On the Viability of Minimal Neutrinophilic Two-Higgs-Doublet Models

We study the constraints that electroweak precision data can impose, after the discovery of the Higgs boson by the LHC, on neutrinophilic two-Higgs-doublet models which comprise one extra $SU(2)\times U(1)$ doublet and a new symmetry, namely a spontaneously broken $\mathbb{Z}_2$ or a softly broken global $U(1)$. In these models the extra Higgs doublet, via its very small vacuum expectation value, is the sole responsible for neutrino masses. We find that the model with a $\mathbb{Z}_2$ symmetry is basically ruled out by electroweak precision data, even if the model is slightly extended to include extra right-handed neutrinos, due to the presence of a very light scalar. While the other model is still perfectly viable, the parameter space is considerably constrained by current data, specially by the $T$ parameter. In particular, the new charged and neutral scalars must have very similar masses.Comment: 22 pages, 3 figures, references and comments added, conclusions unchanged, matches version to appear in JHE

Three-dimensional aspects of fluid flows in channels. II. Effects of Meniscus and Thin Film regimes on Viscous Fingers

We perform a three-dimensional study of steady state viscous fingers that develop in linear channels. By means of a three-dimensional Lattice-Boltzmann scheme that mimics the full macroscopic equations of motion of the fluid momentum and order parameter, we study the effect of the thickness of the channel in two cases. First, for total displacement of the fluids in the channel thickness direction, we find that the steady state finger is effectively two-dimensional and that previous two-dimensional results can be recovered by taking into account the effect of a curved meniscus across the channel thickness as a contribution to surface stresses. Secondly, when a thin film develops in the channel thickness direction, the finger narrows with increasing channel aspect ratio in agreement with experimental results. The effect of the thin film renders the problem three-dimensional and results deviate from the two-dimensional prediction.Comment: 9 pages, 10 figure

A complexity approach for identifying aesthetic composite landscapes

Third European Conference, EvoMUSART 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers[Abstract] The present paper describes a series of features related to complexity which may allow to estimate the complexity of an image as a whole, of all the elements integrating it and of those which are its focus of attention. Using a neural network to create a classifier based on those features an accuracy over 85% in an aesthetic composition binary classification task is achieved. The obtained network seems to be useful for the purpose of assessing the Aesthetic Composition of landscapes. It could be used as part of a media device for facilitating the creation of images or videos with a more professional aesthetic composition.Galicia. Consellería de Innovación, Industria e Comercio; PGIDIT 10TIC105008PRPortugal. Fundação para a Ciência e a Tecnologia; PTDC/EIA-EIA/115667/200

Catching the Bound States in the Continuum of a Phantom Atom in Graphene

We explore theoretically the formation of bound states in the continuum (BICs) in graphene hosting two collinear adatoms situated at different sides of the sheet and at the center of the hexagonal cell, where a phantom atom of a fictitious lattice emulates the six carbons of the cell. We verify that in this configuration the local density of states (LDOS) near the Dirac points exhibits two characteristic features: i) the cubic dependence on energy instead of the linear one for graphene as found in New J. Phys. 16, 013045 (2014) and ii) formation of BICs as aftermath of a Fano destructive interference assisted by the Coulomb correlations in the adatoms. For the geometry where adatoms are collinear to carbon atoms, we report absence of BICs

Quantum phase transition triggering magnetic BICs in graphene

Graphene hosting a pair of collinear adatoms in the phantom atom configuration has pseudogap with cubic scaling on energy, $\Delta\propto|\varepsilon|^{3}$ which leads to the appearance of spin-degenerate bound states in the continuum (BICs) [Phys. Rev. B 92, 045409 (2015)]. In the case when adatoms are locally coupled to a single carbon atom the pseudogap scales linearly with energy, which prevents the formation of BICs. In this Letter, we explore the effects of non-local coupling characterized by the Fano factor of interference $q_{0},$ tunable by changing the slope of the Dirac cones in the graphene band-structure. We demonstrate that three distinct regimes can be identified: i) for $q_{0}<q_{c1}$ (critical point) a mixed pseudogap $\Delta\propto|\varepsilon|,|\varepsilon|^{2}$ appears yielding a phase with spin-degenerate BICs; ii) near $q_{0}=q_{c1}$ when $\Delta\propto|\varepsilon|^{2}$ the system undergoes a quantum phase transition in which the new phase is characterized by magnetic BICs and iii) at a second critical value $q_{0}>q_{c2}$ the cubic scaling of the pseudogap with energy $\Delta\propto|\varepsilon|^{3}$ characteristic to the phantom atom configuration is restored and the phase with non-magnetic BICs is recovered. The phase with magnetic BICs can be described in terms of an effective intrinsic exchange field of ferromagnetic nature between the adatoms mediated by graphene monolayer. We thus propose a new type of quantum phase transition resulting from the competition between the states characterized by spin-degenerate and magnetic BICs

Fast non-negative deconvolution for spike train inference from population calcium imaging

Calcium imaging for observing spiking activity from large populations of neurons are quickly gaining popularity. While the raw data are fluorescence movies, the underlying spike trains are of interest. This work presents a fast non-negative deconvolution filter to infer the approximately most likely spike train for each neuron, given the fluorescence observations. This algorithm outperforms optimal linear deconvolution (Wiener filtering) on both simulated and biological data. The performance gains come from restricting the inferred spike trains to be positive (using an interior-point method), unlike the Wiener filter. The algorithm is fast enough that even when imaging over 100 neurons, inference can be performed on the set of all observed traces faster than real-time. Performing optimal spatial filtering on the images further refines the estimates. Importantly, all the parameters required to perform the inference can be estimated using only the fluorescence data, obviating the need to perform joint electrophysiological and imaging calibration experiments.Comment: 22 pages, 10 figure