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International IEEE/EMBS Conference on Neural Engineering, CNE

作者： Mohammad Ahmadi Abbas Erfanian Brain-Computer Interface Laboratory Neural Technology Research Center Department of Biomedical Engineering Iran University of Science and Technology Tehran Iran

This paper presents a new EEG-based brain-computer interface (BCI) for on-line controlling the hand movement in a virtual reality environment. The goal of this research is to develop an interaction technique that will allow the BCI to be effective in real-world scenarios for hand grasp control. For this purpose, two classifiers are designed. The first classifier which is based on the imagination of right-hand movement is for controlling the hand grasping, holding and opening. The second classifier, which is based on the imagination of left-hand movement is designed for error correction and activating the first classifier. One important issue in developing an on-line BCI is the robust and accurate classification of EEG signal which is characterized with a time-varying distribution. In this work, we present a real-time recurrent probabilistic neural network for classifying the EEG signals. The results show that the subjects were able to achieve an accuracy more than 80% during the first session of experiment without off-line training and 73%-91% during the last session using single-trial classification with no adaptation.

关键词： Control systems Real time systems Neural networks Recurrent neural networks Biological neural networks Electroencephalography brain computer interfaces Virtual reality Grasping Error correction

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Single-Molecule Bioelectronic Sensors with AI-Aided Data Analysis:Convergence and Challenges

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Precision Chemistry 2024年第10期2卷 518-538页

作者： Yuxin Yang Yueqi Li Longhua Tang Jinghong Li State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China Nanhu Brain-Computer Interface Institute HangzhouZhejiang 311100China Center for BioAnalytical Chemistry Hefei National Laboratory of Physical Science at MicroscaleUniversity of Science and Technology of ChinaHefei 230026China Department of Chemistry Center for BioAnalytical ChemistryKey Laboratory of Bioorganic Phosphorus Chemistry&Chemical BiologyTsinghua UniversityBeijing 100084China Beijing Institute of Life Science and Technology Beijing 102206ChinaNew Cornerstone Science InstituteBeijing 102206China

Single-molecule bioelectronic sensing,a groundbreaking domain in biological research,has revolutionized our understanding of molecules by revealing deep insights into fundamental biological *** advent of emergent technologies,such as nanogapped electrodes and nanopores,has greatly enhanced this field,providing exceptional sensitivity,resolution,and integration ***,challenges persist,such as complex data sets with high noise levels and stochastic molecular *** intelligence(AI)has stepped in to address these issues with its powerful data processing *** algorithms effectively extract meaningful features,detect subtle changes,improve signal-to-noise ratios,and uncover hidden patterns in massive *** review explores the synergy between AI and single-molecule bioelectronic sensing,focusing on how AI enhances signal processing and data analysis to boost accuracy and *** also discuss current limitations and future directions for integrating AI,highlighting its potential to advance biological research and technological innovation.

关键词： single-molecule bioelectronic sensing artificial intelligence machine learning signal processing biosensors

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CTFN: Hierarchical learning for multimodal sentiment analysis using coupled-translation fusion network 59

CTFN: Hierarchical learning for multimodal sentiment analysi...

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Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021

作者： Tang, Jiajia Li, Kang Jin, Xuanyu Cichocki, Andrzej Zhao, Qibin Kong, Wanzeng Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province School of Computer Science and Technology Hangzhou Dianzi University China Skolkovo Institute of Science and Technology Moscow Russia Systems Research Institute Polish Academy of Science Warsaw Poland Center for Advanced Intelligence Project RIKEN

ISBN: (纸本)9781954085527

Multimodal sentiment analysis is the challenging research area that attends to the fusion of multiple heterogeneous modalities. The main challenge is the occurrence of some missing modalities during the multimodal fusion procedure. However, the existing techniques require all modalities as input, thus are sensitive to missing modalities at predicting time. In this work, the coupled-translation fusion network (CTFN) is firstly proposed to model bi-direction interplay via couple learning, ensuring the robustness in respect to missing modalities. Specifically, the cyclic consistency constraint is presented to improve the translation performance, allowing us directly to discard decoder and only embraces encoder of Transformer. This could contribute to a much lighter model. Due to the couple learning, CTFN is able to conduct bi-direction cross-modality intercorrelation parallelly. Based on CTFN, a hierarchical architecture is further established to exploit multiple bi-direction translations, leading to double multimodal fusing embeddings compared with traditional translation methods. Moreover, the convolution block is utilized to further highlight explicit interactions among those translations. For evaluation, CTFN was verified on two multimodal benchmarks with extensive ablation studies. The experiments demonstrate that the proposed framework achieves state-of-the-art or often competitive performance. Additionally, CTFN still maintains robustness when considering missing modality. © 2021 Association for Computational Linguistics

