arXiv subjects

Yong Wang

Publications and source records attributed to Yong Wang.

At least 253 records · Page 14Linked to original sources

arXiv2024

Repository-Level Graph Representation Learning for Enhanced Security Patch Detection

Software vendors often silently release security patches without providing sufficient advisories (e.g., Common Vulnerabilities and Exposures) or delayed updates via resources (e.g., National Vulnerability Database). Therefore, it has become crucial to detect these security patches to ensure secure software maintenance. However, existing methods face the following challenges: (1) They primarily focus on the information within the patches themselves, overlooking the complex dependencies in the repository. (2) Security patches typically involve multiple functions and files, increasing the difficulty in well learning the representations. To alleviate the above challenges, this paper proposes a Repository-level Security Patch Detection framework named RepoSPD, which comprises three key components: 1) a repository-level graph construction, RepoCPG, which represents software patches by merging pre-patch and post-patch source code at the repository level; 2) a structure-aware patch representation, which fuses the graph and sequence branch and aims at comprehending the relationship among multiple code changes; 3) progressive learning, which facilitates the model in balancing semantic and structural information. To evaluate RepoSPD, we employ two widely-used datasets in security patch detection: SPI-DB and PatchDB. We further extend these datasets to the repository level, incorporating a total of 20,238 and 28,781 versions of repository in C/C++ programming languages, respectively, denoted as SPI-DB* and PatchDB*. We compare RepoSPD with six existing security patch detection methods and five static tools. Our experimental results demonstrate that RepoSPD outperforms the state-of-the-art baseline, with improvements of 11.90%, and 3.10% in terms of accuracy on the two datasets, respectively.

cs.SE↗

arXiv2024

113 km absolute ranging with nanometer precision

Accurate long-distance ranging is crucial for diverse applications, including satellite formation flying, very-long-baseline interferometry, gravitational-wave observatory, geographical research, etc. The integration of the time-of-flight mesurement with phase interference in dual-comb method enables high-precision ranging with a rapid update rate and an extended ambiguity range. Pioneering experiments have demonstrated unprecedented precision in ranging, achieving 5 nm @ 60 ms for 1.1 m and 200 nm @ 0.5 s for 25 m. However, long-distance ranging remains technically challenging due to high transmission loss and noise. In this letter, we propose a two-way dual-comb ranging (TWDCR) approach that enables successful ranging over a distance of 113 kilometers. We employ air dispersion analysis and synthetic repetition rate technique to extend the ambiguity range of the inherently noisy channel beyond 100 km. The achieved ranging precision is 11.5 $μ$m @ 1.3 ms, 681 nm @ 1 s, and 82 nm @ 21 s, as confirmed through a comparative analysis of two independent systems. The advanced long-distance ranging technology is expected to have immediate implications for space research initiatives, such as the space telescope array and the satellite gravimetry.

physics.optics↗

arXiv2024

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning

Federated Learning (FL) is an innovative distributed machine learning paradigm that enables neural network training across devices without centralizing data. While this addresses issues of information sharing and data privacy, challenges arise from data heterogeneity across clients and increasing network scale, leading to impacts on model performance and training efficiency. Previous research shows that in IID environments, the parameter structure of the model is expected to adhere to certain specific consistency principles. Thus, identifying and regularizing these consistencies can mitigate issues from heterogeneous data. We found that both soft labels derived from knowledge distillation and the classifier head parameter matrix, when multiplied by their own transpose, capture the intrinsic relationships between data classes. These shared relationships suggest inherent consistency. Therefore, the work in this paper identifies the consistency between the two and leverages it to regulate training, underpinning our proposed FedDW framework. Experimental results show FedDW outperforms 10 state-of-the-art FL methods, improving accuracy by an average of 3% in highly heterogeneous settings. Additionally, we provide a theoretical proof that FedDW offers higher efficiency, with the additional computational load from backpropagation being negligible. The code is available at https://github.com/liuvvvvv1/FedDW.

