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Recognition of moyamoya disease and its hemorrhagic risk using deep learning algorithms:sourced from retrospective studies 认领
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作者 Yu Lei Xin Zhang +7 位作者 Wei Ni Heng Yang Jia-Bin Su Bin Xu Liang Chen Jin-Hua Yu Yu-Xiang Gu Ying Mao 《中国神经再生研究:英文版》 SCIE CAS 2021年第5期830-835,共6页
Although intracranial hemorrhage in moyamoya disease can occur repeatedly,predicting the disease is difficult.Deep learning algorithms developed in recent years provide a new angle for identifying hidden risk factors,... Although intracranial hemorrhage in moyamoya disease can occur repeatedly,predicting the disease is difficult.Deep learning algorithms developed in recent years provide a new angle for identifying hidden risk factors,evaluating the weight of different factors,and quantitatively evaluating the risk of intracranial hemorrhage in moyamoya disease.To investigate whether convolutional neural network algorithms can be used to recognize moyamoya disease and predict hemorrhagic episodes,we retrospectively selected 460 adult unilateral hemispheres with moyamoya vasculopathy as positive samples for diagnosis modeling,including 418 hemispheres with moyamoya disease and 42 hemispheres with moyamoya syndromes.Another 500 hemispheres with normal vessel appearance were selected as negative samples.We used deep residual neural network(ResNet-152)algorithms to extract features from raw data obtained from digital subtraction angiography of the internal carotid artery,then trained and validated the model.The accuracy,sensitivity,and specificity of the model in identifying unilateral moyamoya vasculopathy were 97.64±0.87%,96.55±3.44%,and 98.29±0.98%,respectively.The area under the receiver operating characteristic curve was 0.990.We used a combined multi-view conventional neural network algorithm to integrate age,sex,and hemorrhagic factors with features of the digital subtraction angiography.The accuracy of the model in predicting unilateral hemorrhagic risk was 90.69±1.58%and the sensitivity and specificity were 94.12±2.75%and 89.86±3.64%,respectively.The deep learning algorithms we proposed were valuable and might assist in the automatic diagnosis of moyamoya disease and timely recognition of the risk for re-hemorrhage.This study was approved by the Institutional Review Board of Huashan Hospital,Fudan University,China(approved No.2014-278)on January 12,2015. 展开更多
关键词 brain central nervous system deep learning diagnosis HEMORRHAGE machine learning moyamoya disease moyamoya syndrome prediction REBLEEDING
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文章速递Landslide identification using machine learning 认领
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作者 Haojie Wang Limin Zhang +2 位作者 Kesheng Yin Hongyu Luo Jinhui Li 《地学前缘:英文版》 SCIE CAS CSCD 2021年第1期351-364,共14页
Landslide identification is critical for risk assessment and mitigation.This paper proposes a novel machinelearning and deep-learning method to identify natural-terrain landslides using integrated geodatabases.First,l... Landslide identification is critical for risk assessment and mitigation.This paper proposes a novel machinelearning and deep-learning method to identify natural-terrain landslides using integrated geodatabases.First,landslide-related data are compiled,including topographic data,geological data and rainfall-related data.Then,three integrated geodatabases are established;namely,Recent Landslide Database(Rec LD),Relict Landslide Database(Rel LD)and Joint Landslide Database(JLD).After that,five machine learning and deep learning algorithms,including logistic regression(LR),support vector machine(SVM),random forest(RF),boosting methods and convolutional neural network(CNN),are utilized and evaluated on each database.A case study in Lantau,Hong Kong,is conducted to demonstrate the application of the proposed method.From the results of the case study,CNN achieves an identification accuracy of 92.5%on Rec LD,and outperforms other algorithms due to its strengths in feature extraction and multi dimensional data processing.Boosting methods come second in terms of accuracy,followed by RF,LR and SVM.By using machine learning and deep learning techniques,the proposed landslide identification method shows outstanding robustness and great potential in tackling the landslide identification problem. 展开更多
关键词 Landslide risk Landslide identification Machine learning Deep learning Big data Convolutional neural networks
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Astaxanthin alleviates pathological brain aging through the upregulation of hippocampal synaptic proteins 认领
