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Replace2Self: Self-Supervised Denoising based on Voxel Replacing and Image Mixing for Diffusion MRI

IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Wu, Linhai Wang, Lihui Deng, Zeyu Zhu, Yuemin Wei, Hongjiang Guizhou University Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology 550025 China Univ Lyon INSA Lyon CNRS Inserm CREATIS UMR 5220 LyonF-69621 France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200030 China

Low signal to noise ratio (SNR) remains one of the limitations of diffusion weighted (DW) imaging. How to suppress the influence of noise on the subsequent analysis about the tissue microstructure is still challenging. This work proposed a novel self-supervised learning model, Replace2Self, to effectively reduce spatial correlated noise in DW images. Specifically, a voxel replacement strategy based on similar block matching in Q-space was proposed to destroy the correlations of noise in DW image along one diffusion gradient direction. To alleviate the signal gap caused by the voxel replacement, an image mixing strategy based on complementary mask was designed to generate two different noisy DW images. After that, these two noisy DW images were taken as input, and the non-correlated noisy DWimage after voxel replacement was taken as learning target, a denoising network was trained for denoising. To promote the denoising performance, a complementary mask mixing consistency loss and an inverse replacement regularization loss were also proposed. Through the comparisons against several existing DW image denoising methods on extensive simulation data with different noise distributions, noise levels and b-values, as well as the acquisition datasets and the ablation experiments, we verified the effectiveness of the proposed method. Regardless of the noise distribution and noise level, the proposed method achieved the highest PSNR, which was at least 1.9% higher than the suboptimal method when the noise level reaches 10%. Furthermore, our method has superior generalization ability due to the use of the proposed strategies. © 2025 IEEE. All rights reserved.

关键词： Signal to noise ratio

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Multi-modal Self-perception Enhanced Large Language Model for 3D Region-of-Interest Captioning with Limited data

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IEEE Transactions on Multimedia 2025年

作者： Shi, Lu Kan, Shichao Jin, Yi Zhang, Linna Cen, Yigang Bejing Jiaotong University and the Beijing Key Laboratory of Advanced Information Science and Network Technology Institute of Information Science Beijing100044 China Central South University School of Computer Science and Engineering Hunan Changsha0410083 China Beijing Jiaotong University Key Laboratory of Big Data and Artificial Intelligence in Transportation Ministry of Education and the School of Computer and lnformation Technology Beijing100044 China Guizhou University School of Mechanical Engineering Guiyang550025 China

3D Region-of-Interest (RoI) Captioning involves translating a model's understanding of specific objects within a complex 3D scene into descriptive captions. Recent advancements in Large Language Models (LLMs) have shown great potential in this area. Existing methods capture the visual information from RoIs as input tokens for LLMs. However, this approach may not provide enough detailed information for LLMs to generate accurate region-specific captions. In this paper, we introduce Self-RoI, a Large Language Model with multi-modal self-perception capabilities for 3D RoI captioning. To ensure LLMs receive more precise and sufficient information, Self-RoI incorporates Implicit Textual Info. Perception to construct a multi-modal vision-language information. This module utilizes a simple mapping network to generate textual information about basic properties of RoI from vision-following response of LLMs. This textual information is then integrated with the RoI's visual representation to form a comprehensive multi-modal instruction for LLMs. Given the limited availability of 3D RoI-captioning data, we propose a two-stage training strategy to optimize Self-RoI efficiently. In the first stage, we align 3D RoI vision and caption representations. In the second stage, we focus on 3D RoI vision-caption interaction, using a disparate contrastive embedding module to improve the reliability of the implicit textual information and employing language modeling loss to ensure accurate caption generation. Our experiments demonstrate that Self-RoI significantly outperforms previous 3D RoI captioning models. Moreover, the Implicit Textual Info. Perception can be integrated into other multi-modal LLMs for performance enhancement. We will make our code available for further research. © 1999-2012 IEEE.

