La inteligencia cultural y su aplicación en el protocolo de negocios en Asia

La importancia de este estudio radica en que para hacer negocios en Asia con una mínima probabilidad de éxito hay que tener en cuenta la diferencia cultural de los países del área en comparación con los occidentales. Simples gestos o actitudes podrían marcar “el buen o el mal” devenir de nuestros negocios.A lo primero que se debe prestar atención es a la manera en que hacemos uso tanto de la comunicación verbal como de la comunicación no verbal. Trás estudiar el concepto inteligencia cultural se concluirál la importancia real o no, que puede llegar a tener la inteligencia cultural para realizar negocios de una manera positiva, es decir consiguiendo implantar el negocio y obteniendo unos beneficios, en lugares con culturas diferentes.Grado en Comerci

Estudio comparativo de índices espectrales aplicados a los incendios del Alt Empordà en 2000 y 2012 a través de imágenes Modis

[CASTELLÀ]El objetivo del presente estudio es realizar una comparación entre los índices NDVI, EVI, BAIM y NBR aplicados a imágenes Modis de los incendios del Alt Empordà, concretamente el de Cap de Creus en el año 2000 y La Jonquera en el año 2012, con el fin de determinar cuál de ellos es el más idóneo para la cartografía de aéreas quemadas. Los resultados obtenidos del incendio de Cap de Creus en el año 2000 y de La Jonquera en el año 2012 muestran que el BAIM y NBR respectivamente son los índices que cumplen mejor con el objetivo de discriminación de áreas quemadas. Por el contrario, NDVI es el índice que peores resultados obtiene en los dos incendios estudiados. Se ha verificado la exactitud de cada uno de los índices con imágenes Landsat sensor ETM+, las cuales tienen una mejor resolución espacial que las imágenes Modis [ANGLÈS]The aim of this study is a comparison between NDVI, EVI, NBR and BAIM indices applied to Modis images of two important fires in the Alt Empordà, specifically one on the Cap de Creus in 2000 and the other La Jonquera in 2012, with the order to determine which one is most suitable for aerial mapping burned. The results obtained for the burning of Cap de Creus in 2000 and La Jonquera in 2012, show that BAIM and NBR respectively are the indices that best meet the target of discrimination of burned areas. NDVI contrast is the index that gets worse in the two fires studied. It has been verified the accuracy of each indices with Landsat ETM + sensor, which has a better resolution than MODIS

Multinomial logistic regression and stochastic natural gradient descent

Treballs finals del Màster en Matemàtica Avançada, Facultat de matemàtiques, Universitat de Barcelona, Any: 2018, Director: Jesús Cerquides Bueno[en] Function optimization is a widely faced problem nowadays. Its interest, in particular, lies in every learning algorithm in AI, whose achievements are measured by a Loss-Function. On one hand, Multinomial Logistic Regression is a commonly applied model to engage and simplify the problem of predicting a categorical distributed variable which depends on a set of distinct categorical distributed variables. On the other hand, Gradient Descent allows us to reach local extrema of a smooth function. Moreover, large datasets force the use of online optimization. Improving the convergence speed and reducing the computational cost of gradient based online learning algorithms will automatically translate into a significant enhancement on many machine learning processes. In this text, we present a Stochastic Gradient Descent algorithm variant, specifically designed for Multinomial Logistic Regression learning problems by taking advantage of the geometry and the intrinsic metric of the space. We compare it to current most advanced stochastic algorithms, and we provide the favorable experimental results obtained

Efficient and convergent natural gradient based optimization algorithms for machine learning

