Harnessing synthetic gauge fields for maximally entangled state generation

We study the generation of entanglement between two species of neutral cold atoms living on an optical ring lattice, where each group of particles can be described by a $d$-dimensional Hilbert space (qu$d$it). Synthetic magnetic fields are exploited to create an entangled state between the pair of qu$d$its. Maximally entangled eigenstates are found for well defined values of the Aharonov-Bohm phase, which are zero energy eigenstates of both the kinetic and interacting parts of the Bose-Hubbard Hamiltonian, making them quite exceptional and robust against certain non-perturbative fluctuations of the Hamiltonian. We propose a protocol to reach the maximally entangled state (MES) by starting from an initially prepared ground state. Also, an indirect method to detect the MES by measuring the current of the particles is proposed.Comment: 10 pages, 3 figure

Coexistence in neutral theories: interplay of criticality and mild local preferences

Neutral theories have played a crucial and revolutionary role in fields such as population genetics and biogeography. These theories are critical by definition, in the sense that the overall growth rate of each single allele/species/type vanishes. Thus each species in a neutral model sits at the edge between invasion and extinction, allowing for the coexistence of symmetric/neutral types. However, in finite systems, mono-dominated states are ineludibly reached in relatively short times owing to demographic fluctuations, thus leaving us with an unsatisfactory framework to rationalize empirically-observed long-term coexistence. Here, we scrutinize the effect of heterogeneity in quasi-neutral theories, in which there can be a local mild preference for some of the competing species at some sites, even if the overall species symmetry is maintained. As we show here, mild biases at a small fraction of locations suffice to induce overall robust and durable species coexistence, even in regions arbitrarily far apart from the biased locations. This result stems from the long-range nature of the underlying critical bulk dynamics and has a number of implications, for example, in conservation ecology as it suggests that constructing local specific "sanctuaries" for different competing species can result in global enhancement of biodiversity, even in regions arbitrarily distant from the protected refuges.Comment: Accepted for publication in Journal of Statistical Mechanics: Theory and Experimen

Ratchet behavior in nonlinear Klein-Gordon systems with point-like inhomogeneities

We investigate the ratchet dynamics of nonlinear Klein-Gordon kinks in a periodic, asymmetric lattice of point-like inhomogeneities. We explain the underlying rectification mechanism within a collective coordinate framework, which shows that such system behaves as a rocking ratchet for point particles. Careful attention is given to the kink width dynamics and its role in the transport. We also analyze the robustness of our kink rocking ratchet in the presence of noise. We show that the noise activates unidirectional motion in a parameter range where such motion is not observed in the noiseless case. This is subsequently corroborated by the collective variable theory. An explanation for this new phenomenom is given

Testing Constancy in Varying Coefficient Models

This article proposes tests for constancy of coefficients in semi-varying coefficients models. The testing procedure resembles in spirit the union-intersection parameter stability tests in time series, where observations are sorted according to the explanatory variable responsible for the coefficients varying. The test can be applied to model specification checks of interactive effects in linear regression models. Because test statistics are not asymptotically pivotal, critical values and p-values are estimated using a bootstrap technique. The finite sample properties of the test are investigated by means of Monte Carlo experiments, where the new proposal is compared to existing tests based on smooth estimates of the unrestricted model. We also report an application to returns of education modelingResearch funded by Ministerio Economía y Competitividad (Spain), ECO2017-86675-P & MDM 2014-0431, and by Comunidad de Madrid (Spain), MadEco-CM S2015/HUM-3444

A new self-organizing neural gas model based on Bregman divergences

In this paper, a new self-organizing neural gas model that we call Growing Hierarchical Bregman Neural Gas (GHBNG) has been proposed. Our proposal is based on the Growing Hierarchical Neural Gas (GHNG) in which Bregman divergences are incorporated in order to compute the winning neuron. This model has been applied to anomaly detection in video sequences together with a Faster R-CNN as an object detector module. Experimental results not only confirm the effectiveness of the GHBNG for the detection of anomalous object in video sequences but also its selforganization capabilities.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Vehicle Type Detection by Convolutional Neural Networks

In this work a new vehicle type detection procedure for traffic surveillance videos is proposed. A Convolutional Neural Network is integrated into a vehicle tracking system in order to accomplish this task. Solutions for vehicle overlapping, differing vehicle sizes and poor spatial resolution are presented. The system is tested on well known benchmarks, and multiclass recognition performance results are reported. Our proposal is shown to attain good results over a wide range of difficult situations.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Road pollution estimation using static cameras and neural networks

Este artículo presenta una metodología para estimar la contaminación en carreteras mediante el análisis de secuencias de video de tráfico. El objetivo es aprovechar la gran red de cámaras IP existente en el sistema de carreteras de cualquier estado o país para estimar la contaminación en cada área. Esta propuesta utiliza redes neuronales de aprendizaje profundo para la detección de objetos, y un modelo de estimación de contaminación basado en la frecuencia de vehículos y su velocidad. Los experimentos muestran prometedores resultados que sugieren que el sistema se puede usar en solitario o combinado con los sistemas existentes para medir la contaminación en carreteras.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Optimization of soliton ratchets in inhomogeneous sine-Gordon systems

Unidirectional motion of solitons can take place, although the applied force has zero average in time, when the spatial symmetry is broken by introducing a potential $V(x)$, which consists of periodically repeated cells with each cell containing an asymmetric array of strongly localized inhomogeneities at positions $x_{i}$. A collective coordinate approach shows that the positions, heights and widths of the inhomogeneities (in that order) are the crucial parameters so as to obtain an optimal effective potential $U_{opt}$ that yields a maximal average soliton velocity. $U_{opt}$ essentially exhibits two features: double peaks consisting of a positive and a negative peak, and long flat regions between the double peaks. Such a potential can be obtained by choosing inhomogeneities with opposite signs (e.g., microresistors and microshorts in the case of long Josephson junctions) that are positioned close to each other, while the distance between each peak pair is rather large. These results of the collective variables theory are confirmed by full simulations for the inhomogeneous sine-Gordon system

Enhanced transfer learning model by image shifting on a square lattice for skin lesion malignancy assessment

Skin cancer is one of the most prevalent diseases among people. Physicians have a challenge every time they have to determine whether a diseased skin is benign or malign. There exist clinical diagnosis methods (such as the ABCDE rule), but they depend mainly on the physician’s experience and might be imprecise. Deep learning models are very extended in medical image analysis, and several deep models have been proposed for moles classification. In this work, a convolutional neural network is proposed to support the diagnosis procedure. The proposed MobileNetV2-based model is improved by a shifting technique, providing better performance than raw transfer learning models for moles classification. Experiments show that this technique could be applied to the state-of-the-art deep models to improve their results and outperform the training phase.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Asset Management System through the design of a Jadex Agent System

In this paper we have designed an agent system using JADEX platform in order to facilitate the asset management of an institution with several types of assets with some unknown similarities, and a high number of operators. Such environment would justify the use of a recommendation system that would provide an useful plan to be applied over an asset generated from previous maintenance operations hold over other assets. Here we provide the full design of such agent system, including the ontology, beliefs, plans, protocols and goals. This design has been implemented within GAIA: an information & asset management solution developed by Altran