New neighborhood based rough sets

Neighborhood based rough sets are important generalizations of the classical rough sets of Pawlak, as neighborhood operators generalize equivalence classes. In this article, we introduce nine neighborhood based operators and we study the partial order relations between twenty-two different neighborhood operators obtained from one covering. Seven neighborhood operators result in new rough set approximation operators. We study how these operators are related to the other fifteen neighborhood based approximation operators in terms of partial order relations, as well as to seven non-neighborhood-based rough set approximation operators

Leptonic Charged Higgs Decays in the Zee Model

We consider the version of the Zee model where both Higgs doublets couple to leptons. Within this framework we study charged Higgs decays. We focus on a model with minimal number of parameters consistent with experimental neutrino data. Using constraints from neutrino physics we (i) discuss the reconstruction of the parameter space of the model using the leptonic decay patterns of both of the two charged Higgses, $h_{1,2}^{+}\to \ell_{j}^{+}\nu_{i}$, and the decay of the heavier charged Higgs, $h_{2}^{+}\to h^{+}_{1}h^{0}$; (ii) show that the decay rate $\Gamma(h_{1}^{+}\to \mu^{+}\nu_{i})$ in general is enhanced in comparision to the standard two Higgs doublet model while in some regions of parameter space $\Gamma(h_{1}^{+}\to \mu^{+}\nu_{i})$ even dominates over $\Gamma(h_{1}^{+}\to \tau^{+}\nu_{i})$.Comment: 25 pages, 9 figure

Predicting criticality and dynamic range in complex networks: effects of topology

The collective dynamics of a network of coupled excitable systems in response to an external stimulus depends on the topology of the connections in the network. Here we develop a general theoretical approach to study the effects of network topology on dynamic range, which quantifies the range of stimulus intensities resulting in distinguishable network responses. We find that the largest eigenvalue of the weighted network adjacency matrix governs the network dynamic range. Specifically, a largest eigenvalue equal to one corresponds to a critical regime with maximum dynamic range. We gain deeper insight on the effects of network topology using a nonlinear analysis in terms of additional spectral properties of the adjacency matrix. We find that homogeneous networks can reach a higher dynamic range than those with heterogeneous topology. Our analysis, confirmed by numerical simulations, generalizes previous studies in terms of the largest eigenvalue of the adjacency matrix.Comment: 4 pages, 3 figure

Probing neutrino mass with multilepton production at the Tevatron in the simplest R-parity violation model

We analyze the production of multileptons in the simplest supergravity model with bilinear violation of R parity at the Fermilab Tevatron. Despite the small R-parity violating couplings needed to generate the neutrino masses indicated by current atmospheric neutrino data, the lightest supersymmetric particle is unstable and can decay inside the detector. This leads to a phenomenology quite distinct from that of the R-parity conserving scenario. We quantify by how much the supersymmetric multilepton signals differ from the R-parity conserving expectations, displaying our results in the $m_0 \otimes m_{1/2}$ plane. We show that the presence of bilinear R-parity violating interactions enhances the supersymmetric multilepton signals over most of the parameter space, specially at moderate and large $m_0$.Comment: 26 pages, 23 figures. Revised version with some results corrected and references added. Conclusions remain the sam

A model for microinstability destabilization and enhanced transport in the presence of shielded 3-D magnetic perturbations

A mechanism is presented that suggests shielded 3-D magnetic perturbations can destabilize microinstabilities and enhance the associated anomalous transport. Using local 3-D equilibrium theory, shaped tokamak equilibria with small 3-D deformations are constructed. In the vicinity of rational magnetic surfaces, the infinite-n ideal MHD ballooning stability boundary is strongly perturbed by the 3-D modulations of the local magnetic shear associated with the presence of nearresonant Pfirsch-Schluter currents. These currents are driven by 3-D components of the magnetic field spectrum even when there is no resonant radial component. The infinite-n ideal ballooning stability boundary is often used as a proxy for the onset of virulent kinetic ballooning modes (KBM) and associated stiff transport. These results suggest that the achievable pressure gradient may be lowered in the vicinity of low order rational surfaces when 3-D magnetic perturbations are applied. This mechanism may provide an explanation for the observed reduction in the peak pressure gradient at the top of the edge pedestal during experiments where edge localized modes have been completely suppressed by applied 3-D magnetic fields

