The evolutionary dynamics of behavioral traits reflect phenotypic and genetic changes. Methodological difficulties in analyzing the genetic dynamics of complex traits have left open questions on the mechanisms that ha...The evolutionary dynamics of behavioral traits reflect phenotypic and genetic changes. Methodological difficulties in analyzing the genetic dynamics of complex traits have left open questions on the mechanisms that have shaped complex beha- viors and cognitive abilities. A strategy to investigate the change of behavior across generations is to assume that genetic con- straints have a negligible role in evolution （the phenotypic gambit） and focus on the phenotype as a proxy for genetic evolution. Empirical evidence and technologic advances in genomics question the choice of neglecting the genetic underlying the dynamics of behavioral evolution. I first discuss the relevance of genetic factors - e.g. genetic variability, genetic linkage, gene interactions - in shaping evolution, showing the importance of taking genetic factors into account when dealing with evolutionary dynamics. I subsequently describe the recent advancements in genetics and genomics that make the investigation of the ongoing evolutionary process of behavioral traits finally attainable. In particular, by applying genomic resequencing to experimental evolution - a me- thod called Evolve ＆ Resequence - it is possible to monitor at the same time phenotypic and genomie changes in populations exposed to controlled selective pressures. Experimental evolution of associative learning, a well-known trait that promptly re- sponds to selection, is a convenient model to illustrate this approach applied to behavior and cognition. Taking into account the recent achievements of the field, I discuss how to design and conduct an effective Evolve ＆ Resequence study on associative learning in Drosophila. By integrating phenotypic and genomic data in the investigation of evolutionary dynamics, new insights can be gained on longstanding questions such as the modularity of mind and its evolution .展开更多
When a population structure is modelled as a square lattice,the cooperation may be improved for an evolutionary prisoner dilemma game or be inhibited for an evolutionary snowdrift game.In this work,we investigate coop...When a population structure is modelled as a square lattice,the cooperation may be improved for an evolutionary prisoner dilemma game or be inhibited for an evolutionary snowdrift game.In this work,we investigate cooperation in a population on a square lattice where the interaction among players contains both prisoner dilemma game and snowdrift game.The heterogeneity in interaction is introduced to the population in two different ways:the heterogenous character of interaction assigned to every player(HCP) or the heterogenous character of interaction assigned to every link between any two players(HCL).The resonant enhancement of cooperation in the case of HCP is observed while the resonant inhibition of cooperation in the case of HCL is prominent.The explanations on the enhancement or inhibition of cooperation are presented for these two cases.展开更多
Taxonomy is generated to effectively organize and access large volume of data. A taxonomy is a way of representing concepts that exist in data. It needs to continuously evolve to reflect changes in data. Existing auto...Taxonomy is generated to effectively organize and access large volume of data. A taxonomy is a way of representing concepts that exist in data. It needs to continuously evolve to reflect changes in data. Existing automatic taxonomy generation techniques do not handle the evolution of data; therefore, the generated taxonomies do not truly represent the data. The evolution of data can be handled by either regenerating taxonomy from scratch, or allowing taxonomy to incrementally evolve whenever changes occur in the data. The former approach is not economical in terms of time and resources. A taxonomy incremental evolution（TIE） algorithm, as proposed, is a novel attempt to handle the data that evolve in time. It serves as a layer over an existing clustering-based taxonomy generation technique and allows an existing taxonomy to incrementally evolve. The algorithm was evaluated in research articles selected from the computing domain. It was found that the taxonomy using the algorithm that evolved with data needed considerably shorter time, and had better quality per unit time as compared to the taxonomy regenerated from scratch.展开更多
The adiabatic theorem describes the time evolution of the pure state and gives an adiabatic approximate solution to the Schrrdinger equation by choosing a single eigenstate of the Hamiltonian as the initial state. In ...The adiabatic theorem describes the time evolution of the pure state and gives an adiabatic approximate solution to the Schrrdinger equation by choosing a single eigenstate of the Hamiltonian as the initial state. In quantum systems, states are divided into pure states （unite vectors） and mixed states （density matrices, i.e., positive operators with trace one）. Accordingly, mixed states have their own corresponding time evolution, which is described by the von Neumann equation. In this paper, we discuss the quantitative conditions for the time evolution of mixed states in terms of the von Neumann equation. First, we introduce the definitions for uniformly slowly evolving and δ-uniformly slowly evolving with respect to mixed states, then we present a necessary and sufficient condition for the Hamiltonian of the system to be uniformly slowly evolving and we obtain some upper bounds for the adiabatic approximate error. Lastly, we illustrate our results in an example.展开更多
