arXiv subjects

Bin Yuan

Publications and source records attributed to Bin Yuan.

14 recordsLinked to original sources

arXiv2026

Bulkhead: Automated Semantic Detection and Remediation of Container Escape Vulnerabilities

Filesystem isolation in container ecosystems is often weakened by cross-boundary path misresolution, causing path traversal (PaTra) vulnerabilities. These vulnerabilities stem from insecure host-container interactions and have become increasingly pervasive as cloud systems mount shared resources, such as GPUs and agent workspaces, into containers to support AI workloads. Existing defenses remain inadequate. Kernel-level protections are intrusive, can destabilize system calls, and have therefore not been accepted into the Linux mainline. Detection methods rely on static rule matching or manual code auditing. Static rules can flag path-related functions but fail to capture the semantics needed to determine whether a host-container interaction exists, causing many false positives. Manual review requires domain expertise, making it costly, inefficient, and difficult to scale. To address this threat, we present Bulkhead, an automated framework that integrates large language models (LLMs) with formal methods for semantic vulnerability discovery and remediation. Bulkhead uses a multi-agent system to identify and repair PaTra vulnerabilities through multi-dimensional knowledge patterns generalized from known cases. It first applies high-risk functional patterns to locate entry points for cross-boundary interactions in containerized code, then uses call-chain patterns to recover the corresponding execution paths at suitable depth. The Detection pipeline analyzes these call chains against the application scenarios and threat model, identifying vulnerabilities such as missing security checks and TOCTOU flaws in cross-boundary interactions, and generating proof-of-concept (PoC) exploits for validation. These PoCs then guide patch generation. To ensure remediation correctness, the Patch pipeline performs assertion-driven verification using predefined model-checking templates.

cs.CR↗

arXiv2026

CUTEv2: Unified and Configurable Matrix Extension for Diverse CPU Architectures with Minimal Design Overhead

Matrix extensions have emerged as an essential feature in modern CPUs to address the surging demands of AI workloads. However, existing designs often incur substantial hardware and software design overhead. Tight coupling with the CPU pipeline complicates integration across diverse CPUs, while fine-grained synchronous instructions hinder the development of high-performance kernels. This paper proposes a unified and configurable CPU matrix extension architecture. By decoupling matrix units from the CPU pipeline, the design enables low-overhead integration while maintaining close coordination with existing compute and memory resources. The configurable matrix unit supports mixed-precision operations and adapts to diverse compute demands and memory bandwidth constraints. An asynchronous matrix multiplication abstraction with flexible granularity conceals hardware details, simplifies matrix-vector overlap, and supports a unified software stack. The architecture is integrated into four open-source CPU RTL platforms and evaluated on representative AI models. Matrix unit utilization under GEMM workloads exceeds 90% across all platforms. When configured with compute throughput and memory bandwidth comparable to Intel AMX, our design achieves speedups of 1.57x, 1.57x, and 2.31x on ResNet, BERT, and Llama3, with over 30% of the gains attributed to overlapped matrix-vector execution. A 4 TOPS@2GHz matrix unit occupies only 0.53 mm\textsuperscript{2} in 14nm CMOS. These results demonstrate strong cross-platform adaptability and effective hardware-software co-optimization, offering a practical matrix extension for the open-source community.

cs.AR↗

arXiv2025

Fun-ASR Technical Report

In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling, model size scaling, and deep integration with large language models (LLMs). However, LLMs are prone to hallucination, which can significantly degrade user experience in real-world ASR applications. In this paper, we present Fun-ASR, a large-scale, LLM-based ASR system that synergistically combines massive data, large model capacity, LLM integration, and reinforcement learning to achieve state-of-the-art performance across diverse and complex speech recognition scenarios. Moreover, Fun-ASR is specifically optimized for practical deployment, with enhancements in streaming capability, noise robustness, code-switching, hotword customization, and satisfying other real-world application requirements. Experimental results show that while most LLM-based ASR systems achieve strong performance on open-source benchmarks, they often underperform on real industry evaluation sets. Thanks to production-oriented optimizations, Fun-ASR achieves state-of-the-art performance on real application datasets, demonstrating its effectiveness and robustness in practical settings. The code and models are accessible at https://github.com/FunAudioLLM/Fun-ASR .

