International audienceSeveral prior studies have apparently demonstrated implicit learning of a repeated segment in continuous-tracking tasks. In two conceptual replications of these studies, we failed to reproduce the original findings. However, these findings were reproduced in a third experiment, in which we used the same repeated segment as that used in the Wulf et al. studies. Analyses of the velocity and the acceleration of the target suggests that this repeated segment could be easier to track than the random segments serving as control, accounting for the results of Wulf and collaborators. Overall these experiments suggest that learning a repeated segment in continuous-tracking tasks may be much more difficult than learning from a repeated sequence in conventional serial reaction time tasks. A possible explanation for this difference is outlined
One crucial parameter to evaluate the state of the heart after myocardial infarction (MI) is the viability of the myocardial segment, i.e., if the segment recovers its functionality upon revascularization. MRI performed several minutes after the injection of a contrast agent (delayed enhancement-MRI or DE-MRI) is a method of choice to evaluate the extent of MI, and by extension, to assess viable tissues after an injury. The Emidec dataset is composed of a series of exams with DE-MR images in short axis orientation covering the left ventricle from normal cases or patients with myocardial infarction, with the contouring of the myocardium and diseased areas (if present) from experts in the domains. Moreover, classical available clinical parameters when the patient is managed by an emergency department are provided for each case. To the best of our knowledge, the Emidec dataset is the first one where annotated DE-MRI are combined with clinical characteristics of the patient, allowing the development of methodologies for exam classification as for exam quantification.
Existing literature reveals that unsafe worker movement behaviors are one of the major reasons of construction site fatalities resulting in serious collisions with site objects and machinery. For capturing worker movements in dynamic construction environments which involves moving and changing objects, a solution based on semantic trajectories and Hidden Markov Model (HMM) is presented in the form of four subsystems. First, a real-time data collection and trajectory pre-processing subsystem is constructed for extracting multifaceted trajectory characteristics and stay locations of the workers using spatio-temporal data that will help in recognizing the important regions in the building for categorizing the worker movements. Second, to enable the desired semantic insights for better understanding the underlying meaningful worker movements using the contextual data related to the building environment, an ontologybased STriDE (Semantic Trajectories in Dynamic Environments) model is applied which has an ability to track information about the evolution of moving and changing building objects, and outputs semantic trajectories. The third subsystem uses the Hidden Markov Model (HMM) which is the most preferred probabilistic approach in the literature for describing the object behavior in time. An entire set of trajectories belonging to a stay location (a semantic region) is analyzed by categorizing the worker movements into four states using the HMMs along with the Viterbi algorithm. In the end, the output of the Viterbi algorithm is visualized using a BIM model for identifying the most probable high-risk locations involving sharp worker movements and rotations. The developed system will help safety managers in monitoring and controlling building activities remotely in dynamic environments by better understanding the worker behaviors for an improved safety management in day-to-day building operations as well as by preventing other workforce from accessing such hazardous locations which involve risky movements.
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