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Inter-Channel Correlation-Based EMI Noise Removal (ICER) for Shielding-Free Low-Field MRI

IEEE Transactions on Biomedical Engineering 2025年第7期72卷 2095-2104页

作者： Huang, Yiman Qu, Shuxian Xie, Yushu Wang, Hanlei Zhang, Xinlin Zhang, Xiaotong Zhejiang University College of Electrical Engineering MOE Frontier Science Center for Brain Science and Brain-machine Integration Hangzhou China Zhejiang University College of Biomedical Engineering & Instrument Science MOE Frontier Science Center for Brain Science and Brain-machine Integration Hangzhou China Fuzhou University College of Physics and Information Engineering Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology Fuzhou China Zhejiang University College of Electrical Engineering the Second Affiliated Hospital of Zhejiang University School of Medicine the State Key Lab of Brain-Machine Intelligence MOE Frontier Science Center for Brain Science and Brain-machine Integration Hangzhou China

Objective: For low-field magnetic resonance imaging (MRI) in unshielded environment, existing methods have been proposed to eliminate electromagnetic interference (EMI) noise in each single radio-frequency (RF) receive coil. In the present study, we propose to use the EMI information from multiple MRI receive coils collectively in EMI denoising. Methods: The proposed method leverages the information of inter-channel correlation, including EMI detectors and RF receive coils to remove EMI noise. Calibration signals from both EMI detectors and RF receivers are concatenated to determine a de-correlation matrix, which is then used to denoise MRI signals. Results: Saline phantom and in vivo experiments demonstrated the efficacy of the proposed method in EMI elimination, showing that the proposed method outperformed advanced EMI elimination methods with up to 16.34% improvement in EMI noise removal percentage (NRP) and 1.58dB improvement in signal-to-noise ratio (SNR), along with reduced computational times. Conclusion: The proposed method effectively removes EMI noise and shows improved performance by using information from all receive coils. Significance: This method allows for the design of multi-receive coils for low-field MRI such as phased-arrays, which have the potential to enhance the performance in noise removal and improve the SNR in MRI signal acquisition. © 2025 IEEE.

关键词： Signal to noise ratio

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Context Gating in Spiking Neural Networks: Achieving Lifelong Learning through Integration of Local and Global Plasticity

arXiv

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arXiv 2024年

作者： Shen, Jiangrong Ni, Wenyao Xu, Qi Pan, Gang Tang, Huajin The State Key Lab of Brain Machine Intelligence College of Computer Science and Technology Zhejiang University Zhejiang310027 China The Faculty of Electronic Information and Electrical Engineering School of Artificial Intelligence Dalian University of Technology Dalian116024 China

Humans learn multiple tasks in succession with minimal mutual interference, through the context gating mechanism in the prefrontal cortex (PFC). The brain-inspired models of spiking neural networks (SNN) have drawn massive attention for their energy efficiency and biological plausibility. To overcome catastrophic forgetting when learning multiple tasks in sequence, current SNN models for lifelong learning focus on memory reserving or regularization-based modification, while lacking SNN to replicate human experimental behavior. Inspired by biological context-dependent gating mechanisms found in PFC, we propose SNN with context gating trained by the local plasticity rule (CG-SNN) for lifelong learning. The iterative training between global and local plasticity for task units is designed to strengthen the connections between task neurons and hidden neurons and preserve the multi-task relevant information. The experiments show that the proposed model is effective in maintaining the past learning experience and has better task-selectivity than other methods during lifelong learning. Our results provide new insights that the CG-SNN model can extend context gating with good scalability on different SNN architectures with different spike-firing mechanisms. Thus, our models have good potential for parallel implementation on neuromorphic hardware and model human's behavior. Copyright © 2024, The Authors. All rights reserved.

关键词： Iterative methods

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A multi-target regression-based method for multiple orders remaining completion time prediction in discrete manufacturing workshops

A multi-target regression-based method for multiple orders r...

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作者： Liu, Mingyuan Zhang, Jian Qin, Shengfeng Zhang, Kai Wang, Shuying Ding, Guofu Institute of Advance Design and Manufacturing Technology Southwest Jiaotong University Chengdu610031 China Department of Design Northumbria University Newcastle Upon TyneNE1 8ST United Kingdom State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong University Chengdu China Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu61003l China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu610031 China

Accurate prediction of multiple orders remaining completion time (MORCT) is crucial in the make-to-order production model. It enables managers to keep track of production status, make timely decisions, and ensure on-time delivery of orders. However, dynamic production environments, characterized by constantly changing order quantities and relationships, as well as the special temporal features of the production process, pose challenges to existing prediction methods. To address these issues, this paper proposes a novel framework based on multi-target regression. First, production data are collected and standardized from various sources using multiple data transfer protocols. The input dataset is then constructed and dynamically adjusted to accommodate changes in order quantities and priorities. Finally, a prediction model named DMTR-LSA is developed to effectively handle the specific temporal relationships in the production data by integrating long short-term memory (LSTM) and self-attention mechanisms. A case study in a real production workshop demonstrates that the proposed method supports simultaneous prediction of multiple orders. It outperforms existing methods on several evaluation metrics, reducing the average prediction error by more than 8.9%. These results highlight the practical value of the proposed method for predicting MORCT in dynamic production environments and its potential impact to enhance the production decision-making process. © 2024 Elsevier Ltd

