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17th International Convention on Rehabilitation Engineering and Assistive Technology, i-CREATe 2024

作者： Mao, He Peng, Yinghu Zheng, Yue Li, Guanglin Li, Hai Xu, Yongan Fang, Peng Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen518055 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen518055 China Southern Medical University Neurorehabilitation Laboratory Department of Rehabilitation Medicine Shenzhen Hospital Shenzhen518101 China Second Affiliated Hospital Institute of Emergency Medicine Zhejiang University School of Medicine Department of Emergency Medicine Hangzhou310009 China

ISBN: (纸本)9798350355154

Functional electrical stimulation (FES) is an effective therapy for motor function restoration after stroke. To investigate brain activation during FES-induced wrist extension, electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) were simultaneously measured for 6 healthy subjects. We found an increase in oxygenated hemoglobin (HbO) concentration, which was accompanied by a decrease in deoxygenated hemoglobin (HbR) concentration in bilateral sensorimotor cortex (SMC) during movement. We also found event-related desynchronization (ERD) of alpha and beta EEG rhythms during movement and event-related synchronization (ERS) after the cessation of the movement. Neurovascular coupling (NC) was evaluated by the correlation coefficient (cc) between alpha ERD and HbO, and a higher cc value was found on the contralateral hemisphere. These findings demonstrate the potential application of EEG-fNIRS for quantifying NC during FES therapy. © 2024 IEEE.

关键词： Electroencephalography

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Characteristic-Specific Partial Fine-Tuning for Efficient Emotion and Speaker Adaptation in Codec Language Text-to-Speech Models

arXiv

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arXiv 2025年

作者： Wang, Tianrui Ge, Meng Gong, Cheng Qiang, Chunyu Wang, Haoyu Huang, Zikang Jiang, Yu Wang, Xiaobao Chen, Xie Wang, Longbiao Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Guangdong Laboratory of Artificial Intelligence and Digital Economy Guangdong China China Telecom Beijing China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Co. Ltd Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong China

Recently, emotional speech generation and speaker cloning have garnered significant interest in text-to-speech (TTS). With the open-sourcing of codec language TTS models trained on massive datasets with large-scale parameters, adapting these general pre-trained TTS models to generate speech with specific emotional expressions and target speaker characteristics has become a topic of great attention. Common approaches, such as full and adapter-based fine-tuning, often overlook the specific contributions of model parameters to emotion and speaker control. Treating all parameters uniformly during fine-tuning, especially when the target data has limited content diversity compared to the pre-training corpus, results in slow training speed and an increased risk of catastrophic forgetting. To address these challenges, we propose a characteristic-specific partial fine-tuning strategy, short as CSP-FT. First, we use a weighted-sum approach to analyze the contributions of different Transformer layers in a pre-trained codec language TTS model for emotion and speaker control in the generated speech. We then selectively fine-tune the layers with the highest and lowest characteristic-specific contributions to generate speech with target emotional expression and speaker identity. Experimental results demonstrate that our method achieves performance comparable to, or even surpassing, full fine-tuning in generating speech with specific emotional expressions and speaker identities. Additionally, CSP-FT delivers approximately 2× faster training speeds, fine-tunes only around 8% of parameters, and significantly reduces catastrophic forgetting. Furthermore, we show that codec language TTS models perform competitively with self-supervised models in speaker identification and emotion classification tasks, offering valuable insights for developing universal speech processing models. © 2025, CC BY.

关键词： C (programming language)

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Fast reconstruction and optical-sectioning three-dimensional structured illumination microscopy

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The Innovation 2025年第3期6卷 50-58,49页

作者： Ruijie Cao Yaning Li Wenyi Wang Yunzhe Fu Xiaoyu Bu Dilizhatai Saimi Jing Sun Xichuan Ge Shan Jiang Yuru Pei Baoxiang Gao Zhixing Chen Meiqi Li Peng Xi Department of Biomedical Engineering College of Future TechnologyPeking UniversityBeijing 100871China National Biomedical Imaging Center College of Future TechnologyPeking UniversityBeijing 100871China China Academy of Space Technology Beijing Institute of Space Mechanics and ElectricityBeijing 100094China Airy Technologies Co. Ltd.Beijing 100081China Key Laboratory of Analytical Science and Technology of Hebei Province College of Chemistry and Materials ScienceHebei UniversityBaoding 071002China College of Future Technology Institute of Molecular MedicineNational Biomedical Imaging CenterBeijing Key Laboratory of Cardiometabolic Molecular MedicinePeking UniversityBeijing 100871China Institute of Biomedical Engineering Beijing Institute of Collaborative InnovationBeijingChina Key Laboratory of Machine Perception(MOE) Department of Machine IntelligencePeking UniversityBeijing 100871China Peking-Tsinghua Center for Life Science Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing 100871China School of Life Sciences Peking UniversityBeijing 100871China