cs.CL↗

arXiv2025

UltraDfeGAN: Detail-Enhancing Generative Adversarial Networks for High-Fidelity Functional Ultrasound Synthesis

Functional ultrasound (fUS) is a neuroimaging technique known for its high spatiotemporal resolution, enabling non-invasive observation of brain activity through neurovascular coupling. Despite its potential in clinical applications such as neonatal monitoring and intraoperative guidance, the development of fUS faces challenges related to data scarcity and limitations in generating realistic fUS images. This paper explores the use of a generative adversarial network (GAN) framework tailored for fUS image synthesis. The proposed method incorporates architectural enhancements, including feature enhancement modules and normalization techniques, aiming to improve the fidelity and physiological plausibility of generated images. The study evaluates the performance of the framework against existing generative models, demonstrating its capability to produce high-quality fUS images under various experimental conditions. Additionally, the synthesized images are assessed for their utility in downstream tasks, showing improvements in classification accuracy when used for data augmentation. Experimental results are based on publicly available fUS datasets, highlighting the framework's effectiveness in addressing data limitations.

eess.IV↗

arXiv2025

SoK: Automated Vulnerability Repair: Methods, Tools, and Assessments

The increasing complexity of software has led to the steady growth of vulnerabilities. Vulnerability repair investigates how to fix software vulnerabilities. Manual vulnerability repair is labor-intensive and time-consuming because it relies on human experts, highlighting the importance of Automated Vulnerability Repair (AVR). In this SoK, we present the systematization of AVR methods through the three steps of AVR workflow: vulnerability analysis, patch generation, and patch validation. We assess AVR tools for C/C++ and Java programs as they have been widely studied by the community. Since existing AVR tools for C/C++ programs are evaluated with different datasets, which often consist of a few vulnerabilities, we construct the first C/C++ vulnerability repair benchmark dataset, dubbed Vul4C, which contains 144 vulnerabilities as well as their exploits and patches. We use Vul4C to evaluate seven AVR tools for C/C++ programs and use the third-party Vul4J dataset to evaluate two AVR tools for Java programs. We also discuss future research directions.

cs.SE↗

arXiv2024

Identification of important nodes in the information propagation network based on the artificial intelligence method

This study presents an integrated approach for identifying key nodes in information propagation networks using advanced artificial intelligence methods. We introduce a novel technique that combines the Decision-making Trial and Evaluation Laboratory (DEMATEL) method with the Global Structure Model (GSM), creating a synergistic model that effectively captures both local and global influences within a network. This method is applied across various complex networks, such as social, transportation, and communication systems, utilizing the Global Network Influence Dataset (GNID). Our analysis highlights the structural dynamics and resilience of these networks, revealing insights into node connectivity and community formation. The findings demonstrate the effectiveness of our AI-based approach in offering a comprehensive understanding of network behavior, contributing significantly to strategic network analysis and optimization.

cs.SI↗

arXiv2024

Structural Resilience and Connectivity of the IPv6 Internet: An AS-level Topology Examination

The study utilizes a comprehensive dataset informed by IPv6 routing information to provide statistics, degree distribution, joint degree distribution, and clustering analysis of the IPv6 Internet's structure and resilience.The dataset includes 17,232 unique ASes and 10,000 unique IPv6 prefixes. Analysis reveals an interconnected network with an average path length of approximately 3 hops, suggesting a robust and efficient network with potential redundancy and resilience, despite some isolated components. The paper outlines the degree distribution, indicating many peripheral nodes in a sparse network, and a clustering analysis showing a tendency for ASes to form clusters, which is indicative of redundancy and robustness against failures. The connectivity analysis, including path redundancy and reachability, supports the network's resilience.The findings are crucial for network design and strategic planning, particularly as IPv6 adoption increases. The paper emphasizes the importance of continuous monitoring and improvement of network connectivity in the evolving Internet landscape, highlighting the IPv6 Internet's resilience and structured connectivity.

cs.NI↗

arXiv2024

Curriculum Recommendations Using Transformer Base Model with InfoNCE Loss And Language Switching Method

