Quantum corrections to minimal surfaces with mixed three-form flux

We obtain the ratio of semiclassical partition functions for the extension under mixed flux of the minimal surfaces subtending a circumference and a line in Euclidean AdS(3) x S-3 x T-4. We reduce the problem to the computation of a set of functional determinants. If the Ramond-Ramond flux does not vanish, we find that the contribution of the B-field is comprised in the conformal anomaly. In this case, we successively apply the Gel'fand-Yaglom method and the Abel-Plana formula to the flat-measure determinants. To cancel the resultant infrared divergences, we shift the regularization of the sum over half-integers depending on whether it corresponds to massive or massless fermionic modes. We show that the result is compatible with the zeta-function regularization approach. In the limit of pure Neveu-Schwarz-Neveu-Schwarz flux we argue that the computation trivializes. We extend the reasoning to other surfaces with the same behavior in this regime

Evaluation of a Bayesian Algorithm to Detect Burned Areas in the Canary Islands’ Dry Woodlands and Forests Ecoregion Using MODIS Data

Burned Area (BA) is deemed as a primary variable to understand the Earth’s climate system. Satellite remote sensing data have allowed for the development of various burned area detection algorithms that have been globally applied to and assessed in diverse ecosystems, ranging from tropical to boreal. In this paper, we present a Bayesian algorithm (BY-MODIS) that detects burned areas in a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2002 to 2012 of the Canary Islands’ dry woodlands and forests ecoregion (Spain). Based on daily image products MODIS, MOD09GQ (250 m), and MOD11A1 (1 km), the surface spectral reflectance and the land surface temperature, respectively, 10 day composites were built using the maximum temperature criterion. Variables used in BY-MODIS were the Global Environment Monitoring Index (GEMI) and Burn Boreal Forest Index (BBFI), alongside the NIR spectral band, all of which refer to the previous year and the year the fire took place in. Reference polygons for the 14 fires exceeding 100 hectares and identified within the period under analysis were developed using both post-fire LANDSAT images and official information from the forest fires national database by the Ministry of Agriculture and Fisheries, Food and Environment of Spain (MAPAMA). The results obtained by BY-MODIS can be compared to those by official burned area products, MCD45A1 and MCD64A1. Despite that the best overall results correspond to MCD64A1, BY-MODIS proved to be an alternative for burned area mapping in the Canary Islands, a region with a great topographic complexity and diverse types of ecosystems. The total burned area detected by the BY-MODIS classifier was 64.9% of the MAPAMA reference data, and 78.6% according to data obtained from the LANDSAT images, with the lowest average commission error (11%) out of the three products and a correlation (R2) of 0.82. The Bayesian algorithm—originally developed to detect burned areas in North American boreal forests using AVHRR archival data Long-Term Data Record—can be successfully applied to a lower latitude forest ecosystem totally different from the boreal ecosystem and using daily time series of satellite images from MODIS with a 250 m spatial resolution, as long as a set of training areas adequately characterising the dynamics of the forest canopy affected by the fire is defined

THE MOLT ISSUE: WHERE DO WE GO FROM NOW?

No abstract

THE MOLT ISSUE: WHERE DO WE GO FROM NOW?

No abstract

Neural Networks for Aircraft Trajectory Prediction: Answering Open Questions About Their Performance

