Second-to-Fourth Digit Ratio Has a Non-Monotonic Impact on Altruism

Gene-culture co-evolution emphasizes the joint role of culture and genes for the emergence of altruistic and cooperative behaviors and behavioral genetics provides estimates of their relative importance. However, these approaches cannot assess which biological traits determine altruism or how. We analyze the association between altruism in adults and the exposure to prenatal sex hormones, using the second-to-fourth digit ratio. We find an inverted U-shaped relation for left and right hands, which is very consistent for men and less systematic for women. Subjects with both high and low digit ratios give less than individuals with intermediate digit ratios. We repeat the exercise with the same subjects seven months later and find a similar association, even though subjects' behavior differs the second time they play the game. We then construct proxies of the median digit ratio in the population (using more than 1000 different subjects), show that subjects' altruism decreases with the distance of their ratio to these proxies. These results provide direct evidence that prenatal events contribute to the variation of altruistic behavior and that the exposure to fetal hormones is one of the relevant biological factors. In addition, the findings suggest that there might be an optimal level of exposure to these hormones from social perspective.

Deep learning review and its applications

Deep neural networks have recently gained popularity for improv- ing state-of-the-art machine learning algorithms in diverse areas such as speech recognition, computer vision and bioinformatics. Convolutional networks especially have shown prowess in visual recognition tasks such as object recognition and detection in which this work is focused on. Mod- ern award-winning architectures have systematically surpassed previous attempts at tackling computer vision problems and keep winning most current competitions. After a brief study of deep learning architectures and readily available frameworks and libraries, the LeNet handwriting digit recognition network study case is developed, and lastly a deep learn- ing network for playing simple videogames is reviewed.

Análisis predictivo para clasificar dígitos escritos a mano utilizando la base de datos MNIST

En el presente trabajo de fin de máster se realiza una investigación sobre las técnicas de preproceso del dataset de entrenamiento y la aplicación de un modelo de predicción que realice una clasificación de dı́gitos escritos a mano. El conjunto de dataset de train y test son proporcionado en la competencia de Kaggle: Digit Recognizer y provienen de la base de datos de dı́gitos manuscritos MNIST. Por tratarse de imágenes las técnicas de preproceso se concentran en obtener una imagen lo más nı́tida posible y la reducción de tamaño de la misma, objetivos que se logran con técnicas de umbralización por el método de Otsu, transformada de Wavelet de Haar y el análisis de sus componentes principales. Se utiliza Deep Learning como modelo predictivo por ajustarse a este tipo de datos, se emplean además librerı́as de código abierto implementadas en el lenguaje estádisto R. Por último se obtiene una predicción con las técnicas y herramientas mencio- nadas para ser evaluada en la competencia de Kaggle, midiendo y comparando los resultados obtenidos con el resto de participantes.