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Luis Felipe Paes De Almeida - One of the best experts on this subject based on the ideXlab platform.
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Early Evaluation of Camu-Camu Subsamples in Transition Savanna/Forest Area
Journal of Agricultural Science, 2014Co-Authors: Luis Felipe Paes De Almeida, Kaoru Yuyama, Edvan Alves Chagas, Ricardo Bardales Lozano, Teresinha Costa Silveira Albuquerque, Carlos Abanto Rodriguez, Fernando Barreto Diógenes De QueirozAbstract:Camu-camu (Myrciaria dubia (Kunth) McVaugh) is an indigenous fruit of the floodplain and riparian forests of the Amazon region. In Brazil, Roraima state has appropriate conditions for this fruit production. Cultivation outside the floodplains is an alternative to increase the availability of fruits, since flowering occurs almost all year round, and fruit bearing coincides with the end of the dry season and early rain season. The objective of this study was to evaluate the vegetative development of 6 camu-camu Subsamples selected from the Instituto Nacional de Pesquisas da Amazonia- INPA (Amazonas state), in a savanna region near Boa Vista city (Roraima, Brazil). The experiment was conducted at the Serra da Prata Experimental Station of the Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA), with a climate type Am according to Koppen climate classification. Four parameters were evaluated for the growth analysis, the basal stem diameter (mm), the plant height (cm), and numbers of basal and terminal shoots, as well as the numbers of flowers and fruits. Measurements were made at 90 days intervals in order to define the growth curve of each subsample for 30 months Based on the parameters of vegetative growth and early flowering, we recommend the UAT 1096-5 subsample with the best vegetative development for transition forest/Savanna area, presenting moreover higher number of terminal shoots, greater height, and early flowering than the remaining camu-camu Subsamples. At 30 months after planting, the UAT 1896-7 and UAT 0796-8 Subsamples showed no statistical difference from UAT 1096-5 regarding the number of terminal shoots, but showed slightly lower height, with a statistically significant difference. The UAT 1596-7, UAT 1796-7, and URUBU-2 Subsamples showed the lowest number of terminal shoots, although UAT 1596-7 presented greater height than the others. In relation to precocity, peak flowering occurred in January, 30 months after planting, with the UAT 0796-8 and UAT 1096-5 Subsamples excelling over others, with 500 and 435 flowers in total, respectively.
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early evaluation of camu camu Subsamples in transition savanna forest area
The Journal of Agricultural Science, 2014Co-Authors: Luis Felipe Paes De Almeida, Kaoru Yuyama, Edvan Alves Chagas, Ricardo Bardales Lozano, Teresinha Costa Silveira Albuquerque, Carlos Abanto Rodriguez, Fernando Barreto QueirozAbstract:Camu-camu (Myrciaria dubia (Kunth) McVaugh) is an indigenous fruit of the floodplain and riparian forests of the Amazon region. In Brazil, Roraima state has appropriate conditions for this fruit production. Cultivation outside the floodplains is an alternative to increase the availability of fruits, since flowering occurs almost all year round, and fruit bearing coincides with the end of the dry season and early rain season. The objective of this study was to evaluate the vegetative development of 6 camu-camu Subsamples selected from the Instituto Nacional de Pesquisas da Amazonia- INPA (Amazonas state), in a savanna region near Boa Vista city (Roraima, Brazil). The experiment was conducted at the Serra da Prata Experimental Station of the Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA), with a climate type Am according to Koppen climate classification. Four parameters were evaluated for the growth analysis, the basal stem diameter (mm), the plant height (cm), and numbers of basal and terminal shoots, as well as the numbers of flowers and fruits. Measurements were made at 90 days intervals in order to define the growth curve of each subsample for 30 months Based on the parameters of vegetative growth and early flowering, we recommend the UAT 1096-5 subsample with the best vegetative development for transition forest/Savanna area, presenting moreover higher number of terminal shoots, greater height, and early flowering than the remaining camu-camu Subsamples. At 30 months after planting, the UAT 1896-7 and UAT 0796-8 Subsamples showed no statistical difference from UAT 1096-5 regarding the number of terminal shoots, but showed slightly lower height, with a statistically significant difference. The UAT 1596-7, UAT 1796-7, and URUBU-2 Subsamples showed the lowest number of terminal shoots, although UAT 1596-7 presented greater height than the others. In relation to precocity, peak flowering occurred in January, 30 months after planting, with the UAT 0796-8 and UAT 1096-5 Subsamples excelling over others, with 500 and 435 flowers in total, respectively.
Eric Jonas - One of the best experts on this subject based on the ideXlab platform.
