Development of teaching-learning sequences on quantum physics for the Italian secondary school

In the past 15 years, quantum mechanics has been included in most secondary school standards, including the Italian ones, but still in a rather marginal way. The conceptual complexity of quantum mechanics is often a hurdle for students as well as for teachers; as a consequence, most teachers and textbooks opt for narrative/historical approaches which, however, are not sufficient to grasp the deepest conceptual aspects of quantum physics, nor to deal with its technological applications. Teaching quantum physics in secondary school is therefore a challenge that calls for a close collaboration between physics teachers and physics education researchers. The goal of this work is to develop and test research-based teaching-learning sequences (TLS) based on the study of relevant literature in physics education research and on a survey to be conducted with secondary school teachers. More specifically, the thesis work will include the following phases. A review of the literature on the teaching and learning of quantum mechanics, with particular reference to the proposals developed in the Italian context. A survey with a sample of secondary school physics teachers aimed at understanding the needs and difficulties of teaching quantum mechanics. On the basis of the literature and of the results of the survey, development of a teaching-learning sequence (TLS) on quantum mechanics for the fifth year of the Italian “liceo scientifico”. Testing and evaluation of the TLS in a real classroom context

Incremental clustering of continuous perceptions

This thesis aims to develop a framework for the incremental clustering of the perceptions ofan agent. The agent moves in a simulated environment and collects data about its position and its surroundings, in particular the egocentric images seen by its on-board camera. In this context, clustering is useful to group together the perceptions of a same object, in particular to recognize objects which have already been seen by the agent. This task is performed with the aid of a neural network, pre-trained to recognize if two perceptions are associated to a same object. In this thesis we analyze the tuning and training performed for the neural network, the clustering algorithm and its performance

Studio e progettazione di un intervento di rinnovo tecnologico di sottostazione elettrica (SSE) ferroviaria di conversione

L'elaborato fornisce lo studio di una ignota sottostazione elettrica ferroviaria di conversione per la trazione elettrica alla tensione di 3 kVcc, sottoposta ad un intervento di rinnovo tecnologico con finalità di risolvere i problemi emersi durante il suo esercizio attraverso un'analisi e una progettazione secondo le normative vigenti

Top-down processing of noisy and adversarial stimuli in deep generative models

This thesis studies the effect of adding a term usually neglected during the training phase of energy-based models. It is called ``KL term'' because of its dependence on Kullback-Leibler divergence, and it does not have a significant impact on the training in terms of running time and computational cost. I will initially present an analysis of its impact on training stability and some considerations relative to the general structure of the learning model. I will then study the denoising capabilities of the model by implementing top-down processes applied to different types of noisy input. Thirdly, to understand the quality of internal representations emerging in the hidden layers, I will apply a read-out classifier to the deepest hidden layer, calculating psychometric curves produced with different noise values. The final analysis will explore the model capability to resist to adversarial attacks using forward-backward iterations, also considering the spontaneous generative activity of the network by stimulating the read-out neurons relative to a specific class. Interestingly, results of this latter investigation are very encouraging for the MNIST dataset, suggesting that this type of energy-based models has potential to improve current defenses on adversarial attacks. However, for Cifar10 dataset it seems that more powerful computing hardware is needed to train models of larger sizes

Caratterizzazione delle sfere metriche nello spazio iperbolico complesso

Lo scopo di questo lavoro è dimostrare un teorema inedito nella letteratura, che fornisce una caratterizzazione delle superfici sferiche nello spazio iperbolico complesso in analogia all'equazione $A-N(A)=A_{0}$ per la sfera unitaria immersa in $\mathbb{R}^{n}$ di centro $A_{0}$ e direzione normale $N(A)$