Bayesian space-time data fusion for real-time forecasting and map uncertainty

Environmental computer models are deterministic models devoted to predict several environmental phenomena such as air pollution or meteorological events. Numerical model output is given in terms of averages over grid cells, usually at high spatial and temporal resolution. However, these outputs are often biased with unknown calibration and not equipped with any information about the associated uncertainty. Conversely, data collected at monitoring stations is more accurate since they essentially provide the true levels. Due the leading role played by numerical models, it now important to compare model output with observations. Statistical methods developed to combine numerical model output and station data are usually referred to as data fusion. In this work, we first combine ozone monitoring data with ozone predictions from the Eta-CMAQ air quality model in order to forecast real-time current 8-hour average ozone level defined as the average of the previous four hours, current hour, and predictions for the next three hours. We propose a Bayesian downscaler model based on first differences with a flexible coefficient structure and an efficient computational strategy to fit model parameters. Model validation for the eastern United States shows consequential improvement of our fully inferential approach compared with the current real-time forecasting system. Furthermore, we consider the introduction of temperature data from a weather forecast model into the downscaler, showing improved real-time ozone predictions. Finally, we introduce a hierarchical model to obtain spatially varying uncertainty associated with numerical model output. We show how we can learn about such uncertainty through suitable stochastic data fusion modeling using some external validation data. We illustrate our Bayesian model by providing the uncertainty map associated with a temperature output over the northeastern United States.

A bayesian model for mixed responses and skew laten variable to measure HRQoL in children

The aim of the thesi is to formulate a suitable Item Response Theory (IRT) based model to measure HRQoL (as latent variable) using a mixed responses questionnaire and relaxing the hypothesis of normal distributed latent variable. The new model is a combination of two models already presented in literature, that is, a latent trait model for mixed responses and an IRT model for Skew Normal latent variable. It is developed in a Bayesian framework, a Markov chain Monte Carlo procedure is used to generate samples of the posterior distribution of the parameters of interest. The proposed model is test on a questionnaire composed by 5 discrete items and one continuous to measure HRQoL in children, the EQ-5D-Y questionnaire. A large sample of children collected in the schools was used. In comparison with a model for only discrete responses and a model for mixed responses and normal latent variable, the new model has better performances, in term of deviance information criterion (DIC), chain convergences times and precision of the estimates.

Ensemble forecasting in the Mediterranean sea

A new methodology is being devised for ensemble ocean forecasting using distributions of the surface wind field derived from a Bayesian Hierarchical Model (BHM). The ocean members are forced with samples from the posterior distribution of the wind during the assimilation of satellite and in-situ ocean data. The initial condition perturbations are then consistent with the best available knowledge of the ocean state at the beginning of the forecast and amplify the ocean response to uncertainty only in the forcing. The ECMWF Ensemble Prediction System (EPS) surface winds are also used to generate a reference ocean ensemble to evaluate the performance of the BHM method that proves to be eective in concentrating the forecast uncertainty at the ocean meso-scale. An height month experiment of weekly BHM ensemble forecasts was performed in the framework of the operational Mediterranean Forecasting System. The statistical properties of the ensemble are compared with model errors throughout the seasonal cycle proving the existence of a strong relationship between forecast uncertainties due to atmospheric forcing and the seasonal cycle.

Bayesian Analysis of Linear Inverse Problems with Applications in Economics and Finance

