arXiv subjects

Xiao Wang

Publications and source records attributed to Xiao Wang.

At least 343 records · Page 19Linked to original sources

arXiv2025

Mixed anion control of enhanced negative thermal expansion in the oxysulfide of PbTiO3

The rare physical property of negative thermal expansion (NTE) is intriguing because materials with large NTE over a wide temperature range can serve as high-performance thermal expansion compensators. However, applications of NTE are hindered by the fact that most of the available NTE materials show small magnitudes of NTE, and/or NTE occurs only in a narrow temperature range. Herein, for the first time, we investigated the effect of anion substitution instead of general Pb/Ti-site substitutions on the thermal expansion properties of a typical ferroelectric NTE material, PbTiO3. Intriguingly, the substitution of S for O in PbTiO3 further increases the tetragonality of PbTiO3. Consequently, an unusually enhanced NTE with an average volumetric coefficient of thermal expansion $\barα_V$ = -2.50 $\times$ 10$^{-5}$/K was achieved over a wide temperature range (300 -- 790 K), which is contrasted to that of pristine PbTiO3 ($\barα_V$ = -1.99 $\times$ 10$^{-5}$/K RT -- 763 K). The intensified NTE is attributed to the enhanced hybridization between Pb/Ti and O/S atoms by the substitution of S, as evidenced by our theoretical investigations. We therefore demonstrate a new technique for introducing mixed anions to achieve large NTE over a wide temperature range in PbTiO3-based ferroelectrics.

cond-mat.mtrl-sci↗

arXiv2025

Scale-up Unlearnable Examples Learning with High-Performance Computing

Recent advancements in AI models are structured to retain user interactions, which could inadvertently include sensitive healthcare data. In the healthcare field, particularly when radiologists use AI-driven diagnostic tools hosted on online platforms, there is a risk that medical imaging data may be repurposed for future AI training without explicit consent, spotlighting critical privacy and intellectual property concerns around healthcare data usage. Addressing these privacy challenges, a novel approach known as Unlearnable Examples (UEs) has been introduced, aiming to make data unlearnable to deep learning models. A prominent method within this area, called Unlearnable Clustering (UC), has shown improved UE performance with larger batch sizes but was previously limited by computational resources. To push the boundaries of UE performance with theoretically unlimited resources, we scaled up UC learning across various datasets using Distributed Data Parallel (DDP) training on the Summit supercomputer. Our goal was to examine UE efficacy at high-performance computing (HPC) levels to prevent unauthorized learning and enhance data security, particularly exploring the impact of batch size on UE's unlearnability. Utilizing the robust computational capabilities of the Summit, extensive experiments were conducted on diverse datasets such as Pets, MedMNist, Flowers, and Flowers102. Our findings reveal that both overly large and overly small batch sizes can lead to performance instability and affect accuracy. However, the relationship between batch size and unlearnability varied across datasets, highlighting the necessity for tailored batch size strategies to achieve optimal data protection. Our results underscore the critical role of selecting appropriate batch sizes based on the specific characteristics of each dataset to prevent learning and ensure data security in deep learning applications.

cs.LG↗

arXiv2025

Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective

To preserve user privacy in recommender systems, federated recommendation (FR) based on federated learning (FL) emerges, keeping the personal data on the local client and updating a model collaboratively. Unlike FL, FR has a unique sparse aggregation mechanism, where the embedding of each item is updated by only partial clients, instead of full clients in a dense aggregation of general FL. Recently, as an essential principle of FL, model security has received increasing attention, especially for Byzantine attacks, where malicious clients can send arbitrary updates. The problem of exploring the Byzantine robustness of FR is particularly critical since in the domains applying FR, e.g., e-commerce, malicious clients can be injected easily by registering new accounts. However, existing Byzantine works neglect the unique sparse aggregation of FR, making them unsuitable for our problem. Thus, we make the first effort to investigate Byzantine attacks on FR from the perspective of sparse aggregation, which is non-trivial: it is not clear how to define Byzantine robustness under sparse aggregations and design Byzantine attacks under limited knowledge/capability. In this paper, we reformulate the Byzantine robustness under sparse aggregation by defining the aggregation for a single item as the smallest execution unit. Then we propose a family of effective attack strategies, named Spattack, which exploit the vulnerability in sparse aggregation and are categorized along the adversary's knowledge and capability. Extensive experimental results demonstrate that Spattack can effectively prevent convergence and even break down defenses under a few malicious clients, raising alarms for securing FR systems.