cs.LG↗

arXiv2024

An extrapolation-driven network architecture for physics-informed deep learning

Current PINN implementations with sequential learning strategies often experience some weaknesses, such as the failure to reproduce the previous training results when using a single network, the difficulty to strictly ensure continuity and smoothness at the time interval nodes when using multiple networks, and the increase in complexity and computational overhead. To overcome these shortcomings, we first investigate the extrapolation capability of the PINN method for time-dependent PDEs. Taking advantage of this extrapolation property, we generalize the training result obtained in a specific time subinterval to larger intervals by adding a correction term to the network parameters of the subinterval. The correction term is determined by further training with the sample points in the added subinterval. Secondly, by designing an extrapolation control function with special characteristics and combining it with a correction term, we construct a new neural network architecture whose network parameters are coupled with the time variable, which we call the extrapolation-driven network architecture. Based on this architecture, using a single neural network, we can obtain the overall PINN solution of the whole domain with the following two characteristics: (1) it completely inherits the local solution of the interval obtained from the previous training, (2) at the interval node, it strictly maintains the continuity and smoothness that the true solution has. The extrapolation-driven network architecture allows us to divide a large time domain into multiple subintervals and solve the time-dependent PDEs one by one in a chronological order. This training scheme respects the causality principle and effectively overcomes the difficulties of the conventional PINN method in solving the evolution equation on a large time domain. Numerical experiments verify the performance of our method.

math.NA↗

arXiv2024

ChartifyText: Automated Chart Generation from Data-Involved Texts via LLM

Text documents with numerical values involved are widely used in various applications such as scientific research, economy, public health and journalism. However, it is difficult for readers to quickly interpret such data-involved texts and gain deep insights. To fill this research gap, this work aims to automatically generate charts to accurately convey the underlying data and ideas to readers, which is essentially a challenging task. The challenges originate from text ambiguities, intrinsic sparsity and uncertainty of data in text documents, and subjective sentiment differences. Specifically, we propose ChartifyText, a novel fully-automated approach that leverages Large Language Models (LLMs) to convert complex data-involved texts to expressive charts. It consists of two major modules: tabular data inference and expressive chart generation. The tabular data inference module employs systematic prompt engineering to guide the LLM (e.g., GPT-4) to infer table data, where data ranges, uncertainties, missing data values and corresponding subjective sentiments are explicitly considered. The expressive chart generation module augments standard charts with intuitive visual encodings and concise texts to accurately convey the underlying data and insights. We extensively evaluate the effectiveness of ChartifyText on real-world data-involved text documents through case studies, in-depth interviews with three visualization experts, and a carefully-designed user study with 15 participants. The results demonstrate the usefulness and effectiveness of ChartifyText in helping readers efficiently and effectively make sense of data-involved texts.

cs.HC↗

arXiv2024

Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift

Data heterogeneity is one of the key challenges in federated learning, and many efforts have been devoted to tackling this problem. However, distributed concept drift with data heterogeneity, where clients may additionally experience different concept drifts, is a largely unexplored area. In this work, we focus on real drift, where the conditional distribution $P(Y|X)$ changes. We first study how distributed concept drift affects the model training and find that local classifier plays a critical role in drift adaptation. Moreover, to address data heterogeneity, we study the feature alignment under distributed concept drift, and find two factors that are crucial for feature alignment: the conditional distribution $P(Y|X)$ and the degree of data heterogeneity. Motivated by the above findings, we propose FedCCFA, a federated learning framework with classifier clustering and feature alignment. To enhance collaboration under distributed concept drift, FedCCFA clusters local classifiers at class-level and generates clustered feature anchors according to the clustering results. Assisted by these anchors, FedCCFA adaptively aligns clients' feature spaces based on the entropy of label distribution $P(Y)$, alleviating the inconsistency in feature space. Our results demonstrate that FedCCFA significantly outperforms existing methods under various concept drift settings. Code is available at https://github.com/Chen-Junbao/FedCCFA.

cs.LG↗

arXiv2024

Palabos Turret: A Particle-Resolved Numerical Framework for Settling Dynamics of Arbitrary-Shaped Particles

