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Boundaries in the city: space and identity in Italian postcolonial literature
No full textCette thèse est consacrée à la question des rapports entre espace urbain et identité dans la littérature postcoloniale en Italie, et plus précisément, à la question des « frontières dans la ville » dans cette littérature. La notion de frontière dans la ville, relevant des domaines de l’anthropologie, de la géographie urbaine et de la sémiotique, est ici remobilisée et réinterprétée pour analyser la mise en scène de l’espace urbain dans un corpus principalement (mais pas exclusivement) littéraire. A travers les récits d’auteurs migrants et de deuxième génération, les villes d’Italie apparaissent sous une lumière très différente de ce que l’on a pu voir dans le reste de la littérature italienne : ces nouvelles représentations portent un questionnement de fond sur la place des subjectivités migrantes et/ou d’origine étrangère dans les espaces urbains et par extension, dans la nation. Le concept de frontière permet ainsi d’aborder frontalement cette question en s’interrogeant sur les formes d’exclusion qui conditionnent le rapport de sujets minoritaires à la ville, mais aussi les nouvelles manières de la percevoir, de se la réapproprier, de l’habiter et de la représenter que l’on retrouve dans ces œuvres. La frontière revêt ici plusieurs dimensions principales. Elle renvoie à la manière dont les textes postcoloniaux redessinent la « carte » des villes italiennes et réinterprètent la relation entre marges, périphéries et centre. La frontière touche aussi l’inscription dans l’espace social : les textes révèlent la ville comme terrain d’interaction, de discrimination, de rencontre potentielle. Enfin, un dernier aspect concerne le mouvement, la manière dont les personnages traversent la ville et y évoluent, en analysant les parcours urbains à la fois d’un point de vue symbolique et narratif.Questa tesi è dedicata alla questione dei rapporti tra spazio urbano e identità, e più specificamente alla questione dei « confini nella città » nella letteratura postcoloniale italiana. La nozione di confini nella città, ripresa dall’anropologia, la geografia urbana e la semiotica, viene qui rimobilitata e reinterpretata per analizzare la rappresentazione dello spazio urbano in un corpus principalmente (ma non esclusivamente) letterario. Attraverso i racconti di autrici migranti e di seconda generazione, le città italiane appaiono sotto una luce alquanto diversa da quanto si è potuto vedere nel resto della letteratura italiana: queste nuove rappresentazioni dello spazio portano una riflessione di fondo sul posto delle soggettivià migranti e/o di origine straniera negli spazi della città e per estensione, della nazione. Il concetto di confine permette così di affrontare frontalmente la questione, interrogandosi sulle forme di esclusione che condizionano la relazione di soggettività minoritarie alla città, ma anche nuove modalità di percezione, rappresentazione, riappropriazione e abitazione che si riscontrano nelle opere. La nozione di confine riveste qui alcune dimensioni principali. Rimanda innanzitutto alle nuove mappature che i testi postcoloniali operano sulle città italiane, reinterpretando le relazioni tra margini, periferia e centro. Il confine tocca inoltre l’iscrizione nello spazio sociale : i testi rivelano la città come terreno di interazione, discriminazione e incontro potenziale. Infine, un ultimo aspetto riguarda il movimento, il modo in cui i personaggi attraversano la città e vi si muovono, analizzando i percorsi urbani da un punto di vista simbolico e narrativo.This thesis investigates the relationship between space and identity within Italian postcolonial literature, focalizing more specifically on the concept of ''urban boundaries''. This notion, which is borrowed from the fields of urban anthropology, geography and semiotics, is here remobilized to analyze the representation of Italian cities within a literary corpus. The representation of cities through the perspective of migrant and second generation writers is considerably different than in the rest of Italian literature, as it proceed to interrogate the place of new subjectivities within urban spaces and within the nation.The concept of boundaries (which translates the italian ''confini''), allows to examine the way in which this literature stages the forms of exclusion faced by migrant and second-generation youth within the city, but also the new ways in which these subjectivities perceive, inhabit and reappropriate urban space.Several dimensions of the concept are developed in our analysis. We firstly underline the centrality of boundaries in the way in which postcolonial authors manage to remap the Italian city, and reinterpret the relationship between its margins, peripheries and centers. Boundaries also involve the experience of the city as social space, as the theater in which interactions, encounters and discrimination take place. We then examine the rôle of boundaries in relationship to movements within the city, through an analysis of the topos of urban walks. Finally, we pay attention to the way in which urban movements allows to reveal and narrate not only social and racial but also memorial boundaries, by underlining the importance of colonial memories within Italian cities
