The U(1) phase transition on toroidal and spherical lattices

We have studied the properties of the phase transition in the U(1) compact pure gauge model paying special atention to the influence of the topology of the boundary conditions. From the behavior of the energy cumulants and the observation of an effective \nu -> 1/d on toroidal and spherical lattices, we conclude that the transition is first order.Comment: LATTICE98(gauge

First order signatures in 4D pure compact U(1) gauge theory with toroidal and spherical topologies

We study the pure compact U(1) gauge theory with the extended Wilson action (\beta, \gamma couplings) by finite size scaling techniques, in lattices ranging from L=6 to L=24 in the region of \gamma <= 0 with toroidal and spherical topologies. The phase transition presents a double peak structure which survives in the thermodynamical limit in the torus. In the sphere the evidence support the idea of a weaker, but still first order, phase transition. For negative values of gamma the transition becomes weaker and larger lattices are needed to find its asymptotic behaviour. Along the transient region the behaviour is the typical one of a weak first order transition for both topologies, with a region where 1/d < nu < 0.5, which becomes nu compatible with 1/d when larger lattices are used.Comment: Some references added; changes in the text mainly wording. To appear in Phys. Lett.

Converting rain into drinking water: Quality issues and technological advances

With growing pressures on water supplies worldwide, rainwater harvesting is increasingly seen as a viable option to provide drinking water to an ever expanding population, particularly in developing countries. However, rooftop runoff is not without quality issues. Microbiological and chemical contamination have been detected in several studies, well above local and international guidelines, posing a health risk for consumers. Our research explores the use of silver ions, combined with conventional filtration and settling mechanisms, as a safe and affordable model for purification that can be applied on a small scale. The complete systems were installed and tested in rural communities in a Mexican semi-arid region. Efficiencies up to 99.9% were achieved in the removal of indicator microorganisms, with a marked exception where cross-contamination from external seepage occurs. Sites without overhanging branches or with relatively clean surfaces show an absence of total coliforms in the untreated runoff, compared with others where values as high as 1,650 CFU/100 ml were recorded. Thus, given adequate maintenance, the system can successfully deliver high quality drinking water, even when storage is required for long periods of time. © IWA Publishing 2011

Evolutionary constraints on the complexity of genetic regulatory networks allow predictions of the total number of genetic interactions

Genetic regulatory networks (GRNs) have been widely studied, yet there is a lack of understanding with regards to the final size and properties of these networks, mainly due to no network currently being complete. In this study, we analyzed the distribution of GRN structural properties across a large set of distinct prokaryotic organisms and found a set of constrained characteristics such as network density and number of regulators. Our results allowed us to estimate the number of interactions that complete networks would have, a valuable insight that could aid in the daunting task of network curation, prediction, and validation. Using state-of-the-art statistical approaches, we also provided new evidence to settle a previously stated controversy that raised the possibility of complete biological networks being random and therefore attributing the observed scale-free properties to an artifact emerging from the sampling process during network discovery. Furthermore, we identified a set of properties that enabled us to assess the consistency of the connectivity distribution for various GRNs against different alternative statistical distributions. Our results favor the hypothesis that highly connected nodes (hubs) are not a consequence of network incompleteness. Finally, an interaction coverage computed for the GRNs as a proxy for completeness revealed that high-throughput based reconstructions of GRNs could yield biased networks with a low average clustering coefficient, showing that classical targeted discovery of interactions is still needed.Comment: 28 pages, 5 figures, 12 pages supplementary informatio

Three-Dimensional Wave Packet Approach for the Quantum Transport of Atoms through Nanoporous Membranes

Quantum phenomena are relevant to the transport of light atoms and molecules through nanoporous two-dimensional (2D) membranes. Indeed, confinement provided by (sub-)nanometer pores enhances quantum effects such as tunneling and zero point energy (ZPE), even leading to quantum sieving of different isotopes of a given element. However, these features are not always taken into account in approaches where classical theories or approximate quantum models are preferred. In this work we present an exact three-dimensional wave packet propagation treatment for simulating the passage of atoms through periodic 2D membranes. Calculations are reported for the transmission of $^3$He and $^4$He through graphdiyne as well as through a holey graphene model. For He-graphdiyne, estimations based on tunneling-corrected transition state theory are correct: both tunneling and ZPE effects are very important but competition between each other leads to a moderately small $^4$He/$^3$He selectivity. Thus, formulations that neglect one or another quantum effect are inappropriate. For the transport of He isotopes through leaky graphene, the computed transmission probabilities are highly structured suggesting widespread selective adsorption resonances and the resulting rate coefficients and selectivity ratios are not in agreement with predictions from transition state theory. Present approach serves as a benchmark for studies of the range of validity of more approximate methods.Comment: 4 figure

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

Redundancy of stereoscopic images: Experimental Evaluation

With the recent advancement in visualization devices over the last years, we are seeing a growing market for stereoscopic content. In order to convey 3D content by means of stereoscopic displays, one needs to transmit and display at least 2 points of view of the video content. This has profound implications on the resources required to transmit the content, as well as demands on the complexity of the visualization system. It is known that stereoscopic images are redundant, which may prove useful for compression and may have positive effect on the construction of the visualization device. In this paper we describe an experimental evaluation of data redundancy in color stereoscopic images. In the experiments with computer generated and real life and test stereo images, several observers visually tested the stereopsis threshold and accuracy of parallax measuring in anaglyphs and stereograms as functions of the blur degree of one of two stereo images and color saturation threshold in one of two stereo images for which full color 3D perception with no visible color degradations is maintained. The experiments support a theoretical estimate that one has to add, to data required to reproduce one of two stereoscopic images, only several percents of that amount of data in order to achieve stereoscopic perception