A multi-class classification model with parametrized target outputs for randomized-based feedforward neural networks

Randomized-based Feedforward Neural Networks approach regression and classification (binary and multi-class) problems by minimizing the same optimization problem. Specifically, the model parameters are determined through the ridge regression estimator of the patterns projected in the hidden layer space (randomly generated in its neural network version) for models without direct links and the patterns projected in the hidden layer space along with the original input data for models with direct links. The targets are encoded for the multi-class classification problem according to the 1- of-J encoding (J the number of classes), which implies that the model parameters are estimated to project all the patterns belonging to its corresponding class to one and the remaining to zero. This approach has several drawbacks, which motivated us to propose an alternative optimization model for the framework. In the proposed optimization model, model parameters are estimated for each class so that their patterns are projected to a reference point (also optimized during the process), whereas the remaining patterns (not belonging to that class) are projected as far away as possible from the reference point. The final problem is finally presented as a generalized eigenvalue problem. Four models are then presented: the neural network version of the algorithm and its corresponding kernel version for the neural networks models with and without direct links. In addition, the optimization model has also been implemented in randomization-based multi-layer or deep neural networks. The empirical results obtained by the proposed models were compared to those reported by state-ofthe-art models in the correct classification rate and a separability index (which measures the degree of separability in projection terms per class of the patterns belonging to the class of the others). The proposed methods show very competitive performance in the separability index and prediction accuracy compared to the neural networks version of the comparison methods (with and without direct links). Remarkably, the model provides significantly superior performance in deep models with direct links compared to its deep model counterpart

A multi-class classification model with parametrized target outputs for randomized-based feedforward neural networks.

Randomized-based Feedforward Neural Networks approach regression and classification (binary and multi-class) problems by minimizing the same optimization problem. Specifically, the model parameters are determined through the ridge regression estimator of the patterns projected in the hidden layer space (randomly generated in its neural network version) for models without direct links and the patterns projected in the hidden layer space along with the original input data for models with direct links. The targets are encoded for the multi-class classification problem according to the 1-of- encoding ( the number of classes), which implies that the model parameters are estimated to project all the patterns belonging to its corresponding class to one and the remaining to zero. This approach has several drawbacks, which motivated us to propose an alternative optimization model for the framework. In the proposed optimization model, model parameters are estimated for each class so that their patterns are projected to a reference point (also optimized during the process), whereas the remaining patterns (not belonging to that class) are projected as far away as possible from the reference point. The final problem is finally presented as a generalized eigenvalue problem. Four models are then presented: the neural network version of the algorithm and its corresponding kernel version for the neural networks models with and without direct links. In addition, the optimization model has also been implemented in randomization-based multi-layer or deep neural networks.Funding for open access charge: Universidad de Málaga / CBU

Evolutionary q-Gaussian radial basis function neural networks for multiclassification

This paper proposes a radial basis function neural network (RBFNN), called the q-Gaussian RBFNN, that reproduces different radial basis functions (RBFs) by means of a real parameter q. The architecture, weights and node topology are learnt through a hybrid algorithm (HA). In order to test the overall performance, an experimental study with sixteen data sets taken from the UCI repository is presented. The q-Gaussian RBFNN was compared to RBFNNs with Gaussian, Cauchy and inverse multiquadratic RBFs in the hidden layer and to other probabilistic classifiers, including different RBFNN design methods, support vector machines (SVMs), a sparse classifier (sparse multinomial logistic regression, SMLR) and a non-sparse classifier (regularized multinomial logistic regression, RMLR). The results show that the q-Gaussian model can be considered very competitive with the other classification methods. © 2011 Elsevier Ltd

Coordination and load analysis of C-RAN in HetNets by graph-partitioning

In 5G systems, ultra-dense networks are a promising technique to cope strong increase of traffic data in mobile communications. In addition, the deployment of indoor small cells offloads the wireless system from macrocells at the cost of increasing network complexity. In this work, a method for capacity analysis of Centralized Radio Access Networks (C-RANs) comprising macrocells and small cells is proposed. Radio remote heads~(RRH) are grouped to a Base Band Unit~(BBU) pools using graph theory techniques. For this purpose, the impact of Inter-Cell Interference Coordination (ICIC) and Coordinated Multi-Point Transmission/Reception (CoMP) techniques on the network is assessed under different load levels and coordination restrictions. Assessment is carried out by using a radio planning tool that allows to characterize spectral efficiency and allocation of shared resources per cell over a realistic Long-Term Evolution (LTE) heterogeneous network. Results show that load and coordination conditions between cells are key to improve system capacity.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Of Abuses, Dynasties and Celebrations: The Argentine Press Before the Inauguration of the ESMA Memory Site