cs.CR↗

arXiv2025

Activating Associative Disease-Aware Vision Token Memory for LLM-Based X-ray Report Generation

X-ray image based medical report generation achieves significant progress in recent years with the help of the large language model, however, these models have not fully exploited the effective information in visual image regions, resulting in reports that are linguistically sound but insufficient in describing key diseases. In this paper, we propose a novel associative memory-enhanced X-ray report generation model that effectively mimics the process of professional doctors writing medical reports. It considers both the mining of global and local visual information and associates historical report information to better complete the writing of the current report. Specifically, given an X-ray image, we first utilize a classification model along with its activation maps to accomplish the mining of visual regions highly associated with diseases and the learning of disease query tokens. Then, we employ a visual Hopfield network to establish memory associations for disease-related tokens, and a report Hopfield network to retrieve report memory information. This process facilitates the generation of high-quality reports based on a large language model and achieves state-of-the-art performance on multiple benchmark datasets, including the IU X-ray, MIMIC-CXR, and Chexpert Plus. The source code of this work is released on \url{https://github.com/Event-AHU/Medical_Image_Analysis}.

eess.IV↗

arXiv2024

Unsupervised dense retrieval with conterfactual contrastive learning

Efficiently retrieving a concise set of candidates from a large document corpus remains a pivotal challenge in Information Retrieval (IR). Neural retrieval models, particularly dense retrieval models built with transformers and pretrained language models, have been popular due to their superior performance. However, criticisms have also been raised on their lack of explainability and vulnerability to adversarial attacks. In response to these challenges, we propose to improve the robustness of dense retrieval models by enhancing their sensitivity of fine-graned relevance signals. A model achieving sensitivity in this context should exhibit high variances when documents' key passages determining their relevance to queries have been modified, while maintaining low variances for other changes in irrelevant passages. This sensitivity allows a dense retrieval model to produce robust results with respect to attacks that try to promote documents without actually increasing their relevance. It also makes it possible to analyze which part of a document is actually relevant to a query, and thus improve the explainability of the retrieval model. Motivated by causality and counterfactual analysis, we propose a series of counterfactual regularization methods based on game theory and unsupervised learning with counterfactual passages. Experiments show that, our method can extract key passages without reliance on the passage-level relevance annotations. Moreover, the regularized dense retrieval models exhibit heightened robustness against adversarial attacks, surpassing the state-of-the-art anti-attack methods.

cs.IR↗

arXiv2024

VELoRA: A Low-Rank Adaptation Approach for Efficient RGB-Event based Recognition

Pattern recognition leveraging both RGB and Event cameras can significantly enhance performance by deploying deep neural networks that utilize a fine-tuning strategy. Inspired by the successful application of large models, the introduction of such large models can also be considered to further enhance the performance of multi-modal tasks. However, fully fine-tuning these models leads to inefficiency and lightweight fine-tuning methods such as LoRA and Adapter have been proposed to achieve a better balance between efficiency and performance. To our knowledge, there is currently no work that has conducted parameter-efficient fine-tuning (PEFT) for RGB-Event recognition based on pre-trained foundation models. To address this issue, this paper proposes a novel PEFT strategy to adapt the pre-trained foundation vision models for the RGB-Event-based classification. Specifically, given the RGB frames and event streams, we extract the RGB and event features based on the vision foundation model ViT with a modality-specific LoRA tuning strategy. The frame difference of the dual modalities is also considered to capture the motion cues via the frame difference backbone network. These features are concatenated and fed into high-level Transformer layers for efficient multi-modal feature learning via modality-shared LoRA tuning. Finally, we concatenate these features and feed them into a classification head to achieve efficient fine-tuning. The source code and pre-trained models will be released on \url{https://github.com/Event-AHU/VELoRA}.

cs.CV↗

arXiv2024

SafeAligner: Safety Alignment against Jailbreak Attacks via Response Disparity Guidance

As the development of large language models (LLMs) rapidly advances, securing these models effectively without compromising their utility has become a pivotal area of research. However, current defense strategies against jailbreak attacks (i.e., efforts to bypass security protocols) often suffer from limited adaptability, restricted general capability, and high cost. To address these challenges, we introduce SafeAligner, a methodology implemented at the decoding stage to fortify defenses against jailbreak attacks. We begin by developing two specialized models: the Sentinel Model, which is trained to foster safety, and the Intruder Model, designed to generate riskier responses. SafeAligner leverages the disparity in security levels between the responses from these models to differentiate between harmful and beneficial tokens, effectively guiding the safety alignment by altering the output token distribution of the target model. Extensive experiments show that SafeAligner can increase the likelihood of beneficial tokens, while reducing the occurrence of harmful ones, thereby ensuring secure alignment with minimal loss to generality.

cs.CR↗

arXiv2024

Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?

