashboards, often suffer from poor interpretability, monotonous presentation, and limited explainability. To overcome these challenges, we present StoryLensEdu, a narrative-driven multi-agent system that automatically generates intuitive, engaging, and interactive learning reports. StoryLensEdu integrates three agents: a Data Analyst that extracts data insights based on a learning objective centered structure, a Teacher that ensures educational relevance and offers actionable suggestions, and a Storyteller that organizes these insights using the Heroes Journey narrative framework. StoryLensEdu supports post-generation interactive question answering to improve explainability and user engagement. We conducted a formative study in a real high school and iteratively developed StoryLensEdu in collaboration with an e-learning team to inform our design. Evaluation with real users shows that StoryLensEdu enhances engagement and promotes a deeper understanding of the learning process.

cs.HC↗

arXiv2026

Look-Ahead and Look-Back Flows: Training-Free Image Generation with Trajectory Smoothing

Recent advances have reformulated diffusion models as deterministic ordinary differential equations (ODEs) through the framework of flow matching, providing a unified formulation for the noise-to-data generative process. Various training-free flow matching approaches have been developed to improve image generation through flow velocity field adjustment, eliminating the need for costly retraining. However, Modifying the velocity field $v$ introduces errors that propagate through the full generation path, whereas adjustments to the latent trajectory $z$ are naturally corrected by the pretrained velocity network, reducing error accumulation. In this paper, we propose two complementary training-free latent-trajectory adjustment approaches based on future and past velocity $v$ and latent trajectory $z$ information that refine the generative path directly in latent space. We propose two training-free trajectory smoothing schemes: \emph{Look-Ahead}, which averages the current and next-step latents using a curvature-gated weight, and \emph{Look-Back}, which smoothes latents using an exponential moving average with decay. We demonstrate through extensive experiments and comprehensive evaluation metrics that the proposed training-free trajectory smoothing models substantially outperform various state-of-the-art models across multiple datasets including COCO17, CUB-200, and Flickr30K.

cs.CV↗

arXiv2026

Idea2Story: An Automated Pipeline for Transforming Research Concepts into Complete Scientific Narratives

Autonomous scientific discovery with large language model (LLM)-based agents has recently made substantial progress, demonstrating the ability to automate end-to-end research workflows. However, existing systems largely rely on runtime-centric execution paradigms, repeatedly reading, summarizing, and reasoning over large volumes of scientific literature online. This on-the-spot computation strategy incurs high computational cost, suffers from context window limitations, and often leads to brittle reasoning and hallucination. We propose Idea2Story, a pre-computation-driven framework for autonomous scientific discovery that shifts literature understanding from online reasoning to offline knowledge construction. Idea2Story continuously collects peer-reviewed papers together with their review feedback, extracts core methodological units, composes reusable research patterns, and organizes them into a structured methodological knowledge graph. At runtime, underspecified user research intents are aligned to established research paradigms, enabling efficient retrieval and reuse of high-quality research patterns instead of open-ended generation and trial-and-error. By grounding research planning and execution in a pre-built knowledge graph, Idea2Story alleviates the context window bottleneck of LLMs and substantially reduces repeated runtime reasoning over literature. We conduct qualitative analyses and preliminary empirical studies demonstrating that Idea2Story can generate coherent, methodologically grounded, and novel research patterns, and can produce several high-quality research demonstrations in an end-to-end setting. These results suggest that offline knowledge construction provides a practical and scalable foundation for reliable autonomous scientific discovery.

cs.CE↗

arXiv2025

CellScout: Visual Analytics for Mining Biomarkers in Cell State Discovery

Cell state discovery is crucial for understanding biological systems and enhancing medical outcomes. A key aspect of this process is identifying distinct biomarkers that define specific cell states. However, difficulties arise from the co-discovery process of cell states and biomarkers: biologists often use dimensionality reduction to visualize cells in a two-dimensional space. Then they usually interpret visually clustered cells as distinct states, from which they seek to identify unique biomarkers. However, this assumption is often invalid due to internal inconsistencies in a cluster, making the process trial-and-error and highly uncertain. Therefore, biologists urgently need effective tools to help uncover the hidden association relationships between different cell populations and their potential biomarkers. To address this problem, we first designed a machine-learning algorithm based on the Mixture-of-Experts (MoE) technique to identify meaningful associations between cell populations and biomarkers. We further developed a visual analytics system, CellScout, in collaboration with biologists, to help them explore and refine these association relationships to advance cell state discovery. We validated our system through expert interviews, from which we further selected a representative case to demonstrate its effectiveness in discovering new cell states.