In my PhD thesis I propose a Bayesian nonparametric estimation method for structural econometric models where the functional parameter of interest describes the economic agent's behavior. The structural parameter is characterized as the solution of a functional equation, or by using more technical words, as the solution of an inverse problem that can be either ill-posed or well-posed. From a Bayesian point of view, the parameter of interest is a random function and the solution to the inference problem is the posterior distribution of this parameter. A regular version of the posterior distribution in functional spaces is characterized. However, the infinite dimension of the considered spaces causes a problem of non continuity of the solution and then a problem of inconsistency, from a frequentist point of view, of the posterior distribution (i.e. problem of ill-posedness). The contribution of this essay is to propose new methods to deal with this problem of ill-posedness. The first one consists in adopting a Tikhonov regularization scheme in the construction of the posterior distribution so that I end up with a new object that I call regularized posterior distribution and that I guess it is solution of the inverse problem. The second approach consists in specifying a prior distribution on the parameter of interest of the g-prior type. Then, I detect a class of models for which the prior distribution is able to correct for the ill-posedness also in infinite dimensional problems. I study asymptotic properties of these proposed solutions and I prove that, under some regularity condition satisfied by the true value of the parameter of interest, they are consistent in a "frequentist" sense. Once I have set the general theory, I apply my bayesian nonparametric methodology to different estimation problems. First, I apply this estimator to deconvolution and to hazard rate, density and regression estimation. Then, I consider the estimation of an Instrumental Regression that is useful in micro-econometrics when we have to deal with problems of endogeneity. Finally, I develop an application in finance: I get the bayesian estimator for the equilibrium asset pricing functional by using the Euler equation defined in the Lucas'(1978) tree-type models.

A Bayesian changepoint analysis on spatio-temporal point processes

Changepoint analysis is a well established area of statistical research, but in the context of spatio-temporal point processes it is as yet relatively unexplored. Some substantial differences with regard to standard changepoint analysis have to be taken into account: firstly, at every time point the datum is an irregular pattern of points; secondly, in real situations issues of spatial dependence between points and temporal dependence within time segments raise. Our motivating example consists of data concerning the monitoring and recovery of radioactive particles from Sandside beach, North of Scotland; there have been two major changes in the equipment used to detect the particles, representing known potential changepoints in the number of retrieved particles. In addition, offshore particle retrieval campaigns are believed may reduce the particle intensity onshore with an unknown temporal lag; in this latter case, the problem concerns multiple unknown changepoints. We therefore propose a Bayesian approach for detecting multiple changepoints in the intensity function of a spatio-temporal point process, allowing for spatial and temporal dependence within segments. We use Log-Gaussian Cox Processes, a very flexible class of models suitable for environmental applications that can be implemented using integrated nested Laplace approximation (INLA), a computationally efficient alternative to Monte Carlo Markov Chain methods for approximating the posterior distribution of the parameters. Once the posterior curve is obtained, we propose a few methods for detecting significant change points. We present a simulation study, which consists in generating spatio-temporal point pattern series under several scenarios; the performance of the methods is assessed in terms of type I and II errors, detected changepoint locations and accuracy of the segment intensity estimates. We finally apply the above methods to the motivating dataset and find good and sensible results about the presence and quality of changes in the process.

Multilevel Analysis in Household Survey: An Application to Health Condition Data

The aim of this thesis is to apply multilevel regression model in context of household surveys. Hierarchical structure in this type of data is characterized by many small groups. In last years comparative and multilevel analysis in the field of perceived health have grown in size. The purpose of this thesis is to develop a multilevel analysis with three level of hierarchy for Physical Component Summary outcome to: evaluate magnitude of within and between variance at each level (individual, household and municipality); explore which covariates affect on perceived physical health at each level; compare model-based and design-based approach in order to establish informativeness of sampling design; estimate a quantile regression for hierarchical data. The target population are the Italian residents aged 18 years and older. Our study shows a high degree of homogeneity within level 1 units belonging from the same group, with an intraclass correlation of 27% in a level-2 null model. Almost all variance is explained by level 1 covariates. In fact, in our model the explanatory variables having more impact on the outcome are disability, unable to work, age and chronic diseases (18 pathologies). An additional analysis are performed by using novel procedure of analysis :"Linear Quantile Mixed Model", named "Multilevel Linear Quantile Regression", estimate. This give us the possibility to describe more generally the conditional distribution of the response through the estimation of its quantiles, while accounting for the dependence among the observations. This has represented a great advantage of our models with respect to classic multilevel regression. The median regression with random effects reveals to be more efficient than the mean regression in representation of the outcome central tendency. A more detailed analysis of the conditional distribution of the response on other quantiles highlighted a differential effect of some covariate along the distribution.