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作者 Ning Liu Liang Zeng +2 位作者 Yi-Ming Zhang Wang Pan Hong Lai 《中国神经再生研究:英文版》 SCIE CAS 2021年第6期1062-1067,共6页
Oxidative stress is currently considered to be the main cause of brain aging.Astaxanthin can improve oxidative stress under multiple pathological conditions.It is therefore hypothesized that astaxanthin might have the... Oxidative stress is currently considered to be the main cause of brain aging.Astaxanthin can improve oxidative stress under multiple pathological conditions.It is therefore hypothesized that astaxanthin might have therapeutic effects on brain aging.To validate this hypothesis and investigate the underlying mechanisms,a mouse model of brain aging was established by injecting amyloid beta(Aβ)25-35(5μM,3μL/injection,six injections given every other day)into the right lateral ventricle.After 3 days of Aβ25-35 injections,the mouse models were intragastrically administered astaxanthin(0.1 mL/d,10 mg/kg)for 30 successive days.Astaxanthin greatly reduced the latency to find the platform in the Morris water maze,increased the number of crossings of the target platform,and increased the expression of brain-derived neurotrophic factor,synaptophysin,sirtuin 1,and peroxisome proliferator-activated receptor-γ coactivator 1α.Intraperitoneal injection of the sirtuin 1 inhibitor nicotinamide(500μM/d)for 7 successive days after astaxanthin intervention inhibited these phenomena.These findings suggest that astaxanthin can regulate the expression of synaptic proteins in mouse hippocampus through the sirtuin 1/peroxisome proliferator-activated receptor-γcoactivator 1αsignaling pathway,which leads to improvements in the learning,cognitive,and memory abilities of mice.The study was approved by the Animal Ethics Committee,China Medical University,China(approval No.CMU2019294)on January 15,2019. 展开更多
关键词 brain aging cognitive factor HIPPOCAMPUS learning memory oxidative stress pathways SYNAPSE
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Bimanual motor skill learning and robotic assistance for chronic hemiparetic stroke: a randomized controlled trial 认领
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作者 Maral Yeganeh Doost Benoît Herman +6 位作者 Adrien Denis Julien Sapin Daniel Galinski Audrey Riga Patrice Laloux Benoît Bihin Yves Vandermeeren 《中国神经再生研究:英文版》 SCIE CAS 2021年第8期1566-1573,共8页
Using robotic devices might improve recovery post-stroke, but the optimal way to apply robotic assistance has yet to be determined. The current study aimed to investigate whether training under the robotic active-assi... Using robotic devices might improve recovery post-stroke, but the optimal way to apply robotic assistance has yet to be determined. The current study aimed to investigate whether training under the robotic active-assisted mode improves bimanual motor skill learning(biMSkL) more than training under the active mode in stroke patients. Twenty-six healthy individuals(HI) and 23 chronic hemiparetic stroke patients with a detectable lesion on MRI or CT scan, who demonstrated motor deficits in the upper limb, were randomly allocated to two parallel groups. The protocol included a two-day training on a new bimanual cooperative task, LIFT-THE-TRAY, under either the active or activeassisted modes(where assistance decreased in a pre-determined stepwise fashion) with the bimanual version of the REAplan? robotic device. The hypothesis was that the active-assisted mode would result in greater biMSkL than the active mode. The biMSkL was quantified by a speed-accuracy trade-off(SAT) before(T1) and immediately after(T2) training on days 1 and 2(T3 and T4). The change in SAT after 2 days of training(T4/T1) indicated that both HI and stroke patients learned and retained the bimanual cooperative task. After 2 days of training, the active-assisted mode did not improve biMSkL more than the active mode(T4/T1) in HI nor stroke patients. Whereas HI generalized the learned bimanual skill to different execution speeds in both the active and active-assisted subgroups, the stroke patients generalized the learned skill only in the active subgroup. Taken together, the active-assisted mode, applied in a pre-determined stepwise decreasing fashion, did not improve biMSkL more than the active mode in HI and stroke subjects. Stroke subjects might benefit more from robotic assistance when applied "as-needed." This study was approved by the local ethical committee(Comité d'éthique médicale, CHU UCL Namur, MontGodinne, Yvoir, Belgium;Internal number: 54/2010, Eudra CT number: NUB B039201317382) on July 14, 2016 and was registered with ClinicalTrial 展开更多
关键词 BIMANUAL HEMIPARESIS motor learning rehabilitation ROBOTIC robotic assistance slacking stroke retention
