arXiv subjects

Jie Zhang

Publications and source records attributed to Jie Zhang.

At least 415 records · Page 23Linked to original sources

arXiv2025

Self-organization, detailed balance, and stress-structure correlations in 2D granular dynamics

We argue that a number of recent experimental and numerical observations point to an ongoing cooperative stress-structure self-organisation (SO) in quasi-static granular dynamics. These observations include: a) detail-insensitive collapses of certain quantities; b) correlations between stress and structure and evidence of entropy-stability competition in settled packings, which cast doubt on most linear stress theories of granular materials; c) detailed balanced steady states, which seem contradictory to the common belief that only systems in thermal equilibrium satisfy detailed balance, but are not, as we explain. We then propose a new statistical mechanical formulation that takes into account the cooperative SO.

cond-mat.stat-mech↗

arXiv2025

Spider: Any-to-Many Multimodal LLM

Multimodal LLMs (MLLMs) have emerged as an extension of Large Language Models (LLMs), enabling the integration of various modalities. However, Any-to-Any MLLMs are limited to generating pairwise modalities 'Text + X' within a single response, such as Text + {Image or Audio or Video}. To address this limitation, we introduce Spider, a novel efficient Any-to-Many Modalities Generation (AMMG) framework, which can generate an arbitrary combination of modalities 'Text + Xs', such as Text + {Image and Audio and Video}. To achieve efficient AMMG, our Spider integrates three core components: a Base Model for basic X-to-X (i.e., Any-to-Any) modality processing, an Any-to-Many Instruction Template designed for producing Xs signal prompts, and a novel Efficient Decoders-Controller for controlling multimodal Decoders to generate Xs (many-modal) contents. To train Spider, we constructed a novel Text-formatted Many-Modal (TMM) dataset, which facilitates learning the X-to-Xs (i.e., Any-to-Many) capability necessary for AMMG. Ultimately, the well-trained Spider generates a pseudo X-to-Xs dataset, the first-ever X-to-Xs many-modal dataset, enhancing the potential for AMMG tasks in future research. Overall, this work not only pushes the boundary of multimodal interaction but also provides rich data support for advancing the field. Code: https://github.com/Layjins/Spider

cs.CV↗

arXiv2025

BoxSeg: Quality-Aware and Peer-Assisted Learning for Box-supervised Instance Segmentation

Box-supervised instance segmentation methods aim to achieve instance segmentation with only box annotations. Recent methods have demonstrated the effectiveness of acquiring high-quality pseudo masks under the teacher-student framework. Building upon this foundation, we propose a BoxSeg framework involving two novel and general modules named the Quality-Aware Module (QAM) and the Peer-assisted Copy-paste (PC). The QAM obtains high-quality pseudo masks and better measures the mask quality to help reduce the effect of noisy masks, by leveraging the quality-aware multi-mask complementation mechanism. The PC imitates Peer-Assisted Learning to further improve the quality of the low-quality masks with the guidance of the obtained high-quality pseudo masks. Theoretical and experimental analyses demonstrate the proposed QAM and PC are effective. Extensive experimental results show the superiority of our BoxSeg over the state-of-the-art methods, and illustrate the QAM and PC can be applied to improve other models.

cs.CV↗

arXiv2025

Surgical Action Planning with Large Language Models

In robot-assisted minimally invasive surgery, we introduce the Surgical Action Planning (SAP) task, which generates future action plans from visual inputs to address the absence of intraoperative predictive planning in current intelligent applications. SAP shows great potential for enhancing intraoperative guidance and automating procedures. However, it faces challenges such as understanding instrument-action relationships and tracking surgical progress. Large Language Models (LLMs) show promise in understanding surgical video content but remain underexplored for predictive decision-making in SAP, as they focus mainly on retrospective analysis. Challenges like data privacy, computational demands, and modality-specific constraints further highlight significant research gaps. To tackle these challenges, we introduce LLM-SAP, a Large Language Models-based Surgical Action Planning framework that predicts future actions and generates text responses by interpreting natural language prompts of surgical goals. The text responses potentially support surgical education, intraoperative decision-making, procedure documentation, and skill analysis. LLM-SAP integrates two novel modules: the Near-History Focus Memory Module (NHF-MM) for modeling historical states and the prompts factory for action planning. We evaluate LLM-SAP on our constructed CholecT50-SAP dataset using models like Qwen2.5 and Qwen2-VL, demonstrating its effectiveness in next-action prediction. Pre-trained LLMs are tested in a zero-shot setting, and supervised fine-tuning (SFT) with LoRA is implemented. Our experiments show that Qwen2.5-72B-SFT surpasses Qwen2.5-72B with a 19.3% higher accuracy.

