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Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning

Frontiers of Information technology & Electronic Engineering 2023年第11期24卷 1541-1556页

作者： Shihmin WANG Binqi ZHAO Zhengfeng ZHANG Junping ZHANG Jian PU Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Institute of Science and Technology for Brain-inspired Intelligence Fudan UniversityShanghai 200433China

As one of the most fundamental topics in reinforcement learning(RL),sample efficiency is essential to the deployment of deep RL *** most existing exploration methods that sample an action from different types of posterior distributions,we focus on the policy sampling process and propose an efficient selective sampling approach to improve sample efficiency by modeling the internal hierarchy of the ***,we first employ clustering methods in the policy sampling process to generate an action candidate *** we introduce a clustering buffer for modeling the internal hierarchy,which consists of on-policy data,off-policy data,and expert data to evaluate actions from the clusters in the action candidate set in the exploration *** this way,our approach is able to take advantage of the supervision information in the expert demonstration *** on six different continuous locomotion environments demonstrate superior reinforcement learning performance and faster convergence of selective *** particular,on the LGSVL task,our method can reduce the number of convergence steps by 46.7%and the convergence time by 28.5%.Furthermore,our code is open-source for *** code is available at https://***/Shihwin/SelectiveSampling.

关键词： Reinforcement learning Sample eficiency Sampling process Clustering methods Autonomous driving

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Safety and efficacy of Angong Niuhuang Pills in patients with moderate-to-severe acute ischemic stroke (ANGONG TRIAL): A randomized double-blind placebo-controlled pilot clinical trial

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中华医学杂志(英文版) 2025年第5期138卷 579-588页

作者： Li Shengde Wang Anxin Shi Lin Liu Qin Guo Xiaoling Liu Kun Wang Xiaoli Li Jie Zhu Jianming Wu Qiuyi Yang Qingcheng Zhuang Xianbo You Hui Feng Feng Luo Yishan Li Huiling Ni Jun Peng Bin Department of Neurology State Key Laboratory of Complex Severe and Rare Diseases Peking Union Medical College Hospital Chinese Academy of Medical Science and Peking Union Medical College Beijing China Department of Neurology Beijing Tiantan Hospital Capital Medical University Beijing China BrainNow Research Institute Shenzhen Guangdong China Department of Neurology The Second People’s Hospital of Yibin Yibin Sichuan China Department of Neurology The First Affiliated Hospital of Anhui University of Technology Huainan Anhui China Department of Neurology Brain Hospital of Hunan Province (The Second People’s Hospital of Hunan Province) Changsha Hunan China Department of Neurology Hengshui People’s Hospital Hengshui Hebei China Department of Neurology The First People’s Hospital of Yibin Yibin Sichuan China Department of Neurology The First People’s Hospital of Changde City Changde Hunan China Department of Neurology The First People’s Hospital of Zhangjiagang City Suzhou Jiangsu China Department of Neurology The People’s Hospital of Anyang City Anyang Henan China Department of Neurology Liaocheng People’s Hospital Liaocheng Shandong China Department of Radiology State Key Laboratory of Complex Severe and Rare Diseases Peking Union Medical College Hospital Chinese Academy of Medical Science and Peking Union Medical College Beijing China Department of Occupational Medicine and Toxicologym Beijing Chaoyang Hospital Capital Medical University Beijing China

