arXiv subjects

Jian Gao

Publications and source records attributed to Jian Gao.

At least 55 records · Page 3Linked to original sources

arXiv2025

Scheduzz: Constraint-based Fuzz Driver Generation with Dual Scheduling

Fuzzing a library requires experts to understand the library usage well and craft high-quality fuzz drivers, which is tricky and tedious. Therefore, many techniques have been proposed to automatically generate fuzz drivers. However, they fail to generate rational fuzz drivers due to the lack of adherence to proper library usage conventions, such as ensuring a resource is closed after being opened. To make things worse, existing library fuzzing techniques unconditionally execute each driver, resulting in numerous irrational drivers that waste computational resources while contributing little coverage and generating false positive bug reports. To tackle these challenges, we propose a novel automatic library fuzzing technique, Scheduzz, an LLM-based library fuzzing technique. It leverages LLMs to understand rational usage of libraries and extract API combination constraints. To optimize computational resource utilization, a dual scheduling framework is implemented to efficiently manage API combinations and fuzz drivers. The framework models driver generation and the corresponding fuzzing campaign as an online optimization problem. Within the scheduling loop, multiple API combinations are selected to generate fuzz drivers, while simultaneously, various optimized fuzz drivers are scheduled for execution or suspension. We implemented Scheduzz and evaluated it in 33 real-world libraries. Compared to baseline approaches, Scheduzz significantly reduces computational overhead and outperforms UTopia on 16 out of 21 libraries. It achieves 1.62x, 1.50x, and 1.89x higher overall coverage than the state-of-the-art techniques CKGFuzzer, Promptfuzz, and the handcrafted project OSS-Fuzz, respectively. In addition, Scheduzz discovered 33 previously unknown bugs in these well-tested libraries, 3 of which have been assigned CVEs.

cs.SE↗

arXiv2025

Self-similarity Analysis in Deep Neural Networks

Current research has found that some deep neural networks exhibit strong hierarchical self-similarity in feature representation or parameter distribution. However, aside from preliminary studies on how the power-law distribution of weights across different training stages affects model performance,there has been no quantitative analysis on how the self-similarity of hidden space geometry influences model weight optimization, nor is there a clear understanding of the dynamic behavior of internal neurons. Therefore, this paper proposes a complex network modeling method based on the output features of hidden-layer neurons to investigate the self-similarity of feature networks constructed at different hidden layers, and analyzes how adjusting the degree of self-similarity in feature networks can enhance the classification performance of deep neural networks. Validated on three types of networks MLP architectures, convolutional networks, and attention architectures this study reveals that the degree of self-similarity exhibited by feature networks varies across different model architectures. Furthermore, embedding constraints on the self-similarity of feature networks during the training process can improve the performance of self-similar deep neural networks (MLP architectures and attention architectures) by up to 6 percentage points.

cs.LG↗

arXiv2025

Spacetime perturbations and quasi-teleparallel gravity

Gravity is identical to curved spacetime. It is manifested by the curvature of a Riemannian spacetime in general relativity but by torsion or non-metricity in teleparallel gravity models. In this paper, we apply these multiple options to the spacetime perturbation theory and seek the possibilities of representing the gravitation of the background and that of the perturbation in separate ways. We show that the perturbation around a Riemannian background can be described by torsion or non-metricity, so that we have teleparallel like actions for the perturbation.

gr-qc↗

arXiv2025

Human-AI Interactions and Societal Pitfalls

When working with generative artificial intelligence (AI), users may see productivity gains, but the AI-generated content may not match their preferences exactly. To study this effect, we introduce a Bayesian framework in which heterogeneous users choose how much information to share with the AI, facing a trade-off between output fidelity and communication cost. We show that the interplay between these individual-level decisions and AI training may lead to societal challenges. Outputs may become more homogenized, especially when the AI is trained on AI-generated content, potentially triggering a homogenization death spiral. And any AI bias may propagate to become societal bias. A solution to the homogenization and bias issues is to reduce human-AI interaction frictions and enable users to flexibly share information, leading to personalized outputs without sacrificing productivity.

