An unmistakable trend in embedded systems is the growth of soft real-time computing. A soft real-time application is one for which deadlines can occasionally be missed, but the probability of this event has to be controllable and predictable. This work is aimed to close the gap in the research of stochastic real-time analysis related to resource reservation scheduling algorithms. This dissertation attempts to: 1. give a quick overview of classic real-time analysis 2. analyze the problems related to use the well-known techniques in the context of soft real-time applications: • overvalue the assignation of parameters as in hard real- time systems based on worst case execution times • time and memory complexity using the known theoretical stochastic analysis 3. propose solutions able to overcome the limitation showed in point 2 4. show some specific examples (theoretical and practical) in which resource reservation lead to advantages. The novel contributions of this thesis are: • a new bound to predict the probability of a deadline misses in a resource reservation systems • a very efficient numeric solution for matrix generated with well-know abstraction models of reservation based on Quasi Birth Death Markov Process • an analytical solution, with some conservative approximations, for the same models. • a new model for specific applications, like interrupts. • experiments using resource reservation in different contexts The thesis is evolved following two different approaches: 1. the first based on the exact model of reservation, and the contributions is: • define a new pessimistic bound, efficient in term of computation, able to overcome the problem of complete knowledge of the computation time. The solution is an approximation of the real solution of the model. 2. the second based on an approximation model in which the novel contributions are: • presents an exact and numeric efficient solution for the model based on Quasi Birth and Death Markov Process • introduces an approximate analytical solution which can be computed with no complexity and which is reversible These techniques are applicable since the minimum interarrival of a request is greater than a server period. Unfortunately exists situations in which this assumption is not feasible. An important example is using resource reservation to scheduling interrupts. In order to consider also this situation, another important novel result of this thesis is: • to introduce a new model for scheduling interrupts In addition, some practical examples of using resource reservation are presented.

Many real applications consume data that is intrinsically uncertain and error-prone. An uncertain data series is a series whose point values are uncertain. An uncertain data stream is a data stream whose tuples are existentially uncertain and/or have an uncertain value. Typical sources of uncertainty in data series and data streams include sensor data, data synopses, privacy-preserving transformations and forecasting models. In this thesis, we focus on the following three problems: (1) the formulation and the evaluation of similarity search queries in uncertain data series; (2) the evaluation of nearest neighbor search queries in uncertain data series; (3) the adaptation of sliding windows in uncertain data stream processing to accommodate existential and value uncertainty. We demonstrate experimentally that the correlation among neighboring time-stamps in data series can be leveraged to increase the accuracy of the results. We further show that the "possible world" semantics can be used as underlying uncertainty model to formulate nearest neighbor queries that can be evaluated efficiently. Finally, we discuss the relation between existential and value uncertainty in data stream applications, and verify experimentally our proposal of uncertain sliding windows.

Cílem této diplomové práce je praktické i teoretické seznámení uživatelů neparametrických metod s několika vybranými statistickými programy, v nichž je možné neparametrické analýzy provádět. Nedílnou součástí cíle je i porovnání těchto programů. V teoretické části jsou stručně popsány základy testování statistických hypotéz a teorie neparametrických testů. V praktické části jsou prozkoumány programy NCSS 2007, Statistica 7, SPSS 15, Systat 12, Stagraphics Centurion XV, Minitab 15, S-Plus 6.2, SAS 9.1 a StatXact 7. U každého software jsou popsány neparametrické testy, které obsahuje, a zjednodušeně i vkládání dat a výstupy. Nakonec jsou vybrané programy porovnány z několika hledisek. Všechny vybrané programy obsahují značné množství neparametrických testů a liší se většinou jen v detailech. Pro většinu analýz není potřeba hledat speciální programy a dají se použít všechny vybrané, pouze pro porovnávání rozptylů je nutné použít buď SAS nebo StatXact, protože v jiných programech ve výběru tyto testy nenalezneme.

Which event study methods are best in non-U.S. multi-country samples? Nonparametric tests, especially the rank and generalized sign, are better specified and more powerful than common parametric tests, especially in multi-day windows. The generalized sign test is the best statistic but must be applied to buy-and-hold abnormal returns for correct specification. Market-adjusted and market-model methods with local market indexes, without conversion to a common currency, work well. The results are robust to limiting the samples to situations expected to be problematic for test specification or power. Applying the tests that perform best in simulation to merger announcements produces reasonable results.

