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Sustained antidepressant effect of ketamine through NMDAR trapping in the LHb (vol 622, pg 802, 2023)

NATURE 2023年第7988期623卷 E11-E11页

作者： Ma, Shuangshuang Chen, Min Jiang, Yihao Xiang, Xinkuan Wang, Shiqi Wu, Zuohang Li, Shuo Cui, Yihui Wang, Junying Zhu, Yanqing Zhang, Yan Ma, Huan Duan, Shumin Li, Haohong Yang, Yan Lingle, Christopher J. Hu, Hailan Department of Psychiatry and International Institutes of Medicine The Fourth Affiliated Hospital Zhejiang University School of Medicine Yiwu China Nanhu Brain–Computer Interface Institute MOE Frontier Science Center for Brain Science and Brain–Machine Integration State Key Laboratory of Brain–Machine Intelligence New Cornerstone Science Laboratory Zhejiang University Hangzhou China Department of Affiliated Mental Health Center and Hangzhou Seventh People’s Hospital and School of Brain Science and Brain Medicine Zhejiang University School of Medicine Hangzhou China Department of Anesthesiology Washington University School of Medicine St Louis MO USA

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Schizophrenia detection based on EEG using Recurrent Auto-Encoder framework

arXiv

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arXiv 2022年

作者： Wu, Yihan Xia, Min Wang, Xiuzhu Zhang, Yangsong School of Computer Science and Technology Laboratory for Brain Science and Medical Artificial Intelligence Southwest University of Science and Technology Mianyang621010 China MOE Key Lab for Neuroinformation University of Electronic Science and Technology of China Chengdu610054 China

Schizophrenia (SZ) is a serious mental disorder that could seriously affect the patient's quality of life. In recent years, detection of SZ based on deep learning (DL) using electroencephalogram (EEG) has received increasing attention. In this paper, we proposed an end-to-end recurrent auto-encoder (RAE) model to detect SZ. In the RAE model, the raw data was input into one auto-encoder block, and the reconstructed data were recurrently input into the same block. The extracted code by auto-encoder block was simultaneously served as an input of a classifier block to discriminate SZ patients from healthy controls (HC). Evaluated on the dataset containing 14 SZ patients and 14 HC subjects, and the proposed method achieved an average classification accuracy of 81.81% in subject-independent experiment scenario. This study demonstrated that the structure of RAE is able to capture the differential features between SZ patients and HC subjects. Copyright © 2022, The Authors. All rights reserved.

关键词： Electroencephalography

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Sample Set Reduction Method Based on Neighborhood Non-dominated Crowding-distance Sorting

Sample Set Reduction Method Based on Neighborhood Non-domina...

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International Symposium on Computational Intelligence and Design

作者： Mengmeng Li Zhigang Shang Xiaoyang Shen Caitong Yue Haofeng Wang Yonghui Dong Hong Wan Industrial Technology Research Institute Zhengzhou University Zhengzhou China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou China School of Electrical Engineering Zhengzhou University Zhengzhou China

To remove the redundant samples and reduce sample set size of big data analysis while ensuring classification accuracy, a sample set reduction method based on Neighborhood Non-dominated Crowding-distance Sorting (NNCS) is proposed. The proposed method is based on the idea of neighborhood, in which the distance between the samples of one class and the other classes is taken as the evaluation criterion. Furthermore, optimization algorithm NSGA-II is adapted to select the key samples by non-dominated crowding-distance sorting and the purpose of reducing the sample set is achieved finally. The experimental results on nine UCI standard data sets show that the method has good generalization performance and reduces the sample set size effectively under the premise of fully guaranteed classification accuracy.