Background: Preclinical studies have indicated that Angong Niuhuang Pills (ANP) reduce cerebral infarct and edema volumes. This study aimed to investigate whether ANP safely reduces cerebral infarct and edema volumes in patients with moderate to severe acute ischemic ***: This randomized, double-blind, placebo-controlled pilot trial included patients with acute ischemic stroke with National Institutes of Health Stroke Scale (NIHSS) scores ranging from 10 to 20 in 17 centers in China between April 2021 and July 2022. Patients were allocated within 36 h after onset via block randomization to receive ANP or placebo (3 g/day for 5 days). The primary outcomes were changes in cerebral infarct and edema volumes after 14 days of treatment. The primary safety outcome was severe adverse events (SAEs) for 90 ***: There were 57 and 60 patients finally included in the ANP and placebo groups, respectively for modified intention-to-treat analysis. The median age was 66.0 years, and the median NIHSS score at baseline was 12.0. The changes in cerebral infarct volume at day 14 were 0.3 mL and 0.4 mL in the ANP and placebo groups, respectively (median difference: -7.1 mL; interquartile range [IQR]: -18.3 to 2.3 mL,P = 0.30). The changes in cerebral edema volume of the ANP and placebo groups on day 14 were 11.4 mL and 4.0 mL, respectively (median difference: 3.0 mL, IQR: -1.3 to 9.9 mL,P = 0.15). The rates of SAE within 90 days were similar in the ANP (3/57, 5%) and placebo (7/60, 12%) groups (P = 0.36). Changes in serum mercury and arsenic concentrations were comparable. In patients with large artery atherosclerosis, ANP reduced the cerebral infarct volume at 14 days (median difference: -12.3 mL; IQR: -27.7 to -0.3 mL,P = 0.03).Conclusions: ANP showed a similar safety profile to placebo and non-significant tendency to reduce cerebral infarct volume in patients with moderate-to-severe stroke. Further studies are warranted to assess the efficacy of ANP in reducing ce

关键词： Angong Niuhuang Pill Acute ischemic stroke Cerebral infarct Cerebral edema Pharmacology

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Theoretical analyses of multi-objective evolutionary algorithms on multi-modal objectives: (hot-off-the-press track at GECCO 2021) 21

Theoretical analyses of multi-objective evolutionary algorit...

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2021 Genetic and Evolutionary Computation Conference, GECCO 2021

作者： Doerr, Benjamin Zheng, Weijie École Polytechnique Cnrs Institut Polytechnique de Paris Palaiseau France Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9781450383516

Previous theory work on multi-objective evolutionary algorithms considers mostly easy problems that are composed of unimodal objectives. This paper takes a first step towards a deeper understanding of how evolutionary algorithms solve multi-modal multi-objective problems. We propose the OneJumpZeroJump problem, a bi-objective problem with single objectives isomorphic to the classic jump function benchmark. We prove that the simple evolutionary multi-objective optimizer (SEMO) cannot compute the full Pareto front. In contrast, for all problem sizes n and all jump sizes [EQUATION], the global SEMO (GSEMO) covers the Pareto front in ((n-2k)nk) iterations in expectation. To improve the performance, we combine the GSEMO with two approaches, a heavy-tailed mutation operator and a stagnation detection strategy, that showed advantages in single-objective multi-modal problems. Runtime improvements of asymptotic order at least k(k) are shown for both strategies. Our experiments verify the substantial runtime gains already for moderate problem sizes. Overall, these results show that the ideas recently developed for single-objective evolutionary algorithms can be effectively employed also in multi-objective optimization. This Hot-off-the-Press paper summarizes "Theoretical Analyses of Multi-Objective Evolutionary Algorithms on Multi-Modal Objectives"by B. Doerr and W. Zheng, which has been accepted for publication in AAAI 2021 [9]. © 2021 Owner/Author.

关键词： Presses (machine tools)

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Sharp bounds for genetic drift in estimation of distribution algorithms (Hot-off-the-press track at GECCO 2020) 20

Sharp bounds for genetic drift in estimation of distribution...

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2020 Genetic and Evolutionary Computation Conference, GECCO 2020

作者： Doerr, Benjamin Zheng, Weijie École Polytechnique CNRS Institut Polytechnique de Paris Palaiseau France Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9781450371278

Estimation of distribution algorithms (EDAs) are a successful branch of evolutionary algorithms (EAs) that evolve a probabilistic model instead of a population. Analogous to genetic drift in EAs, EDAs also encounter the phenomenon that the random sampling in the model update can move the sampling frequencies to boundary values not justified by the fitness. This can result in a considerable performance loss. This work gives the first tight quantification of this effect for three EDAs and one ant colony optimizer, namely for the univariate marginal distribution algorithm, the compact genetic algorithm, population-based incremental learning, and the max-min ant system with iteration-best update. Our results allow to choose the parameters of these algorithms in such a way that within a desired runtime, no sampling frequency approaches the boundary values without a clear indication from the objective function. This paper for the Hot-off-the-Press track at GECCO 2020 summarizes the work "Sharp Bounds for Genetic Drift in Estimation of Distribution Algorithms" by B. Doerr and W. Zheng, which has been accepted for publication in the IEEE Transactions on Evolutionary Computation [5]. © 2020 Owner/Author.

