Mapping Digital Media Series: Public Interest and Commercial Media – Digital Trends

After nearly 3 years of intensive research across 56 countries the Open Society Foundation released the cross cutting, global findings from its Mapping Digital Media Project last week. In the latest post in our series on this project, Carlos Cortés, digital policy advisor for the Program on Independent Journalism, offers some analysis and recommendations based on the report

Self-Organization Promotes the Evolution of Cooperation with Cultural Propagation

In this paper three computational models for the study of the evolution of cooperation under cultural propagation are studied: Kin Selection, Direct Reciprocity and Indirect Reciprocity. Two analyzes are reported, one comparing their behavior between them and a second one identifying the impact that different parameters have in the model dynamics. The results of these analyzes illustrate how game transitions may occur depending of some parameters within the models and also explain how agents adapt to these transitions by individually choosing their attachment to a cooperative attitude. These parameters regulate how cooperation can self-organize under different circumstances. The emergence of the evolution of cooperation as a result of the agent's adapting processes is also discussed

Herramientas modernas en redes neuronales: la librería Keras

El mundo de las redes neuronales está en auge. Poder simular el cerebro humano en un ordenador, parece ser uno de los hitos más prometedores de la informática. Es cierto que este hito todavía no se ha conseguido, pero mediante algoritmos de machine learning, ya es posible entrenar máquinas para que aprendan de forma parecida a como lo hará nuestro cerebro. El objetivo de este Trabajo de Fin de Grado es poner en práctica estos algoritmos utilizando la librería Keras. En primer lugar, se hará una breve introducción al mundo de las redes neuronales. Se empezará por lo más básico, explicando qué es una red neuronal y definiendo las partes más importantes de su arquitectura. Una vez entendidos los conceptos básicos, se describirán los tres tipos de redes neuronales más extendidas actualmente debido a sus buenos resultados: Perceptrón multicapa, redes convolucionales, y redes LSTM. En segundo lugar, se describirá Keras, una librería Python de deep learning con la cual podremos diseñar nuestros propios modelos de redes neuronales. Se detallarán las clases y funciones más importantes, así como la gran cantidad de posibilidades que nos ofrece. Por último, se aplicarán todos los conocimientos descritos anteriormente para diseñar cuatro tipos de redes neuronales que resolverán dos tipos de problemas distintos. En cuanto a problemas de clasificación de datos, veremos como una maquina es capaz de clasificar si una persona tendrá diabetes a partir de sus datos médicos, y veremos cómo clasificar imágenes en las que aparecen números para determinar que número es el que se representa en la imagen. Por otro lado, se llevarán a cabo dos problemas de predicción de datos. Mediante el primero, estudiaremos como predecir el precio de distintas viviendas según cualidades que se han recogido previamente, y con el segundo, veremos cómo esta máquina es capaz de generar textos una vez haya sido entrenada con ellos.The world of neural networks is growing. Being able to simulate the human brain in a computer seems to be one of the most promising milestones in computing. It is true that it has not been achieved, but using machine learning algorithms, it is already possible to train machines to learn in a similar way as our brain. The objective of this End-of-Grade Work is to implement these algorithms using the Keras library. First of all, a brief introduction to the world of neural networks will be made. It will start with the basics, explaining what is a neural network and de ning the most important parts of its architecture. After understanding the basics, I will describe the three types of neural networks more promising due to their results: Multilayer Perceptron, Convolutional Networks, and LSTM Networks. Secondly, I will describe Keras, a deep learning python library with which we are able to design our own neural network models. I will detail the classes and the most important functions, as well as the great amount of possibilities that this library o ers us. Finally, all previous knowledge is applied to design four types of neural networks that will solve two di erent types of problems. As for the problems of data classi cation, we will see how to clasify a person depending of if she or he will have diabetes or not analyzing their medical data, and we will see how to classify images of handwritten numbers, to determine which number is represented in the picture. On the other hand, data prediction problems will be addressed. Through the rst example, we will be able to predict the prize of several houses according to qualities that have been previously collected, and with the second, we will see how a machine is able to generate texts once he has been trained with them

Acercamiento intuitivo al concepto de función derivada

En el siguiente artículo se propone un acercamiento numérico y gráfico al concepto de derivada y de función derivada. Para ello se propone iniciar introduciendo las ideas de diferencias, incrementos y razón de incrementos. El que esto escribe diseño y desarrollo un software de apoyo a la introducción de estas ideas. Para abordar la temática se exponen ideas teóricas, una exposición de lo propuesto en el software y algunos resultados obtenidos

Fuzzy memoization for floating-point multimedia applications

Instruction memoization is a promising technique to reduce the power consumption and increase the performance of future low-end/mobile multimedia systems. Power and performance efficiency can be improved by reusing instances of an already executed operation. Unfortunately, this technique may not always be worth the effort due to the power consumption and area impact of the tables required to leverage an adequate level of reuse. In this paper, we introduce and evaluate a novel way of understanding multimedia floating-point operations based on the fuzzy computation paradigm: performance and power consumption can be improved at the cost of small precision losses in computation. By exploiting this implicit characteristic of multimedia applications, we propose a new technique called tolerant memoization. This technique expands the capabilities of classic memoization by associating entries with similar inputs to the same output. We evaluate this new technique by measuring the effect of tolerant memoization for floating-point operations in a low-power multimedia processor and discuss the trade-offs between performance and quality of the media outputs. We report energy improvements of 12 percent for a set of key multimedia applications with small LUT of 6 Kbytes, compared to 3 percent obtained using previously proposed techniques.Peer ReviewedPostprint (published version

Consenso no formal de patología urológica para el informe de cáncer de próstata en Colombia

Este informe preliminar resume las conclusiones y recomendaciones generadas del consenso para elaborar una guía de atención integral (GAI) para el manejo del paciente con cáncer de próstata, en Colombia, que comprenda detección temprana, diagnóstico, tratamiento, seguimiento y rehabilitación. El consenso no formal propone unificar el informe del estudio histopatológico para determinar los factores predictivos, pronóstico, grado, estado y facilitar el registro estadístico