Western Sichuan and its vicinity are located in the juncture of three big active blocks, namely, the Chuandian （Sichuan and Yunnan）, the Bayan Har and the South China blocks, on the eastern margin of the Qinghai-Xiz...Western Sichuan and its vicinity are located in the juncture of three big active blocks, namely, the Chuandian （Sichuan and Yunnan）, the Bayan Har and the South China blocks, on the eastern margin of the Qinghai-Xizang（Tibet） Plateau. Many groups of active faults that are capable of generating earthquakes are developed there. Because there exist lateral secondary active faults, the Chuandian block can be further divided into the central Yumlan and northwestern Sichuan sub-blocks; while the Longmenshan sub-block can be divided on the east end of the Bayan Har block. Joint exploration of deep crustal structure shows that there exist low-velocity and high-conductivity layers in the crust of the Chuandian and Bayan Har blocks, which are one of the important factors that make the upper crust prone to earthquake. The results of geological study and modern GPS observation show that blocks of different orders all have SE- or SSE-trending sliding, clockwise rotation and upwelling movement; but there are some differences in amplitude. This paper has also given the geological or GPS slip rates of main active fault zones and discussed the main scientific problems still existing now.展开更多
In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible t...In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.展开更多
The mathematic theory of Brownian passage-time model and its difference from other recurrence models such as Poisson, lognormal, gamma and Weibull, were introduced. We assessed and analyzed the earthquake probabiliti...The mathematic theory of Brownian passage-time model and its difference from other recurrence models such as Poisson, lognormal, gamma and Weibull, were introduced. We assessed and analyzed the earthquake probabilities of the major faults with the elapsed time much greater than the recurrence interval in the northwest region of Bei- jing (China) in 100-year by using both Brownian passage-time model and Poisson model, and concluded that the calculated results obtained from Brownian passage-time model is more reasonable.展开更多
Based on detailed studies of paleoearthquakes along major active faults in the transition area between the north eastern margin of Tibetan Plateau and the Ordos block, this paper discusses temporal and spatial distrib...Based on detailed studies of paleoearthquakes along major active faults in the transition area between the north eastern margin of Tibetan Plateau and the Ordos block, this paper discusses temporal and spatial distribution of paleoearthquakes and their regional recurrent behavior. The regional paleoarthquake recurrence model in the area exhibits features of temporal and spatial clustering, which may be divided into two kinds. One has a time span about 300 years, and the other has about 1 000 years.展开更多
The movement of pedestrians involves temporal continuity,spatial interactivity,and random diversity.As a result,pedestrian trajectory prediction is rather challenging.Most existing trajectory prediction methods tend t...The movement of pedestrians involves temporal continuity,spatial interactivity,and random diversity.As a result,pedestrian trajectory prediction is rather challenging.Most existing trajectory prediction methods tend to focus on just one aspect of these challenges,ignoring the temporal information of the trajectory and making too many assumptions.In this paper,we propose a recurrent attention and interaction(RAI)model to predict pedestrian trajectories.The RAI model consists of a temporal attention module,spatial pooling module,and randomness modeling module.The temporal attention module is proposed to assign different weights to the input sequence of a target,and reduce the speed deviation of different pedestrians.The spatial pooling module is proposed to model not only the social information of neighbors in historical frames,but also the intention of neighbors in the current time.The randomness modeling module is proposed to model the uncertainty and diversity of trajectories by introducing random noise.We conduct extensive experiments on several public datasets.The results demonstrate that our method outperforms many that are state-ofthe-art.展开更多
The recurrent neural network （RNN） model based on projective operator was studied. Different from the former study, the value region of projective operator in the neural network in this paper is a general closed con...The recurrent neural network （RNN） model based on projective operator was studied. Different from the former study, the value region of projective operator in the neural network in this paper is a general closed convex subset of n-dimensional Euclidean space and it is not a compact convex set in general, that is, the value region of projective operator is probably unbounded. It was proved that the network has a global solution and its solution trajectory converges to some equilibrium set whenever objective function satisfies some conditions. After that, the model was applied to continuously differentiable optimization and nonlinear or implicit complementarity problems. In addition, simulation experiments confirm the efficiency of the RNN.展开更多
Hypernym detection and discovery are fundamental tasks in natural language processing.The former task aims to identify all possible hypernyms of a given hyponym term,whereas the latter attempts to determine whether th...Hypernym detection and discovery are fundamental tasks in natural language processing.The former task aims to identify all possible hypernyms of a given hyponym term,whereas the latter attempts to determine whether the given two terms hold a hypernymy relation or not.Existing research on hypernym detection and discovery tasks projects a term into various semantic spaces with single mapping functions.Despite their success,these methods may not be adequate in capturing complex semantic relevance between hyponym/hypernymy pairs in two aspects.First,they may fall short in modeling the hierarchical structure in the hypernymy relations,which may help them learn better term representations.Second,the polysemy phenomenon that hypernyms may express distinct senses is understudied.In this paper,we propose a Multi-Projection Recurrent model(MPR)to simultaneously capture the hierarchical relationships between terms and deal with diverse senses caused by the polysemy phenomenon.Specifically,we build a multi-projection mapping block to deal with the polysemy phenomenon,which learns various word senses by multiple projections.Besides,we adopt a hierarchy-aware recurrent block with the recurrent operation followed by a multi-hop aggregation module to capture the hierarchical structure of hypernym relations.Experiments on 11 benchmark datasets in various task settings illustrate that our multi-projection recurrent model outperforms the baselines.The experimental analysis and case study demonstrate that our multi-projection module and the recurrent structure are effective for hypernym detection and discovery tasks.展开更多