Particles transported in fluids are everywhere, occurring for example in indoor air, the atmosphere, the oceans, and engineering applications. In this study, a novel three-dimensional numerical framework -- the Palabos Turret is presented, which allows fully resolved simulations of the settling dynamics of heavy particles with arbitrary shapes over a wide range of particle Reynolds numbers. The numerical solver is based on the lattice Boltzmann method utilizing immersed-boundary approach and a recursive-regularized collision model to fully resolve the particle-fluid interactions. A predictor-corrector scheme is applied for the robust time integration of the six-degrees-of-freedom (6DOF) rigid-body motion. Finally, the multi-scale nature arising from the long free-fall distances of a particle is addressed through a dynamic memory allocation scheme allowing for a virtually infinite falling distance. This solver allows for the simulation of particles of any arbitrary shape. The proposed framework is validated using the analytical and experimental data of freely-falling spheres, ellipsoids, and an irregular particle in a wide range of Reynolds numbers between $5\times10^{-1}$ and $4\times10^4$. For different Reynolds numbers and particle shapes considered, the Palabos Turret shows excellent agreement compared to theoretical and experimental values with a median relative deviation of $\pm1.5\%$ and a maximum deviation of $\pm5\%$. The Palabos Turret enables an in-depth analysis of the translational and rotational dynamics of particles with complex geometries.

physics.comp-ph↗

arXiv2024

Spectral forms and de-Rham Hodge operator

Motivated by the trilinear functional of differential one-forms, spectral triple and spectral torsion for the Hodge-Dirac operator, we introduce a multilinear functional of differential one-forms for a finitely summable regular spectral triple with a noncommutative residue, which generalize the spectral torsion defined by Dabrowski-Sitarz-Zalecki. The main results of this paper recover two forms, torsion of the linear connection and four forms by the noncommutative residue and perturbed de-Rham Hodge operators, and provides an explicit computation of generalized spectral torsion associated with the perturbed de-Rham Hodge Dirac triple.

math.DG↗

arXiv2024

ChartKG: A Knowledge-Graph-Based Representation for Chart Images

Chart images, such as bar charts, pie charts, and line charts, are explosively produced due to the wide usage of data visualizations. Accordingly, knowledge mining from chart images is becoming increasingly important, which can benefit downstream tasks like chart retrieval and knowledge graph completion. However, existing methods for chart knowledge mining mainly focus on converting chart images into raw data and often ignore their visual encodings and semantic meanings, which can result in information loss for many downstream tasks. In this paper, we propose ChartKG, a novel knowledge graph (KG) based representation for chart images, which can model the visual elements in a chart image and semantic relations among them including visual encodings and visual insights in a unified manner. Further, we develop a general framework to convert chart images to the proposed KG-based representation. It integrates a series of image processing techniques to identify visual elements and relations, e.g., CNNs to classify charts, yolov5 and optical character recognition to parse charts, and rule-based methods to construct graphs. We present four cases to illustrate how our knowledge-graph-based representation can model the detailed visual elements and semantic relations in charts, and further demonstrate how our approach can benefit downstream applications such as semantic-aware chart retrieval and chart question answering. We also conduct quantitative evaluations to assess the two fundamental building blocks of our chart-to-KG framework, i.e., object recognition and optical character recognition. The results provide support for the usefulness and effectiveness of ChartKG.

cs.AI↗

arXiv2024

The pseudo-differential perturbations of the Dirac operator and the Kastler-Kalau-Walze type theorems

We define two types of pseudo-differential perturbations of the Dirac operator within the framework of the noncommutative geometry. And we obtain the noncommutative residue of the inverse square of these perturbations on 4-dimensional compact manifolds without boundary. As a generalization of the result of noncommutative residue on closed manifolds, we prove the Kastler-Kalau-Walze type theorems for these perturbations on 4-dimensional compact manifolds with boundary. Finally, several examples in which we can consider the corresponding pseudo-differential operators and the Kastler-Kalau-Walze type theorems are listed.

