Approximation und Berechnung von Zufallsgrößen anhand der Finite-Elemente-Methode

Es wird ein Verfahren vorgestellt, mit dem stetige Zufallsgrößen rechnerunterstützt dargestellt und miteinander verknüpft werden können. Die Verteilungsfunktionen der Zufallsgrößen werden mit einem Finite-Elemente-Ansatz in einem endlichen Intervall [tmin; tmax] approximiert. Die Addition zweier Zufallsgrößen wird durch numerische Berechnung des Faltungsintegrals durchgeführt.

Discontinuous Galerkin finite element approximation of quasilinear elliptic boundary value problems II: Strongly monotone quasi-Newtonian flows

In this article, we develop the a priori and a posteriori error analysis of hp-version interior penalty discontinuous Galerkin finite element methods for strongly monotone quasi-Newtonian fluid flows in a bounded Lipschitz domain Ω ⊂ ℝd, d = 2, 3. In the latter case, computable upper and lower bounds on the error are derived in terms of a natural energy norm, which are explicit in the local mesh size and local polynomial degree of the approximating finite element method. A series of numerical experiments illustrate the performance of the proposed a posteriori error indicators within an automatic hp-adaptive refinement algorithm.

Regulation and function of {\it Xparaxis\/} during the development of paraxial mesoderm in {\it Xenopus laevis\/}

Genes of the basic helix-loop-helix transcription factor family have been implicated in many different developmental processes from neurogenesis to myogenesis. The recently cloned bHLH transcription factor, paraxis, has been found to be expressed in the paraxial mesoderm of the mouse suggesting a role for paraxis in the development of this mesodermal subtype which gives rise to the axial muscle, skeleton, and dermis of the embryo. In order to perform in vivo gain of function assays and obtain a better understanding of the possible roles of paraxis in mesodermal and somitic development, we have successfully identified homologues of paraxis in the frog, Xenopus laevis, where the process of mesodermal induction and development is best understood. The two homologues, Xparaxis-a and Xparaxis-b, are conserved with respect to their murine homologue in structure and expression within the embryo. Xparaxis genes are expressed immediately after gastrulation in the paraxial mesoderm of Xenopus embryos and are down regulated in the myotome of the mature somite with continued expression in the undifferentiated dermatome. Overexpression of Xparaxis-b in Xenopus embryos caused defects in the organization and morphology of the somites. This effect was not dependent on DNA binding of Xparaxis but is likely due to its dimerization with other bHLH factors. Co-injections with XE12 did not diminish the effects indicating that the defects were not the result of limiting amounts of XE12. We also demonstrated that Xparaxis does not cause obvious defects in the cell adhesions and movements required for proper mesoderm patterning during gastrulation. The paraxis proteins also lacked the ability to activate transcription as GAL4 fusion proteins in a GAL4 reporter assay, indicating that the genes may function more as modulators of the activity of dimerization partners than as positively acting cell determination factors. In agreement with this, Xparaxis is regulated in response to other pathways of bHLH gene action, in that XE12 can activate Xparaxis-b, in vivo. In addition we show regulation of Xparaxis in response to mMyoD induced myogenesis pathways, again suggesting Xparaxis plays an important role in the patterning and organization of the paraxial mesoderm. ^

Gibbs point process approximation: Total variation bounds using Stein's method

We obtain upper bounds for the total variation distance between the distributions of two Gibbs point processes in a very general setting. Applications are provided to various well-known processes and settings from spatial statistics and statistical physics, including the comparison of two Lennard-Jones processes, hard core approximation of an area interaction process and the approximation of lattice processes by a continuous Gibbs process. Our proof of the main results is based on Stein's method. We construct an explicit coupling between two spatial birth-death processes to obtain Stein factors, and employ the Georgii-Nguyen-Zessin equation for the total bound.

