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

作者： Liang, Jizhi He, Hailang Liu, Yuxin Wei, Xiaoling Sun, Liuyang Tao, Tiger H. Zhou, Zhitao 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 State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China Neuroxess Co. Ltd. Shanghai China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China School of Physical Science and Technology ShanghaiTech University Shanghai China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai 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

This paper reports a flexible and bidirectional olfactory neural interface based on microelectromechanical systems fabrication process and the demonstrations of cross-regional synchronous recording and olfactory augmentation on free-moving mice. With overall 4-μm-thick microelectrodes and a compact headstage mounted on the skull, it enables long-term electrophysiological recording and electrostimulation of the olfactory neural pathways of mice, including the olfactory epithelium, the olfactory bulb and the olfactory cortex. Our results demonstrate the broad application potential of this multiregion olfactory neural interface in studying functional connectivity within the olfactory neural network, investigating olfactory navigation behaviors, optimizing parameters for micro-electrostimulation-induced olfactory augmentation, and exploring the underlying mechanisms. © 2025 IEEE.

关键词： Microrobots

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Blind separation of noncooperative paired carrier multiple access signals based on improved quantum-inspired evolutionary algorithm and receding horizon optimization

Blind separation of noncooperative paired carrier multiple a...

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作者： Deng, Qi Zhang, Shanshan Chen, Gang Lu, Huaxiang Lab of Artificial Networks Institute of Semiconductors CAS Beijing China University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China

The single-channel blind source separation of paired carrier multiple access (PCMA) signal is a key technology in satellite communications. Due to the high-order complexity of existing separation methods and the uncertainty of channel parameters, the processing of noncooperative PCMA signals with long memory remains a great challenge. In this article, the blind separation was solved as the joint channel estimation and sequence detection, and a novel blind separation algorithm based on the improved quantum-inspired evolutionary algorithm (IQEA) and receding horizon optimization was proposed. Considering the practical scenarios, a mixed PCMA signal model with noninteger period sampling was presented in advance. Combined with the proposed signal model, the IQEA was applied to recover the uplink signals from the mixed PCMA signals by its iterative estimation. Meanwhile, to reduce the computational complexity caused by long memory of PCMA signals, the receding horizon optimization was introduced to process the whole signal sequence as a dynamic programming problem. The simulation results verified that the proposed RH-IQEA outperforms existing state-of-the-art separation algorithms in the blind separation of noncooperative PCMA signals, with higher accuracy, lower-order complexity and stronger robustness to parameter estimation errors. © 2021 John Wiley & Sons, Ltd.

关键词： Iterative methods

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Mapping the Human brain:What Is the Next Frontier?

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The Innovation 2021年第1期2卷 102-103页

作者： Lingzhong Fan Brainnetome Center Institute of AutomationChinese Academy of SciencesBeijing 100190China National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China CAS Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China

The human brain is considered to be the most complex and mysterious organ of our *** to technological and ethical considerations,we have not been able to fully map the multi-scale structures and understand the organizing *** the origin of human consciousness and intelligence,it inspires awe and *** the beginning of the 21st century,brain science has increasingly become popular around the whole world,and numerous large-scale brain initiatives have been launched to explore the structure and function of the human brain,to decode the nature of consciousness,and to address the problem of neuropsychiatric diseases.

关键词： Mapping consciousness Human

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Design and Construction of a Spherical Magnet Array for Multi-dimensional Tactile Perception

Design and Construction of a Spherical Magnet Array for Mult...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Mei-Jiang Gui Xiao-Hu Zhou Xiao-Liang Xie Shi-Qi Liu Zhen-Qiu Feng Hao Li Tian-Yu Xiang De-Xing Huang Bo-Xian Yao Zeng-Guang Hou State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Macau China

Soft magnetic sensors provide considerable merits for tactile perception, such as high precision, simple wiring, and easy expansion to large sensing areas. However, current magnetic sensors basically rely on data-driven methods, difficult to establish the relationship for the two-stage conversion (i.e., from magnetic signals to spatial displacements, then to force signals). In this study, a spherical magnet array is designed with magnetic powder and hyper-elastic rubber for multi-dimensional tactile perception. Besides, a highly interpretable and calculable method is proposed to analyze the two-stage conversion according to magnetic dipole model and dynamic Young’s modulus. A nine-spherical-magnet array is constructed, and extensive analysis in terms of different magnet numbers are conducted in COMSOL environments. The results demonstrate the proposed method’s high accuracy with mean square errors of 0.02 mT and 9.90 mN for the two stages, respectively. This fact also indicates the great potential of our method to various applications of tactile perception.

