Pitaya is a fruit that is becoming promising for cultivation around the world due to the quality of its fruits in terms of taste and their peculiar external shape. Because it is a recenttly cultivated crop, information about fertilization is essential to enable its production. In this sense, this study was performed aiming to evaluate different doses of K 2 O for pitaya cultivation. Hylocereus undatus and Hylocereus polyrhizus were studied under field conditions. Pitayas were cultivated at a 3 × 3m spacing, and four doses of K 2 O were tested: 0, 50, 100, and 200 g plant-1 , with a 2 × 4 factorial arrangement distributed in a randomized block design. The production per plant (kg and number of fruits) was evaluated in the

In the context of a relativistic mean field theory the delta-resonance matter formation in a highly compressed nuclear medium is investigated. For a given set of nucleonmeson coupling constants, the delta-resonance formation is studied by changing the delta-meson coupling constants. The effect on the equation of state and on the deltaresonance population with respect to changes in the delta-resonance coupling constants values is discussed for very asymmetric and quasi-symmetric nuclear matter, as an extension of works restricted to the symmetric nuclear matter treatment. 5,6The occurrence of highly dense baryon-rich resonant matter in relativistic heavy ion collisions 1-3 leads to a great interest in the investigation of the effects of delta matter transition in nuclear matter. These results of heavy ion collisions require a re-investigation of the transition to delta-matter in a model which could properly deal with the nuclear matter properties. * E-mail: sbd@cbpf.br 1529 Mod. Phys. Lett. A 2000.15:1529-1537. Downloaded from www.worldscientific.com by UNIVERSITY OF CALIFORNIA @ SAN DIEGO on 04/13/15. For personal use only.

We give conditions under which convolutional neural networks (CNNs) define valid sum-product networks (SPNs). One subclass, called convolutional SPNs (CSPNs), can be implemented using tensors, but also can suffer from being too shallow. Fortunately, tensors can be augmented while maintaining valid SPNs. This yields a larger subclass of CNNs, which we call deep convolutional SPNs (DCSPNs), where the convolutional and sum-pooling layers form rich directed acyclic graph structures. One salient feature of DCSPNs is that they are a rigorous probabilistic model. As such, they can exploit multiple kinds of probabilistic reasoning, including marginal inference and most probable explanation (MPE) inference. This allows an alternative method for learning DCSPNs using vectorized differentiable MPE, which plays a similar role to the generator in generative adversarial networks (GANs). Image sampling is yet another application demonstrating the robustness of DCSPNs. Our preliminary results on image sampling are encouraging, since the DCSPN sampled images exhibit variability. Experiments on image completion show that DCSPNs significantly outperform competing methods by achieving several state-of-the-art mean squared error (MSE) scores in both left-completion and bottom-completion in benchmark datasets.

This work shows results for the EXO 0748-676 neutron star structure obtained with the use of a phenomenological quark matter description by taking into account a quark interaction, and observing the asymptotic freedom and the confinement characteristics of the fundamental theory. An appropriated choice of model parameters permit to reach the stability of compact dense stellar objects compatible with the observational data to the neutron star EXO 0748-676. It is assumed a color-flavor locked (CFL) superconducting phase to the ground state of the quark–gluon plasma formed in the object interior. We also discuss the surface properties of these objects, introducing a crust composed by a lattice of neutron-rich nucleus close to the neutron drip line, imbedded in an electron gas.

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