Statistical Properties of Avalanches in Networks

We characterize the distributions of size and duration of avalanches propagating in complex networks. By an avalanche we mean the sequence of events initiated by the externally stimulated `excitation' of a network node, which may, with some probability, then stimulate subsequent firings of the nodes to which it is connected, resulting in a cascade of firings. This type of process is relevant to a wide variety of situations, including neuroscience, cascading failures on electrical power grids, and epidemology. We find that the statistics of avalanches can be characterized in terms of the largest eigenvalue and corresponding eigenvector of an appropriate adjacency matrix which encodes the structure of the network. By using mean-field analyses, previous studies of avalanches in networks have not considered the effect of network structure on the distribution of size and duration of avalanches. Our results apply to individual networks (rather than network ensembles) and provide expressions for the distributions of size and duration of avalanches starting at particular nodes in the network. These findings might find application in the analysis of branching processes in networks, such as cascading power grid failures and critical brain dynamics. In particular, our results show that some experimental signatures of critical brain dynamics (i.e., power-law distributions of size and duration of neuronal avalanches), are robust to complex underlying network topologies.Comment: 11 pages, 7 figure

Comparing the reliability of networks by spectral analysis

We provide a method for the ranking of the reliability of two networks with the same connectance. Our method is based on the Cheeger constant linking the topological property of a network with its spectrum. We first analyze a set of twisted rings with the same connectance and degree distribution, and obtain the ranking of their reliability using their eigenvalue gaps. The results are generalized to general networks using the method of rewiring. The success of our ranking method is verified numerically for the IEEE57, the Erd\H{o}s-R\'enyi, and the Small-World networks.Comment: 7 pages, 3 figure

Neutrino masses in SU(5)×U(1)FSU(5)\times U(1)_F with adjoint flavons

We present a $SU(5)\times U(1)_F$ supersymmetric model for neutrino masses and mixings that implements the seesaw mechanism by means of the heavy SU(2) singlets and triplets states contained in three adjoints of SU(5). We discuss how Abelian $U(1)_F$ symmetries can naturally yield non-hierarchical light neutrinos even when the heavy states are strongly hierarchical, and how it can also ensure that $R$--parity arises as an exact accidental symmetry. By assigning two flavons that break $U(1)_F$ to the adjoint representation of SU(5) and assuming universality for all the fundamental couplings, the coefficients of the effective Yukawa and Majorana mass operators become calculable in terms of group theoretical quantities. There is a single free parameter in the model, however, at leading order the structure of the light neutrinos mass matrix is determined in a parameter independent way.Comment: 16 pages, 9 figures. Included contributions to neutrino masses from the triplet states contained in the three adjoints of SU(5

Dimension reduction for systems with slow relaxation

We develop reduced, stochastic models for high dimensional, dissipative dynamical systems that relax very slowly to equilibrium and can encode long term memory. We present a variety of empirical and first principles approaches for model reduction, and build a mathematical framework for analyzing the reduced models. We introduce the notions of universal and asymptotic filters to characterize `optimal' model reductions for sloppy linear models. We illustrate our methods by applying them to the practically important problem of modeling evaporation in oil spills.Comment: 48 Pages, 13 figures. Paper dedicated to the memory of Leo Kadanof

Incorporating Financial Sector Risk Into Monetary Policy Models: Application to Chile

This paper builds a model of financial sector vulnerability and integrates it into a macroeconomic framework, typically used for monetary policy analysis. The main question to be answered with the integrated model is whether or not the central bank should include explicitly the financial stability indicator in its monetary policy (interest rate) reaction function. It is found in general, that including distance-to-default (dtd) of the banking system in the central bank reaction function reduces both inflation and output volatility. Moreover, the results are robust to different model calibrations. Indeed, it is more efficient to include dtd in the reaction function with higher coefficient of exchange rate pass through, and with a larger impact of financial vulnerability on the exchange rate, as well as on GDP (or the reverse, there is more effect of GDP on bank’s equity—i.e., what we call endogeneity).