The increase in air traffic in the recent years has motivated the development of technologies to monitor air space and warn about possible collisions by predicting the trajectories that will be followed by aircraft. In this field, neural networks have become prominent thanks to their potential to learn to predict maneuvers without providing aspects that are difficult to model such as atmospheric conditions, or detailed aircraft parameters. A variety of models have been proposed; however, these are often tested in very limited setups, leaving many unanswered questions about how they perform in certain conditions, or whether or not their accuracy can be improved by training models for specific trajectories, using additional features, predicting more distant points directly, etc. This may be problematic for researchers or developers of these systems, who have no way of knowing what strategies will yield the best results. We have identified ten open research questions that have not been answered through in-depth testing. This motivated us to carry out a novel experimental study that performs aircraft trajectory prediction with several dozens configuration variants to answer the aforementioned questions by means of a much more complete evaluation. Some of the conclusions of our study stand in contrast with some popular practices in the state of the art, which casts some doubts on the simplicity of their application; for example, differential features are crucial for proper performance but are not mentioned by most studies, while complex, more elaborate models may lead to worse results than simple ones. Other important insights include the benefit from specialized models in more challenging scenarios, the influence of the known trajectory length in said scenarios, the step degradation of predictions when predicting further into the future, or the detrimental effect of adding additional features. These insights should help guide future research about the application of neural networks when it comes to aircraft trajectory prediction and their eventual inclusion in final systems.journal articl

PREFORMATIVE WING MOLT IN 23 NEOTROPICAL RESIDENT PASSERINE SPECIES

ABSTRACT ∙ Quantitative descriptions of wing‐feather replacement during the preformative and prealternate molts of resident Neotropical passerines are deficient: no more than 100 species possess adequate information. Here, we present quantitative molt data for 23 Neotropical passerines in three blocks: wing and tail molt extent, frequency of wing‐molt pattern, and frequency of wing‐feather replacement. We used Bayesian bootstrapping to estimate mean and 95% credible intervals of wing‐ and tail‐molt extent. We found four molt patterns in the preformative molt, of which most species present more than one. Twenty‐one species undergo partial molt, being the general pattern most frequent. Only Northern Beardless‐Tyrannulet (Camptostoma imberbe) and Black‐chested Sparrow (Peucaea humeralis) undergo a complete preformative molt, the latter also undergoing an extensive prealternate molt. Basic life‐history information may inspire hypotheses to explain molt phenomena. In this sense, our results suggest that lack of time constraints has a small influence on completeness of preformative molt, at least in Neotropical passerines.RESUMEN ∙ Muda preformativa de 23 especies residentes de paseriformes Neotropicales La deficiencia de descripciones cuantitativas sobre el reemplazo de plumas del ala en las mudas preformativa y prealterna de paseriformes neotropicales es dramática: no más de 100 especies poseen información adecuada. A continuación, presentamos datos cuantitativos de muda para 23 paseriformes neotropicales en tres bloques: extensión de muda en ala y cola, frecuencia de patrón de muda alar, y frecuencia de reemplazo de plumas del ala. Utilizamos “bootstrapping” bayesiano para estimar la media e intervalos de credibilidad del 95% de la extensión de muda en ala y cola. Encontramos cuatro patrones de muda en la muda preformativa, de los cuales la mayoría de las especies presentan más de uno. Veintiuna especies mostraron patrones parciales, siendo el patrón general el más frecuente. Sólo Camptostoma imberbe y Peucaea humeralis realizan una muda completa (este último también presenta una extensa muda prealterna). La información básica sobre historia natural puede inspirar hipótesis que expliquen los fenómenos de muda. En este sentido, nuestros resultados sugieren que la ausencia de limitaciones temporales tiene una influencia limitada sobre la compleción de la muda preformativa, al menos en paseriformes neotropicales. 

An automatic welding defects classifier system

Radiographic inspection is a well-established testing method to detect weld defects. However, interpretation of radiographic films is a difficult task. The reliability of such interpretation and the expense of training suitable experts have allowed that the efforts being made towards automation in this field. In this paper, we describe an automatic detection system to recognise welding defects in radiographic images. In a first stage, image processing techniques, including noise reduction, contrast enhancement, thresholding and labelling were implemented to help in the recognition of weld regions and the detection of weld defects. In a second stage, a set of geometrical features was proposed and extracted between defect candidates. In a third stage, an artificial neural network for weld defect classification was used under three regularisation process with different architectures. For the input layer, the principal component analysis technique was used in order to reduce the number of feature variables; and, for the hidden layer, a different number of neurons was used in the aim to give better performance for defect classification in both cases