关键词： Digital elevation model

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An Incentive Mechanism for Privacy Preserved data Trading with Verifiable data Disturbance

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Man Li, Xinghua Luo, Bin Ren, Yanbing Miao, Yinbin Liu, Ximeng Deng, Robert H. State Key Laboratory of Integrated Services Networks the School of Cyber Engineering Xidian University Xi’an710071 China Engineering Research Center of Big data Security Ministry of Education Xi’an710071 China The College of Mathematics and Computer Science Fuzhou University Fujian350108 China The Department of Information Systems Singapore Management University 80 Stamford Road Singapore

To motivate data owners’ (DOs’) trading willingness, the existing incentive mechanisms allow DOs to independently disturb data following data consumer’s (DC’s) availability requirement. However, they cannot motivate DOs’ honest disturbance, which is attributed to DOs’ independent disturbance without any supervision. Thus, we implement an incentive mechanism for privacy preserved data trading with verifiable data disturbance where an honest-but-curious disturbance generator (DG) is additionally introduced to supervise DOs’ local disturbance and assist disturbance verification between DOs and DC. Specifically, DG generates the disturbance strategies and secretly distributes to DOs following private information retrieval, guaranteeing DOs’s local disturbance’s privacy and verifiability with our proposed three-level verification algorithm. Subsequently, we model the trading as a game and disturbance verification results determine the compensation and punishment for trading bilateral utilities following Nash Equilibrium where DOs honestly disturb data. Theoretical analysis shows that DOs are motivated to honestly disturb data and their raw data privacy is preserved. Extensive experiments using the real-world dataset demonstrate that the deviating DOs in our scheme can be verified with a probability of more than 90% and the statistical result accuracy can be improved by more than 80% compared with the existing works. © 2004-2012 IEEE. All rights reserved.

关键词： Differential privacy

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Adaptive Activation Steering: A Tuning-Free LLM Truthfulness Improvement Method for Diverse Hallucinations Categories 25

Adaptive Activation Steering: A Tuning-Free LLM Truthfulness...

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Proceedings of the ACM on Web Conference 2025

作者： Tianlong Wang Xianfeng Jiao Yinghao Zhu Zhongzhi Chen Yifan He Xu Chu Junyi Gao Yasha Wang Liantao Ma School of Software and Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Beihang University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Centre for Medical Informatics University of Edinburgh Edinburgh Scotland United Kingdom and Health Data Research UK London United Kingdom Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China and National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (纸本)9798400712746

Recent studies have indicated that Large Language Models (LLMs) harbor an inherent understanding of truthfulness, yet often fail to consistently express it and generate false statements. This gap between ''knowing'' and ''telling'' poses a challenge for ensuring the truthfulness of generated content. Inspired by recent work on the practice of encoding human-interpretable concepts linearly within large language models, we treat truthfulness as a specially linearly encoded concept within LLMs, and introduce Adaptive Activation Steering (ACT), a tuning-free method that adaptively shifts LLM's activations in the ''truthful'' direction during inference. ACT addresses diverse categories of hallucinations by utilizing diverse truthfulness-related steering vectors and adjusting the steering intensity adaptively. Applied as an add-on across various models, ACT significantly improves truthfulness in LLaMA (↑142%), LLaMA2 (↑24%), Alpaca (↑36%), Vicuna (↑28%), LLaMA2-Chat (↑19%), and LLaMA3(↑34%). Furthermore, we verify ACT's scalability across larger models (13B, 33B, 65B), underscoring the adaptability of ACT to large-scale language models. Our code is available at https://***/tianlwang/ACT.

