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arXiv 2024年

作者： Wei, Xin Tao, Yaling Du, Changde Zhao, Gangming Yu, Yizhou Li, Jinpeng The Ningbo Institute of Life and Health Industry UCAS Ningbo315000 China School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou510641 China The Research Center for Brain-Inspired Intelligence State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Department of Computer Science University of Hong Kong Pokfulam Hong Kong

Mammography is the primary imaging tool for breast cancer diagnosis. Despite significant strides in applying deep learning to interpret mammography images, efforts that focus predominantly on visual features often struggle with generalization across datasets. We hypothesize that integrating additional modalities in the radiology practice, notably the linguistic features of reports and manifestation features embodying radiological insights, offers a more powerful, interpretable and generalizable representation. In this paper, we announce MVKL, the first multimodal mammography dataset encompassing multi-view images, detailed manifestations and reports. Based on this dataset, we focus on the challanging task of unsupervised pretraining and propose ViKL, a innovative framework that synergizes Visual, Knowledge, and Linguistic features. This framework relies solely on pairing information without the necessity for pathology labels, which are often challanging to acquire. ViKL employs a triple contrastive learning approach to merge linguistic and knowledge-based insights with visual data, enabling both inter-modality and intra-modality feature enhancement. Our research yields significant findings: 1) Integrating reports and manifestations with unsupervised visual pretraining, ViKL substantially enhances the pathological classification and fosters multimodal interactions. 2) Manifestations can introduce a novel hard negative sample selection mechanism. 3) The multimodal features demonstrate transferability across different datasets. 4) The multimodal pretraining approach curbs miscalibrations and crafts a high-quality representation space. The MVKL dataset and ViKL code are publicly available at https://***/wxwxwwxxx/ViKL to support a broad spectrum of future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Multimodal emotion recognition using deep canonical correlation analysis

arXiv

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arXiv 2019年

作者： Liu, Wei Qiu, Jie-Lin Zheng, Wei-Long Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai200240 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Neurology Massachusetts General Hospital Harvard Medical School BostonMA02114 United States

Multimodal signals are more powerful than unimodal data for emotion recognition since they can represent emotions more comprehensively. In this paper, we introduce deep canonical correlation analysis (DCCA) to multimodal emotion recognition. The basic idea behind DCCA is to transform each modality separately and coordinate different modalities into a hyperspace by using specified canonical correlation analysis constraints. We evaluate the performance of DCCA on five multimodal datasets: the SEED, SEED-IV, SEED-V, DEAP, and DREAMER datasets. Our experimental results demonstrate that DCCA achieves state-of-the-art recognition accuracy rates on all five datasets: 94.58% on the SEED dataset, 87.45% on the SEED-IV dataset, 84.33% and 85.62% for two binary classification tasks and 88.51% for a four-category classification task on the DEAP dataset, 83.08% on the SEED-V dataset, and 88.99%, 90.57%, and 90.67% for three binary classification tasks on the DREAMER dataset. We also compare the noise robustness of DCCA with that of existing methods when adding various amounts of noise to the SEED-V dataset. The experimental results indicate that DCCA has greater robustness. By visualizing feature distributions with t-SNE and calculating the mutual information between different modalities before and after using DCCA, we find that the features transformed by DCCA from different modalities are more homogeneous and discriminative across emotions. Copyright © 2019, The Authors. All rights reserved.

关键词： Eye movements

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Hybrid reconstruction of the physical model with the deep learning that improves structured illumination microscopy

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Advanced Photonics Nexus 2023年第1期2卷 109-117页

作者： Jianyong Wang Junchao Fan Bo Zhou Xiaoshuai Huang Liangyi Chen Peking University Institute of Molecular MedicineCollege of Future TechnologyCenter for Life SciencesState Key Laboratory of Membrane BiologyBeijing Key Laboratory of Cardiometabolic Molecular MedicineBeijingChina Peking University School of Software and MicroelectronicsBeijingChina Chongqing University of Posts and Telecommunications College of Computer Science and TechnologyChongqing Key Laboratory of Image CognitionChongqingChina Peking University Biomedical Engineering DepartmentBeijingChina Peking University International Cancer InstituteBeijingChina PKU-IDG/McGovern Institute for Brain Research BeijingChina Beijing Academy of Artificial Intelligence BeijingChina National Biomedical Imaging Center BeijingChina