关键词： Sentiment analysis

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A biosensing system using a multiparameter nonlinear dynamic analysis of cardiomyocyte beating for drug-induced arrhythmia recognition

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Microsystems & Nanoengineering 2022年第3期8卷 277-290页

作者： Hao Wang Yue Wu Quchao Zou Wenjian Yang Zhongyuan Xu Hao Dong Zhjing Zhu Depeng Wang Tanxing Wang Ning Hu Diming Zhang State Key Laboratory of Optoelectronic Materials and Technologies Guangdong Province Key Laboratory of Display Material and TechnologySchool of Electronics and Information TechnologySun Yat-sen UniversityGuangzhou 510006China Research Center for Itelligent Sensing Systems Zhejiang LabHangzhou 311121China ZJU-Hangzhou Global Scientific and Technological Innovation Center Department of ChemistryThe Second Affiliated Hospital Zhejiang University School of MedicineDepartment of Clinical Medical EngineeringZhejiang UniversityHangzhou 310058China Key Laboratory of Novel Target and Drug Study for Neural Repair of Zhejang Province School of MedicineSchool of Computer&Computing ScienceZhejiang University City CollegeHangzhou 310015China School of Brain Science and Brain Medicine Zhejiang UniversityHangzhou 310058China College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China E-LinkCare Meditech Co LtdHangzhou 310011China State Key Laboratory of Transducer Technology Chinese Academy of SciencesShanghai 200050China

Cardiovascular disease is the number one cause of death in ***,cardiotoxicity is one of the most important adverse effects assessed by arrhythmia recognition in drug ***,cell-based techniques developed for arhythmia recognition primarily employ linear methods such as time-domain analysis that detect and compare individual waveforms and thus fall short in some applications that require automated and efficient arrhythmia recognition from large *** carried out the frst report to develop a biosensing system that integrated impedance measurement and multiparameter nonlinear dynamic algorithm(MNDA)analysis for druginduced arrhythmia recognition and *** biosensing system cultured cardiomyocytes as physiologically relevant models,used interdigitated electrodes to detect the mechanical beating of the cardiomyocytes,and employed MNDA analysis to recognize drug-induced arrhythmia from the cardiomyocyte beating *** best performing MNDA parameter,approximate entropy,enabled the system to recognize the appearance of sertindoleand norepinephrine-induced arrhythmia in the *** MNDA reconstruction in phase space enabled the system to classify the different arrhythmias and quantify the severity of *** new biosensing system utilizing MNDA provides a promising and alternative method for drug-induced arrhythmia recognition and classification in cardiological and pharmaceutical applications.

关键词： arrhythmia cardio analysis

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Dark-based optical sectioning assists background removal in fluorescence microscopy

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Nature methods 2025年 2025 May 12页

作者： Ruijie Cao Yaning Li Yao Zhou Meiqi Li Fangrui Lin Wenyi Wang Guoxun Zhang Gang Wang Boya Jin Wei Ren Yu Sun Zhifeng Zhao Wei Zhang Jing Sun Yiwei Hou Xinzhu Xu Jiakui Hu Wei Shi Shuang Fu Qianxi Liang Yanye Lu Changhui Li Yuxuan Zhao Yiming Li Dong Kuang Jiamin Wu Peng Fei Junle Qu Peng Xi Department of Biomedical Engineering National Biomedical Imaging Center Peking University College of Future Technology Beijing China. School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China. School of Life Sciences Peking University Beijing China. State Key Laboratory of Radio Frequency Heterogeneous Integration College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen China. Airy Technologies Co. Beijing China. Department of Automation Institute for Brain and Cognitive Sciences IDG/McGovern Institute for Brain Research Tsinghua University Beijing China. Department of Computer Technology and Science Anhui University of Finance and Economics Bengbu China. Key Laboratory of Analytical Science and Technology of Hebei Province College of Chemistry and Materials Science Hebei University Hebei China. Institution of Medical Technology Peking University Health Science Center Peking University Beijing China. Department of Biomedical Engineering Southern University of Science and Technology Shenzhen China. Institute of Pathology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China. State Key Laboratory of Radio Frequency Heterogeneous Integration College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen China. jlqu@***. Department of Biomedical Engineering National Biomedical Imaging Center Peking University College of Future Technology Beijing China. xipeng@***.

