Using differential renormalization, we calculate the complete two-point function of the background gauge superfield in pure N=1 supersymmetric Yang-Mills theory to two loops. Ultraviolet and (off-shell) infrared divergences are renormalized in position and momentum space respectively. This allows us to reobtain the beta function from the dependence on the ultraviolet renormalization scale in an infrared-safe way. The two-loop coefficient of the beta function is generated by the one-loop ultraviolet renormalization of the quantum gauge field via nonlocal terms which are infrared divergent on shell. We also discuss the connection of the beta function to the flow of the Wilsonian coupling. *

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En este artículo se describe un sistema identificador de especies de roedores usando herramientas computacionales del paradigma de aprendizaje profundo. Las especies identificadas son 4 tipos diferentes de roedores y la identificación se logra usando técnicas de inteligencia artificial aplicadas a imágenes de estos roedores en su hábitat natural. Estas imágenes fueron captadas, mediante sistemas de cámaras activadas en modo automático, ocultas en el hábitat natural de las especies en estudio, en condiciones, tanto, de luz del día como también nocturnas y etiquetadas por expertos. El conjunto de imágenes acopiada constituye el conjunto de datos para entrenamiento de tipo supervisado, de 1411 imágenes de 4 clases. El identificador desarrollado, es un clasificador multiclase, basado en el paradigma de aprendizaje profundo del amplio tema del aprendizaje automático, lo que permite construir un sistema de altísimo desempeño. El clasificador consta de tres etapas conectadas en cascada, siendo la primera etapa, una etapa de pre-procesamiento, luego, está una red neuronal convolucional (CNN, de sus siglas en inglés) para extracción de rasgos, implementada con una arquitectura pre-entrenada usando el método de aprendizaje por transferencia; específicamente, la CNN usada es la conocida VGG-16; a esta segunda etapa, se le conecta como etapa siguiente y final, una máquina de vectores de soporte (SVM, de sus siglas en inglés) que actúa como la etapa clasificadora. A fines de comparación, los resultados se contrastan contra modelos de identificación automáticos anteriormente publicados, los resultados logrados con nuestro identificador son significativamente superiores a los alcanzados en investigaciones previas en el tema. Palabras claves.Identificación de especies, aprendizaje profundo, red neuronal convolucional preentrenada.Abstract. In this article, we describe a rodent species identification system using computational tools of the deep learning paradigm. The identified species are 4 different types of rodents and the identification is achieved using artificial intelligence techniques applied to images of these rodents in their natural habitat. These images were captured, using camera systems activated in automatic mode, hidden in the natural habitat of the species under study, under both daylight and nighttime conditions and labeled by experts. The collected image set constitutes the data set for supervised training of 1411 images of 4 classes. The identifier developed is a multiclass classifier, based on the deep learning paradigm of the broad topic of machine learning, which allows to build a high performance system. The classifier consists of three stages connected in cascade, being the first stage, a pre-processing stage, then, there is a convolutional neural network (CNN) for feature extraction, implemented with a pre-trained architecture using the method of learning by transfer; specifically, the CNN used is the well-known VGG-16; to this second stage, a support vector machine (SVM) is connected as the next and fin...

Epilepsy is the most common neuropathology. Statistical studies related to the disease reported that 20%-25% of epileptic patients with occurrence of seizures were even under treatment with drugs. This article presents a strategy for improved detection of the neuropathology, based on electroencephalogram (EEG), using a classifier built with support vector machines (SVC). The SVC is designed based on feature extraction of higher order spectra of time series derived from the EEG applied to epileptic patients and control patients. As demonstrated in the study presented, the EEG time series are highly nonlinear and non-Gaussian, therefore, exhibit higher order spectra, which are extracted features that improve the accuracy in the performance of SVC. The results of this study suggest the development of highly accurate computational tools for the diagnosis of this dreaded neuropathology.

In the context of Differential Renormalization, using Constrained Differential Renormalization rules at one loop, we show how to obtain concrete results in two loop calculations without making use of Ward identities. In order to do that, we obtain a list of integrals with overlapping divergences compatible with CDR that can be applied to various two loop background field calculations. As an example, we obtain the two loop coefficient of the beta function of QED, SuperQED and Yang-Mills theory. * cesar@fpaxp1.usc.es