math.DG↗

arXiv2024

The noncommutative residue and sub-Riemannian limits for the twisted BCV spaces

In this paper, we derive the sub-Riemannian version of the Kastler-Kalau-Walze type theorem and the Dabrowski-Sitarz-Zalecki type theorem for the twisted BCV spaces. We also compute the Connes conformal invariants for the twisted product, as well as the sub-Riemannian limits of the Connes conformal invariants for the twisted BCV spaces.

math.DG↗

arXiv2024

Two Distinct Oxidation Dispersion Mechanisms in Pd-CeO2 Mediated by Thermodynamic and Kinetic Behaviors of Single Pd Species

Understanding the dispersion process of supported catalysts is crucial for synthesizing atomic-level dispersed catalysts and precisely manipulating their chemical state. However, the underlying dispersion mechanism remains elusive due to the lack of atomic-level evidence during the dispersion process. Herein, by employing spherical aberration-corrected environmental scanning transmission electron microscopy (ESTEM), first-principles calculations, and a global optimization algorithm, we unraveled the pre-oxidation dispersion and direct dispersion mechanisms in the Pd/CeO2 (100) system, mediated by the thermodynamic and kinetic behaviors of single Pd species. We discovered that at lower temperatures, the Pd nanoparticles first undergo oxidation followed by the dispersion of PdO, while at higher temperatures, the entire dispersion process of Pd remains in a metallic state. The distinct dispersion mechanisms at different temperatures are driven by the thermodynamic and kinetic differences of environment-dependent single Pd species. The nonmobile Pd1O4 species stabilized at lower temperatures obstructs the direct dispersion of Pd nanoparticles, instead triggering a sequence of pre-oxidation followed by limited dispersion. In contrast, the highly mobile Pd1O2 species at higher temperatures facilitates the complete and direct dispersion of Pd nanoparticles. This research illuminates the essential physical mechanisms of oxidative dispersion from both thermodynamic and kinetic perspectives, potentially enabling strategies for precisely controlling the state of highly dispersed catalysts.

cond-mat.mtrl-sci↗

arXiv2024

Textualized Agent-Style Reasoning for Complex Tasks by Multiple Round LLM Generation

Chain-of-thought prompting significantly boosts the reasoning ability of large language models but still faces three issues: hallucination problem, restricted interpretability, and uncontrollable generation. To address these challenges, we present AgentCOT, a llm-based autonomous agent framework, which can solve complex problems in an agent-style manner by multiple round LLM generation. At each step, AgentCOT selects an action and executes it to yield an intermediate result with supporting evidence. In addition, we integrate the step's index into the reasoning process to form a graph structure for complex inference logic. We introduce two new strategies to enhance the performance of AgentCOT.We conduct extensive experiments to verify the effectiveness of our method on six common benchmarks. Results exhibit that our method brings in substantial improvements over current competitive approaches.

cs.CL↗

arXiv2024

The Connes-Chamseddine cycle and the noncommutative integral

In [5], Connes and Chamseddine defined a cycle in the general framework of noncommutative geometry. They computed this cycle for the Dirac operator on 4-dimensioanl manifolds. We propose a way to study the Connes-Chamseddine cycle from the viewpoint of the noncommutative integral on 6-dimensional manifolds in this paper. Furthermore, we compute several interesting noncommutative integral defined in [8] by the normal coodinated way on n-dimensional manifolds. As a corollary, the Connes-Chamseddine cycle on 6-dimensional manifolds is obtained.

math.DG↗

arXiv2024

MA-CDMR: An Intelligent Cross-domain Multicast Routing Method based on Multiagent Deep Reinforcement Learning in Multi-domain SDWN

