Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image.Local features extracted by convolutions,etc.,capture finegrained details such as edges and te...Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image.Local features extracted by convolutions,etc.,capture finegrained details such as edges and textures,while global features extracted by full connection layers,etc.,represent the overall structure and long-range relationships within the image.These features are crucial for accurate object detection,yet most existing methods focus on aggregating local and global features,often overlooking the importance of medium-range dependencies.To address this gap,we propose a novel full perception module(FPModule),a simple yet effective feature extraction module designed to simultaneously capture local details,medium-range dependencies,and long-range dependencies.Building on this,we construct a full perception head(FP-Head)by cascading multiple FP-Modules,enabling the prediction layer to leverage the most informative features.Experimental results in the MS COCO dataset demonstrate that our approach significantly enhances object recognition and localization,achieving 2.7−5.7 APval gains when integrated into standard object detectors.Notably,the FP-Module is a universal solution that can be seamlessly incorporated into existing detectors to boost performance.The code will be released at https://github.com/Idcogroup/FP-Head.展开更多
Dear Editor, Coxsackievirus A16 （CV A16） and enterovirus 71 （EV71） are currently the two primary causative agents of hand- foot-and-mouth disease （HFMD） （Solomon et al., 2010; Mao et al., 2014）, threatening he...Dear Editor, Coxsackievirus A16 （CV A16） and enterovirus 71 （EV71） are currently the two primary causative agents of hand- foot-and-mouth disease （HFMD） （Solomon et al., 2010; Mao et al., 2014）, threatening health of children world- wide. They both belong to the Enterovirus genus of the Picornaviridae family, and have single-stranded positive- sense RNA genomes of about 7.5 kilobases （kb） in length. As with other positive-strand RNA viruses, the genome rep- lication process ofCV A16 is carried out by a membrane- associated replication complex with the virally encoded RNA-dependent RNA polymerase （RdRP） as the essential catalytic enzyme.展开更多
Northeast China, as the most important production base of agriculture, forestry, and livestock-breeding as well as the old industrial base in the whole country, has been playin a key role in the construction and deve...Northeast China, as the most important production base of agriculture, forestry, and livestock-breeding as well as the old industrial base in the whole country, has been playin a key role in the construction and development of China＇s economy. However, after the policy of reform and open-up was taken in China. the economic development speed and efficiency ofthis area have turned to be evidently lower than those of coastal area and the national average level as well, which is so-called ＇Northeast Phenomenon＇ and ＇Neo-Northeast Phenomenon＇. In terms of those phenomena, this paper firstly reviews the spatial and temporal features of the regional evolution of this area so as to unveil the profound forming causes of ＇Northeast Phenomena＇ and ＇Neo-Northeast Phenomena＇. And then the paper makes a further exploration into the status quo of this region and its forming causes by analyzing its economy gross, industrial structure, product structure, regional eco-categories, etc. At the end of the paper, the authors put forward the basic coordinated development strategies for Northeast China. namely we can revitalize this area by means of adjustment of economic structure, regional coordination, planning urban and rural areas as a whole, institutional innovation, etc.展开更多
The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most exi...The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling.However,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps.Furthermore,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between nodes.To overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies simultaneously.Specifically,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process.In each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting operation.Meanwhile,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven manner.Then,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global information.It projects the embedding input repeatedly with multiple different channels.Finally,the predicted values are generated by stacking several multi-stream modules.Extensive experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.展开更多
