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Yu Fu

Publications and source records attributed to Yu Fu.

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

Dynamics of Irreversible Particle Adsorption to Fluid Interfaces

Understanding the dynamic adsorption of colloidal particles at fluid interfaces is essential for applications ranging from emulsion stabilization to interfacial assembly of functional materials. Adsorption dynamics is often described through diffusion-limited models (such as the Ward-Tordai framework) along with assuming dynamic equilibrium between the adsorbed and dispersed particles. However, most experiments show that particle adsorption is irreversible, and diffusion-limited models fail as the surface coverage goes beyond the dilute limit where particle crowding limits further adsorption. Here, we present a unified model that captures the transition from diffusion-limited to kinetic-limited regimes by coupling diffusion with a Random Sequential Adsorption (RSA)-based boundary condition that accounts for irreversible adsorption and particle blocking for a spherical droplet. Using both a microtensiometer and pendant drop tensiometry, we measure dynamic interfacial tension changes for 3-(Trimethoxysilyl)propyl methacrylate (TPM) particles at the toluene/water interface across a range of bulk concentrations, drop sizes, and particle functionalization. Our analysis shows that the adsorption flux becomes increasingly hindered as the surface area fills, in agreement with RSA predictions. Furthermore, we calculate the Thiele modulus as a dimensionless number that quantifies the relative importance of adsorption kinetics to diffusion. We find that above a critical surface coverage, adsorption becomes reaction-limited, marking a transition to kinetically controlled dynamics. This approach provides a predictive framework for particle adsorption at fluid interfaces and highlights the necessity of moving beyond equilibrium diffusion-limited models.

cond-mat.soft↗
arXiv2025

Color Glass Condensate meets High Twist Expansion

We establish the correspondence between two well-known frameworks for QCD multiple scattering in nuclear media: the Color Glass Condensate (CGC) and the High-Twist (HT) expansion formalism. We argue that a consistent matching between both frameworks, in their common domain of validity, is achieved by incorporating the sub-eikonal longitudinal momentum phase in the CGC formalism, which mediates the transition between coherent and incoherent scattering. We perform a detailed calculation and analysis of direct photon production in proton-nucleus scattering as a concrete example to establish the matching between HT and CGC up to twist-4, including initial- and final-state interactions, as well as their interferences. The techniques developed in this work can be adapted to other processes in electron-nucleus and proton-nucleus collisions, and they provide a potential avenue for a unified picture of dilute-dense dynamics in nuclear media.

hep-ph↗
arXiv2025

Density of algebraic points on products of curves

In this paper, we initiate the systematic study of density of algebraic points on surfaces. We give an effective asymptotic range in which the density degree set has regular behavior dictated by the index. By contrast, in small degree, the question of density is subtle and depends on the arithmetic of the curves. We give several explicit examples displaying these different behaviors, including products of genus $2$ curves with and without dense quadratic points. These results for products of curves have applications to questions about algebraic points on closely related surfaces, such as rank growth on abelian surfaces and bielliptic surfaces.

math.NT↗
arXiv2025

Towards a p-adic nowhere density conjecture of Hecke Orbits

We propose a conjecture on the $p$-adic nowhere density of the Hecke orbit of subvarieties of Hodge type Shimura varieties. By investigating the monodromy of $p$-adic Galois representations associated with points on such Shimura varieties, we prove that the locus in a formal $\mathcal{O}_K$-neighborhood of a mod $p$ point that has large monodromy is open dense, where $K$ is a totally ramified finite extension of $\breve{\mathbb{Q}}_p.$

math.NT↗
arXiv2025

The biharmonic hypersurface flow and the Willmore flow in higher dimensions

The biharmonic flow of hypersurfaces $M^n$ immersed in the Euclidean space $\mathbb {R}^{n+1}$ for $n\geq 2$ is given by a fourth order geometric evolution equation, which is similar to the Willmore flow. We apply the Michael-Simon-Sobolev inequality to establish new Gagliardo-Nirenberg inequalities on hypersurfaces. Based on these Gagliardo-Nirenberg inequalities, we apply local energy estimates to extend the solution by a covering argument and obtain an estimate on the maximal existence time of the biharmonic flow of hypersurfaces in higher dimensions. In particular, we solve a problem in \cite{BWW} on the biharmonic hypersurface flow for $n=4$. Finally, we apply our new approach to prove global existence of the Willmore flow in higher dimensions.