Este artículo tiene como objetivo específico indagar el tratamiento mediático de la inauguración del Sitio de Memoria esma, dentro de los festejos por el 205° aniversario de la Revolución de Mayo (Buenos Aires, Argentina). El propósito general es contribuir a un estudio del estado del discurso social en la Argentina contemporánea, tomando en cuenta el escenario de competencia por el verosímil público entre el campo político y los medios de comunicación masivos. Para ello, estudiamos la cobertura de tres de los principales medios de tirada nacional de la Argentina: Clarín, La Nación y Página 12. El texto considera tanto las hipótesis de lectura sugeridas por los medios como los contratos de lectura que cada uno de ellos sostiene con sus destinatarios. Los resultados indican, en líneas generales, que tanto Clarín como La Nación sugieren una instrumentalización de la inauguración por parte del gobierno para impulsar la campaña electoral de 2015, mientras que Página 12 resalta la dimensión popular y ritual de los festejos.This article has as the specific objective of looking into the media coverage of the Sitio de Memoria ESMA’s opening, which was held within the celebration of the 205th anniversary of the May Revolution (Buenos Aires, Argentina). The overall objective is to contribute to the study of the state of social discourse in contemporary Argentina, considering the scenario of competition between the political field and mass media for the public opinion. To this end, we study the coverage by three of the main national newspapers in Argentina: Clarín, La Nación and Página/12. The text considers the reading hypothesis suggested by the media as well as the reading contracts that each of them establishes with their readers. Overall, the results indicate that both Clarín and La Nación suggest an instrumentalization of the inauguration by the government to boost the 2015 election campaign, while Página/12 highlights the popular and ritual dimension of the celebration.Fil: Dagatti, Mariano Jesús. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Centro de Investigaciones sobre Economía y Sociedad en la Argentina Contemporánea; ArgentinaFil: Fernández Navarro, María Belén. Universidad de Buenos Aires. Facultad de Arquitectura, Diseño y Urbanismo; Argentin

A new look at the Feynman ‘hodograph’ approach to the Kepler first law

Producción CientíficaHodographs for the Kepler problem are circles. This fact, known for almost two centuries, still provides the simplest path to derive the Kepler first law. Through Feynman's 'lost lecture', this derivation has now reached a wider audience. Here we look again at Feynman's approach to this problem, as well as the recently suggested modification by van Haandel and Heckman (vHH), with two aims in mind, both of which extend the scope of the approach. First we review the geometric constructions of the Feynman and vHH approaches (that prove the existence of elliptic orbits without making use of integral calculus or differential equations) and then extend the geometric approach to also cover the hyperbolic orbits (corresponding to $E\gt 0$). In the second part we analyse the properties of the director circles of the conics, which are used to simplify the approach, and we relate with the properties of the hodographs and Laplace–Runge–Lenz vector the constant of motion specific to the Kepler problem. Finally, we briefly discuss the generalisation of the geometric method to the Kepler problem in configuration spaces of constant curvature, i.e. in the sphere and the hyperbolic plane.Física Teórica, Atómica y ÓpticaMinisterio de Educación, Cultura y Deporte (project MTM-2012–33575)Gobierno de Aragón (project DGA E24/1)Ministerio de Economía, Industria y Competitividad (project MTM2014–57129

Statistical model for mobile user positioning based on social information.

In spite of the vast set of measurements provided by current mobile networks, cellular operators have problems to pinpoint problematic locations because the origin of such measurements (i.e., user location) is usually not registered. At the same time, social networks generate a huge amount of data that can be used to infer population density. In this work, a data-driven model is proposed to deduce the statistical distribution of connections, exploiting the knowledge of network layout and population density in the sceario. Due to the absence of GPS measurements, the proposed method combines data from radio connection traces stored in the network management system and geolocated posts from social networks. This information is enriched with user context information inferred from their traffic attributes. The method is tested with a large trace dataset from a live Long Term Evolution (LTE) network and a database of geotagged messages from two social networks (Twitter and Flickr).Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

The Machine-Part Cell Formation Problem with Non-Binary Values: A MILP Model and a Case of Study in the Accounting Profession

The traditional machine-part cell formation problem simultaneously clusters machines and parts in different production cells from a zero–one incidence matrix that describes the existing interactions between the elements. This manuscript explores a novel alternative for the well-known machine-part cell formation problem in which the incidence matrix is composed of non-binary values. The model is presented as multiple-ratio fractional programming with binary variables in quadratic terms. A simple reformulation is also implemented in the manuscript to express the model as a mixed-integer linear programming optimization problem. The performance of the proposed model is shown through two types of empirical experiments. In the first group of experiments, the model is tested with a set of randomized matrices, and its performance is compared to the one obtained with a standard greedy algorithm. These experiments showed that the proposed model achieves higher fitness values in all matrices considered than the greedy algorithm. In the second type of experiment, the optimization model is evaluated with a real-world problem belonging to Human Resource Management. The results obtained were in line with previous findings described in the literature about the case study

Asignación de unidades de banda base en redes de acceso radio centralizadas por teoría de grafos

En este trabajo, se presentan varios métodos para planificar la asignación de celdas a unidades de procesado en banda base en una red de acceso radio centralizada que incluye macroceldas y celdas pequeñas de interior. El proceso de asignación se formula como un problema de partición de grafos, que se resuelve mediante algoritmos heurísticos. La validación se realiza con una herramienta de planificación radio que comprueba los niveles de interferencia en un escenario de red LTE heterogénea. Los resultados demuestran que el método multiarranque adaptativo por agrupamiento es el que obtiene mejores resultados.Ministerio de Ciencia, Innovación y Universidades (RTI2018-099148-B-I00). Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech