La elaboración del queso de cabra en la zona de Cameros Viejo base de una economía complementaria artesanal

En número dedicado a: Provincia de Logroñ

Comparing Fixed and Adaptive Computation Time for Recurrent Neural Networks

Adaptive Computation Time for Recurrent Neural Networks (ACT) is one of the most promising architectures for variable computation. ACT adapts to the input sequence by being able to look at each sample more than once, and learn how many times it should do it. In this paper, we compare ACT to Repeat-RNN, a novel architecture based on repeating each sample a fixed number of times. We found surprising results, where Repeat-RNN performs as good as ACT in the selected tasks. Source code in TensorFlow and PyTorch is publicly available at https://imatge-upc.github.io/danifojo-2018-repeatrnn/Comment: Accepted as workshop paper at ICLR 201

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

Phosphate removal from wastewater with phosphateaccumulating bacteria and red soil

Fosfat-akumulirajuće bakterije primjenjuju se u procesu poboljšanog biološkog uklanjanja fosfata iz otpadne vode. Imobilizacijom fosfat-akumulirajućih bakterija na prirodne nosače postiže se veća koncentracija, a time i uspješnije uklanjanje fosfata iz otpadnih voda. Cilj ovog istraživanja bio je utvrditi sposobnost fosfat-akumulirajuće bakterije Acinetobacter junii i mineralnog nosača crvenice za uklanjanje fosfata iz otpadne vode. Korištena su četiri različita uzorka crvenice. Crvenica je pokazala relativno veliki postotak uklanjanja fosfata (25,7 -31,1 %), a u kombinaciji s fosfat-akumulirajućim bakterijama A. junii taj postotak je dvostruko veći (50,0 – 55,6 %).Phosphate-accumulating bacteria have their use in the process of enhanced biological phosphorus removal from wastewater. The immobilization of phosphate-accumulating bacteria on mineral carriers leads to a higher concentration that enables a more effective phosphate removal from wastewater. The aim of this study was to determine the ability of phosphate-accumulating bacteria Acinetobacter junii and mineral carrier to remove phosphate from wastewater. Four different samples of red soil were used. Red soil showed a relatively high percentage of phosphate removal (25,7-31,1 %), in combination with a phosphateaccumulating bacteria A. junii this percentage is two times higher (50,0 – 55,6 %)

Class-Weighted Convolutional Features for Visual Instance Search

Image retrieval in realistic scenarios targets large dynamic datasets of unlabeled images. In these cases, training or fine-tuning a model every time new images are added to the database is neither efficient nor scalable. Convolutional neural networks trained for image classification over large datasets have been proven effective feature extractors for image retrieval. The most successful approaches are based on encoding the activations of convolutional layers, as they convey the image spatial information. In this paper, we go beyond this spatial information and propose a local-aware encoding of convolutional features based on semantic information predicted in the target image. To this end, we obtain the most discriminative regions of an image using Class Activation Maps (CAMs). CAMs are based on the knowledge contained in the network and therefore, our approach, has the additional advantage of not requiring external information. In addition, we use CAMs to generate object proposals during an unsupervised re-ranking stage after a first fast search. Our experiments on two public available datasets for instance retrieval, Oxford5k and Paris6k, demonstrate the competitiveness of our approach outperforming the current state-of-the-art when using off-the-shelf models trained on ImageNet. The source code and model used in this paper are publicly available at http://imatge-upc.github.io/retrieval-2017-cam/.Comment: To appear in the British Machine Vision Conference (BMVC), September 201

Budget-aware Semi-Supervised Semantic and Instance Segmentation

Methods that move towards less supervised scenarios are key for image segmentation, as dense labels demand significant human intervention. Generally, the annotation burden is mitigated by labeling datasets with weaker forms of supervision, e.g. image-level labels or bounding boxes. Another option are semi-supervised settings, that commonly leverage a few strong annotations and a huge number of unlabeled/weakly-labeled data. In this paper, we revisit semi-supervised segmentation schemes and narrow down significantly the annotation budget (in terms of total labeling time of the training set) compared to previous approaches. With a very simple pipeline, we demonstrate that at low annotation budgets, semi-supervised methods outperform by a wide margin weakly-supervised ones for both semantic and instance segmentation. Our approach also outperforms previous semi-supervised works at a much reduced labeling cost. We present results for the Pascal VOC benchmark and unify weakly and semi-supervised approaches by considering the total annotation budget, thus allowing a fairer comparison between methods.Comment: To appear in CVPR-W 2019 (DeepVision workshop

Aplicació de tècniques de gamificació a la resolució de problemes de tecnologia

Aquest projecte planteja el disseny de noves activitats o recursos a l'aula tot emprant les tècniques de gamificació, amb l'objectiu d'implicar als alumnes i oferir-los una forma diferent d'aprenentatg