[eng] Many times machine learning is casted as an optimization problem. This is the case when an objective function assesses the success of an agent in a certain task and hence, learning is accomplished by optimizing that function. Furthermore, gradient descent is an optimization algorithm that has proven to be a powerful tool, becoming the cornerstone to solving most machine learning challenges. Among its strengths, there are the low computational complexity and the convergence guarantee property to the optimum of the function, after certain regularities on the function. Nevertheless, large dimension scenarios show sudden drops in convergence rates which inhibit further improvements in an acceptable amount of time. For this reason, the field has contemplated the natural gradient to tackle this issue. The natural gradient is defined on a Riemannian manifold (M, g). A Riemannian manifold is a manifold M equipped with a metric g. The natural gradient vector of a function f at a point p in (M, g) is a vector in the tangent space at p that points to the direction in which f locally increases its value faster taking into account the metric attached to the manifold. It turns out that the manifold of probability distributions of the same family, usually considered in machine learning, has a natural metric associated, namely the fisher information metric. While natural gradient based algorithms show a better convergence speed in some limited examples, they often fail in providing good estimates or they even diverge. Moreover, they demand more calculations than the ones performed by gradient descent algorithms, increasing the computational complexity order. This thesis explores the natural gradient descent algorithm for the function optimization task. Our research aims at designing a natural gradient based algorithm to solve a function optimization problem, whose computational complexity is comparable to those gradient based and such that it benefits from higher rates of convergence compared to standard gradient based methods. To reach our objectives, the hypothesis formulated in this thesis is that the convergence property guarantee stabilizes natural gradient algorithms and it gives access to fast rates of convergence. Furthermore, the natural gradient can be computed fast for particular manifolds named dually flat manifolds, and hence, fast natural gradient optimization methods become available. The beginning of our research is mainly focused on the convergence property for natural gradient methods. We develop some strategies to define natural gradient methods whose convergence can be proven. The main assumptions require (M, g) to be a Riemannian manifold and f to be a differentiable function on M. Moreover, it turns out that the multinomial logistic regression problem, a widely considered machine learning problem, can be adapted and solved by taking a dually flat manifolds as the model. Hence, this problem is our most promising target in which the objective of the thesis can be completely accomplished.[cat] L’aprenentatge automàtic sovint es relaciona amb un problema d’optimització. Quan l’éxit o l’error d’un agent en una determinada tasca ve donat per una funció, aprendre a realitzar correctament la tasca equival a optimitzar la funció en questió. El descens del gradient és un mètode d’optimització emprat per resoldre la majoria d’aquest tipus de problemes. Aquest algorisme és eficient i, donades certes condicions, convergeix a la solució. No obstant, la convergència pot esdevenir molt lenta en problemes de dimensió alta, on l’algorisme requerix un temps desmesurat. El gradient natural és emprat, sense gaire èxit, per tal d’evitar aquest fet. En una varietat de Riemann (M, g) amb mètrica g, el gradient natural d’una funció "f" en un punt "p" és un vector del espai tangent en "p" que assenyala la direcció on "f" creix localment més intensament, tenint en compte la mètrica del espai. En teoria, el gradient natural té propietats que podrien afavorir la velocitat de convergència, però en problemes pràctics no s’observa cap millora. Alguns algorismes basats en el gradient natural fins i tot divergeixen essent superats pel descens del gradient standard. A més a més, el gradient natural en general té una complexitat computacional més elevada. Aquesta tesis explora els algorismes basats en el gradient natural. En moltes ocasions, l’aprenentatge automàtic es du a terme en families de distribucions de probabilitat, on la mètrica associada a aquest tipus d’espais és la mètrica de Fisher. La nostra hipòtesi és que per obtenir una velocitat de convergència alta és suficient l’assoliment de la propietat de convergència. L’objectiu és definir exemples d’aquest tipus d’algorismes que siguin convergents i amb un cost computacional reduït per tal que pugui ser emprat en problemes actuals de dimensió alta. Per assolir el nostre objectiu, hem trobat indispensable limitar-nos al conjunt de varietats de Riemann anomenades varietats dualment planes. En particular, afrontem el problema de regressió logística multinomial. Aquest espai ens permet definir un algorisme efficient i convergent basat en el gradient natural gràcies a propietats intrínseques de la varietat

Joost Swarte: the "clear line" as a tool in architectural rendering

[EN] The current article explores the contribution to architectural rendering by a professional who is alien to this discipline, the Dutch illustrator and comic author Joost Swarte (born 1947). His collaboration with Mecanoo during the design of the Toneelschuur theatre is analysed an example of the artist s full participation from the beginning of the project to its final definition. The preserved graphic material has allowed an study of the role played by drawings and illustrations in every stage of the process, as well as their importance in the communication among different project agents. The analysis is later extended to other works where Swarte s intervention was either limited to certain stages of the projects or conceived as an artistic adition to architecture.[ES] El texto aborda la aportación a la expresión gráfica arquitectónica de un profesional ajeno a la disciplina, el autor de cómic e ilustrador neerlandés Joost Swarte (n. 1947). Se estudia para ello su colaboración con Mecanoo en el diseño del teatro Toneelschuur, ejemplo de participación del artista desde el embrión del proyecto hasta su definición final. El material gráfico conservado ha permitido analizar el papel que el dibujo y la ilustración jugaron en cada fase del proceso, así como su utilidad en la comunicación entre los distintos agentes participantes. El análisis se extiende a continuación a otras obras donde o bien la intervención de Swarte se limitó a fases concretas del proyecto o bien sus diseños funcionaron como adiciones artísticas a la arquitectura.López Cotelo, BR. (2022). Joost Swarte: la "línea clara" como herramienta en la representación arquitectónica. EGA Expresión Gráfica Arquitectónica. 27(46):156-171. https://doi.org/10.4995/ega.2022.17643156171274

