Mimicry and automatic imitation are not correlated

It is widely known that individuals have a tendency to imitate each other. However, different psychological disciplines assess imitation in different manners. While social psychologists assess mimicry by means of action observation, cognitive psychologists assess automatic imitation with reaction time based measures on a trial-by-trial basis. Although these methods differ in crucial methodological aspects, both phenomena are assumed to rely on similar underlying mechanisms. This raises the fundamental question whether mimicry and automatic imitation are actually correlated. In the present research we assessed both phenomena and did not find a meaningful correlation. Moreover, personality traits such as empathy, autism traits, and traits related to self- versus other-focus did not correlate with mimicry or automatic imitation either. Theoretical implications are discussed

FLAGS : a methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning

Anomalies and faults can be detected, and their causes verified, using both data-driven and knowledge-driven techniques. Data-driven techniques can adapt their internal functioning based on the raw input data but fail to explain the manifestation of any detection. Knowledge-driven techniques inherently deliver the cause of the faults that were detected but require too much human effort to set up. In this paper, we introduce FLAGS, the Fused-AI interpretabLe Anomaly Generation System, and combine both techniques in one methodology to overcome their limitations and optimize them based on limited user feedback. Semantic knowledge is incorporated in a machine learning technique to enhance expressivity. At the same time, feedback about the faults and anomalies that occurred is provided as input to increase adaptiveness using semantic rule mining methods. This new methodology is evaluated on a predictive maintenance case for trains. We show that our method reduces their downtime and provides more insight into frequently occurring problems. (C) 2020 The Authors. Published by Elsevier B.V

M & L Extra-nummer - Maagdentoren

Sonja Vanblaere - VoorwoordKjell Corens - De Maagdentoren. De rijkdom van een vergeten heerlijkheid.Frans Doperé - Bouwde Reinier II van Schoonvorst in Zichem een ongewone woontoren, een militaire versterking of een uniek prestige-symbool?Sofie Debruyne, Geert Vynckier en Marc Brion - Onder den toren. Het archeologisch bodemarchief van de reus van Zichem.Thomas Van Driessche - De Maagdentoren in de 19de en 20ste eeuw: van de aankoop door de Belgische staat in 1859 tot de bescherming als monument in 1962Hilde Thibaut, Marc Vanderauwera en Kristin Van den Abbeele - De restauratie van de MaagdentorenNorbert Provoost, Marc Vanderauwera en Hilde Thibaut - Stabiliteit van de MaagdentorenMarc Vanderauwera en Hilde Thibaut - Behandeling van het parement in Diestiaan ijzerzandsteenLinda Van Dijck - Het interieur van de MaagdentorenKoen Smets, Herman van den Bossche en Jan van Ormelingen - De Demervallei tussen Aarschot en Diest in de onmiddellijke omgeving van de MaagdentorenLiesbeth Tielens - De toekomst van de Maagdentoren als belangrijke toeristische trekpleister voor Zichem en omstrekenSummar

OpenMR Benelux

A repository for operational and educational content for OpenMR Benelux events