Genetic characterisation and global comparison of ancient Mediterranean Great White Sharks population (Carcharodon carcharias, L.1758)

This study poses as its objective the genetic characterization of the ancient population of the Great White shark, Carcharodon carcharias, L.1758, present in the Mediterranean Sea. Using historical evidence, for the most part buccal arches but also whole, stuffed examples from various national museums, research institutes and private collections, a dataset of 18 examples coming from the Mediterranean Sea has been created, in order to increase the informations regarding this species in the Mediterranean. The importance of the Mediterranean provenance derives from the fact that a genetic characterization of this species' population does not exist, and this creates gaps in the knowledge of this species in the Mediterranean. The genetic characterization of the individuals will initially take place by the extraction of the ancient DNA and the analysis of the variations in the sequence markers of the mitochondrial DNA. This approach has allowed the genetic comparison between ancient populations of the Mediterranean and contemporary populations of the same geographical area. In addition, the genetic characterization of the population of white sharks of the Mediterranean, has allowed a genetic comparison with populations from global "hot spots", using published sequences in online databases (NCBI, GenBank). Analyzing the variability of the dataset, both in terms space and time, I assessed the evolutionary relationships of the Mediterranean population of Great Whites with the global populations (Australia/New Zealand, South Africa, Pacific USA, West Atlantic), and the temporal trend of the Mediterranean population variability. This method based on the sequencing of two portions of mitochondrial DNA genes, markers showed us how the population of Great White Sharks in the Mediterranean, is genetically more similar to the populations of the Australia Pacific ocean, American Pacific Ocean, rather than the population of South Africa, and showing also how the population of South Africa is abnormally distant from all other clusters. Interestingly, these results are inconsistent with the results from tagging of this species. In addition, there is evidence of differences between the ancient population of the Mediterranean with the modern one. This differentiation between the ancient and modern population of white shark can be the result of events impacting on this species occurred over the last two centuries.

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.

An inter-comparison between a VIS/IR and a MW satellite-based methods for cloud detection and classification

This thesis is aimed to assess similarities and mismatches between the outputs from two independent methods for the cloud cover quantification and classification based on quite different physical basis. One of them is the SAFNWC software package designed to process radiance data acquired by the SEVIRI sensor in the VIS/IR. The other is the MWCC algorithm, which uses the brightness temperatures acquired by the AMSU-B and MHS sensors in their channels centered in the MW water vapour absorption band. At a first stage their cloud detection capability has been tested, by comparing the Cloud Masks they produced. These showed a good agreement between two methods, although some critical situations stand out. The MWCC, in effect, fails to reveal clouds which according to SAFNWC are fractional, cirrus, very low and high opaque clouds. In the second stage of the inter-comparison the pixels classified as cloudy according to both softwares have been. The overall observed tendency of the MWCC method, is an overestimation of the lower cloud classes. Viceversa, the more the cloud top height grows up, the more the MWCC not reveal a certain cloud portion, rather detected by means of the SAFNWC tool. This is what also emerges from a series of tests carried out by using the cloud top height information in order to evaluate the height ranges in which each MWCC category is defined. Therefore, although the involved methods intend to provide the same kind of information, in reality they return quite different details on the same atmospheric column. The SAFNWC retrieval being very sensitive to the top temperature of a cloud, brings the actual level reached by this. The MWCC, by exploiting the capability of the microwaves, is able to give an information about the levels that are located more deeply within the atmospheric column.