Training cognitivo adattativo mediante Reinforcement Learning

La sclerosi multipla (SM) è una malattia autoimmune che colpisce il sistema nervoso centrale causando varie alterazioni organiche e funzionali. In particolare, una rilevante percentuale di pazienti sviluppa deficit in differenti domini cognitivi. Per limitare la progressione di tali deficit, team specialistici hanno ideato dei protocolli per la riabilitazione cognitiva. Per effettuare le sedute di riabilitazione, i pazienti devono recarsi in cliniche specializzate, necessitando dell'assistenza di personale qualificato e svolgendo gli esercizi tramite scrittura su carta. In seguito, si è iniziato un percorso verso la digitalizzazione di questo genere di esperienze. Un team multidisciplinare composto da ricercatori del DISI - Università di Bologna e da specialisti di vari centri italiani ha progettato un software, MS-Rehab, il cui scopo è fornire alle strutture sanitarie un sistema completo e di facile utilizzo specifico per la riabilitazione della SM. Tale software permette lo svolgimento di numerosi esercizi nei tre domini cognitivi: attenzione, memoria e funzioni esecutive. Questo lavoro di tesi si è concentrato sull'integrazione di metodi di Reinforcement Learning (RL) all'interno di MS-Rehab, allo scopo di realizzare un meccanismo per l'automatizzazione adattiva della difficoltà degli esercizi. Tale soluzione è inedita nell'ambito della riabilitazione cognitiva. Allo scopo di verificare se tale soluzione permettesse un’esperienza riabilitativa pari o superiore a quella fornita attualmente, è stato realizzato un esperimento basato sulla somministrazione ad individui selezionati di un test preliminare, atto a valutare il loro livello nelle funzioni cognitive di attenzione e memoria, seguito poi da un periodo di allenamento su MS-rehab, e infine da una nuova istanza del test iniziale. I risultati ottenuti sono incoraggianti: le prestazioni del test neuro-psicologico hanno evidenziato punteggi sensibilmente più alti per il gruppo che ha utilizzato la versione con RL

Data-driven and data-oriented methods for materials science and technologies

The discovery of new materials and their functions has always been a fundamental component of technological progress. Nowadays, the quest for new materials is stronger than ever: sustainability, medicine, robotics and electronics are all key assets which depend on the ability to create specifically tailored materials. However, designing materials with desired properties is a difficult task, and the complexity of the discipline makes it difficult to identify general criteria. While scientists developed a set of best practices (often based on experience and expertise), this is still a trial-and-error process. This becomes even more complex when dealing with advanced functional materials. Their properties depend on structural and morphological features, which in turn depend on fabrication procedures and environment, and subtle alterations leads to dramatically different results. Because of this, materials modeling and design is one of the most prolific research fields. Many techniques and instruments are continuously developed to enable new possibilities, both in the experimental and computational realms. Scientists strive to enforce cutting-edge technologies in order to make progress. However, the field is strongly affected by unorganized file management, proliferation of custom data formats and storage procedures, both in experimental and computational research. Results are difficult to find, interpret and re-use, and a huge amount of time is spent interpreting and re-organizing data. This also strongly limit the application of data-driven and machine learning techniques. This work introduces possible solutions to the problems described above. Specifically, it talks about developing features for specific classes of advanced materials and use them to train machine learning models and accelerate computational predictions for molecular compounds; developing method for organizing non homogeneous materials data; automate the process of using devices simulations to train machine learning models; dealing with scattered experimental data and use them to discover new patterns

AI and data-driven infrastructures for workflow automation and integration in advanced research and industrial applications

The use of AI and data-driven technologies and infrastructures for innovation and development of advanced research and industrial applications requires a strong degree of integration across a broad range of tools, disciplines and competences. In spite of a huge disruptive potential, the role of AI for research and development in the context of industrial applications is often hampered by the lack of consolidated and shared practices for transforming domain-specific processes for generating knowledge into added value. These issues are particularly striking for small-medium enterprises (SMEs), which must adopt clear and effective policies for implementing successful technology transfer paths for innovation. The activities of the DAIMON Lab of the CNR-ISMN focus on the design, development, implementation and application of integrated modelling, data-driven and AI methods and infrastructures for innovation in hi-tech applications. Our approach is based on the development of horizontal platforms, which can be applied to a broad range of vertical use-cases. Namely, we target the realisation of high-throughput workflows, related to specific domains and use cases, which are able to collect and process simulations and/or physical data and information. The implementation of an interoperable integration framework is a prerequisite for further application of AI tools for predictivity and automation. With a strong focus on the development of key enabling technologies (KETs), such as advanced materials, the approach pursued is extended to a broad range of application fields and scenarios of interest in industry, including electronic and ICT, advanced and sustainable manufacturing, energy, mobilit

Il teorema di Cook-Levin e i SAT-solver

In questa tesi è trattato il tema della soddisfacibilità booleana o proposizionale, detta anche SAT, ovvero il problema di determinare se una formula booleana è soddisfacibile o meno. Soddisfacibile significa che è possibile assegnare le variabili in modo che la formula assuma il valore di verità vero; viceversa si dice insoddisfacibile se tale assegnamento non esiste e se quindi la formula esprime una funzione identicamente falsa. A tal fine si introducono degli strumenti preliminari che permetteranno di affrontare più approfonditamente la questione, partendo dalla definizione basilare di macchina di Turing, affrontando poi le classi di complessità e la riduzione, la nozione di NP-completezza e si dimostra poi che SAT è un problema NP-completo. Infine è fornita una definizione generale di SAT-solver e si discutono due dei principali algoritmi utilizzati a tale scopo

MAMBO

MAMBO is an ontology focused on the organization of concepts and knowledge in the field of materials based on molecules and targeted to applications. MAMBO aims at bridging the gaps of ongoing efforts in the development of ontologies in the materials science domain. By extending current work in the field, the modular nature of MAMBO also allows straightforward extension of concepts and relations to neighboring domains. MAMBO is expected to enable the systematic integration of computational and experimental data in specific domains of interest (nanomaterials, molecular materials, organic an polymeric materials, supramolecular and bio-organic systems, etc.). Moreover, MAMBO can be applied to the development of data-driven integrated predictive frameworks for the design of novel materials with tailored functional properties

Molecular And Materials Basic Ontology: development and first steps

none4nononeF. Le Piane, M. Baldoni, M. Gaspari, F. MercuriF. Le Piane, M. Baldoni, M. Gaspari, F. Mercur

Introducing {MAMBO}: Materials And Molecules Basic Ontology

Recent advances in computational and experimental technologies applied to the design and development of novel materials have brought out the need for systematic, rational and efficient methods for the organization of knowledge in the field. In this work, we present the initial steps carried out in the development of MAMBO - an ontology focused on the organization of concepts and knowledge in the field of materials based on molecules and targeted to applications. Our approach is guided by the needs of the communities involved in the development of novel molecular materials with functional properties at the nanoscale. As such, MAMBO aims at bridging the gaps of ongoing efforts in the development of ontologies in the materials science domain. By extending current work in the field, the modular nature of MAMBO also allows straightforward extension of concepts and relations to neighboring domains. Our work is expected to enable the systematic integration of computational and experimental data in specific domains of interest (nanomaterials, molecular materials, organic an polymeric materials, supramolecular and bio-organic systems, etc.). Moreover, MAMBO can be applied to the development of data-driven integrated predictive frameworks for the design of novel materials with tailored functional properties