This paper deals with the haptic affective social interaction during a greeting handshaking between a human and a humanoid robot. The goal of this work is to study how the haptic interaction conveys emotions, and more precisely, how it influences the perception of the dimensions of emotions expressed through the facial expressions of the robot. Moreover, we examine the benefits of the multimodality (i.e., visuo-haptic) over the monomodality (i.e., visual-only and haptic-only). The experimental results with Meka robot show that the multimodal condition presenting high values for grasping force and joint stiffness are evaluated with higher values for the arousal and dominance dimensions than during the visual condition. Furthermore, the results corresponding to the monomodal haptic condition showed that participants discriminate well the dominance and the arousal dimensions of the haptic behaviours presenting low and high values for grasping force and joint stiffness.
The physics reach of the HL-LHC will be limited by how efficiently the experiments can use the available computing resources, i.e. affordable software and computing are essential. The development of novel methods for charged particle reconstruction at the HL-LHC incorporating machine learning techniques or based entirely on machine learning is a vibrant area of research. In the past two years, algorithms for track pattern recognition based on graph neural networks (GNNs) have emerged as a particularly promising approach. Previous work mainly aimed at establishing proof of principle. In the present document we describe new algorithms that can handle complex realistic detectors. The new algorithms are implemented in ACTS, a common framework for tracking software. This work aims at implementing a realistic GNN-based algorithm that can be deployed in an HL-LHC experiment.
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