关键词： hallucination

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GKG-LLM: A Unified Framework for Generalized knowledge Graph Construction

arXiv

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arXiv 2025年

作者： Zhang, Jian Wei, Bifan Qi, Shihao Zhu, Haiping Liu, Jun Lin, Qika School of Computer Science and Technology Xi’an Jiaotong University Xi’an China School of Continuing Education Xi’an Jiaotong University Xi’an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China National University of Singapore Singapore

The construction of Generalized knowledge Graph (GKG), including knowledge graph, event knowledge graph and commonsense knowledge graph, is fundamental for various natural language processing tasks. Current studies typically construct these types of graph separately, overlooking holistic insights and potential unification that could be beneficial in computing resources and usage perspectives. However, a key challenge in developing a unified framework for GKG is obstacles arising from task-specific differences. In this study, we propose a unified framework for constructing generalized knowledge graphs to address this challenge. First, we collect data from 15 sub-tasks in 29 datasets across the three types of graphs, categorizing them into in-sample, counter-task, and out-of-distribution (OOD) data. Then, we propose a three-stage curriculum learning fine-tuning framework, by iteratively injecting knowledge from the three types of graphs into the Large Language Models. Extensive experiments show that our proposed model improves the construction of all three graph types across in-domain, OOD and counter-task data. Copyright © 2025, The Authors. All rights reserved.

关键词： knowledge graph

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An Anti-Clutter Distance Measurement Method for IoT Linear Frequency Modulation Radar in Heavy Rainfall Environment

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IEEE Internet of Things Journal 2025年

作者： Zhu, Lingzhi Li, Yi Xia, Weijie Chen, Kuiyu Ma, Qun Zhang, Qi Nanjing University of Aeronautics and Astronautics Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing211106 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Nanjing210023 China City University of Macau Faculty of Data Science 999078 China

Linear frequency modulation (LFM) radar which can measure distance of target is the key sensor in autonomous driving system in the context of Internet of Things (IoT). However, huge amount of raindrops in rainfall environment would reflect signal and result in negative influence. In order to improve the detection ability of LFM radar, echo signal model of LFM radar target in rainfall environment is established. Working principle of LFM radar, characteristics of raindrops, influence of rain clutter and comparison with measured data in time-domain and frequency-domain distribution are given in sequence. Secondly, three features are defined and extracted from frequency-domain spectrum and singular-domain spectrum to recognize target echo signal, rain clutter and mixed signal with high precision. When the rainfall rate is 3.33 mm/h, the overall recognition accuracy can still be up to 95.15%. Thirdly, an adaptive ensemble empirical mode decomposition (EEMD) algorithm is proposed to suppress rain clutter in mixed signal. Simulation results prove the effectiveness of EEMD in improving ranging accuracy in rainfall environment. At last, outfield experiments in normal environment and rainfall environment are conducted respectively. Measured distance obtained by proposed method in rainfall environment is still more accurate than that obtained by traditional fast Fourier transform (FFT) method in normal environment, illustrating that the proposed method can ensure the high performance of LFM radar in rainfall environment. Research in this paper has positive influence on improving reliability of LFM radar in inclement environment and can contribute to more stable target information for the whole in-vehicle IoT. © 2014 IEEE.

关键词： Frequency domain analysis

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Particle-Assisted Deep Reinforcement Learning for Quantum State Manipulation

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IEEE Transactions on Evolutionary Computation 2025年

作者： Yu, Haixu Liu, Xiang Wang, Bohui Zhao, Xudong Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian116024 China Dongguan University of Technology Institute of Science and Technology Innovation Dongguan523000 China Talent Big Data Intelligent Analysis and Evaluation Key Laboratory of MIIT Beijing100804 China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China

Applying deep reinforcement learning (DRL) to solve quantum control problems has become a popular research direction. However, the exploration capability and reward design for the learning agent, which usually affect the DRL's application performance, has not been sufficiently emphasized. In this article, we propose a particle-assisted DRL (PDRL) method to address the above concern by enhancing exploration capabilities and designing appropriate reward functions for efficient quantum state manipulation. In PDRL, each episode in the quantum learning process is characterized by three kinds of events, i.e., unidentifiable, identifiable, and successful events. To improve exploration, exploration particles and feedback particles are employed in the early learning phase when episodes end in identifiable and successful events, respectively. To assign rewards, three event-based reward functions are provided for the DRL's agent, exploration particles and feedback particles, respectively. Numerical results on single-qubit, two-qubit, and many-qubit systems validate the effectiveness of PDRL. Comparative results with existing DRL methods demonstrate the superior performance of PDRL for quantum state manipulation. © 1997-2012 IEEE.