Evaluation of automatic speech recognition (ASR) systems is difficult and costly, since it requires manual transcriptions. This evaluation is usually done by computing word error rate (WER) that is the most popular metric in ASR community. Such computation is doable only if the manual references are available, whereas in the real-life applications, it is a too rigid condition. A reference-free metric to evaluate the ASR performance is \textit{confidence measure} which is provided by the ASR decoder. However, the confidence measure is not always available, especially in commercial ASR usages. Even if available, this measure is usually biased towards the decoder. From this perspective, the confidence measure is not suitable for comparison purposes, for example between two ASR systems. These issues motivate the necessity of an automatic quality estimation system for ASR outputs. This thesis explores ASR quality estimation (ASR QE) from different perspectives including: feature engineering, learning algorithms and applications. From feature engineering perspective, a wide range of features extractable from input signal and output transcription are studied. These features represent the quality of the recognition from different aspects and they are divided into four groups: signal, textual, hybrid and word-based features. From learning point of view, we address two main approaches: i) QE via regression, suitable for single hypothesis scenario; ii) QE via machine-learned ranking (MLR), suitable for multiple hypotheses scenario. In the former, a regression model is used to predict the WER score of each single hypothesis that is created through a single automatic transcription channel. In the latter, a ranking model is used to predict the order of multiple hypotheses with respect to their quality. Multiple hypotheses are mainly generated by several ASR systems or several recording microphones. From application point of view, we introduce two applications in which ASR QE makes salient improvement in terms of WER: i) QE-informed data selection for acoustic model adaptation; ii) QE-informed system combination. In the former, we exploit single hypothesis ASR QE methods in order to select the best adaptation data for upgrading the acoustic model. In the latter, we exploit multiple hypotheses ASR QE methods to rank and combine the automatic transcriptions in a supervised manner. The experiments are mostly conducted on CHiME-3 English dataset. CHiME-3 consists of Wall Street Journal utterances, recorded by multiple far distant microphones in noisy environments. The results show that QE-informed acoustic model adaptation leads to 1.8\% absolute WER reduction and QE-informed system combination leads to 1.7% absolute WER reduction in CHiME-3 task. The outcomes of this thesis are packed in the frame of an open source toolkit named TranscRater -transcription rating toolkit- (https://github.com/hlt-mt/TranscRater) which has been developed based on the aforementioned studies. TranscRater can be used to extract informative features, train the QE models and predict the quality of the reference-less recognitions in a variety of ASR tasks.

This dissertation is divided into three different parts. In the first part we analyse collected data on the occurrence of influenza-like illness (ILI) symptoms regarding the 2009 influenza A/H1N1 virus pandemic in two primary schools of Trento, Italy. These data were used to calibrate a discrete-time SIR model, which was designed to estimate the probabilities of influenza transmission within the classes, grades and schools using Markov Chain Monte Carlo (MCMC) methods. We found that the virus was mainly transmitted within class, with lower levels of transmission between students in the same grade and even lower, though not significantly so, among different grades within the schools. We estimated median values of R0 from the epidemic curves in the two schools of 1.16 and 1.40; on the other hand, we estimated the average number of students infected by the first school case to be 0.85 and 1.09 in the two schools. This discrepancy suggests that household and community transmission played an important role in sustaining the school epidemics. The high probability of infection between students in the same class confirms that targeting within-class transmission is key to controlling the spread of influenza in school settings and, as a consequence, in the general population. In the second part, by starting from a basic host-parasitoid model, we study the dynamics of a 2 hosts-1 parasitoid model assuming, for the sake of simplicity, that larval stages have a fixed duration. If each host is subjected to density-dependent mortality in its larval stage, we obtain explicit conditions for coexistence of both hosts, as long as each 1 host-parasitoid system would tend to an equilibrium point. Otherwise, if mortality is density-independent, under the same conditions host coexistence is impossible. On the other hand, if at least one of the 1 host-parasitoid systems has an oscillatory dynamics (which happens under some parameter values), we found, through numerical bifurcation, that coexistence is favoured. It is also possible that coexistence between the two hosts occurs even in the case without density-dependence. Analysis of this case has been based on methods of approximation of the dominant characteristic multipliers of the monodromy operator using a recent method introduced by Breda et al. Models of this type may be relevant for modelling control strategies for Drosophila suzukii, a recently introduced fruit fly that caused severe production losses, based on native parasitoids of indigenous fruit flies. In the third part, we present a starting point to analyse raw data collected by Stacconi et al. in the province of Trento, Italy. We present an extensions of the model presented in Part 2 where we have two hosts and two parasitoids. Since its analysis is complicated, we begin with a simpler one host-one parasitoid model to better understand the possible impact of parasitoids on a host population. We start by considering that the host population is at an equilibrium without parasitoids, which are then introduced as different percentages of initial adult hosts. We compare the times needed by parasitoids to halve host pupae and we found that the best percentage choice is 10%. Thus we decide to fix this percentage of parasitoid introduction and analyse what happens if parasitoids are introduced when the host population is not at equilibrium both by introducing always the same percentage or the same amount of parasitoids. In this case, even if the attack rate is at 1/10 of its maximum value, parasitoids would have a strong effect on host population, shifting it to an oscillatory regime. However we found that this effect would require more than 100 days but we also found that it can faster if parasitoids are introduced before the host population has reached the equilibrium without parasitoids. Thus there could be possible releases when host population is low. Last we investigate also what happens if in nature mortality rates of these species increase and we found that there is not such a big difference respect to the results obtained using laboratory data.