The APSEL4D Monolithic Active Pixel Sensor and its Usage in a Single Electron Interference Experiment

We have realized a Data Acquisition chain for the use and characterization of APSEL4D, a 32 x 128 Monolithic Active Pixel Sensor, developed as a prototype for frontier experiments in high energy particle physics. In particular a transition board was realized for the conversion between the chip and the FPGA voltage levels and for the signal quality enhancing. A Xilinx Spartan-3 FPGA was used for real time data processing, for the chip control and the communication with a Personal Computer through a 2.0 USB port. For this purpose a firmware code, developed in VHDL language, was written. Finally a Graphical User Interface for the online system monitoring, hit display and chip control, based on windows and widgets, was realized developing a C++ code and using Qt and Qwt dedicated libraries. APSEL4D and the full acquisition chain were characterized for the first time with the electron beam of the transmission electron microscope and with 55Fe and 90Sr radioactive sources. In addition, a beam test was performed at the T9 station of the CERN PS, where hadrons of momentum of 12 GeV/c are available. The very high time resolution of APSEL4D (up to 2.5 Mfps, but used at 6 kfps) was fundamental in realizing a single electron Young experiment using nanometric double slits obtained by a FIB technique. On high statistical samples, it was possible to observe the interference and diffractions of single isolated electrons traveling inside a transmission electron microscope. For the first time, the information on the distribution of the arrival time of the single electrons has been extracted.

Integrated communication and learning towards autonomous mobile radio networks

The integration of distributed and ubiquitous intelligence has emerged over the last years as the mainspring of transformative advancements in mobile radio networks. As we approach the era of “mobile for intelligence”, next-generation wireless networks are poised to undergo significant and profound changes. Notably, the overarching challenge that lies ahead is the development and implementation of integrated communication and learning mechanisms that will enable the realization of autonomous mobile radio networks. The ultimate pursuit of eliminating human-in-the-loop constitutes an ambitious challenge, necessitating a meticulous delineation of the fundamental characteristics that artificial intelligence (AI) should possess to effectively achieve this objective. This challenge represents a paradigm shift in the design, deployment, and operation of wireless networks, where conventional, static configurations give way to dynamic, adaptive, and AI-native systems capable of self-optimization, self-sustainment, and learning. This thesis aims to provide a comprehensive exploration of the fundamental principles and practical approaches required to create autonomous mobile radio networks that seamlessly integrate communication and learning components. The first chapter of this thesis introduces the notion of Predictive Quality of Service (PQoS) and adaptive optimization and expands upon the challenge to achieve adaptable, reliable, and robust network performance in dynamic and ever-changing environments. The subsequent chapter delves into the revolutionary role of generative AI in shaping next-generation autonomous networks. This chapter emphasizes achieving trustworthy uncertainty-aware generation processes with the use of approximate Bayesian methods and aims to show how generative AI can improve generalization while reducing data communication costs. Finally, the thesis embarks on the topic of distributed learning over wireless networks. Distributed learning and its declinations, including multi-agent reinforcement learning systems and federated learning, have the potential to meet the scalability demands of modern data-driven applications, enabling efficient and collaborative model training across dynamic scenarios while ensuring data privacy and reducing communication overhead.

Influence of climate changes on cross allergies: possible involvement of pollen transglutaminase

