From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction

Visual multimedia have become an inseparable part of our digital social lives, and they often capture moments tied with deep affections. Automated visual sentiment analysis tools can provide a means of extracting the rich feelings and latent dispositions embedded in these media. In this work, we explore how Convolutional Neural Networks (CNNs), a now de facto computational machine learning tool particularly in the area of Computer Vision, can be specifically applied to the task of visual sentiment prediction. We accomplish this through fine-tuning experiments using a state-of-the-art CNN and via rigorous architecture analysis, we present several modifications that lead to accuracy improvements over prior art on a dataset of images from a popular social media platform. We additionally present visualizations of local patterns that the network learned to associate with image sentiment for insight into how visual positivity (or negativity) is perceived by the model.Comment: Accepted for publication in Image and Vision Computing. Models and source code available at https://github.com/imatge-upc/sentiment-201

Sobre el moviment d'objectes en fluids

Un assaig de divulgació d'una qüestió de física quotidiana poc tractada a l'escol

Conèixer els superfluids

La física quàntica s'enfronta a sorprenents propietats de la matèria que es donen només a nivell microscòpic i que desafien la realitat que podem observar a la nostra escala. La superfluïdesa és un fenomen quàntic a escala macroscòpica: els superfluids tenen una gran capacitat per transportar la calor o per fluir per capil·lars molt fins. Investigadors de la UAB i de la Universitat de Palerm estan treballant en millorar l'observació de les seves propietats