Three-dimensional structured illumination microscopy(3DSIM)is a popular method for observing subcellular/cellular structures or animal/plant tissues with gentle phototoxicity and 3D ***,its time-consuming reconstruction process poses challenges for high-throughput imaging and real-time ***,traditional 3DSIM typically requires more than six z layers for successful reconstruction and is susceptible to defocused *** poses a great gap between single-layer 2DSIM and 6-layer 3DSIM,and limits the observation of thicker *** address these limitations,we developed FO-3DSIM,a novel method that integrates spatial-domain reconstruction with optical-sectioning ***-3DSIM enhances reconstruction speed by up to 855.7 times with superior performance with limited z layers and under high defocused *** retains the high-fidelity,low-photon reconstruction capabilities of our previously proposed *** fast reconstruction and optical sectioning,we achieved large field-of-view(FOV)3D super-resolution imaging of mouse kidney actin,covering a region of 0.453 mm×0.453 mm×2.75μm within 23 min of acquisition and 13 min of *** real-time performance was demonstrated in live actin imaging with *** approach reduces photodamage through limited z layer reconstruction,allowing the observation of ER tubes with just three *** anticipate that FO-3DSIM will pave the way for near real-time,large FOV 6D imaging,encompassing xyz super-resolution,multi-color,long-term,and polarization imaging with less photodamage,removed defocused backgrounds,and reduced reconstruction time.

关键词： gentle phototoxicity phototoxicity high throughput imaging fast reconstruction real time observation optical sectioning three dimensional structured illumination microscopy reconstruction process

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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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Emotion Recognition from Eye Movements Using Multi-way Autoregressive Model

Emotion Recognition from Eye Movements Using Multi-way Autor...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tian-Fang Ma Xuan-Hao Liu Wei-Long Zheng Bao-Liang Lu Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The application of physiological signals in emotion recognition is a popular research topic in human-computer interactions. Eye movement, as an important physiological signal, plays an essential role in medicine, psychology, cognitive science, and other scientific research fields. Previous studies have successfully identified human emotions by combining various eye-related measurements, selecting features, and utilizing machine learning techniques. However, the exploration of eye movement signals in emotion recognition remains insufficient. In this study, we utilize eye tracking heatmap and eye movement trajectory data for emotion recognition for the first time. Based on the two types of eye movement data, we develop a multi-way autoregressive model capable of processing multi-view eye movement data. Compared to traditional deep learning baseline models, our model better adapts to the structure of eye movement data and significantly improves the classification performance. Furthermore, we integrate heatmap and trajectory data with commonly used eye-related measurement features, which further enhance the performance of emotion recognition beyond previous methods.

关键词： Heating systems Emotion recognition Adaptation models Accuracy Psychology Gaze tracking Biomedical measurement Data models Physiology Trajectory

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Fast peer adaptation with context-aware exploration 24

Fast peer adaptation with context-aware exploration

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Proceedings of the 41st International Conference on machine Learning

作者： Long Ma Yuanfei Wang Fangwei Zhong Song-Chun Zhu Yizhou Wang Academy for Advanced Interdisciplinary Studies Peking University and Nat'l Key Laboratory of General Artificial Intelligence BIGAI&PKU Center on Frontiers of Computing Studies School of Computer Science Peking University and Nat'l Key Laboratory of General Artificial Intelligence BIGAI&PKU School of Intelligence Science and Technology Peking University and Nat'l Key Laboratory of General Artificial Intelligence BIGAI&PKU Inst. for Artificial Intelligence and School of Intelligence Science and Technology Peking University and Nat'l Key Laboratory of General Artificial Intelligence BIGAI&PKU Center on Frontiers of Computing Studies School of Computer Science and Inst. for Artificial Intelligence and Nat'l Eng. Research Center of Visual Technology Peking University and Nat'l Key Laboratory of General Artificial Intelligence BIGAI&PKU

Fast adapting to unknown peers (partners or opponents) with different strategies is a key challenge in multi-agent games. To do so, it is crucial for the agent to probe and identify the peer's strategy efficiently, as this is the prerequisite for carrying out the best response in adaptation. However, exploring the strategies of unknown peers is difficult, especially when the games are partially observable and have a long horizon. In this paper, we propose a peer identification reward, which rewards the learning agent based on how well it can identify the behavior pattern of the peer over the historical context, such as the observation over multiple episodes. This reward motivates the agent to learn a context-aware policy for effective exploration and fast adaptation, i.e., to actively seek and collect informative feedback from peers when uncertain about their policies and to exploit the context to perform the best response when confident. We evaluate our method on diverse testbeds that involve competitive (Kuhn Poker), cooperative (PO-Overcooked), or mixed (Predator-Prey-W) games with peer agents. We demonstrate that our method induces more active exploration behavior, achieving faster adaptation and better outcomes than existing methods.

