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

作者： Zhou, Erda Liu, Changjiang Wang, Xiner 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 State Key Laboratory of Transducer Technology 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 Neuroxess Co. Ltd. Shanghai China Guangdong Institute of Intelligence Science and Technology Zhuhai Guangdong Hengqin China Tianqiao and Chrissy Chen Institute for Translation Research Shanghai China

ISBN: (纸本)9798331508890

In this study, we present a high-density, high-channel-count micro-electrocorticographic (μECoG) electrode array for real-time motor decoding. The 256-channel μECoG electrode arrays, based on MEMS processes, possess excellent flexibility and mechanical robustness, allowing them to conform to the cortical surface and enabling the acquisition of high-quality ECoG signals. The advanced brain-computer interface (BCI) system was applied to a Labrador dog and high accurate real-time motor decoding was achieved, showing the advantages of high-resolution ECoG sampling. Our method demonstrates the potential for controlling a cursor with the ECoG signals, offering the possibility of reconstructing motor functions and synthesizing avatars. © 2025 IEEE.

关键词： brain computer interface

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Instance-aware prompt learning for language understanding and generation

arXiv

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arXiv 2022年

作者： Jin, Feihu Lu, Jinliang Zhang, Jiajun Zong, Chengqing National Laboratory of Pattern Recognition Institute of Automation CAS China School of Artificial Intelligence University of Chinese Academy of Sciences China CAS Center for Excellence in Brain Science and Intelligence Technology China

Recently, prompt learning has become a new paradigm to utilize pre-trained language models (PLMs) and achieves promising results in downstream tasks with a negligible increase of parameters. The current usage of discrete and continuous prompts assumes that the prompt is fixed for a specific task and all samples in the task share the same prompt. However, a task may contain quite diverse samples in which some are easy and others are difficult, and diverse prompts are desirable. In this paper, we propose an instance-aware prompt learning method that learns a different prompt for each instance. Specifically, we suppose that each learnable prompt token has a different contribution to different instances, and we learn the contribution by calculating the relevance score between an instance and each prompt token. The contribution weighted prompt would be instance aware. We apply our method to both unidirectional and bidirectional PLMs on both language understanding and generation tasks. Extensive experiments demonstrate that our method obtains considerable improvements compared to strong baselines. Especially, our method achieves the state-of-the-art on the SuperGLUE few-shot learning benchmark. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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ViLEM: Visual-Language Error Modeling for Image-Text Retrieval

ViLEM: Visual-Language Error Modeling for Image-Text Retriev...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yuxin Chen Zongyang Ma Ziqi Zhang Zhongang Qi Chunfeng Yuan Ying Shan Bing Li Weiming HU Xiaohu Qie JianPing Wu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences ARC Lab School of Artificial Intelligence University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology Tencent PCG Tsinghua University

Dominant pre-training works for image-text retrieval adopt “dual-encoder” architecture to enable high efficiency, where two encoders are used to extract image and text representations and contrastive learning is employed for global alignment. However, coarse-grained global alignment ignores detailed semantic associations between image and text. In this work, we propose a novel proxy task, named Visual-Language Error Modeling (ViLEM), to inject detailed image-text association into “dual-encoder” model by “proofreading” each word in the text against the corresponding image. Specifically, we first edit the image-paired text to automatically generate diverse plausible negative texts with pre-trained language models. ViLEM then enforces the model to discriminate the correctness of each word in the plausible negative texts and further correct the wrong words via resorting to image information. Further-more, we propose a multi-granularity interaction framework to perform ViLEM via interacting text features with both global and local image features, which associates local text semantics with both high-level visual context and multi-level local visual information. Our method surpasses state-of-the-art “dual-encoder” methods by a large margin on the image-text retrieval task and significantly improves discriminativeness to local textual semantics. Our model can also generalize well to video-text retrieval.

