arXiv subjects

Jie Zhang

Publications and source records attributed to Jie Zhang.

At least 325 records · Page 18Linked to original sources

arXiv2025

ASTROFLOW: A Real-Time End-to-End Pipeline for Radio Single-Pulse Searches

Fast radio bursts (FRBs) are extremely bright, millisecond duration cosmic transients of unknown origin. The growing number of wide-field and high-time-resolution radio surveys, particularly with next-generation facilities such as the SKA and MeerKAT, will dramatically increase FRB discovery rates, but also produce data volumes that overwhelm conventional search pipelines. Real-time detection thus demands software that is both algorithmically robust and computationally efficient. We present Astroflow, an end-to-end, GPU-accelerated pipeline for single-pulse detection in radio time-frequency data. Built on a unified C++/CUDA core with a Python interface, Astroflow integrates RFI excision, incoherent dedispersion, dynamic-spectrum tiling, and a YOLO-based deep detector. Through vectorized memory access, shared-memory tiling, and OpenMP parallelism, it achieves 10x faster-than-real-time processing on consumer GPUs for a typical 150 s, 2048-channel observation, while preserving high sensitivity across a wide range of pulse widths and dispersion measures. These results establish the feasibility of a fully integrated, GPU-accelerated single-pulse search stack, capable of scaling to the data volumes expected from upcoming large-scale surveys. Astroflow offers a reusable and deployable solution for real-time transient discovery, and provides a framework that can be continuously refined with new data and models.

astro-ph.IM↗

arXiv2025

DepthVanish: Optimizing Adversarial Interval Structures for Stereo-Depth-Invisible Patches

Stereo depth estimation is a critical task in autonomous driving and robotics, where inaccuracies (such as misidentifying nearby objects as distant) can lead to dangerous situations. Adversarial attacks against stereo depth estimation can help reveal vulnerabilities before deployment. Previous works have shown that repeating optimized textures can effectively mislead stereo depth estimation in digital settings. However, our research reveals that these naively repeated textures perform poorly in physical implementations, i.e., when deployed as patches, limiting their practical utility for stress-testing stereo depth estimation systems. In this work, for the first time, we discover that introducing regular intervals among the repeated textures, creating a grid structure, significantly enhances the patch's attack performance. Through extensive experimentation, we analyze how variations of this novel structure influence the adversarial effectiveness. Based on these insights, we develop a novel stereo depth attack that jointly optimizes both the interval structure and texture elements. Our generated adversarial patches can be inserted into any scenes and successfully attack advanced stereo depth estimation methods of different paradigms, i.e., RAFT-Stereo and STTR. Most critically, our patch can also attack commercial RGB-D cameras (Intel RealSense) in real-world conditions, demonstrating their practical relevance for security assessment of stereo systems. The code is officially released at: https://github.com/WiWiN42/DepthVanish

cs.CV↗

arXiv2025

When is the operation $τ_{T,L}$ a triangle function on $\Del^+$?

This paper resolves an open problem posed by Schweizer and Sklar in 1983. We establish that the binary operation $\tauTL$ is a triangle function on $\Delp$ if and only if the following three conditions hold: (a) $L$ is a continuous t-conorm on $[0, \infty]$ satisfying $(LCS)$; (b) $T$ is a t-norm on $[0, 1]$; and (c) $T$ is weakly left continuous, with left continuity required when $L$ is non-Archimedean.

math.GN↗

arXiv2025

Synergistic Tensor and Pipeline Parallelism

In the machine learning system, the hybrid model parallelism combining tensor parallelism (TP) and pipeline parallelism (PP) has become the dominant solution for distributed training of Large Language Models~(LLMs) and Multimodal LLMs (MLLMs). However, TP introduces significant collective communication overheads, while PP suffers from synchronization inefficiencies such as pipeline bubbles. Existing works primarily address these challenges from isolated perspectives, focusing either on overlapping TP communication or on flexible PP scheduling to mitigate pipeline bubbles. In this paper, we propose a new synergistic tensor and pipeline parallelism schedule that simultaneously reduces both types of bubbles. Our proposed schedule decouples the forward and backward passes in PP into fine-grained computation units, which are then braided to form a composite computation sequence. This compositional structure enables near-complete elimination of TP-related bubbles. Building upon this structure, we further design the PP schedule to minimize PP bubbles. Experimental results demonstrate that our approach improves training throughput by up to 12% for LLMs and 16% for MLLMs compared to existing scheduling methods. Our source code is avaiable at https://github.com/MICLAB-BUPT/STP.