Firstly, I proposed a very simple SIS/SIR model for a general vector-borne disease transmission considering constant population sizes over the season, where contact between the host and the vector responsible of the transmission is assumed to occur only during the summer of each year. I discussed two different types of threshold for pathogen persistence that I explicitly computed: a "short-term threshold" and a "long-term threshold". Later, I took into account the seasonality of the populations involved in the transmission. For a single season, the model consists of system of non linear differential equations considering the various stages of the infection transmission between the vector and the host population. Assuming the overwintering in the mosquito populations, I simulated the model for several years. Finally, I studied the spatial spread of a vector-borne disease throught an impusive reaction-diffusion model and I showed some simulations.

Grape (Vitis vinifera L.) is among the most cultivated plants in the world. Its origin traces back to the Neolithic era, when the first human communities started to domesticate wild Vitis sylvestris L. grapes to produce wines. Domestication modified Vitis vinifera to assume characteristics imparted from the humans, selecting desired traits (e.g. specific aromas), and excluding the undesired ones. This process made this species very different from all the other wild grape species existing around the world, including its progenitor, Vitis sylvestris. Metabolomics is a field of the sciences that comparatively studies the whole metabolite set of two (or more) groups of samples, to point out the chemical diversity and infer on the variability in the metabolic pathways between the groups. Crude metabolomics observation can be often used for hypotheses generation, which need to be confirmed by further experiments. In my case, starting from the grape metabolome project (Mattivi et al. unpublished data), I had the opportunity to put hands on a huge dataset built on the berries of over 100 Vitis vinifera grape varieties, tens of grape interspecific hybrids and few wild grape species analyzed per four years; all included in a single experiment. Starting from this data handling, I designed specific experiments to confirm the hypotheses generated from the observation of the data, to improve compound identification, to give statistical meaning to the differences, to localize the metabolites in the berries and extrapolate further information on the variability existing among the grape genus. The hypotheses formulated were two: 1) several glyco-conjugated volatiles can be detected, identified and quantified in untargeted reverses-phase liquid chromatography-mass spectrometry; 2) The chemical difference between Vitis vinifera and wild grape berries is wider than reported in literature. Furthermore, handling a huge dataset of chemical standards injected under the same conditions of the sample set, I also formulated a third hypothesis: 3) metabolites with similar chemical structures are more likely to generate similar signals in LC-MS, therefore the combined use of the signals can predict the more likely chemical structure of unknown markers. In the first study (chapter 5), the signals putatively corresponding to glycoconjugated volatiles have been first enclosed in a specific portion of the temporal and spectrometric space of the LC-HRMS chromatograms, then they have been subjected to MS/MS analysis and lastly their putative identity have been confirmed through peak intensity correlation between the signals measured in LC-HRMS and GC-MS. In the second study (chapter 6), a multivariate regression model has been built between LC-HRMS signals and the substructures composing the molecular structure of the compounds and its accuracy and efficacy in substructure prediction have been demonstrated. In the third study (chapter 7), I comparatively studied some wild grapes versus some Vitis vinifera varieties separating the basic components of the grape berry (skin, flesh and seeds), with the aim to identify all the detected metabolites that differentiate the two groups, which determine a difference in quality between the wild versus domesticated grapes, especially regarding wine production.