cs.AI↗

arXiv2025

The Mini-SiTian Array: Light Curves Analysis of Asteroids

The SiTian project, with its vast field of view, will become an ideal platform for asteroid scientific research. In this study, we develop a pipeline to analyze the photometry of asteroids and derive their periods from the data collected by the SiTian pathfinder project Mini-SiTian (MST). The pipeline is applied to the MST f02 region, a MST test region with a sky area of $2.29^{\circ} \times 1.53^{\circ}$. Rotation periods of 22 asteroids are derived by the obtained light curves analysis. Among them, there are 8 asteroids available in the Asteroid Lightcurve Photometry Database (ALCDEF), and 6 of them with more photometric points ($>$200) have similar period parameters as the ones in ALCDEF. Additionally, the periods for 14 of these asteroids are newly obtained and are not listed in ALCDEF. This study demonstrates the feasibility of asteroid photometric research by the SiTian project. It shows that future observations from the SiTian project will provide even more photometry of asteroids, significantly increasing the number of available light curves. The potential vast photometric data of asteroids will help us to further understand the physics of asteroids, their material composition, and the formation and evolution of the solar system.

astro-ph.EP↗

arXiv2025

Extinction Distributions in Nearby Star-resolved Galaxies. I. M31

An extinction distribution of the Andromeda Galaxy (M31) is constructed with member stars as tracers by fitting multiband photometric data from UKIRT/WFCAM, PS1, and Gaia DR3. The resulting extinction distribution covers approximately 10 deg$^2$ of M31 with a resolution of approximately 50 arcsec, providing the largest coverage to date based on stellar observations. The derived average extinction, $A_V = 1.17$ mag, agrees well with previous studies. To account for foreground extinction, an extinction map of the Milky Way toward M31 with a resolution of $\sim$ 1.7 arcmin is also constructed, yielding an average extinction of $A_V \approx 0.185$ mag. The results offer a valuable tool for extinction correction in future observations, such as those from the China Space Station Telescope, and provide insights for improving dust models based on the spatial distribution of dust in galaxies like M31.

astro-ph.GA↗

arXiv2025

Enhancing elusive clues in knowledge learning by contrasting attention of language models

Causal language models acquire vast amount of knowledge from general text corpus during pretraining, but the efficiency of knowledge learning is known to be unsatisfactory, especially when learning from knowledge-dense and small-sized corpora. The deficiency can come from long-distance dependencies which are hard to capture by language models, and overfitting to co-occurrence patterns and distracting clues in the training text. To address these issues, the paper proposes a method to enhance knowledge learning during language model pretraining, by enhancing elusive but important clues in text discovered by the language model themselves. We found that larger language models pay more attention to non-obvious but important clues, which are often overlooked by smaller language models. Therefore, we can identify these clues by contrasting the attention weights of large and small language models. We use the identified clues as a guide to perform token-dropout data augmentation on the training text, and observed a significant boost in both small and large models' performance in fact memorization. This shows that the behavior contrast between more and less-performant language models contains important clues for knowledge learning, and it can be ``amplified" for a straight-forward improvement in knowledge learning efficiency.

cs.AI↗

arXiv2025

A Novel Trustworthy Video Summarization Algorithm Through a Mixture of LoRA Experts

With the exponential growth of user-generated content on video-sharing platforms, the challenge of facilitating efficient searching and browsing of videos has garnered significant attention. To enhance users' ability to swiftly locate and review pertinent videos, the creation of concise and informative video summaries has become increasingly important. Video-llama is an effective tool for generating video summarization, but it cannot effectively unify and optimize the modeling of temporal and spatial features and requires a lot of computational resources and time. Therefore, we propose MiLoRA-ViSum to more efficiently capture complex temporal dynamics and spatial relationships inherent in video data and to control the number of parameters for training. By extending traditional Low-Rank Adaptation (LoRA) into a sophisticated mixture-of-experts paradigm, MiLoRA-ViSum incorporates a dual temporal-spatial adaptation mechanism tailored specifically for video summarization tasks. This approach dynamically integrates specialized LoRA experts, each fine-tuned to address distinct temporal or spatial dimensions. Extensive evaluations of the VideoXum and ActivityNet datasets demonstrate that MiLoRA-ViSum achieves the best summarization performance compared to state-of-the-art models, while maintaining significantly lower computational costs. The proposed mixture-of-experts strategy, combined with the dual adaptation mechanism, highlights the model's potential to enhance video summarization capabilities, particularly in large-scale applications requiring both efficiency and precision.

