A Genetic Tuning to Improve the Performance of Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets: Degree of Ignorance and Lateral Position

Fuzzy Rule-Based Systems are appropriate tools to deal with classification problems due to their good properties. However, they can suffer a lack of system accuracy as a result of the uncertainty inherent in the definition of the membership functions and the limitation of the homogeneous distribution of the linguistic labels. The aim of the paper is to improve the performance of Fuzzy Rule-Based Classification Systems by means of the Theory of Interval-Valued Fuzzy Sets and a post-processing genetic tuning step. In order to build the Interval-Valued Fuzzy Sets we define a new function called weak ignorance for modeling the uncertainty associated with the definition of the membership functions. Next, we adapt the fuzzy partitions to the problem in an optimal way through a cooperative evolutionary tuning in which we handle both the degree of ignorance and the lateral position (based on the 2-tuples fuzzy linguistic representation) of the linguistic labels. The experimental study is carried out over a large collection of data-sets and it is supported by a statistical analysis. Our results show empirically that the use of our methodology outperforms the initial Fuzzy Rule-Based Classification System. The application of our cooperative tuning enhances the results provided by the use of the isolated tuning approaches and also improves the behavior of the genetic tuning based on the 3-tuples fuzzy linguistic representation.Spanish Government TIN2008-06681-C06-01 TIN2010-1505

MRQAR: A generic MapReduce framework to discover quantitative association rules in big data problems

Many algorithms have emerged to address the discovery of quantitative association rules from datasets in the last years. However, this task is becoming a challenge because the processing power of most existing techniques is not enough to handle the large amount of data generated nowadays. These vast amounts of data are known as Big Data. A number of previous studies have been focused on mining boolean or nominal association rules from Big Data problems, nevertheless, the data in real-world applications usually consist of quantitative values and designing data mining algorithms able to extract quantitative association rules presents a challenge to workers in this research field. In spite of the fact that we can find classical methods to discover boolean or nominal association rules in the most well-known repositories of Big Data algorithms, such repositories do not provide methods to discover quantitative association rules. Indeed, no methodologies have been proposed in the literature without prior discretization in Big Data. Hence, this work proposes MRQAR, a new generic parallel framework to discover quantitative association rules in large amounts of data, designed following the MapReduce paradigm using Apache Spark. MRQAR performs an incremental learning able to run any sequential quantitative association rule algorithm in Big Data problems without needing to redesign such algorithms. As a case study, we have integrated the multiobjective evolutionary algorithm MOPNAR into MRQAR to validate the generic MapReduce framework proposed in this work. The results obtained in the experimental study performed on five Big Data problems prove the capability of MRQAR to obtain reduced set of high quality rules in reasonable time.Ministerio de Economía y Competitividad TIN2017-89517-PMinisterio de Economía y Competitividad TIN2014-55894-C2-1-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-

Process design for the manufacturing of soft X-ray gratings in single-crystal diamond by high-energy heavy-ion irradiation

The dataset that supports the findings of this study are archived in the Universidad Autónoma de Madrid data repository e‐cienciaDatos in https://doi.org/10.21950/ARZSJ1This paper describes in detail a novel manufacturing process for optical gratings suitable for use in the UV and soft X-ray regimes in a single-crystal diamond substrate based on highly focused swift heavy-ion irradiation. This type of grating is extensively used in light source facilities such as synchrotrons or free electron lasers, with ever-increasing demands in terms of thermal loads, depending on beamline operational parameters and architecture. The process proposed in this paper may be a future alternative to current manufacturing techniques, providing the advantage of being applicable to single-crystal diamond substrates, with their unique properties in terms of heat conductivity and radiation hardness. The paper summarizes the physical principle used for the grating patterns produced by swift heavy-ion irradiation and provides full details for the manufacturing process for a specific grating configuration, inspired in one of the beamlines at the ALBA synchrotron light source, while stressing the most challenging points for a potential implementation. Preliminary proof-of-concept experimental results are presented, showing the practical implementation of the methodology proposed herei

An insight into imbalanced Big Data classification: outcomes and challenges

Big Data applications are emerging during the last years, and researchers from many disciplines are aware of the high advantages related to the knowledge extraction from this type of problem. However, traditional learning approaches cannot be directly applied due to scalability issues. To overcome this issue, the MapReduce framework has arisen as a “de facto” solution. Basically, it carries out a “divide-and-conquer” distributed procedure in a fault-tolerant way to adapt for commodity hardware. Being still a recent discipline, few research has been conducted on imbalanced classification for Big Data. The reasons behind this are mainly the difficulties in adapting standard techniques to the MapReduce programming style. Additionally, inner problems of imbalanced data, namely lack of data and small disjuncts, are accentuated during the data partitioning to fit the MapReduce programming style. This paper is designed under three main pillars. First, to present the first outcomes for imbalanced classification in Big Data problems, introducing the current research state of this area. Second, to analyze the behavior of standard pre-processing techniques in this particular framework. Finally, taking into account the experimental results obtained throughout this work, we will carry out a discussion on the challenges and future directions for the topic.This work has been partially supported by the Spanish Ministry of Science and Technology under Projects TIN2014-57251-P and TIN2015-68454-R, the Andalusian Research Plan P11-TIC-7765, the Foundation BBVA Project 75/2016 BigDaPTOOLS, and the National Science Foundation (NSF) Grant IIS-1447795

Incidence, clinical characteristics and management of inflammatory bowel disease in Spain: large-scale epidemiological study