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电针干预放射性脑损伤小鼠海马区突触可塑性相关蛋白的表达 认领
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作者 王冬慧 武鑫 +2 位作者 孙宁宁 张晗 高剑峰 《中国组织工程研究》 CAS 北大核心 2021年第14期2205-2210,共6页
背景:放射性脑损伤作为放疗后的严重并发症之一,严重危害人们的健康,损害学习及记忆功能,但是关于电针干预防治放射性脑损伤的报道相对较少。目的:探讨电针干预对放射性脑损伤小鼠突触可塑性相关蛋白表达的影响。方法:将30日龄C57BL/6J... 背景:放射性脑损伤作为放疗后的严重并发症之一,严重危害人们的健康,损害学习及记忆功能,但是关于电针干预防治放射性脑损伤的报道相对较少。目的:探讨电针干预对放射性脑损伤小鼠突触可塑性相关蛋白表达的影响。方法:将30日龄C57BL/6J小鼠随机分为空白组、模型组和电针组。除空白组外,其余各组给予8 Gy放射剂量构建放射性脑损伤模型,电针组给予针刺"百会""风府"及双侧"肾俞"穴干预21 d,同时腹腔注射BrdU。电针结束后,采用Morris水迷宫实验和T迷宫实验检测小鼠学习记忆能力,免疫组化法检测海马区BrdU阳性表达,Western blot检测海马区Notch信号通路相关蛋白Notch1和Hes1以及突触可塑性相关蛋白突触素、突触后致密蛋白95、脑源性神经营养因子的表达。结果与结论:①电针干预显著改善放射性脑损伤小鼠学习记忆障碍;②模型组BrdU阳性表达较空白组显著减少(P <0.01),电针组BrdU阳性表达较模型组显著增加(P <0.01);③模型组Notch1、Hes1、突触后致密蛋白95、突触素和脑源性神经营养因子表达量较空白组降低(P <0.01,P <0.01,P <0.01,P <0.05,P <0.01);与模型组比较,电针组Notch1、突触后致密蛋白95、突触素和脑源性神经营养因子表达量增加(P <0.01,P <0.01,P <0.05,P <0.01),Hes1表达量明显降低(P <0.05);④结果表明,电针干预改善放射性脑损伤小鼠学习记忆功能的机制可能与Notch信号通路以及电针调节突触可塑性蛋白表达增加有关。 展开更多
关键词 电针 放射性脑损伤 突触可塑性 NOTCH 信号通路 C57BL/6J小鼠 学习 记忆
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文章速递Advanced prediction of tunnel boring machine performance based on big data 认领
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作者 Jinhui Li Pengxi Li +2 位作者 Dong Guo Xu Li Zuyu Chen 《地学前缘:英文版》 SCIE CAS CSCD 2021年第1期331-338,共8页
Predicting the performance of a tunneling boring machine is vitally important to avoid any possible accidents during tunneling boring.The prediction is not straightforward due to the uncertain geological conditions an... Predicting the performance of a tunneling boring machine is vitally important to avoid any possible accidents during tunneling boring.The prediction is not straightforward due to the uncertain geological conditions and the complex rock-machine interactions.Based on the big data obtained from the 72.1 km long tunnel in the Yin-Song Diversion Project in China,this study developed a machine learning model to predict the TBM performance in a real-time manner.The total thrust and the cutterhead torque during a stable period in a boring cycle was predicted in advance by using the machine-returned parameters in the rising period.A long short-term memory model was developed and its accuracy was evaluated.The results show that the variation in the total thrust and cutterhead torque with various geological conditions can be well reflected by the proposed model.This real-time predication shows superior performance than the classical theoretical model in which only a single value can be obtained based on the single measurement of the rock properties.To improve the accuracy of the model a filtering process was proposed.Results indicate that filtering the unnecessary parameters can enhance both the accuracy and the computational efficiency.Finally,the data deficiency was discussed by assuming a parameter was missing.It is found that the missing of a key parameter can significantly reduce the accuracy of the model,while the supplement of a parameter that highly-correlated with the missing one can improve the prediction. 展开更多
关键词 TBM Big data Machine learning LSTM neural network Data efficiency Data deficiency
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文章速递Estimation of diaphragm wall deflections for deep braced excavation in anisotropic clays using ensemble learning 认领
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作者 Runhong Zhang Chongzhi Wu +2 位作者 Anthony T.C.Goh Thomas Bohlke Wengang Zhang 《地学前缘:英文版》 SCIE CAS CSCD 2021年第1期365-373,共9页
This paper adopts the NGI-ADP soil model to carry out finite element analysis,based on which the effects of soft clay anisotropy on the diaphragm wall deflections in the braced excavation were evaluated.More than one ... This paper adopts the NGI-ADP soil model to carry out finite element analysis,based on which the effects of soft clay anisotropy on the diaphragm wall deflections in the braced excavation were evaluated.More than one thousand finite element cases were numerically analyzed,followed by extensive parametric studies.Surrogate models were developed via ensemble learning methods(ELMs),including the e Xtreme Gradient Boosting(XGBoost),and Random Forest Regression(RFR)to predict the maximum lateral wall deformation(δhmax).Then the results of ELMs were compared with conventional soft computing methods such as Decision Tree Regression(DTR),Multilayer Perceptron Regression(MLPR),and Multivariate Adaptive Regression Splines(MARS).This study presents a cutting-edge application of ensemble learning in geotechnical engineering and a reasonable methodology that allows engineers to determine the wall deflection in a fast,alternative way. 展开更多