cs.HC↗

arXiv2025

ADPretrain: Advancing Industrial Anomaly Detection via Anomaly Representation Pretraining

The current mainstream and state-of-the-art anomaly detection (AD) methods are substantially established on pretrained feature networks yielded by ImageNet pretraining. However, regardless of supervised or self-supervised pretraining, the pretraining process on ImageNet does not match the goal of anomaly detection (i.e., pretraining in natural images doesn't aim to distinguish between normal and abnormal). Moreover, natural images and industrial image data in AD scenarios typically have the distribution shift. The two issues can cause ImageNet-pretrained features to be suboptimal for AD tasks. To further promote the development of the AD field, pretrained representations specially for AD tasks are eager and very valuable. To this end, we propose a novel AD representation learning framework specially designed for learning robust and discriminative pretrained representations for industrial anomaly detection. Specifically, closely surrounding the goal of anomaly detection (i.e., focus on discrepancies between normals and anomalies), we propose angle- and norm-oriented contrastive losses to maximize the angle size and norm difference between normal and abnormal features simultaneously. To avoid the distribution shift from natural images to AD images, our pretraining is performed on a large-scale AD dataset, RealIAD. To further alleviate the potential shift between pretraining data and downstream AD datasets, we learn the pretrained AD representations based on the class-generalizable representation, residual features. For evaluation, based on five embedding-based AD methods, we simply replace their original features with our pretrained representations. Extensive experiments on five AD datasets and five backbones consistently show the superiority of our pretrained features. The code is available at https://github.com/xcyao00/ADPretrain.

cs.CV↗

arXiv2025

Save It for the "Hot" Day: An LLM-Empowered Visual Analytics System for Heat Risk Management

The escalating frequency and intensity of heat-related climate events, particularly heatwaves, emphasize the pressing need for advanced heat risk management strategies. Current approaches, primarily relying on numerical models, face challenges in spatial-temporal resolution and in capturing the dynamic interplay of environmental, social, and behavioral factors affecting heat risks. This has led to difficulties in translating risk assessments into effective mitigation actions. Recognizing these problems, we introduce a novel approach leveraging the burgeoning capabilities of Large Language Models (LLMs) to extract rich and contextual insights from news reports. We hence propose an LLM-empowered visual analytics system, Havior, that integrates the precise, data-driven insights of numerical models with nuanced news report information. This hybrid approach enables a more comprehensive assessment of heat risks and better identification, assessment, and mitigation of heat-related threats. The system incorporates novel visualization designs, such as "thermoglyph" and news glyph, enhancing intuitive understanding and analysis of heat risks. The integration of LLM-based techniques also enables advanced information retrieval and semantic knowledge extraction that can be guided by experts' analytics needs. Our case studies on two cities that faced significant heatwave events and interviews with five experts have demonstrated the usefulness of our system in providing in-depth and actionable insights for heat risk management.