关键词： Iterative methods

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A machine learning technique to detect and predict parkinson's disease

A machine learning technique to detect and predict parkinson...

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2009 International Conference on Image Processing, computer Vision, and Pattern Recognition, IPCV 2009

作者： Yuan, Peijiang Zhang, Bo Li, Jianmin Wang, Julei Gao, Guodong State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing 100084 China TangDu Hospital Institute of Functional Brain Disorders Fourth Military Medical University Xi'an China

ISBN: (纸本)9781601321190

Parkinson's Disease (PD) is a neurological disorder that has been a hot topic worldwide. Human neurological disorders can be modeled in animals like rats and monkeys using standardized procedures that recreate specific pathogenic events and their behavioral outcomes. Different methods have been proposed to detect and verify the efficiency and effectiveness of such models. However, the inner scheme to detect and predict PD at the early stage is still a difficult problem. In this paper, a Conditional Random Fields (CRFs) based approach for PD image detection and prediction is presented. Machine learning techniques are discussed that proved to be useful in detecting and predicting PD in animal models.

关键词： Animals

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Generative Adversarial Networks with Joint Distribution Moment Matching

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Journal of the Operations Research Society of China 2019年第4期7卷 579-597页

作者： Yi-Ying Zhang Chao-Min Shen Hao Feng Preston Thomas Fletcher Gui-Xu Zhang Department of Computer Science East China Normal UniversityShanghai 200062China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal UniversityShanghai 200062China Westlake Institute for Brain-Like Science and Technology Hangzhou 310027China Didi Chuxing Science and Technology Co.Ltd. Beijing 100193China Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVA 22904USA

Generative adversarial networks(GANs)have shown impressive power in the field of machine *** GANs have focused on unsupervised learning *** recent years,conditional GANs that can generate data with labels have been proposed in semi-supervised learning and have achieved better image quality than traditional *** GANs,however,generally only minimize the difference between marginal distributions of real and generated data,neglecting the difference with respect to each class of the *** address this challenge,we propose the GAN with joint distribution moment matching(JDMM-GAN)for matching the joint distribution based on maximum mean discrepancy,which minimizes the differences of both the marginal and conditional *** learning procedure is iteratively conducted by the stochastic gradient descent and *** evaluate JDMM-GAN on several benchmark datasets,including MNIST,CIFAR-10 and the Extended Yale *** with the state-of-the-art GANs,JDMM-GAN generates more realistic images and achieves the best inception score for CIFAR-10 dataset.

关键词： Generative Adversarial Networks Joint Distribution Moment Matching Maximum mean discrepancy

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State-sensitive convolutional sparse coding for potential biomarker identification in brain signals

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science China(Information sciences) 2024年第5期67卷 270-286页

作者： Puli WANG Yu QI Gang PAN College of Computer Science and Technology Zhejiang University State Key Laboratory of Brain-Machine Intelligence Zhejiang University MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University Affiliated Mental Health Center & Hangzhou Seventh People's Hospital Zhejiang University School of Medicine

The identification of prototypical waveforms, such as sleep spindles and epileptic spikes, is crucial for the diagnosis of neurological disorders. These prototypical waveforms are usually recurrently presented in certain brain states, serving as potential biomarkers for clinical evaluations. Convolutional sparse coding(CSC) approaches have demonstrated strength in identifying recurrent patterns in time-series. However,existing CSC approaches do not explicitly explore state-specific patterns, making it difficult to identify state-related biomarkers. To address this problem, we propose state-sensitive CSC to learn state-specific prototypical waveforms. Specifically, we model signals of a certain state with specific waveforms that only appear frequently in this state and background waveforms that are independent of states. Based on this,state-sensitive CSC separates state-specific waveforms from background ones explicitly by incorporating incoherence constraints into optimizations. Experiments with epilepsy brain signals demonstrate that our approach can effectively identify prototypical waveforms in pre-ictal states, providing potential biomarkers for seizure prediction. Our approach provides a promising tool for automatic biomarker candidate identification.

