The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical engineers.In this study,a new approach is proposed to predict Dh using gene expression programming(...The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical engineers.In this study,a new approach is proposed to predict Dh using gene expression programming(GEP).Based on statistical reasoning,individual models were developed for two topographies:free-face and gently sloping ground.Along with a comparison with conventional approaches for predicting the Dh,four additional regression-based soft computing models,i.e.Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimization regression(SMOR),and M5-tree,were developed and compared with the GEP model.The results indicate that the GEP models predict Dh with less bias,as evidenced by the root mean square error(RMSE)and mean absolute error(MAE)for training(i.e.1.092 and 0.815;and 0.643 and 0.526)and for testing(i.e.0.89 and 0.705;and 0.773 and 0.573)in free-face and gently sloping ground topographies,respectively.The overall performance for the free-face topology was ranked as follows:GEP>RVM>M5-tree>GPR>SMOR,with a total score of 40,32,24,15,and 10,respectively.For the gently sloping condition,the performance was ranked as follows:GEP>RVM>GPR>M5-tree>SMOR with a total score of 40,32,21,19,and 8,respectively.Finally,the results of the sensitivity analysis showed that for both free-face and gently sloping ground,the liquefiable layer thickness(T_(15))was the major parameter with percentage deterioration(%D)value of 99.15 and 90.72,respectively.展开更多
The coronavirus(COVID-19)is a disease declared a global pan-demic that threatens the whole world.Since then,research has accelerated and varied to find practical solutions for the early detection and correct identific...The coronavirus(COVID-19)is a disease declared a global pan-demic that threatens the whole world.Since then,research has accelerated and varied to find practical solutions for the early detection and correct identification of this disease.Several researchers have focused on using the potential of Artificial Intelligence(AI)techniques in disease diagnosis to diagnose and detect the coronavirus.This paper developed deep learning(DL)and machine learning(ML)-based models using laboratory findings to diagnose COVID-19.Six different methods are used in this study:K-nearest neighbor(KNN),Decision Tree(DT)and Naive Bayes(NB)as a machine learning method,and Deep Neural Network(DNN),Convolutional Neural Network(CNN),and Long-term memory(LSTM)as DL methods.These approaches are evaluated using a dataset obtained from the Israelita Albert Einstein Hospital in Sao Paulo,Brazil.This data consists of 5644 laboratory results from different patients,with 10%being Covid-19 positive cases.The dataset includes 18 attributes that characterize COVID-19.We used accuracy,f1-score,recall and precision to evaluate the different developed systems.The obtained results confirmed these approaches’effectiveness in identifying COVID-19,However,ML-based classifiers couldn’t perform up to the standards achieved by DL-based models.Among all,NB performed worst by hardly achieving accuracy above 76%,Whereas KNN and DT compete by securing 84.56%and 85%accuracies,respectively.Besides these,DL models attained better performance as CNN,DNN and LSTM secured more than 90%accuracies.The LTSM outperformed all by achieving an accuracy of 96.78%and an F1-score of 96.58%.展开更多
Out of the plethora of peace theories,two stand out in particular:(a)the Kantian democratic theory of peace,which argues that peace depends on a league of democracies,and(b)the liberal economic theory of peace,that a ...Out of the plethora of peace theories,two stand out in particular:(a)the Kantian democratic theory of peace,which argues that peace depends on a league of democracies,and(b)the liberal economic theory of peace,that a free,open world market conduces to peace.In this essay,concrete examples are cited that would raise doubt on the validity of these theories.It then proceeds to examine whether culture would make a difference on the incidence of war.In this light,the Westphalian system of states is compared with the historical Chinese“tribute system”qua an inherent system of international relations.One distinct difference is found in the much lower incidence of wars in the latter system,as David Kang’s study identified that in 5 centuries(1368-1841)the Chinese tribute system had only two interstate wars within its circle of members,not counting the wars initiated by external,ex-regional Western powers.And,the religious wars that plagued the West were never found in the Chinese tribute system.In search of an answer to this almost incredible record of low incidence of war,this essay finds that the Confucian culture,with its emphasis on harmony and harmonization of opposites,in contradistinction to the teachings on conflict in Abrahamic cultures,seems to hold the key to an answer.If so,culture,rather than institutions(such as democracies,open world market,etc.)deserves to be seriously considered as a relevant factor contributing to peace.Most importantly,culture as such can be taught and disseminated,including through the classroom.展开更多
Recent work in decision neuroscience suggests that visual saliency can interact with reward-based choice,and the lateral intraparietal cortex(LIP)is implicated in this process.In this study,we recorded from LIP neuron...Recent work in decision neuroscience suggests that visual saliency can interact with reward-based choice,and the lateral intraparietal cortex(LIP)is implicated in this process.In this study,we recorded from LIP neurons while monkeys performed a two alternative choice task in which the reward and luminance associated with each offer were varied independently.We discovered that the animal’s choice was dictated by the reward amount while the luminance had a marginal effect.In the LIP,neuronal activity corresponded well with the animal’s choice pattern,in that a majority of reward-modulated neurons encoded the reward amount in the neuron’s preferred hemifield with a positive slope.In contrast,compared to their responses to low luminance,an approximately equal proportion of luminance-sensitive neurons responded to high luminance with increased or decreased activity,leading to a much weaker population-level response.Meanwhile,in the non-preferred hemifield,the strength of encoding for reward amount and luminance was positively correlated,suggesting the integration of these two factors in the LIP.Moreover,neurons encoding reward and luminance were homogeneously distributed along the anterior-posterior axis of the LIP.Overall,our study provides further evidence supporting the neural instantiation of a priority map in the LIP in reward-based decisions.展开更多
文摘The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical engineers.In this study,a new approach is proposed to predict Dh using gene expression programming(GEP).Based on statistical reasoning,individual models were developed for two topographies:free-face and gently sloping ground.Along with a comparison with conventional approaches for predicting the Dh,four additional regression-based soft computing models,i.e.Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimization regression(SMOR),and M5-tree,were developed and compared with the GEP model.The results indicate that the GEP models predict Dh with less bias,as evidenced by the root mean square error(RMSE)and mean absolute error(MAE)for training(i.e.1.092 and 0.815;and 0.643 and 0.526)and for testing(i.e.0.89 and 0.705;and 0.773 and 0.573)in free-face and gently sloping ground topographies,respectively.The overall performance for the free-face topology was ranked as follows:GEP>RVM>M5-tree>GPR>SMOR,with a total score of 40,32,24,15,and 10,respectively.For the gently sloping condition,the performance was ranked as follows:GEP>RVM>GPR>M5-tree>SMOR with a total score of 40,32,21,19,and 8,respectively.Finally,the results of the sensitivity analysis showed that for both free-face and gently sloping ground,the liquefiable layer thickness(T_(15))was the major parameter with percentage deterioration(%D)value of 99.15 and 90.72,respectively.