cs.CV↗

arXiv2025

AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologies

The massive and large-scale design of foundational semiconductor integrated circuits (ICs) is crucial to sustaining the advancement of many emerging and future technologies, such as generative AI, 5G/6G, and quantum computing. Excitingly, recent studies have shown the great capabilities of foundational models in expediting the design of digital ICs. Yet, applying generative AI techniques to accelerate the design of analog ICs remains a significant challenge due to critical domain-specific issues, such as the lack of a comprehensive dataset and effective representation methods for analog circuits. This paper proposes, $\textbf{AnalogGenie}$, a $\underline{\textbf{Gen}}$erat$\underline{\textbf{i}}$ve $\underline{\textbf{e}}$ngine for automatic design/discovery of $\underline{\textbf{Analog}}$ circuit topologies--the most challenging and creative task in the conventional manual design flow of analog ICs. AnalogGenie addresses two key gaps in the field: building a foundational comprehensive dataset of analog circuit topology and developing a scalable sequence-based graph representation universal to analog circuits. Experimental results show the remarkable generation performance of AnalogGenie in broadening the variety of analog ICs, increasing the number of devices within a single design, and discovering unseen circuit topologies far beyond any prior arts. Our work paves the way to transform the longstanding time-consuming manual design flow of analog ICs to an automatic and massive manner powered by generative AI. Our source code is available at https://github.com/xz-group/AnalogGenie.

cs.LG↗

arXiv2025

New Red Supergiant Stars in the other side of our Galaxy

Red supergiant stars (RSGs) are massive stars in a late stage of evolution, crucial for understanding stellar life cycles and Galactic structure. However, RSGs on the far side of our Galaxy have been underexplored due to observational challenges. In this study, we introduce a novel method and present a new catalogue comprising 474 RSGs situated on the far side of the Milky Way, sourced from the OGLE-III catalogue of Variable Stars (OIII-CVS). The identification of these RSGs was made possible by analyzing the granulation parameters extracted from OGLE I-band time-series data and the stellar parameters from Gaia DR3. Additionally, we estimate the distances to these RSGs using an empirical relation between their characteristic amplitude, absolute magnitude, and intrinsic color, achieving a distance uncertainty of 13%. These newly identified RSGs are distributed at Galactocentric distances between 0 and 30kpc, and reside roughly 1 to 4kpc above and below the Galactic plane. This distribution provides new insights into the structure of the Milky Way, particularly at its outer boundaries. Our results reveal that the vertical distribution of these RSGs is consistent with the flare structure of the Galactic disk, confirming that the far side of the Milky Way exhibits a similar flaring pattern to the near side. This catalogue offers a valuable resource for future detailed studies of RSGs and contributes to a more comprehensive understanding of the Galactic structure and stellar evolution.

astro-ph.GA↗

arXiv2024

4D SlingBAG: spatial-temporal coupled Gaussian ball for large-scale dynamic 3D photoacoustic iterative reconstruction

Large-scale dynamic three-dimensional (3D) photoacoustic imaging (PAI) is significantly important in clinical applications. In practical implementations, large-scale 3D real-time PAI systems typically utilize sparse two-dimensional (2D) sensor arrays with certain angular deficiencies, necessitating advanced iterative reconstruction (IR) algorithms to achieve quantitative PAI and reduce reconstruction artifacts. However, for existing IR algorithms, multi-frame 3D reconstruction leads to extremely high memory consumption and prolonged computation time, with limited consideration of the spatial-temporal continuity between data frames. Here, we propose a novel method, named the 4D sliding Gaussian ball adaptive growth (4D SlingBAG) algorithm, based on the current point cloud-based IR algorithm sliding Gaussian ball adaptive growth (SlingBAG), which has minimal memory consumption among IR methods. Our 4D SlingBAG method applies spatial-temporal coupled deformation functions to each Gaussian sphere in point cloud, thus explicitly learning the deformations features of the dynamic 3D PA scene. This allows for the efficient representation of various physiological processes (such as pulsation) or external pressures (e.g., blood perfusion experiments) contributing to changes in vessel morphology and blood flow during dynamic 3D PAI, enabling highly efficient IR for dynamic 3D PAI. Simulation experiments demonstrate that 4D SlingBAG achieves high-quality dynamic 3D PA reconstruction. Compared to performing reconstructions by using SlingBAG algorithm individually for each frame, our method significantly reduces computational time and keeps a extremely low memory consumption. The project for 4D SlingBAG can be found in the following GitHub repository: \href{https://github.com/JaegerCQ/4D-SlingBAG}{https://github.com/JaegerCQ/4D-SlingBAG}.

