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We give a new and more manageable characterization for Cyclic Pólya Frequency functions of order 3 (CPF3). Our result also improves present knowledge concerning smoothness properties in CPF. In particular, a conjecture of Mairhuber, Schoenberg, and Williamson, On variation-diminishing transformations on the circle, Rend. Circ. Mat. Palermo (2) 8 (1959), 1-30, about discontinuous CPF functions is established.

In this paper, a novel expression for the convergence of an iterative learning control algorithm for sampled linear multivariable systems is stated. The convergence analysis shows that, applying this algorithm, the input sequence converges to the system output inverse sequence, specified as a finite-time output trajectory, with zero tracking error on all the sampled points. Also, it gives insight on the learning gain matrix selection to act on the convergence speed or the decoupling of inputs, allowing for an easy tuning using methods from modern control theory. The results are illustrated by some examples, showing a number of options to be investigated.

We found a new important tree-level contribution to muon–electron nuclear conversion from neutrino exchange between two quarks in the same nucleon and demonstrated that this process, contrary to common belief, can be observed in the near future experiments if there exists a mixed sterile-active neutrino state νh heavier than the quark confinement scale Λc ∼ 1 GeV. From the present non-observation of muon–electron conversion we derive new experimental constraints on νh − νe,µ mixing

We study the process pp¯ → γγγ as a signal for associated photon–technipion production at the Tevatron. This is a clean signature with relatively low background. Resonant and non-resonant contributions are included and we show that technicolor models can be effectively probed in this mode

The precise knowledge of the statistical properties of synthetic aperture radar (SAR) data plays a central role in image processing and understanding. These properties can be used for discriminating types of land uses and to develop specialized filters for speckle noise reduction, among other applications. In this work we assume the distribution G0 A as the universal model for multilook amplitude SAR images under the multiplicative model. We study some important properties of this distribution and some classical estimators for its parameters, such as Maximum Likelihood (ML) estimators, but they can be highly influenced by small percentages of ‘outliers’, i.e., observations that do not fully obey the basic assumptions. Hence, it is important to find Robust Estimators. One of the best known classes of robust techniques is that of M estimators, which are an extension of the ML estimation method. We compare those estimation procedures by means of a Monte Carlo experiment.

We study the static quantum potential for a theory of anti-symmetric tensor fields that results from the condensation of topological defects, within the framework of the gauge-invariant but path-dependent variables formalism. Our calculations show that the interaction energy is the sum of a Yukawa and a linear potentials, leading to the confinement of static probe charges.

The Self Organizing Map (SOM) model is an unsupervised learning neural network that has been successfully applied as a data mining tool. The advantages of the SOMs are that they preserve the topology of the data space, they project high dimensional data to a lower dimension representation scheme, and are able to find similarities in the data. However, the learning algorithm of the SOM is sensitive to the presence of noise and outliers as we will show in this paper. Due to the influence of the outliers in the learning process, some neurons (prototypes) of the ordered map get located far from the majority of data, and therefore, the network will not effectively represent the topological structure of the data under study. In this paper, we propose a variant to the learning algorithm that is robust under the presence of outliers in the data by being resistant to these deviations. We call this algorithm Robust SOM (RSOM). We will illustrate our technique on synthetic and real data sets.

Nanotubes have been proved as promising candidates for many technological applications in the nanoscale word and different physical properties have been studied and measured along the few recent years. Here we investigate the role played by external magnetic and electric fields on the electronic properties of toroidal and cylindrical straight carbon nanotubes. A single-π band tight-binding Hamiltonian is used and two types of model-calculations are adopted: real-space renormalization techniques, based on Green function formalism, and straight diagonalization calculation. Both electric and magnetic fields may be properly applied, in different configurations, to modify the energy spectra and transport properties, providing metal-insulator transitions for particular tube geometries.

We study nuclear µ−–e− conversion in the general framework of an effective Lagrangian approach without referring to any specific realization of the physics beyond the Standard Model (SM) responsible for lepton flavor violation (L/f ). We show that vector meson exchange between lepton and nucleon currents plays an important role in this process. A new issue of this mechanism is the presence of the strange quark vector current contribution induced by the φ meson. This allows us to extract new limits on the L/f lepton–quark effective couplings from the existing experimental data.

It has been noticed that confinement effects can be described by the addition of a (√−FaμνFaμν) term in the Lagrangian density. We now study the combined effect of such "confinement term" and that of a mass term. The surprising result is that the interplay between these two terms gives rise to a Coulomb interaction. Our picture has a certain correspondence with the quasiconfinement picture described by Giles, Jaffe and de Rujula for QCD with symmetry breaking.

There exists a substantial literature dealing with the problem of errors-in-variables identification. It is known, for example, that there is an equivalence class of models that give compatible descriptions of the input-output data. In the current paper, we impose a mild restriction so as to avoid certain singular possibilities. This leads to a parameterization of the equivalence class of models via a single real parameter. We then use this result to show that there exists a model which is optimal in the sense that minimizes the maximal weighted infinity norm of the error between the chosen model and all members of the equivalence class. This model is unique and is independent of the weighting function used in the infinity norm. It is thus the natural choice to be used in applications such as robust control. The result is also compared with more conventional estimates provided by prediction error methods.

