Rare bird has long been considered an important in the field of airport security,biological conservation,environmental monitoring,and so on.With the development and popularization of IOT-based video surveillance,all d...Rare bird has long been considered an important in the field of airport security,biological conservation,environmental monitoring,and so on.With the development and popularization of IOT-based video surveillance,all day and weather unattended bird monitoring becomes possible.However,the current mainstream bird recognition methods are mostly based on deep learning.These will be appropriate for big data applications,but the training sample size for rare bird is usually very short.Therefore,this paper presents a new sparse recognition model via improved part detection and our previous dictionary learning.There are two achievements in our work:(1)after the part localization with selective search,the gist feature of all bird image parts will be fused as data description;(2)the fused gist feature needs to be learned through our proposed intraclass dictionary learning with regularized K-singular value decomposition.According to above two innovations,the rare bird sparse recognition will be implemented by solving one l1-norm optimization.In the experiment with Caltech-UCSD Birds-200-2011 dataset,results show the proposed method can have better recognition performance than other SR methods for rare bird task with small sample size.展开更多
The paper considers a high-dimensional likelihood ratio(LR)test on the intraclass correlation structure of the multivariate normal population.When the dimension p and sample size N satisfy N−1>p→∞,it is proved th...The paper considers a high-dimensional likelihood ratio(LR)test on the intraclass correlation structure of the multivariate normal population.When the dimension p and sample size N satisfy N−1>p→∞,it is proved that the logarithmic LR statistic asymptotically obeys Gaussian distribution,and the explicit expressions of the mean and the variance are also obtained.The simulations demonstrate that our high-dimensional LR test method outperforms the traditional Chi-square approximation method or F-approximation method,and performs as efficient as the accurate high-dimensional Edgeworth expansion method and the more accurate high-dimensional Edgeworth expansion method in analyzing the intraclass covariance structure of highdimensional data.展开更多
The intraclass correlation coefficient (ICC) plays an important role in various fields of study asa coefficient of reliability. In this paper, we consider objective Bayesian analysis for the ICCin the context of norma...The intraclass correlation coefficient (ICC) plays an important role in various fields of study asa coefficient of reliability. In this paper, we consider objective Bayesian analysis for the ICCin the context of normal linear regression model. We first derive two objective priors for theunknown parameters and show that both result in proper posterior distributions. Within aBayesian decision-theoretic framework, we then propose an objective Bayesian solution to theproblems of hypothesis testing and point estimation of the ICC based on a combined use of theintrinsic discrepancy loss function and objective priors. The proposed solution has an appealinginvariance property under one-to-one reparametrisation of the quantity of interest. Simulationstudies are conducted to investigate the performance the proposed solution. Finally, a real dataapplication is provided for illustrative purposes.展开更多
Multilevel modeling (MLM) has emerged as a powerful statistical framework for analyzing complex data structures with nested relationships. With its hierarchical modeling approach, MLM enables researchers to account fo...Multilevel modeling (MLM) has emerged as a powerful statistical framework for analyzing complex data structures with nested relationships. With its hierarchical modeling approach, MLM enables researchers to account for dependencies and variations within and between different levels of a hierarchy. By explicitly modeling these relationships, MLM provides a robust and accurate analysis of data. It has become increasingly popular in the field of education. MLM enables the investigation of various research issues, the evaluation of individual and group-level indicators, and the calculation of both fixed and random effects. Overall, MLM revolutionizes data analysis by uncovering patterns, understanding contextual effects, and making more precise statistical inferences in complex datasets. For fitting multilevel models in R, use lmer function provided by lme4 package. Through this examination, the use of a multilevel model is expected to increase and revolutionize data analysis and decision-making. The Constrained Intermediate Model (CIM) and Augmented Intermediate model (AIM) deviation are compared using the Likelihood-ratio (LR) test and the ANOVA function. This study analyzes student results from the University of Agriculture Faisalabad, collected via stratified random sampling. A linear mixed-effect model under multilevel modeling estimates the impact on CGPA, considering department, gender, intermediate marks, and entry test scores. These results indicate that Entry test is a significant predictor of CGPA, but the effect of department identifier CMC on CGPA is not statistically significant.展开更多
Objective To evaluate the reliability of three dimensional spiral fast spin echo pseudo-continuous arterial spin labeling(3 D pc-ASL) in measuring cerebral blood flow(CBF) with different post-labeling delay time(PLD) ...Objective To evaluate the reliability of three dimensional spiral fast spin echo pseudo-continuous arterial spin labeling(3 D pc-ASL) in measuring cerebral blood flow(CBF) with different post-labeling delay time(PLD) in the resting state and the right finger taping state.Methods 3 D pc-ASL and three dimensional T1-weighted fast spoiled gradient recalled echo(3 D T1-FSPGR) sequence were applied to eight healthy subjects twice at the same time each day for one week interval. ASL data acquisition was performed with post-labeling delay time(PLD) 1.5 seconds and 2.0 seconds in the resting state and the right finger taping state respectively. CBF mapping was calculated and CBF value of both the gray matter(GM) and white matter(WM) was automatically extracted. The reliability was evaluated using the intraclass correlation coefficient(ICC) and Bland and Altman plot.Results ICC of the GM(0.84) and WM(0.92) was lower at PLD 1.5 seconds than that(GM, 0.88; WM, 0.94) at PLD 2.0 seconds in the resting state, and ICC of GM(0.88) was higher in the right finger taping state than that in the resting state at PLD 1.5 seconds. ICC of the GM and WM was 0.71 and 0.78 for PLD 1.5 seconds and PLD 2.0 seconds in the resting state at the first scan, and ICC of the GM and WM was 0.83 and 0.79 at the second scan, respectively.Conclusion This work demonstrated that 3 D pc-ASL might be a reliable imaging technique to measure CBF over the whole brain at different PLD in the resting state or controlled state.展开更多
文摘Rare bird has long been considered an important in the field of airport security,biological conservation,environmental monitoring,and so on.With the development and popularization of IOT-based video surveillance,all day and weather unattended bird monitoring becomes possible.However,the current mainstream bird recognition methods are mostly based on deep learning.These will be appropriate for big data applications,but the training sample size for rare bird is usually very short.Therefore,this paper presents a new sparse recognition model via improved part detection and our previous dictionary learning.There are two achievements in our work:(1)after the part localization with selective search,the gist feature of all bird image parts will be fused as data description;(2)the fused gist feature needs to be learned through our proposed intraclass dictionary learning with regularized K-singular value decomposition.According to above two innovations,the rare bird sparse recognition will be implemented by solving one l1-norm optimization.In the experiment with Caltech-UCSD Birds-200-2011 dataset,results show the proposed method can have better recognition performance than other SR methods for rare bird task with small sample size.