cs.CV↗

arXiv2024

Sliding Gaussian ball adaptive growth (SlingBAG): point cloud-based iterative algorithm for large-scale 3D photoacoustic imaging

Large-scale 3D photoacoustic (PA) imaging has become increasingly important for both clinical and pre-clinical applications. Limited by cost and system complexity, only systems with sparsely-distributed sensors can be widely implemented, which desires advanced reconstruction algorithms to reduce artifacts. However, high computing memory and time consumption of traditional iterative reconstruction (IR) algorithms is practically unacceptable for large-scale 3D PA imaging. Here, we propose a point cloud-based IR algorithm that reduces memory consumption by several orders, wherein the 3D PA scene is modeled as a series of Gaussian-distributed spherical sources stored in form of point cloud. During the IR process, not only are properties of each Gaussian source, including its peak intensity (initial pressure value), standard deviation (size) and mean (position) continuously optimized, but also each Gaussian source itself adaptively undergoes destroying, splitting, and duplication along the gradient direction. This method, named the sliding Gaussian ball adaptive growth (SlingBAG) algorithm, enables high-quality large-scale 3D PA reconstruction with fast iteration and extremely low memory usage. We validated SlingBAG algorithm in both simulation study and in vivo animal experiments. The source code and data for SlingBAG, along with supplementary materials and demonstration videos, are now available in the following GitHub repository: https://github.com/JaegerCQ/SlingBAG.

cs.CV↗

arXiv2024

Mass-loss Rate of Highly Evolved Stars in the Magellanic Clouds

Asymptotic giant branch stars (AGBs) and red supergiant stars (RSGs) exhibit significant mass loss phenomena and are considered important sources of interstellar dust. In this work, we employed an uniform method of spectral energy distribution fitting to analyze a large, and hence statistically significant, sample of approximately 40,000 RSGs and AGBs in the Magellanic Clouds (MCs), providing a new catalog of evolved stars that includes stellar parameters and dust properties. Our results reveal that the total dust-production rate (DPR) of the Large Magellanic Cloud is approximately $9.69\times10^{-6}\,\rm{M_{\odot }\, yr^{-1}}$, while it is around $1.75\times10^{-6}\,\rm{M_{\odot }\,yr^{-1}}$ for the Small Magellanic Cloud, with a few stars significantly contributing to the total DPR. No significant differences were observed in the contributions to DPR from carbon-rich and oxygen-rich (O-rich) evolved stars in the MCs. We explored the relations between stellar parameters (luminosity, infrared color, period, amplitude) and mass-loss rate (MLR) for evolved stars. A prominent turning point at $\log{(L/L_{\odot})} \approx 4.4$ appears in the luminosity-MLR diagram of RSGs, potentially related to the mass-loss mechanism of RSGs. The luminosity-MLR relation of AGBs is highly scattered. The DPR of AGBs shows a clear change with pulsation period and amplitude, with DPR exhibiting a drastic increase at pulsation periods of approximately 300 days and I-band amplitudes greater than 0.5 mag. Metallicity has some impact on the DPR of O-rich stars, with lower metallicity seeming to result in lower mean DPR and a higher proportion of optically thin stars.

astro-ph.SR↗

arXiv2024

reCSE: Portable Reshaping Features for Sentence Embedding in Self-supervised Contrastive Learning

We propose reCSE, a self supervised contrastive learning sentence representation framework based on feature reshaping. This framework is different from the current advanced models that use discrete data augmentation methods, but instead reshapes the input features of the original sentence, aggregates the global information of each token in the sentence, and alleviates the common problems of representation polarity and GPU memory consumption linear increase in current advanced models. In addition, our reCSE has achieved competitive performance in semantic similarity tasks. And the experiment proves that our proposed feature reshaping method has strong universality, which can be transplanted to other self supervised contrastive learning frameworks and enhance their representation ability, even achieving state-of-the-art performance. Our code is available at https://github.com/heavenhellchen/reCSE.

cs.CL↗

arXiv2024

Relightable 3D Gaussians: Realistic Point Cloud Relighting with BRDF Decomposition and Ray Tracing