This paper explores the robustness issues that arise in the identification of continuous-time systems from sampled data. A key observation is that, in practice, one cannot rely upon the fidelity of the model at high frequencies. This implies that any result which implicitly or explicitly depends upon the folding of high frequency components down to lower frequencies will be inherently non-robust. We illustrate this point by referring to the identification of continuous-time auto-regressive stochastic models from sampled data. We argue that traditional approaches to this problem are sensitive to high frequency modelling errors. We also propose an alternative maximum likelihood procedure in the frequency domain, which is robust to high frequency modelling errors.

Magnetars are a subclass of neutron stars whose intense soft-gamma-ray bursts and quiescent X-ray emission are believed to be powered by the decay of a strong internal magnetic field. We reanalyze neutrino emission in such stars in the plausibly relevant regime in which the Landau band spacing $\Delta E$ of both protons and electrons is much larger than kT (where k is the Boltzmann constant and T is the temperature), but still much smaller than the Fermi energies. Focusing on the direct Urca process, we find that the emissivity oscillates as a function of density or magnetic field, peaking when the Fermi level of the protons or electrons lies about $\sim$3 kT above the bottom of any of their Landau bands. The oscillation amplitude is comparable to the average emissivity when $\Delta E$ is roughly the geometric mean of kT and the Fermi energy (excluding mass), i.e., at fields much weaker than required to confine all particles to the lowest Landau band. Since the density and magnetic field strength vary continuously inside the neutron star, there will be alternating surfaces of high and low emissivity. Globally, these oscillations tend to average out, making it unclear whether there will be any observable effects.

In this paper we present a method to detect edges in images. The method consists of using a 3x3 pixel mask to scan the image, moving it from left to right and from top to bottom, one pixel at a time. Each time it is placed on the image, an agglomerative hierarchical cluster analysis is applied to the eight outer pixels. When there is more than one cluster, it means that window is on an edge, and the central pixel is marked as an edge point. After scanning all the image, we obtain a new image showing the marked pixels around the existing edges of the image. Then a thinning algorithm is applied so that the edges are well defined. The method results to be particularly efficient when the image is contaminated. In those cases, a previous restoration method is applied.

In his thesis, S.P. Robinson made a conjecture concerning the polynomials namely that zPβ n is prestarlike of order (3− β)/2. These polynomials are closely related to the de la Vallée Poussin means (the case β= 1). We prove this conjecture in a more general form and show that these functions constitute a sort of two-dimensional subordination chain. These results are then compared with similar ones for Cesáro means of various orders.

The Neural Gas (NG) is a Vector Quantization technique where a set of prototypes self organize to represent the topology structure of the data. The learning algorithm of the Neural Gas consists in the estimation of the prototypes location in the feature space based in the stochastic gradient descent of an Energy function. In this paper we show that when deviations from idealized distribution function assumptions occur, the behavior of the Neural Gas model can be drastically affected and will not preserve the topology of the feature space as desired. In particular, we show that the learning algorithm of the NG is sensitive to the presence of outliers due to their influence over the adaptation step. We incorporate a robust strategy to the learning algorithm based on M-estimators where the influence of outlying observations are bounded. Finally we make a comparative study of several estimators where we show the superior performance of our proposed method over the original NG, in static data clustering tasks on both synthetic and real data sets.

We present a description of the effect of the surface roughness on the energy straggling associated to the energy loss distributions of protons transmitted through a self supported metallic thin foil. For this purpose we prepared a polycrystalline gold thin films using the standard sputtering method with different deposition rates. The statistics of the surface height distribution induced in these thin films were determined using Atomic Force Microscopy. The measured surface roughness allowed us to quantify the ion energy loss straggling in these samples for different deposition parameters and as a function of the incident ion energy.

The Neural Gas (NG) is a Vector Quantization technique where a set of prototypes self organize to represent the topology structure of the data. The learning algorithm of the Neural Gas consists in the estimation of the prototypes location in the feature space based in the stochastic gradient descent of an Energy function. In this paper we show that when deviations from idealized distribution function assumptions occur, the behavior of the Neural Gas model can be drastically affected and will not preserve the topology of the feature space as desired. In particular, we show that the learning algorithm of the NG is sensitive to the presence of outliers due to their influence over the adaptation step. We incorporate a robust strategy to the learning algorithm based on M-estimators where the influence of outlying observations are bounded. Finally we make a comparative study of several estimators where we show the superior performance of our proposed method over the original NG, in static data clustering tasks on both synthetic and real data sets.

We analyze double Higgs boson production at the Large Hadron Collider in the context of Little Higgs models. In double Higgs production, the diagrams involved are directly related to those that cause the cancellation of the quadratic divergence of the Higgs self-energy, providing a robust prediction for this class of models. We find that in extensions of this model with the inclusion of a so-called T-parity, there is a significant enhancement in the cross sections as compared to the Standard Model.