Mushrooms have a remarkable scientific value due to their nutritional, medicinal properties and industrial applications in enzyme production, so that effort in the maintenance of native wild mushroom varieties is incr...Mushrooms have a remarkable scientific value due to their nutritional, medicinal properties and industrial applications in enzyme production, so that effort in the maintenance of native wild mushroom varieties is increasing. The present study focuses on the use of Random Amplified Polymorphic DNA (RAPD) markers for biodiversity measure of wild mushroom species of the Northwest mountainous region of Greece. Data mining of similarity matrices from RAPD analysis was used to extract measurable entropy parameters for mushroom biodiversity monitoring based on Shannon’s information entropy. Shannon information index provides an easy assessment of the entropy of the genetic information of the germplasm per mushroom species while the total equitability index (E<sub>H</sub>) = 0.871 offers an overall estimation of the genetic variation evenness of all species in the population of the studied mushrooms. Application of RAPDs with parallel entropy analysis is an easily applicable and low-cost valuable technology in environmental monitoring, using genetic information of wild mushroom species as an indicator that can lead to future actions in biodiversity maintenance and germplasm protection. The provided methodology can serve as a pilot procedure enriched with other environmental factors to monitor and protect wild mushroom communities native to the Greek countryside or in any part of the world and provide comparable results about biodiversity from different regions using common entropy indices.展开更多
Shannon’s information measure is a crucial concept in Information Theory. And the research, for the mathematics structure of Shannon’s information measure, is to recognize the essence of information measure. The lin...Shannon’s information measure is a crucial concept in Information Theory. And the research, for the mathematics structure of Shannon’s information measure, is to recognize the essence of information measure. The linear relation between Shannon’s information measures and some signed measure space by using the formal symbols substitution rule is discussed. Furthermore, the coefficient matrix recurrent formula of the linear relation is obtained. Then the coefficient matrix is proved to be invertible via mathematical induction. This shows that the linear relation is one-to-one, and according to this, it can be concluded that a compact space can be generated from Shannon’s information measures.展开更多
This paper derives the variance of the information content and develops its statistical inference method. We describe the relations between information content and sensitivity, specificity, efficiency, prevalence rate...This paper derives the variance of the information content and develops its statistical inference method. We describe the relations between information content and sensitivity, specificity, efficiency, prevalence rate. If sensitivity, specificity and efficiency are fixed, the closer to 0. 5 the prevalence rate is, the more the information content. If prevalence rate and efficiency are fixed, the closer to each other the sensitivity and specificity are, the more the information content. We compare the power of information content method, efficiecy test, Youden's index test and kappa coefficient method. The information content method has higher power than the other methods in most conditions. It is especially sensitive to the difference between two sensitivities. It comes to conclusion that the information content method has more virtues than the other methods mentioned in this paper.展开更多
Federated learning(FL)is an emerging privacy-preserving distributed computing paradigm,enabling numerous clients to collaboratively train machine learning models without the necessity of transmitting clients’private ...Federated learning(FL)is an emerging privacy-preserving distributed computing paradigm,enabling numerous clients to collaboratively train machine learning models without the necessity of transmitting clients’private datasets to the central server.Unlike most existing research where the local datasets of clients are assumed to be unchanged over time throughout the whole FL process,our study addresses such scenarios in this paper where clients’datasets need to be updated periodically,and the server can incentivize clients to employ as fresh as possible datasets for local model training.Our primary objective is to design a client selection strategy to minimize the loss of the global model for FL loss within a constrained budget.To this end,we introduce the concept of“Age of Information”(AoI)to quantitatively assess the freshness of local datasets and conduct a theoretical analysis of the convergence bound in our AoI-aware FL system.Based on the convergence bound,we further formulate our problem as a restless multi-armed bandit(RMAB)problem.Next,we relax the RMAB problem and apply the Lagrangian Dual approach to decouple it into multiple subproblems.Finally,we propose a Whittle’s Index Based Client Selection(WICS)algorithm to determine the set of selected clients.In addition,comprehensive simulations substantiate that the proposed algorithm can effectively reduce training loss and enhance the learning accuracy compared with some state-of-the-art methods.展开更多
