Many garbage collection algorithms have been proposed, but few address the special needs of long-running server programs. Server applications usually run for years and spawn many threads, so they create and discard ...Many garbage collection algorithms have been proposed, but few address the special needs of long-running server programs. Server applications usually run for years and spawn many threads, so they create and discard thousands of objects. Therefore, efficient garbage collection is especially important for those applications. This paper presents a class-based garbage collector for object-oriented programming environments that classifies objects by their types to achieve better gradualness. Grouping objects of the same type into a group, with a limited type-lock, a mutator cache and the lease protocol will reduce memory fragmentation, which is especially important for the efficiency of long-running server applications. This class-based collector partitions the heap space by type, which provides better concurrency than the traditional mark-sweep collector, and its reusable garbaged object pool also reduces the object allocation overhead. This paper also discusses the implementation details, such as the mutator cache and the lease protocol, and techniques to achieve better accuracy.展开更多
In this study,the impact of the training sample selection method on the performance of fuzzy-based Possibilistic c-means(PCM)and Noise Clustering(NC)classifiers were examined and mapped the cumin and fennel rabi crop....In this study,the impact of the training sample selection method on the performance of fuzzy-based Possibilistic c-means(PCM)and Noise Clustering(NC)classifiers were examined and mapped the cumin and fennel rabi crop.Two training sample selection approaches that have been investigated in this study are“mean”and“individual sample as mean”.Both training sample techniques were applied to the PCM and NC classifiers to classify the two indices approach.Both approaches have been studied to decrease spectral information in temporal data processing.The Modified Soil Adjusted Vegetation Index 2(MSAVI-2)and Class-Based Sensor Independent Modified Soil Adjusted Vegetation Index-2(CBSI-MSAVI-2)have been considered to minimize soil background effects,enhancing vegetation detection accuracy,particularly in areas with sparse vegetation cover.The MMD(MeanMembership Difference)and RMSE(RootMean Square Error)approaches were used to measure the study’s accuracy.To illustrate that the classifier successfully describes classes,cluster validity(SSE)was also performed,and the variance parameter was computed to handle heterogeneity within cumin and fennel crop fields.For the calculation of RMSE,Sentinel-2 data was used as classified,whereas PlanetScope satellite data was utilized as the reference data set.The best result was obtained using the NC classifier with“individual sample as mean”using CBSI-MSAVI-2 temporal indices.For Fuzziness Factor(m)=1.1,the RMSE,MMD,Variance,and SSE values for the NC classifier using“individual sample as mean”on the CBSI-MSAVI-2 temporal indices for cumin were 0.00098,0.00162,0.02857,and 0.97143,respectively and for fennel were 0.00025,0.00248,0.10420,and 3.54286,respectively.展开更多
Background:There is growing evidence that an acute bout of exercise positively influences executive function(EF).However,the existing evidence primarily originates from laboratory-based studies,and only a limited numb...Background:There is growing evidence that an acute bout of exercise positively influences executive function(EF).However,the existing evidence primarily originates from laboratory-based studies,and only a limited number of studies have extended this work to real-world classroom settings.Accordingly,in the present study,we aimed to employ a real classroom setting to determine whether acute exercise-induced effects on EF emerged.Methods:All 49 students who enrolled in a real-world course agreed to participate in the experimental protocol and the final sample was composed of 43 individuals(13 male and 30 female participants).Participants were asked to perform an acute bout of exercise(i.e,10 min at moderate intensity)before a real classroom,and on a separate day,complete a non-exercise control condition.EF was assessed via Naming,Inhibition,and Switching variants of the Stroop task.We used a paired-samples t-test to compare participants’cognitive load between two conditions and a repeated-measures ANOVA to investigate changes in RPE.What’s more,a repeated measures ANOVA was used to examine potential pre-to post-class changes in EF-related parameters(e.g.,inverse efficiency scores,reaction times,and error rates).Results:A pre-to post-class benefit in performance efficiency across all Stroop task variants was shown.In both exercise and control conditions,there was a significant main effect of time,with lower inverse efficiency scores(IES)(p=0.003)and shorter reaction times(RT)(p<0.001)observed from pre-to post-class.Moreover,performance gains varied by Stroop task-type,with the Switching task showing the longest RTs and largest IES,reflecting its greater cognitive demands.Importantly,a marginally significant three-way interaction among task-type,intervention,and time(p=0.052)indicated that the exercise intervention enhanced post-class performance on the Switching task.Post-hoc analyses revealed significantly lower IES and faster RTs at post-class for both the Naming and Switching tasks,particularly in the exercise group(e.g.,Switching IES:p<0.001;Switching RT:p<0.001).Conclusions:These findings suggest that pre-class acute exercise enhances EF and provides a benefit to cognitive flexibility.Accordingly,our results extend previous knowledge by indicating that the cognitive benefits of acute exercise observed primarily in laboratory settings can be translated to real-world educational contexts.展开更多
