Interpretazione per i media: il caso particolare delle interviste sul podio della Formula 1

This dissertation analyses the live simultaneous interpretation from English into Italian of six 2013 Formula 1 World Championship podium interviews and focuses on four main aspects: how the interpreter handled the décalage at the end of the interview and during the turn-taking; if he used any marker to indicate that he was starting to translate a new turn of the source text; what he did when overlapped speech in the source texts occurred; what happened when the Italian commentators talked during the interpreter’s translation. In the first chapter a description mainly of what a Formula 1 podium interview is and what an interpreter translates during the Formula 1 weekends is present. In the second chapter a literature review on media interpreting, with particular attention put on Straniero Sergio’s paper on translating Formula 1 press-conferences (2003), and turn-taking is provided. In the third chapter the methodology used to obtain and process the video and audio files of source and target texts and to transcribe them is described. We concentrated primarily on Thibault’s multimodal text transcription techniques (2000) and on how they were used and adapted to fit the purposes of this dissertation. In the fourth chapter the results obtained through the analysis of the source and target texts are shown and described, focusing only on the objectives of the dissertation, without aiming to provide a qualitative evaluation of the interpretations. In the fifth and last chapter the conclusions and some final remarks are made, based on the results obtained during the analysis and the hope for a more in depth knowledge of Italian Formula 1 interpreter’s working conditions.

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.

Catene di Markov nascoste e giochi stocastici.

In questa trattazione si introduce il concetto di catena di Markov nascosta: una coppia di processi stocastici (X,O), dove X è una catena di Markov non osservabile direttamente e O è il processo stocastico delle osservazioni, dipendente istante per istante solo dallo stato corrente della catena X. In prima istanza si illustrano i metodi per la soluzione di tre problemi classici, dato un modello di Markov nascosto e una sequenza di segnali osservati: valutare la probabilità della osservazione nel modello, trovare la sequenza nascosta di stati più probabile e aggiornare il modello per rendere più probabile l'osservazione. In secondo luogo si applica il modello ai giochi stocastici, nel caso in cui solo uno dei giocatori non è a conoscenza del gioco in ogni turno, ma può cercare di ottenere informazioni utili osservando le mosse dell'avversario informato. In particolare si cercano strategie basate sul concetto di catena di Markov nascoste e si analizzano i risultati ottenuti per valutare l'efficienza dell'approccio.

Catene di Markov reversibili e applicazioni al metodo Montecarlo basato sulle catene di Markov

Gli argomenti trattati in questa tesi sono le catene di Markov reversibili e alcune applicazioni al metodo Montecarlo basato sulle catene di Markov. Inizialmente vengono descritte alcune delle proprietà fondamentali delle catene di Markov e in particolare delle catene di Markov reversibili. In seguito viene descritto il metodo Montecarlo basato sulle catene di Markov, il quale attraverso la simulazione di catene di Markov cerca di stimare la distribuzione di una variabile casuale o di un vettore di variabili casuali con una certa distribuzione di probabilità. La parte finale è dedicata ad un esempio in cui utilizzando Matlab sono evidenziati alcuni aspetti studiati nel corso della tesi.

Caratterizzazione di sequenze di DNA mediante catene di Markov

Questa tesi si inserisce nell’ambito di studio dei modelli stocastici applicati alle sequenze di DNA. I random walk e le catene di Markov sono tra i processi aleatori che hanno trovato maggiore diffusione in ambito applicativo grazie alla loro capacità di cogliere le caratteristiche salienti di molti sistemi complessi, pur mantenendo semplice la descrizione di questi. Nello specifico, la trattazione si concentra sull’applicazione di questi nel contesto dell’analisi statistica delle sequenze genomiche. Il DNA può essere rappresentato in prima approssimazione da una sequenza di nucleotidi che risulta ben riprodotta dal modello a catena di Markov; ciò rappresenta il punto di partenza per andare a studiare le proprietà statistiche delle catene di DNA. Si approfondisce questo discorso andando ad analizzare uno studio che si ripropone di caratterizzare le sequenze di DNA tramite le distribuzioni delle distanze inter-dinucleotidiche. Se ne commentano i risultati, al fine di mostrare le potenzialità di questi modelli nel fare emergere caratteristiche rilevanti in altri ambiti, in questo caso quello biologico.