关键词 Anisotropic clay NGI-ADP Wall deflection Ensemble learning eXtreme gradient boosting Random forest regression
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文章速递Modelling of shallow landslides with machine learning algorithms 认领
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作者 Zhongqiang Liu Graham Gilbert +4 位作者 Jose Mauricio Cepeda Asgeir Olaf Kydland Lysdahl Luca Piciullo Heidi Hefre Suzanne Lacasse 《地学前缘:英文版》 SCIE CAS CSCD 2021年第1期385-393,共9页
This paper introduces three machine learning(ML)algorithms,the‘ensemble'Random Forest(RF),the‘ensemble'Gradient Boosted Regression Tree(GBRT)and the Multi Layer Perceptron neural network(MLP)and applies them... This paper introduces three machine learning(ML)algorithms,the‘ensemble'Random Forest(RF),the‘ensemble'Gradient Boosted Regression Tree(GBRT)and the Multi Layer Perceptron neural network(MLP)and applies them to the spatial modelling of shallow landslides near Kvam in Norway.In the development of the ML models,a total of 11 significant landslide controlling factors were selected.The controlling factors relate to the geomorphology,geology,geo-environment and anthropogenic effects:slope angle,aspect,plan curvature,profile curvature,flow accumulation,flow direction,distance to rivers,water content,saturation,rainfall and distance to roads.It is observed that slope angle was the most significant controlling factor in the ML analyses.The performance of the three ML models was evaluated quantitatively based on the Receiver Operating Characteristic(ROC)analysis.The results show that the‘ensemble'GBRT machine learning model yielded the most promising results for the spatial prediction of shallow landslides,with a 95%probability of landslide detection and 87%prediction efficiency. 展开更多
关键词 Shallow landslide Spatial modelling Machine learning GIS
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文章速递Probabilistic outlier detection for sparse multivariate geotechnical site investigation data using Bayesian learning 认领
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作者 Shuo Zheng Yu-Xin Zhu +3 位作者 Dian-Qing Li Zi-Jun Cao Qin-Xuan Deng Kok-Kwang Phoon 《地学前缘:英文版》 SCIE CAS CSCD 2021年第1期425-439,共15页
Various uncertainties arising during acquisition process of geoscience data may result in anomalous data instances(i.e.,outliers)that do not conform with the expected pattern of regular data instances.With sparse mult... Various uncertainties arising during acquisition process of geoscience data may result in anomalous data instances(i.e.,outliers)that do not conform with the expected pattern of regular data instances.With sparse multivariate data obtained from geotechnical site investigation,it is impossible to identify outliers with certainty due to the distortion of statistics of geotechnical parameters caused by outliers and their associated statistical uncertainty resulted from data sparsity.This paper develops a probabilistic outlier detection method for sparse multivariate data obtained from geotechnical site investigation.The proposed approach quantifies the outlying probability of each data instance based on Mahalanobis distance and determines outliers as those data instances with outlying probabilities greater than 0.5.It tackles the distortion issue of statistics estimated from the dataset with outliers by a re-sampling technique and accounts,rationally,for the statistical uncertainty by Bayesian machine learning.Moreover,the proposed approach also suggests an exclusive method to determine outlying components of each outlier.The proposed approach is illustrated and verified using simulated and real-life dataset.It showed that the proposed approach properly identifies outliers among sparse multivariate data and their corresponding outlying components in a probabilistic manner.It can significantly reduce the masking effect(i.e.,missing some actual outliers due to the distortion of statistics by the outliers and statistical uncertainty).It also found that outliers among sparse multivariate data instances affect significantly the construction of multivariate distribution of geotechnical parameters for uncertainty quantification.This emphasizes the necessity of data cleaning process(e.g.,outlier detection)for uncertainty quantification based on geoscience data. 展开更多
关键词 Outlier detection Site investigation Sparse multivariate data Mahalanobis distance Resampling by half-means Bayesian machine learning
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文章速递Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms 认领
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作者 Pin Zhang Zhen-Yu Yin +2 位作者 Yin-Fu Jin Tommy HTChan Fu-Ping Gao 《地学前缘:英文版》 SCIE CAS CSCD 2021年第1期441-452,共12页