cs.HC↗

arXiv2025

VRS-UIE: Value-Driven Reordering Scanning for Underwater Image Enhancement

State Space Models (SSMs) have emerged as a promising backbone for vision tasks due to their linear complexity and global receptive field. However, in the context of Underwater Image Enhancement (UIE), the standard sequential scanning mechanism is fundamentally challenged by the unique statistical distribution characteristics of underwater scenes. The predominance of large-portion, homogeneous but useless oceanic backgrounds can dilute the feature representation responses of sparse yet valuable targets, thereby impeding effective state propagation and compromising the model's ability to preserve both local semantics and global structure. To address this limitation, we propose a novel Value-Driven Reordering Scanning framework for UIE, termed VRS-UIE. Its core innovation is a Multi-Granularity Value Guidance Learning (MVGL) module that generates a pixel-aligned value map to dynamically reorder the SSM's scanning sequence. This prioritizes informative regions to facilitate the long-range state propagation of salient features. Building upon the MVGL, we design a Mamba-Conv Mixer (MCM) block that synergistically integrates priority-driven global sequencing with dynamically adjusted local convolutions, thereby effectively modeling both large-portion oceanic backgrounds and high-value semantic targets. A Cross-Feature Bridge (CFB) further refines multi-level feature fusion. Extensive experiments demonstrate that our VRS-UIE framework sets a new state-of-the-art, delivering superior enhancement performance (surpassing WMamba by 0.89 dB on average) by effectively suppressing water bias and preserving structural and color fidelity. Furthermore, by incorporating efficient convolutional operators and resolution rescaling, we construct a light-weight yet effective scheme, VRS-UIE-S, suitable for real-time UIE applications.

cs.CV↗

arXiv2025

Radio Galaxy Zoo EMU: Harnessing Citizen Science and AI to Advance Open Science Catalogues

Over the past decades, significant efforts have been devoted to developing sophisticated algorithms for automatically identifying and classifying radio sources in large surveys. However, even the most advanced methods face challenges in recognising complex radio structures and accurately associating radio emission with their host galaxies. Leveraging data from the ASKAP telescope and the Evolutionary Map of the Universe (EMU) survey, Radio Galaxy Zoo EMU (RGZ EMU) was created to generate high-quality radio source classifications for training deep learning models and cataloging millions of radio sources in the southern sky. By integrating novel machine learning techniques, including anomaly detection and natural language processing, our workflow actively engages citizen scientists to enhance classification accuracy. We present results from Phase I of the project and discuss how these data will contribute to improving open science catalogues like EMUCAT.

astro-ph.GA↗

arXiv2025

An approximate solution of a case of perturbed Fokker-Planck equation

This paper focuses on finding an approximate solution of a kind of Fokker-Planck equation with time-dependent perturbations. A formulation of the approximate solution of the equation is constructed, and then the existence of the formulation is proved. The related Hamiltonian dynamical system explains the estimations. Our work provides a more comprehensive understanding of the behaviour of systems described by this Fokker-Planck equation and the corresponding stochastic differential equation.

math.PR↗

arXiv2025

Bifurcations of a nonlinear spherical pendulum with vibrating suspension point

This paper considers a nonlinear spherical pendulum whose suspension point performs high-frequency spatial vibrations. The dynamics of this pendulum can be described by averaging its Hamiltonian over phases of vibrations. Rotationally symmetric conditions on vibrations are assumed in the averaged Hamiltonian. Under these conditions, a bifurcation diagram for the phase portraits of the averaged system is presented. Numerical simulations of different examples of vibrations are performed. The case of proper degeneration in KAM theory guarantees the coherence of dynamical characteristics between the averaged and exact systems.

math.GM↗

arXiv2025

CurveFlow: Curvature-Guided Flow Matching for Image Generation

Existing rectified flow models are based on linear trajectories between data and noise distributions. This linearity enforces zero curvature, which can inadvertently force the image generation process through low-probability regions of the data manifold. A key question remains underexplored: how does the curvature of these trajectories correlate with the semantic alignment between generated images and their corresponding captions, i.e., instructional compliance? To address this, we introduce CurveFlow, a novel flow matching framework designed to learn smooth, non-linear trajectories by directly incorporating curvature guidance into the flow path. Our method features a robust curvature regularization technique that penalizes abrupt changes in the trajectory's intrinsic dynamics.Extensive experiments on MS COCO 2014 and 2017 demonstrate that CurveFlow achieves state-of-the-art performance in text-to-image generation, significantly outperforming both standard rectified flow variants and other non-linear baselines like Rectified Diffusion. The improvements are especially evident in semantic consistency metrics such as BLEU, METEOR, ROUGE, and CLAIR. This confirms that our curvature-aware modeling substantially enhances the model's ability to faithfully follow complex instructions while simultaneously maintaining high image quality. The code is made publicly available at https://github.com/Harvard-AI-and-Robotics-Lab/CurveFlow.

