Machine learning techniques to select Be star candidates. An application in the OGLE-IV Gaia south ecliptic pole field

Statistical pattern recognition methods have provided competitive solutions for variable star classification at a relatively low computational cost. In order to perform supervised classification, a set of features is proposed and used to train an automatic classification system. Quantities related to the magnitude density of the light curves and their Fourier coefficients have been chosen as features in previous studies. However, some of these features are not robust to the presence of outliers and the calculation of Fourier coefficients is computationally expensive for large data sets. We propose and evaluate the performance of a new robust set of features using supervised classifiers in order to look for new Be star candidates in the OGLE-IV Gaia south ecliptic pole field. We calculated the proposed set of features on six types of variable stars and on a set of Be star candidates reported in the literature. We evaluated the performance of these features using classification trees and random forests along with K-nearest neighbours, support vector machines, and gradient boosted trees methods. We tuned the classifiers with a 10-fold cross-validation and grid search. We validated the performance of the best classifier on a set of OGLE-IV light curves and applied this to find new Be star candidates. The random forest classifier outperformed the others. By using the random forest classifier and colour criteria we found 50 Be star candidates in the direction of the Gaia south ecliptic pole field, four of which have infrared colours consistent with Herbig Ae/Be stars. Supervised methods are very useful in order to obtain preliminary samples of variable stars extracted from large databases. As usual, the stars classified as Be stars candidates must be checked for the colours and spectroscopic characteristics expected for them

Functionalization of p-activated alcohols by trapping carbocations in pure water under smooth conditions

Acetic acid as catalyst in pure water was found to be an excellent reaction medium for the direct dehydrative functionalization of p-activated alcohols using a wide variety of interesting C-, P-, and S-centered nucleophiles, such as indoles, pyrrole, anilines, 1, 3-dicarbonyl compounds, diphenyl phosphite and pyridine-2-thiol. The smooth reaction conditions, along with high yields, short reaction times, clean reaction crudes, an easy product isolation procedure, plus the reusability of the catalyst and the use of no excess of nucleophiles, make this approach an atom economical, green and appealing method to efficiently trap carbocations in pure water, leading to new Csp3 X bonds (X = Csp2, Csp3, P and S)

TrueLearn: A family of bayesian algorithms to match lifelong learners to open educational resources

The recent advances in computer-assisted learning systems and the availability of open educational resources today promise a pathway to providing cost-efficient high-quality education to large masses of learners. One of the most ambitious use cases of computer-assisted learning is to build a lifelong learning recommendation system. Unlike short-term courses, lifelong learning presents unique challenges, requiring sophisticated recommendation models that account for a wide range of factors such as background knowledge of learners or novelty of the material while effectively maintaining knowledge states of masses of learners for significantly longer periods of time (ideally, a lifetime). This work presents the foundations towards building a dynamic, scalable and transparent recommendation system for education, modelling learner’s knowledge from implicit data in the form of engagement with open educational resources. We i) use a text ontology based on Wikipedia to automatically extract knowledge components of educational resources and, ii) propose a set of online Bayesian strategies inspired by the well-known areas of item response theory and knowledge tracing. Our proposal, TrueLearn, focuses on recommendations for which the learner has enough background knowledge (so they are able to understand and learn from the material), and the material has enough novelty that would help the learner improve their knowledge about the subject and keep them engaged. We further construct a large open educational video lectures dataset and test the performance of the proposed algorithms, which show clear promise towards building an effective educational recommendation system

Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems

In informational recommenders, many challenges arise from the need to handle the semantic and hierarchical structure between knowledge areas. This work aims to advance towards building a state-aware educational recommendation system that incorporates semantic relatedness between knowledge topics, propagating latent information across semantically related topics. We introduce a novel learner model that exploits this semantic relatedness between knowledge components in learning resources using the Wikipedia link graph, with the aim to better predict learner engagement and latent knowledge in a lifelong learning scenario. In this sense, Semantic TrueLearn builds a humanly intuitive knowledge representation while leveraging Bayesian machine learning to improve the predictive performance of the educational engagement. Our experiments with a large dataset demonstrate that this new semantic version of TrueLearn algorithm achieves statistically significant improvements in terms of predictive performance with a simple extension that adds semantic awareness to the model

Geocronología de la Terraza Compleja de Arganda en el valle del río Jarama (Madrid, España)

La Terraza Compleja de Arganda (TCA), situada en el tramo bajo del río Jarama (Madrid), está formada por sucesivos apilamientos de secuencias fluviales denominados de abajo a arriba Arganda I, II, III y IV, en los que se han encontrado importantes yacimientos arqueológicos y paleontológicos del Pleistoceno (Áridos 1 y 2, Valdocarros o HAT), y numerosos conjuntos de industria lítica del Paleolítico inferior y medio. Hasta ahora, la única referencia cronológica disponible para la TCA era la proporcionada por el estadio evolutivo de los micromamíferos de los yacimientos Áridos 1 en Arganda I y Valdocarros en Arganda II. En este trabajo, se propone la equivalencia de las distintas unidades de la TCA con terrazas escalonadas y se establece un marco cronológico numérico, obtenido mediante dataciones de termoluminiscencia, luminiscencia ópticamente estimulada y racemización de aminoácidos. Arganda I (≈ T+30-32 m) se situaría hacia el final del MIS 11 o en el inicio del MIS 9, Arganda II (≈T+23-24 m) se correspondería con el inicio del MIS 7, Arganda III (≈T+18-20 m) se situaría entre el MIS 7 y el MIS 5, y Arganda IV comenzaría su deposición en el MIS 5 finalizando su sedimentación en el MIS 1 al sur de Arganda del Rey (Madrid)

Abscesos cerebrales por Nocardia spp. en una paciente inmunocompetente

The infection by Nocardia spp is not common in immunocompetent patients. The empirical antimicrobial treatment directed by anatomical regions does not contemplate the particularities of the germ and the microbiological analysis is necessary for the specific treatment. We present the case of a previously healthy and immunocompetent patient, without known risk factors for Nocardia spp. infection, with evidence of involvement of the pulmonary parenchyma and the skin and subsequent development of multiple brain abscesses. © 2020 Instituto Nacional de Salud

Effect of diet on live weight and egg weight of backyard hens during the rainy season

The objective was to determine the effect of diet on live weight (LW) and egg weight (EW) of backyard hens (BH) during the rainy season in the Bajío region of the state of Michoacán, Mexico. Seventeen municipalities were sampled, where 101 BH (six hens/municipality) were captured and weighed and 101 eggs (six eggs/municipality) were harvested and weighed. The crops of the captured hens were removed Post-sacrifice, the organic content/crop (OCC) was classified and weighed by components, to later perform chemical compositional analysis. Data were analysed using generalised linear models and the differences between municipalities were obtained by the method of least squares means. The weight of the OCC (36.4 ± 22.4 g) was affected by the municipality (P<0.001), but not by the LW of the BH (P>0.05). Commercial feed (8.1 ± 6.0 g), grains: maize and sorghum (13.9 ± 13.5 g) kitchen waste (1.5 ± 2.9 g), herbaceous (0.6 ± 0.9 g) and insects (0.3 ± 0.7 g) were found in the crop. According to the commercial feed (COF) component, two feeding systems (FS) were identified: traditional FS, without COF and nontraditional FS, with COF. The diets of both FS were similar (P>0.05) in nutritional composition and do not meet the nutritional requirements of the hens. The LW (1.567 ± 0.316 kg) and EW (51.3 ± 1.0 g) of the BH cannot be completely attributed to the diet consumed during the rainy season