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Order-Prompted Tag Sequence Generation for Video Tagging

Order-Prompted Tag Sequence Generation for Video Tagging

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International Conference on Computer Vision (ICCV)

作者： Zongyang Ma Ziqi Zhang Yuxin Chen Zhongang Qi Yingmin Luo Zekun Li Chunfeng Yuan Bing Li Xiaohu Qie Ying Shan Weiming Hu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences ARC Lab School of Artificial Intelligence University of Chinese Academy of Sciences CNCERT/CC Tencent PCG CAS Center for Excellence in Brain Science and Intelligence Technology

Video Tagging intends to infer multiple tags spanning relevant content for a given video. Typically, video tags are freely defined and uploaded by a variety of users, so they have two characteristics: abundant in quantity and disordered intra-video. It is difficult for the existing multilabel classification and generation methods to adapt directly to this task. This paper proposes a novel generative model, Order-Prompted Tag Sequence Generation (OP-TSG), according to the above characteristics. It regards video tagging as a tag sequence generation problem guided by sample-dependent order prompts. These prompts are semantically aligned with tags and enable to decouple tag generation order, making the model focus on modeling the tag dependencies. Moreover, the word-based generation strategy enables the model to generate novel tags. To verify the effectiveness and generalization of the proposed method, a Chinese video tagging benchmark CREATE-tagging, and an English image tagging benchmark Pexel-tagging are established. Extensive results show that OP-TSG is significantly superior to other methods, especially the results on rare tags improve by 3.3% and 3% over SOTA methods on CREATE-tagging and Pexel-tagging, and novel tags generated on CREATE-tagging exhibit a tag gain of 7.04%.

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Deep learning-based optical aberration estimation enables offline digital adaptive optics and super-resolution imaging

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Photonics Research 2024年第3期12卷 474-484页

作者： CHANG QIAO HAOYU CHEN RUN WANG TAO JIANG YUWANG WANG DONG LI Department of Automation Tsinghua UniversityBeijing 100084China 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 Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China

Optical aberrations degrade the performance of fluorescence *** adaptive optics(AO)leverages specific devices,such as the Shack–Hartmann wavefront sensor and deformable mirror,to measure and correct optical ***,conventional AO requires either additional hardware or a more complicated imaging procedure,resulting in higher cost or a lower acquisition *** this study,we proposed a novel space-frequency encoding network(SFE-Net)that can directly estimate the aberrated point spread functions(PSFs)from biological images,enabling fast optical aberration estimation with high accuracy without engaging extra optics and image *** showed that with the estimated PSFs,the optical aberration can be computationally removed by the deconvolution ***,to fully exploit the benefits of SFE-Net,we incorporated the estimated PSF with neural network architecture design to devise an aberration-aware deeplearning super-resolution model,dubbed *** demonstrated that the combination of SFE-Net and SFT-DFCAN enables instant digital AO and optical aberration-aware super-resolution reconstruction for live-cell imaging.

关键词： optics resolution enable

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HANDWRITING TRAJECTORY RECOVERY FROM OFF-LINE MULTI-STROKE CHARACTERS BY DEEP ORDERING PREDICTION AND HEURISTIC SEARCH

HANDWRITING TRAJECTORY RECOVERY FROM OFF-LINE MULTI-STROKE C...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Wang, Tie-Qiang Liu, Cheng-Lin Institute of Automation of 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

Stroke order recovery from off-line multi-stroke characters is a great challenge due to the ambiguity in intersection and connection among strokes. In this paper, we propose a novel framework for handwriting trajectory recovery from off-line handwritten characters by deep neural network (DNN) based ordering prediction and heuristic search, where several DNN modules are designed to extract stroke skeleton, ambiguous zones and starting points, respectively. Then, the ordering matrix Mo among all the stroke segments is calculated by a pointer network (Ptr-Net). Besides, a convolutional neural network (CNN) is used to measure the time adjacency between two arbitrary segments. Based on these necessary measurements, the final writing order is decided via searching for the optimal permutation by heuristic A∗ search. Experiments on handwriting images synthesized from the public online handwriting datasets casIA-OLHWDB1.1, ICDAR13-Online and UNIPEN show that the proposed method yields superior performance on Chinese and English/Arabic handwriting. © 2021 IEEE

关键词： Recovery

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Real-Time Adjustment of Gait Training Patterns Based on Muscle Activation for Lower Limb Exoskeleton

Real-Time Adjustment of Gait Training Patterns Based on Musc...

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Chinese Control Conference (CCC)

作者： Weiguo Shi Weiqun Wang Jiaxing Wang Shixin Ren Zeng-Guang Hou School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China

Lower limb exoskeleton (LLE) has been widely studied recently. While how to adjust the subject-specific gait training patterns for LLE is one of the key problems that have not been well addressed. In this paper, a real-time method based on the muscle activation extracted from sEMG signals is proposed to adjust the gait training patterns, to maintain the given level of muscle activation and to enhance the subjects' engagement during gait training. Firstly, the muscle activation is calculated based on the previous research results. Then, in order to simplify the joint trajectory representation, the hip, knee, and ankle joint trajectories are parameterized based on finite Fourier series (FFS). Therefore, the gait training patterns can be represented by the coefficients and periods. Moreover, a real-time strategy is proposed to adjust the parameters of FFS based on the feedback of subjects' muscle activation during gait training. Finally, the performance of the proposed method is demonstrated by the experiment.

