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Search Results: 1 - 10 of 228152 matches for " R. Martinez "
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Innervation of the hair cell and the auditory neuron
Martinez Monedero R
Revista de la Sociedad Otorrinolaringológica de Castilla y León, Cantabria y La Rioja , 2010,
Abstract: The knowledge of the molecular biology of the synapses between the hair cell and theauditory neurons is important for the developing of new strategies in the therapy ofthe sensorineural hearing loss.
Multi-spin correlation functions for the Z-Invariant Ising model
J. R. Reyes Martinez
Physics , 1997,
Abstract: Continuing our work hep-th/9609135 where a explicit formula for the two-point functions of the two dimensional Z-invariant Ising model were found. I obtain here different results for the higher correlation functions and several consistency checks are done.
Possibilities of MgB2/Cu Wires Fabricated by the in-situ Reaction Technique
E. Martinez,R. Navarro
Physics , 2003,
Abstract: The superconducting properties of copper-sheathed MgB2 wires fabricated by conventional powder-in-tube techniques and the in-situ reaction procedure are analysed. The influence of the processing conditions and initial (1+x)Mg + 2B (x = 0, 0.1, 0.2) proportions of the precursors on the critical current values of the wires have been studied. In particular, the limits of the available temperatures and times for heat treatments imposed by the chemical reaction between Mg and Cu, and their effect on the superconducting properties of the wires, are discussed. The analysis includes the study of the sample microstructure and phase composition as well as of the critical current temperature and field dependences. The wires show high thermal stability during direct transport measurements and carry a critical current density of 1.3x109 A/m2 at 15 K in the self-field for optimised processing conditions.
Transfer Functions of Generalized Bessel Polynomials
Jose R. Martinez
Mathematics , 2014,
Abstract: The stability and approximation properties of transfer functions of generalized Bessel polynomials (GBP) are investigated. Sufficient conditions are established for the GBP to be Hurwitz. It is shown that the Pad\'e approximants of $e^{-s}$ are related to the GBP. An infinite subset of stable Pad\'e functions useful for approximating a constant time delay is defined and its approximation properties examined. The lowpass Pad\'e functions are compared with an approximating function suggested by Budak. Basic limitations of Budak's approximation are derived.
Increase in water column denitrification during the last deglaciation: the influence of oxygen demand in the eastern equatorial Pacific
P. Martinez,R. S. Robinson
Biogeosciences (BG) & Discussions (BGD) , 2010,
Abstract: Here we present organic export production and nitrogen isotope results spanning the last 30 000 years from a core recovered off Costa Rica (Ocean Drilling Program (ODP) Site 1242) on the leading edge of the oxygen minimum zone of the Eastern Tropical North Pacific. Marine export production reveals glacial-interglacial variations with low organic matter (total organic carbon and total nitrogen) contents during warm intervals, twice more during cold episodes and double peaked maximum during the deglaciation, between ~15.5–18.5 and 11–13 ka B.P. When this new export production record is compared with four nearby cores from within the Eastern Pacific along the Equatorial divergence, good agreement between all the cores is observed. The major feature is a maximum of export during the early deglaciation. As for export production, water-column denitrification, represented by sedimentary δ15N records, along the Eastern tropical North and South Pacific between 15° N and 36° S is also coherent over the last deglaciation. Each of the nitrogen isotope profiles indicate that denitrification increased abruptly at 19 ka B.P to a maximum during the early deglaciation, confirming a typical Antarctic timing. It is proposed that the increase in export production and then in subsurface oxygen demand lead to an intensification of water-column denitrification within the oxygen minimum zones in the easternmost Pacific at the time of the last deglaciation. The triggering mechanism would have been primarily linked to an increase in preformed nutrients contents feeding the Equatorial Undercurrent driven by the resumption of overturning in the Southern Ocean and the return of nutrients from the deep ocean to the sea-surface. An increase in equatorial wind-driven upwelling of sub-surface nutrient-rich waters could have played the role of an amplifier.