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scaling nonparametric bayesian inference via subsample annealing
International Conference on Artificial Intelligence and Statistics, 2014Co-Authors: Fritz Obermeyer, Jonathan Glidden, Eric JonasAbstract:We describe an adaptation of the simulated annealing algorithm to nonparametric clustering and related probabilistic models. This new algorithm learns nonparametric latent structure over a growing and constantly churning subsample of training data, where the portion of data subsampled can be interpreted as the inverse temperature (t) in an annealing schedule. Gibbs sampling at high temperature (i.e., with a very small subsample) can more quickly explore sketches of the nal latent state by (a) making longer jumps around latent space (as in block Gibbs) and (b) lowering energy barriers (as in simulated annealing). We prove subsample annealing speeds up mixing timeN 2 ! N in a simple clustering model and exp(N)! N in another class of models, where N is data size. Empirically subsample-annealing outperforms naive Gibbs sampling in accuracyper-wallclock time, and can scale to larger datasets and deeper hierarchical models. We demonstrate improved inference on million-row Subsamples of US Census data and network log data and a 307-row hospital rating dataset, using a Pitman-Yor generalization of the Cross Categorization model.
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scaling nonparametric bayesian inference via subsample annealing
arXiv: Machine Learning, 2014Co-Authors: Fritz Obermeyer, Jonathan Glidden, Eric JonasAbstract:We describe an adaptation of the simulated annealing algorithm to nonparametric clustering and related probabilistic models. This new algorithm learns nonparametric latent structure over a growing and constantly churning subsample of training data, where the portion of data subsampled can be interpreted as the inverse temperature beta(t) in an annealing schedule. Gibbs sampling at high temperature (i.e., with a very small subsample) can more quickly explore sketches of the final latent state by (a) making longer jumps around latent space (as in block Gibbs) and (b) lowering energy barriers (as in simulated annealing). We prove subsample annealing speeds up mixing time N^2 -> N in a simple clustering model and exp(N) -> N in another class of models, where N is data size. Empirically subsample-annealing outperforms naive Gibbs sampling in accuracy-per-wallclock time, and can scale to larger datasets and deeper hierarchical models. We demonstrate improved inference on million-row Subsamples of US Census data and network log data and a 307-row hospital rating dataset, using a Pitman-Yor generalization of the Cross Categorization model.
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AISTATS - Scaling Nonparametric Bayesian Inference via Subsample-Annealing
2014Co-Authors: Fritz Obermeyer, Jonathan Glidden, Eric JonasAbstract:We describe an adaptation of the simulated annealing algorithm to nonparametric clustering and related probabilistic models. This new algorithm learns nonparametric latent structure over a growing and constantly churning subsample of training data, where the portion of data subsampled can be interpreted as the inverse temperature (t) in an annealing schedule. Gibbs sampling at high temperature (i.e., with a very small subsample) can more quickly explore sketches of the nal latent state by (a) making longer jumps around latent space (as in block Gibbs) and (b) lowering energy barriers (as in simulated annealing). We prove subsample annealing speeds up mixing timeN 2 ! N in a simple clustering model and exp(N)! N in another class of models, where N is data size. Empirically subsample-annealing outperforms naive Gibbs sampling in accuracyper-wallclock time, and can scale to larger datasets and deeper hierarchical models. We demonstrate improved inference on million-row Subsamples of US Census data and network log data and a 307-row hospital rating dataset, using a Pitman-Yor generalization of the Cross Categorization model.
Tae-hwy Lee - One of the best experts on this subject based on the ideXlab platform.
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Forecasting Realized Volatility Using Subsample Averaging
2014Co-Authors: Tae-hwy Lee, Huiyu HuangAbstract:When the observed price process is the true underlying price process plus microstructure noise, it is known that realized volatility (RV) estimates will be overwhelmed by the noise when the sampling frequency approaches infinity. Therefore, it may be optimal to sample less frequently, and averaging the less frequently sampled Subsamples can improve estimation for quadratic variation. In this paper, we extend this idea to forecasting daily realized volatility. While the subsample-averaging has been proposed and used in estimating RV, this paper is the first that uses the subsample-averaging for forecasting RV. The subsample averaging method we examine incorporates the high frequency data in different levels of systematic sampling. It first pools the high frequency data into several Subsamples, that generates forecasts from each subsample, and then combine these forecasts. We find that, in daily S&P 500 return RV forecasts, subsample-averaging generates better forecasts than those using only one subsample without averaging over all Subsamples.
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Forecasting Realized Volatility Using Subsample Averaging
Open Journal of Statistics, 2013Co-Authors: Huiyu Huang, Tae-hwy LeeAbstract:When the observed price process is the true underlying price process plus microstructure noise, it is known that realized volatility (RV) estimates will be overwhelmed by the noise when the sampling frequency approaches infinity. Therefore, it may be optimal to sample less frequently, and averaging the less frequently sampled Subsamples can improve estimation for quadratic variation. In this paper, we extend this idea to forecasting daily realized volatility. While subsample averaging has been proposed and used in estimating RV, this paper is the first that uses subsample averaging for forecasting RV. The subsample averaging method we examine incorporates the high frequency data in different levels of systematic sampling. It first pools the high frequency data into several Subsamples, then generates forecasts from each subsample, and then combines these forecasts. We find that in daily S&P 500 return realized volatility forecasts, subsample averaging generates better forecasts than those using only one subsample.