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CALIBRATION FOR NON-EXEMPLAR BASED CLASS-INCREMENTAL LEARNING

CALIBRATION FOR NON-EXEMPLAR BASED CLASS-INCREMENTAL LEARNIN...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Zhu, Fei Zhang, Xu-Yao Liu, Cheng-Lin NLPR Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence of Brain Science and Intelligence Technology Beijing100190 China

ISBN: (纸本)9781665438643

Catastrophic forgetting is the central challenge in incremental learning. Notable studies address the problem by using regularization or experience replay strategies. However, the performance is far from ideal without storing previous data, especially in the scenario of class-incremental learning (CIL). In CIL setting, an important factor causing catastrophic forgetting is the severe bias between the new and previously learned classes, in both classifier and feature extractor. In this paper, we propose calibrateCIL which contains two simple modifications to calibrate the bias in non-exemplar based CIL. Specifically, local softmax is proposed to calibrate the classifier, and cutout training is used to calibrate the feature extractor by learning richer, more generalizable and transferable features. Our method can give balance class scores without any post-processing technique. We show that our method outperforms state-of-the-art non-exemplar based methods on the challenging problem of CIL, and the ablation study demonstrates the effectiveness of the two modifications. © 2021 IEEE

关键词： Computer vision

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Engineered High-Fidelity cas13 Variants with Minimal Collateral RNA Targeting

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Bulletin of the Chinese Academy of sciences 2022年第3期36卷 181-182页

作者： YANG Hui Center for Excellence in Brain Science and Intelligence Technology(Institute of Neuroscience) Chinese Academy of Sciences

Using CRISPR-cas13 for targeted RNA degradation is hindered by the collateral damages induced by *** scientists screened hundreds of engineered cas13 variants and obtained several hits with markedly reduced collateral... 详细信息

关键词： markedly collateral targeted

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Soft threshold ternary networks 29

Soft threshold ternary networks

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29th International Joint Conference on Artificial intelligence, IJCAI 2020

作者： Xu, Weixiang He, Xiangyu Zhao, Tianli Hu, Qinghao Wang, Peisong Cheng, Jian Institute of Automation Chinese Academy of Sciences China CAS Center for Excellence in Brain Science and Intelligence Technology

ISBN: (纸本)9780999241165

Large neural networks are difficult to deploy on mobile devices because of intensive computation and storage. To alleviate it, we study ternarization, a balance between efficiency and accuracy that quantizes both weights and activations into ternary values. In previous ternarized neural networks, a hard threshold ? is introduced to determine quantization intervals. Although the selection of ? greatly affects the training results, previous works estimate ? via an approximation or treat it as a hyper-parameter, which is suboptimal. In this paper, we present the Soft Threshold Ternary Networks (STTN), which enables the model to automatically determine quantization intervals instead of depending on a hard threshold. Concretely, we replace the original ternary kernel with the addition of two binary kernels at training time, where ternary values are determined by the combination of two corresponding binary values. At inference time, we add up the two binary kernels to obtain a single ternary kernel. Our method dramatically outperforms current state-of-the-arts, lowering the performance gap between full-precision networks and extreme low bit networks. Experiments on ImageNet with AlexNet (Top-1 55.6%), ResNet-18 (Top-1 66.2%) achieves new state-of-the-art. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

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Multimodal emotion recognition and sentiment analysis via attention enhanced recurrent model 2

Multimodal emotion recognition and sentiment analysis via at...

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2nd Multimodal Sentiment Analysis Challenge and Workshop, MuSe 2021, held in conjunction with the ACM Multimedia 2021

作者： Sun, Licai Xu, Mingyu Lian, Zheng Liu, Bin Tao, Jianhua Wang, Meng Cheng, Yuan School of Artificial Intelligence University of Chinese Academy of Sciences China NLPR Institute of Automation Chinese Academy of Sciences China CAS Center for Excellence in Brain Science and Intelligence Technology China Ant Financial Services Group Beijing China

ISBN: (纸本)9781450386784

With the proliferation of user-generated videos in online websites, it becomes particularly important to achieve automatic perception and understanding of human emotion/sentiment from these videos. In this paper, we present our solutions to the MuSe-Wilder and MuSe-Sent sub-challenges in MuSe 2021 Multimodal Sentiment Analysis Challenge. MuSe-Wilder focuses on continuous emotion (i.e., arousal and valence) recognition while the task of MuSe-Sent concentrates on discrete sentiment classification. To this end, we first extract a variety of features from three common modalities (i.e., audio, visual, and text), including both low-level handcrafted features and high-level deep representations from supervised/unsupervised pre-trained models. Then, the long short-term memory recurrent neural network, as well as the self-attention mechanism is employed to model the complex temporal dependencies in the feature sequence. The concordance correlation coefficient (CCC) loss and F1-loss are used to guide continuous regression and discrete classification, respectively. To further boost the model's performance, we adopt late fusion to exploit complementary information from different modalities. Our proposed method achieves CCCs of 0.4117 and 0.6649 for arousal and valence respectively on the test set of MuSe-Wilder, which outperforms the baseline system (i.e., 0.3386 and 0.5974) by a large margin. For MuSe-Sent, F1-scores of 0.3614 and 0.4451 for arousal and valence are obtained, which also outperforms the baseline system significantly (i.e., 0.3512 and 0.3291). With these promising results, we ranked top3 in both sub-challenges. © 2021 ACM.

