The cartography of dreams: application of computational linguistics to the study of sleep conscious experiences

The study of dreams represents a crucial intersection between philosophical, psychological, neuroscientific, and clinical interests. Since dreams are subjective experiences spontaneously generated by the brain when it is partially disconnected from the external environment and thus is let free to operate in an unconstrained manner, their study could reveal specific mental processes that are different from those occurring during wakefulness and might provide crucial insights into brain functioning, both in physiological and pathological conditions. Given the high cost of sleep and dream research in terms of human effort and funding, open science and the building of large- scale datasets and repositories will constitute a key for significant advances in the field. At the same time, the analysis of large datasets will require a methodological shift, from human-based assessments to more automated approaches. For instance, methods based on natural language processing (NLP) could replace manual scales and rating approaches for the assessment of dream content. Such a methodological shift could also have positive consequences concerning the reproducibility and reliability of scientific results. Based on the above premises, we created Somnieve, a multimodal, open-source database collecting dream reports along with demographic information and psychometric, cognitive, and electroencephalographic measures obtained from a representative sample of the healthy Italian adult population. In particular, participants were asked to wear an actigraph and to record a report of their last dream experience each morning upon awakening for 14 days. Moreover, they completed a battery of questionnaires and cognitive tests. The database currently includes 1324 dream reports obtained from 161 healthy adult individuals (66M, 18-65y). Beside presenting and describing the Somnieve database, this Thesis work exploited the database to investigate the individual determinants of physiological dream content and recall frequency. We relied on computational linguistics to test whether it might be possible to implement computational linguistics based tools to automatically and objectively code dream content and verify the existence of generalizable semantic patterns in dream narratives. Moreover, we evaluated the inter- and intra-individual factors affecting dream recall frequency. Present results highlight the potential benefits that large multimodal databases like Somnieve could bring for the field of dream research. It is our hope that this, and similar independent efforts by other laboratories, will contribute to improve reproducibility in dream research and identify the individual determinants of dream content and recall frequency in physiological conditions, as well as quantify their possible pathological alterations

Phase Field methods for Fracture Mechanics in coupled problems

Nowadays, cutting-edge industry processes cannot thrive with- out the integration of multidisciplinary perspectives in all its associated processes. Even the field of Mechanics is not ex- empt from such approaches, since most recent studies now incorporate considerations spanning multiple size scales (multi- scale) and encompassing various branches such as Chemistry, Biology, Electricity, and Magnetism, among others (multi-physics). This is what constitutes the very essence of a coupled prob- lem in Mechanics. The principal objective of this thesis is to specifically explore their impact on structural integrity and reliability in the field of Fracture Mechanics. Consequently, it is necessary to establish a robust mathematical framework to assess the mechanical behavior and failure strength, con- sidering the intricate influence of the multi-scale and multi- physics fields associated with each problem. To accomplish this mission, we have primarily utilized the phase-field ap- proach for fracture, alongside the continuum damage me- chanics technique. Our efforts have been devoted to shed light on representative coupled problems in Fracture Mechanics. To exemplify the breadth of this field, our research comprises a diverse spec- trum of topics. First, the research deals on the problem of hy- drogen embrittlement in polycrystalline materials. Moreover, the residual stress influence on the integrity of soft cylindrical tubes has been investigated. Furthermore, a computational framework for incompressible materials has been proposed. The final topic concerns the application of this last formula- tion in the simulation of swelling of thermoresponsive hydro- gels

On dissonance and fascist heritage in Italy. An analysis on the reuse of ex-Case del Fascio in three provinces

In Italy the current debate over the reuse of fascist heritage is, on the one side, incapable of answering to contemporary needs and criticalities raised by international movements and on the other side, it is raising a growing academic interest. This research aims to introduce critical heritage studies in the Italian context and update the current debate over difficult heritage. Moreover, this research enriches the interdisciplinary approach of critical heritage studies by integrating a new perspective taken from organisation studies. The research focuses on the concept of dissonance linked to difficult heritage by testing the dissonant heritage theory and proposing a new and productive concept of dissonance. Are the preservation of fascist heritage and the use of fascist architecture generating dissonance? The objective is to understand how fascist heritage is preserved and reused in Italy, how this approach has changed over time and how it should be approached now. The object of the research are the reuses of case del fascio (for their capillary diffusion, representativeness of the regime, and ordinary characteristic) in three Italian provinces (Latina, Livorno, Treviso). The issue of the reuse, demolition or neglect of fascist-built architectures is carried out on a twofold level: a material one, studying the construction, modifications and reuse of case del fascio through archival sources and on-site inspections; and a public discourse one, applying the economies of worth by Boltanski and Thevenot to debates over the preservation and reuse of fascist heritage in Italy. The innovations of the research can be found in (1) testing the dissonant heritage theory to the Italian case, finding that the relationship between the remains and reuse of fascist-built architectures is not linear, is more complex and dependent on inertia and local dynamics. It outlines also (2) a new perspective for the critical reuse of fascist-built architectures based on a positive concept of dissonance. An (3) analysis of how dissonance works and how can be activated and silenced is paralleled with suggestions on how organising dissonance as a new way of taking decisions over the reuse of ex-fascist public buildings