Graph neural networks (GNNs) are vulnerable to adversarial attacks, especially for topology perturbations, and many methods that improve the robustness of GNNs have received considerable attention. Recently, we have witnessed the significant success of large language models (LLMs), leading many to explore the great potential of LLMs on GNNs. However, they mainly focus on improving the performance of GNNs by utilizing LLMs to enhance the node features. Therefore, we ask: Will the robustness of GNNs also be enhanced with the powerful understanding and inference capabilities of LLMs? By presenting the empirical results, we find that despite that LLMs can improve the robustness of GNNs, there is still an average decrease of 23.1% in accuracy, implying that the GNNs remain extremely vulnerable against topology attacks. Therefore, another question is how to extend the capabilities of LLMs on graph adversarial robustness. In this paper, we propose an LLM-based robust graph structure inference framework, LLM4RGNN, which distills the inference capabilities of GPT-4 into a local LLM for identifying malicious edges and an LM-based edge predictor for finding missing important edges, so as to recover a robust graph structure. Extensive experiments demonstrate that LLM4RGNN consistently improves the robustness across various GNNs. Even in some cases where the perturbation ratio increases to 40%, the accuracy of GNNs is still better than that on the clean graph. The source code can be found in https://github.com/zhongjian-zhang/LLM4RGNN.

cs.LG↗

arXiv2024

ECAFormer: Low-light Image Enhancement using Cross Attention

Low-light image enhancement (LLIE) is critical in computer vision. Existing LLIE methods often fail to discover the underlying relationships between different sub-components, causing the loss of complementary information between multiple modules and network layers, ultimately resulting in the loss of image details. To beat this shortage, we design a hierarchical mutual Enhancement via a Cross Attention transformer (ECAFormer), which introduces an architecture that enables concurrent propagation and interaction of multiple features. The model preserves detailed information by introducing a Dual Multi-head self-attention (DMSA), which leverages visual and semantic features across different scales, allowing them to guide and complement each other. Besides, a Cross-Scale DMSA block is introduced to capture the residual connection, integrating cross-layer information to further enhance image detail. Experimental results show that ECAFormer reaches competitive performance across multiple benchmarks, yielding nearly a 3% improvement in PSNR over the suboptimal method, demonstrating the effectiveness of information interaction in LLIE.

cs.CV↗

arXiv2024

Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset

Object detection in event streams has emerged as a cutting-edge research area, demonstrating superior performance in low-light conditions, scenarios with motion blur, and rapid movements. Current detectors leverage spiking neural networks, Transformers, or convolutional neural networks as their core architectures, each with its own set of limitations including restricted performance, high computational overhead, or limited local receptive fields. This paper introduces a novel MoE (Mixture of Experts) heat conduction-based object detection algorithm that strikingly balances accuracy and computational efficiency. Initially, we employ a stem network for event data embedding, followed by processing through our innovative MoE-HCO blocks. Each block integrates various expert modules to mimic heat conduction within event streams. Subsequently, an IoU-based query selection module is utilized for efficient token extraction, which is then channeled into a detection head for the final object detection process. Furthermore, we are pleased to introduce EvDET200K, a novel benchmark dataset for event-based object detection. Captured with a high-definition Prophesee EVK4-HD event camera, this dataset encompasses 10 distinct categories, 200,000 bounding boxes, and 10,054 samples, each spanning 2 to 5 seconds. We also provide comprehensive results from over 15 state-of-the-art detectors, offering a solid foundation for future research and comparison. The source code of this paper will be released on: https://github.com/Event-AHU/OpenEvDET

cs.CV↗

arXiv2024

Blockchain Data Analysis in the Era of Large-Language Models

Blockchain data analysis is essential for deriving insights, tracking transactions, identifying patterns, and ensuring the integrity and security of decentralized networks. It plays a key role in various areas, such as fraud detection, regulatory compliance, smart contract auditing, and decentralized finance (DeFi) risk management. However, existing blockchain data analysis tools face challenges, including data scarcity, the lack of generalizability, and the lack of reasoning capability. We believe large language models (LLMs) can mitigate these challenges; however, we have not seen papers discussing LLM integration in blockchain data analysis in a comprehensive and systematic way. This paper systematically explores potential techniques and design patterns in LLM-integrated blockchain data analysis. We also outline prospective research opportunities and challenges, emphasizing the need for further exploration in this promising field. This paper aims to benefit a diverse audience spanning academia, industry, and policy-making, offering valuable insights into the integration of LLMs in blockchain data analysis.