cs.CL↗

arXiv2025

Blind Baselines Beat Membership Inference Attacks for Foundation Models

Membership inference (MI) attacks try to determine if a data sample was used to train a machine learning model. For foundation models trained on unknown Web data, MI attacks are often used to detect copyrighted training materials, measure test set contamination, or audit machine unlearning. Unfortunately, we find that evaluations of MI attacks for foundation models are flawed, because they sample members and non-members from different distributions. For 8 published MI evaluation datasets, we show that blind attacks -- that distinguish the member and non-member distributions without looking at any trained model -- outperform state-of-the-art MI attacks. Existing evaluations thus tell us nothing about membership leakage of a foundation model's training data.

cs.CR↗

arXiv2025

Unlearning during Learning: An Efficient Federated Machine Unlearning Method

In recent years, Federated Learning (FL) has garnered significant attention as a distributed machine learning paradigm. To facilitate the implementation of the right to be forgotten, the concept of federated machine unlearning (FMU) has also emerged. However, current FMU approaches often involve additional time-consuming steps and may not offer comprehensive unlearning capabilities, which renders them less practical in real FL scenarios. In this paper, we introduce FedAU, an innovative and efficient FMU framework aimed at overcoming these limitations. Specifically, FedAU incorporates a lightweight auxiliary unlearning module into the learning process and employs a straightforward linear operation to facilitate unlearning. This approach eliminates the requirement for extra time-consuming steps, rendering it well-suited for FL. Furthermore, FedAU exhibits remarkable versatility. It not only enables multiple clients to carry out unlearning tasks concurrently but also supports unlearning at various levels of granularity, including individual data samples, specific classes, and even at the client level. We conducted extensive experiments on MNIST, CIFAR10, and CIFAR100 datasets to evaluate the performance of FedAU. The results demonstrate that FedAU effectively achieves the desired unlearning effect while maintaining model accuracy. Our code is availiable at https://github.com/Liar-Mask/FedAU.

cs.LG↗

arXiv2025

Clean Image May be Dangerous: Data Poisoning Attacks Against Deep Hashing

Large-scale image retrieval using deep hashing has become increasingly popular due to the exponential growth of image data and the remarkable feature extraction capabilities of deep neural networks (DNNs). However, deep hashing methods are vulnerable to malicious attacks, including adversarial and backdoor attacks. It is worth noting that these attacks typically involve altering the query images, which is not a practical concern in real-world scenarios. In this paper, we point out that even clean query images can be dangerous, inducing malicious target retrieval results, like undesired or illegal images. To the best of our knowledge, we are the first to study data \textbf{p}oisoning \textbf{a}ttacks against \textbf{d}eep \textbf{hash}ing \textbf{(\textit{PADHASH})}. Specifically, we first train a surrogate model to simulate the behavior of the target deep hashing model. Then, a strict gradient matching strategy is proposed to generate the poisoned images. Extensive experiments on different models, datasets, hash methods, and hash code lengths demonstrate the effectiveness and generality of our attack method.

cs.CV↗

arXiv2025

Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging

Logs produced by extensive software systems are integral to monitoring system behaviors. Advanced log analysis facilitates the detection, alerting, and diagnosis of system faults. Log parsing, which entails transforming raw log messages into structured templates, constitutes a critical phase in the automation of log analytics. Existing log parsers fail to identify the correct templates due to reliance on human-made rules. Besides, these methods focus on statistical features while ignoring semantic information in log messages. To address these challenges, we introduce a cutting-edge \textbf{L}og parsing framework with \textbf{E}ntropy sampling and chain-of-thought \textbf{M}erging (\model{}). Specifically, to discard the tedious manual rules, we propose a novel sampling method inspired by information entropy, which efficiently clusters typical logs. Furthermore, to enhance the merging of log templates, we design a chain-of-thought method for large language models (LLMs). LLMs exhibit exceptional semantic comprehension and deftly distinguish between parameters and invariant tokens. We have conducted experiments on large-scale public datasets. Extensive evaluation demonstrates that \model{} achieves state-of-the-art performance and impressive efficiency. The Code is available at https://github.com/zwpride/lemur.