Economic evaluation of policy measures for animal welfare and biosecurity in animal breeding, and their effect on the reduction of antimicrobial resistance in the environment and along the food supply chain
No full textThis study aims to analyze the public costs associated with the ClassyFarm system as a tool for monitoring antibiotic use in livestock farming. ClassyFarm is a digital platform developed in 2018 which operates by applying business intelligence processes to farm-level data to generate scores related to proper drug use, the implementation of adequate biosecurity protocols and compliance with animal welfare standards. Specifically, assessing the prudence of veterinary drug use by farmers allows for the estimation of each farm's risk of developing and spreading AMR microbial strains. Based on the system’s structure, the economic evaluation considered the following parameters related to costs: - Development and updating of the ClassyFarm system; - Control activities carried out by official veterinarians for data collection on ClassyFarm farms; - Acquisition and integration of information from other official databases. Although including a benefits assessment would have strengthened the study, such analysis was limited to a qualitative approach due to the nature of the ClassyFarm system. Although the system is intended to support farmers with decision-making data to improve farm management, its use is not legally mandated, making it challenging to directly link observed improvements to its implementation. Potential benefits for the public sector—such are difficult to quantify, as they are influenced by various factors beyond the system. A theoretical monetary assessment of the system’s benefits would require estimating the worth of the information it provides, based on users’ willingness to pay for access in the absence of a centralized infrastructure. However, this approach is prone to valuation bias, shaped more by users’ perceptions of pharmacosurveillance policies than by the system itself. Despite the technical limitations described above, this economic assessment offers an original, scientifically grounded insight into public spending on surveillance programs, which is crucial for future policy-making in the context of shrinking global healthcare budgets
Digital transformation of medical imaging: from quantitative diagnostic to surgical training
No full textMedical imaging is the prominent way of knowing internal structure, morphology and functional aspects of a living human body and its widely adopted in clinical practice to diagnose, support and plan treatments. This thesis explores the digital transformation of medical imaging, demonstrating how pixel-intensity analysis and advanced computational methodologies can enhance diagnostic precision and support surgical training. Leveraging data-rich imaging modalities such as computed tomography (CT) and infrared imaging, this work develops and validates quantitative pipelines to extract robust imaging biomarkers and reconstruct anatomically accurate models for both clinical and educational purposes. From a diagnostic perspective, novel radiodensitometric and radiomic techniques were applied across three clinical domains. First, CT-based quantitative analysis was used to characterize skeletal muscle aging, introducing biomarkers of sarcopenia and fat infiltration. Second, the concept of “virtual cardiac histology” was developed, where high-dimensional radiomic features from cardiac CT enabled the non-invasive differentiation of healthy myocardium from pathological conditions, including hypertrophic cardiomyopathy (HCM) and acute myocardial infarction (AMI). Third, automated meibography analysis was proposed to quantify morphological changes in Meibomian glands from infrared eyelid imaging, offering objective metrics for ocular surface diseases. From an educational and surgical standpoint, this research introduces the Radio Anatomical Interactive Library (RAIL), an innovative platform that integrates clinical imaging data, 3D reconstructions, and mixed reality tools to support anatomical learning, preoperative planning, and surgical rehearsal. By combining radiological data with immersive technologies, RAIL enhances comprehension of complex anatomies and promotes interactive, case-based training. Collectively, the studies presented demonstrate that pixel-level analysis of medical images can yield reproducible and clinically meaningful biomarkers, bridging the gap between qualitative interpretation and quantitative, data-driven diagnostics. This thesis highlights the potential of integrating AI-based radiomics, 3D visualization, and extended reality into the healthcare continuum, enabling personalized diagnostics, improving training efficiency, and advancing the paradigm of precision medicine
