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arXiv 2023年

作者： Zhao, Yuxuan Lu, Enmeng Zeng, Yi Brain-inspired Cognitive Intelligence Lab Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China Center for Long-term Artificial Intelligence Beijing China

At the core of bodily self-consciousness is the perception of the ownership of one’s body. Recent efforts to gain a deeper understanding of the mechanisms behind the brain’s encoding of the self-body have led to various attempts to develop a unified theoretical framework to explain related behavioral and neurophysiological phenomena. A central question to be explained is how body illusions such as the rubber hand illusion actually occur. Despite the conceptual descriptions of the mechanisms of bodily self-consciousness and the possible relevant brain areas, the existing theoretical models still lack an explanation of the computational mechanisms by which the brain encodes the perception of one’s body and how our subjectively perceived body illusions can be generated by neural networks. Here we integrate the biological findings of bodily self-consciousness to propose a brain-inspired bodily self-perception model, by which perceptions of bodily self can be autonomously constructed without any supervision signals. We successfully validated our computational model with six rubber hand illusion experiments and a disability experiment on platforms including a iCub humanoid robot and simulated environments. The experimental results show that our model can not only well replicate the behavioral and neural data of monkeys in biological experiments, but also reasonably explain the causes and results of the rubber hand illusion from the neuronal level due to advantages in biological interpretability, thus contributing to the revealing of the computational and neural mechanisms underlying the occurrence of the rubber hand illusion. © 2023, CC BY.

关键词： Rubber

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Decreased peripheral mtDNA in methamphetamine use disorder

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science China(Life sciences) 2022年第3期65卷 648-650页

作者： Ling-Yan Su Yuan Li Qianjin Liu Lijin Jiao Jing Shen Lu-Xiu Yang Ti-Fei Yuan Yong-Gang Yao Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences&Yunnan Province and KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common DiseasesKunming Institute of ZoologyKunming 650204China Kunming College of Life Science University of Chinese Academy of SciencesKunming 650204China Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghai 200030China Hubei Shizishan Drug Rehabilitation Center Wuhan 426070China Co-innovation Center of Neuroregeneration Nantong UniversityNantong 226019China Translational Research Institute of Brain and Brain-Like Intelligence Shanghai Fourth People’s Hospital Affiliated to Tongji University School of MedicineShanghai 200434China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China

Dear Editor,Methamphetamine(METH)is the most prevalent addictive psychostimulant in *** use disorder(MUD)and its associated disorders constitute a significant burden to the affected individuals and their families(Paulus and Stewart,2020).Neurotransmitter system aberrations,mitochondrial dysfunction,oxidative stress,and inflammasome activation have been involved in METH-induced neurotoxicity(Shin et al.,2018;Wang et al.,2015;Wei et al.,2020).

关键词： China Stewart

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A Survey on Open-Set Image Recognition

arXiv

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arXiv 2023年

作者： Sun, Jiayin Dong, Qiulei The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Beijing100190 China

Open-set image recognition (OSR) aims to both classify known-class samples and identify unknown-class samples in the testing set, which supports robust classifiers in many realistic applications, such as autonomous driving, medical diagnosis, security monitoring, etc. In recent years, open-set recognition methods have achieved more and more attention, since it is usually difficult to obtain holistic information about the open world for model training. In this paper, we aim to summarize the up-to-date development of recent OSR methods, considering their rapid development in recent two or three years. Specifically, we firstly introduce a new taxonomy, under which we comprehensively review the existing DNN-based OSR methods. Then, we compare the performances of some typical and state-of-the-art OSR methods on both coarse-grained datasets and fine-grained datasets under both standard-dataset setting and cross-dataset setting, and further give the analysis of the comparison. Finally, we discuss some open issues and possible future directions in this community. Copyright © 2023, The Authors. All rights reserved.

关键词： Image recognition

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Spatial and Semantic Consistency Regularizations for Pedestrian Attribute Recognition

Spatial and Semantic Consistency Regularizations for Pedestr...

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International Conference on Computer Vision (ICCV)

作者： Jian Jia Xiaotang Chen Kaiqi Huang the School of Artificial Intelligence University of Chinese Academy of Sciences CRISE Institute of Automation Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

ISBN: (纸本)9781665428132

While recent studies on pedestrian attribute recognition have shown remarkable progress in leveraging complicated networks and attention mechanisms, most of them neglect the inter-image relations and an important prior: spatial consistency and semantic consistency of attributes under surveillance scenarios. The spatial locations of the same attribute should be consistent between different pedestrian images, e.g., the "hat" attribute and the "boots" attribute are always located at the top and bottom of the picture respectively. In addition, the inherent semantic feature of the "hat" attribute should be consistent, whether it is a baseball cap, beret, or helmet. To fully exploit inter-image relations and aggregate human prior in the model learning process, we construct a Spatial and Semantic Consistency (SSC) framework that consists of two complementary regularizations to achieve spatial and semantic consistency for each attribute. Specifically, we first propose a spatial consistency regularization to focus on reliable and stable attribute-related regions. Based on the precise attribute locations, we further propose a semantic consistency regularization to extract intrinsic and discriminative semantic features. We conduct extensive experiments on popular benchmarks including PA100K, RAP, and PETA. Results show that the proposed method performs favorably against state- of-the-art methods without increasing parameters.