Fernando Barreto Diógenes De Queiroz - One of the best experts on this subject based on the ideXlab platform.
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Early Evaluation of Camu-Camu Subsamples in Transition Savanna/Forest Area
Journal of Agricultural Science, 2014Co-Authors: Luis Felipe Paes De Almeida, Kaoru Yuyama, Edvan Alves Chagas, Ricardo Bardales Lozano, Teresinha Costa Silveira Albuquerque, Carlos Abanto Rodriguez, Fernando Barreto Diógenes De QueirozAbstract:Camu-camu (Myrciaria dubia (Kunth) McVaugh) is an indigenous fruit of the floodplain and riparian forests of the Amazon region. In Brazil, Roraima state has appropriate conditions for this fruit production. Cultivation outside the floodplains is an alternative to increase the availability of fruits, since flowering occurs almost all year round, and fruit bearing coincides with the end of the dry season and early rain season. The objective of this study was to evaluate the vegetative development of 6 camu-camu Subsamples selected from the Instituto Nacional de Pesquisas da Amazonia- INPA (Amazonas state), in a savanna region near Boa Vista city (Roraima, Brazil). The experiment was conducted at the Serra da Prata Experimental Station of the Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA), with a climate type Am according to Koppen climate classification. Four parameters were evaluated for the growth analysis, the basal stem diameter (mm), the plant height (cm), and numbers of basal and terminal shoots, as well as the numbers of flowers and fruits. Measurements were made at 90 days intervals in order to define the growth curve of each subsample for 30 months Based on the parameters of vegetative growth and early flowering, we recommend the UAT 1096-5 subsample with the best vegetative development for transition forest/Savanna area, presenting moreover higher number of terminal shoots, greater height, and early flowering than the remaining camu-camu Subsamples. At 30 months after planting, the UAT 1896-7 and UAT 0796-8 Subsamples showed no statistical difference from UAT 1096-5 regarding the number of terminal shoots, but showed slightly lower height, with a statistically significant difference. The UAT 1596-7, UAT 1796-7, and URUBU-2 Subsamples showed the lowest number of terminal shoots, although UAT 1596-7 presented greater height than the others. In relation to precocity, peak flowering occurred in January, 30 months after planting, with the UAT 0796-8 and UAT 1096-5 Subsamples excelling over others, with 500 and 435 flowers in total, respectively.
Fernando Barreto Queiroz - One of the best experts on this subject based on the ideXlab platform.
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early evaluation of camu camu Subsamples in transition savanna forest area
The Journal of Agricultural Science, 2014Co-Authors: Luis Felipe Paes De Almeida, Kaoru Yuyama, Edvan Alves Chagas, Ricardo Bardales Lozano, Teresinha Costa Silveira Albuquerque, Carlos Abanto Rodriguez, Fernando Barreto QueirozAbstract:Camu-camu (Myrciaria dubia (Kunth) McVaugh) is an indigenous fruit of the floodplain and riparian forests of the Amazon region. In Brazil, Roraima state has appropriate conditions for this fruit production. Cultivation outside the floodplains is an alternative to increase the availability of fruits, since flowering occurs almost all year round, and fruit bearing coincides with the end of the dry season and early rain season. The objective of this study was to evaluate the vegetative development of 6 camu-camu Subsamples selected from the Instituto Nacional de Pesquisas da Amazonia- INPA (Amazonas state), in a savanna region near Boa Vista city (Roraima, Brazil). The experiment was conducted at the Serra da Prata Experimental Station of the Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA), with a climate type Am according to Koppen climate classification. Four parameters were evaluated for the growth analysis, the basal stem diameter (mm), the plant height (cm), and numbers of basal and terminal shoots, as well as the numbers of flowers and fruits. Measurements were made at 90 days intervals in order to define the growth curve of each subsample for 30 months Based on the parameters of vegetative growth and early flowering, we recommend the UAT 1096-5 subsample with the best vegetative development for transition forest/Savanna area, presenting moreover higher number of terminal shoots, greater height, and early flowering than the remaining camu-camu Subsamples. At 30 months after planting, the UAT 1896-7 and UAT 0796-8 Subsamples showed no statistical difference from UAT 1096-5 regarding the number of terminal shoots, but showed slightly lower height, with a statistically significant difference. The UAT 1596-7, UAT 1796-7, and URUBU-2 Subsamples showed the lowest number of terminal shoots, although UAT 1596-7 presented greater height than the others. In relation to precocity, peak flowering occurred in January, 30 months after planting, with the UAT 0796-8 and UAT 1096-5 Subsamples excelling over others, with 500 and 435 flowers in total, respectively.
