Author: Letizia Pizzini

The flower paradigm and a transformer classifier for EEG dataThe primary objective of the project is the development of a transformer model for the classification of EEG-related data. Subsequently, our inquiry delves into the crucial aspect of preprocessing, seeking to elucidate its pivotal role in influencing the overall efficacy and performance of the aforementioned architecture. To rigorously examine this dimension, we intend to undertake a systematic comparative analysis. This entails the initial classification task on extensively preprocessed data, followed by a parallel evaluation on data subjected to minimal preprocessing, and ultimately, the assessment of the classifier's effectiveness on raw, unprocessed data. Looking beyond the immediate confines of our research focus, the broader vision of the work is oriented towards pioneering a fresh and innovative paradigm within the domain of assistive communication technology, particularly within the context of speller technology designed for individuals with communication impairments as ALS patients. This paradigm represents a pivotal shift, characterized by a fundamental reliance on the...

by Luca Bottero Our interests center around the intriguing domain of Continual Learning, a subset of machine learning focused on training models to adapt and evolve over time. This involves enabling these models to learn from new data while retaining previously acquired knowledge. In today’s fast-paced world, characterized by constant changes in data and knowledge, Continual Learning assumes primary importance. Traditional machine learning models often encounter challenges when confronted with novel information, a phenomenon commonly referred to as catastrophic forgetting. Continual Learning presents a remedy to this issue by facilitating continuous learning, rendering models adaptable, efficient, and adept at handling evolving data environments. Currently, we are working in a project that applies these Continual Learning techniques to gravitational wave interferometer data. We aspire to develop adaptive models that can learn from new experimental runs while retaining knowledge learned from prior detections. In a world characterized by relentless technological advancements and cease- less data generation, the concept of Continual Learning remains pivotal. The Turin...

by Beatrice Villata Tumor treatment can be achieved with radiotherapy techniques by irradiating the target volumes with ionizing radiation. Conventionally, photons are the selected particles for the treatment, but in recent years interest in the use of charged particles has been steadily growing in medical physics. The energy deposition of particle therapy allows to spare the organ at risk around the target volume. At the same time, the energy deposition of particle therapy is greatly influenced by range uncertainties provoked by motion or variations in tissue density along the path, requiring precise treatment planning and monitoring techniques to ensure accurate and effective therapy delivery.One way to achieve this is by analyzing the secondary particles created in the interactions between the charged particles and the tissues. Prompt gammas are candidates for this technique because their creation occurs instantaneously on the interaction site and can escape the patient. For these reasons, prompt gammas are not affected by washout effects and can be easily...

An outstanding opportunity to learn Machine Learning with an "hands-on" approach and get in touch with the Machine Learning Journal Club association. Introduction to Machine Learning Language: Italian Corso articolato in 10 lezioni di circa 2h tenuto presso il Dipartimento di Fisica in orario 16-18.Dopo una prima introduzione al Machine Learning, il corso si focalizzerà sulla comprensione e l'implementazione di algoritmi in linguaggio Python, dai più semplici quali Linear e Logistic Regression fino alle Reti Neurali. Nell'ultima parte del corso verrà introdotto il tema del Continual Learning.Rendiamo disponibili le registrazioni delle lezioni sul nostro canale YouTube.Per iscriversi inviare una email a info (at) mljc (dot) it , le date verranno comunicate man mano agli iscritti. Introduction to ML for Brain Computer Interfaces Language: Italian Corso articolato in 5 lezioni di circa 2h tenuto presso il Dipartimento di Fisica in orario 17-19.Si parte dalle basi dell'analisi di segnali EEG, illustrando varie tecniche e aspetti del preprocessing per arrivare a comprendere e implementare algoritmi di feature extraction e ...