Recurrence model for strong earthquakes on Fen Wei seismic belt is proposed on the basis of the collection and analysis of fault slip rate, paleoearthquake sequence, maximum displacement of each event etc. on 21 faul...Recurrence model for strong earthquakes on Fen Wei seismic belt is proposed on the basis of the collection and analysis of fault slip rate, paleoearthquake sequence, maximum displacement of each event etc. on 21 fault segments of the belt, which are active since late Late Pleistocene. And the long and intermediate term seismic potential of the belt has been evaluated through four approaches.展开更多
Background:Breast cancer with low-positive human epidermal growth factor receptor 2(HER2)expression has triggered further refinement of evaluation criteria for HER2 expression.We studied the clinicopathological featur...Background:Breast cancer with low-positive human epidermal growth factor receptor 2(HER2)expression has triggered further refinement of evaluation criteria for HER2 expression.We studied the clinicopathological features of early-stage breast cancer with low-positive HER2 expression in China and analyzed prognostic factors.Methods:Clinical and pathological data and prognostic information of patients with early-stage breast cancer with low-positive HER2 expression treated by the member units of the Chinese Society of Breast Surgery and Chinese Society of Surgery of Chinese Medical Association,from January 2015 to December 2016 were collected.The prognostic factors of these patients were analyzed.Results:Twenty-nine hospitals provided valid cases.From 2015 to 2016,a total of 25,096 cases of early-stage breast cancer were treated,7642(30.5%)of which had low-positive HER2 expression and were included in the study.After ineligible cases were excluded,6486 patients were included in the study.The median follow-up time was 57 months(4-76 months).The disease-free survival rate was 92.1%at 5 years,and the overall survival rate was 97.4%at 5 years.At the follow-up,506(7.8%)cases of metastasis and 167(2.6%)deaths were noted.Multivariate Cox regression analysis showed that tumor stage,lymphvascular invasion,and the Ki67 index were related to recurrence and metastasis(P<0.05).The recurrence risk prediction model was established using a machine learning model and showed that the area under the receiving operator characteristic curve was 0.815(95%confidence interval:0.750-0.880).Conclusions:Early-stage breast cancer patients with low-positive HER2 expression account for 30.5%of all patients.Tumor stage,lymphvascular invasion,and the Ki67 index are factors affecting prognosis.The recurrence prediction model for breast cancer with low-positive HER2 expression based on a machine learning model had a good clinical reference value for predicting the recurrence risk at 5 years.Trial registration:ChiCTR.org.cn,ChiCTR2100046766.展开更多
Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environme...Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environment.At present,the monitoring method of seawater pH has been matured.However,how to accurately predict future changes has been lacking effective solutions.Based on this,the model of bidirectional gated recurrent neural network with multi-headed self-attention based on improved complete ensemble empirical mode decomposition with adaptive noise combined with phase space reconstruction(ICPBGA)is proposed to achieve seawater pH prediction.To verify the validity of this model,pH data of two monitoring sites in the coastal sea area of Beihai,China are selected to verify the effect.At the same time,the ICPBGA model is compared with other excellent models for predicting chaotic time series,and root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2)are used as performance evaluation indicators.The R2 of the ICPBGA model at Sites 1 and 2 are above 0.9,and the prediction errors are also the smallest.The results show that the ICPBGA model has a wide range of applicability and the most satisfactory prediction effect.The prediction method in this paper can be further expanded and used to predict other marine environmental indicators.展开更多
Recurrent events data and gap times between recurrent events are frequently encountered in many clinical and observational studies,and often more than one type of recurrent events is of interest.In this paper,we consi...Recurrent events data and gap times between recurrent events are frequently encountered in many clinical and observational studies,and often more than one type of recurrent events is of interest.In this paper,we consider a proportional hazards model for multiple type recurrent gap times data to assess the effect of covaxiates on the censored event processes of interest.An estimating equation approach is used to obtain the estimators of regression coefficients and baseline cumulative hazard functions.We examine asymptotic properties of the proposed estimators.Finite sample properties of these estimators are demonstrated by simulations.展开更多
文摘Western Sichuan and its vicinity are located in the juncture of three big active blocks, namely, the Chuandian （Sichuan and Yunnan）, the Bayan Har and the South China blocks, on the eastern margin of the Qinghai-Xizang（Tibet） Plateau. Many groups of active faults that are capable of generating earthquakes are developed there. Because there exist lateral secondary active faults, the Chuandian block can be further divided into the central Yumlan and northwestern Sichuan sub-blocks; while the Longmenshan sub-block can be divided on the east end of the Bayan Har block. Joint exploration of deep crustal structure shows that there exist low-velocity and high-conductivity layers in the crust of the Chuandian and Bayan Har blocks, which are one of the important factors that make the upper crust prone to earthquake. The results of geological study and modern GPS observation show that blocks of different orders all have SE- or SSE-trending sliding, clockwise rotation and upwelling movement; but there are some differences in amplitude. This paper has also given the geological or GPS slip rates of main active fault zones and discussed the main scientific problems still existing now.