cs.DC↗

arXiv2025

IRCopilot: Automated Incident Response with Large Language Models

Incident response plays a pivotal role in mitigating the impact of cyber attacks. In recent years, the intensity and complexity of global cyber threats have grown significantly, making it increasingly challenging for traditional threat detection and incident response methods to operate effectively in complex network environments. While Large Language Models (LLMs) have shown great potential in early threat detection, their capabilities remain limited when it comes to automated incident response after an intrusion. To address this gap, we construct an incremental benchmark based on real-world incident response tasks to thoroughly evaluate the performance of LLMs in this domain. Our analysis reveals several key challenges that hinder the practical application of contemporary LLMs, including context loss, hallucinations, privacy protection concerns, and their limited ability to provide accurate, context-specific recommendations. In response to these challenges, we propose IRCopilot, a novel framework for automated incident response powered by LLMs. IRCopilot mimics the three dynamic phases of a real-world incident response team using four collaborative LLM-based session components. These components are designed with clear divisions of responsibility, reducing issues such as hallucinations and context loss. Our method leverages diverse prompt designs and strategic responsibility segmentation, significantly improving the system's practicality and efficiency. Experimental results demonstrate that IRCopilot outperforms baseline LLMs across key benchmarks, achieving sub-task completion rates of 150%, 138%, 136%, 119%, and 114% for various response tasks. Moreover, IRCopilot exhibits robust performance on public incident response platforms and in real-world attack scenarios, showcasing its strong applicability.

cs.CR↗

arXiv2025

Engulfment of Eccentric Planets by Giant Stars: Hydrodynamics and Light Curves

Recent observations suggest that planetary engulfment by a giant star may produce radiation that resembles subluminous red novae. We present three-dimensional hydrodynamical simulations of the interaction between an eccentric $5 \,M_J$ giant planet and its $1\,M_\odot$ red-giant host star. The planet's pericenter is initially $60\%$ of the stellar radius and is fully engulfed after tens of orbits. Once inside the stellar envelope, the planet generates pressure disturbances that steepen into shocks, ejecting material from the envelope. We use post-processing to calculate the light curves produced by planetary engulfment. We find that the hot stellar ejecta enhances the stellar luminosity by several orders of magnitude. A prolonged hydrogen recombination plateau appears when the ejecta cools to about $10^4\,\rm{K}$. The late-time rapid dimming of the light curve follows dust formation, which obscures the radiation. For planets with lower eccentricity, the orbital decay proceeds more slowly, although the observable properties remain similar.

astro-ph.EP↗

arXiv2025

Coherently Enhanced Axion-Photon Conversion via Seeded Photons for Short-Pulse Axion Detection

We propose a seeded axion-photon conversion scheme to enhance the sensitivity of light-shining-through-a-wall (LSW) experiments for axion detection, where the axions are generated from short pulse lasers and the usual resonant cavity is not applicable. By injecting a weak, coherent seed electromagnetic (EM) field into the axion-photon conversion region, the axion-induced EM field can constructively interfere with the seed field, amplifying the number of regenerated photons to a level exceeding that of the unseeded scenario. We evaluate the expected signal enhancement, statistical limits from Poisson counting with seed fluctuations and background, and the potential improvement in coupling sensitivity. Compared to a standard LSW setup, the seeded scheme can achieve orders-of-magnitude higher photon yield per axion, potentially surpassing resonance-enhanced experiments in certain parameter regimes. This approach presents a promising pathway to extend the reach of laboratory axion searches, particularly in scenarios where the resonant cavities are impractical.

hep-ph↗

arXiv2025

FaceTracer: Unveiling Source Identities from Swapped Face Images and Videos for Fraud Prevention