During the last decades public health policy makers have been increasingly turning to mathematical modeling to support their decisions. This trend has been calling for the introduction of a new class of models that not only are capable to explain qualitatively the dynamics of infectious diseases, but also have the capability to provide quantitatively reliable and accurate results. To this aim models are becoming more and more detailed and informed with data. However, there is still much to be done in order to capture the individual and population features that shape the spread of infectious diseases. This thesis addresses some issues in epidemiological modeling that warrant further investigation. In Chapter 1 we introduce an age-structured individual-based stochastic model of Varicella Zoster Virus (VZV) transmission, whose main novelty is the inclusion of realistic population dynamics over the last century. This chapter represents an attempt to answer the need pointed out by recent studies for a better understanding of the role of demographic processes in shaping the circulation of infectious diseases. In Chapter 2 we use the model for VZV transmission developed in Chapter 1 to evaluate the effectiveness of varicella and HZ vaccination programs in Italy. With a view to the support of public health decisions, the epidemiological model is coupled with a cost-effectiveness analysis. To the best of our knowledge, this work represents the first attempt to evaluate the post-vaccination trends in varicella and HZ, both from an epidemiological and economic perspective, in light of the underlying effect of demographic processes. Another novelty of this study is that we take into account the uncertainty regarding the mechanism of VZV reactivation, by comparing results obtained using two different modeling assumptions on exogenous boosting. In Chapter 3 we retrospectively analyze the spatiotemporal dynamics of the 2009 H1N1 influenza pandemic in England, by using a spatially-explicit model of influenza transmission, accounting for socio-demographic and disease natural history data. The aim of this work is to investigate whether the observed spatiotemporal dynamics of the epidemic was shaped by a spontaneous behavioral response to the pandemic threat. This chapter, represents an attempt to contribute to the challenge of understanding and quantifying the effect of human behavioral changes on the spread of epidemics. In Chapter 4 we investigate the current epidemiology of measles in Italy, by using a detailed computational model for measles transmission, informed with regional heterogeneities in the age-specific seroprevalence profiles. The analysis performed in this chapter tries to fill some of the existing gaps in the knowledge of the epidemiological features of vaccine preventable diseases in frameworks characterized by a low circulation of the virus.

The works presented in this thesis are very different one from the other but they all deal with the mathematical modelling of emerging infectious diseases which, beyond being the leitmotiv of this thesis, is an important research area in the field of epidemiology and public health. A minor but significant part of the thesis has a theoretical flavour. This part is dedicated to the mathematical analysis of the competition model between two HIV subtypes in presence of vaccination and cross-immunity proposed by Porco and Blower (1998). We find the sharp conditions under which vaccination leads to the coexistence of the strains and using arguments from bifurcation theory, draw conclusions on the equilibria stability and find that a rather unusual behaviour of histeresis-type might emerge after repeated variations of the vaccination rate within a certain range. The most of this thesis has been inspired by real outbreaks occurred in Italy over the last 10 years and is about the modelling of the 1999-2000 H7N1 avian influenza outbreak and of the 2009-2010 H1N1 pandemic influenza. From an applied perspective, parameter estimation is a key part of the modelling process and in this thesis statistical inference has been performed within both a classical framework (i.e. by maximum likelihood and least square methods) and a Bayesian setting (i.e. by Markov Chain Monte Carlo techniques). However, my contribution goes beyond the application of inferential techniques to specific case studies. The stochastic, spatially explicit, between-farm transmission model developed for the transmission of the H7N1 virus has indeed been used to simulate different control strategies and asses their relative effectiveness. The modelling framework presented here for the H1N1 pandemic in Italy constitutes a novel approach that can be applied to a variety of different infections detected by surveillance system in many countries. We have coupled a deterministic compartmental model with a statistical description of the reporting process and have taken into account for the presence of stochasticity in the surveillance system. We thus tackled some statistical challenging issues (such as the estimation of the fraction of H1N1 cases reporting influenza-like-illness symptoms) that had not been addressed before. Last, we apply different estimation methods usually adopted in epidemiology to real and simulated school outbreaks, in the attempt to explore the suitability of a specific individual-based model at reproducing empirically observed epidemics in specific social contexts.