Gravity and known size calibrate visual information to time parabolic trajectories

Catching a ball in a parabolic flight is a complex task in which the time and area of interception are strongly coupled, making interception possible for a short period. Although this makes the estimation of time-to-contact (TTC) from visual information in parabolic trajectories very useful, previous attempts to explain our precision in interceptive tasks circumvent the need to estimate TTC to guide our action. Obtaining TTC from optical variables alone in parabolic trajectories would imply very complex transformations from 2D retinal images to a 3D layout. We propose based on previous work and show by using simulations that exploiting prior distributions of gravity and known physical size makes these transformations much simpler, enabling predictive capacities from minimal early visual information. Optical information is inherently ambiguous, and therefore, it is necessary to explain how these prior distributions generate predictions. Here is where the role of prior information comes into play: it could help to interpret and calibrate visual information to yield meaningful predictions of the remaining TTC. The objective of this work is: (1) to describe the primary sources of information available to the observer in parabolic trajectories; (2) unveil how prior information can be used to disambiguate the sources of visual information within a Bayesian encoding-decoding framework; (3) show that such predictions might be robust against complex dynamic environments; and (4) indicate future lines of research to scrutinize the role of prior knowledge calibrating visual information and prediction for action control

Malthusianism of the 21st century

This paper has arisen from a reflection of the current reality in terms of the postulations about what is known as the Malthusian economy. Since the Industrial Revolution to the present day, despite there having been a high increase in world population, technology has played a fundamental role in the increase in the level of wealth per capita. However, the continued growth of the population and the future forecasts indicate that technology with have the responsibility of continually being the key piece of economic growth. This paper seeks to analyse the future scenarios in order to better understand the conditioning factors of the sustainability of our population growth. If technology were not able to maintain production growth rates higher than the population growth rates, we would return to the scenario described by Malthus

Dual Stochastic Natural Gradient Descent

Although theoretically appealing, Stochastic Natural Gradient Descent (SNGD) is computationally expensive, it has been shown to be highly sensitive to the learning rate, and it is not guaranteed to be convergent. Convergent Stochastic Natural Gradient Descent (CSNGD) aims at solving the last two problems. However, the computational expense of CSNGD is still unacceptable when the number of parameters is large. In this paper we introduce the Dual Stochastic Natural Gradient Descent (DSNGD) where we take benefit of dually flat manifolds to obtain a robust alternative to SNGD which is also computationally feasible.Comment: 16 page

Incidence and type of bicuspid aortic valve in two model species

Incidence and type of bicuspid aortic valve in two model species. MC Fernández 1,2, A López-García 1,2, MT Soto 1, AC Durán 1,2 and B Fernández 1,2. 1 Department of Animal Biology, Faculty of Science, University of Málaga, Spain. 2 Biomedical Research Institute of Málaga (IBIMA), University of Málaga, Spain. Bicuspid aortic valve (BAV) is the most frequent human congenital cardiac malformation, with an incidence of 1–2% worldwide. Two morphological types exist: type A (incidence 0.75–1.25%) and type B (incidence 0.25–0.5%), each with a distinct aetiology and natural history. Currently, ten animal models of BAV have been described in two different rodent species: one spontaneous Syrian hamster (Mesocricetus auratus) model of BAV type A and nine mutant laboratory mouse (Mus musculus) models of BAV type B. It remains to be elucidated whether the mutations leading to BAV in these models are typespecific or whether there are inter-specific differences regarding the type of BAV that hamsters, mice and humans may develop. To solve this issue, we have characterized the incidence and types of BAVs in four inbred, two outbred and two hybrid lines of Syrian hamsters (n=4,340) and in three inbred, three outbred and one hybrid lines of laboratory mice (n=1,661) by means of stereomicroscopy and scanning electron microscopy. In addition, we have reviewed and calculated the incidence and type of BAVs in the published papers dealing with this anomaly in mice. Our results indicate that the Syrian hamster develops BAVs type A and B including a variety of morphologies comparable to those of humans, whereas the mouse develops only BAVs type B with a short spectrum of valve morphologies. Thus, inter-specific differences between human and mouse aortic valves must be taken into consideration when studying valve disease in murine models. This work was supported by P10-CTS-6068.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. P10-CTS-6068

Effect of Industry 4.0 on Education Systems: An Outlook

Congreso Universitario de Innovación Educativa En las Enseñanzas Técnicas, CUIEET (26º. 2018. Gijón