CALA: Classifying Links Automatically based on their URL

Web page classification refers to the problem of automatically assigning a web page to one or moreclasses after analysing its features. Automated web page classifiers have many applications, and many re- searchers have proposed techniques and tools to perform web page classification. Unfortunately, the ex- isting tools have a number of drawbacks that makes them unappealing for real-world scenarios, namely:they require a previous extensive crawling, they are supervised, they need to download a page beforeclassifying it, or they are site-, language-, or domain-dependent. In this article, we propose CALA, a toolfor URL-based web page classification. The strongest features of our tool are that it does not require aprevious extensive crawling to achieve good classification results, it is unsupervised, it is based exclu- sively on URL features, which means that pages can be classified without downloading them, and it issite-, language-, and domain-independent, which makes it generally applicable. We have validated ourtool with 22 real-world web sites from multiple domains and languages, and our conclusion is that CALAis very effective and efficient in practice.Ministerio de Educación y Ciencia TIN2007-64119Junta de Andalucía P07-TIC-2602Junta de Andalucía P08-TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-EMinisterio de Ciencia e Innovación TIN2010-21744Ministerio de Ciencia e Innovación TIN2010-09809-EMinisterio de Ciencia e Innovación TIN2010-10811-EMinisterio de Ciencia e Innovación TIN2010-09988-EMinisterio de Economía y Competitividad TIN2011-15497-EMinisterio de Economía y Competitividad TIN2013-40848-

Towards Discovering Conceptual Models behind Web Sites

Deep Web sites expose data from a database, whose conceptual model remains hidden. Having access to that model is mandatory to perform several tasks, such as integrating different web sites; extracting information from the web unsupervisedly; or creating ontologies. In this paper, we propose a technique to discover the conceptual model behind a web site in the Deep Web, using a statistical approach to discover relationships between entities. Our proposal is unsupervised, not requiring the user to have expert knowledge; and it does not focus on a single view on the database, instead it integrates all views containing entities and relationships that are exposed in the web site.Ministerio de Educación y Ciencia TIN2007-64119Junta de Andalucía P07-TIC-2602Junta de Andalucía P08-TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-EMinisterio de Ciencia e Innovación TIN2010-10811-EMinisterio de Ciencia e Innovación TIN2010-21744Ministerio de Ciencia e Innovación TIN2010-09809-EMinisterio de Ciencia e Innovación TIN2010-09988-

MostoDEx: A tool to exchange RDF data using exchange samples

The Web is evolving into a Web of Data in which RDF data are becoming pervasive, and it is organised into datasets that share a common purpose but have been developed in isolation. This motivates the need to devise complex integration tasks, which are usually performed using schema mappings; generating them automatically is appealing to relieve users from the burden of handcrafting them. Many tools are based on the data models to be integrated: classes, properties, and constraints. Unfortunately, many data models in the Web of Data comprise very few or no constraints at all, so relying on constraints to generate schema mappings is not appealing. Other tools rely on handcrafting the schema mappings, which is not appealing at all. A few other tools rely on exchange samples but require user intervention, or are hybrid and require constraints to be available. In this article, we present MostoDEx, a tool to generate schema mappings between two RDF datasets. It uses a single exchange sample and a set of correspondences, but does not require any constraints to be available or any user intervention. We validated and evaluated MostoDEx using many experiments that prove its effectiveness and efficiency in practice.Ministerio de Educación y Ciencia TIN2007-64119Junta de Andalucía P07-TIC-2602Junta de Andalucía P08- TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-EMinisterio de Ciencia e Innovación TIN2010-21744Ministerio de Ciencia e Innovación TIN2010-09809-EMinisterio de Ciencia e Innovación TIN2010-10811-EMinisterio de Ciencia e Innovación TIN2010-09988-EMinisterio de Economía y Competitividad TIN2011-15497-EMinisterio de Economía y Competitividad TIN2013-40848-