cs.CR↗

arXiv2024

SignAvatar: Sign Language 3D Motion Reconstruction and Generation

Achieving expressive 3D motion reconstruction and automatic generation for isolated sign words can be challenging, due to the lack of real-world 3D sign-word data, the complex nuances of signing motions, and the cross-modal understanding of sign language semantics. To address these challenges, we introduce SignAvatar, a framework capable of both word-level sign language reconstruction and generation. SignAvatar employs a transformer-based conditional variational autoencoder architecture, effectively establishing relationships across different semantic modalities. Additionally, this approach incorporates a curriculum learning strategy to enhance the model's robustness and generalization, resulting in more realistic motions. Furthermore, we contribute the ASL3DWord dataset, composed of 3D joint rotation data for the body, hands, and face, for unique sign words. We demonstrate the effectiveness of SignAvatar through extensive experiments, showcasing its superior reconstruction and automatic generation capabilities. The code and dataset are available on the project page.

cs.CV↗

arXiv2024

Theoretical Studies on Sodium Storage Mechanism in Hard Carbon Anodes of Sodium-Ion Batteries: Molecular Simulations Based on Machine Learning Force Fields

Sodium-ion batteries (SIBs) have garnered significant attention in recent years as a promising alternative to lithium-ion batteries (LIBs) due to their low cost, abundant sodium resources, and excellent cycling performance. Hard carbon materials, characterized by their high specific capacity, outstanding cycling stability, and low cost, have emerged as potential candidates for SIB anodes. However, the sodium storage mechanism in hard carbon anodes remains highly complex, especially in disordered structures, and is yet to be fully understood. To address this, we employed relative machine learning force fields (MLFFs) in conjunction with multiscale simulation techniques to systematically investigate the sodium storage behavior in hard carbon. By integrating simulations, this study provides a detailed exploration of sodium adsorption, intercalation, and filling mechanisms. High-precision, large-scale simulations reveal the dynamic behavior and distribution patterns of sodium ions in hard carbon. The findings not only deepen our understanding of sodium storage mechanisms in hard carbon anodes, but also offer a theoretical foundation for optimizing future SIB designs, while introducing novel simulation methodologies and technical frameworks to enhance battery performance.

cond-mat.mtrl-sci↗

arXiv2024

PaliGemma 2: A Family of Versatile VLMs for Transfer

PaliGemma 2 is an upgrade of the PaliGemma open Vision-Language Model (VLM) based on the Gemma 2 family of language models. We combine the SigLIP-So400m vision encoder that was also used by PaliGemma with the whole range of Gemma 2 models, from the 2B one all the way up to the 27B model. We train these models at three resolutions (224px, 448px, and 896px) in multiple stages to equip them with broad knowledge for transfer via fine-tuning. The resulting family of base models covering different model sizes and resolutions allows us to investigate factors impacting transfer performance (such as learning rate) and to analyze the interplay between the type of task, model size, and resolution. We further increase the number and breadth of transfer tasks beyond the scope of PaliGemma including different OCR-related tasks such as table structure recognition, molecular structure recognition, music score recognition, as well as long fine-grained captioning and radiography report generation, on which PaliGemma 2 obtains state-of-the-art results.

cs.CV↗

arXiv2024

An Empirical Study of Mamba-based Pedestrian Attribute Recognition

Current strong pedestrian attribute recognition models are developed based on Transformer networks, which are computationally heavy. Recently proposed models with linear complexity (e.g., Mamba) have garnered significant attention and have achieved a good balance between accuracy and computational cost across a variety of visual tasks. Relevant review articles also suggest that while these models can perform well on some pedestrian attribute recognition datasets, they are generally weaker than the corresponding Transformer models. To further tap into the potential of the novel Mamba architecture for PAR tasks, this paper designs and adapts Mamba into two typical PAR frameworks, i.e., the text-image fusion approach and pure vision Mamba multi-label recognition framework. It is found that interacting with attribute tags as additional input does not always lead to an improvement, specifically, Vim can be enhanced, but VMamba cannot. This paper further designs various hybrid Mamba-Transformer variants and conducts thorough experimental validations. These experimental results indicate that simply enhancing Mamba with a Transformer does not always lead to performance improvements but yields better results under certain settings. We hope this empirical study can further inspire research in Mamba for PAR, and even extend into the domain of multi-label recognition, through the design of these network structures and comprehensive experimentation. The source code of this work will be released at \url{https://github.com/Event-AHU/OpenPAR}

cs.CV↗

arXiv2024

Enhancing LLM Reasoning via Critique Models with Test-Time and Training-Time Supervision

