Introduction

En este volumen 30 de la revista Arquitectonics hay una serie de artículos relacionados con el análisis de los lugares y espacios públicos de diferentes países, un tema de investigación muy importante en los últimos años, tal vez relacionado con la fuerte crisis financiera en todo el mundo vinculada a la vivienda.There is a set of articles related to the analysis of architectural and urban places from different countries, a very important research subject in the last years, perhaps related to the strong financial crisis around the world linked to housing.Peer Reviewe

On the search of a lost urban planning modernity: throughout the legacy of Lewis Mumford

Postprint (published version

Dos miradas teóricas de la evaluación formativa en el caso del aprendizaje por indagación: contraste entre la visión socio-constructivista y la visión socio-cultural

El aprendizaje de las ciencias y las matemáticas por indagación (ECMBI) es una tendencia en educación apoyada a nivel mundial por educadores, científicos, empresarios y políticos. Una gran cantidad de maestros en diversidad de culturas y medios sociales (Europa, América Latina, USA, Asia) están utilizando los principios de ECMBI en sus clases . Esta tendencia basa su propuesta de enseñanza-aprendizaje en supuestos que salen de una visión socio constructivista del aprendizaje. Dentro de este movimiento, diferentes investigadores han formulado propuestas y modelos de evaluación formativa que buscan apoyar el proceso de aprendizaje de los estudiantes. La presentación muestra que estas propuestas no dan cuenta de procesos sociales y culturales identificados por las visiones socio culturales del aprendizaje como constitutivos del proceso de enseñanza-aprendizaje. En primera instancia presentaré los modelos de evaluación formativa y los supuestos que las sustentan para enseguida introducir un modelo que tenga en cuenta las visiones socio-culturales del aprendizaje. La presentación está construida en el área de las ciencias pero con posibilidades de tejer vínculos con el área de las matemáticas

A fractal fragmentation model for rockfalls

The final publication is available at Springer via http://dx.doi.org/10.1007/s10346-016-0773-8The impact-induced rock mass fragmentation in a rockfall is analyzed by comparing the in situ block size distribution (IBSD) of the rock mass detached from the cliff face and the resultant rockfall block size distribution (RBSD) of the rockfall fragments on the slope. The analysis of several inventoried rockfall events suggests that the volumes of the rockfall fragments can be characterized by a power law distribution. We propose the application of a three-parameter rockfall fractal fragmentation model (RFFM) for the transformation of the IBSD into the RBSD. A discrete fracture network model is used to simulate the discontinuity pattern of the detached rock mass and to generate the IBSD. Each block of the IBSD of the detached rock mass is an initiator. A survival rate is included to express the proportion of the unbroken blocks after the impact on the ground surface. The model was calibrated using the volume distribution of a rockfall event in Vilanova de Banat in the Cadí Sierra, Eastern Pyrenees, Spain. The RBSD was obtained directly in the field, by measuring the rock block fragments deposited on the slope. The IBSD and the RBSD were fitted by exponential and power law functions, respectively. The results show that the proposed fractal model can successfully generate the RBSD from the IBSD and indicate the model parameter values for the case study.Peer ReviewedPostprint (author's final draft

El Mirascope

El Mirascope és un giny que ens pot ajudar a introduir el tema de l'òptica geomètrica. Produeix una imatge real, tan real que ens indueix a creure que l'objecte és vertaderament allà. Només quan intentem agafar aquest objecte fantasmal ens adonem que és una pura il•lusió òptica

Invariant Models for Causal Transfer Learning

Methods of transfer learning try to combine knowledge from several related tasks (or domains) to improve performance on a test task. Inspired by causal methodology, we relax the usual covariate shift assumption and assume that it holds true for a subset of predictor variables: the conditional distribution of the target variable given this subset of predictors is invariant over all tasks. We show how this assumption can be motivated from ideas in the field of causality. We focus on the problem of Domain Generalization, in which no examples from the test task are observed. We prove that in an adversarial setting using this subset for prediction is optimal in Domain Generalization; we further provide examples, in which the tasks are sufficiently diverse and the estimator therefore outperforms pooling the data, even on average. If examples from the test task are available, we also provide a method to transfer knowledge from the training tasks and exploit all available features for prediction. However, we provide no guarantees for this method. We introduce a practical method which allows for automatic inference of the above subset and provide corresponding code. We present results on synthetic data sets and a gene deletion data set

Learning Independent Causal Mechanisms

Statistical learning relies upon data sampled from a distribution, and we usually do not care what actually generated it in the first place. From the point of view of causal modeling, the structure of each distribution is induced by physical mechanisms that give rise to dependences between observables. Mechanisms, however, can be meaningful autonomous modules of generative models that make sense beyond a particular entailed data distribution, lending themselves to transfer between problems. We develop an algorithm to recover a set of independent (inverse) mechanisms from a set of transformed data points. The approach is unsupervised and based on a set of experts that compete for data generated by the mechanisms, driving specialization. We analyze the proposed method in a series of experiments on image data. Each expert learns to map a subset of the transformed data back to a reference distribution. The learned mechanisms generalize to novel domains. We discuss implications for transfer learning and links to recent trends in generative modeling.Comment: ICML 201