Compression index Ccis an essential parameter in geotechnical design for which the effectiveness of correlation is still a challenge.This paper suggests a novel modelling approach using machine learning(ML)technique.T... Compression index Ccis an essential parameter in geotechnical design for which the effectiveness of correlation is still a challenge.This paper suggests a novel modelling approach using machine learning(ML)technique.The performance of five commonly used machine learning(ML)algorithms,i.e.back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),random forest(RF)and evolutionary polynomial regression(EPR)in predicting Cc is comprehensively investigated.A database with a total number of 311 datasets including three input variables,i.e.initial void ratio e0,liquid limit water content wL,plasticity index Ip,and one output variable Cc is first established.Genetic algorithm(GA)is used to optimize the hyper-parameters in five ML algorithms,and the average prediction error for the 10-fold cross-validation(CV)sets is set as thefitness function in the GA for enhancing the robustness of ML models.The results indicate that ML models outperform empirical prediction formulations with lower prediction error.RF yields the lowest error followed by BPNN,ELM,EPR and SVM.If the ranges of input variables in the database are large enough,BPNN and RF models are recommended to predict Cc.Furthermore,if the distribution of input variables is continuous,RF model is the best one.Otherwise,EPR model is recommended if the ranges of input variables are small.The predicted correlations between input and output variables using five ML models show great agreement with the physical explanation. 展开更多
关键词 Compressibility Clays Machine learning Optimization Random forest Genetic algorithm
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文章速递Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models 认领
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作者 Hugo K.H.Olierook Richard Scalzo +5 位作者 David Kohn Rohitash Chandra Ehsan Farahbakhsh Chris Clark Steven M.Reddy R.Dietmar Müller 《地学前缘:英文版》 SCIE CAS CSCD 2021年第1期479-493,共15页
Traditional approaches to develop 3D geological models employ a mix of quantitative and qualitative scientific techniques,which do not fully provide quantification of uncertainty in the constructed models and fail to ... Traditional approaches to develop 3D geological models employ a mix of quantitative and qualitative scientific techniques,which do not fully provide quantification of uncertainty in the constructed models and fail to optimally weight geological field observations against constraints from geophysical data.Here,using the Bayesian Obsidian software package,we develop a methodology to fuse lithostratigraphic field observations with aeromagnetic and gravity data to build a 3D model in a small(13.5 km×13.5 km)region of the Gascoyne Province,Western Australia.Our approach is validated by comparing 3D model results to independently-constrained geological maps and cross-sections produced by the Geological Survey of Western Australia.By fusing geological field data with aeromagnetic and gravity surveys,we show that 89%of the modelled region has>95%certainty for a particular geological unit for the given model and data.The boundaries between geological units are characterized by narrow regions with<95%certainty,which are typically 400-1000 m wide at the Earth's surface and 500-2000 m wide at depth.Beyond~4 km depth,the model requires geophysical survey data with longer wavelengths(e.g.,active seismic)to constrain the deeper subsurface.Although Obsidian was originally built for sedimentary basin problems,there is reasonable applicability to deformed terranes such as the Gascoyne Province.Ultimately,modification of the Bayesian engine to incorporate structural data will aid in developing more robust 3D models.Nevertheless,our results show that surface geological observations fused with geophysical survey data can yield reasonable 3D geological models with narrow uncertainty regions at the surface and shallow subsurface,which will be especially valuable for mineral exploration and the development of 3D geological models under cover. 展开更多
关键词 Capricorn orogen Machine learning Bayesian inference Markov chain Monte Carlo Solid earth Mineral exploration
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文章速递基于“学”“做”“讲”的教具制作与难点突破研究 认领
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作者 朱雪 《成才之路》 2021年第2期92-93,共2页
在“学讲”课堂教学中,教师往往更注重学生的“学”——自主学、合作学和“讲”——讲出来、教别人,而忽略“做”,但“做”是“学习金字塔”中的实践部分。文章以“肾单位”教学为例,探讨“学”“做”“讲”相结合突破教学重难点的策略... 在“学讲”课堂教学中,教师往往更注重学生的“学”——自主学、合作学和“讲”——讲出来、教别人,而忽略“做”,但“做”是“学习金字塔”中的实践部分。文章以“肾单位”教学为例,探讨“学”“做”“讲”相结合突破教学重难点的策略,以培养学生合作探究能力,提高学生生物学科核心素养。 展开更多