Neural signatures of auditory statistics: a window into auditory computations and their interactions with other modalities

The auditory system processes information at high temporal resolutions, extracting fine-grained details from complex sounds. However, this ability comes at a cost as the acoustic information often exceeds memory storage capacity. To keep track of sound changes occurring over several seconds, the auditory system abstracts local features into compact representations (summary statistics). This thesis addresses three questions: (i) whether it is possible to distinguish from neural activity the processing of local features or summary statistics; (ii) whether the brain is endowed with distinct structures for computations based on local features or summary statistics; (iii) whether these basic computations are affected by other sensory modalities. First, we designed a protocol for the EEG. Participants were exposed to streams comprising triplets of synthetic sound excerpts. Two sounds were identical, while the third could vary for its local features or summary statistics. We presented sounds of different durations to manipulate the similarity of statistics measured from the repeated and novel sounds. Results showed that local details and summary statistics are processed automatically and encoded by different neural oscillatory profiles. Second, we collected MEG data with the same protocol and performed source reconstruction of the evoked response to the novel sounds. This analysis revealed functional cortical specializations and hemispheric asymmetries for the processing of computations occurring at high or low temporal resolutions. Third, we tested three groups of individuals, congenitally (CB), late- onset blinds (LB), and sighted controls (SC) in two behavioral experiments. One benefitted from the processing of local features, the other from summary statistics. CB performed as SC in both tasks, showing that both computations can develop independently from vision. Conversely, LB’s performance was impaired when relying on local features, with no alterations in summary statistics processing. These findings suggest an audiovisual interplay selectively for processing auditory details, which emerges only in late development. Overall, these findings demonstrate that the auditory system utilizes distinct neural processes and dedicated brain structures to encode local features and summary statistics of sound and emphasize the role of visual experience in the processing of local features. By unraveling these fundamental aspects of auditory perception, this thesis expands our knowledge in the context of auditory cognition and its complex interplay with other sensory modalities

Exploring the Relationship Between Electroconvulsive Therapy and Reward Processing in Major Depressive Disorder

Depression affects over ten percent of the population worldwide, with a huge toll for patients, their families, and the whole society. Around one third of patients with depression does not respond satisfactorily or at all to either pharmacological or psychological therapy. Electro- convulsive therapy (ECT) is an established treatment for severe mental illnesses, in particular treatment-resistant depression, a leading contributor to global disease. Despite its proven effectiveness in treating depression, the underlying mechanisms of ECT are not yet fully understood. This thesis examines the potential differences between patients who respond positively to electroconvulsive therapy (ECT) and those who do not, providing novel insights into their relationship with the reward processing in the brain. While this thesis expands our knowledge of ECT effect in the treatment of depression, it is important to acknowledge that the study comes with at least two major limitations. (i) The small sample size may impact on the statistical power of the study; (ii) the computer task used to assess the reward processing may not be sensitive to detect subtle differences between groups or changes over time. It is possible that other measures will provide a more comprehensive assessment of reward functions in future evaluations. Future research with larger samples and more sensitive measures could build upon these findings and further advance our understanding of the mechanisms underlying ECT and treatment response in depression

Legislative and Policy Responses to the Illicit Trafficking of Cultural Property in the European Union An historical inquiry into the legal means and methods employed by the EU and its northern Member states to protect cultural property from illicit trafficking

This doctoral dissertation is an historical analysis of the legislative and policy responses to the phenomenon that is illicit trafficking and the illegal movement of cultural property to, from and within the European continent in the 20th and 21st centuries. Its intent is to illustrate the evolution of the historic means used the restrain the illicit trafficking of culture property, ascertain if they work(ed), and understand the extent to which they influence the current EU legal order. Using archival resources, comparisons of national, European and international legislation, policy, codes of conduct, and contemporary media commentary, this dissertation illustrates that illicit trafficking is an old and complex illegal trade that has long posed legal and policy headaches for governments; though the types of objects being trafficked differ from state to state, this dissertation illustrates that the problems faced by governments in addressing this phenomenon are often similar. Export controls are historically the main means by which states protect heritage from trafficking, and this dissertation agrees with this observation. However, the EU decision to complement export controls with import controls appears to suggest the inability of these traditionally accepted methods to fully restrain trafficking. The most surprising findings of this work are the extent to which museums have influenced national and EU policy; and early stage which the EU engaged in finding solutions to illicit trade, earlier than originally presumed. Finally, the innovative responses by the EU are ground- breaking, and in this sense, this dissertation further demonstrates the potential of the EU as an emerging major partner and forward-thinking actor in the fight against illicit trafficking