(1) Aims: To assess the incidence of inflammatory bowel disease (IBD) in Spain, to describe the main epidemiological and clinical characteristics at diagnosis and the evolution of the disease, and to explore the use of drug treatments. (2) Methods: Prospective, population-based nationwide registry. Adult patients diagnosed with IBD—Crohn’s disease (CD), ulcerative colitis (UC) or IBD unclassified (IBD-U)—during 2017 in Spain were included and were followed-up for 1 year. (3) Results: We identified 3611 incident cases of IBD diagnosed during 2017 in 108 hospitals covering over 22 million inhabitants. The overall incidence (cases/100, 000 person-years) was 16 for IBD, 7.5 for CD, 8 for UC, and 0.5 for IBD-U; 53% of patients were male and median age was 43 years (interquartile range = 31–56 years). During a median 12-month follow-up, 34% of patients were treated with systemic steroids, 25% with immunomodulators, 15% with biologics and 5.6% underwent surgery. The percentage of patients under these treatments was significantly higher in CD than UC and IBD-U. Use of systemic steroids and biologics was significantly higher in hospitals with high resources. In total, 28% of patients were hospitalized (35% CD and 22% UC patients, p < 0.01). (4) Conclusion: The incidence of IBD in Spain is rather high and similar to that reported in Northern Europe. IBD patients require substantial therapeutic resources, which are greater in CD and in hospitals with high resources, and much higher than previously reported. One third of patients are hospitalized in the first year after diagnosis and a relevant proportion undergo surgery. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Psychophysics with children: Investigating the effects of attentional lapses on threshold estimates

When assessing the perceptual abilities of children, researchers tend to use psychophysical techniques designed for use with adults. However, children’s poorer attentiveness might bias the threshold estimates obtained by these methods. Here, we obtained speed discrimination threshold estimates in 6- to 7-year-old children in UK Key Stage 1 (KS1), 7- to 9-year-old children in Key Stage 2 (KS2), and adults using three psychophysical procedures: QUEST, a 1-up 2-down Levitt staircase, and Method of Constant Stimuli (MCS). We estimated inattentiveness using responses to “easy” catch trials. As expected, children had higher threshold estimates and made more errors on catch trials than adults. Lower threshold estimates were obtained from psychometric functions fit to the data in the QUEST condition than the MCS and Levitt staircases, and the threshold estimates obtained when fitting a psychometric function to the QUEST data were also lower than when using the QUEST mode. This suggests that threshold estimates cannot be compared directly across methods. Differences between the procedures did not vary significantly with age group. Simulations indicated that inattentiveness biased threshold estimates particularly when threshold estimates were computed as the QUEST mode or the average of staircase reversals. In contrast, thresholds estimated by post-hoc psychometric function fitting were less biased by attentional lapses. Our results suggest that some psychophysical methods are more robust to attentiveness, which has important implications for assessing the perception of children and clinical groups

Age and date for early arrival of the Acheulian in Europe (Barranc de la Boella, la Canonja, Spain)

The first arrivals of hominin populations into Eurasia during the Early Pleistocene are currently considered to have occurred as short and poorly dated biological dispersions. Questions as to the tempo and mode of these early prehistoric settlements have given rise to debates concerning the taxonomic significance of the lithic assemblages, as trace fossils, and the geographical distribution of the technological traditions found in the Lower Palaeolithic record. Here, we report on the Barranc de la Boella site which has yielded a lithic assemblage dating to ,1 million years ago that includes large cutting tools (LCT). We argue that distinct technological traditions coexisted in the Iberian archaeological repertoires of the late Early Pleistocene age in a similar way to the earliest sub-Saharan African artefact assemblages. These differences between stone tool assemblages may be attributed to the different chronologies of hominin dispersal events. The archaeological record of Barranc de la Boella completes the geographical distribution of LCT assemblages across southern Eurasia during the EMPT (Early-Middle Pleistocene Transition, circa 942 to 641 kyr). Up to now, chronology of the earliest European LCT assemblages is based on the abundant Palaeolithic record found in terrace river sequences which have been dated to the end of the EMPT and later. However, the findings at Barranc de la Boella suggest that early LCT lithic assemblages appeared in the SW of Europe during earlier hominin dispersal episodes before the definitive colonization of temperate Eurasia took place.The research at Barranc de la Boella has been carried out with the financial support of the Spanish Ministerio de Economı´a y Competitividad (CGL2012- 36682; CGL2012-38358, CGL2012-38434-C03-03 and CGL2010-15326; MICINN project HAR2009-7223/HIST), Generalitat de Catalunya, AGAUR agence (projects 2014SGR-901; 2014SGR-899; 2009SGR-324, 2009PBR-0033 and 2009SGR-188) and Junta de Castilla y Leo´n BU1004A09. Financial support for Barranc de la Boella field work and archaeological excavations is provided by the Ajuntament de la Canonja and Departament de Cultura (Servei d’Arqueologia i Paleontologia) de la Generalitat de Catalunya. A. Carrancho’s research was funded by the International Excellence Programme, Reinforcement subprogramme of the Spanish Ministry of Education. I. Lozano-Ferna´ndez acknowledges the pre-doctoral grant from the Fundacio´n Atapuerca. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Ensemble and fuzzy techniques applied to imbalanced traffic congestion datasets a comparative study

Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. Among the different kind of classification problems that have been studied in the literature, the imbalanced ones, particularly those that represents real-world problems, have attracted the interest of many researchers in recent years. In order to face this problems, different approaches have been used or proposed in the literature, between then, soft computing and ensemble techniques. In this work, ensembles and fuzzy techniques have been applied to real-world traffic datasets in order to study their performance in imbalanced real-world scenarios. KEEL platform is used to carried out this study. The results show that different ensemble techniques obtain the best results in the proposed datasets. Document type: Part of book or chapter of boo