MAIN PhD Seminars 2024
| Date |
Speaker(s) |
| January, 15th |
Sandro Gabriele Tiddia |
| January, 29th |
Andrea Azzarelli |
| February, 12nd |
Valentino Artizzu |
| February, 26th |
Simone Pusceddu |
| March, 12nd |
Nicola Piras |
| March, 26th |
Sara Vergallo
Giorgia Nieddu |
| April, 9th |
Matteo Mocci |
| April, 23rd |
Michele Faedda |
All the seminars at 13:00 in Aula Magna di Fisica.
Sandro Gabriele Tiddia: LLM Agents: Definitions and Real-World Applications
In this seminar, we explore the concept of ‘agents’ in artificial intelligence (AI), with a particular focus on the role of Large Language Models (LLMs) in powering these systems. The seminar begins by discussing a real-world application where LLMs are used to build a question-answering (QA) system, showing how LLMs can function as ‘agents’ within such a system. We then examine various definitions of ‘agent’ across AI subfields and consider how agents interact with their environment, make decisions, and pursue goals autonomously. Additionally, we revisit earlier works on agency in AI, reflecting on their original, more profound ideas, and connect them to recent developments and applications of LLM-powered agents. The seminar concludes by exploring experiments and use cases from recent literature, highlighting the capabilities and potential of LLM agents across different domains. The goal is to provide a clear introduction to the concept of LLM-powered agents, their role in AI systems, and how this concept has evolved from theoretical foundations to practical applications.
Andrea Azzarelli: Fractional Laplacian and ADMM for glyph extraction
In archaeology it is a common task to extract incisions or glyphs from a surface. This procedure is usually done manually and, therefore, it is prone to errors and it can be extremely time consuming. In this talk we present a variational model to automatically extract these incisions from a smooth surface. We provide a procedure to generate realistic synthetic data and we show the performances of the proposed method on this kind of data.
Valentino Artizzu: End-User Development for Extended Reality: Empowering Users to Create and Understand XR Environments
In this seminar we will see how to enable individuals without prior XR development experience to create and understand XR environments. It focuses on using End-User Development (EUD) techniques to allow users to design, build, and adapt XR systems. The research specifically explores methodologies and tools for non-programmers to construct XR environments. It examines how EUD can facilitate novice developers in comprehending existing VR environments for enhancement purposes. The study further investigates how EUD can empower domain experts to tailor these environments to meet diverse requirements. Additionally, it delves into how EUD can guide domain experts in configuring XR environments to support task learning and demonstration. The goal is to provide a clear overview of the topic using the experiences and the applications developed during the three years of a PhD career.
Nicola Piras: Global and local fit measures for latent class models and extensions
Latent class (LC) analysis is a powerful and flexible statistical tool for model-based clustering with categorical data. An important task in LC analysis is the choice of the number of clusters or classes. The choice of the number of classes is a selection model problem and usually Information Criteria are considered for this purpose. These are measures that weigh model fit (log-likelihood) and model complexity (based on the number of free parameters). The LC models formulation is subject to an assumption of conditional independence between the variables involved. Adherence to this assumption and the correct estimation of the parameters is central to evaluate how well the model fits to the data. While global selection and the goodness of fit is verified through Information Criteria, model conditions must also be checked. Specific statistics can be defined that allow to verify the local independence assumption. In the literature of LC analysis the statistics considered are the Bivariate residuals. The standard LC model can be modified to handle more complex data structures, and fit measures must be adapted to the new formulations. In this talk, after having briefly discussed the results in the standard formulation, I will also present the extension to the case in which data have a multilevel cross-classified structure. This structure is present when observations are simultaneously nested within two groups, for example, children nested within both schools and neighborhoods. An application is illustrated using an Italian dataset on the evaluation of students of their degree programmes, where degree programmes are nested in both universities and fields of study.