Face-swapping techniques have advanced rapidly with the evolution of deep learning, leading to widespread use and growing concerns about potential misuse, especially in cases of fraud. While many efforts have focused on detecting swapped face images or videos, these methods are insufficient for tracing the malicious users behind fraudulent activities. Intrusive watermark-based approaches also fail to trace unmarked identities, limiting their practical utility. To address these challenges, we introduce FaceTracer, the first non-intrusive framework specifically designed to trace the identity of the source person from swapped face images or videos. Specifically, FaceTracer leverages a disentanglement module that effectively suppresses identity information related to the target person while isolating the identity features of the source person. This allows us to extract robust identity information that can directly link the swapped face back to the original individual, aiding in uncovering the actors behind fraudulent activities. Extensive experiments demonstrate FaceTracer's effectiveness across various face-swapping techniques, successfully identifying the source person in swapped content and enabling the tracing of malicious actors involved in fraudulent activities. Additionally, FaceTracer shows strong transferability to unseen face-swapping methods including commercial applications and robustness against transmission distortions and adaptive attacks.Our code is available at: https://github.com/zzy224/FaceTracer.

cs.CV↗

arXiv2025

QAE-BAC: Achieving Quantifiable Anonymity and Efficiency in Blockchain-Based Access Control with Attribute

Blockchain-based Attribute-Based Access Control (BC-ABAC) offers a decentralized paradigm for secure data governance but faces two inherent challenges: the transparency of blockchain ledgers threatens user privacy by enabling reidentification attacks through attribute analysis, while the computational complexity of policy matching clashes with blockchain's performance constraints. Existing solutions, such as those employing Zero-Knowledge Proofs (ZKPs), often incur high overhead and lack measurable anonymity guarantees, while efficiency optimizations frequently ignore privacy implications. To address these dual challenges, this paper proposes QAEBAC (Quantifiable Anonymity and Efficiency in Blockchain-Based Access Control with Attribute). QAE-BAC introduces a formal (r, t)-anonymity model to dynamically quantify the re-identification risk of users based on their access attributes and history. Furthermore, it features an Entropy-Weighted Path Tree (EWPT) that optimizes policy structure based on realtime anonymity metrics, drastically reducing policy matching complexity. Implemented and evaluated on Hyperledger Fabric, QAE-BAC demonstrates a superior balance between privacy and performance. Experimental results show that it effectively mitigates re-identification risks and outperforms state-of-the-art baselines, achieving up to an 11x improvement in throughput and an 87% reduction in latency, proving its practicality for privacy-sensitive decentralized applications.

cs.CR↗

arXiv2025

Mask Image Watermarking

We present MaskWM, a simple, efficient, and flexible framework for image watermarking. MaskWM has two variants: (1) MaskWM-D, which supports global watermark embedding, watermark localization, and local watermark extraction for applications such as tamper detection; (2) MaskWM-ED, which focuses on local watermark embedding and extraction, offering enhanced robustness in small regions to support fine-grined image protection. MaskWM-D builds on the classical encoder-distortion layer-decoder training paradigm. In MaskWM-D, we introduce a simple masking mechanism during the decoding stage that enables both global and local watermark extraction. During training, the decoder is guided by various types of masks applied to watermarked images before extraction, helping it learn to localize watermarks and extract them from the corresponding local areas. MaskWM-ED extends this design by incorporating the mask into the encoding stage as well, guiding the encoder to embed the watermark in designated local regions, which improves robustness under regional attacks. Extensive experiments show that MaskWM achieves state-of-the-art performance in global and local watermark extraction, watermark localization, and multi-watermark embedding. It outperforms all existing baselines, including the recent leading model WAM for local watermarking, while preserving high visual quality of the watermarked images. In addition, MaskWM is highly efficient and adaptable. It requires only 20 hours of training on a single A6000 GPU, achieving 15x computational efficiency compared to WAM. By simply adjusting the distortion layer, MaskWM can be quickly fine-tuned to meet varying robustness requirements.

cs.CV↗

arXiv2025

RealMath: A Continuous Benchmark for Evaluating Language Models on Research-Level Mathematics

Existing benchmarks for evaluating mathematical reasoning in large language models (LLMs) rely primarily on competition problems, formal proofs, or artificially challenging questions -- failing to capture the nature of mathematics encountered in actual research environments. We introduce RealMath, a novel benchmark derived directly from research papers and mathematical forums that assesses LLMs' abilities on authentic mathematical tasks. Our approach addresses three critical challenges: sourcing diverse research-level content, enabling reliable automated evaluation through verifiable statements, and designing a continually refreshable dataset to mitigate contamination risks. Experimental results across multiple LLMs reveal surprising capabilities in handling research mathematics compared to competition problems, suggesting current models may already serve as valuable assistants for working mathematicians despite limitations on highly challenging problems. The code and dataset for RealMath are publicly available.