Current Chinese event detection methods commonly use word embedding to capture semantic representation,but these methods find it difficult to capture the dependence relationship between the trigger words and other wor...Current Chinese event detection methods commonly use word embedding to capture semantic representation,but these methods find it difficult to capture the dependence relationship between the trigger words and other words in the same sentence.Based on the simple evaluation,it is known that a dependency parser can effectively capture dependency relationships and improve the accuracy of event categorisation.This study proposes a novel architecture that models a hybrid representation to summarise semantic and structural information from both characters and words.This model can capture rich semantic features for the event detection task by incorporating the semantic representation generated from the dependency parser.The authors evaluate different models on kbp 2017 corpus.The experimental results show that the proposed method can significantly improve performance in Chinese event detection.展开更多
An objective identification technique is used to detect regional extreme low temperature events （RELTE） in China during 1960-2009. Their spatial-temporal characteristics are analyzed. The results indicate that the l...An objective identification technique is used to detect regional extreme low temperature events （RELTE） in China during 1960-2009. Their spatial-temporal characteristics are analyzed. The results indicate that the lowest temperatures of RELTE, together with the frequency distribution of the geometric latitude center, exhibit a double-peak feature. The RELTE frequently happen near the geometric area of 30°N and 42°N before the mid-1980s, but shifted afterwards to 30°N. During 1960-2009, the frequency~ intensity, and the maximum impacted area of RELTE show overall decreasing trends. Due to the contribution of RELTE, with long duratioh and large spatial range, which account for 10% of the total RELTE, there is a significant turning point in the late 1980s. A change to a much more steady state after the late 1990s is identified. In addition, the integrated indices of RELTE are classified and analyzed.展开更多
Marine oil spill emulsions are difficult to recover,and the damage to the environment is not easy to eliminate.The use of remote sensing to accurately identify oil spill emulsions is highly important for the protectio...Marine oil spill emulsions are difficult to recover,and the damage to the environment is not easy to eliminate.The use of remote sensing to accurately identify oil spill emulsions is highly important for the protection of marine environments.However,the spectrum of oil emulsions changes due to different water content.Hyperspectral remote sensing and deep learning can use spectral and spatial information to identify different types of oil emulsions.Nonetheless,hyperspectral data can also cause information redundancy,reducing classification accuracy and efficiency,and even overfitting in machine learning models.To address these problems,an oil emulsion deep-learning identification model with spatial-spectral feature fusion is established,and feature bands that can distinguish between crude oil,seawater,water-in-oil emulsion(WO),and oil-in-water emulsion(OW)are filtered based on a standard deviation threshold–mutual information method.Using oil spill airborne hyperspectral data,we conducted identification experiments on oil emulsions in different background waters and under different spatial and temporal conditions,analyzed the transferability of the model,and explored the effects of feature band selection and spectral resolution on the identification of oil emulsions.The results show the following.(1)The standard deviation–mutual information feature selection method is able to effectively extract feature bands that can distinguish between WO,OW,oil slick,and seawater.The number of bands was reduced from 224 to 134 after feature selection on the Airborne Visible Infrared Imaging Spectrometer(AVIRIS)data and from 126 to 100 on the S185 data.(2)With feature selection,the overall accuracy and Kappa of the identification results for the training area are 91.80%and 0.86,respectively,improved by 2.62%and 0.04,and the overall accuracy and Kappa of the identification results for the migration area are 86.53%and 0.80,respectively,improved by 3.45%and 0.05.(3)The oil emulsion identification model has a certain degree of transferability and can effectively identify oil spill emulsions for AVIRIS data at different times and locations,with an overall accuracy of more than 80%,Kappa coefficient of more than 0.7,and F1 score of 0.75 or more for each category.