关键词 生物教学 教具
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Practical Meta-Reinforcement Learning of Evolutionary Strategy with Quantum Neural Networks for Stock Trading 认领
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作者 Erik Sorensen Wei Hu 《量子信息科学期刊(英文)》 2020年第3期43-71,共29页
We show the practicality of two existing meta-learning algorithms Model-</span></span><span><span><span> </span></span></span><span><span><span><spa... We show the practicality of two existing meta-learning algorithms Model-</span></span><span><span><span> </span></span></span><span><span><span><span style="font-family:Verdana;">Agnostic Meta-Learning and Fast Context Adaptation Via Meta-learning using an evolutionary strategy for parameter optimization, as well as propose two novel quantum adaptations of those algorithms using continuous quantum neural networks, for learning to trade portfolios of stocks on the stock market. The goal of meta-learning is to train a model on a variety of tasks, such that it can solve new learning tasks using only a small number of training samples. In our classical approach, we trained our meta-learning models on a variety of portfolios that contained 5 randomly sampled Consumer Cyclical stocks from a pool of 60. In our quantum approach, we trained our </span><span style="font-family:Verdana;">quantum meta-learning models on a simulated quantum computer with</span><span style="font-family:Verdana;"> portfolios containing 2 randomly sampled Consumer Cyclical stocks. Our findings suggest that both classical models could learn a new portfolio with 0.01% of the number of training samples to learn the original portfolios and can achieve a comparable performance within 0.1% Return on Investment of the Buy and Hold strategy. We also show that our much smaller quantum meta-learned models with only 60 model parameters and 25 training epochs </span><span style="font-family:Verdana;">have a similar learning pattern to our much larger classical meta-learned</span><span style="font-family:Verdana;"> models that have over 250,000 model parameters and 2500 training epochs. Given these findings</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">,</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> we also discuss the benefits of scaling up our experiments from a simulated quantum computer to a 展开更多
关键词 Reinforcement Learning Deep Learning META-LEARNING Evolutionary Strategy Quantum Computing Quantum Machine Learning Stock Market Algorithmic Trading
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An Environmental Learning Support System Incorporating the Life Cycle Concept 认领
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作者 Akira Shirato Kayoko Yamamoto 《环境保护(英文)》 2020年第6期491-508,共18页
The need for environmental education, which incorporates the life cycle concept into the learning program, will become increasingly greater all over the world. In the present study, an e-learning system, which is made... The need for environmental education, which incorporates the life cycle concept into the learning program, will become increasingly greater all over the world. In the present study, an e-learning system, which is made up of 3 parts including text-based learning materials, quizzes to review the content of the learning materials and CO<sub>2</sub> emission simulation, was designed and developed with the purpose of supporting environmental learning. Targeting a wide range of people, the operation period of this system was 1 month. Based on the results of questionnaire survey for users, it was evident that the quiz function and the simulation function of CO<sub>2</sub> emission contributed to the efficiency in environmental learning, and the format of the e-learning system was effective and helpful for environmental learning. Additionally, with the users’ awareness related to environmental conservation before and after using the system, significant changes in awareness were seen in areas such as behavioral intention, sense of urgency and sense of connection. Furthermore, as it was revealed that 62% of the total access numbers were from mobile devices, it was effective to prepare an interface optimized for mobile devices enabling users to use the system from their smartphones and tablet PCs. 展开更多
关键词 Environmental Learning Life Cycle Assessment (LCA) Life Cycle Concept Environmental Education Sustainable Development Goals (SDGs) E-Learning System
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Game-Based Learning for Competency Abilities in Blended Museum Contexts for Diverse Learners 认领
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作者 Hsin-Yi Liang Tien-Yu Hsu 《Psychology Research》 2020年第9期338-348,共11页