The Curriculum Recommendations paradigm is dedicated to fostering learning equality within the ever-evolving realms of educational technology and curriculum development. In acknowledging the inherent obstacles posed by existing methodologies, such as content conflicts and disruptions from language translation, this paradigm aims to confront and overcome these challenges. Notably, it addresses content conflicts and disruptions introduced by language translation, hindrances that can impede the creation of an all-encompassing and personalized learning experience. The paradigm's objective is to cultivate an educational environment that not only embraces diversity but also customizes learning experiences to suit the distinct needs of each learner. To overcome these challenges, our approach builds upon notable contributions in curriculum development and personalized learning, introducing three key innovations. These include the integration of Transformer Base Model to enhance computational efficiency, the implementation of InfoNCE Loss for accurate content-topic matching, and the adoption of a language switching strategy to alleviate translation-related ambiguities. Together, these innovations aim to collectively tackle inherent challenges and contribute to forging a more equitable and effective learning journey for a diverse range of learners. Competitive cross-validation scores underscore the efficacy of sentence-transformers/LaBSE, achieving 0.66314, showcasing our methodology's effectiveness in diverse linguistic nuances for content alignment prediction. Index Terms-Curriculum Recommendation, Transformer model with InfoNCE Loss, Language Switching.

cs.CL↗

arXiv2024

Automated Scoring of Clinical Patient Notes using Advanced NLP and Pseudo Labeling

Clinical patient notes are critical for documenting patient interactions, diagnoses, and treatment plans in medical practice. Ensuring accurate evaluation of these notes is essential for medical education and certification. However, manual evaluation is complex and time-consuming, often resulting in variability and resource-intensive assessments. To tackle these challenges, this research introduces an approach leveraging state-of-the-art Natural Language Processing (NLP) techniques, specifically Masked Language Modeling (MLM) pretraining, and pseudo labeling. Our methodology enhances efficiency and effectiveness, significantly reducing training time without compromising performance. Experimental results showcase improved model performance, indicating a potential transformation in clinical note assessment.

cs.CL↗

arXiv2023

Rechargeable Li/Cl$_2$ battery down to -80 °C

Low temperature rechargeable batteries are important to life in cold climates, polar/deep-sea expeditions and space explorations. Here, we report ~ 3.5 - 4 V rechargeable lithium/chlorine (Li/Cl2) batteries operating down to -80 °C, employing Li metal negative electrode, a novel CO2 activated porous carbon (KJCO2) as the positive electrode, and a high ionic conductivity (~ 5 to 20 mS cm-1 from -80 °C to 25 °C) electrolyte comprised of 1 M aluminum chloride (AlCl3), 0.95 M lithium chloride (LiCl), and 0.05 M lithium bis(fluorosulfonyl)imide (LiFSI) in low melting point (-104.5 °C) thionyl chloride (SOCl2). Between room-temperature and -80 °C, the Li/Cl2 battery delivered up to ~ 30,000 - 4,500 mAh g-1 first discharge capacity and a 1,200 - 5,000 mAh g-1 reversible capacity (discharge voltages in ~ 3.5 to 3.1 V) over up to 130 charge-discharge cycles. Mass spectrometry and X-ray photoelectron spectroscopy (XPS) probed Cl2 trapped in the porous carbon upon LiCl electro-oxidation during charging. At lower temperature down to -80 °C, SCl2/S2Cl2 and Cl2 generated by electro-oxidation in the charging step were trapped in porous KJCO2 carbon, allowing for reversible reduction to afford a high discharge voltage plateau near ~ 4 V with up to ~ 1000 mAh g-1 capacity for SCl2/S2Cl2 reduction and up to ~ 4000 mAh g-1 capacity at ~ 3.1 V plateau for Cl2 reduction. Towards practical use, we made CR2032 Li/Cl2 battery cells to drive digital watches at -40 °C and light emitting diode at -80 °C, opening Li/Cl2 secondary batteries for ultra-cold conditions.