Training large language models (LLMs) to spend more time thinking and reflection before responding is crucial for effectively solving complex reasoning tasks in fields such as science, coding, and mathematics. However, the effectiveness of mechanisms like self-reflection and self-correction depends on the model's capacity to accurately assess its own performance, which can be limited by factors such as initial accuracy, question difficulty, and the lack of external feedback. In this paper, we delve into a two-player paradigm that separates the roles of reasoning and critique models, where the critique model provides step-level feedback to supervise the reasoning (actor) model during both test-time and train-time. We first propose AutoMathCritique, an automated and scalable framework for collecting critique data, resulting in a dataset of $76,321$ responses paired with step-level feedback. Fine-tuning language models with this dataset enables them to generate natural language feedback for mathematical reasoning. We demonstrate that the critique models consistently improve the actor's performance on difficult queries at test-time, especially when scaling up inference-time computation. Motivated by these findings, we introduce the critique-based supervision to the actor's self-training process, and propose a critique-in-the-loop self-improvement method. Experiments show that the method improves the actor's exploration efficiency and solution diversity, especially on challenging queries, leading to a stronger reasoning model. Lastly, we take the preliminary step to explore training self-talk reasoning models via critique supervision and showcase its potential. Our code and datasets are at \href{https://mathcritique.github.io/}{https://mathcritique.github.io/}.

cs.CL↗

arXiv2024

Nimbus: Secure and Efficient Two-Party Inference for Transformers

Transformer models have gained significant attention due to their power in machine learning tasks. Their extensive deployment has raised concerns about the potential leakage of sensitive information during inference. However, when being applied to Transformers, existing approaches based on secure two-party computation (2PC) bring about efficiency limitations in two folds: (1) resource-intensive matrix multiplications in linear layers, and (2) complex non-linear activation functions like $\mathsf{GELU}$ and $\mathsf{Softmax}$. This work presents a new two-party inference framework $\mathsf{Nimbus}$ for Transformer models. For the linear layer, we propose a new 2PC paradigm along with an encoding approach to securely compute matrix multiplications based on an outer-product insight, which achieves $2.9\times \sim 12.5\times$ performance improvements compared to the state-of-the-art (SOTA) protocol. For the non-linear layer, through a new observation of utilizing the input distribution, we propose an approach of low-degree polynomial approximation for $\mathsf{GELU}$ and $\mathsf{Softmax}$, which improves the performance of the SOTA polynomial approximation by $2.9\times \sim 4.0\times$, where the average accuracy loss of our approach is 0.08\% compared to the non-2PC inference without privacy. Compared with the SOTA two-party inference, $\mathsf{Nimbus}$ improves the end-to-end performance of \bert{} inference by $2.7\times \sim 4.7\times$ across different network settings.

cs.CR↗

arXiv2024

EMatch: A Unified Framework for Event-based Optical Flow and Stereo Matching

Event cameras have shown promise in vision applications like optical flow estimation and stereo matching, with many specialized architectures leveraging the asynchronous and sparse nature of event data. However, existing works only focus event data within the confines of task-specific domains, overlooking how tasks across the temporal and spatial domains can reinforce each other. In this paper, we reformulate event-based flow estimation and stereo matching as a unified dense correspondence matching problem, enabling us to solve both tasks within a single model by directly matching features in a shared representation space. Specifically, our method utilizes a Temporal Recurrent Network to aggregate event features across temporal or spatial domains, and a Spatial Contextual Attention to enhance knowledge transfer across event flows via temporal or spatial interactions. By utilizing a shared feature similarities module that integrates knowledge from event streams via temporal or spatial interactions, our network performs optical flow estimation from temporal event segment inputs and stereo matching from spatial event segment inputs simultaneously. We demonstrate that our unified model inherently supports multi-task fusion and cross-task transfer. Without the need for retraining for specific task, our model can effectively handle both optical flow and stereo estimation, achieving state-of-the-art performance on both tasks.

cs.CV↗