(4)As the spectral resolution decreasing,the model yields different degrees of misclassification for areas with a mixed distribution of oil slick and seawater or mixed distribution of WO and OW.Based on the above experimental results,we demonstrate that the oil emulsion identification model with spatial–spectral feature fusion achieves a high accuracy rate in identifying oil emulsion using airborne hyperspectral data,and can be applied to images under different spatial and temporal conditions.Furthermore,we also elucidate the impact of factors such as spectral resolution and background water bodies on the identification process.These findings provide new reference for future endeavors in automated marine oil spill detection.展开更多
Background Lip reading uses lip images for visual speech recognition.Deep-learning-based lip reading has greatly improved performance in current datasets;however,most existing research ignores the significance of shor...Background Lip reading uses lip images for visual speech recognition.Deep-learning-based lip reading has greatly improved performance in current datasets;however,most existing research ignores the significance of short-term temporal dependencies of lip-shape variations between adjacent frames,which leaves space for further improvement in feature extraction.Methods This article presents a spatiotemporal feature fusion network(STDNet)that compensates for the deficiencies of current lip-reading approaches in short-term temporal dependency modeling.Specifically,to distinguish more similar and intricate content,STDNet adds a temporal feature extraction branch based on a 3D-CNN,which enhances the learning of dynamic lip movements in adjacent frames while not affecting spatial feature extraction.In particular,we designed a local–temporal block,which aggregates interframe differences,strengthening the relationship between various local lip regions through multiscale convolution.We incorporated the squeeze-and-excitation mechanism into the Global-Temporal Block,which processes a single frame as an independent unitto learn temporal variations across the entire lip region more effectively.Furthermore,attention pooling was introduced to highlight meaningful frames containing key semantic information for the target word.Results Experimental results demonstrated STDNet's superior performance on the LRW and LRW-1000,achieving word-level recognition accuracies of 90.2% and 53.56%,respectively.Extensive ablation experiments verified the rationality and effectiveness of its modules.Conclusions The proposed model effectively addresses short-term temporal dependency limitations in lip reading,and improves the temporal robustness of the model against variable-length sequences.These advancements validate the importance of explicit short-term dynamics modeling for practical lip-reading systems.展开更多
提出一种利用神经网络(ANN)模型获取不同特征尺寸静态随机存储器(static random access memory,SRAM)单粒子翻转截面数据的方法。利用SRAM单粒子翻转历史数据建立了SRAM单粒子翻转机器学习预测模型,并基于该模型研究了器件的电压相关性...提出一种利用神经网络(ANN)模型获取不同特征尺寸静态随机存储器(static random access memory,SRAM)单粒子翻转截面数据的方法。利用SRAM单粒子翻转历史数据建立了SRAM单粒子翻转机器学习预测模型,并基于该模型研究了器件的电压相关性。研究中系统梳理了来自不同文献重离子实验的历史数据,并开展重离子实验进行数据的补充。结合输出参数的特点对相关数据进行处理,在饱和截面与形状参数的基础上引入特征因子以表征不同器件之间的系统偏差,优化并训练了双隐层前馈神经网络,该网络能够准确预测SRAM单粒子翻转截面并探究单粒子翻转电压相关性的规律。利用神经网络丰富了不同特征尺寸SRAM在低电压下单粒子翻转截面数据,通过机理分析发现了灵敏体积减小是造成小尺寸器件单粒子翻转电压相关性增加的主要原因,器件的电压相关性与工作电压和LET值之间均存在指数关系等规律。展开更多
A new three-dimensional porous lanthanide coordination polymer{[Dy(L)(μ2-bpdo)0.5(μ_(4)-bpdo)0.5(CH_(3)OH)]·ClO_(4)·3CH_(3)OH}_(n)(2)featuring slow magnetic relaxation has been successfully assembled by re...A new three-dimensional porous lanthanide coordination polymer{[Dy(L)(μ2-bpdo)0.5(μ_(4)-bpdo)0.5(CH_(3)OH)]·ClO_(4)·3CH_(3)OH}_(n)(2)featuring slow magnetic relaxation has been successfully assembled by reacting a superparamagnetic chain complex{[Dy(HL)(H_(2)O)_(2)(CH_(3)OH)_(2)]·2Cl·CH_(3)OH}_(n)(1)with an organic bridging ligand,namely,_(4),_(4)’-bipyridine-N,N’-dioxide(bpdo)(H2L=N’-(2-hydroxybenzylidene)-picolinohydrazide).展开更多
针对目前方法大多未能充分利用跨度语义信息和局部上下文隐含信息等问题,提出基于跨度和多层次特征融合的实体关系联合抽取模型。该模型首先将文本输入到预训练语言模型(Bidirectional Encoder Representations from Transformer,BERT)...针对目前方法大多未能充分利用跨度语义信息和局部上下文隐含信息等问题,提出基于跨度和多层次特征融合的实体关系联合抽取模型。该模型首先将文本输入到预训练语言模型(Bidirectional Encoder Representations from Transformer,BERT)转换为词向量后,将其与通过图卷积获得的句法依赖信息进行融合,形成更丰富的文本特征;然后通过多头注意力层对文本特征进行加权处理,以此抑制噪声特征的干扰,并促进特征之间的交互,随后根据跨度将文本信息分割成跨度序列进行实体识别;最后使用双向门控循环单元提取局部上下文隐含信息,将与实体类型信息融合到候选实体跨度对并使用sigmoid函数进行关系分类。实验表明,该模型在SciERC数据集和CoNLL04数据集上取得良好的提升效果。展开更多
基金supported by the National Natural Science Foundation of China(62371350,62171324,62471338,U1903214).