Allergies are a complex of symptoms derived from altered IgE-mediated reactions of the immune system towards substances known as allergens. Allergic sensibilization can be of food or respiratory origin and, in particular, apple and hazelnut allergens have been identified in pollens or fruits. Allergic cross-reactivity can occur in a patient reacting to similar allergens from different origins, justifying the research in both systems as in Europe a greater number of people suffers from apple fruit allergy, but little evidence exists about pollen. Apple fruit allergies are due to four different classes of allergens (Mal d 1, 2, 3, 4), whose allergenicity is related both to genotype and tissue specificity; therefore I have investigated their presence also in pollen at different time of germination to clarify the apple pollen allergenic potential. I have observed that the same four classes of allergens found in fruit are expressed at different levels also in pollen, and their presence might support that the apple pollen can be considered allergenic as the fruit, deducing that apple allergy could also be indirectly caused by sensitization to pollen. Climate changes resulting from increases in temperature and air pollution influence pollen allergenicity, responsible for the dramatic raise in respiratory allergies (hay fever, bronchial asthma, conjunctivitis). Although the link between climate change and pollen allergenicity is proven, the underlying mechanism is little understood. Transglutaminases (TGases), a class of enzymes able to post-translationally modify proteins, are activated under stress and involved in some inflammatory responses, enhancing the activity of pro-inflammatory phospholipase A2, suggesting a role in allergies. Recently, a calcium-dependent TGase activity has been identified in the pollen cell wall, raising the possibility that pollen TGase may have a role in the modification of pollen allergens reported above, thus stabilizing them against proteases. This enzyme can be involved also in the transamidation of proteins present in the human mucosa interacting with surface pollen or, finally, the enzyme itself can represent an allergen, as suggested by studies on celiac desease. I have hypothesized that this pollen enzyme can be affected by climate changes and be involved in exhacerbating allergy response. The data presented in this thesis represent a scientific basis for future development of studies devoted to verify the hypothesis set out here. First, I have demonstrated the presence of an extracellular TGase on the surface of the grain observed either at the apical or the proximal parts of the pollen-tube by laser confocal microscopy (Iorio et al., 2008), that plays an essential role in apple pollen-tube growth, as suggested by the arrest of tube elongation by TGase inhibitors, such as EGTA or R281. Its involvement in pollen tube growth is mainly confirmed by the data of activity and gene expression, because TGase showed a peak between 15 min and 30 min of germination, when this process is well established, and an optimal pH around 6.5, which is close to that recorded for the germination medium. Moreover, data show that pollen TGase can be a glycoprotein as the glycosylation profile is linked both with the activation of the enzyme and with its localization at the pollen cell wall during germination, because from the data presented seems that the active form of TGase involved in pollen tube growth and pollen-stylar interaction is more exposed and more weakly bound to the cell wall. Interestingly, TGase interacts with fibronectin (FN), a putative SAMs or psECM component, inducing possibly intracellular signal transduction during the interaction between pollen-stylar occuring in the germination process, since a protein immunorecognised by anti-FN antibody is also present in pollen, in particular at the level of pollen grain cell wall in a punctuate pattern, but also along the shank of the pollen tube wall, in a similar pattern that recalls the signal obtained with the antibody anti TGase. FN represents a good substrate for the enzyme activity, better than DMC usually used as standard substrate for animal TGase. Thus, this pollen enzyme, necessary for its germination, is exposed on the pollen surface and consequently can easily interact with mucosal proteins, as it has been found germinated pollen in studies conducted on human mucus (Forlani, personal communication). I have obtained data that TGase activity increases in a very remarkable way when pollen is exposed to stressful conditions, such as climate changes and environmental pollution. I have used two different species of pollen, an aero allergenic (hazelnut, Corylus avellana) pollen, whose allergenicity is well documented, and an enthomophylus (apple, Malus domestica) pollen, which is not yet well characterized, to compare data on their mechanism of action in response to stressors. The two pollens have been exposed to climate changes (different temperatures, relative humidity (rH), acid rain at pH 5.6 and copper pollution (3.10 µg/l)) and showed an increase in pollen surface TGase activity that is not accompanied to an induced expression of TGase immunoreactive protein with AtPNG1p. Probably, climate change induce an alteration or damage to pollen cell wall that carries the pollen grains to release their content in the medium including TGase enzyme, that can be free to carry out its function as confirmed by the immunolocalisation and by the in situ TGase activity assay data; morphological examination indicated pollen damage, viability significantly reduced and in acid rain conditions an early germination of apple pollen, thus possibly enhancing the TGase exposure on pollen surface. Several pollen proteins were post-translationally modified, as well as mammalian sPLA2 especially with Corylus pollen, which results in its activation, potentially altering pollen allergenicity and inflammation. Pollen TGase activity mimicked the behaviour of gpl TGase and AtPNG1p in the stimulation of sPLA2, even if the regulatory mechanism seems different to gpl TGase, because pollen TGase favours an intermolecular cross-linking between various molecules of sPLA2, giving rise to high-molecular protein networks normally more stable. In general, pollens exhibited a significant endogenous phospholipase activity and it has been observed differences according to the allergenic (Corylus) or not-well characterized allergenic (Malus) attitude of the pollen. However, even if with a different intensity level in activation, pollen enzyme share the ability to activate the sPLA2, thus suggesting an important regulatory role for the activation of a key enzyme of the inflammatory response, among which my interest was addressed to pollen allergy. In conclusion, from all the data presented, mainly presence of allergens, presence of an extracellular TGase, increasing in its activity following exposure to environmental pollution and PLA2 activation, I can conclude that also Malus pollen can behave as potentially allergenic. The mechanisms described here that could affect the allergenicity of pollen, maybe could be the same occurring in fruit, paving the way for future studies in the identification of hyper- and hypo- allergenic cultivars, in preventing environmental stressor effects and, possibly, in the production of transgenic plants.