math.DG↗
arXiv2025

UniDiffGrasp: A Unified Framework Integrating VLM Reasoning and VLM-Guided Part Diffusion for Open-Vocabulary Constrained Grasping with Dual Arms

Open-vocabulary, task-oriented grasping of specific functional parts, particularly with dual arms, remains a key challenge, as current Vision-Language Models (VLMs), while enhancing task understanding, often struggle with precise grasp generation within defined constraints and effective dual-arm coordination. We innovatively propose UniDiffGrasp, a unified framework integrating VLM reasoning with guided part diffusion to address these limitations. UniDiffGrasp leverages a VLM to interpret user input and identify semantic targets (object, part(s), mode), which are then grounded via open-vocabulary segmentation. Critically, the identified parts directly provide geometric constraints for a Constrained Grasp Diffusion Field (CGDF) using its Part-Guided Diffusion, enabling efficient, high-quality 6-DoF grasps without retraining. For dual-arm tasks, UniDiffGrasp defines distinct target regions, applies part-guided diffusion per arm, and selects stable cooperative grasps. Through extensive real-world deployment, UniDiffGrasp achieves grasp success rates of 0.876 in single-arm and 0.767 in dual-arm scenarios, significantly surpassing existing state-of-the-art methods, demonstrating its capability to enable precise and coordinated open-vocabulary grasping in complex real-world scenarios.

cs.RO↗
arXiv2025

Meta-RTL: Reinforcement-Based Meta-Transfer Learning for Low-Resource Commonsense Reasoning

Meta learning has been widely used to exploit rich-resource source tasks to improve the performance of low-resource target tasks. Unfortunately, most existing meta learning approaches treat different source tasks equally, ignoring the relatedness of source tasks to the target task in knowledge transfer. To mitigate this issue, we propose a reinforcement-based multi-source meta-transfer learning framework (Meta-RTL) for low-resource commonsense reasoning. In this framework, we present a reinforcement-based approach to dynamically estimating source task weights that measure the contribution of the corresponding tasks to the target task in the meta-transfer learning. The differences between the general loss of the meta model and task-specific losses of source-specific temporal meta models on sampled target data are fed into the policy network of the reinforcement learning module as rewards. The policy network is built upon LSTMs that capture long-term dependencies on source task weight estimation across meta learning iterations. We evaluate the proposed Meta-RTL using both BERT and ALBERT as the backbone of the meta model on three commonsense reasoning benchmark datasets. Experimental results demonstrate that Meta-RTL substantially outperforms strong baselines and previous task selection strategies and achieves larger improvements on extremely low-resource settings.

cs.CL↗
arXiv2025

DATAWEAVER: Authoring Data-Driven Narratives through the Integrated Composition of Visualization and Text

Data-driven storytelling has gained prominence in journalism and other data reporting fields. However, the process of creating these stories remains challenging, often requiring the integration of effective visualizations with compelling narratives to form a cohesive, interactive presentation. To help streamline this process, we present an integrated authoring framework and system, DataWeaver, that supports both visualization-to-text and text-to-visualization composition. DataWeaver enables users to create data narratives anchored to data facts derived from "call-out" interactions, i.e., user-initiated highlights of visualization elements that prompt relevant narrative content. In addition to this "vis-to-text" composition, DataWeaver also supports a "text-initiated" approach, generating relevant interactive visualizations from existing narratives. Key findings from an evaluation with 13 participants highlighted the utility and usability of DataWeaver and the effectiveness of its integrated authoring framework. The evaluation also revealed opportunities to enhance the framework by refining filtering mechanisms and visualization recommendations and better support authoring creativity by introducing advanced customization options.

cs.HC↗
arXiv2025

MetaXCR: Reinforcement-Based Meta-Transfer Learning for Cross-Lingual Commonsense Reasoning