Structured illumination microscopy(SIM)has been widely used in live-cell superresolution(SR)***,conventional physical model-based SIM SR reconstruction algorithms are prone to artifacts in handling raw images with low signal-to-noise ratios(SNRs).Deep-learning(DL)-based methods can address this challenge but may lead to degradation and *** combining the physical inversion model with a total deep variation(TDV)regularization,we propose a hybrid restoration method(TDV-SIM)that outperforms conventional or DL methods in suppressing artifacts and hallucinations while maintaining *** demonstrate the performance superiority of TDV-SIM in restoring actin filaments,endoplasmic reticulum,and mitochondrial cristae from extremely low SNR raw *** TDV-SIM represents the ideal method for prolonged live-cell SR imaging with minimal exposure and ***,TDV-SIM proves the power of integrating model-based reconstruction methods with DL ones,possibly leading to the rapid exploration of similar strategies in high-fidelity reconstructions of other microscopy methods.

关键词： structured illumination microscopy superresolution reconstruction deep learning

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Loss-Controlling Calibration for Predictive Models

arXiv

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arXiv 2023年

作者： Wang, Di Shi, Junzhi Wang, Pingping Zhuang, Shuo Li, Hongyue School of Electrical and Information Engineering Tianjin University Tianjin300072 China Tianjin Key Laboratory of Brain-inspired Intelligence Technology School of Electrical and Information Engineering Tianjin University Tianjin300072 China School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China Qingdao Academy of Chinese Medical Science Shandong University of Traditional Chinese Medicine Shandong Qingdao266112 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

We propose a learning framework for calibrating predictive models to make loss-controlling prediction for exchangeable data, which extends our recently proposed conformal loss-controlling prediction for more general cases. By comparison, the predictors built by the proposed loss-controlling approach are not limited to set predictors, and the loss function can be any measurable function without the monotone assumption. To control the loss values in an efficient way, we introduce transformations preserving exchangeability to prove finite-sample controlling guarantee when the test label is obtained, and then develop an approximation approach to construct predictors. The transformations can be built on any predefined function, which include using optimization algorithms for parameter searching. This approach is a natural extension of conformal loss-controlling prediction, since it can be reduced to the latter when the set predictors have the nesting property and the loss functions are monotone. Our proposed method is applied to selective regression and high-impact weather forecasting problems, which demonstrates its effectiveness for general loss-controlling prediction. Copyright © 2023, The Authors. All rights reserved.

关键词： Calibration

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Fast Multi-grid Methods for Minimizing Curvature Energies

arXiv

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arXiv 2022年

作者： Zhang, Zhenwei Chen, Ke Tang, Ke Duan, Yuping Center for Applied Mathematics Tianjin University Tianjin300072 China Liverpool Centre of Mathematics for Healthcare Centre for Mathematical Imaging Techniques Department of Mathematical Sciences University of Liverpool LiverpoolL69 7ZL United Kingdom The Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China

The geometric high-order regularization methods such as mean curvature and Gaussian curvature, have been intensively studied during the last decades due to their abilities in preserving geometric properties including image edges, corners, and contrast. However, the dilemma between restoration quality and computational efficiency is an essential roadblock for high-order methods. In this paper, we propose fast multi-grid algorithms for minimizing both mean curvature and Gaussian curvature energy functionals without sacrificing accuracy for efficiency. Unlike the existing approaches based on operator splitting and the Augmented Lagrangian method (ALM), no artificial parameters are introduced in our formulation, which guarantees the robustness of the proposed algorithm. Meanwhile, we adopt the domain decomposition method to promote parallel computing and use the fine-to-coarse structure to accelerate convergence. Numerical experiments are presented on image denoising, CT, and MRI reconstruction problems to demonstrate the superiority of our method in preserving geometric structures and fine details. The proposed method is also shown effective in dealing with large-scale image processing problems by recovering an image of size 1024×1024 within 40s, while the ALM method requires around 200s. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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Neuromodulated Meta-Learning

arXiv

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arXiv 2024年

作者： Wang, Jingyao Guo, Huijie Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Xiong, Hui Hua, Gang University of Chinese Academy of Sciences Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Wormpex AI Research LLC BellevueWA98004 United States

Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for distinct tasks. Meta-learning similarly trains machines to handle multiple tasks but relies on a fixed network structure, not as flexible as BNS. To investigate the role of flexible network structure (FNS) in meta-learning, we conduct extensive empirical and theoretical analyses, finding that model performance is tied to structure, with no universally optimal pattern across tasks. This reveals the crucial role of FNS in meta-learning, ensuring meta-learning to generate the optimal structure for each task, thereby maximizing the performance and learning efficiency of meta-learning. Motivated by this insight, we propose to define, measure, and model FNS in meta-learning. First, we define that an effective FNS should possess frugality, plasticity, and sensitivity. Then, to quantify FNS in practice, we present three measurements for these properties, collectively forming the structure constraint with theoretical supports. Building on this, we finally propose Neuromodulated Meta-Learning (NeuronML) to model FNS in meta-learning. It utilizes bi-level optimization to update both weights and structure with the structure constraint. Extensive theoretical and empirical evaluations demonstrate the effectiveness of NeuronML on various tasks. Code is publicly available at https://***/WangJingyao07/NeuronML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Noncontact Doppler Radar-based Heart Rate Detection on the SVD and ANC

Noncontact Doppler Radar-based Heart Rate Detection on the S...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiuxia Shi Bin Hu Fuze Tian Qinglin Zhao Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University School of Computer and Communication Lanzhou University of Technology Ministry of Education Key Laboratory of Special Functional Materials and Structural Design (Lanzhou University) Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Chinese Academy of Sciences Engineering Research Center of Open Source Software and Real-Time System(Lanzhou University) Ministry of Education Key Laboratory of Special Functional Materials and Structural Design Ministry of Education Lanzhou University Lanzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short Time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar.

关键词： Biomedical measurement Noise cancellation Time measurement Doppler radar Recording Doppler effect Biosensors

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多肽靶向嵌合体通过促进tau蛋白特异性去磷酸化治疗阿尔茨海默病和其他tau蛋白病

引用

science Bulletin 2024年第8期69卷 1137-1152页

作者：苏静芬肖越魏林郁雷慧杨孙飞王围霞尹君熊瑞李师宏张配周颖王小川郑杰王建枝 Department of Pathophysiology School of Basic MedicineKey Laboratory of Education Ministry of China/Hubei Province for Neurological DisordersTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430030China Sino-UK Joint Laboratory of Brain Function and Injury of Henan Province Department of Physiology and PathophysiologySchool of Basic Medical SciencesXinxiang Medical UniversityXinxiang 453003China Department of Pathophysiology School of Basic Medical SciencesWuhan UniversityWuhan 430030China Department of Anesthesiology The First Affiliated Hospital of Gannan Medical UniversityGanzhou 341000China The Core Facility and Technical Support Wuhan Institute of VirologyChinese Academy of SciencesWuhan 430030China Research Center for Medicine and Structural Biology Wuhan UniversityWuhan 430030China Neuroscience Research Institute and Department of Neurobiology School of Basic Medical SciencesPeking UniversityKey Laboratory for NeuroscienceMinistry of Education/National Health CommissionBeijing 100083China Co-innovation Center of Neuroregeneration Nantong UniversityNantong 226000China

Abnormal hyperphosphorylation and accumulation of tau protein play a pivotal role in neurodegeneration in Alzheimer’s disease(AD)and many other *** elimination of hyperphosphorylated tau is promising for the therapy of these *** have conceptualized a strategy,named dephosphorylation-targeting chimeras(DEPTACs),for specifically hijacking phosphatases to tau to debilitate its ***,we conducted the step-by-step optimization of each constituent motif to generate DEPTACs with reasonable effectiveness in facilitating the dephosphorylation and subsequent clearance of pathological ***,for one of the selected chimeras,D16,we demonstrated its significant efficiency in rescuing the neurodegeneration caused by neurotoxic K18-tau seeds in ***,intravenous administration of D16 also alleviated tau pathologies in the brain and improved memory deficits in AD *** results suggested DEPTACs as targeted modulators of tau phosphorylation,which hold therapeutic potential for AD and other tauopathies.