Exploiting Process Algebras and BPM Techniques for Guaranteeing Success of Distributed Activities

The communications and collaborations among activities, pro- cesses, or systems, in general, are the base of complex sys- tems defined as distributed systems. Given the increasing complexity of their structure, interactions, and functionali- ties, many research areas are interested in providing mod- elling techniques and verification capabilities to guarantee their correctness and satisfaction of properties. In particular, the formal methods community provides robust verification techniques to prove system properties. However, most ap- proaches rely on manually designed formal models, making the analysis process challenging because it requires an expert in the field. On the other hand, the BPM community pro- vides a widely used graphical notation (i.e., BPMN) to design internal behaviour and interactions of complex distributed systems that can be enhanced with additional features (e.g., privacy technologies). Furthermore, BPM uses process min- ing techniques to automatically discover these models from events observation. However, verifying properties and ex- pected behaviour, especially in collaborations, still needs a solid methodology. This thesis aims at exploiting the features of the formal meth- ods and BPM communities to provide approaches that en- able formal verification over distributed systems. In this con- text, we propose two approaches. The modelling-based ap- proach starts from BPMN models and produces process al- gebra specifications to enable formal verification of system properties, including privacy-related ones. The process mining- based approach starts from logs observations to automati- xv cally generate process algebra specifications to enable veri- fication capabilities

Learning-based Stochastic Model Predictive Control for Autonomous Driving

Autonomous driving in urban environments requires safe control policies that account for the non-determinism of moving\ud obstacles, for instance, the intention of other vehicles while crossing an uncontrolled intersection. This thesis addresses the aforementioned problem by proposing a stochastic model predictive control (SMPC) approach. In this approach, we consider robust collision avoidance as a constraint to guarantee safety and a stochastic performance index that will increase the quality of the closed-loop tracking by ignoring the unlikely obstacle configurations that could occur. We compute the probabilities associated with different obstacle trajectories by training a classifier on a realistic dataset generated by the microscopic traffic simulator SUMO and show the benefits of the proposed stochastic MPC formulation in a simulated real intersection. This thesis is divided into two parts: first, discuss the formulation of the existing control algorithm and our proposed approach, and second, the scenario prediction of the obstacle vehicles

Automatic and Accurate Performance Prediction in Distributed Systems

System performance is getting attention by industry as it affects user experience, and much research focused on performance evaluation approaches. Profiling is the most straightforward approach to performance evaluation of software systems, despite being limited to shallow analyses. Conversely, software performance models excel in representing complex interactions between components. Still, practitioners do not integrate performance models in the software development cycle, as the learning curve is too steep, and the approaches do not adapt well to incremental development practices. In this thesis, we propose three approaches towards automatic learning of performance models. The first approach employs a Recurrent Neural Network (RNN) to extract a full Queueing Network (QN) model of the system; the second one calibrates a Layered Queueing Network (LQN) using an RNN; the third one presents μP, a framework that allows the user to develop microservice systems and obtain the corresponding LQN model from source code analysis. We considered the microservices architecture as it is embraced by influential players (e.g., Amazon, Netflix). Those approaches have two advantages: i) minimal user intervention to flatten the learning curve; ii) continuous synchronization between software and performance model, such as each software development iteration is reflected on the model. We validated our approaches on several benchmarks taken from the literature. The models we generate can be queried to predict the system behavior under conditions significantly different from the learning setting, and the results show sensible advancements in the quality of the predictions

Essays on the Evolution of Prosocial Behaviors

Prosocial behaviors – such as helping others, donating, and cooperating – are often considered key to evolutionary suc- cess. Therefore, it is of great interest to understand under what conditions these behaviors can emerge and/or can be sustained at a population level. Following a dual process approach, I study whether and how cognition can affect the evolution of collaboration, cooperation, and generosity. I do this by employing stochastic stability analysis techniques and agent-based simulations. For each prosocial behavior consid-ered, I find that cognition can play an important role in the diffusion of prosocial behaviors, sometimes fostering them and other times hampering them. These results shed light on recent experimental evidence and, at the same time, suggest new interesting research avenues