Gravitational Lensing as a Tool on Galactic and Cosmological Scales

This work considers the reconstruction of strong gravitational lenses from their observed effects on the light distribution of background sources. After reviewing the formalism of gravitational lensing and the most common and relevant lens models, new analytical results on the elliptical power law lens are presented, including new expressions for the deflection, potential, shear and magnification, which naturally lead to a fast numerical scheme for practical calculation. The main part of the thesis investigates lens reconstruction with extended sources by means of the forward reconstruction method, in which the lenses and sources are given by parametric models. The numerical realities of the problem make it necessary to find targeted optimisations for the forward method, in order to make it feasible for general applications to modern, high resolution images. The result of these optimisations is presented in the \textsc{Lensed} algorithm. Subsequently, a number of tests for general forward reconstruction methods are created to decouple the influence of sourced from lens reconstructions, in order to objectively demonstrate the constraining power of the reconstruction. The final chapters on lens reconstruction contain two sample applications of the forward method. One is the analysis of images from a strong lensing survey. Such surveys today contain $\sim 100$ strong lenses, and much larger sample sizes are expected in the future, making it necessary to quickly and reliably analyse catalogues of lenses with a fixed model. The second application deals with the opposite situation of a single observation that is to be confronted with different lens models, where the forward method allows for natural model-building. This is demonstrated using an example reconstruction of the ``Cosmic Horseshoe''. An appendix presents an independent work on the use of weak gravitational lensing to investigate theories of modified gravity which exhibit screening in the non-linear regime of structure formation.

Sleep disorders in Multiple System Atrophy: a longitudinal study

Introduction: A higher frequency of sleep and breathing disorders in Multiple System Atrophy (MSA) populations is documented in literature. The analysis of disease progression and prognosis in patients with sleep and breathing disorders could shed light on specific neuropathology and pathophysiology of MSA. Objective: To characterize sleep disorders and their longitudinal modifications during disease course in MSA patients, and to determine their prognostic value. Methods: This is a retrospective and prospective cohort study including 182 MSA patients (58.8% males). Type of onset was defined by the first reported motor or autonomic symptom/sign related to MSA. The occurrence of symptoms/signs and milestones of disease progression and their latency were collected. REM sleep behaviour disorder (RBD) and stridor were video-polysomnography (VPSG)-confirmed. VPSG recordings were analysed in a standardized fashion during the disease course. Survival data were based on time to death from the first symptom of disease. Results: Isolated RBD represented the first MSA symptom in 30% of patients, preceding disease onset according to international criteria with a median of 3(1–5) years. Patients developing early stridor or presenting with RBD at disease onset showed a more rapid and severe disease progression. These features had independent negative prognostic value for survival. Sleep architecture was characterized by peculiar features which could represent negative markers in MSA prognosis. Patients with stridor treated with tracheostomy showed a reduced risk of death. Conclusions: This is one of the first studies focusing on longitudinal progression of sleep in MSA. Sleep disorders are key features of disease, playing a role in presentation, prognosis and progression. In our MSA cohort, RBD represented the most frequent mode of disease presentation. Moreover, some specific clinical and instrumental sleep features could represent a hallmark of MSA and could be involved in prognosis and, in particular, in sudden death and death during sleep.