Semantic-based searching in peer-to-peer （P2P） networks has drawn significant attention recently. A number of semantic searching schemes, such as GES proposed by Zhu Y et al., employ search models in Information Ret...Semantic-based searching in peer-to-peer （P2P） networks has drawn significant attention recently. A number of semantic searching schemes, such as GES proposed by Zhu Y et al., employ search models in Information Retrieval （IR）. All these IR-based schemes use one vector to summarize semantic contents of all documents on a single node. For example, GES derives a node vector based on the IR model： VSM （Vector Space Model）. A topology adaptation algorithm and a search protocol are then designed according to the similarity between node vectors of different nodes. Although the single semantic vector is suitable when the distribution of documents in each node is uniform, it may not be efficient when the distribution is diverse. When there are many categories of documents at each node, the node vector representation may be inaccurate. We extend the idea of GES and present a new class-based semantic searching scheme （CSS） specifically designed for unstructured P2P networks with heterogeneous single-node document collection. It makes use of a state-of-the-art data clustering algorithm, online spherical k-means clustering （OSKM）, to cluster all documents on a node into several classes. Each class can be viewed as a virtual node. Virtual nodes are connected through virtual links. As a result, the class vector replaces the node vector and plays an important role in the class-based topology adaptation and search process. This makes CSS very efficient. Our simulation using the IR benchmark TREC collection demonstrates that CSS outperforms GES in terms of higher recall, higher precision, and lower search cost.展开更多
Contextual logic provides a mechanism to reason about modules. In this paper, this theory of modules is extended to a context theory of classes where class is in the true spirit of object-oriented databases. The logic...Contextual logic provides a mechanism to reason about modules. In this paper, this theory of modules is extended to a context theory of classes where class is in the true spirit of object-oriented databases. The logic, referred to as CLOG,is class-based. CLOG supports class, object identity, multiple role of object,monotonic and non-monotonic inheritance of data a-nd method, method factor-ing, views, derived and query classes. Views and derived classes are queries in themselves- Objects are pure data terms representing the ground instances of facts in the class. 'Object identity is a first class term in the logic. Inheritance is handled through delegation.展开更多
基金the National High- Tech Research andDevelopm ent Program of China(No. 30 6 - 0 1- 0 3- 11- 9)
文摘Many garbage collection algorithms have been proposed, but few address the special needs of long-running server programs. Server applications usually run for years and spawn many threads, so they create and discard thousands of objects. Therefore, efficient garbage collection is especially important for those applications. This paper presents a class-based garbage collector for object-oriented programming environments that classifies objects by their types to achieve better gradualness. Grouping objects of the same type into a group, with a limited type-lock, a mutator cache and the lease protocol will reduce memory fragmentation, which is especially important for the efficiency of long-running server applications. This class-based collector partitions the heap space by type, which provides better concurrency than the traditional mark-sweep collector, and its reusable garbaged object pool also reduces the object allocation overhead. This paper also discusses the implementation details, such as the mutator cache and the lease protocol, and techniques to achieve better accuracy.
文摘In this study,the impact of the training sample selection method on the performance of fuzzy-based Possibilistic c-means(PCM)and Noise Clustering(NC)classifiers were examined and mapped the cumin and fennel rabi crop.Two training sample selection approaches that have been investigated in this study are“mean”and“individual sample as mean”.Both training sample techniques were applied to the PCM and NC classifiers to classify the two indices approach.Both approaches have been studied to decrease spectral information in temporal data processing.The Modified Soil Adjusted Vegetation Index 2(MSAVI-2)and Class-Based Sensor Independent Modified Soil Adjusted Vegetation Index-2(CBSI-MSAVI-2)have been considered to minimize soil background effects,enhancing vegetation detection accuracy,particularly in areas with sparse vegetation cover.The MMD(MeanMembership Difference)and RMSE(RootMean Square Error)approaches were used to measure the study’s accuracy.To illustrate that the classifier successfully describes classes,cluster validity(SSE)was also performed,and the variance parameter was computed to handle heterogeneity within cumin and fennel crop fields.For the calculation of RMSE,Sentinel-2 data was used as classified,whereas PlanetScope satellite data was utilized as the reference data set.The best result was obtained using the NC classifier with“individual sample as mean”using CBSI-MSAVI-2 temporal indices.For Fuzziness Factor(m)=1.1,the RMSE,MMD,Variance,and SSE values for the NC classifier using“individual sample as mean”on the CBSI-MSAVI-2 temporal indices for cumin were 0.00098,0.00162,0.02857,and 0.97143,respectively and for fennel were 0.00025,0.00248,0.10420,and 3.54286,respectively.