关键词： Sample set reduction key sample Neighborhood Non-dominated sorting Crowding-distance sorting

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Action recognition in realistic scenes via local spatio-temporal representation

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Journal of Information and Computational science 2014年第1期11卷 275-286页

作者： Lei, Qing Li, Shaozi Zhang, Hongbo Cognitive Science Department Xiamen University Xiamen 361005 China Fujian Key Laboratory of the Brain-like Intelligent Systems Xiamen 361005 China College of Computer Science and Technology Huaqiao University Xiamen 361021 China

In this paper, we consider the problem of human action recognition in realistic scenes. Mainstream methods adopt local spatio-temporal patterns matching strategy that detect STIPs (Spatio-Temporal Interest Points) and compute features from raw video frames, and then classify the features into some predefined action categories. However, these researches have made certain assumptions seldom hold in the real-world environment such as small scale, without viewpoint changes and background motion. To obtain more effective detection and description for human action videos of complex scenes, we improve the performance of STIPs detection in realistic scenes by filtering out noises resulted from low resolution or cluttered background. In addition, we propose a new feature description method that uses 3D local self-similarities to compensate for motion estimation error due to camera motion. BoW (Bag-of-Words) model is applied to learn a codebook in training stage, and a nonlinear SVM classifier is used for action recognition finally. Experimental results show that our approach achieve superior performance on more challenge dataset (Youtube) in comparison to baseline method. 1548-7741/Copyright © 2014 Binary Information Press.

关键词： Motion estimation

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Point, Segment and Count: A Generalized Framework for Object Counting

arXiv

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arXiv 2023年

作者： Huang, Zhizhong Dai, Mingliang Zhang, Yi Zhang, Junping Shan, Hongming Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China School of Cyber Science and Engineering Sichuan University Chengdu610065 China Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Shanghai200433 China

Class-agnostic object counting aims to count all objects in an image with respect to example boxes or class names, a.k.a few-shot and zero-shot counting. In this paper, we propose a generalized framework for both few-shot and zero-shot object counting based on detection. Our framework combines the superior advantages of two foundation models without compromising their zero-shot capability: (i) SAM to segment all possible objects as mask proposals, and (ii) CLIP to classify proposals to obtain accurate object counts. However, this strategy meets the obstacles of efficiency overhead and the small crowded objects that cannot be localized and distinguished. To address these issues, our framework, termed PseCo, follows three steps: point, segment, and count. Specifically, we first propose a class-agnostic object localization to provide accurate but least point prompts for SAM, which consequently not only reduces computation costs but also avoids missing small objects. Furthermore, we propose a generalized object classification that leverages CLIP image/text embeddings as the classifier, following a hierarchical knowledge distillation to obtain discriminative classifications among hierarchical mask proposals. Extensive experimental results on FSC-147, COCO, and LVIS demonstrate that PseCo achieves state-of-the-art performance in both few-shot/zero-shot object counting/detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Automatic creation of artistic Chinese calligraphy

Journal of Software

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Journal of Software 2013年第12期8卷 3048-3054页

作者： Li, Wei Zhou, Changle Department of Computer Science and Technology Xiamen University of Technology Xiamen 361024 China Cognitive Science Department Xiamen University Xiamen 361005 China Fujian Key Laboratory of the Brain-like Intelligent Systems Xiamen University Xiamen 361005 China

We present an approach to creating Chinese calligraphy with particular style from learning author's written works automatically. Our first contribution is a method to represent Chinese character topology via fuzzy relative position. Our second contribution is an algorithm to take topological features as features fed into the evaluation model, which is a decision tree algorithm. Our third contribution is an algorithm of heuristic search in the optimization glyph phase. To improve the converge speed, we introduce hypothesis testing and the decay function of transformation amplitude. The experiments demonstrate our algorithm can obtain the similar style Chinese calligraphy with training samples. © 2013 Academy Publisher.

关键词： Decision trees

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When non-elitism meets time-linkage problems

arXiv

引用

arXiv 2021年

作者： Zheng, Weijie Zhang, Qiaozhi Chen, Huanhuan Yao, Xin Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Computer Science and Technology University of Science and Technology of China Hefei China CERCIA School of Computer Science University of Birmingham Birmingham United Kingdom

Many real-world applications have the time-linkage property, and the only theoretical analysis is recently given by Zheng, et al. (TEVC 2021) on their proposed time-linkage OneMax problem, OneMax(0,1n). However, only two elitist algorithms (1 + 1) EA and (µ + 1) EA are analyzed, and it is unknown whether the non-elitism mechanism could help to escape the local optima existed in OneMax(0,1n). In general, there are few theoretical results on the benefits of the non-elitism in evolutionary algorithms. In this work, we analyze on the influence of the non-elitism via comparing the performance of the elitist (1 + λ) EA and its non-elitist counterpart (1, λ) EA. We prove that with probability 1 − o(1) (1 + λ) EA will get stuck in the local optima and cannot find the global optimum, but with probability 1, (1, λ) EA can reach the global optimum and its expected runtime is O(n3+c log n) with λ = c log e n for the constant c ≥ 1. Noting that a smaller offspring size is e−1 helpful for escaping from the local optima, we further resort to the compact genetic algorithm where only two individuals are sampled to update the probabilistic model, and prove its expected runtime of O(n3 log n). Our computational experiments also verify the efficiency of the two non-elitist algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Genetic algorithms

