Abstract:Time irreversibility, i.e. the lack of invariance of the statistical properties of a system 1 under time reversal, is a fundamental property of all systems operating out of equilibrium. Time 2 reversal symmetry is associated with important statistical and physical properties and is related to the 3 predictability of the system generating the time series. Over the past fifteen years, various methods 4 to quantify time irreversibility in time series have been proposed, but these can be computationally 5 expensive. Here we propose a new method, based on permutation entropy, which is essentially 6 parameter-free, temporally local, yields straightforward statistical tests, and has fast convergence 7 properties. We apply this method to the study of financial time series, showing that stocks and indices 8 present a rich irreversibility dynamics. We illustrate the comparative methodological advantages of 9 our method with respect to a recently proposed method based on visibility graphs, and discuss the 10 implications of our results for financial data analysis and interpretation.

Abstract:The new generation of artificial satellites is providing a huge amount of Earth observation images whose exploitation can report invaluable benefits, both economical and environmental. However, only a small fraction of this data volume has been analyzed, mainly due to the large human resources needed for that task. In this sense, the development of unsupervised methodologies for the analysis of these images is a priority. In this work, a new unsupervised segmentation algorithm for satellite images is proposed. This algorithm is based on the rough-set theory, and it is inspired by a previous segmentation algorithm defined in the RGB color domain. The main contributions of the new algorithm are: (i) extending the original algorithm to four spectral bands; (ii) the concept of the superpixel is used in order to define the neighborhood similarity of a pixel adapted to the local characteristics of each image; (iii) and two new region merged strategies are proposed and evaluated in order to establish the final number of regions in the segmented image. The experimental results show that the proposed approach improves the results provided by the original method when both are applied to satellite images with different spectral and spatial resolutions.

No abstract

No abstract

Graphical abstract

One of the hottest topics being researched in the field of IoT relates to making connected devices smarter, by locally computing relevant information and integrating data coming from other sensors through a local network. Such works are still in their early stages either by lack of access to data or, on the other hand, by the lack of simple test cases with a clear added value. This contribution aims at shading some light on how knowledge can be obtained, using a simple use case. It focuses on the feasibility of having a home refrigerator performing temperature forecasts, using information provided by both internal and external sensors. The problem is reviewed for both its potential applications and to compare the use of different algorithms, from simple linear correlations to ARIMA models. We analyse the precision and computational cost using real data from a refrigerator. Results indicate that small average errors, down to ≈0.09 °C, can be obtained. Lastly, it is devised how can the scenario be improved, and, most importantly, how this work can be extended in the future.

The increasing availability of biological data is improving our understanding of diseases and providing new insight into their underlying relationships. Thanks to the improvements on both text mining techniques and computational capacity, the combination of biological data with semantic information obtained from medical publications has proven to be a very promising path. However, the limitations in the access to these data and their lack of structure pose challenges to this approach. In this document we propose the use of Wikipedia -the free online encyclopedia -as a source of accessible textual information for disease understanding research. To check its validity, we compare its performance in the determination of relationships between diseases with that of PubMed, one of the most consulted data sources of medical texts. The obtained results suggest that the information extracted from Wikipedia is as relevant as that obtained from PubMed abstracts (i.e. the free access portion of its articles), although further research is proposed to verify its reliability for medical studies.

Cancer remains one of the major public health challenges worldwide. After cardiovascular diseases, cancer is one of the first causes of death and morbidity in Europe, with more than 4 million new cases and 1.9 million deaths per year. The suboptimal management of cancer patients during treatment and subsequent follows up are major obstacles in achieving better outcomes of the patients and especially regarding cost and quality of life In this paper, we present an initial data-driven approach to analyze the resources and services that are used more frequently by lung-cancer patients with the aim of identifying where the care process can be improved by paying a special attention on services before diagnosis to being able to identify possible lung-cancer patients before they are diagnosed and by reducing the length of stay in the hospital. Our approach has been built by analyzing the clinical notes of those oncological patients to extract this information and their relationships with other variables of the patient. Although the approach shown in this manuscript is very preliminary, it shows that quite interesting outcomes can be derived from further analysis.