为了认识痕量元素As在飞灰中的富集特性,利用密度泛函理论研究了砷的典型氧化物AsO在飞灰中的主要成分SiO_2模型上的吸附机理,对优化后的吸附构型进行能量计算、AIM理论、Mulliken电荷分析以及定域化轨道指示函数(LOL)填色图分析,剖析了...为了认识痕量元素As在飞灰中的富集特性,利用密度泛函理论研究了砷的典型氧化物AsO在飞灰中的主要成分SiO_2模型上的吸附机理,对优化后的吸附构型进行能量计算、AIM理论、Mulliken电荷分析以及定域化轨道指示函数(LOL)填色图分析,剖析了AsO与SiO_2表面的相互作用。结果表明,AsO在无定型SiO_2表面的缺陷位的吸附能均大于50 k J/mol,吸附构型均为典型的化学吸附。在无定型SiO_2缺陷活性位点形成的As-Si键、Si-O键和As-O键强度较大,均属于共价键;SiO_2与AsO之间为共价相互作用。展开更多
It is unpractical to learn the optimal structure of a big Bayesian network(BN)by exhausting the feasible structures,since the number of feasible structures is super exponential on the number of nodes.This paper propos...It is unpractical to learn the optimal structure of a big Bayesian network(BN)by exhausting the feasible structures,since the number of feasible structures is super exponential on the number of nodes.This paper proposes an approach to layer nodes of a BN by using the conditional independence testing.The parents of a node layer only belong to the layer,or layers who have priority over the layer.When a set of nodes has been layered,the number of feasible structures over the nodes can be remarkably reduced,which makes it possible to learn optimal BN structures for bigger sizes of nodes by accurate algorithms.Integrating the dynamic programming(DP)algorithm with the layering approach,we propose a hybrid algorithm—layered optimal learning(LOL)to learn BN structures.Benefitted by the layering approach,the complexity of the DP algorithm reduces to O(ρ2^n?1)from O(n2^n?1),whereρ<n.Meanwhile,the memory requirements for storing intermediate results are limited to O(C k#/k#^2 )from O(Cn/n^2 ),where k#<n.A case study on learning a standard BN with 50 nodes is conducted.The results demonstrate the superiority of the LOL algorithm,with respect to the Bayesian information criterion(BIC)score criterion,over the hill-climbing,max-min hill-climbing,PC,and three-phrase dependency analysis algorithms.展开更多
In large-scale distributed simulation, thousands of objects keep moving and interacting in a virtual environment, which produces a mass of messages. High level architecture （HLA） is the prevailing standard for model...In large-scale distributed simulation, thousands of objects keep moving and interacting in a virtual environment, which produces a mass of messages. High level architecture （HLA） is the prevailing standard for modeling and simulation. It specifies two publish-subscribe mechanisms for message filtering： class-based and value-based. However, the two mechanisms can only judge whether a message is relevant to a subscriber or not. Lacking of the ability to evaluate the relevance, all relevant messages are delivered with the same priority even when congestion occurs. It significantly limits the scalability and performance of distributed simulation. Aiming to solve the relevance evaluation problem, speed up message filtering, and filter more unnecessary messages, a new relevance evaluation mechanism Layer of Interest （Lol） was proposed by this paper. Lol defines a relevance classifier based on the impact of spatial distance on receiving attributes and attribute values. An adaptive publish-subscribe scheme was built on the basis of Loh This scheme can abandon most irrelevant messages directly. Run-time infrastructure （RTI） can also apply congestion control by reducing the frequency of sending or receiving object messages based on each objects＇ Loh The experiment results verify the efficiency of message filtering and RTI congestion control.展开更多
《HON》原英文名《Heroes of Newerth》,是由美国S2 Games公司研发的一款MOBA网游。这款游戏是世界上第一款MOBA独立客户端网游,S2 Games多年前联系到Dota地图作者lcefrog寻求他同意来制作产品。他没有意见,认为竞争和长期效应是好的,只...《HON》原英文名《Heroes of Newerth》,是由美国S2 Games公司研发的一款MOBA网游。这款游戏是世界上第一款MOBA独立客户端网游,S2 Games多年前联系到Dota地图作者lcefrog寻求他同意来制作产品。他没有意见,认为竞争和长期效应是好的,只要Dot A有人关注,他就开发下去。2008年7月左右,社区网站出现消息。目前国服已经上线,并有了参与国际比赛的队伍WE。展开更多
文摘为了认识痕量元素As在飞灰中的富集特性,利用密度泛函理论研究了砷的典型氧化物AsO在飞灰中的主要成分SiO_2模型上的吸附机理,对优化后的吸附构型进行能量计算、AIM理论、Mulliken电荷分析以及定域化轨道指示函数(LOL)填色图分析,剖析了AsO与SiO_2表面的相互作用。结果表明,AsO在无定型SiO_2表面的缺陷位的吸附能均大于50 k J/mol,吸附构型均为典型的化学吸附。在无定型SiO_2缺陷活性位点形成的As-Si键、Si-O键和As-O键强度较大,均属于共价键;SiO_2与AsO之间为共价相互作用。
基金supported by the National Natural Science Foundation of China(61573285)
文摘It is unpractical to learn the optimal structure of a big Bayesian network(BN)by exhausting the feasible structures,since the number of feasible structures is super exponential on the number of nodes.This paper proposes an approach to layer nodes of a BN by using the conditional independence testing.The parents of a node layer only belong to the layer,or layers who have priority over the layer.When a set of nodes has been layered,the number of feasible structures over the nodes can be remarkably reduced,which makes it possible to learn optimal BN structures for bigger sizes of nodes by accurate algorithms.Integrating the dynamic programming(DP)algorithm with the layering approach,we propose a hybrid algorithm—layered optimal learning(LOL)to learn BN structures.Benefitted by the layering approach,the complexity of the DP algorithm reduces to O(ρ2^n?1)from O(n2^n?1),whereρ<n.Meanwhile,the memory requirements for storing intermediate results are limited to O(C k#/k#^2 )from O(Cn/n^2 ),where k#<n.A case study on learning a standard BN with 50 nodes is conducted.The results demonstrate the superiority of the LOL algorithm,with respect to the Bayesian information criterion(BIC)score criterion,over the hill-climbing,max-min hill-climbing,PC,and three-phrase dependency analysis algorithms.
基金Supported by the National Basic Research Program of China (Grant No. 2009CB320805)the National Natural Science Foundation of China(Grant No. 60603084)the National High-Tech Research & Development Program of China (Grant No. 2006AA01Z331)
文摘In large-scale distributed simulation, thousands of objects keep moving and interacting in a virtual environment, which produces a mass of messages. High level architecture （HLA） is the prevailing standard for modeling and simulation. It specifies two publish-subscribe mechanisms for message filtering： class-based and value-based. However, the two mechanisms can only judge whether a message is relevant to a subscriber or not. Lacking of the ability to evaluate the relevance, all relevant messages are delivered with the same priority even when congestion occurs. It significantly limits the scalability and performance of distributed simulation. Aiming to solve the relevance evaluation problem, speed up message filtering, and filter more unnecessary messages, a new relevance evaluation mechanism Layer of Interest （Lol） was proposed by this paper. Lol defines a relevance classifier based on the impact of spatial distance on receiving attributes and attribute values. An adaptive publish-subscribe scheme was built on the basis of Loh This scheme can abandon most irrelevant messages directly. Run-time infrastructure （RTI） can also apply congestion control by reducing the frequency of sending or receiving object messages based on each objects＇ Loh The experiment results verify the efficiency of message filtering and RTI congestion control.