physics.app-ph↗

arXiv2020

Liquid-like growth of colloidal nanocrystals of coalescence

Our understanding of the growth of crystals is dominated by the classical description according to which individual atoms or molecules, driven by supersaturation, add to crystal facets. As a result, the growth of hard matter is still mostly considered to be fundamentally incomparable to the growth of soft matter, like polymers or liquids. By a combination of experiment and modeling we here show how amine-capped PbS colloidal nanoparticles grow in the absence of supersaturation by coalescence, like droplets in an emulsion. Specifically, we (i) determine that the rates of crystal-crystal coalescence are remarkably high (10^-2 to 10^1 M^-1*s^-1) in spite of the steric stabilization of the particles, and are comparable to those of bimolecular reactions, (thereby providing a new avenue for the development of a form of chemistry where the reactants are colloids rather than molecules), (ii) elucidate the rate limiting steps of crystal-crystal coalescence leading us to propose design rules to control it, and (iii) demonstrate a simple, two-parameter model that predicts quantitatively this process and its dependence on the ligands. Lastly, we use Brownian dynamics simulations to show how crowding effects and the relatively large size of the particles compared to their mean free path explain these remarkably large rates of coalescence and, at the same time, the puzzlingly low values of activation energy previously reported for oriented attachment processes.

physics.chem-ph↗

arXiv2020

The Lunar Lander Neutron and Dosimetry (LND) Experiment on Chang'E 4

Chang'E 4 is the first mission to the far side of the Moon and consists of a lander, a rover, and a relay spacecraft. Lander and rover were launched at 18:23 UTC on December 7, 2018 and landed in the von Kármán crater at 02:26 UTC on January 3, 2019. Here we describe the Lunar Lander Neutron \& Dosimetry experiment (LND) which is part of the Chang'E 4 Lander scientific payload. Its chief scientific goal is to obtain first active dosimetric measurements on the surface of the Moon. LND also provides observations of fast neutrons which are a result of the interaction of high-energy particle radiation with the lunar regolith and of their thermalized counterpart, thermal neutrons, which are a sensitive indicator of subsurface water content.

astro-ph.IM↗

arXiv2019

Simultaneous generation of two THz waves with bulk LiNbO3 and four THz waves with PPLN by coupled optical parametric generation

We present a theoretical research concerning simultaneous generation of two terahertz (THz) waves with bulk LiNbO3 and four THz waves with periodically poled LiNbO3 (PPLN) by coupled optical parametric generation (COPG). First, we investigate collinear phase matching of COPG generating two orthogonally polarized THz waves with two types of phase matching of o = e + e and o = e + o with bulk LiNbO3. The two orthogonally polarized THz waves are generated from stimulated polariton scattering (SPS) with A1 and E symmetric transverse optical (TO) modes in bulk LiNbO3, respectively. Then, we find that perturbations of phase mismatch for o = e + e and o = e + o can be compensated by a same grating vector of PPLN. As a result, four THz waves are simultaneously generated with a PPLN crystal and a pump laser. We calculate third-order nonlinear optical coefficients of o = e + o generating THz waves from E symmetric TO modes. The intensities of four THz waves are calculated by solving coupled wave equations. The calculation results demonstrate that the COPG generating four THz waves have high photon conversion efficiencies.

physics.optics↗

arXiv2017

3D Printing of Flexible Room-Temperature Liquid Metal Battery

The entirely soft and transformable room-temperature liquid metal battery based on 3D hybrid printing was proposed and experimentally demonstrated. Liquid metal gallium and conductive gel were employed as the negative and positive electrode material, respectively. The discharge process of batteries with different structure, single-cell and multi-cell, were clarified. The results indicated that the obtained battery possessed rather stable and persisted discharge property, but the multi-cell battery presented stair-step decrease overtime. SEM and EDS images were obtained to evaluate the morphological change of the electrode during the discharge process, and the liquid metal was found to be easily exposed and active according to the solubility of the alkaline electrolyte to oxidized gallium. The successful 3D hybrid printing allowed different polymers, including PLA, ABS and TPU, to be precisely patterned in any required battery body structures, with liquid electrodes in appropriate specified positions. The flexibility and practicability of this new generation battery was illustrated through working devices. The present soft battery offers important power supply for future wearable and epidermal electronics.

physics.app-ph↗