cs.CV↗

arXiv2025

FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA

Fairness remains a critical concern in healthcare, where unequal access to services and treatment outcomes can adversely affect patient health. While Federated Learning (FL) presents a collaborative and privacy-preserving approach to model training, ensuring fairness is challenging due to heterogeneous data across institutions, and current research primarily addresses non-medical applications. To fill this gap, we establish the first experimental benchmark for fairness in medical FL, evaluating six representative FL methods across diverse demographic attributes and imaging modalities. We introduce FairFedMed, the first medical FL dataset specifically designed to study group fairness (i.e., demographics). It comprises two parts: FairFedMed-Oph, featuring 2D fundus and 3D OCT ophthalmology samples with six demographic attributes; and FairFedMed-Chest, which simulates real cross-institutional FL using subsets of CheXpert and MIMIC-CXR. Together, they support both simulated and real-world FL across diverse medical modalities and demographic groups. Existing FL models often underperform on medical images and overlook fairness across demographic groups. To address this, we propose FairLoRA, a fairness-aware FL framework based on SVD-based low-rank approximation. It customizes singular value matrices per demographic group while sharing singular vectors, ensuring both fairness and efficiency. Experimental results on the FairFedMed dataset demonstrate that FairLoRA not only achieves state-of-the-art performance in medical image classification but also significantly improves fairness across diverse populations. Our code and dataset can be accessible via link: https://wang.hms.harvard.edu/fairfedmed/.

cs.CY↗

arXiv2025

NTIRE 2025 Image Shadow Removal Challenge Report

This work examines the findings of the NTIRE 2025 Shadow Removal Challenge. A total of 306 participants have registered, with 17 teams successfully submitting their solutions during the final evaluation phase. Following the last two editions, this challenge had two evaluation tracks: one focusing on reconstruction fidelity and the other on visual perception through a user study. Both tracks were evaluated with images from the WSRD+ dataset, simulating interactions between self- and cast-shadows with a large number of diverse objects, textures, and materials.

cs.CV↗

arXiv2025

Spontaneous Enhancement of Dzyaloshinskii-Moriya Interaction via Field-Cooling-Induced Interface Engineering in 2D van der Waals Ferromagnetic ternary Tellurides

The emergence of two-dimensional (2D) van der Waals (vdW) ferromagnets has opened new avenues for exploring topological spin textures and their applications in next-generation spintronics. Among these materials, Fe3GaTe2 (FGaT) emerges as a model system due to its room-temperature skyrmion phases, which are stabilized by strong Dzyaloshinskii-Moriya interaction (DMI). However, the atomistic origins of DMI in centrosymmetric vdW lattices remain elusive. Here, we report a spontaneous DMI enhancement mechanism driven by FC in FGaT and its analog Fe3GeTe2 (FGeT). Combining Raman spectroscopy and scanning transmission electron microscopy (STEM), we have observed the irreversible precipitation of FeTe2 in annealed FGaT. The resulting FeTe2/FGaT heterostructure is considered to break the symmetry and significantly enhance the DMI. Furthermore, similar phenomenon has been observed in the family ferromagnetic material FGeT as well. Additionally, the precipitation of FeTe2 varies significantly with different thicknesses of FGaT, aligning closely with the reported behavior of skyrmions. This discovery provides new insights into the mechanisms behind the origin of the DMI in ternary tellurides, paving the way for advanced spintronic applications.

cond-mat.mtrl-sci↗

arXiv2025

Strain Induced Robust Skyrmion lattice at Room Temperature in van der Waals Ferromagnet