文摘Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image.Local features extracted by convolutions,etc.,capture finegrained details such as edges and textures,while global features extracted by full connection layers,etc.,represent the overall structure and long-range relationships within the image.These features are crucial for accurate object detection,yet most existing methods focus on aggregating local and global features,often overlooking the importance of medium-range dependencies.To address this gap,we propose a novel full perception module(FPModule),a simple yet effective feature extraction module designed to simultaneously capture local details,medium-range dependencies,and long-range dependencies.Building on this,we construct a full perception head(FP-Head)by cascading multiple FP-Modules,enabling the prediction layer to leverage the most informative features.Experimental results in the MS COCO dataset demonstrate that our approach significantly enhances object recognition and localization,achieving 2.7−5.7 APval gains when integrated into standard object detectors.Notably,the FP-Module is a universal solution that can be seamlessly incorporated into existing detectors to boost performance.The code will be released at https://github.com/Idcogroup/FP-Head.
文摘Dear Editor, Coxsackievirus A16 （CV A16） and enterovirus 71 （EV71） are currently the two primary causative agents of hand- foot-and-mouth disease （HFMD） （Solomon et al., 2010; Mao et al., 2014）, threatening health of children world- wide. They both belong to the Enterovirus genus of the Picornaviridae family, and have single-stranded positive- sense RNA genomes of about 7.5 kilobases （kb） in length. As with other positive-strand RNA viruses, the genome rep- lication process ofCV A16 is carried out by a membrane- associated replication complex with the virally encoded RNA-dependent RNA polymerase （RdRP） as the essential catalytic enzyme.
基金Under the auspices of National Natural Science Foundation of China (No. 40471040)
文摘Northeast China, as the most important production base of agriculture, forestry, and livestock-breeding as well as the old industrial base in the whole country, has been playin a key role in the construction and development of China＇s economy. However, after the policy of reform and open-up was taken in China. the economic development speed and efficiency ofthis area have turned to be evidently lower than those of coastal area and the national average level as well, which is so-called ＇Northeast Phenomenon＇ and ＇Neo-Northeast Phenomenon＇. In terms of those phenomena, this paper firstly reviews the spatial and temporal features of the regional evolution of this area so as to unveil the profound forming causes of ＇Northeast Phenomena＇ and ＇Neo-Northeast Phenomena＇. And then the paper makes a further exploration into the status quo of this region and its forming causes by analyzing its economy gross, industrial structure, product structure, regional eco-categories, etc. At the end of the paper, the authors put forward the basic coordinated development strategies for Northeast China. namely we can revitalize this area by means of adjustment of economic structure, regional coordination, planning urban and rural areas as a whole, institutional innovation, etc.
基金partially supported by the National Key Research and Development Program of China(2020YFB2104001)。
文摘The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling.However,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps.Furthermore,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between nodes.To overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies simultaneously.Specifically,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process.In each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting operation.Meanwhile,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven manner.Then,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global information.It projects the embedding input repeatedly with multiple different channels.Finally,the predicted values are generated by stacking several multi-stream modules.Extensive experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.
基金973 Program,Grant/Award Number:2014CB340504The State Key Program of National Natural Science of China,Grant/Award Number:61533018+3 种基金National Natural Science Foundation of China,Grant/Award Number:61402220The Philosophy and Social Science Foundation of Hunan Province,Grant/Award Number:16YBA323Natural Science Foundation of Hunan Province,Grant/Award Number:2020JJ4525Scientific Research Fund of Hunan Provincial Education Department,Grant/Award Number:18B279,19A439。
文摘Current Chinese event detection methods commonly use word embedding to capture semantic representation,but these methods find it difficult to capture the dependence relationship between the trigger words and other words in the same sentence.Based on the simple evaluation,it is known that a dependency parser can effectively capture dependency relationships and improve the accuracy of event categorisation.This study proposes a novel architecture that models a hybrid representation to summarise semantic and structural information from both characters and words.This model can capture rich semantic features for the event detection task by incorporating the semantic representation generated from the dependency parser.The authors evaluate different models on kbp 2017 corpus.The experimental results show that the proposed method can significantly improve performance in Chinese event detection.