关键词： Prediction models

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Graph Attention Memory for Visual Navigation 4

Graph Attention Memory for Visual Navigation

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4th International Conference on Data-Driven Optimization of Complex systems, DOCS 2022

作者： Li, Dong Zhang, Qichao Zhao, Dongbin Noah's Ark Lab Huawei Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex System China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Visual navigation in complex environments is inefficient with traditional reactive policy or general-purposed recurrent policy due to the long-term memory problem. To address this issue, this paper proposes a graph attention memory (GAM) architecture consisting of memory construction module, graph attention module, and control module. The memory construction module builds the topological graph based on supervised learning by taking the exploration prior. Then, guided attention features to reach the goal are extracted with the graph attention module. Finally, the deep reinforcement learning based control module makes decisions based on visual observations and guided attention features. In addition, the convergence of the proposed GAM module for recurrent attention operation is analyzed in this paper. We evaluate GAM-based navigation system in two complex 3D ViZDoom environments. Experimental results show that the GAM-based navigation system outperforms all baselines in both success rate and navigation efficiency, and significantly improves the generalization. © 2022 IEEE.

关键词： Deep learning

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Design of a Novel Autonomous Docking system With a Supporting Robotic Fish

Design of a Novel Autonomous Docking System With a Supportin...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Dong, Huijie Zhao, Chunyang Qiao, Tiezhu Wang, Jian Chen, Di Meng, Yan Yu, Junzhi Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Chinese Academy of Sciences Laboratory of Cognitive and Decision Intelligence for Complex System Institute of Automation Beijing100190 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Guangdong Ocean University College of Electronics and Information Engineering Zhanjiang524088 China

ISBN: (纸本)9798350327182

Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel autonomous docking system centered on a designed supporting robotic fish named 'CourierFish'. Specifically, CourierFish is capable of docking with a surface dock station for supplying itself and docking with a seafloor platform for supporting equipment in the platform. A visual navigation scheme integrating LED and ArUco markers is presented for accurate localization. The control approach for docking motion is also illustrated. Simulations and aquatic experiments are performed to verify the feasibility of the proposed docking system. The obtained results lay a solid foundation for the construction of various underwater equipment and robot networks. © 2023 IEEE.

关键词： Fish

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Achieving Nearly-Optimal Regret and Sample Complexity in Dueling Bandits with Applications in Online Recommendations 25

Achieving Nearly-Optimal Regret and Sample Complexity in Due...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Lanjihong Ma Yao-Xiang Ding Zhen-Yu Zhang Zhi-Hua Zhou National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing University Nanjing Jiangsu China State Key Lab of CAD & CG Zhejiang University Hangzhou Zhejiang China Center for Advanced Intelligence Project RIKEN Tokyo Tokyo Japan

ISBN: (纸本)9798400712456

We focus on the dueling bandits problem, which has recently drawn significant attention due to its wide-ranging applications in online recommendation systems and the alignment of large language models (LLMs), considers an online preference learning scenario where the learner iteratively selects arms based on pairwise comparison feedback to infer user preferences. Two primary objectives are typically considered in dueling bandits: Regret Minimization (RM), which aims to improve the overall quality of selected arms over time, and Best Arm Identification (BAI), which seeks to efficiently identify the best item with minimal user feedback. For instance, RM is exemplified by the objective of consistently providing high-quality items, while BAI reduces the required human feedback by minimizing the number of necessary comparisons. Conventional research treats RM and BAI as two conflicting objectives, optimizing one at the expense of the other. In this paper, we propose a novel framework that demonstrates the near-consistency of RM and BAI in dueling bandits by reducing the BAI in dueling bandits into a sequential noisy identification problem. Based on our formulation, we propose a black-box reduction technique that transforms any RM algorithm into a BAI algorithm, and prove that such reduction with optimal RM algorithm achieves optimal sample complexity and nearly-optimal cumulative weak regret simultaneously. Our proposed algorithm acheives a nearly-optimal BAI sample complexity and attains a cumulative weak regret that is order-wise equivalent to the best-known result simultaneously. Experiments on both synthetic benchmarks and real-world online recommendation tasks validate the effectiveness of the proposed method, providing empirical evidences for our theoretical findings.