Manipulating topological magnetic orders of two-dimensional (2D) magnets by strain, once achieved, offers enormous potential for future low-power flexible spintronic applications. In this work, by placing Fe3GaTe2 (FGaT), a room-temperature 2D ferromagnet, on flexible substrate, we demonstrate a field-free and robust formation of skyrmion lattice induced by strain. By applying a minimal strain of ~0.80% to pre-annealed FGaT flakes, the Magnetic Force Microscopy (MFM) tip directly triggers the transition from maze-like domains to an ordered skyrmion lattice while scanning the sample surface. The skyrmion lattice is rather stable against extensive cyclic mechanical testing (stretching, bending, and twisting over 2000 cycles each). It also exhibited stability across a wide range of magnetic fields (~2.9 kOe) and temperatures (~ 323 K), as well as long-term retention stability, highlighting its robustness and field free stabilization. The strain effect reduces the lattice symmetry and enhances the Dzyaloshinskii-Moriya interaction (DMI) of FGaT, thus stabilizing the skyrmion lattice. Our findings highlight the potential of FGaT for integrating magnetic skyrmions into future low-power-consumption flexible spintronics devices.

cond-mat.mtrl-sci↗

arXiv2025

Prismatic: Interactive Multi-View Cluster Analysis of Concept Stocks

Financial cluster analysis allows investors to discover investment alternatives and avoid undertaking excessive risks. However, this analytical task faces substantial challenges arising from many pairwise comparisons, the dynamic correlations across time spans, and the ambiguity in deriving implications from business relational knowledge. We propose Prismatic, a visual analytics system that integrates quantitative analysis of historical performance and qualitative analysis of business relational knowledge to cluster correlated businesses interactively. Prismatic features three clustering processes: dynamic cluster generation, knowledge-based cluster exploration, and correlation-based cluster validation. Utilizing a multi-view clustering approach, it enriches data-driven clusters with knowledge-driven similarity, providing a nuanced understanding of business correlations. Through well-coordinated visual views, Prismatic facilitates a comprehensive interpretation of intertwined quantitative and qualitative features, demonstrating its usefulness and effectiveness via case studies on formulating concept stocks and extensive interviews with domain experts.

cs.HC↗

arXiv2025

Long-lived Sterile Neutrino Searches at Future Muon Colliders

We explore the potential of studying sterile neutrinos at a future high-energy muon collider, where these particles can generate small active neutrino masses via the seesaw mechanism and exhibit long-lived particle signatures. A Dirac sterile neutrino model with ${\rm U(1)}_{L_μ-L_τ}$ symmetry is introduced, where the heavy right-handed neutrino ($N_R$) produces tiny active neutrino masses, and the light left-handed neutrino ($N_L$) naturally behaves as a long-lived particle. The ${\rm U(1)}_{L_μ-L_τ}$ gauge symmetry also enhances sterile neutrino pair production at a future high-energy muon collider. Using the displaced vertex method, the muon collider can search for heavy sterile neutrino, especially for $m_L> m_W$. We find that a muon collider with $\sqrt{s} = 3~ (10)$ TeV and luminosity $L=1~(10)$ ab$^{-1}$ can probe $N_L$ masses of $m_L \in [100,~1500~(5000)]$ GeV and mixing angles $θ_{νL} \in [10^{-13},~10^{-6}]$.

hep-ph↗

arXiv2025

FairDiffusion: Enhancing Equity in Latent Diffusion Models via Fair Bayesian Perturbation

Recent progress in generative AI, especially diffusion models, has demonstrated significant utility in text-to-image synthesis. Particularly in healthcare, these models offer immense potential in generating synthetic datasets and training medical students. However, despite these strong performances, it remains uncertain if the image generation quality is consistent across different demographic subgroups. To address this critical concern, we present the first comprehensive study on the fairness of medical text-to-image diffusion models. Our extensive evaluations of the popular Stable Diffusion model reveal significant disparities across gender, race, and ethnicity. To mitigate these biases, we introduce FairDiffusion, an equity-aware latent diffusion model that enhances fairness in both image generation quality as well as the semantic correlation of clinical features. In addition, we also design and curate FairGenMed, the first dataset for studying the fairness of medical generative models. Complementing this effort, we further evaluate FairDiffusion on two widely-used external medical datasets: HAM10000 (dermatoscopic images) and CheXpert (chest X-rays) to demonstrate FairDiffusion's effectiveness in addressing fairness concerns across diverse medical imaging modalities. Together, FairDiffusion and FairGenMed significantly advance research in fair generative learning, promoting equitable benefits of generative AI in healthcare.

cs.CV↗