cs.AI↗

arXiv2025

Black-box Optimization of LLM Outputs by Asking for Directions

We present a novel approach for attacking black-box large language models (LLMs) by exploiting their ability to express confidence in natural language. Existing black-box attacks require either access to continuous model outputs like logits or confidence scores (which are rarely available in practice), or rely on proxy signals from other models. Instead, we demonstrate how to prompt LLMs to express their internal confidence in a way that is sufficiently calibrated to enable effective adversarial optimization. We apply our general method to three attack scenarios: adversarial examples for vision-LLMs, jailbreaks and prompt injections. Our attacks successfully generate malicious inputs against systems that only expose textual outputs, thereby dramatically expanding the attack surface for deployed LLMs. We further find that better and larger models exhibit superior calibration when expressing confidence, creating a concerning security paradox where model capability improvements directly enhance vulnerability. Our code is available at this [link](https://github.com/zj-jayzhang/black_box_llm_optimization).

cs.CR↗

arXiv2025

EditMark: Watermarking Large Language Models based on Model Editing

Large Language Models (LLMs) have demonstrated remarkable capabilities, but their training requires extensive data and computational resources, rendering them valuable digital assets. Therefore, it is essential to watermark LLMs to protect their copyright and trace unauthorized use or resale. Existing methods for watermarking LLMs primarily rely on training LLMs with a watermarked dataset, which entails burdensome training costs and negatively impacts the LLM's performance. In addition, their watermarked texts are not logical or natural, thereby reducing the stealthiness of the watermark. To address these issues, we propose EditMark, the first watermarking method that leverages model editing to embed a training-free, stealthy, and performance-lossless watermark for LLMs. We observe that some questions have multiple correct answers. Therefore, we assign each answer a unique watermark and update the weights of LLMs to generate corresponding questions and answers through the model editing technique. In addition, we refine the model editing technique to align with the requirements of watermark embedding. Specifically, we introduce an adaptive multi-round stable editing strategy, coupled with the injection of a noise matrix, to improve both the effectiveness and robustness of the watermark embedding. Extensive experiments indicate that EditMark can embed 32-bit watermarks into LLMs within 20 seconds (Fine-tuning: 6875 seconds) with a watermark extraction success rate of 100%, which demonstrates its effectiveness and efficiency. External experiments further demonstrate that EditMark has fidelity, stealthiness, and a certain degree of robustness against common attacks.

cs.CR↗

arXiv2025

Patronus: Safeguarding Text-to-Image Models against White-Box Adversaries

Text-to-image (T2I) models, though exhibiting remarkable creativity in image generation, can be exploited to produce unsafe images. Existing safety measures, e.g., content moderation or model alignment, fail in the presence of white-box adversaries who know and can adjust model parameters, e.g., by fine-tuning. This paper presents a novel defensive framework, named Patronus, which equips T2I models with holistic protection to defend against white-box adversaries. Specifically, we design an internal moderator that decodes unsafe input features into zero vectors while ensuring the decoding performance of benign input features. Furthermore, we strengthen the model alignment with a carefully designed non-fine-tunable learning mechanism, ensuring the T2I model will not be compromised by malicious fine-tuning. We conduct extensive experiments to validate the intactness of the performance on safe content generation and the effectiveness of rejecting unsafe content generation. Results also confirm the resilience of Patronus against various fine-tuning attacks by white-box adversaries.

cs.CR↗

arXiv2025

DCMIL: A Progressive Representation Learning of Whole Slide Images for Cancer Prognosis Analysis

The burgeoning discipline of computational pathology shows promise in harnessing whole slide images (WSIs) to quantify morphological heterogeneity and develop objective prognostic modes for human cancers. However, progress is impeded by the computational bottleneck of gigapixel-size inputs and the scarcity of dense manual annotations. Current methods often overlook fine-grained information across multi-magnification WSIs and variations in tumor microenvironments. Here, we propose an easy-to-hard progressive representation learning, termed dual-curriculum contrastive multi-instance learning (DCMIL), to efficiently process WSIs for cancer prognosis. The model does not rely on dense annotations and enables the direct transformation of gigapixel-size WSIs into outcome predictions. Extensive experiments on twelve cancer types (5,954 patients, 12.54 million tiles) demonstrate that DCMIL outperforms standard WSI-based prognostic models. Additionally, DCMIL identifies fine-grained prognosis-salient regions, provides robust instance uncertainty estimation, and captures morphological differences between normal and tumor tissues, with the potential to generate new biological insights. All codes have been made publicly accessible at https://github.com/tuuuc/DCMIL.

