New markov chain based methods for single and cross-domain sentiment classification

Nowadays communication is switching from a centralized scenario, where communication media like newspapers, radio, TV programs produce information and people are just consumers, to a completely different decentralized scenario, where everyone is potentially an information producer through the use of social networks, blogs, forums that allow a real-time worldwide information exchange. These new instruments, as a result of their widespread diffusion, have started playing an important socio-economic role. They are the most used communication media and, as a consequence, they constitute the main source of information enterprises, political parties and other organizations can rely on. Analyzing data stored in servers all over the world is feasible by means of Text Mining techniques like Sentiment Analysis, which aims to extract opinions from huge amount of unstructured texts. This could lead to determine, for instance, the user satisfaction degree about products, services, politicians and so on. In this context, this dissertation presents new Document Sentiment Classification methods based on the mathematical theory of Markov Chains. All these approaches bank on a Markov Chain based model, which is language independent and whose killing features are simplicity and generality, which make it interesting with respect to previous sophisticated techniques. Every discussed technique has been tested in both Single-Domain and Cross-Domain Sentiment Classification areas, comparing performance with those of other two previous works. The performed analysis shows that some of the examined algorithms produce results comparable with the best methods in literature, with reference to both single-domain and cross-domain tasks, in $2$-classes (i.e. positive and negative) Document Sentiment Classification. However, there is still room for improvement, because this work also shows the way to walk in order to enhance performance, that is, a good novel feature selection process would be enough to outperform the state of the art. Furthermore, since some of the proposed approaches show promising results in $2$-classes Single-Domain Sentiment Classification, another future work will regard validating these results also in tasks with more than $2$ classes.

Anisotropic seismic tomography with the reversible jump Markov chain Monte Carlo

The established isotropic tomographic models show the features of subduction zones in terms of seismic velocity anomalies, but they are generally subjected to the generation of artifacts due to the lack of anisotropy in forward modelling. There is evidence for the significant influence of seismic anisotropy in the mid-upper mantle, especially for boundary layers like subducting slabs. As consequence, in isotropic models artifacts may be misinterpreted as compositional or thermal heterogeneities. In this thesis project the application of a trans-dimensional Metropolis-Hastings method is investigated in the context of anisotropic seismic tomography. This choice arises as a response to the important limitations introduced by traditional inversion methods which use iterative procedures of optimization of a function object of the inversion. On the basis of a first implementation of the Bayesian sampling algorithm, the code is tested with some cartesian two-dimensional models, and then extended to polar coordinates and dimensions typical of subduction zones, the main focus proposed for this method. Synthetic experiments with increasing complexity are realized to test the performance of the method and the precautions for multiple contexts, taking into account also the possibility to apply seismic ray-tracing iteratively. The code developed is tested mainly for 2D inversions, future extensions will allow the anisotropic inversion of seismological data to provide more realistic imaging of real subduction zones, less subjected to generation of artifacts.

Spectral distortions of the cosmic microwave background and small-scale gravitational waves as new windows on the Early Universe

In this thesis, we explore constraints which can be put on the primordial power spectrum of curvature perturbations beyond the scales probed by anisotropies of the cosmic microwave background and galaxy surveys. We exploit present and future measurements of CMB spectral distortions, and their synergy with CMB anisotropies, as well existing and future upper limits on the stochastic background of gravitational waves. We derive for the first time phenomenological templates that fit small-scale bumps in the primordial power spectrum generated in multi-field models of inflation. By using such templates, we study for the first time imprints of primordial peaks on anisotropies and spectral distortions of the cosmic microwave background and we investigate their contribution to the stochastic background of gravitational waves. Through a Monte Carlo Markov Chain analysis we infer for the first time the constraints on the amplitude, the width and the location of such bumps using Planck and FIRAS data. We also forecast how a future spectrometer like PIXIE could improve FIRAS boundaries. The results derived in this thesis have implications for the possibility of primordial black holes from inflation.

Evolution of the structural and fracture design of the ISS payloads due to the new launchers, specifically applied to Biolab IEC-2 projects

The relatively young discipline of astronautics represents one of the scientifically most fascinating and technologically advanced achievements of our time. The human exploration in space does not offer only extraordinary research possibilities but also demands high requirements from man and technology. The space environment provides a lot of attractive experimental tools towards the understanding of fundamental mechanism in natural sciences. It has been shown that especially reduced gravity and elevated radiation, two distinctive factors in space, influence the behavior of biological systems significantly. For this reason one of the key objectives on board of an earth orbiting laboratory is the research in the field of life sciences, covering the broad range from botany, human physiology and crew health up to biotechnology. The Columbus Module is the only European low gravity platform that allows researchers to perform ambitious experiments in a continuous time frame up to several months. Biolab is part of the initial outfitting of the Columbus Laboratory; it is a multi-user facility supporting research in the field of biology, e.g. effect of microgravity and space radiation on cell cultures, micro-organisms, small plants and small invertebrates. The Biolab IEC are projects designed to work in the automatic part of Biolab. In this moment in the TO-53 department of Airbus Defence & Space (formerly Astrium) there are two experiments that are in phase C/D of the development and they are the subject of this thesis: CELLRAD and CYTOSKELETON. They will be launched in soft configuration, that means packed inside a block of foam that has the task to reduce the launch loads on the payload. Until 10 years ago the payloads which were launched in soft configuration were supposed to be structural safe by themselves and a specific structural analysis could be waived on them; with the opening of the launchers market to private companies (that are not under the direct control of the international space agencies), the requirements on the verifications of payloads are changed and they have become much more conservative. In 2012 a new random environment has been introduced due to the new Space-X launch specification that results to be particularly challenging for the soft launched payloads. The last ESA specification requires to perform structural analysis on the payload for combined loads (random vibration, quasi-steady acceleration and pressure). The aim of this thesis is to create FEM models able to reproduce the launch configuration and to verify that all the margins of safety are positive and to show how they change because of the new Space-X random environment. In case the results are negative, improved design solution are implemented. Based on the FEM result a study of the joins has been carried out and, when needed, a crack growth analysis has been performed.

Stationary states in random walks on networks

In this thesis we dealt with the problem of describing a transportation network in which the objects in movement were subject to both finite transportation capacity and finite accomodation capacity. The movements across such a system are realistically of a simultaneous nature which poses some challenges when formulating a mathematical description. We tried to derive such a general modellization from one posed on a simplified problem based on asyncronicity in particle transitions. We did so considering one-step processes based on the assumption that the system could be describable through discrete time Markov processes with finite state space. After describing the pre-established dynamics in terms of master equations we determined stationary states for the considered processes. Numerical simulations then led to the conclusion that a general system naturally evolves toward a congestion state when its particle transition simultaneously and we consider one single constraint in the form of network node capacity. Moreover the congested nodes of a system tend to be located in adjacent spots in the network, thus forming local clusters of congested nodes.