Analytical Models of Exoplanetary Atmospheres. II. Radiative Transfer via the Two-Stream Approximation

We present a comprehensive analytical study of radiative transfer using the method of moments and include the effects of non-isotropic scattering in the coherent limit. Within this unified formalism, we derive the governing equations and solutions describing two-stream radiative transfer (which approximates the passage of radiation as a pair of outgoing and incoming fluxes), flux-limited diffusion (which describes radiative transfer in the deep interior) and solutions for the temperature-pressure profiles. Generally, the problem is mathematically under-determined unless a set of closures (Eddington coefficients) is specified. We demonstrate that the hemispheric (or hemi-isotropic) closure naturally derives from the radiative transfer equation if energy conservation is obeyed, while the Eddington closure produces spurious enhancements of both reflected light and thermal emission. We concoct recipes for implementing two-stream radiative transfer in stand-alone numerical calculations and general circulation models. We use our two-stream solutions to construct toy models of the runaway greenhouse effect. We present a new solution for temperature-pressure profiles with a non-constant optical opacity and elucidate the effects of non-isotropic scattering in the optical and infrared. We derive generalized expressions for the spherical and Bond albedos and the photon deposition depth. We demonstrate that the value of the optical depth corresponding to the photosphere is not always 2/3 (Milne's solution) and depends on a combination of stellar irradiation, internal heat and the properties of scattering both in optical and infrared. Finally, we derive generalized expressions for the total, net, outgoing and incoming fluxes in the convective regime.

Experimental dissolution of molybdenum-sulphides at low oxygen concentrations: A first-order approximation of late Archean atmospheric conditions

The abundance of atmospheric oxygen and its evolution through Earth's history is a highly debated topic. The earliest change of the Mo concentration and isotope composition of marine sediments are interpreted to be linked to the onset of the accumulation of free O2 in Earth's atmosphere. The O2 concentration needed to dissolve significant amounts of Mo in water is not yet quantified, however. We present laboratory experiments on pulverized and surface-cleaned molybdenite (MoS2) and a hydrothermal breccia enriched in Mo-bearing sulphides using a glove box setup. Duration of an experiment was 14 days, and first signs of oxidation and subsequent dissolution of Mo compounds start to occur above an atmospheric oxygen concentration of 72 ± 20 ppmv (i.e., 2.6 to 4.6 × 10−4 present atmospheric level (PAL)). This experimentally determined value coincides with published model calculations supporting atmospheric O2 concentrations between 1 × 10−5 to 3 × 10−4 PAL prior to the Great Oxidation Event and sets an upper limit to the molecular oxygen needed to trigger Mo accumulation and Mo isotope variations recorded in sediments. In combination with the published Mo isotope composition of the rock record, this result implies an atmospheric oxygen concentration prior to 2.76 Ga of below 72 ± 20 ppmv.

Polynomial approximation using particle swarm optimization of lineal enhanced neural networks with no hidden layers.

This paper presents some ideas about a new neural network architecture that can be compared to a Taylor analysis when dealing with patterns. Such architecture is based on lineal activation functions with an axo-axonic architecture. A biological axo-axonic connection between two neurons is defined as the weight in a connection in given by the output of another third neuron. This idea can be implemented in the so called Enhanced Neural Networks in which two Multilayer Perceptrons are used; the first one will output the weights that the second MLP uses to computed the desired output. This kind of neural network has universal approximation properties even with lineal activation functions. There exists a clear difference between cooperative and competitive strategies. The former ones are based on the swarm colonies, in which all individuals share its knowledge about the goal in order to pass such information to other individuals to get optimum solution. The latter ones are based on genetic models, that is, individuals can die and new individuals are created combining information of alive one; or are based on molecular/celular behaviour passing information from one structure to another. A swarm-based model is applied to obtain the Neural Network, training the net with a Particle Swarm algorithm.

Approximation to earth material from international normative

For centuries, earth has been used as a construction material. Nevertheless, the normative in this matter is very scattered, and the most developed countries, to carry out a construction with this material implies a variety of technical and legal problems. In this paper we review, in an international level, the normative panorama about earth constructions. It analyzes ninety one standards and regulations of countries all around the five continents. These standards represent the state of art that normalizes the earth as a construction material. In this research we analyze the international standards to earth construction, focusing on durability test (spray and drip erosion tests). It analyzes the differences between methods of test. Also we show all results about these tests in two types of compressed earth block.

Diffusion Gradient Temporal Difference for Cooperative Reinforcement Learning with Linear Function Approximation

We introduce a diffusion-based algorithm in which multiple agents cooperate to predict a common and global statevalue function by sharing local estimates and local gradient information among neighbors. Our algorithm is a fully distributed implementation of the gradient temporal difference with linear function approximation, to make it applicable to multiagent settings. Simulations illustrate the benefit of cooperation in learning, as made possible by the proposed algorithm.