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Adaptive initialization method for K-means algorithm

arXiv

引用

arXiv 2019年

作者： Yang, Jie Wang, Yu-Kai Yao, Xin Lin, Chin-Teng Computational Intelligence and Brain Computer Interface Lab Centre for AI FEIT University of Technology Sydney Australia Shenzhen Key Laboratory of Computational Intelligence Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China CERCIA School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses the random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. Many initialization methods have been proposed, but none of them can dynamically adapt to datasets with various characteristics. In our previous research, an initialization method for K-means based on hybrid distance was proposed, and this algorithm can adapt to datasets with different characteristics. However, it has the following drawbacks: (a) When calculating density, the threshold cannot be uniquely determined, resulting in unstable results. (b) Heavily depending on adjusting the parameter, the parameter must be adjusted five times to obtain better clustering results. (c) The time complexity of the algorithm is quadratic, which is difficult to apply to large datasets. In the current paper, we proposed an adaptive initialization method for the K-means algorithm (AIMK) to improve our previous work. AIMK can not only adapt to datasets with various characteristics but also obtain better clustering results within two interactions. In addition, we then leverage random sampling in AIMK, which is named as AIMK-RS, to reduce the time complexity. AIMK-RS is easily applied to large and high-dimensional datasets. We compared AIMK and AIMK-RS with 10 different algorithms on 16 normal and six extra-large datasets. The experimental results show that AIMK and AIMK-RS outperform the current initialization methods and several well-known clustering algorithms. Furthermore, AIMK-RS can significantly reduce the complexity of applying it to extra-large datasets with high dimensions. The time complexity of AIMK-RS is O(n). Copyright © 2019, The Authors. All rights reserved.

关键词： K-means clustering

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Fusion Model for Speech Emotion Recognition with Low Level Descriptor Features

Fusion Model for Speech Emotion Recognition with Low Level D...

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第十四届全国人机语音通讯学术会议

作者： Cheng Chang Huifeng Zhang Zhangxuan Gu Yanmin Qian Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University

Speech emotion recognition is one of the most challenging speech processing tasks,with many applications in the field of Human-Machine Interaction(HMI).Traditional works have been using Gaussian Mixture Models(GMM) for *** recent researches,Deep Neural Networks(DNN) have shown strong ability in feature learning and modeling in many *** this paper,we present to utilize the DNN to learn extra features from Low Level Descriptor(LLD) features for other classifiers,and propose a decision-level fusion model for speech emotion *** carry out experiments and evaluations on a public German corpus called FAU-AIBO with five *** our experimental results using LLD features demonstrate that our proposed approach improves the recognition performance on unweighted results and outperforms the baseline using GMM significantly.

关键词： speech emotion recognition deep neural network low level descriptor fusion

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Speech Emotion Recognition Based on SVM and GMM-HMM Hybrid System

Speech Emotion Recognition Based on SVM and GMM-HMM Hybrid S...

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第十四届全国人机语音通讯学术会议

作者： Kaiyu Shi Xuan Liu Yanmin Qian Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University

Speech emotion recognition is one of the latest challenges in speech processing,we implemented a hybrid system for speech emotion *** methods were proposed,compared and *** first,we utilized GMM-HMM model fed with MFCC feature to exploit the dynamics of emotional *** a balanced SVM classifier was applied on the static LLD *** order to take advantage of generative model and discriminate model,these two methods were combined by taking the probability representation of the GMM-HMM model as the additional features for the SVM *** hybrid system makes full use of both MFCC feature and LLD feature,the performance of the hybrid system also proves the effectiveness of our proposed system.

关键词： speech emotion recognition SVM GMMHMM hybrid system

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