Scaling performance at the end of moore's law: a programmer's perspective
Get PDFComputer architectures face a fundamental shift as Moore's law and Dennard scaling reach their technological limits. This evolution sparked a Cambrian explosion of specialized hardware designs: computing is now a power-bound challenge. While applications still struggle to scale on exascale systems, future HPC systems must integrate an increasingly diverse spectrum of accelerators, while software stacks must adapt to heterogeneous platforms. The need for domain-specific features is driving the advent of the RISC-V architecture: its flexible ISA could be the answer to the evolutionary challenges faced by computing. The adoption of RISC-V in HPC is still uncharted territory, bringing new challenges for system integration and software stacks. This thesis focuses on three ideas. Embarrassingly parallel, task-based workloads must explore throughput-optimized GPU kernel designs to unlock drug discovery campaigns on current TOP500 systems. HPC systems must overcome design and integration challenges to prepare for increasingly diverse post-exascale clusters, where RISC-V could be an answer. The hardware/software interface must adapt: target-specific components of the compilation stack must evolve to sustain domain-specific code generation. The first part of this thesis involves implementing and scaling drug discovery simulations on GPU-accelerated TOP500 systems, focusing on efficient acceleration of task-based workloads that scale to trillions of molecules, enabling the largest drug discovery simulation for SARS-CoV-2 ever performed. The second part centers on designing, building, and evaluating Monte Cimone, the world's first RISC-V HPC production cluster: its successful deployment proves the production readiness of RISC-V for HPC, paving the way for future RISC-V supercomputers. The third part focuses on the collective endeavor of developing an MLIR-based compiler backend for Snitch, a novel RISC-V streaming accelerator for machine learning, applying a progressive lowering approach to the compiler backend and enabling efficient micro-kernel code generation for application-specific RISC-V accelerators
The luminosity measurement at the LHC with the ATLAS experiment: upgrade for the LUCID detector for high luminosity-LHC and Z cross section measurement with early Run3-data for luminosity validation
Get PDFThe knowledge of the luminosity is crucial for measuring the cross sections of all physical processes and provides real-time information about the performance of the accelerator. In this thesis, luminosity will be discussed both from a technical point of view and as one of the main systematic uncertainties in physics analysis. In the first part of this thesis, the measurement of luminosity is discussed, starting with a theoretical overview and introducing various experimental techniques. The focus then shifts to the LUCID detector, the primary luminometer of the ATLAS experiment, used during LHC Run-2 (2015–2018) and Run-3 (2022–present). Given the challenging conditions expected in the High-Luminosity LHC, LUCID requires a complete redesign. Three prototypes of the future LUCID have been installed and are currently under tests to evaluate the performances of the new LUCID using data collected in Run-3. In the second part of the thesis, the measurement of the inclusive Z-boson production cross section at a center-of-mass energy of = 13.6 TeV is presented, along with its ratio to the W and t\Bar{t} production cross sections, using early ATLAS Run-3 data. This analysis is among the first conducted at the start of each data-taking period, as it allows for checks on detector calibration, alignment, and luminosity measurement via the Z-counting technique and, in this case, testing theoretical predictions at a new center-of-mass energy
Deep learning for new physics search