In fluorescence microscopy, a persistent challenge is the defocused background that obscures cellular details and introduces artifacts. Here, we introduce Dark sectioning, a method inspired by natural image dehazing for removing backgrounds that leverages dark channel prior and dual frequency separation to provide single-frame optical sectioning. Unlike denoising or deconvolution, Dark sectioning specifically targets and removes out-of-focus backgrounds, stably improving the signal-to-background ratio by nearly 10 dB and structural similarity index measure of images by approximately tenfold. Dark sectioning was validated using wide-field, confocal, two/three-dimensional structured illumination and one/two-photon microscopy with high-fidelity reconstruction. We further demonstrate its potential to improve the segmentation accuracy in deep tissues, resulting in better recognition of neurons in the mouse brain and accurate assessment of nuclei in prostate lesions or mouse brain sections. Dark sectioning is compatible with many other microscopy modalities, including light-sheet and light-field microscopy, as well as processing algorithms, including deconvolution and super-resolution optical fluctuation imaging.

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Anatomical-Functional Fusion Network for Lesion Segmentation Using Dual-View CEUS 9th

Anatomical-Functional Fusion Network for Lesion Segmentatio...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Wan, Peng Liu, Chunrui Zhang, Daoqiang Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Department of Ultrasound Affiliated Drum Tower Hospital Medical School of Nanjing University Nanjing China

ISBN: (纸本)9783031466632

Dual-view contrast-enhanced ultrasound (CEUS) has been widely applied in lesion detection and characterization due to the provided anatomical and functional information of lesions. Accurate delineation of lesion contour is important to assess lesion morphology and perfusion dynamics. Although the last decade has witnessed the unprecedented progress of deep learning methods in 2D ultrasound imaging segmentation, there are few attempts to discriminate tissue perfusion discrepancy using dynamic CEUS imaging. Combined with the side-by-side gray-scale US view, we propose a novel anatomical-functional fusion network (AFF-Net) to fuse complementary imaging characteristics from dual-view dynamic CEUS imaging. Towards a comprehensive characterization of lesions, our method mainly tackles with two challenges: 1) how to effectively represent and aggregate enhancement features of the dynamic CEUS view;2) how to efficiently fuse them with the morphology features of the US view. Correspondingly, we design the channel-wise perfusion (PE) gate and anatomical-functional fusion (AFF) module with the goal to exploit dynamic blood flow characteristics and perform layer-level fusion of the two modalities, respectively. The effectiveness of the AFF-Net method on lesion segmentation is validated on our collected thyroid nodule dataset with superior performance compared with existing methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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A New Approach to Detecting and Analyzing Monkey Activities Using the Improved YOLOv5 3

A New Approach to Detecting and Analyzing Monkey Activities ...

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3rd International Conference on Machine Learning and computer Application, ICMLCA 2022

作者： Shen, Biao Zhang, Zhiyi Wang, Qishun Tu, Zhengzheng Liu, Ning Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China State Key Laboratory of Brain and Cognitive Science Institute of Biophysics Chinese Academy of Sciences Beijing100101 China

ISBN: (纸本)9781510664814

With the development of science and technology, video equipment has become popular in laboratories. Researchers using animals (e.g., macaques) as models often collect a large number of animal behavior videos for the research aims. Researchers often need to watch numerous videos to record and analyze animal movements and behaviors for subsequent analysis. Manual analysis of videos is time-consuming and inefficient. To resolve this problem, we developed an approach to detect macaques in video and then capture information about their location. Using this information, we can quickly analyze animal behaviors, such as movement preferences and behavior patterns. The method was developed based on the YOLOv5 algorithm and had excellent accuracy and robustness. In addition, this method can automatically process videos and generate statistical results and analysis charts. Furthermore, all settings are manually configurable and easy to use to meet the different requirements of researchers. We believe that this new approach will benefit many researchers. © 2023 SPIE.