Increase in water column denitrification during the deglaciation controlled by oxygen demand in the eastern equatorial Pacific
P. Martinez,R. S. Robinson
Biogeosciences Discussions , 2009,
Abstract: Here we present organic export production and isotopic nitrogen results over the last 30 000 years from one core localized off Costa Rica (ODP Site 1242) on the leading edge of the oxygen minimum zone of the Eastern Tropical North Pacific. Marine export production reveals glacial-interglacial variations with low organic matter (total organic carbon and total nitrogen) contents during warm intervals, twice more during cold episodes and double peaked maximum during the deglaciation, between ~15.5–18.5 and 11–13 ka BP. When this new export production record is compared with four nearby cores localized within the Eastern Pacific along the Equatorial divergence, a good agreement between all the cores is observed, with the major feature being a maximum of export during the early deglaciation. As for export production, water-column denitrification represented by sedimentary δ15N records along the Eastern tropical North and South Pacific between 15° N and 36° S is coherent as well over the last deglaciation period. The whole isotopic nitrogen profiles indicate that denitrification increased abruptly at 19 ka BP to a maximum during the early deglaciation, confirming a typical Antarctic timing. It is proposed that the increase in export production and then in subsurface oxygen demand lead to an intensification of water-column denitrification within the oxygen minimum zones in the easternmost Pacific at the time of the last deglaciation. The triggering mechanism would have been primarily linked to an increase in preformed nutrients contents feeding the Equatorial Undercurrent driven by the resumption of overturning in the Southern Ocean and the return of nutrients from the deep ocean to the sea-surface. An increase in equatorial wind-driven upwelling of sub-surface nutrient-rich waters could have played the role of an amplifier.
SUSY QM, symmetries and spectrum generating algebras for two-dimensional systems
D Martinez,R D Mota
Physics , 2008, DOI: 10.1016/j.aop.2007.07.001
Abstract: We show in a systematic and clear way how factorization methods can be used to construct the generators for hidden and dynamical symmetries. This is shown by studying the 2D problems of hydrogen atom, the isotropic harmonic oscillator and the radial potential $A\rho^{2\zeta-2}-B\rho^{\zeta-2}$. We show that in these cases the non-compact (compact) algebra corresponds to so(2,1) (su(2)).
Killing of supports on graded algebras
R. Martinez Villa,M. Saorin
Mathematics , 2005,
Abstract: Killing of supports along subsets $\mathcal{U}$ of a group $G$ and regradings along certain maps of groups $\phi :G'\longrightarrow G$ are studied, in the context of group-graded algebras. We show that, under precise condition on $\mathcal{U}$ and $\phi$, the graded module theories over the initial and the final algebras are functorially well-connected. Special attention is paid to $G=\mathbf{Z}$, in which case the results can be applied to $n$-Koszul algebras.
An Extensive Evaluation of Filtering Misclassified Instances in Supervised Classification Tasks
Michael R. Smith,Tony Martinez
Computer Science , 2013,
Abstract: Removing or filtering outliers and mislabeled instances prior to training a learning algorithm has been shown to increase classification accuracy. A popular approach for handling outliers and mislabeled instances is to remove any instance that is misclassified by a learning algorithm. However, an examination of which learning algorithms to use for filtering as well as their effects on multiple learning algorithms over a large set of data sets has not been done. Previous work has generally been limited due to the large computational requirements to run such an experiment, and, thus, the examination has generally been limited to learning algorithms that are computationally inexpensive and using a small number of data sets. In this paper, we examine 9 learning algorithms as filtering algorithms as well as examining the effects of filtering in the 9 chosen learning algorithms on a set of 54 data sets. In addition to using each learning algorithm individually as a filter, we also use the set of learning algorithms as an ensemble filter and use an adaptive algorithm that selects a subset of the learning algorithms for filtering for a specific task and learning algorithm. We find that for most cases, using an ensemble of learning algorithms for filtering produces the greatest increase in classification accuracy. We also compare filtering with a majority voting ensemble. The voting ensemble significantly outperforms filtering unless there are high amounts of noise present in the data set. Additionally, we find that a majority voting ensemble is robust to noise as filtering with a voting ensemble does not increase the classification accuracy of the voting ensemble.
A Comparative Evaluation of Curriculum Learning with Filtering and Boosting
Michael R. Smith,Tony Martinez
Computer Science , 2013,
Abstract: Not all instances in a data set are equally beneficial for inferring a model of the data. Some instances (such as outliers) are detrimental to inferring a model of the data. Several machine learning techniques treat instances in a data set differently during training such as curriculum learning, filtering, and boosting. However, an automated method for determining how beneficial an instance is for inferring a model of the data does not exist. In this paper, we present an automated method that orders the instances in a data set by complexity based on the their likelihood of being misclassified (instance hardness). The underlying assumption of this method is that instances with a high likelihood of being misclassified represent more complex concepts in a data set. Ordering the instances in a data set allows a learning algorithm to focus on the most beneficial instances and ignore the detrimental ones. We compare ordering the instances in a data set in curriculum learning, filtering and boosting. We find that ordering the instances significantly increases classification accuracy and that filtering has the largest impact on classification accuracy. On a set of 52 data sets, ordering the instances increases the average accuracy from 81% to 84%.
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