865 resultados para Aberdeen Proving Ground (Md.)--Maps.


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The ground state (J = 0) electronic correlation energy of the 4-electron Be-sequence is calculated in the Multi-Configuration Dirac-Fock approximation for Z = 4-20. The 4 electrons were distributed over the configurations arising from the 1s, 2s, 2p, 3s, 3p and 3d orbitals. Theoretical values obtained here are in good agreement with experimental correlation energies.

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Self-consistent relativistic Dirac-Hartree-Fock calculations have been made of some lowlying electronic energies for the atoms of all elements in ground-state ds^2 electron configurations. The results indicate that, contrary to some previous estimates, the ground electronic state of atomic Lr could be in either the 5f^14 6d7s^2 or the 5f^14 7p 7s^2 electron configuration. The separation between the lowest energy level of the 5f^14 6d7s^2 configuration and the lowest energy level of the 5f^14 7p7s^2 configuration is estimated to be (0 ± 3) x 10^3 cm^-1 for atomic Lr.

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Correlation energies for all isoelectronic sequences of 2 to 20 electrons and Z = 2 to 25 are obtained by taking differences between theoretical total energies of Dirac-Fock calculations and experimental total energies. These are pure relativistic correlation energies because relativistic and QED effects are already taken care of. The theoretical as well as the experimental values are analysed critically in order to get values as accurate as possible. The correlation energies obtained show an essentially consistent behaviour from Z = 2 to 17. For Z > 17 inconsistencies occur indicating errors in the experimental values which become very large for Z > 25.

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The first direct observation of a hyperfine splitting in the optical regime is reported. The wavelength of the M1 transition between the F = 4 and F = 5 hyperfine levels of the ground state of hydrogenlike ^209 Bi^82+ was measured to be \lamda_0 = 243.87(4) nm by detection of laser induced fluorescence at the heavy-ion storage ring ESR at GSI. In addition, the lifetime of the laser excited F = 5 sublevel was determined to be \tau_0 = 0.351(16) ms. The method can be applied to a number of other nuclei and should allow a novel test of QED corrections in the previously unexplored combination of strong magnetic and electric fields in highly charged ions.

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The starting point of our reflections is a classroom situation in grade 12 in which it was to be proved intuitively that non-trivial solutions of the differential equation f' = f have no zeros. We give a working definition of the concept of preformal proving, as well as three examples of preformal proofs. Then we furnish several such proofs of the aforesaid fact, and we analyse these proofs in detail. Finally, we draw some conclusions for mathematics in school and in teacher training.

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Baylis & Driver (Nature Neuroscience, 2001) have recently presented data on the response of neurons in macaque inferotemporal cortex (IT) to various stimulus transformations. They report that neurons can generalize over contrast and mirror reversal, but not over figure-ground reversal. This finding is taken to demonstrate that ``the selectivity of IT neurons is not determined simply by the distinctive contours in a display, contrary to simple edge-based models of shape recognition'', citing our recently presented model of object recognition in cortex (Riesenhuber & Poggio, Nature Neuroscience, 1999). In this memo, I show that the main effects of the experiment can be obtained by performing the appropriate simulations in our simple feedforward model. This suggests for IT cell tuning that the possible contributions of explicit edge assignment processes postulated in (Baylis & Driver, 2001) might be smaller than expected.

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Starting with logratio biplots for compositional data, which are based on the principle of subcompositional coherence, and then adding weights, as in correspondence analysis, we rediscover Lewi's spectral map and many connections to analyses of two-way tables of non-negative data. Thanks to the weighting, the method also achieves the property of distributional equivalence

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Self-organizing maps (Kohonen 1997) is a type of artificial neural network developed to explore patterns in high-dimensional multivariate data. The conventional version of the algorithm involves the use of Euclidean metric in the process of adaptation of the model vectors, thus rendering in theory a whole methodology incompatible with non-Euclidean geometries. In this contribution we explore the two main aspects of the problem: 1. Whether the conventional approach using Euclidean metric can shed valid results with compositional data. 2. If a modification of the conventional approach replacing vectorial sum and scalar multiplication by the canonical operators in the simplex (i.e. perturbation and powering) can converge to an adequate solution. Preliminary tests showed that both methodologies can be used on compositional data. However, the modified version of the algorithm performs poorer than the conventional version, in particular, when the data is pathological. Moreover, the conventional ap- proach converges faster to a solution, when data is \well-behaved". Key words: Self Organizing Map; Artificial Neural networks; Compositional data

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Es presenta la primera part de l'Estudi del fons pictòric, en la major part, d'època barroca que procedeix del Museo del Prado. Fons dipositat a l'Antic Museu Provincial per Reial Ordre entre 1880 i 1882

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Es presenta la segona part de l'Estudi del fons pictòric, en la major part, d'època contemporània que procedeix del Museo del Prado. Fons dipositat a l'Antic Museu Provincial per Reial Ordre entre 1876

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Es presenta la segona part de l'Estudi del fons pictòric, en la major part, d'època contemporània que procedeix del Museo del Prado. Fons dipositat a l'Antic Museu Provincial per Reial Ordre entre 1876

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Es presenta la primera part de l'Estudi del fons pictòric, en la major part, d'època barroca que procedeix del Museo del Prado. Fons dipositat a l'Antic Museu Provincial per Reial Ordre entre 1880 i 1882

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Wikiloc es un servicio web gratuito para visualizar y compartir rutas y puntos de interés GPS. Utilizando software libre y la API de Google Maps, Wikiloc hace la función de base de datos personal de localizaciones GPS. Desde cualquier acceso a Internet un usuario de GPS puede cargar sus datos GPS y al momento visualizar la ruta y waypoints con distinta cartografía de fondo, incluidos servidores de mapas externos WMS (Web Map Service) o descargarlo a Google Earth para ver en 3D. Paralelamente se muestra el perfil de altura, distancia, desniveles acumulados y las fotos o comentarios que el usuario quiera añadir