文摘The evolutionary dynamics of behavioral traits reflect phenotypic and genetic changes. Methodological difficulties in analyzing the genetic dynamics of complex traits have left open questions on the mechanisms that have shaped complex beha- viors and cognitive abilities. A strategy to investigate the change of behavior across generations is to assume that genetic con- straints have a negligible role in evolution （the phenotypic gambit） and focus on the phenotype as a proxy for genetic evolution. Empirical evidence and technologic advances in genomics question the choice of neglecting the genetic underlying the dynamics of behavioral evolution. I first discuss the relevance of genetic factors - e.g. genetic variability, genetic linkage, gene interactions - in shaping evolution, showing the importance of taking genetic factors into account when dealing with evolutionary dynamics. I subsequently describe the recent advancements in genetics and genomics that make the investigation of the ongoing evolutionary process of behavioral traits finally attainable. In particular, by applying genomic resequencing to experimental evolution - a me- thod called Evolve ＆ Resequence - it is possible to monitor at the same time phenotypic and genomie changes in populations exposed to controlled selective pressures. Experimental evolution of associative learning, a well-known trait that promptly re- sponds to selection, is a convenient model to illustrate this approach applied to behavior and cognition. Taking into account the recent achievements of the field, I discuss how to design and conduct an effective Evolve ＆ Resequence study on associative learning in Drosophila. By integrating phenotypic and genomic data in the investigation of evolutionary dynamics, new insights can be gained on longstanding questions such as the modularity of mind and its evolution .
基金Supported by Natural Science Foundation of China under Grant No. 11147112
文摘When a population structure is modelled as a square lattice,the cooperation may be improved for an evolutionary prisoner dilemma game or be inhibited for an evolutionary snowdrift game.In this work,we investigate cooperation in a population on a square lattice where the interaction among players contains both prisoner dilemma game and snowdrift game.The heterogeneity in interaction is introduced to the population in two different ways:the heterogenous character of interaction assigned to every player(HCP) or the heterogenous character of interaction assigned to every link between any two players(HCL).The resonant enhancement of cooperation in the case of HCP is observed while the resonant inhibition of cooperation in the case of HCL is prominent.The explanations on the enhancement or inhibition of cooperation are presented for these two cases.
文摘Taxonomy is generated to effectively organize and access large volume of data. A taxonomy is a way of representing concepts that exist in data. It needs to continuously evolve to reflect changes in data. Existing automatic taxonomy generation techniques do not handle the evolution of data; therefore, the generated taxonomies do not truly represent the data. The evolution of data can be handled by either regenerating taxonomy from scratch, or allowing taxonomy to incrementally evolve whenever changes occur in the data. The former approach is not economical in terms of time and resources. A taxonomy incremental evolution（TIE） algorithm, as proposed, is a novel attempt to handle the data that evolve in time. It serves as a layer over an existing clustering-based taxonomy generation technique and allows an existing taxonomy to incrementally evolve. The algorithm was evaluated in research articles selected from the computing domain. It was found that the taxonomy using the algorithm that evolved with data needed considerably shorter time, and had better quality per unit time as compared to the taxonomy regenerated from scratch.
基金supported by the National Natural Science Foundation of China(Grant Nos.11601300,and 11571213)the Fundamental Research Funds for the Central Universities(Grant No.GK201703093)
文摘The adiabatic theorem describes the time evolution of the pure state and gives an adiabatic approximate solution to the Schrrdinger equation by choosing a single eigenstate of the Hamiltonian as the initial state. In quantum systems, states are divided into pure states （unite vectors） and mixed states （density matrices, i.e., positive operators with trace one）. Accordingly, mixed states have their own corresponding time evolution, which is described by the von Neumann equation. In this paper, we discuss the quantitative conditions for the time evolution of mixed states in terms of the von Neumann equation. First, we introduce the definitions for uniformly slowly evolving and δ-uniformly slowly evolving with respect to mixed states, then we present a necessary and sufficient condition for the Hamiltonian of the system to be uniformly slowly evolving and we obtain some upper bounds for the adiabatic approximate error. Lastly, we illustrate our results in an example.
