High-Dimensional Repeated Measures

Recently, new tests for main and simple treatment effects, time effects, and treatment by time interactions in possibly high-dimensional multigroup repeated-measures designs with unequal covariance matrices have been proposed. Technical details for using more than one between-subject and more than one within-subject factor are presented in this article. Furthermore, application to electroencephalography (EEG) data of a neurological study with two whole-plot factors (diagnosis and sex) and two subplot factors (variable and region) is shown with the R package HRM (high-dimensional repeated measures)

Pseudo-Ranks: How to Calculate Them Efficiently in R

Many popular nonparametric inferential methods are based on ranks. Among the most commonly used and most famous tests are for example the Wilcoxon-Mann-Whitney test for two independent samples, and the Kruskal-Wallis test for multiple independent groups. However, recently, it has become clear that the use of ranks may lead to paradoxical results in case of more than two groups. Luckily, these problems can be avoided simply by using pseudo-ranks instead of ranks. These pseudo-ranks, however, suffer from being (a) at first less intuitive and not as straightforward in their interpretation, (b) computationally much more expensive to calculate. The computational cost has been prohibitive, for example, for large-scale simulative evaluations or application of resampling-based pseudorank procedures. In this paper, we provide different algorithms to calculate pseudo-ranks efficiently in order to solve problem (b) and thus render it possible to overcome the current limitations of procedures based on pseudo-ranks

2-[1-(1-Naphth­yl)-1H-1,2,3-triazol-4-yl]pyridine

In the crystal structure of the title compound, C17H12N4, the angle between the naphthalene and 1H-1,2,3-triazole ring systems is 71.02 (4)° and that between the pyridine and triazole rings is 8.30 (9)°

The graph neural networking challenge: a worldwide competition for education in AI/ML for networks

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge", an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020". We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.This work has received funding from the European Union’s H2020 research and innovation programme within the framework of the NGI-POINTER Project funded under grant agreement No. 871528. This paper reflects only the authors’ view; the European Commission is not responsible for any use that may be made of the information it contains. This work was also supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE), the Catalan Institution for Research and Advanced Studies (ICREA), and by FI-AGAUR grant by the Catalan Government. Salzburg Research is grateful for the support by the WISS 2025 (Science and Innovation Strategy Salzburg 2025) project ”IDALab Salzburg” (20204-WISS/225/197-2019 and 20102-F1901166-KZP) and the 5G-AI-MLab by the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK) and the Austrian state Salzburg.Peer ReviewedArticle escrit per 24 autors/autores: José Suárez-Varela (1), Miquel Ferriol-Galmés (1), Albert López (1), Paul Almasan (1), Guillermo Bernárdez (1), David Pujol-Perich (1), Krzysztof Rusek (1,2), Loïck Bonniot (3, 4), Christoph Neumann (3), François Schnitzler (3), François Taïani (4), Martin Happ (5, 6), Christian Maier (5), Jia Lei Du (5), Matthias Herlich (5), Peter Dorfinger (5), Nick Vincent Hainke (7), Stefan Venz (7), Johannes Wegener (7), Henrike Wissing (7), Bo Wu (8), Shihan Xiao (8), Pere Barlet-Ros (1), Albert Cabellos-Aparicio (1). 1- Barcelona Neural Networking center, Universitat Politècnica de Catalunya, Spain. 2- AGH University of Science and Technology, Department of Telecommunications, Poland. 3- InterDigital, France. 4- Univ. Rennes, Inria, CNRS, IRISA, France. 5- Salzburg Research Forschungsgesellschaft mbH, Austria. 6- IDA Lab, University of Salzburg, Austria. 7- Fraunhofer HHI, Germany. 8- Network Technology Lab., Huawei Technologies Co., Ltd., China.Postprint (author's final draft

Ski touring: Analyzing risk-taking behavior and risk avoidance associated with an emerging outdoor activity in the Alps

Ski touring is an emerging trend in the Alps, defined as ascending and descending a slope/backcountry with ski-touring equipment. The aim of this research is to understand risk-taking behavior, risk-avoidance behavior and assessed expertise (ascending/descending) of backcountry ski tourers. A cross-sectional survey design was chosen (N ​= ​300). Results show that e.g. genderwise, the difference is statistically significant in terms of ‘I consciously expose myself to danger’ t(158) ​= ​2.94, and e.g. skillwise, for ‘I check every piece of equipment before use’ t(152) ​= ​−2.98 – providing insights into the risk behavior of BCSTers in terms of gender and skills

Global e-Learning in Early Nutrition and Lifestyle for International Healthcare Professionals: Design and Evaluation of the Early Nutrition Specialist Programme (ENS)

Background: Every encounter a healthcare professional has with new or expecting parents offers an opportunity for addressing improved early nutrition and lifestyle. Evidence-based qualification programmes via e-learning offer valuable tools for attenuating the world’s huge double burden of both under- and overnutrition in early childhood. We evaluated use and learner satisfaction of a global e-learning programme on early nutrition and lifestyle addressing international healthcare professionals. Methods: We implemented the Early Nutrition Specialist Programme (ENS) with six interactive e-learning courses on early nutrition building on more than ten years of experience with global e-learning platforms, expert knowledge and an international network in the subject field. We collected descriptive and explorative evaluation data on usage and learner satisfaction with a questionnaire and log data over three years among 4003 learners from 48 countries. Results: Results show high completion of the ENS programme, with 85.5% of learners finalizing the programme after enrollment into the first of six courses. Very good results were provided for learner satisfaction with the courses (96.7% of users), for increasing understanding of the topic (97.4%) and matching the indicated time investment (94.4%). Most predominant themes in the open text fields of user feedback questionnaires were “Increase interactivity or number of audio-visuals”, “Content suggestions or more examples” and “Technical (quality) issues or navigation problems”. Conclusions: The ENS programme evaluation shows high completion rates and level of satisfaction by learners from numerous countries. The different needs for Continuing Medical Education (CME) of healthcare professionals in diverse healthcare system settings can be met by a joint e-learning qualification programme. Further optimizations will be implemented based on user feedback. More research with a learning analytics approach may help to further identify the most effective and efficient didactic and pedagogic elements of e-learning