OL\'E: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning

Deep neural networks trained using a softmax layer at the top and the cross-entropy loss are ubiquitous tools for image classification. Yet, this does not naturally enforce intra-class similarity nor inter-class margin of the learned deep representations. To simultaneously achieve these two goals, different solutions have been proposed in the literature, such as the pairwise or triplet losses. However, such solutions carry the extra task of selecting pairs or triplets, and the extra computational burden of computing and learning for many combinations of them. In this paper, we propose a plug-and-play loss term for deep networks that explicitly reduces intra-class variance and enforces inter-class margin simultaneously, in a simple and elegant geometric manner. For each class, the deep features are collapsed into a learned linear subspace, or union of them, and inter-class subspaces are pushed to be as orthogonal as possible. Our proposed Orthogonal Low-rank Embedding (OL\'E) does not require carefully crafting pairs or triplets of samples for training, and works standalone as a classification loss, being the first reported deep metric learning framework of its kind. Because of the improved margin between features of different classes, the resulting deep networks generalize better, are more discriminative, and more robust. We demonstrate improved classification performance in general object recognition, plugging the proposed loss term into existing off-the-shelf architectures. In particular, we show the advantage of the proposed loss in the small data/model scenario, and we significantly advance the state-of-the-art on the Stanford STL-10 benchmark

Plan de prevención de riesgos laborales en la Bodega de la Alcaldía de Juigalpa-Chontales, ubicada a 700 metros al suroeste de la Gasolinera PUMA, correspondiente al segundo semestre del año 2013

La seguridad e higiene se ocupa de proteger la salud e integridad física de los trabajadores, controlando el entorno del trabajo para reducir o eliminar riesgos. Los accidentes laborales o las condiciones de trabajo poco seguras pueden provocar enfermedades y lesiones temporales o permanentes e incluso causar la muerte. La seguridad y la higiene aplicada a los centros de trabajo, tienen como objetivo mejorar las condiciones de trabajo, por medio del dictado de normas encaminadas tanto a que se les proporcionen las condiciones adecuadas para el trabajo. Dentro de la Bodega de la Alcaldía de Juigalpa se encuentran muchas deficiencias tanto estructural como de condiciones de trabajo, para darnos cuenta de todas estas debilidades aplicamos algunos instrumentos, estos confirman que las paredes no son adecuadas, los circuitos eléctricos están viejos y mal distribuidos, las áreas no están delimitadas y hay muchos objetos que estorban el paso. Se les dejan recomendaciones muy precisas en los puntos en donde hay debilidad para que de esta manera se puedan proporcionar a los trabajadores las condiciones necesarias para desempeñar correctamente sus funciones. Esta investigación fue de corte transversal, puesto que se recoge información en oportunidad única y de tipo descriptiva, ya que se aborda y pretende medir las características de cada una de las medidas de higiene y seguridad Es una investigación cualitativa porque se centra principalmente en aspectos observables de ergonomía, seguridad e higiene laboral La población de estudio está compuesta por 240 trabajadores de la Alcaldía de Juigalpa, Chontales, pero la muestra seleccionada está constituida por 7 trabajadores

Mapas de distribución de algas marinas de la Península Ibérica y las Islas Baleares. XXIV. Catenella caespitosa, Caulacanthus ustulatus y Feldmannophycus rayssiae (Caulacanthaceae, Rhodophyta)

Se presentan los mapas de distribución en la Península Ibérica y las Islas Baleares de los géneros Catenella Greville, Caulacanthus Kützing y Feldmannophycus Augier & Boudouresque, cada uno de ellos representado en nuestras costas por una sola especie: Catenella caespitosa, Caulacanthus ustulatus y Feldmannophycus rayssiae.We publish here the distribution maps along the Iberian Peninsula and the Balearic Islands of the genera Catenella Greville, Caulacanthus Kützing and Feldmannophycus Augier & Boudouresque. Each one of these genera is represented in this geographical area only by one species: Catenella caespitosa, Caulacanthus ustulatus and Feldmannophycus rayssiae