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Disturbance Observer Based Control for an Underwater Biomimetic Vehicle-Manipulator System with Mismatched Disturbances 10

Disturbance Observer Based Control for an Underwater Biomime...

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10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021

作者： Lv, Jiaqi Wang, Yu Wang, Shuo Wang, Rui Cheng, Long Tan, Min School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Cas Center for Excellence in Brain Science and Intelligence Technology Shanghai200031 China

ISBN: (纸本)9781665424233

In this paper, a disturbance observer based control (DOBC) framework is proposed to achieve the motion control of an underwater biomimetic vehicle-manipulator system (UBVMS) driven by bionic flippers with mismatched disturbances. First, a disturbance observer is established to estimate the mismatched disturbances in finite time. Then, a novel arctangent non-singularity sliding mode manifold incorporating the disturbance observer is proposed to counteract the lumped mismatched disturbances. The stability of the system is validated by the Lyapunov theory. Finally, various comparative simulations are carried out to validate the performance of our proposed DOBC framework in the presence of mismatched disturbances. © 2021 IEEE.

关键词： Biomimetics

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Dynamic surface control for an underactuated underwater biomimetic vehicle-manipulator system

Dynamic surface control for an underactuated underwater biom...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Bai, Xuejian Wang, Yu Wang, Rui Wang, Shuo Tan, Min School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Automation Chinese Academy of Sciences Beijing100190 China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai200031 China

ISBN: (纸本)9781665436786

In this paper, a position control method based on an improved dynamic surface control is proposed for an underactuated underwater biomimetic vehicle-manipulator system (UBVMS) driven by undulatory fin propulsors. A surge force adaptive process is designed to solve the underactuated problem of the UBVMS. And the proposed closed-loop control system is verified to be stable in terms of Lyapunov theory. Simulation results indicate that the proposed control method has good control effectiveness. Finally, experiments in an indoor experiment pool are implemented to validate the control method's feasibility and robustness in the applications of UBVMSs. © 2021 IEEE.

关键词： Biomimetics

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Ttm50 facilitates calpain activation by anchoring it to calcium stores and increasing its sensitivity to calcium

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细胞研究（英文版） 2021年第4期31卷 433-449页

作者： Elsayed Metwally Guoli Zhao Qifu Wang Yong Q.Zhang State Key Laboratory for Molecular and Developmental Biology CAS Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Genetics and Developmental BiologyChinese Academy of SciencesBeijing 100101China International College University of Chinese Academy of SciencesBeijing 10080China Department of Cytology and Histology Faculty of Veterinary MedicineSuez Canal UniversityIsmailia 41522Egypt State Key Laboratory for Molecular and Developmental Biology CAS Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Genetics and Developmental BiologyChinese Academy of SciencesBeijing 100101China F.M.Kirby Neurobiology Center Boston Children's HospitalBostonMA 02115USA Department of Neurobiology Harvard Medical SchoolBostonMA 02115USA State Key Laboratory for Molecular and Developmental Biology CAS Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Genetics and Developmental BiologyChinese Academy of SciencesBeijing 100101China State Key Laboratory for Molecular and Developmental Biology CAS Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Genetics and Developmental BiologyChinese Academy of SciencesBeijing 100101China International College University of Chinese Academy of SciencesBeijing 10080China

Calcium-dependent proteolytic calpains are implicated in a variety of physiological processes,as well as pathologies associated with calcium ***,the mechanism by which calpain is activated remains elusive since intracellular calcium levels under physiological conditions do not reach the high concentration range required to trigger calpain *** a candidate screening using the abundance of the calpain target glutamate receptor GluRIIA at the Drosophila neuromuscular junction as a readout,we uncovered that calpain activity was inhibited upon knockdown of Ttm50,a subunit of the Tim23 complex known to be involved in the import of proteins across the mitochondrial inner ***,Ttm50 and calpain are co-localized at calcium stores Golgi and endoplasmic reticulum(ER),and Ttm50 interacts with calpain via its C-terminal *** interaction is required for calpain localization at Golgi/ER,and increases calcium sensitivity of calpain by roughly an order of *** findings reveal the regulation of calpain activation by Ttm50,and shed new light on calpain-associated pathologies.

关键词： Developmental biology Proteolysis

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