Towards a Multimodal Time-Based Empathy Prediction System

We describe our system for empathic emotion recognition. It is based on deep learning on multiple modalities in a late fusion architecture. We describe the modules of our system and discuss the evaluation results. Our code is also available for the research community

Anticipating the effects of visual gravity during simulated self-motion: estimates of time-to-passage along vertical and horizontal paths

By simulating self-motion on a virtual rollercoaster, we investigated whether acceleration cued by the optic flow affected the estimate of time-to-passage (TTP) to a target. In particular, we studied the role of a visual acceleration (1 g = 9.8 m/s(2)) simulating the effects of gravity in the scene, by manipulating motion law (accelerated or decelerated at 1 g, constant speed) and motion orientation (vertical, horizontal). Thus, 1-g-accelerated motion in the downward direction or decelerated motion in the upward direction was congruent with the effects of visual gravity. We found that acceleration (positive or negative) is taken into account but is overestimated in module in the calculation of TTP, independently of orientation. In addition, participants signaled TTP earlier when the rollercoaster accelerated downward at 1 g (as during free fall), with respect to when the same acceleration occurred along the horizontal orientation. This time shift indicates an influence of the orientation relative to visual gravity on response timing that could be attributed to the anticipation of the effects of visual gravity on self-motion along the vertical, but not the horizontal orientation. Finally, precision in TTP estimates was higher during vertical fall than when traveling at constant speed along the vertical orientation, consistent with a higher noise in TTP estimates when the motion violates gravity constraints

On the Design of Constructively Aligned Educational Unit

Modern pedagogy is moving away from traditional transmissive approaches, and it is extensively embracing constructive theory of learning. A prominent practical embodiment of this paradigm shift is a method called Constructive Alignment (CA). This approach focuses on learners’ actions and starts from a clear communication of the Intended Learning Outcomes (ILOs) of the focal unit. ILOs are made of content, a context, and an Educational Goal Verb (EGV). According to the Bloom Taxonomy, the EGV is the core of an ILO and refers to the action the learners are expected to be able to master after completing the educational unit. The ILO is then aligned to the course activity using the EGV (i.e., EGVs are enacted through Teaching and Learning Activities (TLAs) and verified through Assessment Tasks (ATs)). Despite the ILO definition being extensively investigated and described, the extant literature has poorly explored how to devise suitable TLAs and ATs, lacking comprehensive contributions that identify and describe the different kinds of TLAs and ATs available to course designers. In view of the above gap, the authors searched and reviewed the literature (scientific papers (i.e., top-down, deductive approach)) and practices in higher education (university websites and blogs (i.e., bottom-up, inductive approach)) to identify all the possible sources of TLA and AT descriptions available. The results propose standardized templates that support the course design process, providing extensive descriptions of TLA and AT based on the best practices identified. The proposed templates include the core dimensions that proved to be suitable for designing traditional and remote-learning activities. Finally, the examples provided in the paper show how to use these templates on a few kinds of selected on-campus and digital TLAs and ATs from the educational units identified in the Erasmus+ MAESTRO project, which is based on Industry 4.0 technological enablers and their application in support of manufacturing sustainability

Probing neutrino masses with CMB lensing extraction

We evaluate the ability of future cosmic microwave background (CMB) experiments to measure the power spectrum of large scale structure using quadratic estimators of the weak lensing deflection field. We calculate the sensitivity of upcoming CMB experiments such as BICEP, QUaD, BRAIN, ClOVER and PLANCK to the non-zero total neutrino mass M_nu indicated by current neutrino oscillation data. We find that these experiments greatly benefit from lensing extraction techniques, improving their one-sigma sensitivity to M_nu by a factor of order four. The combination of data from PLANCK and the SAMPAN mini-satellite project would lead to sigma(M_nu) = 0.1 eV, while a value as small as sigma(M_nu) = 0.035 eV is within the reach of a space mission based on bolometers with a passively cooled 3-4 m aperture telescope, representative of the most ambitious projects currently under investigation. We show that our results are robust not only considering possible difficulties in subtracting astrophysical foregrounds from the primary CMB signal but also when the minimal cosmological model (Lambda Mixed Dark Matter) is generalized in order to include a possible scalar tilt running, a constant equation of state parameter for the dark energy and/or extra relativistic degrees of freedom.Comment: 13 pages, 4 figures. One new figure and references added. Version accepted for publicatio