文摘Mushrooms have a remarkable scientific value due to their nutritional, medicinal properties and industrial applications in enzyme production, so that effort in the maintenance of native wild mushroom varieties is increasing. The present study focuses on the use of Random Amplified Polymorphic DNA (RAPD) markers for biodiversity measure of wild mushroom species of the Northwest mountainous region of Greece. Data mining of similarity matrices from RAPD analysis was used to extract measurable entropy parameters for mushroom biodiversity monitoring based on Shannon’s information entropy. Shannon information index provides an easy assessment of the entropy of the genetic information of the germplasm per mushroom species while the total equitability index (E<sub>H</sub>) = 0.871 offers an overall estimation of the genetic variation evenness of all species in the population of the studied mushrooms. Application of RAPDs with parallel entropy analysis is an easily applicable and low-cost valuable technology in environmental monitoring, using genetic information of wild mushroom species as an indicator that can lead to future actions in biodiversity maintenance and germplasm protection. The provided methodology can serve as a pilot procedure enriched with other environmental factors to monitor and protect wild mushroom communities native to the Greek countryside or in any part of the world and provide comparable results about biodiversity from different regions using common entropy indices.
基金the Science and Technology Research Project of Education Department, Heilongjiang Province (Grant No.11513095)the Science andTechnology Foundation of Heilongjiang Institute of Science and Technology(Grant No.04 -25).
文摘Shannon’s information measure is a crucial concept in Information Theory. And the research, for the mathematics structure of Shannon’s information measure, is to recognize the essence of information measure. The linear relation between Shannon’s information measures and some signed measure space by using the formal symbols substitution rule is discussed. Furthermore, the coefficient matrix recurrent formula of the linear relation is obtained. Then the coefficient matrix is proved to be invertible via mathematical induction. This shows that the linear relation is one-to-one, and according to this, it can be concluded that a compact space can be generated from Shannon’s information measures.
文摘This paper derives the variance of the information content and develops its statistical inference method. We describe the relations between information content and sensitivity, specificity, efficiency, prevalence rate. If sensitivity, specificity and efficiency are fixed, the closer to 0. 5 the prevalence rate is, the more the information content. If prevalence rate and efficiency are fixed, the closer to each other the sensitivity and specificity are, the more the information content. We compare the power of information content method, efficiecy test, Youden's index test and kappa coefficient method. The information content method has higher power than the other methods in most conditions. It is especially sensitive to the difference between two sensitivities. It comes to conclusion that the information content method has more virtues than the other methods mentioned in this paper.
基金supported by the National Natural Science Foundation of China under Grant No.62172386the Natural Science Foundation of Jiangsu Province of China under Grant No.BK20231212the Teaching Research Project of the Education Department of Anhui Province of China under Grant No.2021jyxm1738.
文摘Federated learning(FL)is an emerging privacy-preserving distributed computing paradigm,enabling numerous clients to collaboratively train machine learning models without the necessity of transmitting clients’private datasets to the central server.Unlike most existing research where the local datasets of clients are assumed to be unchanged over time throughout the whole FL process,our study addresses such scenarios in this paper where clients’datasets need to be updated periodically,and the server can incentivize clients to employ as fresh as possible datasets for local model training.Our primary objective is to design a client selection strategy to minimize the loss of the global model for FL loss within a constrained budget.To this end,we introduce the concept of“Age of Information”(AoI)to quantitatively assess the freshness of local datasets and conduct a theoretical analysis of the convergence bound in our AoI-aware FL system.Based on the convergence bound,we further formulate our problem as a restless multi-armed bandit(RMAB)problem.Next,we relax the RMAB problem and apply the Lagrangian Dual approach to decouple it into multiple subproblems.Finally,we propose a Whittle’s Index Based Client Selection(WICS)algorithm to determine the set of selected clients.In addition,comprehensive simulations substantiate that the proposed algorithm can effectively reduce training loss and enhance the learning accuracy compared with some state-of-the-art methods.