关键词： Emotion Recognition

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Population genetics of marmosets in Asian primate research centers and loci associated with epileptic risk revealed by whole-genome sequencing

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Zoological Research 2023年第5期44卷 837-847页

作者： Xiangyu Yang Yafei Mao Xuan-Kai Wang Dong-Ni Ma Zhen Xu Neng Gong Barbara Henning Xu Zhang Guang He Yong-Yong Shi Evan EEichler Zhi-Qiang Li Eiki Takahashi Wei-Dong Li Bio-X Institutes Key Laboratory for the Genetics of Development and Neuropsychiatric Disorders(Ministry of Education)Shanghai Jiao Tong UniversityShanghai 200240China Institute of Psychology and Behavioral Science Shanghai Jiao Tong UniversityShanghai 200030China WLA Laboratories World Laureates AssociationShanghai 201203China Center for Brain Health and Brain Technology Global Institute of Future TechnologyShanghai Jiao Tong UniversityShanghai 200240China Department of Genome Sciences University of Washington School of MedicineSeattle WA 98195USA Institute of Neuroscience Key Laboratory of Primate NeurobiologyCAS Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai 200031China Affiliated Hospital of Qingdao University&Biomedical Sciences Institute of Qingdao University Qingdao Branch of SJTU Bio-X InstitutesQingdao UniversityQingdaoShandong 266003China Howard Hughes Medical Institute University of WashingtonSeattle WA 98195USA Department of Biomedicine Graduate School of Medical SciencesKyushu UniversityFukuoka 812-8582Japan

The common marmoset(Callithrix jacchus)has emerged as a valuable nonhuman primate model in biomedical research with the recent release of high-quality reference genome *** marmosets have been independently reported in two Asian primate research ***,the population genetics within these primate centers and the specific genetic variants associated with epilepsy in marmosets have not yet been ***,we characterized the genetic relationships and risk variants for epilepsy in 41 samples from two epileptic marmoset pedigrees using whole-genome *** identified 14558184 single nucleotide polymorphisms(SNPs)from the 41 samples and found higher chimerism levels in blood samples than in fingernail *** analysis showed fourth-degree of relatedness among marmosets at the primate *** addition,SNP and copy number variation(CNV)analyses suggested that the WW domain-containing oxidoreductase(WWOX)and Tyrosine-protein phosphatase nonreceptor type 21(PTPN21)genes may be associated with epilepsy in ***,KCTD18-like gene deletion was more common in epileptic marmosets than control *** study provides valuable population genomic resources for marmosets in two Asian primate *** analyses identified a reasonable breeding strategy for genetic diversity maintenance in the two centers,while the case-control study revealed potential risk genes/variants associated with epilepsy in marmosets.

关键词： Common marmoset(Callithrix jacchus) Population genetics Whole-genome sequencing Genetic chimerism Epilepsy Risk locus

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Self-supervised denoising for multimodal structured illumination microscopy enables long-term super-resolution live-cell imaging

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PhotoniX 2024年第1期5卷 654-675页

作者： Xingye Chen Chang Qiao Tao Jiang Jiahao Liu Quan Meng Yunmin Zeng Haoyu Chen Hui Qiao Dong Li Jiamin Wu Department of Automation Tsinghua UniversityBeijing 100084China Research Institute for Frontier Science Beihang UniversityBeijing 100083China Institute for Brain and Cognitive Sciences Tsinghua UniversityBeijing 100084China National Laboratory of Biomacromolecules New Cornerstone Science LaboratoryCAS Center for Excellence in BiomacromoleculesInstitute of BiophysicsChinese Academy of SciencesBeijing 100101China College of Life Sciences University of Chinese Academy of SciencesBeijing 100049China Laboratory of Image Processing and Intelligent Control of Ministry of Education of China School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

Detection noise significantly degrades the quality of structured illumination microscopy(SIM)images,especially under low-light *** supervised learning based denoising methods have shown prominent advances in eliminating the noise-induced artifacts,the requirement of a large amount of high-quality training data severely limits their *** we developed a pixel-realignmentbased self-supervised denoising framework for SIM(PRS-SIM)that trains an SIM image denoiser with only noisy data and substantially removes the reconstruction *** demonstrated that PRS-SIM generates artifact-free images with 20-fold less fluorescence than ordinary imaging conditions while achieving comparable super-resolution capability to the ground truth(GT).Moreover,we developed an easy-to-use plugin that enables both training and implementation of PRS-SIM for multimodal SIM platforms including 2D/3D and linear/nonlinear *** PRS-SIM,we achieved longterm super-resolution live-cell imaging of various vulnerable bioprocesses,revealing the clustered distribution of Clathrin-coated pits and detailed interaction dynamics of multiple organelles and the cytoskeleton.

关键词： Super-resolution microscopy Structured illumination microscopy Deep learning Self-supervised learning Live-cell imaging

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