关键词： convolutional sparse coding prototypical waveforms state-specific dictionary biomarker identification neural signal processing

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Computational Methods for Prediction of Human Protein-Phenotype Associations:A Review

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Phenomics 2021年第4期1卷 171-185页

作者： Lizhi Liu Shanfeng Zhu School of Computer Science Fudan UniversityShanghai 200433China Institute of Science and Technology for Brain-Inspired Intelligence Fudan UniversityShanghai 200433China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence(Fudan University) Ministry of EducationShanghai 200433China MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200433China Zhangjiang Fudan International Innovation Center Shanghai 200433China Shanghai Key Lab of Intelligent Information Processing Fudan UniversityShanghai 200433China

Deciphering the relationship between human proteins(genes)and phenotypes is one of the fundamental tasks in phenomics *** Human Phenotype Ontology(HPO)builds upon a standardized logical vocabulary to describe the abnormal phenotypes encountered in human diseases and paves the way towards the computational analysis of their genetic *** date,many computational methods have been proposed to predict the HPO annotations of *** this paper,we conduct a comprehensive review of the existing approaches to predicting HPO annotations of novel proteins,identifying missing HPO annotations,and prioritizing candidate proteins with respect to a certain HPO *** each topic,we first give the formalized description of the problem,and then systematically revisit the published literatures highlighting their advantages and disadvantages,followed by the discussion on the challenges and promising future *** addition,we point out several potential topics to be worthy of exploration including the selection of negative HPO annotations and detecting HPO *** believe that this review will provide insight to the researchers in the field of computational phenotype analyses in terms of comprehending and developing novel prediction algorithms.

关键词： Human Phenotype Ontology(HPO) Human protein-phenotype association HPO annotation Machine learning Deep learning

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DDGNet-YOLO: A Target Detection Network for Dangerous Driving Gestures

DDGNet-YOLO: A Target Detection Network for Dangerous Drivin...

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2020 Chinese Automation Congress, CAC 2020

作者： Zhou, Yefan Lv, Zhao Zhou, Yongtai Anhui University Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei230601 China Institute of Physical Science and Information Technology Hefei230601 China Zhejiang Key Laboratory for Brain-Machine Collaborative Intelligence Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9781728176871

As we all know, some bad driving behaviors such as using mobile phone, eating, drinking etc., may lead to traffic accidents. In order to reduce these potential dangers, we proposed a target detection network to detect the abovementioned dangerous driving gestures. We have improved the YOLOv4 target detection network and designed the DDGNet- YOLO target detection network, so as to reduce the detection time of dangerous driving gesture. Our algorithm have been performed on the CVRR-HANDS 3D dataset. The mAP@0.75 of dangerous driving gesture is 76.4%, and the average real-time processing speed of the algorithm was 143 frames per second. © 2020 IEEE.

关键词： dangerous driving gesture target detection YOLOv4

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brain Image Parcellation Using Fully Convolutional Network with Adaptively Selected Features from brain Atlases 9

Brain Image Parcellation Using Fully Convolutional Network w...

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9th International Conference on Bioinformatics and Biomedical science, ICBBS 2020

作者： Zhang, Xiao Zhao, Haifeng Tang, Zhenyu Zhang, Shaojie Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

ISBN: (纸本)9781450388658

brain image parcellation is an important data processing step in neuroscience. Since multi-atlas based parcellation (MAP) uses prior information from brain atlases (i.e., manually labeled brainregions), it can provide accurate brain parcellation and has been widely adopted. Recently, some deep learning based brain image parcellation (DLP) methods using fully convolutional network (FCN) have been proposed. Compared with MAP, DLP has high computational efficiency, making it more applicable in practice. However, existing DLP methods either neglect or partially utilize brain atlases, making it difficult to get comparable parcellation accuracy as MAP. In this paper, we propose a new DLP method which is able to use brain atlases in an effective way. The network is based on FCN and non-local block based channel attention module (NL module). The input of our network is the target brain image to be parcellated as well as available brain atlases, and the parcellation result is produced through the FCN guided by the features of brain atlases selected by NL modules at different scales. In the experiments using two public MR brain image datasets (LPBA40 and NIREP-NA0), our method outperforms MAP and the state-of-the-art DLP methods due to the effective usage of brain atlases. © 2020 ACM.

关键词： Computational efficiency

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