Desenvolvimento inicial do arroz com a antecipação da adubação de nitrogênio e seu efeito na produtividade do arroz no município de Paragominas, PA.

Analisou-se a resposta do arroz de terras altas cultivado em Latossolo Amarelo distrofico na região Nordeste Paraense, ao parcelamento e épocas de aplicação de fertilizante nitrogenado. Observou-se também as formas que permitam adaptar o sistema de produção do arroz de terras altas ao plantio direto de forma a permitir que faca parte de sistemas agrícolas em rotação com soja, adequando ao manejo de adubação, a relação de formas de N no solo, aos tipos e época de manejo das coberturas de solo e a qualidade de semeadura

Utilizing Mobile Devices in Education: Faculty Experiences

The use of technology in higher education has become the rule rather than the exception; advancing technologies, such as mobile devices, is an inescapable transition for faculty members. This study explored the lived-experiences of eight full time faculty members who adopted a mobile device to support teaching and learning. The study focused on understanding how training was experienced, how mobile devices were adopted, and how the use of mobile devices affected engagement practices

Differentially Private Synthetic Data Using KD-Trees

Creation of a synthetic dataset that faithfully represents the data distribution and simultaneously preserves privacy is a major research challenge. Many space partitioning based approaches have emerged in recent years for answering statistical queries in a differentially private manner. However, for synthetic data generation problem, recent research has been mainly focused on deep generative models. In contrast, we exploit space partitioning techniques together with noise perturbation and thus achieve intuitive and transparent algorithms. We propose both data independent and data dependent algorithms for $\epsilon$-differentially private synthetic data generation whose kernel density resembles that of the real dataset. Additionally, we provide theoretical results on the utility-privacy trade-offs and show how our data dependent approach overcomes the curse of dimensionality and leads to a scalable algorithm. We show empirical utility improvements over the prior work, and discuss performance of our algorithm on a downstream classification task on a real dataset

Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe

Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data. Multidimensional Hawkes processes model both the self and cross-excitation between different types of events and have been applied successfully in various domain such as finance, epidemiology and personalized recommendations, among others. In this work we present an adaptation of the Frank-Wolfe algorithm for learning multidimensional Hawkes processes. Experimental results show that our approach has better or on par accuracy in terms of parameter estimation than other first order methods, while enjoying a significantly faster runtime.Comment: Presented at the NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research. 9 pages, 3 figures, 4 table

On the Complexity of Case-Based Planning

We analyze the computational complexity of problems related to case-based planning: planning when a plan for a similar instance is known, and planning from a library of plans. We prove that planning from a single case has the same complexity than generative planning (i.e., planning "from scratch"); using an extended definition of cases, complexity is reduced if the domain stored in the case is similar to the one to search plans for. Planning from a library of cases is shown to have the same complexity. In both cases, the complexity of planning remains, in the worst case, PSPACE-complete

Online Learning for Mixture of Multivariate Hawkes Processes

Online learning of Hawkes processes has received increasing attention in the last couple of years especially for modeling a network of actors. However, these works typically either model the rich interaction between the events or the latent cluster of the actors or the network structure between the actors. We propose to model the latent structure of the network of actors as well as their rich interaction across events for real-world settings of medical and financial applications. Experimental results on both synthetic and real-world data showcase the efficacy of our approach.Comment: 12 pages, 6 figures, 3 table

Alterações na fertilidade do solo após cinco anos de cultivo do mogno africano (Khaya ivorensis) em latossolo amarelo de Paragominas.

O sistema de produção implantado em 2009 na Fazenda Vitória teve como objetivo avaliar o crescimento da espécie Khaya ivorensis no sistema Integração Lavoura-Pecuária-Floresta iLPF e no sistema em monocultivo, para recuperar áreas de pastagens degradadas, avaliar o crescimento de espécie potenciais para a região, para suprir a demanda por madeira e agregar valor à terra com o plantio do K. ivorensis e melhorar a fertilidade do solo. O primeiro ciclo de cultivo de grãos foi com milho, o segundo foi à soja, a forragem foi com Brachiaria ruziziensis e a espécie florestal foi a K. ivorensis. O Sistema Integração Lavoura-Pecuária-Floresta (ILPF) a produção de grãos, de forragem e de madeira numa mesma área, em consórcio, em rotação ou em sucessão de culturas, adotando-se, preferencialmente, o plantio direto, tendo, assim, uma diversidade de opções de cultivo. O crescimento do mogno africano foi mensurado até o quinto ano, o DAP foi mensurado a partir do segundo ano da instalação do sistema iLPF. Houve recuperação e manutenção da capacidade produtiva solo, além da redução da erosão dos solos e redução de carbono com a profundidade