mHealth-based Methods for Neuromotor Assessment and Rehabilitation

Recent years observed massive growth in wearable technology, everything can be smart: phones, watches, glasses, shirts, etc. These technologies are prevalent in various fields: from wellness/sports/fitness to the healthcare domain. The spread of this phenomenon led the World-Health-Organization to define the term 'mHealth' as "medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices". Furthermore, mHealth solutions are suitable to perform real-time wearable Biofeedback (BF) systems: sensors in the body area network connected to a processing unit (smartphone) and a feedback device (loudspeaker) to measure human functions and return them to the user as (bio)feedback signal. During the COVID-19 pandemic, this transformation of the healthcare system has been dramatically accelerated by new clinical demands, including the need to prevent hospital surges and to assure continuity of clinical care services, allowing pervasive healthcare. Never as of today, we can say that the integration of mHealth technologies will be the basis of this new era of clinical practice. In this scenario, this PhD thesis's primary goal is to investigate new and innovative mHealth solutions for the Assessment and Rehabilitation of different neuromotor functions and diseases. For the clinical assessment, there is the need to overcome the limitations of subjective clinical scales. Creating new pervasive and self-administrable mHealth solutions, this thesis investigates the possibility of employing innovative systems for objective clinical evaluation. For rehabilitation, we explored the clinical feasibility and effectiveness of mHealth systems. In particular, we developed innovative mHealth solutions with BF capability to allow tailored rehabilitation. The main goal that a mHealth-system should have is improving the person's quality of life, increasing or maintaining his autonomy and independence. To this end, inclusive design principles might be crucial, next to the technical and technological ones, to improve mHealth-systems usability.

Colocated multiple-input multiple-output radars for smart mobility

In recent years, radars have been used in many applications such as precision agriculture and advanced driver assistant systems. Optimal techniques for the estimation of the number of targets and of their coordinates require solving multidimensional optimization problems entailing huge computational efforts. This has motivated the development of sub-optimal estimation techniques able to achieve good accuracy at a manageable computational cost. Another technical issue in advanced driver assistant systems is the tracking of multiple targets. Even if various filtering techniques have been developed, new efficient and robust algorithms for target tracking can be devised exploiting a probabilistic approach, based on the use of the factor graph and the sum-product algorithm. The two contributions provided by this dissertation are the investigation of the filtering and smoothing problems from a factor graph perspective and the development of efficient algorithms for two and three-dimensional radar imaging. Concerning the first contribution, a new factor graph for filtering is derived and the sum-product rule is applied to this graphical model; this allows to interpret known algorithms and to develop new filtering techniques. Then, a general method, based on graphical modelling, is proposed to derive filtering algorithms that involve a network of interconnected Bayesian filters. Finally, the proposed graphical approach is exploited to devise a new smoothing algorithm. Numerical results for dynamic systems evidence that our algorithms can achieve a better complexity-accuracy tradeoff and tracking capability than other techniques in the literature. Regarding radar imaging, various algorithms are developed for frequency modulated continuous wave radars; these algorithms rely on novel and efficient methods for the detection and estimation of multiple superimposed tones in noise. The accuracy achieved in the presence of multiple closely spaced targets is assessed on the basis of both synthetically generated data and of the measurements acquired through two commercial multiple-input multiple-output radars.

Ecological responses of Western Palearctic avian species to late Quaternary climate oscillations

This PhD thesis explores the ecological responses of bird species to glacial-interglacial transitions during the late Quaternary in the Western Palearctic, using multiple approaches and at different scales, enhancing the importance of the bird fossil record and quantitative methods to elucidate biotic trends in relation to long-term climate changes. The taxonomic and taphonomic analyses of the avian fossil assemblages from four Italian Middle and Upper Pleistocene sedimentary successions (Grotta del Cavallo, Grotta di Fumane, Grotta di Castelcivita, and Grotta di Uluzzo C) allowed us to reconstruct local-scale patterns in birds’ response to climate changes. These bird assemblages are characterized by the presence of temperate species and by the occasional presence of cold-dwelling species during glacials, related to range shifts. These local patterns are supported by those identified at the continental scale. In this respect, I mapped the present-day and LGM climatic envelopes of species with different climatic requirements. The results show a substantial stability in the range of temperate species and pronounced changes in the range of cold-dwelling species, supported by their fossil records. Therefore, the responses to climate oscillations are highly related to the thermal niches of investigated species. I also clarified the dynamics of the presence of boreal and arctic bird species in Mediterranean Europe, due to southern range shifts, during the glacial phases. After a reassessment of the reliability of the existing fossil evidence, I show that this phenomenon is not as common as previously thought, with important implications for the paleoclimatic and paleoenvironmental significance of the targeted species. I have also been able to explore the potential of multivariate and rarefaction methods in the analyses of avian fossils from Grotta del Cavallo. These approaches helped to delineate the main drivers of taphonomic damages and the dynamics of species diversity in relation to climate-driven paleoenvironmental changes.