Museums offer a lifelong edutainment environment with flexible choices for the public and provide fruitful interdisciplinary learning resources to support competency-based education.However,the lack of proper scaffold... Museums offer a lifelong edutainment environment with flexible choices for the public and provide fruitful interdisciplinary learning resources to support competency-based education.However,the lack of proper scaffolding and supports in museums negatively affect learner learning.Further,the individual differences need to be considered to effectively support the diverse learners learning in museums.In this study,an innovative learning model to support competency education for lifelong learning in museums is proposed.A game-based learning service named CoboFun that offers various types of problem-solving activities was developed to facilitate learners’interaction with exhibits and their peers in the museum.To examine the service design of CoboFun,learners’perceptions were evaluated and the differences in their cognitive styles were examined(Field Independent(FI)and Field Dependent(FD)).The results showed that both FI and FD learners enjoyed learning with CoboFun but that flexible learning tools needed to be provided to satisfy the different needs for the learners with different cognitive styles. 展开更多
关键词 competency-based learning museum learning game-based learning virtual and physical lifelong learning
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An Introduction to The Construction of English Multiplex Teaching Evaluation System in Secondary Vocational Schools 认领
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作者 许恩萍 《海外英语》 2020年第10期279-280,共2页
In view of the current situation that the evaluation mode of English Teaching in secondary vocational school is still dominated by the summative evaluation,the new evaluation mode,the multiple evaluation system,is app... In view of the current situation that the evaluation mode of English Teaching in secondary vocational school is still dominated by the summative evaluation,the new evaluation mode,the multiple evaluation system,is applied to the English teaching practice of secondary vocational school in the form of experiment,and the situation of studentst learning strategies,autonomous learning,interest in learning and English level after the implementation of the multiple evaluation system is analyzed.The results show that the multiple evaluation system of English Teaching in secondary vocational school has a positive impact on studentst English learning strategies,learning autonomy and learning interest,and also plays a certain role in promoting the improvement of students’ overall English level. 展开更多
关键词 Secondary Vocational English teaching multiple evaluation system learning strategy learning autonomy learning interest
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Enhanced Learning Resource Recommendation Based on Online Learning Style Model 认领
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作者 Hui Chen Chuantao Yin +3 位作者 Rumei Li Wenge Rong Zhang Xiong Bertrand David 《清华大学学报自然科学版(英文版)》 SCIE EI CAS CSCD 2020年第3期348-356,共9页
Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences.This paper introduces a learning style model to rep... Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences.This paper introduces a learning style model to represent features of online learners.It also presents an enhanced recommendation method named Adaptive Recommendation based on Online Learning Style(AROLS),which implements learning resource adaptation by mining learners’behavioral data.First,AROLS creates learner clusters according to their online learning styles.Second,it applies Collaborative Filtering(CF)and association rule mining to extract the preferences and behavioral patterns of each cluster.Finally,it generates a personalized recommendation set of variable size.A real-world dataset is employed for some experiments.Results show that our online learning style model is conducive to the learners’data mining,and AROLS evidently outperforms the traditional CF method. 展开更多
关键词 smart LEARNING E-LEARNING ONLINE LEARNING style adaptive RECOMMENDATION COLLABORATIVE Filtering(CF)
Active Learning Query Strategies for Classification,Regression,and Clustering:A Survey 认领
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作者 Punit Kumar Atul Gupta 《计算机科学技术学报:英文版》 SCIE EI CSCD 2020年第4期913-945,共33页