Diagnostic techniques for EHD and MHD interaction

The impact of plasma technologies is growing both in the academic and in the industrial fields. Nowadays, a great interest is focused in plasma applications in aeronautics and astronautics domains. Plasma actuators based on the Magneto-Hydro-Dynamic (MHD) and Electro- Hydro-Dynamic (EHD) interactions are potentially able to suitably modify the fluid-dynamics characteristics around a flying body without utilizing moving parts. This could lead to the control of an aircraft with negligible response time, more reliability and improvements of the performance. In order to study the aforementioned interactions, a series of experiments and a wide number of diagnostic techniques have been utilized. The EHD interaction, realized by means of a Dielectric Barrier Discharge (DBD) actuator, and its impact on the boundary layer have been evaluated by means of two different experiments. In the first one a three phase multi-electrode flat panel actuator is used. Different external flow velocities (from 1 to 20m/s) and different values of the supplied voltage and frequency have been considered. Moreover a change of the phase sequence has been done to verify the influence of the electric field existing between successive phases. Measurements of the induced speed had shown the effect of the supply voltage and the frequency, and the phase order in the momentum transfer phenomenon. Gains in velocity, inside the boundary layer, of about 5m/s have been obtained. Spectroscopic measurements allowed to determine the rotational and the vibrational temperature of the plasma which lie in the range of 320 ÷ 440°K and of 3000 ÷ 3900°K respectively. A deviation from thermodynamic equilibrium had been found. The second EHD experiment is realized on a single electrode pair DBD actuator driven by nano-pulses superimposed to a DC or an AC bias. This new supply system separates the plasma formation mechanism from the acceleration action on the fluid, leading to an higher degree of the control of the process. Both the voltage and the frequency of the nano-pulses and the amplitude and the waveform of the bias have been varied during the experiment. Plasma jets and vortex behavior had been observed by means of fast Schlieren imaging. This allowed a deeper understanding of the EHD interaction process. A velocity increase in the boundary layer of about 2m/s had been measured. Thrust measurements have been performed by means of a scales and compared with experimental data reported in the literature. For similar voltage amplitudes thrust larger than those of the literature, had been observed. Surface charge measurements led to realize a modified DBD actuator able to obtain similar performances when compared with that of other experiments. However in this case a DC bias replacing the AC bias had been used. MHD interaction experiments had been carried out in a hypersonic wind tunnel in argon with a flow of Mach 6. Before the MHD experiments a thermal, fluid-dynamic and plasma characterization of the hypersonic argon plasma flow have been done. The electron temperature and the electron number density had been determined by means of emission spectroscopy and microwave absorption measurements. A deviation from thermodynamic equilibrium had been observed. The electron number density showed to be frozen at the stagnation region condition in the expansion through the nozzle. MHD experiments have been performed using two axial symmetric test bodies. Similar magnetic configurations were used. Permanent magnets inserted into the test body allowed to generate inside the plasma azimuthal currents around the conical shape of the body. These Faraday currents are responsible of the MHD body force which acts against the flow. The MHD interaction process has been observed by means of fast imaging, pressure and electrical measurements. Images showed bright rings due to the Faraday currents heating and exciting the plasma particles. Pressure measurements showed increases of the pressure in the regions where the MHD interaction is large. The pressure is 10 to 15% larger than when the MHD interaction process is silent. Finally by means of electrostatic probes mounted flush on the test body lateral surface Hall fields of about 500V/m had been measured. These results have been used for the validation of a numerical MHD code.

