2,247 research outputs found
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
Full-scale combination of anaerobic digestion and concentration by evaporation in Garrigues (Lleida, Spain): evaluation after 2 years of operation
Evaporation process is a reliable strategy to manage pig slurry, removing water and recovering
nutrients by concentration. The described treatment plant, TRACJUSA, applies the VALPURENTM
process, which is based on the combination of anaerobic digestion and evaporation. The benefits of
this combined process have been previously reported at laboratory scalePostprint (published version
Los moriscos de Castellón (1525-1609). Del bautismo forzoso a la expulsión
Evolución del grupo de los antiguos mudéjares de Castellón convertidos en 1525 en cristianos nuevos o moriscos hasta su expulsión en 1609. (A
More cat than cute? Interpretable Prediction of Adjective-Noun Pairs
The increasing availability of affect-rich multimedia resources has bolstered
interest in understanding sentiment and emotions in and from visual content.
Adjective-noun pairs (ANP) are a popular mid-level semantic construct for
capturing affect via visually detectable concepts such as "cute dog" or
"beautiful landscape". Current state-of-the-art methods approach ANP prediction
by considering each of these compound concepts as individual tokens, ignoring
the underlying relationships in ANPs. This work aims at disentangling the
contributions of the `adjectives' and `nouns' in the visual prediction of ANPs.
Two specialised classifiers, one trained for detecting adjectives and another
for nouns, are fused to predict 553 different ANPs. The resulting ANP
prediction model is more interpretable as it allows us to study contributions
of the adjective and noun components. Source code and models are available at
https://imatge-upc.github.io/affective-2017-musa2/ .Comment: Oral paper at ACM Multimedia 2017 Workshop on Multimodal
Understanding of Social, Affective and Subjective Attributes (MUSA2
Identification of Orthotropic Elastic Properties of Wood by a Synthetic Image Approach Based on Digital Image Correlation
No 888153
CENTRO-01-0145-FEDER-029713
POCI-01-0145-FEDER-031243
POCI-01-0145-FEDER-030592
ENTRO-01-0145-FEDER-022083This work aims to determine the orthotropic linear elastic constitutive parameters of Pinus pinaster Ait. wood from a single uniaxial compressive experimental test, under quasi-static loading conditions, based on two different specimen configurations: (a) on-axis rectangular specimens oriented on the radial-tangential plane, (b) off-axis specimens with a grain angle of about 60◦ (radial-tangential plane). Using digital image correlation (DIC), full-field displacement and strain maps are obtained and used to identify the four orthotropic elastic parameters using the finite element model updating (FEMU) technique. Based on the FE data, a synthetic image reconstruction approach is proposed by coupling the inverse identification method with synthetically deformed images, which are then processed by DIC and compared with the experimental results. The proposed methodology is first validated by employing a DIC-levelled FEA reference in the identification procedure. The impact of the DIC setting parameters on the identification results is systematically investigated. This influence appears to be stronger when the parameter is less sensitive to the experimental setup used. When using on-axis specimen configuration, three orthotropic parameters of Pinus pinaster (ER, ET and νRT ) are correctly identified, while the shear modulus (GRT ) is robustly identified when using off-axis specimen configuration.publishersversionpublishe
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