Add acknowledgments

Signed-off-by: Riccardo Finotello <riccardo.finotello@gmail.com>

README.md

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 # Ph.D. Thesis
 
-This project contains the LaTeX code of my Ph.D. defence thesis.
+This project contains the LaTeX code of the thesis of my Ph.D. defence.
 
 The LaTeX file compiles using PDFLaTeX as backend.
+Make sure to download all the style files (`debug.sty` and `sciencestuff.sty`) and the class `thesis.cls`.
+
+## Abstract
+
+We present topics of (semi-)phenomenological relevance in string theory ranging from particle physics amplitudes and Big Bang-like singularities to the study of state-of-the-art deep learning techniques for string compactifications based on recent advancements in artificial intelligence.
+
+We show the computation of the leading contribution to amplitudes in the presence of non Abelian twist fields in intersecting D-branes scenarios in non factorised tori.
+This is a generalisation to the current literature which mainly covers factorised internal spaces.
+We also study a new method to compute amplitudes in the presence of an arbitrary number of spin fields introducing point-like defects on the string worldsheet.
+The procedure can then be treated as an alternative computation with respect to bosonization and approaches based on the Reggeon vertex.
+We then present an analysis of Big Bang-like cosmological divergences in string theory on time-dependent orbifolds.
+We show that divergences are not due to gravitational feedback but to the lack of an underlying effective field theory.
+We also introduce a new orbifold structure capable of fixing the issue and reinstate a distributional interpretation to field theory amplitudes.
+
+We finally present a new artificial intelligence approach to algebraic geometry and string compactifications.
+We compute the Hodge numbers of Complete Intersection Calabi--Yau 3-folds using deep learning techniques based on computer vision and object recognition techniques.
+We also include a methodological study of machine learning applied to data in string theory: as in most applications machine learning almost never relies on the blind application of algorithms to the data but it requires a careful exploratory analysis and feature engineering.
+We thus show how such an approach can help in improving results by processing the data before utilising them.
+We then show that deep learning the configuration matrix of the manifolds reaches the highest accuracy in the task with smaller networks, less parameters and less data.
+This is a novel approach to the task: differently from previous attempts we focus on using convolutional neural networks capable of reaching higher accuracy on the predictions and ensuring phenomenological relevance to results.
+In fact parameter sharing and concurrent scans of the configuration matrix retain better generalisation properties and adapt better to the task than fully connected networks.

sec/acknowledgments.tex

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+This manuscript signals the end of a fascinating journey I went through together with great people who supported me when I was at my worst and built in me the motivation to keep going.
+They were capable of giving me consciousness of what I was doing and guided me to this very moment.
+Words will certainly not render justice to their role, but I shall try nonetheless.
+
+An important mention goes to my advisor Igor who taught me most of what I know now: with the trust he put in me, he made me feel what research is like, and introduced me to the dedication and determination needed to succeed.
+I most certainly would not be here without him, and this I shall always remember.
+I cannot help expressing the deepest gratitude to Harold: even though he was not my advisor, he trusted me, he guided me towards the best of all the worlds, and he supported me in every (im)possible way to the point words will never be enough.
+He is surely a brilliant scientist and a great collaborator, but if he is not first of all a friend then I do not know who can be.
+I also wish to thank Marco and Carlo for all the support they gave me whenever they could and the advice they provided at all times.
+
+Life as a Ph.D.\ student would have been incredibly tough were it not for the great people who were with me all the time.
+I was not yet officially inside my Ph.D.\ programme when Alberto welcomed me to the office and showed me how to survive as a student and get the most from the experience as a person.
+Even though he will not admit it, Riccardo helped me to discover dedication in the darkest moments, while contextually Giovanni taught me how to bring light and humanity in those, in the face of adversities.
+In all this, with his silence \emph{à la Piemontese}, Francesco represented the needed balance.
+Life in the office would not have been the same were it not for the people who supported me in this adventure, from the overly calm Chiara to the extremely excitable Kostas, to colleagues such as Andrea with whom I was honoured to collaborate directly.
+Students and researchers met during these three years were absolutely great.
+Confrontation with them helped me discover a vast number of interconnected topics and fascinating subtleties, and it allowed me to meet and get to know fellow students such as Paolo, trusted and worthy accomplice in many adventures.
+
+Even with all the help of an incredibly large number of great people, I do not think I could be here writing these without the support of Alessandra and my family.
+They had the most difficult job of all: they had to put up with me at my worst and to help me get through the difficulties.
+My parents and my brother showed me all the affection they possibly could, they provided me with everything I needed, and they unconditionally made sure I could be as happy as possible to succeed in what I really wanted to do.
+Not only did they succeed, they were absolutely amazing.
+Alessandra is my hero: though she went through the toughness of her Ph.D.\ programme as well, she was able to be there for me every time I needed confrontation or consolation, every time I wanted to share good or bad news, every single time I was happy for something or in distress.
+Alessandra is the strongest person I know.
+There are definitely no words to express how grateful I am to all of them, but I hope I can repay them in time as I could not be more joyfully blessed than I am right now.
+
+\noindent\emph{To all of you: thank you.}
+
+
+% vim: ft=tex

thesis.cls

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