关键词： Computer vision Head Surveillance Aggregates Semantics Benchmark testing Feature extraction

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Prevalence of developmental dyslexia in primary school children:a protocol for systematic review and meta-analysis

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World Journal of Pediatrics 2022年第12期18卷 804-809页

作者： Li-Ping Yang Chun-Bo Li Xiu-Mei Li Man-Man Zhai Jing Zhao Xu-Chu Weng Key Laboratory of Brain Cognition and Education SciencesMinistry of Education55 Zhongshan Avenue WestTianhe DistrictGuangzhou510631China School of Psychology South China Normal UniversityGuangzhou510631China Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghai200030China CAS Center for Excellence in Brain Science and Intelligence Technology(CEBSIT) Chinese Academy of ScienceShanghai200031China Institute of Psychology and Behavioral Science Shanghai Jiao Tong UniversityShanghai200030China Institutes of Psychological Sciences Hangzhou Normal UniversityRoom 30119 Shuyuan Building2318 Yuhangtang RoadCangqianYuhang DistrictHangzhou311121China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou311121China Institute for Brain Research and Rehabilitation South China Normal UniversityGuangzhou510631China

Background Developmental dyslexia(DD)is a specific impairment during the acquisition of reading skills and may have a lifelong negative impact on *** estimates of the prevalence of DD serve as the basis for evidence-based health resource allocation and policy ***,the prevalence of DD in primary school children varies largely across ***,it is unclear whether there are differences in prevalence in different genders and writing ***,the present study aims to conduct a systematic review and meta-analysis to assess the global prevalence of DD and to explore related *** We will undertake a comprehensive literature search in 14 databases,including EMBASE,PubMed,Web of science,China National Knowledge Infrastructure and Cochrane,from their inception to June ***-sectional and longitudinal studies that describe the prevalence of DD will be *** quality of the included observational studies will be assessed using the Strengthening the Reporting of Observational Studies in Epidemiology *** risk of bias will be determined by sensitivity analysis to identify publication *** One meta-analysis will be conducted to estimate the prevalence of DD in primary school *** will be assessed in terms of the properties of subjects(e.g.,gender,grade and writing system)and method of diagnosis in the included primary *** analyses will also be performed for population and secondary *** The results will synthesize the prevalence of DD and provide information for policy-makers and public health specialists.

关键词： Developmental dyslexia Prevalence Primary school children Protocol

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Prototype of A Novel Visuotactile Endoscope Probe

Prototype of A Novel Visuotactile Endoscope Probe

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IEEE International Conference on Cyber technology in automation, Control, and Intelligent Systems

作者： Shaowei Cui Yuan Tian Jingyi Hu Rui Wang Yu Wang Shuo Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing University of Science and Technology Beijing China Institute of Automation Chinese Academy of Sciences School of Future Technology University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China 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 Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China

Endoscopes incorporating tactile sensing can significantly improve the efficiency of endoscopic diagnosis and treatment. Unfortunately, traditional tactile endoscopes are difficult to guarantee the accuracy of tactile signals and high-quality visual images at the same time. In this paper, a novel visuotactile endoscope probe is proposed to obtain clear external visual images and rich tactile information simultaneously. The probe is composed of two parts: a miniatured camera and a gel module which is made of highly clear silicone with markers painted on its surface. The visual and tactile information can be extracted from the images captured by the vision system. Specifically, a marker detection and tracking algorithm is used to extract tactile features, and an image inpainting method is proposed to obtain clear vision effect. This study is a preliminary exploration of the visuotactile endoscope, which holds promising potential for in vivo palpation and broad applications in the medical field.

关键词：

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Fully Automated End-to-End Fake Audio Detection

arXiv

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arXiv 2022年

作者： Wang, Chenglong Yi, Jiangyan Tao, Jianhua Sun, Haiyang Chen, Xun Tian, Zhengkun Ma, Haoxin Fan, Cunhang Fu, Ruibo University of Science and Technology of China Hefei China NLPR 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 School of Computer Science and Technology Anhui University Hefei China

The existing fake audio detection systems often rely on expert experience to design the acoustic features or manually design the hyperparameters of the network structure. However, artificial adjustment of the parameters can have a relatively obvious influence on the results. It is almost impossible to manually set the best set of parameters. Therefore this paper proposes a fully automated end-to-end fake audio detection method. We first use wav2vec pre-trained model to obtain a high-level representation of the speech. Furthermore, for the network structure, we use a modified version of the differentiable architecture search (DARTS) named light-DARTS. It learns deep speech representations while automatically learning and optimizing complex neural structures consisting of convolutional operations and residual blocks. The experimental results on the ASVspoof 2019 LA dataset show that our proposed system achieves an equal error rate (EER) of 1.08%, which outperforms the state-of-the-art single system. Copyright © 2022, The Authors. All rights reserved.