Commonsense reasoning (CR) has been studied in many pieces of domain and has achieved great progress with the aid of large datasets. Unfortunately, most existing CR datasets are built in English, so most previous work focus on English. Furthermore, as the annotation of commonsense reasoning is costly, it is impossible to build a large dataset for every novel task. Therefore, there are growing appeals for Cross-lingual Low-Resource Commonsense Reasoning, which aims to leverage diverse existed English datasets to help the model adapt to new cross-lingual target datasets with limited labeled data. In this paper, we propose a multi-source adapter for cross-lingual low-resource Commonsense Reasoning (MetaXCR). In this framework, we first extend meta learning by incorporating multiple training datasets to learn a generalized task adapters across different tasks. Then, we further introduce a reinforcement-based sampling strategy to help the model sample the source task that is the most helpful to the target task. Finally, we introduce two types of cross-lingual meta-adaption methods to enhance the performance of models on target languages. Extensive experiments demonstrate MetaXCR is superior over state-of-the-arts, while being trained with fewer parameters than other work.

cs.CL↗
arXiv2025

SemiSAM+: Rethinking Semi-Supervised Medical Image Segmentation in the Era of Foundation Models

Deep learning-based medical image segmentation typically requires large amount of labeled data for training, making it less applicable in clinical settings due to high annotation cost. Semi-supervised learning (SSL) has emerged as an appealing strategy due to its less dependence on acquiring abundant annotations from experts compared to fully supervised methods. Beyond existing model-centric advancements of SSL by designing novel regularization strategies, we anticipate a paradigmatic shift due to the emergence of promptable segmentation foundation models with universal segmentation capabilities using positional prompts represented by Segment Anything Model (SAM). In this paper, we present SemiSAM+, a foundation model-driven SSL framework to efficiently learn from limited labeled data for medical image segmentation. SemiSAM+ consists of one or multiple promptable foundation models as generalist models, and a trainable task-specific segmentation model as specialist model. For a given new segmentation task, the training is based on the specialist-generalist collaborative learning procedure, where the trainable specialist model delivers positional prompts to interact with the frozen generalist models to acquire pseudo-labels, and then the generalist model output provides the specialist model with informative and efficient supervision which benefits the automatic segmentation and prompt generation in turn. Extensive experiments on two public datasets and one in-house clinical dataset demonstrate that SemiSAM+ achieves significant performance improvement, especially under extremely limited annotation scenarios, and shows strong efficiency as a plug-and-play strategy that can be easily adapted to different specialist and generalist models.

eess.IV↗
arXiv2025

TRAWL: Tensor Reduced and Approximated Weights for Large Language Models

Recent research has shown that pruning large-scale language models for inference is an effective approach to improving model efficiency, significantly reducing model weights with minimal impact on performance. Interestingly, pruning can sometimes even enhance accuracy by removing noise that accumulates during training, particularly through matrix decompositions. However, recent work has primarily focused on single matrix decompositions or lower precision techniques, which may fail to fully capture structural patterns. To address these limitations, we introduce TRAWL (Tensor Reduced and Approximated Weights for Large Language Models), a technique that applies tensor decomposition across multiple weight matrices to effectively denoise LLMs by capturing global structural patterns. Our experiments show that TRAWL improves model performance by up to 16% over baseline models on benchmark datasets, without requiring additional data, training, or fine-tuning.

cs.CL↗
arXiv2025

Studying the divisibility of power LCM matrics by power GCD matrices on gcd-closed sets

Let $S=\{x_1,\ldots, x_n\}$ be a gcd-closed set (i.e. $(x_i,x_j)\in S $ for all $1\le i,j\le n$). In 2002, Hong proposed the divisibility problem of characterizing all gcd-closed sets $S$ with $|S|\ge 4$ such that the GCD matrix $(S)$ divides the LCM matrix $[S]$ in the ring $M_{n}(\mathbb{Z})$. For $x\in S,$ let $G_S(x):=\{z\in S: z<x, z|x \text{ and } (z|y|x, y\in S)\Rightarrow y\in\{z,x\}\}$. In 2009, Feng, Hong and Zhao answered this problem in the context where $\max_{x \in S}\{|G_S(x)|\} \leq 2$. In 2022, Zhao, Chen and Hong obtained a necessary and sufficient condition on the gcd-closed set $S$ with $\max_{x \in S}\{|G_S(x)|\}=3$ such that $(S)|\left[S\right].$ Meanwhile, they raised a conjecture on the necessary and sufficient condition such that $(S)|\left[S\right]$ holds for the remaining case $\max_{x \in S}\{|G_S(x)|\}\ge 4$. In this papar, we confirm the Zhao-Chen-Hong conjecture from a novel perspective, consequently solve Hong's open problem completely.

math.CO↗
arXiv2024

"The Data Says Otherwise"-Towards Automated Fact-checking and Communication of Data Claims

