Giovani in un'ora - Ciclo di seminari - Prima parte

Day - Time: 02 October 2024, h.11:00

Place: Area della Ricerca CNR di Pisa - Room: C-29

Speakers

• Alì Reza Omrani
• Laura Bussi
• Francesco Laccone

Referent

Fabio Carrara

Abstract

Abstract: Repeated measurement of behavior is a process central to behavior analysis, but its implementation occasionally requires hiring observers dedicated exclusively to data collection, which may increase the cost of providing services and conducting research. One potential solution to reduce resources necessary to conduct behavioral observations involves machine learning. Using data previously published by Dufour et al. (2020), we developed and tested novel models to automatically measure vocal stereotypy in eight children with autism. In addition to accuracy, we examined session-by-session correlation between values measured by machine learning and those recorded by a human observer. Nearly all our models produced correlations similar to those between continuous and discontinuous methods of measurements (i.e., .90 or more) and resulted in better metrics than those reported by Dufour et al. (2020). Although practitioners and researchers should continue examining their accuracy in measuring vocal stereotypy, the adoption of the proposed models may prove useful.

Laura Bussi - "Logics of polyhedral reachability"

Abstract: Polyhedral semantics is a recently introduced branch of spatial modal logic, in which modal formulas are interpreted as piecewise linear subsets of an Euclidean space. For many practical applications of polyhedral semantics, it is advantageous to enrich the basic modal language with a reachability modality. Recently, a language with an Until-like spatial modality has been introduced, with demonstrated applicability to the analysis of 3D meshes via model checking. We exhibit an axiom system for this logic, and show that it is complete with respect to polyhedral semantics.

Abstract: Grid shell design is a complex task balancing aesthetic and engineering quality to which structural engineers and architects jointly collaborate since the earliest conceptual design phases. The structural efficiency is a byproduct of a careful shape design and selection of a suitable grid topology. However, in sculptural architecture the artistic intent is prevailing, and free-form shapes are usually the outcome. Moreover, large openings creating spectacular effects are desired. In these cases, bending forces arise and shape optimization methods are required to mitigate this source of inefficiency. Fulfilling this task may alter the initial surface or its aesthetics or violate some design constraints if the modifications are not bounded. We propose a computational method for form-finding and shape optimization of triangular grid shells. The objective is to improve the performance of the grid shell while ensuring small changes in the aesthetics. Shape alterations are provided by a graph neural network that adopts several geometric input features and the structural analysis of the starting grid shell. Then, it solves a displacement learning problem, which updates the nodal coordinates of the grid shell, with a target goal of reducing a loss function that considers the strain energy as a measure of structural efficiency and the total weight of the structure as a measure of sustainability. The freeGrid benchmark baseline structures are adopted as dataset to prove the effectiveness of the method.