关键词： tau Dephosphorylation-targeting chimeras Therapeutic potential Tauopathy

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Inverse design of artificial skins

arXiv

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arXiv 2023年

作者： Liu, Zhiguang Cai, Minkun Hong, Shenda Shi, Junli Xie, Sai Liu, Chang Du, Huifeng Morin, James D. Li, Gang Liu, Wang Wang, Hong Tang, Ke Fang, Nicholas X. Guo, Chuan Fei Department of Materials Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States National Institute of Health Data Science Peking University Beijing100191 China Centers for Mechanical Engineering Research Education at MIT and SUSTech Southern University of Science and Technology Shenzhen518055 China Department of Mechanical Engineering The University of Hong Kong Hong Kong999077 Hong Kong Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Modern Mechanics University of Science and Technology of China Hefei231600 China

Mimicking the perceptual functions of human cutaneous mechanoreceptors, artificial skins or flexible pressure sensors can transduce tactile stimuli to quantitative electrical signals1-3. Conventional methods to design such devices follow a forward structure-to-property routine based on trial-and-error experiments/simulations, which take months or longer to determine one solution valid for one specific material. Target-oriented inverse design that shows far higher output efficiency has proven effective in other fields4,5, but is still absent for artificial skins because of the difficulties in acquiring big data. Here, we report a property-to-structure inverse design of artificial skins based on small dataset machine learning, exhibiting a comprehensive efficiency at least four orders of magnitude higher than the conventional routine. The inverse routine can predict hundreds of solutions that overcome the intrinsic signal saturation problem for linear response in hours, and the solutions are valid to a variety of materials. Our results demonstrate that the inverse design allowed by small dataset is an efficient and powerful tool to target multifarious applications of artificial skins, which can potentially advance the fields of intelligent robots, advanced healthcare, and human-machine interfaces. Copyright © 2023, The Authors. All rights reserved.

关键词： Intelligent robots

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Slow and weak attractor computation embedded in fast and strong E-I balanced neural dynamics 23

Slow and weak attractor computation embedded in fast and str...

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xiaohan Lin Liyuan Li Boxin Shi Tiejun Huang Yuanyuan Mi Si Wu Nat'l Key Lab for Multimedia Information Processing Nat'l Eng. Lab of Visual Technology School of Computer Science Peking University and Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies School of Psychological and Cognitive Sciences Beijing Key Laboratory of Behavior and Mental Health IDG/McGovern Institute for Brain Research Center of Quantitative Biology Peking University Department of Mathematics Peking University and Institute of Neuroinformatics University of Zurich Nat'l Key Lab for Multimedia Information Processing Nat'l Eng. Lab of Visual Technology School of Computer Science Peking University and Institute for Artificial Intelligence Peking University Department of Psychology Tsinghua University Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies School of Psychological and Cognitive Sciences Beijing Key Laboratory of Behavior and Mental Health IDG/McGovern Institute for Brain Research Center of Quantitative Biology Peking University

Attractor networks require neuronal connections to be highly structured in order to maintain attractor states that represent information, while excitation and inhibition balanced networks (E-INNs) require neuronal connections to be random and sparse to generate irregular neuronal firing. Despite being regarded as canonical models of neural circuits, both types of networks are usually studied independently, and it remains unclear how they coexist in the brain, given their very different structural demands. In this study, we investigate the compatibility of continuous attractor neural networks (CANNs) and E-INNs. In line with recent experimental data, we find that a neural circuit can exhibit both the traits of CANNs and E-INNs when the neuronal synapses consist of two sets: one set is strong and fast for irregular firing, and the other set is weak and slow for attractor dynamics. In addition, both simulations and theoretical analysis reveal that the network exhibits enhanced performance compared to the case of using only one set of synapses, with accelerated convergence of attractor states and retained E-I balanced condition for localized input. We hope that this study provides insight into how structured neural computations are realized by irregular firings of neurons.

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