Generally,data is available abundantly in unlabeled form,and its annotation requires some cost.The labeling,as well as learning cost,can be minimized by learning with the minimum labeled data instances.Active learning... Generally,data is available abundantly in unlabeled form,and its annotation requires some cost.The labeling,as well as learning cost,can be minimized by learning with the minimum labeled data instances.Active learning(AL),learns from a few labeled data instances with the additional facility of querying the labels of instances from an expert annotator or oracle.The active learner uses an instance selection strategy for selecting those critical query instances,which reduce the generalization error as fast as possible.This process results in a refined training dataset,which helps in minimizing the overall cost.The key to the success of AL is query strategies that select the candidate query instances and help the learner in learning a valid hypothesis.This survey reviews AL query strategies for classification,regression,and clustering under the pool-based AL scenario.The query strategies under classification are further divided into:informative-based,representative-based,informative-and representative-based,and others.Also,more advanced query strategies based on reinforcement learning and deep learning,along with query strategies under the realistic environment setting,are presented.After a rigorous mathematical analysis of AL strategies,this work presents a comparative analysis of these strategies.Finally,implementation guide,applications,and challenges of AL are discussed. 展开更多
关键词 ACTIVE LEARNING ACTIVE LEARNING QUERY strategy ACTIVE CLASSIFICATION ACTIVE regression ACTIVE CLUSTERING DEEP ACTIVE LEARNING
Joint User Selection and Resource Allocation for Fast Federated Edge Learning 认领
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作者 JIANG Zhihui HE Yinghui YU Guanding 《中兴通讯技术:英文版》 2020年第2期20-30,共11页
By periodically aggregating local learning updates from edge users, federated edge learning (FEEL) is envisioned as a promising means to reap the benefit of local rich da?ta and protect users'privacy. However, the... By periodically aggregating local learning updates from edge users, federated edge learning (FEEL) is envisioned as a promising means to reap the benefit of local rich da?ta and protect users'privacy. However, the scarce wireless communication resource greatly limits the number of participated users and is regarded as the main bottleneck which hin?ders the development of FEEL. To tackle this issue, we propose a user selection policy based on data importance for FEEL system. In order to quantify the data importance of each user, we first analyze the relationship between the loss decay and the squared norm of gradi?ent. Then, we formulate a combinatorial optimization problem to maximize the learning effi?ciency by jointly considering user selection and communication resource allocation. By problem transformation and relaxation, the optimal user selection policy and resource alloca?tion are derived, and a polynomial-time optimal algorithm is developed. Finally, we deploy two commonly used deep neural network (DNN) models for simulation. The results validate that our proposed algorithm has strong generalization ability and can attain higher learning efficiency compared with other traditional algorithms. 展开更多
关键词 data importance federated edge learning learning accuracy learning efficien?cy resource allocation user selection
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Robot learning from demonstration for path planning: A review 认领
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作者 XIE ZongWu ZHANG Qi +1 位作者 JIANG ZaiNan LIU Hong 《中国科学:技术科学英文版》 SCIE EI CAS CSCD 2020年第8期1325-1334,共10页
Learning from demonstration(LfD)is an appealing method of helping robots learn new skills.Numerous papers have presented methods of LfD with good performance in robotics.However,complicated robot tasks that need to ca... Learning from demonstration(LfD)is an appealing method of helping robots learn new skills.Numerous papers have presented methods of LfD with good performance in robotics.However,complicated robot tasks that need to carefully regulate path planning strategies remain unanswered.Contact or non-contact constraints in specific robot tasks make the path planning problem more difficult,as the interaction between the robot and the environment is time-varying.In this paper,we focus on the path planning of complex robot tasks in the domain of LfD and give a novel perspective for classifying imitation learning and inverse reinforcement learning.This classification is based on constraints and obstacle avoidance.Finally,we summarize these methods and present promising directions for robot application and LfD theory. 展开更多
关键词 learning from demonstration path planning imitation learning inverse reinforcement learning obstacle avoidance
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