Perspectivas antropológicas sobre Andalucía

En un reciente coloquio sobre la identidad andaluza, me hacía una y otra vez la siguiente pregunta: pero ¿qué puede aportar la antropología al conocimiento de nuestra tierra? Mientras que otros participantes exponían sus bien documentadas disertaciones en torno a áreas lingüísticas o regiones económicas andaluzas, o al origen de lo andaluz, una cosa al menos se me iba haciendo cada vez más patente: desde una perspectiva local, de pueblo, es difícil, por no decir imposible, hablar del conjunto. Tal vez, a lo más, sospechar su complejidad o su heterogeneidad

From Leviathan to Toro of Wall Street

¿Cual es en nuestros días el antagonista principal de la gran metáfora hobbesiana? En términos jakobsonianos, el Toro de Wall Street parece situarse mucho más próximo al polo metonímico que al metafórico. Alguien ha venido a equiparar el famoso Toro a Leviatán en un doble sentido: primero, porque ha venido a representar la autonomía de la economía —la fuerza de recuperación interna del mercado tras las fases bajistas de la bolsa—, del mismo modo que Leviatán representó la autonomización de la esfera política respecto a los controles eclesiásticos; y, en segundo lugar, porque como el Estado que encarna Leviatán termina por adquirir atributos sagrados, el Toro, que ha venido a identificarse con el mercado de valores, acaba por ser igualmente reverenciado.What is nowadays the main antagonist to the great Hobbesian metaphor? In Jakobsian terms, the Bull of Wall Street seems to be much closer to the metonymical pole than to the metaphorical one. Somebody have equated the famous bull to Leviathan in a dual sense: first, since it has come to represent the empowerment of economy, the strength of the internal recovery phase of the stock market after periodic turmoils, just as Leviathan represented both the state autonomy and its political independece from church control; and, secondly, because if the Leviathan State eventually acquired sacred attributes, the Bull has come too to be identified with the stock market and ending up beeing equally revered

Consideraciones tecnológicas sobre la talla laminar por presión: sistemas de sujeción

El empleo de modelos experimentales de talla en la reproducción de artefactos líticos, permite una aproximación a la dinámica de fabricación de los objetos y a la comprensión de ciertos aspectos técnicos integrados en el proceso de reducción. La obtención de láminas por presión, exige el conocimiento y control de toda úna serie de mecanismos de fabricación cuya aplicación y combinación implican un grado de conocimiento técnico preciso. En este avance presentamos alguno de los sistemas experimentales clásicos de laminación, prestando especial atención a los sistemas de inmovilización del núcleo y al modo de ejecución del esfuerzo. El control ejercido sobre alguno de los mecanismos de fabricación empleados (parámetros mecánicos o variables independientes) y los resultados obtenidos (parámetros morfológicos o variables dependientes), nos está permitiendo comprender y explicar la morfología de algunas láminas y la formación de ciertas fracturas y accidentes de talla como resultado de la aplicación incorrecta de ciertos mecanismos.Experimental too! replication allow us to a better understanding of teclmical issues in prehistory. One of the more complex teclmic in lithic reduction is blade flaking by pressure. In this paper we show sorne of the technical procedures employed, specially the core holding methods. The application of this method in the courses developed at the University Autónoma of Madrid allow us for a better understanding of different morphologies, fractures and accidents in the blade production with pressure flaking

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

Addressing the 5G cell switch-off problem with a multi-objective cellular genetic algorithm

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The power consumption foreseen for 5G networks is expected to be substantially greater than that of 4G systems, mainly because of the ultra-dense deployments required to meet the upcoming traffic demands. This paper deals with a multi- objective formulation of the Cell Switch-Off (CSO) problem, a well-known and effective approach to save energy in such dense scenarios, which is addressed with an accurate, yet rather unknown multi-objective metaheuristic called MOCell (multi- objective cellular genetic algorithm). It has been evaluated over a different set of networks of increasing densification levels. The results have shown that MOCell is able to reach major energy savings when compared to a widely used multi-objective algorithm.TIN2016-75097-P Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Pixel Features for Self-organizing Map Based Detection of Foreground Objects in Dynamic Environments

Among current foreground detection algorithms for video sequences, methods based on self-organizing maps are obtaining a greater relevance. In this work we propose a probabilistic self-organising map based model, which uses a uniform distribution to represent the foreground. A suitable set of characteristic pixel features is chosen to train the probabilistic model. Our approach has been compared to some competing methods on a test set of benchmark videos, with favorable results.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

Blood Cell Classification Using the Hough Transform and Convolutional Neural Networks

https://doi.org/10.1007/978-3-319-77712-2_62The detection of red blood cells in blood samples can be crucial for the disease detection in its early stages. The use of image processing techniques can accelerate and improve the effectiveness and efficiency of this detection. In this work, the use of the Circle Hough transform for cell detection and artificial neural networks for their identification as a red blood cell is proposed. Specifically, the application of neural networks (MLP) as a standard classification technique with (MLP) is compared with new proposals related to deep learning such as convolutional neural networks (CNNs). The different experiments carried out reveal the high classification ratio and show promising results after the application of the CNNs.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Improved detection of small objects in road network sequences using CNN and super resolution

The detection of small objects is one of the problems present in deep learning due to the context of the scene or the low number of pixels of the objects to be detected. According to these problems, current pre-trained models based on convolutional neural networks usually give a poor average precision, highlighting some as CenterNet HourGlass104 with a mean average precision of 25.6%, or SSD-512 with 9%. This work focuses on the detection of small objects. In particular, our proposal aims to vehicle detection from images captured by video surveillance cameras with pretrained models without modifying their structures, so it does not require retraining the network to improve the detection rate of the elements. For better performance, a technique has been developed which, starting from certain initial regions, detects a higher number of objects and improves their class inference without modifying or retraining the network. The neural network is integrated with processes that are in charge of increasing the resolution of the images to improve the object detection performance. This solution has been tested for a set of traffic images containing elements of different scales to check the efficiency depending on the detections obtained by the model. Our proposal achieves good results in a wide range of situations, obtaining, for example, an average score of 45.1% with the EfficientDet-D4 model for the first video sequence, compared to the 24.3% accuracy initially provided by the pre-trained model.This work is partially supported by the Ministry of Science, Innovation and Universities of Spain [grant number RTI2018-094645-B-I00], project name Automated detection with low-cost hardware of unusual activities in video sequences. It is also partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084, project name Detection of anomalous behaviour agents by deep learning in low-cost video surveillance intelligent systems. All of them include funds from the European Regional Development Fund (ERDF). It is also partially supported by the University of Málaga (Spain) under grants B1-2019_01, project name Anomaly detection on roads by moving cameras, and B1-2019_02, project name Self-Organizing Neural Systems for Non-Stationary Environments. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. The authors acknowledge the funding from the Universidad de Málaga. I.G.-A. is funded by a scholarship from the Autonomous Government of Andalusia (Spain) under the Young Employment operative program [grant number SNGJ5Y6-15]. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs. Funding for open access charge: Universidad de Málaga / CBUA