La gitana dormida

Ensayo de José Lezama Lim

Robust Energy Resource Management Incorporating Risk Analysis Using Conditional Value-at-Risk

The energy resource management (ERM) problem in today’s energy systems is complex and challenging due to the increasing penetration of distributed energy resources with uncertain behavior. Despite the improvement of forecasting tools, and the development of strategies to deal with this uncertainty (for instance, considering Monte Carlo simulation to generate a set of different possible scenarios), the risk associated with such variable resources cannot be neglected and deserves proper attention to guarantee the correct functioning of the entire system. This paper proposes a risk-based optimization approach for the centralized day-ahead ERM taking into account extreme events. Risk-neutral and risk-averse methodologies are implemented, where the risk-averse strategy considers the worst scenario costs through the conditional value-at-risk ( CVaR ) method. The model is formulated from the perspective of an aggregator that manages multiple technologies such as distributed generation, demand response, energy storage systems, among others. The case study analysis the aggregator’s management inserted in a 13-bus distribution network in the smart grid context with high penetration of renewable energy and electric vehicles. Results show an increase of nearly 4% in the day-ahead operational costs comparing the risk-neutral to the risk-averse strategy, but a reduction of up to 14% in the worst-case scenario cost. Thus, the proposed model can provide safer and more robust solutions incorporating the CVaR tool into the day-ahead management.This work was supported in part by the European Regional Development Fund (FEDER) through the Operational Program for Competitiveness and Internationalization (COMPETE 2020), under Project POCI-01-0145-FEDER-028983; and in part by the National Funds through the Fundação para a Ciância e Tecnologia (FCT) Portuguese Foundation for Science and Technology, under Project PTDC/EEI-EEE/28983/2017(CENERGETIC), Project CEECIND/02814/2017, Project UIDB/000760/2020, and Project UIDP/00760/2020.info:eu-repo/semantics/publishedVersio

Acerca de la importancia de la educación moral: Un examen de algunos argumentos a favor y en contra de la educación moral formal

En este artículo, su autor examina algunos argumentos a favor y en contra de la educación moral formal. En relación con los argumentos a favor de la educación moral formal, dicho examen se enfoca en dos argumentos: El ‘argumento sociológico’ –o argumento sobre los beneficios sociales de la educación moral–, y el ‘argumento naturalista’. En cuanto a los argumentos en contra de la educación moral formal, el análisis se centra también en dos argumentos: el ‘argumento sobre el adoctrinamiento’, así como en el ‘argumento sobre las prioridades académicas’. Estos dos argumentos contra la educación moral formal serán finalmente contestados para concluir que la educación moral formal cuenta hoy con razones más poderosas para ser defendida y fomentada que para ser relegada o rechazada

Scaling Painting Style Transfer

Neural style transfer is a deep learning technique that produces an unprecedentedly rich style transfer from a style image to a content image and is particularly impressive when it comes to transferring style from a painting to an image. It was originally achieved by solving an optimization problem to match the global style statistics of the style image while preserving the local geometric features of the content image. The two main drawbacks of this original approach is that it is computationally expensive and that the resolution of the output images is limited by high GPU memory requirements. Many solutions have been proposed to both accelerate neural style transfer and increase its resolution, but they all compromise the quality of the produced images. Indeed, transferring the style of a painting is a complex task involving features at different scales, from the color palette and compositional style to the fine brushstrokes and texture of the canvas. This paper provides a solution to solve the original global optimization for ultra-high resolution images, enabling multiscale style transfer at unprecedented image sizes. This is achieved by spatially localizing the computation of each forward and backward passes through the VGG network. Extensive qualitative and quantitative comparisons show that our method produces a style transfer of unmatched quality for such high resolution painting styles.Comment: 10 pages, 5 figure