Heart monitoring improves life quality.Electrocardiograms(ECGs or EKGs)detect heart irregularities.Machine learning algorithms can create a few ECG diagnosis processing methods.The first method uses raw ECG and time-s...Heart monitoring improves life quality.Electrocardiograms(ECGs or EKGs)detect heart irregularities.Machine learning algorithms can create a few ECG diagnosis processing methods.The first method uses raw ECG and time-series data.The second method classifies the ECG by patient experience.The third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer information.Because ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and diagnosis.Classifications using all three approaches have not been examined till now.Several researchers found that Machine Learning(ML)techniques can improve ECG classification.This study will compare popular machine learning techniques to evaluate ECG features.Four algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization results.SVM plus prior knowledge has the highest accuracy(99%)of the four ML methods.QRS characteristics failed to identify signals without chaos theory.With 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.展开更多
In this article, we review our recent work on quantum phase transition in two-dimensional strongly correlated fermion systems. We discuss the metal insulator transition properties of these systems by calculating the d...In this article, we review our recent work on quantum phase transition in two-dimensional strongly correlated fermion systems. We discuss the metal insulator transition properties of these systems by calculating the density of states, double occupancy, and Fermi surface evolution using a com- bination of the cellular dynamical mean-field theory （CDMFT） and the continuous-time quantum Monte Carlo algorithm. Furthermore, we explore the magnetic properties of each state by defining magnetic order parameters. Rich phase diagrams with many intriguing quantum states, including antiferromagnetic metal, paramagnetic metal, Kondo metal, and ferromagnetic insulator, were found for the two-dimensional lattices with strongly correlated fermions. We believe that our results would lead to a better understanding of the properties of real materials.展开更多
For a revised model of Caldentey and Stacchetti(Econometrica,2010)in continuous-time insider trading with a random deadline which allows market makers to observe some information on a risky asset,a closed form of its ...For a revised model of Caldentey and Stacchetti(Econometrica,2010)in continuous-time insider trading with a random deadline which allows market makers to observe some information on a risky asset,a closed form of its market equilibrium consisting of optimal insider trading intensity and market liquidity is obtained by maximum principle method.It shows that in the equilibrium,(i)as time goes by,the optimal insider trading intensity is exponentially increasing even up to infinity while both the market liquidity and the residual information are exponentially decreasing even down to zero;(ii)the more accurate information observed by market makers,the stronger optimal insider trading intensity is such that the total expect profit of the insider is decreasing even go to zero while both the market liquidity and the residual information are decreasing;(iii)the longer the mean of random time,the weaker the optimal insider trading intensity is while the more both the residual information and the expected profit are,but there is a threshold of trading time,half of the mean of the random time,such that if and only if after it the market liquidity is increasing with the mean of random time increasing.展开更多
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups(Grant Number RGP.2/246/44),B.B.,and https://www.kku.edu.sa/en.
文摘Heart monitoring improves life quality.Electrocardiograms(ECGs or EKGs)detect heart irregularities.Machine learning algorithms can create a few ECG diagnosis processing methods.The first method uses raw ECG and time-series data.The second method classifies the ECG by patient experience.The third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer information.Because ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and diagnosis.Classifications using all three approaches have not been examined till now.Several researchers found that Machine Learning(ML)techniques can improve ECG classification.This study will compare popular machine learning techniques to evaluate ECG features.Four algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization results.SVM plus prior knowledge has the highest accuracy(99%)of the four ML methods.QRS characteristics failed to identify signals without chaos theory.With 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.
基金I am so grateful for the great contribu- tions and beneficial communications from Yao-Hua Chen, Hai-Di Liu, and Heng-Fu Lin while I am preparing this review paper. This work was supported by the National Science Foundation of China （Grant Nos. 11174169, 11234007, and 51471093）.
文摘In this article, we review our recent work on quantum phase transition in two-dimensional strongly correlated fermion systems. We discuss the metal insulator transition properties of these systems by calculating the density of states, double occupancy, and Fermi surface evolution using a com- bination of the cellular dynamical mean-field theory （CDMFT） and the continuous-time quantum Monte Carlo algorithm. Furthermore, we explore the magnetic properties of each state by defining magnetic order parameters. Rich phase diagrams with many intriguing quantum states, including antiferromagnetic metal, paramagnetic metal, Kondo metal, and ferromagnetic insulator, were found for the two-dimensional lattices with strongly correlated fermions. We believe that our results would lead to a better understanding of the properties of real materials.
基金Supported by the National Natural Science Foundation of China(11861025)Guizhou QKHPTRC[2018]5769。
文摘For a revised model of Caldentey and Stacchetti(Econometrica,2010)in continuous-time insider trading with a random deadline which allows market makers to observe some information on a risky asset,a closed form of its market equilibrium consisting of optimal insider trading intensity and market liquidity is obtained by maximum principle method.It shows that in the equilibrium,(i)as time goes by,the optimal insider trading intensity is exponentially increasing even up to infinity while both the market liquidity and the residual information are exponentially decreasing even down to zero;(ii)the more accurate information observed by market makers,the stronger optimal insider trading intensity is such that the total expect profit of the insider is decreasing even go to zero while both the market liquidity and the residual information are decreasing;(iii)the longer the mean of random time,the weaker the optimal insider trading intensity is while the more both the residual information and the expected profit are,but there is a threshold of trading time,half of the mean of the random time,such that if and only if after it the market liquidity is increasing with the mean of random time increasing.