关键词： Fake detection

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End-to-end Alternating Optimization for Real-World Blind Super Resolution

arXiv

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arXiv 2023年

作者： Luo, Zhengxiong Huang, Yan Li, Shang Wang, Liang Tan, Tieniu Artificial Intelligence School University of Chinese Academy of Sciences Beijing100049 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Center for Research on Intelligent Perception and Computing Beijing China Center for Excellence in Brain Science and Intelligence Technology Beijing China Nanjing University Nanjing China

Blind Super-Resolution (SR) usually involves two sub-problems: 1) estimating the degradation of the given low-resolution (LR) image;2) super-resolving the LR image to its high-resolution (HR) counterpart. Both problems are ill-posed due to the information loss in the degrading process. Most previous methods try to solve the two problems independently, but often fall into a dilemma: a good super-resolved HR result requires an accurate degradation estimation, which however, is difficult to be obtained without the help of original HR information. To address this issue, instead of considering these two problems independently, we adopt an alternating optimization algorithm, which can estimate the degradation and restore the SR image in a single model. Specifically, we design two convolutional neural modules, namely Restorer and Estimator. Restorer restores the SR image based on the estimated degradation, and Estimator estimates the degradation with the help of the restored SR image. We alternate these two modules repeatedly and unfold this process to form an end-to-end trainable network. In this way, both Restorer and Estimator could get benefited from the intermediate results of each other, and make each sub-problem easier. Moreover, Restorer and Estimator are optimized in an end-to-end manner, thus they could get more tolerant of the estimation deviations of each other and cooperate better to achieve more robust and accurate final results. Extensive experiments on both synthetic datasets and real-world images show that the proposed method can largely outperform state-of-the-art methods and produce more visually favorable results. The codes are rleased at https://***/greatlog/***. © 2023, CC BY.

关键词： Restoration

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Dynamic Dual Gating Neural Networks

Dynamic Dual Gating Neural Networks

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International Conference on Computer Vision (ICCV)

作者： Fanrong Li Gang Li Xiangyu He Jian Cheng Institute of Automation Chinese Academy of Sciences School of Future Technology University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

ISBN: (纸本)9781665428132

In dynamic neural networks that adapt computations to different inputs, gating-based methods have demonstrated notable generality and applicability in trading-off the model complexity and accuracy. However, existing works only explore the redundancy from a single point of the network, limiting the performance. In this paper, we propose dual gating, a new dynamic computing method, to reduce the model complexity at run-time. For each convolutional block, dual gating identifies the informative features along two separate dimensions, spatial and channel. Specifically, the spatial gating module estimates which areas are essential, and the channel gating module predicts the salient channels that contribute more to the results. Then the computation of both unimportant regions and irrelevant channels can be skipped dynamically during inference. Extensive experiments on a variety of datasets demonstrate that our method can achieve higher accuracy under similar computing budgets compared with other dynamic execution methods. In particular, dynamic dual gating can provide 59.7% saving in computing of ResNet50 with 76.41% top-1 accuracy on ImageNet, which has advanced the state-of-the-art. Codes are available at https://***/lfr-0531/DGNet.

关键词： Computer vision Limiting Computational modeling Neural networks Redundancy Channel estimation Network architecture

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A Comparative Study on Autism Spectrum Disorder Detection via 3D Convolutional Neural Networks 25th

A Comparative Study on Autism Spectrum Disorder Detection vi...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Zhang, Kaijie Wang, Wei Guo, Yijun Shan, Caifeng Wang, Liang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Beijing China Center for Excellence in Brain Science and Intelligence Technology Institute of Automation Chinese Academy of Sciences Beijing China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Artificial Intelligence Research Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030687625

The prevalence of Autism Spectrum Disorder (ASD) in the United States has increased by 178% from 2000 to 2016. However, due to the lack of well-trained specialists and the time-consuming diagnostic process, many children are not able to be promptly diagnosed. Recently, several research have taken steps to explore automatic video-based ASD detection systems with the help of machine learning and deep learning models, such as support vector machine (SVM) and long short-term memory (LSTM) model. However, the models mentioned above could not extract effective features directly from raw videos. In this study, we aim to take advantages of 3D convolution-based deep learning models to aid video-based ASD detection. We explore three representative 3D convolutional neural networks (CNNs), including C3D, I3D and 3D ResNet. In addition, a new 3D convolutional model, called 3D ResNeSt, is also proposed based on ResNeSt. We evaluate these models on an ASD detection dataset. The experimental results show that, on average, all of the four 3D convolutional models can obtain competitive results when compared to the baseline using LSTM model. Our proposed 3D ResNeSt model achieves the best performance, which improves the average detection accuracy from 0.72 to 0.85. © 2021, Springer Nature Switzerland AG.

关键词： Convolution

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