基金supported by the Special Scientific Research Projects for Public Interest(No.GYHY201006021 and GYHY201106016)the National Natural Science Foundation of China(No.41205040 and 40930952)
文摘An objective identification technique is used to detect regional extreme low temperature events （RELTE） in China during 1960-2009. Their spatial-temporal characteristics are analyzed. The results indicate that the lowest temperatures of RELTE, together with the frequency distribution of the geometric latitude center, exhibit a double-peak feature. The RELTE frequently happen near the geometric area of 30°N and 42°N before the mid-1980s, but shifted afterwards to 30°N. During 1960-2009, the frequency~ intensity, and the maximum impacted area of RELTE show overall decreasing trends. Due to the contribution of RELTE, with long duratioh and large spatial range, which account for 10% of the total RELTE, there is a significant turning point in the late 1980s. A change to a much more steady state after the late 1990s is identified. In addition, the integrated indices of RELTE are classified and analyzed.
基金The National Natural Science Foundation of China under contract Nos 61890964 and 42206177the Joint Funds of the National Natural Science Foundation of China under contract No.U1906217.
文摘Marine oil spill emulsions are difficult to recover,and the damage to the environment is not easy to eliminate.The use of remote sensing to accurately identify oil spill emulsions is highly important for the protection of marine environments.However,the spectrum of oil emulsions changes due to different water content.Hyperspectral remote sensing and deep learning can use spectral and spatial information to identify different types of oil emulsions.Nonetheless,hyperspectral data can also cause information redundancy,reducing classification accuracy and efficiency,and even overfitting in machine learning models.To address these problems,an oil emulsion deep-learning identification model with spatial-spectral feature fusion is established,and feature bands that can distinguish between crude oil,seawater,water-in-oil emulsion(WO),and oil-in-water emulsion(OW)are filtered based on a standard deviation threshold–mutual information method.Using oil spill airborne hyperspectral data,we conducted identification experiments on oil emulsions in different background waters and under different spatial and temporal conditions,analyzed the transferability of the model,and explored the effects of feature band selection and spectral resolution on the identification of oil emulsions.The results show the following.(1)The standard deviation–mutual information feature selection method is able to effectively extract feature bands that can distinguish between WO,OW,oil slick,and seawater.The number of bands was reduced from 224 to 134 after feature selection on the Airborne Visible Infrared Imaging Spectrometer(AVIRIS)data and from 126 to 100 on the S185 data.(2)With feature selection,the overall accuracy and Kappa of the identification results for the training area are 91.80%and 0.86,respectively,improved by 2.62%and 0.04,and the overall accuracy and Kappa of the identification results for the migration area are 86.53%and 0.80,respectively,improved by 3.45%and 0.05.(3)The oil emulsion identification model has a certain degree of transferability and can effectively identify oil spill emulsions for AVIRIS data at different times and locations,with an overall accuracy of more than 80%,Kappa coefficient of more than 0.7,and F1 score of 0.75 or more for each category.(4)As the spectral resolution decreasing,the model yields different degrees of misclassification for areas with a mixed distribution of oil slick and seawater or mixed distribution of WO and OW.Based on the above experimental results,we demonstrate that the oil emulsion identification model with spatial–spectral feature fusion achieves a high accuracy rate in identifying oil emulsion using airborne hyperspectral data,and can be applied to images under different spatial and temporal conditions.Furthermore,we also elucidate the impact of factors such as spectral resolution and background water bodies on the identification process.These findings provide new reference for future endeavors in automated marine oil spill detection.
基金Supported by the National Key Research and Development Program of China(2023YFC3306201)the National Natural Science Foundation of China(61772125)the Fundamental Research Funds for the Central Universities(N2317004).