关键词： best arm identification

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Spatio-Temporal Field Neural Networks for Air Quality Inference

arXiv

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arXiv 2024年

作者： Feng, Yutong Wang, Qiongyan Xia, Yutong Huang, Junlin Zhong, Siru Liang, Yuxuan Hong Kong University of Science and Technology Guangzhou China National University of Singapore Singapore State Key Lab of Resources and Environmental Information System China

Air quality inference aims to utilize historical data from a limited number of observation sites to infer the air quality index at unknown locations. Considering data sparsity due to the high maintenance cost of stations, good inference algorithms can effectively save the cost and refine the data granularity. While spatio-temporal graph neural networks have made excellent progress on this problem, their non-Euclidean and discrete data structure modeling of reality limits its potential. In this work, we make the first attempt to combine two different spatio-temporal perspectives, fields and graphs, by proposing a new model, Spatio-Temporal Field Neural Network, and its corresponding new framework, Pyramidal Inference. Extensive experiments validate that our model achieves state-of-the-art performance in nationwide air quality inference in the Chinese Mainland, demonstrating the superiority of our proposed model and framework. Copyright © 2024, The Authors. All rights reserved.

关键词： Inference engines

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Talk With Human-like Agents: Empathetic Dialogue Through Perceptible Acoustic Reception and Reaction

arXiv

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arXiv 2024年

作者： Yan, Haoqiu Zhu, Yongxin Zheng, Kai Liu, Bing Cao, Haoyu Jiang, Deqiang Xu, Linli School of Computer Science and Technology University of Science and Technology China School of Data Science University of Science and Technology China State Key Laboratory of Cognitive Intelligence China Tencent Youtu Lab China

Large Language Model (LLM)-enhanced agents become increasingly prevalent in Human-AI communication, offering vast potential from entertainment to professional domains. However, current multi-modal dialogue systems overlook the acoustic information present in speech, which is crucial for understanding human communication nuances. This oversight can lead to misinterpretations of speakers' intentions, resulting in inconsistent or even contradictory responses within dialogues. To bridge this gap, in this paper, we propose PerceptiveAgent, an empathetic multi-modal dialogue system designed to discern deeper or more subtle meanings beyond the literal interpretations of words through the integration of speech modality perception. Employing LLMs as a cognitive core, PerceptiveAgent perceives acoustic information from input speech and generates empathetic responses based on speaking styles described in natural language. Experimental results indicate that PerceptiveAgent excels in contextual understanding by accurately discerning the speakers' true intentions in scenarios where the linguistic meaning is either contrary to or inconsistent with the speaker's true feelings, producing more nuanced and expressive spoken dialogues. Code is publicly available at: https://***/Haoqiu-Yan/PerceptiveAgent. Copyright © 2024, The Authors. All rights reserved.

关键词： Speech communication

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A Flexible and Ultrasensitive Artificial Compound Eye Using Bionic Micro-Lens Array for Drone Vision 38

A Flexible and Ultrasensitive Artificial Compound Eye Using ...

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38th IEEE International Conference on Micro Electro Mechanical systems, MEMS 2025

作者： Wang, Jiachuang Zhao, Fangyu Liu, Wenyuan Qin, Nan Tao, Tiger H. State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China 2020 X-Lab Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Graduate Study University of Chinese Academy of Sciences Beijing China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Jiangxi Nanchang China Guangdong Institute of Intelligence Science and Technology Zhuhai Guangdong Hengqin China Tianqiao and Chrissy Chen Institute for Translational Research Shanghai China

ISBN: (纸本)9798331508890

We report a bionic artificial compound eye based on flexible quantum dots film and micro electromechanical system (MEMS) fabrication process. The device integrates organic photodetector and micro-lens array, featuring the high-speed response, ultrathin thickness and extensive curvature radius. Our artificial eye exhibits significant discriminative detection to different targets with varying shapes and distances. The insect scale biomimetic compound eye provides a novel precision integration route for flexible optoelectronic devices and micro-optics, showing great potential applications in drone vision and bionic robot navigation. © 2025 IEEE.

关键词： Semiconductor quantum dots

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Fight Back Against Jailbreaking via Prompt Adversarial Tuning

arXiv

引用

arXiv 2024年

作者： Mo, Yichuan Wang, Yuji Wei, Zeming Wang, Yisen State Key Lab of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Shanghai Jiao Tong University China School of Mathematical Sciences Peking University China Institute for Artificial Intelligence Peking University China

While Large Language Models (LLMs) have achieved tremendous success in various applications, they are also susceptible to jailbreaking attacks. Several primary defense strategies have been proposed to protect LLMs from producing harmful information, mostly focusing on model fine-tuning or heuristical defense designs. However, how to achieve intrinsic robustness through prompt optimization remains an open problem. In this paper, motivated by adversarial training paradigms for achieving reliable robustness, we propose an approach named Prompt Adversarial Tuning (PAT) that trains a prompt control attached to the user prompt as a guard prefix. To achieve our defense goal whilst maintaining natural performance, we optimize the control prompt with both adversarial and benign prompts. Comprehensive experiments show that our method is effective against both grey-box and black-box attacks, reducing the success rate of advanced attacks to nearly 0%, while maintaining the model’s utility on the benign task and incurring only negligible computational overhead, charting a new perspective for future explorations in LLM security. Our code is available at https://***/PKU-ML/PAT. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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