465 research outputs found
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 AlcaldÃa 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 fÃsica 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 AlcaldÃa 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 caracterÃsticas de cada una de las medidas de higiene y seguridad
Es una investigación cualitativa porque se centra principalmente en aspectos observables de ergonomÃa, seguridad e higiene laboral
La población de estudio está compuesta por 240 trabajadores de la AlcaldÃa de Juigalpa, Chontales, pero la muestra seleccionada está constituida por 7 trabajadores
Mapas de distribución de algas marinas de la PenÃnsula Ibérica y las Islas Baleares. XXIV. Catenella caespitosa, Caulacanthus ustulatus y Feldmannophycus rayssiae (Caulacanthaceae, Rhodophyta)
Se presentan los mapas de distribución en la PenÃnsula 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
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
La gitana dormida
Ensayo de José Lezama Lim
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 artÃculo, 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’, asà 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
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