The cross-domain multicast routing problem in a software-defined wireless network with multiple controllers is a classic NP-hard optimization problem. As the network size increases, designing and implementing cross-domain multicast routing paths in the network requires not only designing efficient solution algorithms to obtain the optimal cross-domain multicast tree but also ensuring the timely and flexible acquisition and maintenance of global network state information. However, existing solutions have a limited ability to sense the network traffic state, affecting the quality of service of multicast services. In addition, these methods have difficulty adapting to the highly dynamically changing network states and have slow convergence speeds. To this end, this paper aims to design and implement a multiagent deep reinforcement learning based cross-domain multicast routing method for SDWN with multicontroller domains. First, a multicontroller communication mechanism and a multicast group management module are designed to transfer and synchronize network information between different control domains of the SDWN, thus effectively managing the joining and classification of members in the cross-domain multicast group. Second, a theoretical analysis and proof show that the optimal cross-domain multicast tree includes an interdomain multicast tree and an intradomain multicast tree. An agent is established for each controller, and a cooperation mechanism between multiple agents is designed to effectively optimize cross-domain multicast routing and ensure consistency and validity in the representation of network state information for cross-domain multicast routing decisions. Third, a multiagent reinforcement learning-based method that combines online and offline training is designed to reduce the dependence on the real-time environment and increase the convergence speed of multiple agents.

cs.NI↗

arXiv2024

AdaMotif: Graph Simplification via Adaptive Motif Design

With the increase of graph size, it becomes difficult or even impossible to visualize graph structures clearly within the limited screen space. Consequently, it is crucial to design effective visual representations for large graphs. In this paper, we propose AdaMotif, a novel approach that can capture the essential structure patterns of large graphs and effectively reveal the overall structures via adaptive motif designs. Specifically, our approach involves partitioning a given large graph into multiple subgraphs, then clustering similar subgraphs and extracting similar structural information within each cluster. Subsequently, adaptive motifs representing each cluster are generated and utilized to replace the corresponding subgraphs, leading to a simplified visualization. Our approach aims to preserve as much information as possible from the subgraphs while simplifying the graph efficiently. Notably, our approach successfully visualizes crucial community information within a large graph. We conduct case studies and a user study using real-world graphs to validate the effectiveness of our proposed approach. The results demonstrate the capability of our approach in simplifying graphs while retaining important structural and community information.

cs.SI↗

arXiv2024

Twist, turn and encounter: the trajectories of small atmospheric particles unravelled

Every solid particle in the atmosphere, from ice crystals and pollen to dust, ash, and microplastics, is non-spherical. These particles play significant roles in Earth's climate system, influencing temperature, weather patterns, natural ecosystems, human health, and pollution levels. However, our understanding of these particles is largely based on the theories for extremely small particles and experiments conducted in liquid mediums. In this study, we used an innovative experimental setup and particle-resolved numerical simulations to investigate the behaviour of sub-millimetre ellipsoids of varying shapes in the air. Our results revealed complex decaying oscillation patterns involving numerous twists and turns in these particles, starkly contrasting their dynamics in liquid mediums. We found that the frequency and decay rate of these oscillations have a strong dependence on the particle shape. Interestingly, disk-shaped particles oscillated at nearly twice the frequency of rod-shaped particles, though their oscillations also decayed more rapidly. During oscillation, even subtly non-spherical particles can drift laterally up to ten times their volume-equivalent spherical diameter. This behaviour enables particles to sweep through four times more air both vertically and laterally compared to a volume-equivalent sphere, significantly increasing their encounter rate and aggregation possibility. Our findings provide an explanation for the long-range transport and naturally occurring aggregate formation of highly non-spherical particles such as snowflakes and volcanic ash.

physics.flu-dyn↗

arXiv2024

Large-Scale Metric Computation in Online Controlled Experiment Platform

Online controlled experiment (also called A/B test or experiment) is the most important tool for decision-making at a wide range of data-driven companies like Microsoft, Google, Meta, etc. Metric computation is the core procedure for reaching a conclusion during an experiment. With the growth of experiments and metrics in an experiment platform, computing metrics efficiently at scale becomes a non-trivial challenge. This work shows how metric computation in WeChat experiment platform can be done efficiently using bit-sliced index (BSI) arithmetic. This approach has been implemented in a real world system and the performance results are presented, showing that the BSI arithmetic approach is very suitable for large-scale metric computation scenarios.

cs.DC↗