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Efficient Multi-Query Oriented Continuous Subgraph Matching 40

Efficient Multi-Query Oriented Continuous Subgraph Matching

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Ma, Ziyi Yang, Jianye Zhou, Xu Xiao, Guoqing Wang, Jianhua Yang, Liang Li, Kenli Lin, Xuemin School of Artificial Intelligence Hebei University of Technology China Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control China Cyberspace Institute of Advanced Technology Guangzhou University China PengCheng Laboratory Department of New Networks China College of Computer Science and Electronic Engineering Hunan University China Shenzhen Research Institute Hunan University China Antai College of Economics and Management Shanghai Jiao Tong University China

ISBN: (纸本)9798350317152

Continuous subgraph matching (CSM) is a critical task for analyzing dynamic graphs and has a wide range of applications, such as merchant fraud detection, cyber-attack hunting, and rumor detection. Although many efficient CSM algorithms have been recently proposed, they are mainly designed to process a single query. However, in some application scenarios, multi-query oriented continuous subgraph matching (MQCSM) may be of more interest. To our knowledge, the two existing solutions to MQCSM are outdated due to unsatisfactory performance. In this paper, we propose MQ-Match, an efficient approach to MQCSM. First, we design a compact yet effective index structure CCG, which maintains the local matching result of vertices in the data graph using a directed graph. The directed edges in CCG can be utilized as an effective pruning rule for the subsequent incremental matching algorithm when expanding a partial match. Then, we develop a computation sharing incremental matching algorithm. In specific, a set of matching trees is constructed based on the depth-first search trees of the query graphs. By utilizing CCG, we conduct subgraph matching for the matching tree to collect the incremental matches for the query graphs, where the common structures of query graphs are matched only once. Extensive experiments show that MQ-Match can achieve 3.1x-7071.4x speedup over the competitors, and consumes much less memory under the majority of the experiment settings. © 2024 IEEE.

关键词： Directed graphs

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LinNet: Linear Network for Efficient Point Cloud Representation Learning 38

LinNet: Linear Network for Efficient Point Cloud Representat...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Deng, Hao Jing, Kunlei Cheng, Shengmei Liu, Cheng Ru, Jiawei Bo, Jiang Wang, Lin State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University China Shaanxi Key Laboratory of Higher Education Institution of Generative Artificial Intelligence and Mixed Reality China School of Software Engineering Xi'an Jiaotong University China Department of Computing The Hong Kong Polytechnic University Hong Kong

Point-based methods have made significant progress, but improving their scalability in large-scale 3D scenes is still a challenging problem. In this paper, we delve into the point-based method and develop a simpler, faster, stronger variant model, dubbed as LinNet. In particular, we first propose the disassembled set abstraction (DSA) module, which is more effective than the previous version of set abstraction. It achieves more efficient local aggregation by leveraging spatial anisotropy and channel anisotropy separately. Additionally, by mapping 3D point clouds onto 1D space-filling curves, we enable parallelization of downsampling and neighborhood queries on GPUs with linear complexity. LinNet, as a purely point-based method, outperforms most previous methods in both indoor and outdoor scenes without any extra attention, and sparse convolution but merely relying on a simple MLP. It achieves the mIoU of 73.7%, 81.4%, and 69.1% on the S3DIS Area5, NuScenes, and SemanticKITTI validation benchmarks, respectively, while speeding up almost 10x times over PointNeXt. Our work further reveals both the efficacy and efficiency potential of the vanilla point-based models in large-scale representation learning. Our code will be available at https://***/DengH293/LinNet. © 2024 Neural information processing systems foundation. All rights reserved.

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Image-Based Visual Servoing under Field of View Constraints for Robot Manipulators 12

Image-Based Visual Servoing under Field of View Constraints ...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Zuo, Guoyu Liu, Yan Huang, Gao Gong, Daoxiong Faculty of Information Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

The challenge in image-based visual servoing is to deal with the visibility constraints, which require the image features to always remain in the field of view (FOV) of the camera. In this paper, a novel constraint function is proposed to address this problem. The FOV constraint function represented by the coordinates of the features and the region boundaries defined in the image is added to the classical proportional controller, which leads the image features from the margin to the center of the image. Simulation results verify the efficiency of the modified controller by controlling a robot manipulator with eye-in-hand configuration. © 2022 IEEE.

关键词： Robot applications

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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