文摘Background Lip reading uses lip images for visual speech recognition.Deep-learning-based lip reading has greatly improved performance in current datasets;however,most existing research ignores the significance of short-term temporal dependencies of lip-shape variations between adjacent frames,which leaves space for further improvement in feature extraction.Methods This article presents a spatiotemporal feature fusion network(STDNet)that compensates for the deficiencies of current lip-reading approaches in short-term temporal dependency modeling.Specifically,to distinguish more similar and intricate content,STDNet adds a temporal feature extraction branch based on a 3D-CNN,which enhances the learning of dynamic lip movements in adjacent frames while not affecting spatial feature extraction.In particular,we designed a local–temporal block,which aggregates interframe differences,strengthening the relationship between various local lip regions through multiscale convolution.We incorporated the squeeze-and-excitation mechanism into the Global-Temporal Block,which processes a single frame as an independent unitto learn temporal variations across the entire lip region more effectively.Furthermore,attention pooling was introduced to highlight meaningful frames containing key semantic information for the target word.Results Experimental results demonstrated STDNet's superior performance on the LRW and LRW-1000,achieving word-level recognition accuracies of 90.2% and 53.56%,respectively.Extensive ablation experiments verified the rationality and effectiveness of its modules.Conclusions The proposed model effectively addresses short-term temporal dependency limitations in lip reading,and improves the temporal robustness of the model against variable-length sequences.These advancements validate the importance of explicit short-term dynamics modeling for practical lip-reading systems.
文摘提出一种利用神经网络(ANN)模型获取不同特征尺寸静态随机存储器(static random access memory,SRAM)单粒子翻转截面数据的方法。利用SRAM单粒子翻转历史数据建立了SRAM单粒子翻转机器学习预测模型,并基于该模型研究了器件的电压相关性。研究中系统梳理了来自不同文献重离子实验的历史数据,并开展重离子实验进行数据的补充。结合输出参数的特点对相关数据进行处理,在饱和截面与形状参数的基础上引入特征因子以表征不同器件之间的系统偏差,优化并训练了双隐层前馈神经网络,该网络能够准确预测SRAM单粒子翻转截面并探究单粒子翻转电压相关性的规律。利用神经网络丰富了不同特征尺寸SRAM在低电压下单粒子翻转截面数据,通过机理分析发现了灵敏体积减小是造成小尺寸器件单粒子翻转电压相关性增加的主要原因,器件的电压相关性与工作电压和LET值之间均存在指数关系等规律。
基金supported by the National Natural Science Foundation of China(No.21671024)the National Key Basic Research Program of China(2013CB933402).We thank Prof.Zhenqiang Wang of the University of South Dakota for helpful comments.
文摘A new three-dimensional porous lanthanide coordination polymer{[Dy(L)(μ2-bpdo)0.5(μ_(4)-bpdo)0.5(CH_(3)OH)]·ClO_(4)·3CH_(3)OH}_(n)(2)featuring slow magnetic relaxation has been successfully assembled by reacting a superparamagnetic chain complex{[Dy(HL)(H_(2)O)_(2)(CH_(3)OH)_(2)]·2Cl·CH_(3)OH}_(n)(1)with an organic bridging ligand,namely,_(4),_(4)’-bipyridine-N,N’-dioxide(bpdo)(H2L=N’-(2-hydroxybenzylidene)-picolinohydrazide).
文摘针对目前方法大多未能充分利用跨度语义信息和局部上下文隐含信息等问题,提出基于跨度和多层次特征融合的实体关系联合抽取模型。该模型首先将文本输入到预训练语言模型(Bidirectional Encoder Representations from Transformer,BERT)转换为词向量后,将其与通过图卷积获得的句法依赖信息进行融合,形成更丰富的文本特征;然后通过多头注意力层对文本特征进行加权处理,以此抑制噪声特征的干扰,并促进特征之间的交互,随后根据跨度将文本信息分割成跨度序列进行实体识别;最后使用双向门控循环单元提取局部上下文隐含信息,将与实体类型信息融合到候选实体跨度对并使用sigmoid函数进行关系分类。实验表明,该模型在SciERC数据集和CoNLL04数据集上取得良好的提升效果。
