Mapping the spatial distribution of entanglement in optical lattices

We study the entangled states that can be generated using two species of atoms trapped in independently movable, two-dimensional optical lattices. We show that using two sets of measurements it is possible to measure a set of entanglement witness operators distributed over arbitrarily large regions of the lattice, and use these witnesses to produce two-dimensional plots of the entanglement content of these states. We also discuss the influence of noise on the states and on the witnesses, as well as connections to ongoing experiments.Comment: 2 figures, 6 page

Seeing Majorana fermions in time-of-flight images of spinless fermions coupled by s-wave pairing

The Chern number, nu, as a topological invariant that identifies the winding of the ground state in the particle-hole space, is a definitive theoretical signature that determines whether a given superconducting system can support Majorana zero modes. Here we show that such a winding can be faithfully identified for any superconducting system (p-wave or s-wave with spin-orbit coupling) through a set of time-of-flight measurements, making it a diagnostic tool also in actual cold atom experiments. As an application, we specialize the measurement scheme for a chiral topological model of spinless fermions. The proposed model only requires the experimentally accessible s-wave pairing and staggered tunnelling that mimics spin-orbit coupling. By adiabatically connecting this model to Kitaev's honeycomb lattice model, we show that it gives rise to nu = \pm 1 phases, where vortices bind Majorana fermions, and nu=\pm 2 phases that emerge as the unique collective state of such vortices. Hence, the preparation of these phases and the detection of their Chern numbers provide an unambiguous signature for the presence of Majorana modes. Finally, we demonstrate that our detection procedure is resilient against most inaccuracies in experimental control parameters as well as finite temperature.Comment: 9+4 pages, 11 figures, expanded versio

Generating and verifying graph states for fault-tolerant topological measurement-based quantum computing in 2D optical lattices

We propose two schemes for implementing graph states useful for fault-tolerant topological measurement-based quantum computation in 2D optical lattices. We show that bilayer cluster and surface code states can be created by global single-row and controlled-Z operations. The schemes benefit from the accessibility of atom addressing on 2D optical lattices and the existence of an efficient verification protocol which allows us to ensure the experimental feasibility of measuring the fidelity of the system against the ideal graph state. The simulation results show potential for a physical realization toward fault-tolerant measurement-based quantum computation against dephasing and unitary phase errors in optical lattices.Comment: 6 pages and 4 figures (minor changed

Individual differences in frustration responses

El Contraste Sucesivo Negativo consumatorio (CSNc) es uno de los procedimientos más utilizados para el estudio de las respuestas de frustración en ratas. En este trabajo se presentan dos experimentos en los que se estudian las asociaciones entre la intensidad y duración del CSNc y las diferencias individuales en el miedo incondicionado / ansiedad, la actividad locomotora, la búsqueda de novedad y la sensibilidad al dolor físico. Los resultados son discutidos en torno a las teorías de la frustración. Al igual que en estudios previos, los análisis no arrojaron asociaciones consistentes entre las variables estudiadas, aunque se propone que la respuesta inicial ante la devaluación del incentivo podría estar relacionada con los niveles de búsqueda de novedad.The consummatory Successive Negative Contrast (cSNC) is one of the most used methods for studying frustration responses in rats. In this paper we present two experiments in which we study the associations between the intensity and duration of cSNC and individual differences in unconditioned fear / anxiety, locomotor activity, novelty seeking and sensitivity to physical pain. The results are discussed in the context of the theories on frustration. As in previous studies, the analysis yielded no consistent associations between the variables studied, although it is proposed that initial response to incentive devaluation could be related to novelty seeking levels.Fil: Cuenya, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Médicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; ArgentinaFil: Fosacheca, Sandro Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Médicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; ArgentinaFil: Mustaca, Alba Elisabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Médicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina. Universidad Abierta Interamericana; Argentin