Mechanisms Contributing to Tyrosin Kinase Inhibitor Resistance in GISTs: Toward a Personalized Therapy

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors in the gastrointestinal tract. This work considers the pharmacological response in GIST patients treated with imatinib by two different angles: the genetic and somatic point of view. We analyzed polymorphisms influence on treatment outcome, keeping in consideration SNPs in genes involved in drug transport and folate pathway. Naturally, all these intriguing results cannot be considered as the only main mechanism in imatinib response. GIST mainly depends by oncogenic gain of function mutations in tyrosin kinase receptor genes, KIT or PDGFRA, and the mutational status of these two genes or acquisition of secondary mutation is considered the main player in GIST development and progression. To this purpose we analyzed the secondary mutations to better understand how these are involved in imatinib resistance. In our analysis we considered both imatinib and the second line treatment, sunitinib, in a subset of progressive patients. KIT/PDGFRA mutation analysis is an important tool for physicians, as specific mutations may guide therapeutic choices. Currently, the only adaptations in treatment strategy include imatinib starting dose of 800 mg/daily in KIT exon-9-mutated GISTs. In the attempt to individualize treatment, genetic polymorphisms represent a novelty in the definition of biomarkers of imatinib response in addition to the use of tumor genotype. Accumulating data indicate a contributing role of pharmacokinetics in imatinib efficacy, as well as initial response, time to progression and acquired resistance. At the same time it is becoming evident that genetic host factors may contribute to the observed pharmacokinetic inter-patient variability. Genetic polymorphisms in transporters and metabolism may affect the activity or stability of the encoded enzymes. Thus, integrating pharmacogenetic data of imatinib transporters and metabolizing genes, whose interplay has yet to be fully unraveled, has the potential to provide further insight into imatinib response/resistance mechanisms.

Real-time anomaly detection of gamma-ray bursts for the Cherenkov Telescope Array using deep learning

The Cherenkov Telescope Array (CTA) will be the next-generation ground-based observatory to study the universe in the very-high-energy domain. The observatory will rely on a Science Alert Generation (SAG) system to analyze the real-time data from the telescopes and generate science alerts. The SAG system will play a crucial role in the search and follow-up of transients from external alerts, enabling multi-wavelength and multi-messenger collaborations. It will maximize the potential for the detection of the rarest phenomena, such as gamma-ray bursts (GRBs), which are the science case for this study. This study presents an anomaly detection method based on deep learning for detecting gamma-ray burst events in real-time. The performance of the proposed method is evaluated and compared against the Li&Ma standard technique in two use cases of serendipitous discoveries and follow-up observations, using short exposure times. The method shows promising results in detecting GRBs and is flexible enough to allow real-time search for transient events on multiple time scales. The method does not assume background nor source models and doe not require a minimum number of photon counts to perform analysis, making it well-suited for real-time analysis. Future improvements involve further tests, relaxing some of the assumptions made in this study as well as post-trials correction of the detection significance. Moreover, the ability to detect other transient classes in different scenarios must be investigated for completeness. The system can be integrated within the SAG system of CTA and deployed on the onsite computing clusters. This would provide valuable insights into the method's performance in a real-world setting and be another valuable tool for discovering new transient events in real-time. Overall, this study makes a significant contribution to the field of astrophysics by demonstrating the effectiveness of deep learning-based anomaly detection techniques for real-time source detection in gamma-ray astronomy.