Feature Selection for Interpatient Supervised Heart Beat Classification

Supervised and interpatient classification of heart beats is primordial in many applications requiring long-term monitoring of the cardiac function. Several classification models able to cope with the strong class unbalance and a large variety of feature sets have been proposed for this task. In practice, over 200 features are often considered, and the features retained in the final model are either chosen using domain knowledge or an exhaustive search in the feature sets without evaluating the relevance of each individual feature included in the classifier. As a consequence, the results obtained by these models can be suboptimal and difficult to interpret. In this work, feature selection techniques are considered to extract optimal feature subsets for state-of-the-art ECG classification models. The performances are evaluated on real ambulatory recordings and compared to previously reported feature choices using the same models. Results indicate that a small number of individual features actually serve the classification and that better performances can be achieved by removing useless features

Machine Learning and Data Analysis in Astroinformatics

Astroinformatics is a new discipline at the cross-road of astronomy, advanced statistics and computer science. With next generation sky surveys, space missions and modern instrumentation astronomy will enter the Petascale regime raising the demand for advanced computer science techniques with hard- and software solutions for data management, analysis, efficient automation and knowledge discovery. This tutorial reviews important developments in astroinformatics over the past years and discusses some relevant research questions and concrete problems. The contribution ends with a short review of the special session papers in these proceedings, as well as perspectives and challenges for the near future

Flemish Normative Data for the Buschke Selective Reminding Test

The purpose of this study was to provide normative data for a Flemish version of the Buschke Selective Reminding Test (SRT). The SRT allows for the simultaneous analysis of several components of verbal memory, such as short and long term retrieval. The Flemish SRT was administered to 3257 neurologically healthy adults (1627 men and 1630 women, age range = 18–94 years). Effects of age, sex and education on SRT performance were assessed. Results indicate that SRT performance decreased with age and that this decline accelerated in men compared to women. Furthermore, an effect of education was found favoring participants who completed a higher education. Normative data quantified through percentile ranks and stratified by age, sex and education level are provided

The Flow of Trust: A Visualization Framework to Externalize, Explore, and Explain Trust in ML Applications

We present a conceptual framework for the development of visual interactive techniques to formalize and externalize trust in machine learning (ML) workflows. Currently, trust in ML applications is an implicit process that takes place in the user's mind. As such, there is no method of feedback or communication of trust that can be acted upon. Our framework will be instrumental in developing interactive visualization approaches that will help users to efficiently and effectively build and communicate trust in ways that fit each of the ML process stages. We formulate several research questions and directions that include: 1) a typology/taxonomy of trust objects, trust issues, and possible reasons for (mis)trust; 2) formalisms to represent trust in machine-readable form; 3) means by which users can express their state of trust by interacting with a computer system (e.g., text, drawing, marking); 4) ways in which a system can facilitate users' expression and communication of the state of trust; and 5) creation of visual interactive techniques for representation and exploration of trust over all stages of an ML pipeline

Analyzing subcomponents of affective dysregulation in borderline personality disorder in comparison to other clinical groups using multiple e-diary datasets

Background: Affective dysregulation is widely regarded as being the core problem in patients with borderline personality disorder (BPD). Moreover, BPD is the disorder mainly associated with affective dysregulation. However, the empirical confirmation of the specificity of affective dysregulation for BPD is still pending. We used a validated approach from basic affective science that allows for simultaneously analyzing three interdependent components of affective dysregulation that are disturbed in patients with BPD: homebase, variability, and attractor strength (return to baseline). Methods: We applied two types of multilevel models on two e-diary datasets to investigate group differences regarding three subcomponents between BPD patients (n =43; n =51) and patients with posttraumatic stress disorder (PTSD; n= 28) and those with bulimia nervosa (BN; n= 20) as clinical control groups in dataset 1, and patients with panic disorder (PD; n= 26) and those with major depression (MD; n =25) as clinical control groups in dataset 2. In addition, healthy controls (n= 28; n= 40) were included in the analyses. In both studies, e-diaries were used to repeatedly collect data about affective experiences during participants’ daily lives. In study 1 a high-frequency sampling strategy with assessments in 15 min-intervals over 24 h was applied, whereas the assessments occurred every waking hour over 48 h in study 2. The local ethics committees approved both studies, and all participants provided written informed consent. Results: In contradiction to our hypotheses, BPD patients did not consistently show altered affective dysregulation compared to the clinical patient groups. The only differences in affective dynamics in BPD patients emerged with regard to one of three subcomponents, affective homebase. However, these results were not even consistent. Conversely, comparing the patients to healthy controls revealed a pattern of more negative affective homebases, higher levels of affective variability, and (partially) reduced returns to baseline in the patient groups. Conclusions: Our results indicate that affective dysregulation constitutes a transdiagnostic mechanism that manifests in similar ways in several different mental disorders. We point out promising prospects that might help to elucidate the common and distinctive mechanisms that underlie several different disorders and that should be addressed in future studies

Reviewing, indicating, and counting books for modern research evaluation systems

In this chapter, we focus on the specialists who have helped to improve the conditions for book assessments in research evaluation exercises, with empirically based data and insights supporting their greater integration. Our review highlights the research carried out by four types of expert communities, referred to as the monitors, the subject classifiers, the indexers and the indicator constructionists. Many challenges lie ahead for scholars affiliated with these communities, particularly the latter three. By acknowledging their unique, yet interrelated roles, we show where the greatest potential is for both quantitative and qualitative indicator advancements in book-inclusive evaluation systems.Comment: Forthcoming in Glanzel, W., Moed, H.F., Schmoch U., Thelwall, M. (2018). Springer Handbook of Science and Technology Indicators. Springer Some corrections made in subsection 'Publisher prestige or quality

A novel high-speed bulge test to identify the large deformation behavior of sheet metals

Background Despite the widespread use of quasi-static bulge tests to investigate the plastic deformation of sheet metals, a dynamic counterpart able to provide reliable measurements of the bulge pressure, displacement and strain fields at the sample surface is still missing.Objective Aiming at an in-depth identification of the mechanical response of sheet metals at high strain rates under nearly equibiaxial stresses, a novel high-speed bulge (HSB) test was developed.Method The working principle of the HSB setup combines the strengths of conventional split Hopkinson pressure bar (SHPB) and static hydraulic bulge facilities. The main innovation of the HSB test facility, compared to existing setups, is the possibility to implement high-speed stereo digital image correlation (DIC) measurements of the 3D-displacement and in-plane strain fields at the sample surface. Moreover, from strain measurements on the output Hopkinson bar, the time history of the pressure imposed to the sample is obtained.Results The potential of the novel technique is demonstrated by experiments on AA2024-T3 sheets. The measurements reveal a nearly oscillation-free pressure signal which indicates a stable sample loading. The material is deformed up to large levels of plastic strain at strain rates of about 300 to 350 s(-1). The strain rate at the sample apex has a stable value during most of the experiment. From the measurements, the material flow curves are calculated using the methodology presented in the ISO-16808:2014 standard for bulge experiments.Conclusion The ability of the proposed HSB test facility to capture bulge pressure, displacement and deformation fields of the entire sample surface, provides unique opportunities to investigate sheet metals behaviour under a nearly biaxial stress state at high strain rates. Furthermore, the available measurement data can be used to calibrate and validate complex, strain rate dependent plasticity models