Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

De-authoring or Authoring?: A plunge into the question of authorship and language in J.M. Coetzee's Foe

J. M. Coetzee's Foe is not only a post-colonial novel, but it is also a re-writing of a classic, and its main themes are language, authorship, power and identity. Moreover, Foe is narrated by a woman, while written by a male, Nobel prize winning South African author. The aim of my tesina is to focus on the question of authorship and the role of language in Foe. Without any claim to be exhaustive, in the first section I will examine some selected extracts of Coetzee's book, in order to provide an analysis of the novel. These quotations will mainly be its metalinguistic parts and will be analysed in the “theory” sections of my work, relying on literary theory and on previous works on the novel. Among others, I will cover themes such as the relationship between speech and writing, the connection between writing, history, and memory, the role of silence and alternative ways of communicating and the relationship between literary authority and truth. These arguments will be the foundation for my second section, in which I will attempt to shed a light on the importance of the novel from a linguistic point of view, but always keeping an eye on the implication that this has on authorship. While it is true that it is less politically-permeated than Coetzee's previous works, Foe is above all a “journey of discovery” in the world of language and authorship. In fact, it becomes a warning for any person immersed in the ocean of language since, while everyone naturally tends to trust speech and writing as the only medium through which one can get closer to the truth, authority never is a synonym of reliability, and language is a system of communication behind which structures of power, misconceptions, lies, and treacherous tides easily hide.

A new concept for a Sport-Coupè car developed with the Industrial Design Structure method (IDeS)

The aim of this study, conducted in collaboration with Lawrence Technological University in Detroit, is to create, through the method of the Industrial Design Structure (IDeS), a new concept for a sport-coupe car, based on a restyling of a retro model (Ford Mustang 1967). To date, vintage models of cars always arouse great interest both for the history behind them and for the classic and elegant style. Designing a model of a vehicle that can combine the charm of retro style with the innovation and comfort of modern cars would allow to meet the needs and desires of a large segment of the market that today is forced to choose between past and future. Thanks to a well-conceived concept car an automaker company is able to express its future policy, to make a statement of intent as, such a prototype, ticks all the boxes, from glamour and visual wow-factor to technical intrigue and design fascination. IDeS is an approach that makes use of many engineering tools to realize a study developed on several steps that must be meticulously organized and timed. With a deep analysis of the trends dominating the automotive industry it is possible to identify a series of product requirements using quality function deployment (QFD). The considerations from this first evaluation led to the definition of the technical specifications via benchmarking (BM) and top-flop analysis (TFA). Then, the structured methodology of stylistic design engineering (SDE) is applied through six phases: (1) stylistic trends analysis; (2) sketches; (3) 2D CAD drawings; (4) 3D CAD models; (5) virtual prototyping; (6) solid stylistic model. Finally, Developing the IDeS method up to the final stages of Prototypes and Testing you get a product as close as possible to the ideal vehicle conceptualized in the initial analysis.