文摘The coronavirus(COVID-19)is a disease declared a global pan-demic that threatens the whole world.Since then,research has accelerated and varied to find practical solutions for the early detection and correct identification of this disease.Several researchers have focused on using the potential of Artificial Intelligence(AI)techniques in disease diagnosis to diagnose and detect the coronavirus.This paper developed deep learning(DL)and machine learning(ML)-based models using laboratory findings to diagnose COVID-19.Six different methods are used in this study:K-nearest neighbor(KNN),Decision Tree(DT)and Naive Bayes(NB)as a machine learning method,and Deep Neural Network(DNN),Convolutional Neural Network(CNN),and Long-term memory(LSTM)as DL methods.These approaches are evaluated using a dataset obtained from the Israelita Albert Einstein Hospital in Sao Paulo,Brazil.This data consists of 5644 laboratory results from different patients,with 10%being Covid-19 positive cases.The dataset includes 18 attributes that characterize COVID-19.We used accuracy,f1-score,recall and precision to evaluate the different developed systems.The obtained results confirmed these approaches’effectiveness in identifying COVID-19,However,ML-based classifiers couldn’t perform up to the standards achieved by DL-based models.Among all,NB performed worst by hardly achieving accuracy above 76%,Whereas KNN and DT compete by securing 84.56%and 85%accuracies,respectively.Besides these,DL models attained better performance as CNN,DNN and LSTM secured more than 90%accuracies.The LTSM outperformed all by achieving an accuracy of 96.78%and an F1-score of 96.58%.
文摘Out of the plethora of peace theories,two stand out in particular:(a)the Kantian democratic theory of peace,which argues that peace depends on a league of democracies,and(b)the liberal economic theory of peace,that a free,open world market conduces to peace.In this essay,concrete examples are cited that would raise doubt on the validity of these theories.It then proceeds to examine whether culture would make a difference on the incidence of war.In this light,the Westphalian system of states is compared with the historical Chinese“tribute system”qua an inherent system of international relations.One distinct difference is found in the much lower incidence of wars in the latter system,as David Kang’s study identified that in 5 centuries(1368-1841)the Chinese tribute system had only two interstate wars within its circle of members,not counting the wars initiated by external,ex-regional Western powers.And,the religious wars that plagued the West were never found in the Chinese tribute system.In search of an answer to this almost incredible record of low incidence of war,this essay finds that the Confucian culture,with its emphasis on harmony and harmonization of opposites,in contradistinction to the teachings on conflict in Abrahamic cultures,seems to hold the key to an answer.If so,culture,rather than institutions(such as democracies,open world market,etc.)deserves to be seriously considered as a relevant factor contributing to peace.Most importantly,culture as such can be taught and disseminated,including through the classroom.
基金supported by the National Science and Technology Innovation 2030 Major Program(2021ZD0203700/2021ZD0203702)the Shanghai Municipal Science and Technology Major Project(2018SHZDZX05)+1 种基金the Program of Introducing Talents of Discipline to Universities(Ministry of Education of China,Base B16018)NYU Shanghai Boost Fund.
文摘Recent work in decision neuroscience suggests that visual saliency can interact with reward-based choice,and the lateral intraparietal cortex(LIP)is implicated in this process.In this study,we recorded from LIP neurons while monkeys performed a two alternative choice task in which the reward and luminance associated with each offer were varied independently.We discovered that the animal’s choice was dictated by the reward amount while the luminance had a marginal effect.In the LIP,neuronal activity corresponded well with the animal’s choice pattern,in that a majority of reward-modulated neurons encoded the reward amount in the neuron’s preferred hemifield with a positive slope.In contrast,compared to their responses to low luminance,an approximately equal proportion of luminance-sensitive neurons responded to high luminance with increased or decreased activity,leading to a much weaker population-level response.Meanwhile,in the non-preferred hemifield,the strength of encoding for reward amount and luminance was positively correlated,suggesting the integration of these two factors in the LIP.Moreover,neurons encoding reward and luminance were homogeneously distributed along the anterior-posterior axis of the LIP.Overall,our study provides further evidence supporting the neural instantiation of a priority map in the LIP in reward-based decisions.