An efficient Jeans modelling of axisymmetric galaxies with multiple stellar components

Dynamical models of stellar systems represent a powerful tool to study their internal structure and dynamics, to interpret the observed morphological and kinematical fields, and also to support numerical simulations of their evolution. We present a method especially designed to build axisymmetric Jeans models of galaxies, assumed as stationary and collisionless stellar systems. The aim is the development of a rigorous and flexible modelling procedure of multicomponent galaxies, composed of different stellar and dark matter distributions, and a central supermassive black hole. The stellar components, in particular, are intended to represent different galaxy structures, such as discs, bulges, halos, and can then have different structural (density profile, flattening, mass, scale-length), dynamical (rotation, velocity dispersion anisotropy), and population (age, metallicity, initial mass function, mass-to-light ratio) properties. The theoretical framework supporting the modelling procedure is presented, with the introduction of a suitable nomenclature, and its numerical implementation is discussed, with particular reference to the numerical code JASMINE2, developed for this purpose. We propose an approach for efficiently scaling the contributions in mass, luminosity, and rotational support, of the different matter components, allowing for fast and flexible explorations of the model parameter space. We also offer different methods of the computation of the gravitational potentials associated of the density components, especially convenient for their easier numerical tractability. A few galaxy models are studied, showing internal, and projected, structural and dynamical properties of multicomponent galaxies, with a focus on axisymmetric early-type galaxies with complex kinematical morphologies. The application of galaxy models to the study of initial conditions for hydro-dynamical and $N$-body simulations of galaxy evolution is also addressed, allowing in particular to investigate the large number of interesting combinations of the parameters which determine the structure and dynamics of complex multicomponent stellar systems.

Analytical methods for modeling elastic metasurfaces

This dissertation aims at developing advanced analytical tools able to model surface waves propagating in elastic metasurfaces. In particular, four different objectives are defined and pursued throughout this work to enrich the description of the metasurface dynamics. First, a theoretical framework is developed to describe the dispersion properties of a seismic metasurface composed of discrete resonators placed on a porous medium considering part of it fully saturated. Such a model combines classical elasticity theory, Biot’s poroelasticity and an effective medium approach to describe the metasurface dynamics and its coupling with the poroelastic substrate. Second, an exact formulation based on the multiple scattering theory is developed to extend the two-dimensional classical Lamb’s problem to the case of an elastic half-space coupled to an arbitrary number of discrete surface resonators. To this purpose, the incident wavefield generated by a harmonic source and the scattered field generated by each resonator are calculated. The substrate wavefield is then obtained as solutions of the coupled problem due to the interference of the incident field and the multiple scattered fields of the oscillators. Third, the above discussed formulation is extended to three-dimensional contexts. The purpose here is to investigate the dynamic behavior and the topological properties of quasiperiodic elastic metasurfaces. Finally, the multiple scattering formulation is extended to model flexural metasurfaces, i.e., an array of thin plates. To this end, the resonant plates are modeled by means of their equivalent impedance, derived by exploiting the Kirchhoff plate theory. The proposed formulation permits the treatment of a general flexural metasurface, with no limitation on the number of plates and the configuration taken into account. Overall, the proposed analytical tools could pave the way for a better understanding of metasurface dynamics and their implementation in engineered devices.

User scheduling for low earth orbit multi-user multiple-input-multiple-output non-terrestrial network systems

