Period Estimation in Astronomical Time Series Using Slotted Correntropy

In this letter, we propose a method for period estimation in light curves from periodic variable stars using correntropy. Light curves are astronomical time series of stellar brightness over time, and are characterized as being noisy and unevenly sampled. We propose to use slotted time lags in order to estimate correntropy directly from irregularly sampled time series. A new information theoretic metric is proposed for discriminating among the peaks of the correntropy spectral density. The slotted correntropy method outperformed slotted correlation, string length, VarTools (Lomb-Scargle periodogram and Analysis of Variance), and SigSpec applications on a set of light curves drawn from the MACHO survey

RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection

The brain electrical activity presents several short events during sleep that can be observed as distinctive micro-structures in the electroencephalogram (EEG), such as sleep spindles and K-complexes. These events have been associated with biological processes and neurological disorders, making them a research topic in sleep medicine. However, manual detection limits their study because it is time-consuming and affected by significant inter-expert variability, motivating automatic approaches. We propose a deep learning approach based on convolutional and recurrent neural networks for sleep EEG event detection called Recurrent Event Detector (RED). RED uses one of two input representations: a) the time-domain EEG signal, or b) a complex spectrogram of the signal obtained with the Continuous Wavelet Transform (CWT). Unlike previous approaches, a fixed time window is avoided and temporal context is integrated to better emulate the visual criteria of experts. When evaluated on the MASS dataset, our detectors outperform the state of the art in both sleep spindle and K-complex detection with a mean F1-score of at least 80.9% and 82.6%, respectively. Although the CWT-domain model obtained a similar performance than its time-domain counterpart, the former allows in principle a more interpretable input representation due to the use of a spectrogram. The proposed approach is event-agnostic and can be used directly to detect other types of sleep events.Comment: 8 pages, 5 figures. In proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN 2020

Aplicaciones de las redes neuronales en las finanzas

Se estudian las Redes Neuronales Supervisadas como herramientas para la predicción de tendencias y como clasificadoras de conjuntos de datos en los analisis financieros

Análisis cualitativo en las calificaciones del crédito público: Propuesta de un enfoque complementario utilizando Mapas Auto-organizados (SOM)

The financial crisis that began in late 2007 has raised awareness on the need to properly measure credit risk, placing a significant focus on the accuracy of public credit ratings. The objective of this paper is to present an automated credit rating model that dispenses with the excessive qualitative input that, during the years leading to the 2007 crisis, may have yielded results inconsistent with true counterparty risk levels. Our model is based on a mix of relevant credit ratios, historical data on a corporate universe comprising the global pharmaceutical, chemicals and Oil & Gas industries and a powerful clustering mathematical algorithm, Self-Organising Maps, a type of neural network.La crisis financiera que comenzó a finales de 2007 ha incrementado la concienciación sobre la necesidad de medir adecuadamente el riesgo del crédito, haciendo mayor hincapié en la precisión de las calificaciones públicas. El objetivo de este trabajo es presentar un modelo automatizado de calificación crediticia que prescinda del exceso de lo cualitativo, habitual durante los años previos a la crisis de 2007, y que pudo haber provocado resultados inconsistentes con los niveles reales del riesgo de crédito. Nuestro modelo se basa en una combinación de las ratios crediticias relevantes, los datos históricos relativos a un universo empresarial que incluye a las industrias farmacéuticas, químicas y petrolíferas, y un potente algoritmo matemático de agrupación, SOM, que constituye un tipo de red neuronal

Discriminating Variable Star Candidates in Large Image Databases from the HiTS Survey Using NMF

AbstractNew instruments and technologies are allowing the acquisition of large amounts of data from astronomical surveys. Nowadays there is a pressing need for autonomous methods to discriminate the interesting astronomical objects in the vast sky. The High Cadence Transient Survey (HiTS) project is an astronomical survey that is trying to find a rare transient event that occurs during the first instants of a supernova. In this paper we propose an autonomous method to discriminate stellar variability from the HiTS database, that uses a feature extraction scheme based on Non-negative matrix factorization (NMF). Using NMF, dictionaries of image prototypes that represent the data in a compact way are obtained. The projections of the dataset into these dictionaries are fed into a random forest classifier. NMF is compared with other feature extraction schemes, on a subset of 500,000 transient candidates from the HiTS survey. With NMF a better class separability at feature level is obtained which enhances the classification accuracy significantly. Using the NMF features less than 4% of the true stellar transients are lost, at a manageable false positive rate of 0.1%

Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection

We introduce Deep-HiTS, a rotation invariant convolutional neural network (CNN) model for classifying images of transients candidates into artifacts or real sources for the High cadence Transient Survey (HiTS). CNNs have the advantage of learning the features automatically from the data while achieving high performance. We compare our CNN model against a feature engineering approach using random forests (RF). We show that our CNN significantly outperforms the RF model reducing the error by almost half. Furthermore, for a fixed number of approximately 2,000 allowed false transient candidates per night we are able to reduce the miss-classified real transients by approximately 1/5. To the best of our knowledge, this is the first time CNNs have been used to detect astronomical transient events. Our approach will be very useful when processing images from next generation instruments such as the Large Synoptic Survey Telescope (LSST). We have made all our code and data available to the community for the sake of allowing further developments and comparisons at https://github.com/guille-c/Deep-HiTS

Formal specifications in component-based development

Software engineering has entered a new era, the Internet and its associated technologies require a different conceptual framework for building and understanding software solutions. Users ask to develop applications more rapidly, and software engineers need to ensamble systems from preexisting parts. Components and Components-Based Development( CBD), are the approaches that provide solutions to these arising needs. Components are the way to encapsulate existing functionality, acquire third-party solutions, and build new services to support emerging business processes. Component-based development provides a design paradigm that is well suited to the new requirements, were the traditional design and build has been replaced by select and integrate. Within this approach, the specification of components plays a crucial role. If we are working on the development of components in order to construct a library for general use, we need to start from a concrete and complete specification of what we are going to construct. If we are assembling our application from pre-existing components, we need a precise specification of the behaviour of the component in order to select it from the library.Eje: Ingeniería de Software y Base de DatosRed de Universidades con Carreras en Informática (RedUNCI