基金supported by the National Science and Technology Council under grants NSTC 112-2221-E-320-002the Buddhist Tzu Chi Medical Foundation in Taiwan under Grant TCMMP 112-02-02.
文摘In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.
基金Joint Seismological Science Foundation of China (103034) and Key Project ″Assessment of Seismic Safety″ from China Earthquake Administration during the tenth Five-year Plan.
文摘The mathematic theory of Brownian passage-time model and its difference from other recurrence models such as Poisson, lognormal, gamma and Weibull, were introduced. We assessed and analyzed the earthquake probabilities of the major faults with the elapsed time much greater than the recurrence interval in the northwest region of Bei- jing (China) in 100-year by using both Brownian passage-time model and Poisson model, and concluded that the calculated results obtained from Brownian passage-time model is more reasonable.
文摘Based on detailed studies of paleoearthquakes along major active faults in the transition area between the north eastern margin of Tibetan Plateau and the Ordos block, this paper discusses temporal and spatial distribution of paleoearthquakes and their regional recurrent behavior. The regional paleoarthquake recurrence model in the area exhibits features of temporal and spatial clustering, which may be divided into two kinds. One has a time span about 300 years, and the other has about 1 000 years.
基金supported by the National NaturalScience Foundation of China(U1811463)the Fundamental Research Funds for the Central Universities(12060093192)。
文摘The movement of pedestrians involves temporal continuity,spatial interactivity,and random diversity.As a result,pedestrian trajectory prediction is rather challenging.Most existing trajectory prediction methods tend to focus on just one aspect of these challenges,ignoring the temporal information of the trajectory and making too many assumptions.In this paper,we propose a recurrent attention and interaction(RAI)model to predict pedestrian trajectories.The RAI model consists of a temporal attention module,spatial pooling module,and randomness modeling module.The temporal attention module is proposed to assign different weights to the input sequence of a target,and reduce the speed deviation of different pedestrians.The spatial pooling module is proposed to model not only the social information of neighbors in historical frames,but also the intention of neighbors in the current time.The randomness modeling module is proposed to model the uncertainty and diversity of trajectories by introducing random noise.We conduct extensive experiments on several public datasets.The results demonstrate that our method outperforms many that are state-ofthe-art.
文摘The recurrent neural network （RNN） model based on projective operator was studied. Different from the former study, the value region of projective operator in the neural network in this paper is a general closed convex subset of n-dimensional Euclidean space and it is not a compact convex set in general, that is, the value region of projective operator is probably unbounded. It was proved that the network has a global solution and its solution trajectory converges to some equilibrium set whenever objective function satisfies some conditions. After that, the model was applied to continuously differentiable optimization and nonlinear or implicit complementarity problems. In addition, simulation experiments confirm the efficiency of the RNN.
基金supported by the National Science and Technology Major Project of China(2022ZD0120202)the Natural Natural Science Foundation of China(Grant No.U23B2056).
文摘Hypernym detection and discovery are fundamental tasks in natural language processing.The former task aims to identify all possible hypernyms of a given hyponym term,whereas the latter attempts to determine whether the given two terms hold a hypernymy relation or not.Existing research on hypernym detection and discovery tasks projects a term into various semantic spaces with single mapping functions.Despite their success,these methods may not be adequate in capturing complex semantic relevance between hyponym/hypernymy pairs in two aspects.First,they may fall short in modeling the hierarchical structure in the hypernymy relations,which may help them learn better term representations.Second,the polysemy phenomenon that hypernyms may express distinct senses is understudied.In this paper,we propose a Multi-Projection Recurrent model(MPR)to simultaneously capture the hierarchical relationships between terms and deal with diverse senses caused by the polysemy phenomenon.Specifically,we build a multi-projection mapping block to deal with the polysemy phenomenon,which learns various word senses by multiple projections.Besides,we adopt a hierarchy-aware recurrent block with the recurrent operation followed by a multi-hop aggregation module to capture the hierarchical structure of hypernym relations.Experiments on 11 benchmark datasets in various task settings illustrate that our multi-projection recurrent model outperforms the baselines.The experimental analysis and case study demonstrate that our multi-projection module and the recurrent structure are effective for hypernym detection and discovery tasks.