In this paper, we present a novel differentiable point-based rendering framework to achieve photo-realistic relighting. To make the reconstructed scene relightable, we enhance vanilla 3D Gaussians by associating extra properties, including normal vectors, BRDF parameters, and incident lighting from various directions. From a collection of multi-view images, the 3D scene is optimized through 3D Gaussian Splatting while BRDF and lighting are decomposed by physically based differentiable rendering. To produce plausible shadow effects in photo-realistic relighting, we introduce an innovative point-based ray tracing with the bounding volume hierarchies for efficient visibility pre-computation. Extensive experiments demonstrate our improved BRDF estimation, novel view synthesis and relighting results compared to state-of-the-art approaches. The proposed framework showcases the potential to revolutionize the mesh-based graphics pipeline with a point-based pipeline enabling editing, tracing, and relighting.

cs.CV↗

arXiv2024

RoSE-Opt: Robust and Efficient Analog Circuit Parameter Optimization with Knowledge-infused Reinforcement Learning

This paper proposes a learning framework, RoSE-Opt, to achieve robust and efficient analog circuit parameter optimization. RoSE-Opt has two important features. First, it incorporates key domain knowledge of analog circuit design, such as circuit topology, couplings between circuit specifications, and variations of process, supply voltage, and temperature, into the learning loop. This strategy facilitates the training of an artificial agent capable of achieving design goals by identifying device parameters that are optimal and robust. Second, it exploits a two-level optimization method, that is, integrating Bayesian optimization (BO) with reinforcement learning (RL) to improve sample efficiency. In particular, BO is used for a coarse yet quick search of an initial starting point for optimization. This sets a solid foundation to efficiently train the RL agent with fewer samples. Experimental evaluations on benchmarking circuits show promising sample efficiency, extraordinary figure-of-merit in terms of design efficiency and design success rate, and Pareto optimality in circuit performance of our framework, compared to previous methods. Furthermore, this work thoroughly studies the performance of different RL optimization algorithms, such as Deep Deterministic Policy Gradients (DDPG) with an off-policy learning mechanism and Proximal Policy Optimization (PPO) with an on-policy learning mechanism. This investigation provides users with guidance on choosing the appropriate RL algorithms to optimize the device parameters of analog circuits. Finally, our study also demonstrates RoSE-Opt's promise in parasitic-aware device optimization for analog circuits. In summary, our work reports a knowledge-infused BO-RL design automation framework for reliable and efficient optimization of analog circuits' device parameters.

cs.AR↗

arXiv2024

Making Task-Oriented Dialogue Datasets More Natural by Synthetically Generating Indirect User Requests

Indirect User Requests (IURs), such as "It's cold in here" instead of "Could you please increase the temperature?" are common in human-human task-oriented dialogue and require world knowledge and pragmatic reasoning from the listener. While large language models (LLMs) can handle these requests effectively, smaller models deployed on virtual assistants often struggle due to resource constraints. Moreover, existing task-oriented dialogue benchmarks lack sufficient examples of complex discourse phenomena such as indirectness. To address this, we propose a set of linguistic criteria along with an LLM-based pipeline for generating realistic IURs to test natural language understanding (NLU) and dialogue state tracking (DST) models before deployment in a new domain. We also release IndirectRequests, a dataset of IURs based on the Schema Guided Dialog (SGD) corpus, as a comparative testbed for evaluating the performance of smaller models in handling indirect requests.

cs.CL↗

arXiv2024

Probing the distinct extinction law of the Pillars of Creation in M16 with JWST

Investigating the extinction law in regions of high dust extinction, such as the Pillars of Creation within the M16 region, is crucial for understanding the densest parts of the interstellar medium (ISM). In this study, we utilize observations from the Near-Infrared Camera (NIRCam) and the Mid-Infrared Instrument (MIRI) onboard the James Webb Space Telescope (JWST) to analyze the color-excess ratios $E(F090W-λ)/E(F090W-F200W)$ across a wavelength range of $0.9-7.7\,μ\mathrm{m}$. Our method involves performing linear regression on color-color diagrams to derive these ratios. The enhanced detection capabilities of JWST data allow us to probe the distinct extinction law to the densest regions in M16 corresponding to an extinction depth up to $A_V \sim 60$\,mag. Remarkably, the resultant color-excess ratio curve exhibits a flatter profile than predicted by typical dust extinction models with $R_V = 5.5$ for dense ISM environments. Moreover, we observe that the mid-infrared (MIR) extinction law diverges from the near-infrared (NIR) power-law, showing a tendency for the slope to flatten as the wavelength increases. These findings have significant implications for our understanding of the dust properties in dense interstellar environments.

astro-ph.GA↗