基金supported by the Shenzhen Educational Research Funding(grant number zdzb2014)Shenzhen Science and Technology Innovation Commission(grant number 202307313000096)+3 种基金Social Science Foundation from China’s Ministry of Education(grant number 23YJA880093)Post-doctoral Fellowship(grant number 2022M711174)National Center for Mental Health(grant number Z014)Research Excellence Scholarships of Shenzhen University(grant number ZYZD2305).
文摘Background:There is growing evidence that an acute bout of exercise positively influences executive function(EF).However,the existing evidence primarily originates from laboratory-based studies,and only a limited number of studies have extended this work to real-world classroom settings.Accordingly,in the present study,we aimed to employ a real classroom setting to determine whether acute exercise-induced effects on EF emerged.Methods:All 49 students who enrolled in a real-world course agreed to participate in the experimental protocol and the final sample was composed of 43 individuals(13 male and 30 female participants).Participants were asked to perform an acute bout of exercise(i.e,10 min at moderate intensity)before a real classroom,and on a separate day,complete a non-exercise control condition.EF was assessed via Naming,Inhibition,and Switching variants of the Stroop task.We used a paired-samples t-test to compare participants’cognitive load between two conditions and a repeated-measures ANOVA to investigate changes in RPE.What’s more,a repeated measures ANOVA was used to examine potential pre-to post-class changes in EF-related parameters(e.g.,inverse efficiency scores,reaction times,and error rates).Results:A pre-to post-class benefit in performance efficiency across all Stroop task variants was shown.In both exercise and control conditions,there was a significant main effect of time,with lower inverse efficiency scores(IES)(p=0.003)and shorter reaction times(RT)(p<0.001)observed from pre-to post-class.Moreover,performance gains varied by Stroop task-type,with the Switching task showing the longest RTs and largest IES,reflecting its greater cognitive demands.Importantly,a marginally significant three-way interaction among task-type,intervention,and time(p=0.052)indicated that the exercise intervention enhanced post-class performance on the Switching task.Post-hoc analyses revealed significantly lower IES and faster RTs at post-class for both the Naming and Switching tasks,particularly in the exercise group(e.g.,Switching IES:p<0.001;Switching RT:p<0.001).Conclusions:These findings suggest that pre-class acute exercise enhances EF and provides a benefit to cognitive flexibility.Accordingly,our results extend previous knowledge by indicating that the cognitive benefits of acute exercise observed primarily in laboratory settings can be translated to real-world educational contexts.
基金supported in part by the National Science Foundation of USA under Grant Nos.ANI 0073736,EIA 0130806,CCR0329741,CNS 0422762,CNS 0434533,CNS 0531410,CNS 0626240,CCF 0830289,and CNS 0948184
文摘Semantic-based searching in peer-to-peer （P2P） networks has drawn significant attention recently. A number of semantic searching schemes, such as GES proposed by Zhu Y et al., employ search models in Information Retrieval （IR）. All these IR-based schemes use one vector to summarize semantic contents of all documents on a single node. For example, GES derives a node vector based on the IR model： VSM （Vector Space Model）. A topology adaptation algorithm and a search protocol are then designed according to the similarity between node vectors of different nodes. Although the single semantic vector is suitable when the distribution of documents in each node is uniform, it may not be efficient when the distribution is diverse. When there are many categories of documents at each node, the node vector representation may be inaccurate. We extend the idea of GES and present a new class-based semantic searching scheme （CSS） specifically designed for unstructured P2P networks with heterogeneous single-node document collection. It makes use of a state-of-the-art data clustering algorithm, online spherical k-means clustering （OSKM）, to cluster all documents on a node into several classes. Each class can be viewed as a virtual node. Virtual nodes are connected through virtual links. As a result, the class vector replaces the node vector and plays an important role in the class-based topology adaptation and search process. This makes CSS very efficient. Our simulation using the IR benchmark TREC collection demonstrates that CSS outperforms GES in terms of higher recall, higher precision, and lower search cost.
文摘Contextual logic provides a mechanism to reason about modules. In this paper, this theory of modules is extended to a context theory of classes where class is in the true spirit of object-oriented databases. The logic, referred to as CLOG,is class-based. CLOG supports class, object identity, multiple role of object,monotonic and non-monotonic inheritance of data a-nd method, method factor-ing, views, derived and query classes. Views and derived classes are queries in themselves- Objects are pure data terms representing the ground instances of facts in the class. 'Object identity is a first class term in the logic. Inheritance is handled through delegation.