文摘Recurrence model for strong earthquakes on Fen Wei seismic belt is proposed on the basis of the collection and analysis of fault slip rate, paleoearthquake sequence, maximum displacement of each event etc. on 21 fault segments of the belt, which are active since late Late Pleistocene. And the long and intermediate term seismic potential of the belt has been evaluated through four approaches.
基金supported by grants from the Youth Cultivation Fund of Beijing Medical Ward Foundation(No.20180502)Beijing Medical Ward Foundation(No.YXJL-2020-0941-0736)。
文摘Background:Breast cancer with low-positive human epidermal growth factor receptor 2(HER2)expression has triggered further refinement of evaluation criteria for HER2 expression.We studied the clinicopathological features of early-stage breast cancer with low-positive HER2 expression in China and analyzed prognostic factors.Methods:Clinical and pathological data and prognostic information of patients with early-stage breast cancer with low-positive HER2 expression treated by the member units of the Chinese Society of Breast Surgery and Chinese Society of Surgery of Chinese Medical Association,from January 2015 to December 2016 were collected.The prognostic factors of these patients were analyzed.Results:Twenty-nine hospitals provided valid cases.From 2015 to 2016,a total of 25,096 cases of early-stage breast cancer were treated,7642(30.5%)of which had low-positive HER2 expression and were included in the study.After ineligible cases were excluded,6486 patients were included in the study.The median follow-up time was 57 months(4-76 months).The disease-free survival rate was 92.1%at 5 years,and the overall survival rate was 97.4%at 5 years.At the follow-up,506(7.8%)cases of metastasis and 167(2.6%)deaths were noted.Multivariate Cox regression analysis showed that tumor stage,lymphvascular invasion,and the Ki67 index were related to recurrence and metastasis(P<0.05).The recurrence risk prediction model was established using a machine learning model and showed that the area under the receiving operator characteristic curve was 0.815(95%confidence interval:0.750-0.880).Conclusions:Early-stage breast cancer patients with low-positive HER2 expression account for 30.5%of all patients.Tumor stage,lymphvascular invasion,and the Ki67 index are factors affecting prognosis.The recurrence prediction model for breast cancer with low-positive HER2 expression based on a machine learning model had a good clinical reference value for predicting the recurrence risk at 5 years.Trial registration:ChiCTR.org.cn,ChiCTR2100046766.
基金The National Natural Science Foundation of China under contract No.62275228the S&T Program of Hebei under contract Nos 19273901D and 20373301Dthe Hebei Natural Science Foundation under contract No.F2020203066.
文摘Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environment.At present,the monitoring method of seawater pH has been matured.However,how to accurately predict future changes has been lacking effective solutions.Based on this,the model of bidirectional gated recurrent neural network with multi-headed self-attention based on improved complete ensemble empirical mode decomposition with adaptive noise combined with phase space reconstruction(ICPBGA)is proposed to achieve seawater pH prediction.To verify the validity of this model,pH data of two monitoring sites in the coastal sea area of Beihai,China are selected to verify the effect.At the same time,the ICPBGA model is compared with other excellent models for predicting chaotic time series,and root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2)are used as performance evaluation indicators.The R2 of the ICPBGA model at Sites 1 and 2 are above 0.9,and the prediction errors are also the smallest.The results show that the ICPBGA model has a wide range of applicability and the most satisfactory prediction effect.The prediction method in this paper can be further expanded and used to predict other marine environmental indicators.
基金supported in part by Natural Science Foundation of Hubei(08BA164)Major Research Program of Hubei Provincial Department of Education(09B2001)+2 种基金supported in part by National Natural Science Foundation of China(1117112)Doctoral Fund of Ministry of Education of China(20090076110001)National Statistical Science Research Major Program of China(2011LZ051)
文摘Recurrent events data and gap times between recurrent events are frequently encountered in many clinical and observational studies,and often more than one type of recurrent events is of interest.In this paper,we consider a proportional hazards model for multiple type recurrent gap times data to assess the effect of covaxiates on the censored event processes of interest.An estimating equation approach is used to obtain the estimators of regression coefficients and baseline cumulative hazard functions.We examine asymptotic properties of the proposed estimators.Finite sample properties of these estimators are demonstrated by simulations.