Formula Zero: Traduzione trilingue inglese-italiano-spagnolo di un policy statement sulla sicurezza stradale della Federazione Internazionale dell'Automobile (FIA)

The aim of this dissertation is to provide a trilingual translation from English into Italian and from Italian into Spanish of a policy statement from the Fédération Internationale de l’Automobile (FIA) regarding road safety. The document, named “Formula Zero: a strategy for reducing fatalities and injuries on track and road”, was published in June 2000 and involves an approach about road safety inspired by another approach introduced in Sweden called ‘Vision Zero’. This work consists of six sections. The first chapter introduces the main purposes and activities of the Federation, as well as the institutions related to it and Vision Zero. The second chapter presents the main lexical, morphosyntactic and stylistic features of the institutional texts and special languages. In particular, the text contains technical nomenclature of transports and elements of sport language, especially regarding motor sport and Formula One. In the third chapter, the methodology is explained, with all the resources used during the preliminary phase and the translation, including corpora, glossaries, expert consultancy and specialised sites. The fourth chapter focuses on the morphosyntactic and terminology features contained in the text, while the fifth chapter presents the source text and the target texts. The final chapter deals with all the translation strategies that are applied, alongside with all the challenging elements detected. Therefore, the dissertation concludes with some theoretical and practical considerations about the role of inverse translation and English as Lingua Franca (ELF), by comparing the text translated into Spanish to the original in English, using Italian as a lingua franca.

Formula integrale di Poisson

Il tema centrale di questa tesi è lo studio del problema di Dirichlet per il Laplaciano in R^2 usando le serie di Fourier. Il problema di Dirichlet per il Laplaciano consiste nel determinare una funzione f armonica e regolare in un dominio limitato D quando sono noti i valori che f assume sul suo bordo. Ammette una sola soluzione, ma non esistono criteri generali per ricavarla. In questa tesi si mostra come la formula integrale di Poisson, sotto determinate condizioni, risolva il problema di Dirichlet in R^2 e in R^n.

Atlas-Based Segmentation of Brain Tumor Images Using a Markov Random Field-Based Tumor Growth Model and Non-Rigid Registration

We propose a new and clinically oriented approach to perform atlas-based segmentation of brain tumor images. A mesh-free method is used to model tumor-induced soft tissue deformations in a healthy brain atlas image with subsequent registration of the modified atlas to a pathologic patient image. The atlas is seeded with a tumor position prior and tumor growth simulating the tumor mass effect is performed with the aim of improving the registration accuracy in case of patients with space-occupying lesions. We perform tests on 2D axial slices of five different patient data sets and show that the approach gives good results for the segmentation of white matter, grey matter, cerebrospinal fluid and the tumor.

Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model

We present an automatic method to segment brain tissues from volumetric MRI brain tumor images. The method is based on non-rigid registration of an average atlas in combination with a biomechanically justified tumor growth model to simulate soft-tissue deformations caused by the tumor mass-effect. The tumor growth model, which is formulated as a mesh-free Markov Random Field energy minimization problem, ensures correspondence between the atlas and the patient image, prior to the registration step. The method is non-parametric, simple and fast compared to other approaches while maintaining similar accuracy. It has been evaluated qualitatively and quantitatively with promising results on eight datasets comprising simulated images and real patient data.

Computation of discrete cosine transform using Clenshaw's recurence formula

Clenshaw’s recurrenee formula is used to derive recursive algorithms for the discrete cosine transform @CT) and the inverse discrete cosine transform (IDCT). The recursive DCT algorithm presented here requires one fewer delay element per coefficient and one fewer multiply operation per coeflident compared with two recently proposed methods. Clenshaw’s recurrence formula provides a unified development for the recursive DCT and IDCT algorithms. The M v e al gorithms apply to arbitrary lengtb algorithms and are appropriate for VLSI implementation.