关键词： Deep reinforcement learning

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TFS-Net: Temporal first simulation network for video saliency prediction

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Expert Systems with Applications 2025年 282卷

作者： Li, Longyi Dong, Liyan Zhang, Hao Qin, Jun Zhang, Zhengtai Sun, Minghui Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China School of Computer Science and Technology Changchun University of Science and Technology Changchun130013 China Research Institute of Jilin University in Shenzhen Shenzhen China

Video saliency prediction (VSP) plays a critical role in modern video processing systems by optimizing computational resource allocation and enhancing overall system performance. However, existing VSP methods either lack effective temporal modeling or incur high computational costs, particularly struggling with the initialization of video sequences. This paper presents TFS-Net, a novel temporal-first simulation network for VSP that integrates both static and dynamic modeling via parallel-optimized self-attention mechanisms. Specifically, TFS-Net addresses the challenge of initial frame processing with the innovative F31 algorithm and improves multi-scale spatiotemporal feature integration through a Hierarchical Decoder with Multi-dimensional Attention (HDMA). Drawing inspiration from primate saccadic behavior, the F31 algorithm optimizes processing efficiency during both training and inference phases, demonstrating particular effectiveness in unmanned aerial vehicle (UAV) real-time applications. Extensive evaluations on public datasets demonstrate that TFS-Net achieves significant improvements over state-of-the-art methods, with gains of 14.6%, 12.0%, and 11.2% in AUC-J, CC, and SIM metrics, respectively. Further experiments on UAV video analysis validate the model's robustness and practicality in real-world scenarios. © 2025

关键词： Spatio-temporal data

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Multi-omics synergy in oncology: Unraveling the complex interplay of radiomic, genoproteomic, and pathological data

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Intelligent Oncology 2025年第1期1卷 17-30页

作者： Yang Luo Yilin Li Mengjie Fang Shuo Wang Lizhi Shao Ruiyang Zou Di Dong Zhenyu Liu Jingwei Wei Jie Tian Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing 100190 China Beijing Key Laboratory of Molecular Imaging Beijing 100190 China School of Life Science and Technology Xidian University Xi’an Shaanxi 710126 China School of Public Health Anhui Medical University Hefei Anhui 230032 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering Medicine Beihang University Beijing 100191 China School of Engineering Medicine Beihang University Beijing 100191 China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology Beihang University Beijing 100191 China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi’an Shaanxi 710126 China

The advent of multi-omics approaches has revolutionized the field of oncology by enabling a comprehensive understanding of cancer biology through the integration of diverse biological data. This review aims to explore the synergy between three key omics domains: radiomics, genoproteomics, and pathomics. Radiomics involves extracting high-dimensional data from medical images, providing valuable insights into tumor heterogeneity and treatment response. Genoproteomics, encompassing both genomic and proteomic analyses, delves into the molecular mechanisms driving cancer progression and therapeutic resistance. Pathomics leverages advanced digital pathology techniques to quantitatively analyze tissue architecture and cellular morphology. We provide an in-depth overview of the methodologies and tools employed in each omics field, highlighting their specific applications in oncology, including cancer diagnosis, biomarker discovery, and prediction of treatment outcomes. Furthermore, we discuss the integration of multi-omics data, addressing the challenges and innovative solutions for harmonizing these complex datasets. Through an examination of recent advancements and case studies, we underscore the critical role of multi-omics in advancing our understanding of cancer and paving the way for more effective and personalized therapeutic strategies.

关键词： Oncology Multi-omics Radiomics Genoproteomics Pathomics

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on Network and Service Management 2025年

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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