cs.SE↗

arXiv2025

Experimental Evidence of Vortex $γ$ Photons in All-Optical Inverse Compton Scattering

Vortex $γ$ photons carrying orbital angular momenta (OAM) hold great potential for various applications. However, their generation remains a great challenge. Here, we successfully generate sub-MeV vortex $γ$ photons via all-optical inverse Compton scattering of relativistic electrons colliding with a sub-relativistic Laguerre-Gaussian laser. In principle, directly measuring the OAM of $γ$ photons is challenging due to their incoherence and extremely short wavelength. Therein, we put forward a novel method to determine the OAM properties by revealing the quantum opening angle of vortex $γ$ photons, since vortex particles exhibit not only a spiral phase but also transverse momentum according to the quantum electrodynamics theory. Thus,$γ$ photons carrying OAM anifest a much larger angular distribution than those without OAM, which has been clearly observed in our experiments. This angular expansion is considered as an overall effect lying beyond classical theory. Our method provides the first experimental evidence for detecting vortex $γ$ photons and opens a new perspective for investigating OAM-induced quantum phenomena in broad fields.

physics.plasm-ph↗

arXiv2025

Exploring Prosocial Irrationality for LLM Agents: A Social Cognition View

Large language models (LLMs) have been shown to face hallucination issues due to the data they trained on often containing human bias; whether this is reflected in the decision-making process of LLM Agents remains under-explored. As LLM Agents are increasingly employed in intricate social environments, a pressing and natural question emerges: Can we utilize LLM Agents' systematic hallucinations to mirror human cognitive biases, thus exhibiting irrational social intelligence? In this paper, we probe the irrational behavior among contemporary LLM Agents by melding practical social science experiments with theoretical insights. Specifically, We propose CogMir, an open-ended Multi-LLM Agents framework that utilizes hallucination properties to assess and enhance LLM Agents' social intelligence through cognitive biases. Experimental results on CogMir subsets show that LLM Agents and humans exhibit high consistency in irrational and prosocial decision-making under uncertain conditions, underscoring the prosociality of LLM Agents as social entities and highlighting the significance of hallucination properties. Additionally, the CogMir framework demonstrates its potential as a valuable platform for encouraging more research into the social intelligence of LLM Agents.

cs.CY↗

arXiv2025

REVAL: A Comprehension Evaluation on Reliability and Values of Large Vision-Language Models

The rapid evolution of Large Vision-Language Models (LVLMs) has highlighted the necessity for comprehensive evaluation frameworks that assess these models across diverse dimensions. While existing benchmarks focus on specific aspects such as perceptual abilities, cognitive capabilities, and safety against adversarial attacks, they often lack the breadth and depth required to provide a holistic understanding of LVLMs' strengths and limitations. To address this gap, we introduce REVAL, a comprehensive benchmark designed to evaluate the \textbf{RE}liability and \textbf{VAL}ue of LVLMs. REVAL encompasses over 144K image-text Visual Question Answering (VQA) samples, structured into two primary sections: Reliability, which assesses truthfulness (\eg, perceptual accuracy and hallucination tendencies) and robustness (\eg, resilience to adversarial attacks, typographic attacks, and image corruption), and Values, which evaluates ethical concerns (\eg, bias and moral understanding), safety issues (\eg, toxicity and jailbreak vulnerabilities), and privacy problems (\eg, privacy awareness and privacy leakage). We evaluate 26 models, including mainstream open-source LVLMs and prominent closed-source models like GPT-4o and Gemini-1.5-Pro. Our findings reveal that while current LVLMs excel in perceptual tasks and toxicity avoidance, they exhibit significant vulnerabilities in adversarial scenarios, privacy preservation, and ethical reasoning. These insights underscore critical areas for future improvements, guiding the development of more secure, reliable, and ethically aligned LVLMs. REVAL provides a robust framework for researchers to systematically assess and compare LVLMs, fostering advancements in the field.