Fact-checking data claims requires data evidence retrieval and analysis, which can become tedious and intractable when done manually. This work presents Aletheia, an automated fact-checking prototype designed to facilitate data claims verification and enhance data evidence communication. For verification, we utilize a pre-trained LLM to parse the semantics for evidence retrieval. To effectively communicate the data evidence, we design representations in two forms: data tables and visualizations, tailored to various data fact types. Additionally, we design interactions that showcase a real-world application of these techniques. We evaluate the performance of two core NLP tasks with a curated dataset comprising 400 data claims and compare the two representation forms regarding viewers' assessment time, confidence, and preference via a user study with 20 participants. The evaluation offers insights into the feasibility and bottlenecks of using LLMs for data fact-checking tasks, potential advantages and disadvantages of using visualizations over data tables, and design recommendations for presenting data evidence.

cs.HC↗
arXiv2024

Managing Project Teams in an Online Class of 1000+ Students

Team projects in Computer Science (CS) help students build collaboration skills, apply theory, and prepare for real-world software development. Online classes present unique opportunities to transform the accessibility of CS education at scale. Still, the geographical distribution of students and staff adds complexity to forming effective teams, providing consistent feedback, and facilitating peer interactions. We discuss our approach of managing, evaluating, and providing constructive feedback to over 200 project teams, comprising 1000+ graduate students distributed globally, two professors, and 25+ teaching assistants. We deployed and iteratively refined this approach over 10 years while offering the Data and Visual Analytics course (CSE 6242) at Georgia Institute of Technology. Our approach and insights can help others striving to make CS education accessible, especially in online and large-scale settings.

cs.CY↗
arXiv2024

Free Lunch in Pathology Foundation Model: Task-specific Model Adaptation with Concept-Guided Feature Enhancement

Whole slide image (WSI) analysis is gaining prominence within the medical imaging field. Recent advances in pathology foundation models have shown the potential to extract powerful feature representations from WSIs for downstream tasks. However, these foundation models are usually designed for general-purpose pathology image analysis and may not be optimal for specific downstream tasks or cancer types. In this work, we present Concept Anchor-guided Task-specific Feature Enhancement (CATE), an adaptable paradigm that can boost the expressivity and discriminativeness of pathology foundation models for specific downstream tasks. Based on a set of task-specific concepts derived from the pathology vision-language model with expert-designed prompts, we introduce two interconnected modules to dynamically calibrate the generic image features extracted by foundation models for certain tasks or cancer types. Specifically, we design a Concept-guided Information Bottleneck module to enhance task-relevant characteristics by maximizing the mutual information between image features and concept anchors while suppressing superfluous information. Moreover, a Concept-Feature Interference module is proposed to utilize the similarity between calibrated features and concept anchors to further generate discriminative task-specific features. The extensive experiments on public WSI datasets demonstrate that CATE significantly enhances the performance and generalizability of MIL models. Additionally, heatmap and umap visualization results also reveal the effectiveness and interpretability of CATE. The source code is available at https://github.com/HKU-MedAI/CATE.

cs.CV↗
arXiv2024

Modification of the Jet Energy-Energy Correlator in Cold Nuclear Matter

We compute medium corrections to the energy-energy correlator (EEC) for jets in electron-nucleus collisions at leading order in the QCD coupling and the interaction of the jet with the medium. We derive an analytical expression for the modification of the EEC as a function of the opening angle and show that the modification is strongest at large angles within the jet cone. We obtain explicit results for the dependence of the modification on the jet energy, the scattering power of cold nuclear matter, and the path length within the medium. We extend our calculations to gluon jets in proton-nucleus collisions and compare our results with recent preliminary data for proton-lead collisions at the LHC. We also discuss the role of comovers on the EEC in p+Pb collisions.

nucl-th↗
arXiv2024

Euler characteristics of the generalized Kloosterman sheaves for symplectic and orthogonal groups

We study the monodromy of certain $\ell$-adic local systems attached to the generalized Kloosterman sheaves constructed by Yun and calculate their Euler characteristics under standard representations in the cases of symplectic and split/quasi-split orthogonal groups. This provides evidence for the conjectural description of their Swan conductors at $\infty$ which is predicted by Reeder-Yu on the Langlands parameters attached to the epipelagic representations.

math.NT↗