基金Supported by National Natural Science Foundation of China(Grant No.11401169)Natural Science Foundation of Henan Province of China(Grant No.202300410089).
文摘The paper considers a high-dimensional likelihood ratio(LR)test on the intraclass correlation structure of the multivariate normal population.When the dimension p and sample size N satisfy N−1>p→∞,it is proved that the logarithmic LR statistic asymptotically obeys Gaussian distribution,and the explicit expressions of the mean and the variance are also obtained.The simulations demonstrate that our high-dimensional LR test method outperforms the traditional Chi-square approximation method or F-approximation method,and performs as efficient as the accurate high-dimensional Edgeworth expansion method and the more accurate high-dimensional Edgeworth expansion method in analyzing the intraclass covariance structure of highdimensional data.
文摘The intraclass correlation coefficient (ICC) plays an important role in various fields of study asa coefficient of reliability. In this paper, we consider objective Bayesian analysis for the ICCin the context of normal linear regression model. We first derive two objective priors for theunknown parameters and show that both result in proper posterior distributions. Within aBayesian decision-theoretic framework, we then propose an objective Bayesian solution to theproblems of hypothesis testing and point estimation of the ICC based on a combined use of theintrinsic discrepancy loss function and objective priors. The proposed solution has an appealinginvariance property under one-to-one reparametrisation of the quantity of interest. Simulationstudies are conducted to investigate the performance the proposed solution. Finally, a real dataapplication is provided for illustrative purposes.
文摘Multilevel modeling (MLM) has emerged as a powerful statistical framework for analyzing complex data structures with nested relationships. With its hierarchical modeling approach, MLM enables researchers to account for dependencies and variations within and between different levels of a hierarchy. By explicitly modeling these relationships, MLM provides a robust and accurate analysis of data. It has become increasingly popular in the field of education. MLM enables the investigation of various research issues, the evaluation of individual and group-level indicators, and the calculation of both fixed and random effects. Overall, MLM revolutionizes data analysis by uncovering patterns, understanding contextual effects, and making more precise statistical inferences in complex datasets. For fitting multilevel models in R, use lmer function provided by lme4 package. Through this examination, the use of a multilevel model is expected to increase and revolutionize data analysis and decision-making. The Constrained Intermediate Model (CIM) and Augmented Intermediate model (AIM) deviation are compared using the Likelihood-ratio (LR) test and the ANOVA function. This study analyzes student results from the University of Agriculture Faisalabad, collected via stratified random sampling. A linear mixed-effect model under multilevel modeling estimates the impact on CGPA, considering department, gender, intermediate marks, and entry test scores. These results indicate that Entry test is a significant predictor of CGPA, but the effect of department identifier CMC on CGPA is not statistically significant.
基金Supported by the Foundation for Medical and Health Sci&Tech Innovation Project of Sanya(2016YW37)the Special Financial Grant from China Postdoctoral Science Foundation(2014T70960)
文摘Objective To evaluate the reliability of three dimensional spiral fast spin echo pseudo-continuous arterial spin labeling(3 D pc-ASL) in measuring cerebral blood flow(CBF) with different post-labeling delay time(PLD) in the resting state and the right finger taping state.Methods 3 D pc-ASL and three dimensional T1-weighted fast spoiled gradient recalled echo(3 D T1-FSPGR) sequence were applied to eight healthy subjects twice at the same time each day for one week interval. ASL data acquisition was performed with post-labeling delay time(PLD) 1.5 seconds and 2.0 seconds in the resting state and the right finger taping state respectively. CBF mapping was calculated and CBF value of both the gray matter(GM) and white matter(WM) was automatically extracted. The reliability was evaluated using the intraclass correlation coefficient(ICC) and Bland and Altman plot.Results ICC of the GM(0.84) and WM(0.92) was lower at PLD 1.5 seconds than that(GM, 0.88; WM, 0.94) at PLD 2.0 seconds in the resting state, and ICC of GM(0.88) was higher in the right finger taping state than that in the resting state at PLD 1.5 seconds. ICC of the GM and WM was 0.71 and 0.78 for PLD 1.5 seconds and PLD 2.0 seconds in the resting state at the first scan, and ICC of the GM and WM was 0.83 and 0.79 at the second scan, respectively.Conclusion This work demonstrated that 3 D pc-ASL might be a reliable imaging technique to measure CBF over the whole brain at different PLD in the resting state or controlled state.