cs.CV↗

arXiv2025

MERGE: Multi-faceted Hierarchical Graph-based GNN for Gene Expression Prediction from Whole Slide Histopathology Images

Recent advances in Spatial Transcriptomics (ST) pair histology images with spatially resolved gene expression profiles, enabling predictions of gene expression across different tissue locations based on image patches. This opens up new possibilities for enhancing whole slide image (WSI) prediction tasks with localized gene expression. However, existing methods fail to fully leverage the interactions between different tissue locations, which are crucial for accurate joint prediction. To address this, we introduce MERGE (Multi-faceted hiErarchical gRaph for Gene Expressions), which combines a multi-faceted hierarchical graph construction strategy with graph neural networks (GNN) to improve gene expression predictions from WSIs. By clustering tissue image patches based on both spatial and morphological features, and incorporating intra- and inter-cluster edges, our approach fosters interactions between distant tissue locations during GNN learning. As an additional contribution, we evaluate different data smoothing techniques that are necessary to mitigate artifacts in ST data, often caused by technical imperfections. We advocate for adopting gene-aware smoothing methods that are more biologically justified. Experimental results on gene expression prediction show that our GNN method outperforms state-of-the-art techniques across multiple metrics.

cs.CV↗

arXiv2025

Power Spectra of Velocity Fluctuations in Granular Heap Flow

This study used Speckle Visibility Spectroscopy to examine velocity fluctuations in a three-dimensional granular heap flow, where the mean velocity profile consists of a fast-flow surface layer and a creep layer beneath. The velocity spectra follow power-law scalings, $E(f) \propto f^α$, with $α\approx -0.85$ in the surface flow layer -- matching the Self-Organized Criticality (SOC) model with open boundaries ($α\approx -0.95$). In the creep layer, $α$ decreases with depth, reaching $α\approx -1.5$ at approximately 55 mean particle diameters, consistent with the SOC model with closed boundaries ($α\approx -1.58$). Analysis of the fluctuation velocity distributions offers additional insights into the microscopic origins of the spectrum's characteristics. These findings help resolve the long-standing puzzle of flow localization in gravity-driven granular flows despite a constant shear stress-to-pressure ratio throughout the material.

cond-mat.soft↗

arXiv2025

JailGuard: A Universal Detection Framework for LLM Prompt-based Attacks

The systems and software powered by Large Language Models (LLMs) and Multi-Modal LLMs (MLLMs) have played a critical role in numerous scenarios. However, current LLM systems are vulnerable to prompt-based attacks, with jailbreaking attacks enabling the LLM system to generate harmful content, while hijacking attacks manipulate the LLM system to perform attacker-desired tasks, underscoring the necessity for detection tools. Unfortunately, existing detecting approaches are usually tailored to specific attacks, resulting in poor generalization in detecting various attacks across different modalities. To address it, we propose JailGuard, a universal detection framework deployed on top of LLM systems for prompt-based attacks across text and image modalities. JailGuard operates on the principle that attacks are inherently less robust than benign ones. Specifically, JailGuard mutates untrusted inputs to generate variants and leverages the discrepancy of the variants' responses on the target model to distinguish attack samples from benign samples. We implement 18 mutators for text and image inputs and design a mutator combination policy to further improve detection generalization. The evaluation on the dataset containing 15 known attack types suggests that JailGuard achieves the best detection accuracy of 86.14%/82.90% on text and image inputs, outperforming state-of-the-art methods by 11.81%-25.73% and 12.20%-21.40%.

cs.CR↗

arXiv2025

Optimizing 6G Dense Network Deployment for the Metaverse Using Deep Reinforcement Learning