In rural and isolated areas without cellular coverage, Satellite Communication (SatCom) is the best candidate to complement terrestrial coverage. However, the main challenge for future generations of wireless networks will be to meet the growing demand for new services while dealing with the scarcity of frequency spectrum. As a result, it is critical to investigate more efficient methods of utilizing the limited bandwidth; and resource sharing is likely the only choice. The research community’s focus has recently shifted towards the interference management and exploitation paradigm to meet the increasing data traffic demands. In the Downlink (DL) and Feedspace (FS), LEO satellites with an on-board antenna array can offer service to numerous User Terminals (UTs) (VSAT or Handhelds) on-ground in FFR schemes by using cutting-edge digital beamforming techniques. Considering this setup, the adoption of an effective user scheduling approach is a critical aspect given the unusually high density of User terminals on the ground as compared to the on-board available satellite antennas. In this context, one possibility is that of exploiting clustering algorithms for scheduling in LEO MU-MIMO systems in which several users within the same group are simultaneously served by the satellite via Space Division Multiplexing (SDM), and then these different user groups are served in different time slots via Time Division Multiplexing (TDM). This thesis addresses this problem by defining a user scheduling problem as an optimization problem and discusses several algorithms to solve it. In particular, focusing on the FS and user service link (i.e., DL) of a single MB-LEO satellite operating below 6 GHz, the user scheduling problem in the Frequency Division Duplex (FDD) mode is addressed. The proposed State-of-the-Art scheduling approaches are based on graph theory. The proposed solution offers high performance in terms of per-user capacity, Sum-rate capacity, SINR, and Spectral Efficiency.

Computational methods applied to undeciphered scripts from the Aegean and Cyprus: Linear A and Cypro-Minoan

The study of ancient, undeciphered scripts presents unique challenges, that depend both on the nature of the problem and on the peculiarities of each writing system. In this thesis, I present two computational approaches that are tailored to two different tasks and writing systems. The first of these methods is aimed at the decipherment of the Linear A afraction signs, in order to discover their numerical values. This is achieved with a combination of constraint programming, ad-hoc metrics and paleographic considerations. The second main contribution of this thesis regards the creation of an unsupervised deep learning model which uses drawings of signs from ancient writing system to learn to distinguish different graphemes in the vector space. This system, which is based on techniques used in the field of computer vision, is adapted to the study of ancient writing systems by incorporating information about sequences in the model, mirroring what is often done in natural language processing. In order to develop this model, the Cypriot Greek Syllabary is used as a target, since this is a deciphered writing system. Finally, this unsupervised model is adapted to the undeciphered Cypro-Minoan and it is used to answer open questions about this script. In particular, by reconstructing multiple allographs that are not agreed upon by paleographers, it supports the idea that Cypro-Minoan is a single script and not a collection of three script like it was proposed in the literature. These results on two different tasks shows that computational methods can be applied to undeciphered scripts, despite the relatively low amount of available data, paving the way for further advancement in paleography using these methods.

Development of machine learning methods for multi-modal biomarkers detection and integration

In medicine, innovation depends on a better knowledge of the human body mechanism, which represents a complex system of multi-scale constituents. Unraveling the complexity underneath diseases proves to be challenging. A deep understanding of the inner workings comes with dealing with many heterogeneous information. Exploring the molecular status and the organization of genes, proteins, metabolites provides insights on what is driving a disease, from aggressiveness to curability. Molecular constituents, however, are only the building blocks of the human body and cannot currently tell the whole story of diseases. This is why nowadays attention is growing towards the contemporary exploitation of multi-scale information. Holistic methods are then drawing interest to address the problem of integrating heterogeneous data. The heterogeneity may derive from the diversity across data types and from the diversity within diseases. Here, four studies conducted data integration using customly designed workflows that implement novel methods and views to tackle the heterogeneous characterization of diseases. The first study devoted to determine shared gene regulatory signatures for onco-hematology and it showed partial co-regulation across blood-related diseases. The second study focused on Acute Myeloid Leukemia and refined the unsupervised integration of genomic alterations, which turned out to better resemble clinical practice. In the third study, network integration for artherosclerosis demonstrated, as a proof of concept, the impact of network intelligibility when it comes to model heterogeneous data, which showed to accelerate the identification of new potential pharmaceutical targets. Lastly, the fourth study introduced a new method to integrate multiple data types in a unique latent heterogeneous-representation that facilitated the selection of important data types to predict the tumour stage of invasive ductal carcinoma. The results of these four studies laid the groundwork to ease the detection of new biomarkers ultimately beneficial to medical practice and to the ever-growing field of Personalized Medicine.