Structural design, manufacturing and testing of the new wing for the CSIR's Modular UAS in composite materials

In the framework of an international collaboration with South Africa CSIR, the structural design, manufacturing and testing of the new wing for the Modular UAS in composite materials has been performed.

Modular catalyst: from the tiling of a water plane to the design of a floating structure

La tesi nasce dalla volontà di agire sull’area della Darsena di Ravenna, strategica in quanto via d’acqua navigabile che congiunge il mare con il centro città ma dal potenziale ancora poco sfruttato. Il progetto è studiato per essere inserito come catalizzatore urbano, creando spazi di interazione attraverso elementi modulari galleggianti e riconfigurabili per adattarsi a programmi d’uso flessibili; tali elementi si aggregano formando un sistema che ristruttura lo spazio dell’attuale banchina, cambiandone la percezione da barriera a waterfront urbano. La necessità di ottenere una struttura con capacità di crescita e flessibilità programmatica sfocia in un approccio modulare seguendo il principio massima variazione/minimo numero di elementi i cui principi aggregativi si basano sulla tassellazione “Cairo”. Vengono studiate le possibilità di incorporare variazione ed eterogeneità all’interno del sistema senza comprometterne la modularità fino ad integrare percorsi multilivello. La definizione delle morfologie delle parti che compongono i moduli si basano sullo studio dei principi di galleggiamento, stabilità e yacht design: a partire dalla forma dello scafo adatta ai principi di tiling definiti in precedenza, tutte le parti che compongono le varie tipologie di modulo sono progettate cercando continuità e integrazione tettonica (geometrica, strutturale, funzionale e percettiva). Vengono proposte soluzioni integrate sia per le problematiche tipiche delle strutture galleggianti sia per l’inserimento di attività all’interno della soluzione architettonica. Vengono prototipati di una serie di moduli, scelti in modo da dimostrare i principi di ricombinazione, continuità, modularità e tiling.

Edge-aware Graph Attention Networks: Joint Reasoning On Text and Knowledge Graphs for Biomedical Question Answering

Much of the real-world dataset, including textual data, can be represented using graph structures. The use of graphs to represent textual data has many advantages, mainly related to maintaining a more significant amount of information, such as the relationships between words and their types. In recent years, many neural network architectures have been proposed to deal with tasks on graphs. Many of them consider only node features, ignoring or not giving the proper relevance to relationships between them. However, in many node classification tasks, they play a fundamental role. This thesis aims to analyze the main GNNs, evaluate their advantages and disadvantages, propose an innovative solution considered as an extension of GAT, and apply them to a case study in the biomedical field. We propose the reference GNNs, implemented with methodologies later analyzed, and then applied to a question answering system in the biomedical field as a replacement for the pre-existing GNN. We attempt to obtain better results by using models that can accept as input both node and edge features. As shown later, our proposed models can beat the original solution and define the state-of-the-art for the task under analysis.

Elementary properties of Modular curves and Modular forms

Lo scopo di questa tesi è introdurre in breve le prime proprietà delle curve modulari e delle forme modulari, per poi mostrarne alcune applicazioni archetipiche. Per farlo, dopo aver richiamato alcune nozioni utili nel primo capitolo, sviluppiamo, nel secondo capitolo, la teoria di base delle curve modulari compatte come superfici di Riemann, calcolandone il genere nel caso dei sottogruppi principali di congruenza. Dunque, nel terzo capitolo, dopo un estesa trattazione dell'esempio delle forme modulari rispetto al gruppo modulare, viene calcolata la dimensione degli spazi delle forme intere e delle forme cuspidali rispetto a un sottogruppo di indice finito del gruppo modulare. Questo capitolo si conclude con tre esempi di applicazione della teoria esposta, tra i quali spiccano la dimostrazione del Grande Teorema di Picard e del Teorema dei quattro quadrati di Jacobi.

Bio-QA-GNN: Reasoning with Language Models and Knowledge Graphs for Interpretable Biomedical Question Answering

Nowadays the idea of injecting world or domain-specific structured knowledge into pre-trained language models (PLMs) is becoming an increasingly popular approach for solving problems such as biases, hallucinations, huge architectural sizes, and explainability lack—critical for real-world natural language processing applications in sensitive fields like bioinformatics. One recent work that has garnered much attention in Neuro-symbolic AI is QA-GNN, an end-to-end model for multiple-choice open-domain question answering (MCOQA) tasks via interpretable text-graph reasoning. Unlike previous publications, QA-GNN mutually informs PLMs and graph neural networks (GNNs) on top of relevant facts retrieved from knowledge graphs (KGs). However, taking a more holistic view, existing PLM+KG contributions mainly consider commonsense benchmarks and ignore or shallowly analyze performances on biomedical datasets. This thesis start from a propose of a deep investigation of QA-GNN for biomedicine, comparing existing or brand-new PLMs, KGs, edge-aware GNNs, preprocessing techniques, and initialization strategies. By combining the insights emerged in DISI's research, we introduce Bio-QA-GNN that include a KG. Working with this part has led to an improvement in state-of-the-art of MCOQA model on biomedical/clinical text, largely outperforming the original one (+3.63\% accuracy on MedQA). Our findings also contribute to a better understanding of the explanation degree allowed by joint text-graph reasoning architectures and their effectiveness on different medical subjects and reasoning types. Codes, models, datasets, and demos to reproduce the results are freely available at: \url{https://github.com/disi-unibo-nlp/bio-qagnn}.

Design and specification of a modular automatic testing solution

Electric vehicles and electronic components inside the vehicle are becoming increasingly important. The software as well starts to have a significant impact on modern high-end cars therefore a careful validation process needs to be implemented with the aim of having a bug free product when it is released. The software complexity increases and thus also the testing phases is more demanding. Test can be troublesome and, in some cases, boring and easy. The intelligence can be moved in test definition and writing rather than on test execution. The aim of this document is to start the definition of an automatic modular testing system capable to execute test cycles on systems that interacts with the CAN networks and with DUT that can be touched with a robotic arm. The document defines a first version of the system, in particular the hardware interface part with the aim of taking logs and execute test in an automated fashion with the test engineer can have a higher focus on the test definition and analysis rather than execution.

Robotic grasping: State of the art and modular real-world implementation

Robotic Grasping is an important research topic in robotics since for robots to attain more general-purpose utility, grasping is a necessary skill, but very challenging to master. In general the robots may use their perception abilities like an image from a camera to identify grasps for a given object usually unknown. A grasp describes how a robotic end-effector need to be positioned to securely grab an object and successfully lift it without lost it, at the moment state of the arts solutions are still far behind humans. In the last 5–10 years, deep learning methods take the scene to overcome classical problem like the arduous and time-consuming approach to form a task-specific algorithm analytically. In this thesis are present the progress and the approaches in the robotic grasping field and the potential of the deep learning methods in robotic grasping. Based on that, an implementation of a Convolutional Neural Network (CNN) as a starting point for generation of a grasp pose from camera view has been implemented inside a ROS environment. The developed technologies have been integrated into a pick-and-place application for a Panda robot from Franka Emika. The application includes various features related to object detection and selection. Additionally, the features have been kept as generic as possible to allow for easy replacement or removal if needed, without losing time for improvement or new testing.