cs.CV↗

arXiv2025

Multi-agent Power Grid Restoration Under Uncertainty Considering Coupled Transportation-Power Networks

Restoring power distribution systems after extreme events such as tornadoes presents significant logistical and computational challenges. The complexity arises from the need to coordinate multiple repair crews under uncertainty, manage interdependent infrastructure failures, and respect strict sequencing and routing constraints. Existing methods often rely on deterministic heuristics or simplified models that fail to capture the interdependencies between power and transportation networks, do not adequately model uncertainty, and lack representation of the interrelated dynamics and dependencies among different types of repair crews--leading to suboptimal restoration outcomes. To address these limitations, we develop a stochastic two-stage mixed-integer programming framework for proactive crew allocation, assignment, and routing in power grid restoration. The primary objective of our framework is to minimize service downtime and enhance power restoration by efficiently coordinating repair operations under uncertainty. Multiple repair crews are modeled as distinct agents, enabling decentralized coordination and efficient task allocation across the network. To validate our approach, we conduct a case study using the IEEE 8500-node test feeder integrated with a real transportation network from the Dallas-Fort Worth (DFW) region. Additionally, we use tornado event data from the DFW area to construct realistic failure scenarios involving damaged grid components and transportation links. Results from our case study demonstrate that the proposed method enables more coordinated and efficient restoration strategies. The model facilitates real-time disaster response by supporting timely and practical power grid restoration, with a strong emphasis on interoperability and crew schedule coordination.

math.OC↗

arXiv2025

LazyEviction: Lagged KV Eviction with Attention Pattern Observation for Efficient Long Reasoning

Large Language Models (LLMs) exhibit enhanced capabilities by Chain-of-Thought reasoning. However, the extended reasoning sequences introduce significant GPU memory overhead due to increased key-value (KV) cache. Existing KV cache compression methods mitigate memory bottlenecks but struggle in long reasoning tasks. In this paper, we analyze attention patterns in reasoning tasks and reveal a Token Importance Recurrence phenomenon: a large proportion of tokens regain high attention after multiple decoding steps, which is failed to capture by existing works and may lead to unpredictable eviction on such periodically critical tokens. To address this, we propose LazyEviction, an observation window-based lagged eviction framework retaining latent recurring tokens by prioritized eviction based on tokens' recurrence patterns. Extensive experiments demonstrate that LazyEviction reduces KV cache by 50%~70% while maintaining comparable accuracy, outperforming existing KV cache compression baselines. Our implementation code can be found at https://github.com/Halo-949/LazyEviction.

cs.LG↗

arXiv2025

Magnetically controllable nonlinear valley Hall effect in centrosymmetric ferromagnets

Valley Hall effect is fundamental to valleytronics and provides a promising avenue for advancing information technology. While conventional valley Hall effect requires the inversion symmetry breaking, the recently proposed nonlinear valley Hall (NVH) effect removes the symmetry constraint, and broaden material choices. However, existing studies are limited to nonmagnetic materials without spin involvement and rely on external strain to break rotational symmetry. Here, to address these limitations, we design a magnetically controllable NVH effect in centrosymmetric ferromagnets, by the tight-binding model and first-principles calculations. The model calculations demonstrate nonvanishing NVH conductivities can emerge in pristine hexagonal lattice without external strain, with the magnitude, sign, and spin polarization of the conductivities being all dependent on the magnetization orientation. The effect thus generates various spin-polarized valley Hall currents, characterized by distinct combinations of current direction and spin polarization. First-principle results on a ferromagnetic VSi$_2$N$_4$ bilayer confirm considerable NVH conductivities and their dependence on the magnetization. The magnetically controllable NVH effect unlocks the potential of centrosymmetric magnets for valleytronics, and offer opportunities for novel spintronic and valleytronic devices.

cond-mat.mes-hall↗