Get PDFThe ultimate goal of high-energy physics is searching for novel elementary particles and fundamental interactions, which may enable the formulation of a more comprehensive physics Beyond the Standard Model (BSM) able to explain a wider range of phenomena compared to the actual Standard Model (SM) of particle physics. Physics analyses are numerous and complex; so far, no theory tested against the SM has proven to be the right one. There could be various reasons for this: the detector apparatus of particle colliders is not able to reveal the new physics; the trigger system discards the almost totality of interesting, i.e., potentially new physics, events; lastly, the space of plausible alternative hypotheses to the Standard Model is so vast that we may be looking in the wrong directions. Assuming the detector is sufficiently expressive to capture the potential new physics, this thesis provides novel data-driven tools, powered by deep learning, to improve various aspects of particle physics analyses resulting in better sensitivity to the new physics. In particular, the signal sensitivity can be increased either: directly, by leveraging parameter-conditional neural networks that can incorporate domain knowledge by conditioning the architecture on physics parameters, or by anomaly detection implemented with auto-encoders that can identify unexpected, out-of-distribution, and rare events from a known background; indirectly, by improving physics pipelines (e.g., accelerator control, data quality monitoring, particle reconstruction and tracking) with reinforcement learning, replacing sub-optimal hand-defined heuristics; or jointly, since a foundation model can be pre-trained on the entire physics domain, fine-tuning it on multiple downstream tasks, achieving superior performance even with few labeled data. All the methods presented in this thesis are generally applicable. They may help particle physicists conduct better analyses by leveraging the increasing amount of big data that particle accelerators like the Large Hadron Collider can produce when collisions occur
Physics Education and identity development: the "personal-epistemological consonance/dissonance" construct
Get PDFThis dissertation explores the interplay between physics education and identity development, introducing the concept of Personal-Epistemological Consonance/Dissonance as a lens to analyze students’ engagement with physics. The research is grounded in the tension between the epistemological foundations of physics—its methods, practices, and values—and the ways students construct their personal and social identities through science learning. The study, developed over three years, is structured into four parts. The first part establishes a theoretical foundation by reviewing identity studies in science education and identifying a research blind spot: the need to investigate how epistemological aspects of physics shape students’ identity trajectories. The research is contextualized within the H2020 FEDORA project, a European initiative aimed at rethinking science education to promote critical thinking and engagement. Parts two and three focus on three empirical case studies conducted in upper secondary school settings. These studies explore classroom interventions, such as the “Physics of Clouds” and “Kairòs” activities, designed to integrate physics concepts with broader existential and epistemological reflections. The final case study involves an orientation course on scientific uncertainty and climate change, highlighting how students negotiate their identities through engagement with contemporary scientific challenges. The findings reveal that physics education impacts students beyond knowledge acquisition—it also shapes their sense of self and agency. The concept of personal-epistemological consonance/dissonance is introduced to describe the alignment or misalignment between students’ personal epistemologies and the disciplinary values of physics. The research contributes to the field of physics education research (PER) by proposing a novel framework for analyzing identity formation and offering recommendations for a more personally relevant science education
Searching for high-redshift progenitors of massive galaxies
Get PDFThis Thesis focuses on the process of star formation and its relationship with the formation and evolution of the most massive galaxies in the Universe. The main hypothesis behind this work is that the picture of the high-z Universe based on deep optical/NIR surveys is potentially incomplete, since it misses most of the contribution from dusty sources that can be unveiled only with multi-wavelength data. In this Thesis, I follow two complementary approaches to support this hypothesis. In the initial chapters, I focus on the radio-selected (RS-) NIR-dark galaxies: a population of likely dusty star-forming galaxies selected at radio frequencies. I characterize these sources by taking advantage of the almost complete photometric coverage offered by the COSMOS field and involve in the analysis state-of-the-art facilities such as ALMA and JWST. The main result of this first part of the Thesis is the determination of the physical properties of the RS-NIRdark galaxies, picturing them as a population of highly dust-obscured (Av~4 mag), massive (M~10^(10.5-11) Msun) and star-forming (SFR~300-500 Msun/yr) galaxies mainly located at z~3 and beyond. These properties are then employed to estimate the expected contribution of these sources to the cosmic star formation rate density. The second approach, followed in the fifth chapter of the Thesis, focuses on a class of extremely red sources unveiled in the first data collected with JWST and missed by old-generation facilities. The analysis presented in this work pictures them as extremely massive (M~10^11 Msun) sources at z~5-7. The existence of such massive galaxies in the first Gyr of cosmic history suggests a much more efficient star formation in the early Universe than what is commonly derived by observing low-z galaxies. These two approaches, combined, shed new light on the evolutionary paths that bring to the formation of massive galaxies in the low-z Universe