El fraude fiscal en España. Una estimación con datos de contabilidad nacional

[ES]Análisis del fraude fiscal en España atendiendo a tres pilares fundamentales: analizar las causas, proponer soluciones y cuantificar su magnitud. Todo el estudio culmina con el núcleo de la tesis cuyo objetivo es ofrecer una magnitud del fraude fiscal en los dos impuestos que más aportan a los ingresos tributarios, el IVA y el IRPF. Pretendemos ofrecer un índice de fraude fiscal lo más real posible para cada uno de estos dos impuestos y, para ello, la metodología a utilizar se basa en el estudio de la contabilidad nacional. De esta manera obtenemos el índice de fraude en términos de discrepancia, determinada por la diferencia entre los datos declarados y los datos que debieron ser declarados

The origin and effect of small RNA signaling in plants

Given their sessile condition, land plants need to integrate environmental cues rapidly and send signal throughout the organism to modify their metabolism accordingly. Small RNA (sRNA) molecules are among the messengers that plant cells use to carry such signals. These molecules originate from fold-back stem-loops transcribed from endogenous loci or from perfect double-stranded RNA produced through the action of RNA-dependent RNA polymerases. Once produced, sRNAs associate with Argonaute (AGO) and other proteins to form the RNA-induced silencing complex (RISC) that executes silencing of complementary RNA molecules. Depending on the nature of the RNA target and the AGO protein involved, RISC triggers either DNA methylation or chromatin modification (leading to transcriptional gene silencing, TGS) or RNA cleavage or translational inhibition (leading to post-transcriptional gene silencing, PTGS). In some cases, sRNAs move to neighboring cells and/or to the vascular tissues for long-distance trafficking. Many genes are involved in the biogenesis of sRNAs and recent studies have shown that both their origin and their protein partners have great influence on their activity and range. Here we summarize the work done to uncover the mode of action of the different classes of sRNA with special emphasis on their movement and how plants can take advantage of their mobility. We also review the various genetic requirements needed for production, movement and perception of the silencing signal

Robust newsvendor problem with autoregressive demand

This paper explores the classic single-item newsvendor problem under a novel setting which combines temporal dependence and tractable robust optimization. First, the demand is modeled as a time series which follows an autoregressive process AR(p), p ≥ 1. Second, a robust approach to maximize the worst-case revenue is proposed: a robust distribution-free autoregressive forecasting method, which copes with non-stationary time series, is formulated. A closed-form expression for the optimal solution is found for the problem for p = 1; for the remaining values of p, the problem is expressed as a nonlinear convex optimization program, to be solved numerically. The optimal solution under the robust method is compared with those obtained under two versions of the classic approach, in which either the demand distribution is unknown, and assumed to have no autocorrelation, or it is assumed to follow an AR(p) process with normal error terms. Numerical experiments show that our proposal usually outperforms the previous benchmarks, not only with regard to robustness, but also in terms of the average revenue.Ministerio de Economía y CompetitividadJunta de Andalucí

A sparsity-controlled vector autoregressive model

Vector autoregressive (VAR) models constitute a powerful and well studied tool to analyze multivariate time series. Since sparseness, crucial to identify and visualize joint dependencies and relevant causalities, is not expected to happen in the standard VAR model, several sparse variants have been introduced in the literature. However, in some cases it might be of interest to control some dimensions of the sparsity, as e.g. the number of causal features allowed in the prediction. To authors extent none of the existent methods endows the user with full control over the different aspects of the sparsity of the solution. In this paper we propose a sparsity-controlled VAR model which allows to control different dimensions of the sparsity, enabling a proper visualization of potential causalities and dependencies. The model coefficients are found as the solution to a mathematical optimization problem, solvable by standard numerical optimization routines. The tests performed on both simulated and real-life multivariate time series show that our approach may outperform both the standard and Group Lasso in terms of prediction errors specially when highly sparse graphs are sought, while avoiding the VAR’s overfitting for more dense graphs. Causality; Mixed Integer Non Linear Programming; multivariate time series; sparse models; Vector autoregressive process.Ministerio de Econom´ıa y CompetitividadJunta de Andalucí