关键词： Motion analysis

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Enhanced exciton diffusion from interlayer charge-transfer transitions in PtSe2/MoSe_(2) van der Waals heterojunction

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Nano Research 2023年第11期16卷 12809-12816页

作者： Jiarong Wang Dawei He Zhiying Bai Guili Li Jinxuan Bai Keqin Liu Fangying Ren Xiaojing Liu Jiaqi He Weiya Zhou Jianlin Sun Yongsheng Wang Xiaoxian Zhang Yuchao Yang Key Laboratory of Luminescence and Optical Information Ministry of EducationInstitute of Optoelectronic TechnologyBeijing Jiaotong UniversityBeijing 100044China Beijing Advanced Innovation Center for Integrated Circuits School of Integrated CircuitsPeking UniversityBeijing 100871China School of Electronic and Computer Engineering Peking UniversityShenzhen 518055China Center for Brain Inspired Chips Institute for Artificial IntelligenceFrontiers Science Center for Nano-optoelectronicsPeking UniversityBeijing 100871China Center for Brain Inspired Intelligence Chinese Institute for Brain Research(CIBR)Beijing 102206China College of Mathematics and Physics Beijing University of Chemical TechnologyBeijing 100029China Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of SciencesBeijing 100190China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100049China

Artificial van der Waals(vdWs)heterostructures offer unprecedented opportunities to explore and reveal novel synergistic electronic and optical phenomena,which are beneficial for the development of novel optoelectronic devices at atomic ***,due to the damage caused by the device fabrication process,their inherent properties such as carrier mobility are obscured,which hinders the improvement of device performance and the incorporation of vdWs materials into next-generation integrated ***,combining pump-probe spectroscopic and scanning probe microscopic techniques,the intrinsic optoelectronic properties of PtSe_(2)/MoSe_(2)heterojunction were nondestructively and systematically *** heterojunction exhibits a broad-spectrum optical response and maintains ultrafast carrier dynamics(interfacial charge transfer~0.8 ps and carrier lifetime~38.2 ps)*** in-plane exciton diffusion coefficient of the heterojunction was extracted(19.4±7.6 cm^(2)∙s^(−1)),and its exciton mobility as high as 756.8 cm^(2)∙V−1∙s^(−1)was deduced,exceeding the value of its *** enhancement was attributed to the formation of an n-type Schottky junction between PtSe_(2)and MoSe_(2),and its built-in electric field assisted the ultrafast transfer of photogenerated carriers from MoSe_(2)to PtSe_(2),enhancing the in-plane exciton diffusion of the *** results demonstrate that PtSe_(2)/MoSe_(2)is suitable for the development of broadspectrum and sensitive optoelectronic ***,the results contribute to a fundamental understanding of the performance of various optoelectronic devices based on such PtSe_(2)two-dimensional(2D)heterostructures.

关键词： van der Waals heterostructures ultrafast carrier dynamics exciton diffusion pump-probe spectroscopy scanning probe microscopy

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Exploiting Correlation Subspace to Predict Heterogeneous Cross-Project Defects

Exploiting Correlation Subspace to Predict Heterogeneous Cro...

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作者： Cheng, Ming Wu, Guoqing Wan, Hongyan You, Guoan Yuan, Mengting Jiang, Min State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan Hubei China Department of Cognitive Science and Technology School of Information Science and Engineering Xiamen University Xiamen Fujian China Fujian Key Laboratory of Brain-Like Intelligent Systems School of Information Science and Engineering Xiamen University Xiamen Fujian China

Cross-project defect prediction trains a prediction model using historical data from source projects and applies the model to target projects. Most previous efforts assumed the cross-project data have the same metrics set, which means the metrics used and the size of metrics set are the same. However, this assumption may not hold in practical scenarios. In addition, software defect datasets have the class-imbalance problem which increases the difficulty for the learner to predict defects. In this paper, we advance canonical correlation analysis by deriving a joint feature space for associating cross-project data. We also propose a novel support vector machine algorithm which incorporates the correlation transfer information into classifier design for cross-project prediction. Moreover, we take different misclassification costs into consideration to make the classification inclining to classify a module as a defective one, alleviating the impact of imbalanced data. The experimental results show that our method is more effective compared to state-of-the-art methods. © 2016 World Scientific Publishing Company.

关键词： Support vector machines

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Face-sketch learning with human sketch-drawing order enforcement

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science China(Information sciences) 2020年第11期63卷 298-311页

作者： Liang CHANG Lihua JIN Lifen WENG Wentao CHAO Xuguang WANG Xiaoming DENG Qiulei DONG School of Artificial Intelligence Beijing Normal University Department of Design Art Xiamen University of Technology Department of Automation North China Electric Power University Beijing Key Laboratory of Human Computer Interactions Institute of Software Chinese Academy of Sciences National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences

Dear editor,Although face-sketch synthesis generates a sketch from a given face photo automatically [1], it is an open research problem in computer vision [2–4]. Recently, several deep neural network (DNN)methods for... 详细信息

关键词： face sketch synthesis deep neural network order enforcement image synthesis generative adversarial network

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