Cognitive control across multiple domains: semantic conflict, emotion, and dishonesty
Get PDFCognitive control refers to the ability to manage behaviors towards specific goals. This ability can involve overriding automatic/habitual responses and adapting to new information (Diamond, 2013). The present thesis investigates how cognitive control is modulated in different conflict contexts: Semantic, emotional, and conflict arising from dishonest responses. Our findings show that cognitive control – operationalized here as sequential modulation of interference – is activated during semantic interference and dishonest contexts, but not during emotional distractors. In the semantic context, we employed a Picture Word Interference task to study whether the repetition of superordinate categorical versus perceptual features affects cognitive control. Binding-retrieval accounts suggest that trial features that occur in the same time frame are bound together in an episodic representation. If a feature of episodic representation is repeated in the next trial, the previous control state is also reactivated (Frings et al., 2020). Our results showed that the effect of the sensory/perceptual repetition did not add to the repetition of the category, suggesting that only the superordinate category feature is involved in episodic representation here. In a dishonest context, we employed a Reaction Time Concealed Information Test. Participants were required to respond either truthfully or dishonestly to photos of previously seen and new items. We observed slower responses in dishonest conditions compared to honest ones, suggesting conflict monitoring (Foerster et al., 2023). The interference from dishonest responses decreased in trials following dishonest conditions compared to those following honest conditions. Contrary to semantic and dishonest contexts, a pattern of reduction of emotional interference was not observed. In conclusion, semantic and dishonest conflicts may activate cognitive stability, a cognitive control component for maintaining focus despite distractions (Egner, 2023) In our experiments, this pattern does not apply to emotional conflict that could be more related to attentional capture than to response conflict
Transient deformations: understanding Earth's interior through climate change
Get PDFTransient effects in the response of the Earth to surface unloading are still a matter of debate in the geophysical community. Recognizing their presence in the context of Glacial Isostatic Adjustment would represent a significant step towards a comprehensive understanding of mantle relaxation mechanisms at intermediate time scales. This Thesis explores the Andrade rheology, a transient model that has gained much success in planetary sciences. In this regard, two of the main results are the recovery of the analytical expressions of the relaxation modulus in the time domain of the Andrade model and the Love numbers for a homogeneous Andrade planet. Then, I examine the response of several Earth models including layers with Andrade rheology to different types of (glaciers) unloading. The aim is outlining the most favorable conditions under which transient deformations following an unloading event can be observed through geodetic techniques. My findings indicate that rapid changes in the load history and the position of the observation point with respect to the melting masses are parameters of utmost relevance. A shallow elastic lithosphere enables transient features to emerge more clearly, and the displacement rates reach the maximum difference from their non-transient counterparts in the regions right beneath the shrinking load, suggesting that, in the future, sub-glacial geodesy may improve the identification of transient features in the Earth’s response. Finally, even for medium-scale glaciers, the viscoelastic contributions to displacement may be significant already over short time periods (<15 yr). These kinds of studies become even more crucial in the context of present-day climate change. Hence, the growth both in number and magnitude of phenomena like extreme melting events and calvings, may increase our possibility to observe transient signals in the Earth’s response