As the Metaverse envisions deeply immersive and pervasive connectivity in 6G networks, Integrated Access and Backhaul (IAB) emerges as a critical enabler to meet the demanding requirements of massive and immersive communications. IAB networks offer a scalable solution for expanding broadband coverage in urban environments. However, optimizing IAB node deployment to ensure reliable coverage while minimizing costs remains challenging due to location constraints and the dynamic nature of cities. Existing heuristic methods, such as Greedy Algorithms, have been employed to address these optimization problems. This work presents a novel Deep Reinforcement Learning ( DRL) approach for IAB network planning, tailored to future 6G scenarios that seek to support ultra-high data rates and dense device connectivity required by immersive Metaverse applications. We utilize Deep Q-Network (DQN) with action elimination and integrate DQN, Double Deep Q-Network ( DDQN), and Dueling DQN architectures to effectively manage large state and action spaces. Simulations with various initial donor configurations demonstrate the effectiveness of our DRL approach, with Dueling DQN reducing node count by an average of 12.3% compared to traditional heuristics. The study underscores how advanced DRL techniques can address complex network planning challenges in 6G-enabled Metaverse contexts, providing an efficient and adaptive solution for IAB deployment in diverse urban environments.

cs.NI↗

arXiv2025

Unsaturated Dinitrogen Difluoride under Pressure: toward high-Energy Density Polymerized NF Chains

Based on first-principles calculations and ab initio molecular dynamics simulations, the polymerisation of the unsaturated cis dinitrogen-difluoride (cis-N2F2) molecular compound is investigated. The thermodynamic, dynamical and thermal stabilities of the nitrogen fluorine NF system are investigated at conditions of 0-3000 K and 0-200 GPa. The cis-N2F2 molecule is a suitable precursor to obtain one-dimensional polymerized nitrogen-fluorine (poly-NF) chains at a pressure above 90 GPa and at a temperature around 1900 K. Importantly, these poly-NF chains can be quenched to room conditions, and potentially serve as a High-energy-density materials (HEDM). It has been established that when Al is utilised as a reducing agent, poly-NF chains exhibit a gravimetric energy density of 13.55 kJ/g, which exceeds that of cubic gauche nitrogen (cg-N, 9.70 kJ/g). This is attributable to the presence of both polymerised nitrogen and strong oxidising F atoms.

cond-mat.mtrl-sci↗

arXiv2025

SSD: A State-based Stealthy Backdoor Attack For Navigation System in UAV Route Planning

Unmanned aerial vehicles (UAVs) are increasingly employed to perform high-risk tasks that require minimal human intervention. However, UAVs face escalating cybersecurity threats, particularly from GNSS spoofing attacks. While previous studies have extensively investigated the impacts of GNSS spoofing on UAVs, few have focused on its effects on specific tasks. Moreover, the influence of UAV motion states on the assessment of network security risks is often overlooked. To address these gaps, we first provide a detailed evaluation of how motion states affect the effectiveness of network attacks. We demonstrate that nonlinear motion states not only enhance the effectiveness of position spoofing in GNSS spoofing attacks but also reduce the probability of speed-related attack detection. Building upon this, we propose a state-triggered backdoor attack method (SSD) to deceive GNSS systems and assess its risk to trajectory planning tasks. Extensive validation of SSD's effectiveness and stealthiness is conducted. Experimental results show that, with appropriately tuned hyperparameters, SSD significantly increases positioning errors and the risk of task failure, while maintaining 100% stealth across three state-of-the-art detectors.

cs.CR↗

arXiv2025

FpgaHub: Fpga-centric Hyper-heterogeneous Computing Platform for Big Data Analytics

Modern data analytics requires a huge amount of computing power and processes a massive amount of data. At the same time, the underlying computing platform is becoming much more heterogeneous on both hardware and software. Even though specialized hardware, e.g., FPGA- or GPU- or TPU-based systems, often achieves better performance than a CPU-only system due to the slowing of Moore's law, such systems are limited in what they can do. For example, GPU-only approaches suffer from severe IO limitations. To truly exploit the potential of hardware heterogeneity, we present FpgaHub, an FPGA-centric hyper-heterogeneous computing platform for big data analytics. The key idea of FpgaHub is to use reconfigurable computing to implement a versatile hub complementing other processors (CPUs, GPUs, DPUs, programmable switches, computational storage, etc.). Using an FPGA as the basis, we can take advantage of its highly reconfigurable nature and rich IO interfaces such as PCIe, networking, and on-board memory, to place it at the center of the architecture and use it as a data and control plane for data movement, scheduling, pre-processing, etc. FpgaHub enables architectural flexibility to allow exploring the rich design space of heterogeneous computing platforms.

cs.DC↗