Construction and test of a cosmic ray telescope based on CMS Drift Tube chambers and data acquisition prototypes of the Phase-2 upgrade

In this thesis work, a cosmic-ray telescope was set up in the INFN laboratories in Bologna using smaller size replicas of CMS Drift Tubes chambers, called MiniDTs, to test and develop new electronics for the CMS Phase-2 upgrade. The MiniDTs were assembled in INFN National Laboratory in Legnaro, Italy. Scintillator tiles complete the telescope, providing a signal independent of the MiniDTs for offline analysis. The telescope readout is a test system for the CMS Phase-2 upgrade data acquisition design. The readout is based on the early prototype of a radiation-hard FPGA-based board developed for the High Luminosity LHC CMS upgrade, called On Board electronics for Drift Tubes. Once the set-up was operational, we developed an online monitor to display in real-time the most important observables to check the quality of the data acquisition. We performed an offline analysis of the collected data using a custom version of CMS software tools, which allowed us to estimate the time pedestal and drift velocity in each chamber, evaluate the efficiency of the different DT cells, and measure the space and time resolution of the telescope system.

Effects of nutrients and sea-level rise on salt marshes of different soil types

The present thesis analyses the effects of the enrichment of the soil with fertilizer and sea level rise (SLR) on salt marsh vegetation. We simulated different conditions of the salt marshes under current and projected sea level rise. These habitats are colonised by various types of plants, we focused on species belonging to the genus Spartina. This plant seems to be particularly sensitive to eutrophication due to human activities, as experiments have documented a loss of habitat associated with altered nutrient conditions. We manipulated experimentally the types of sediment, the concentration of nutrients and sea level rise. We wanted to test whether eutrophication can affect the aboveground/belowground growth of the vegetation, and indirectly the erosion of the sediment, with potentially interacting effects with soil type and SLR in affecting the loss of the habitats and species. The study lasted from July to October. The data were analysed using Permanova. The results showed that the plants were placed in growth spiked sediment different from those raised in the untreated sediment. Furthermore, the sediment underwent a level of erosion differently depending on the growth of plants and the condition they were in the pots, current or future sea levers. These results suggest that the total salt marsh habitat is very sensitive to changes caused by human activities, and that excessive eutrophication, combined with SLR will likely facilitate further loss of salt marsh vegetation.

"Offshore tower or platform foundations: numerical analysis of a laterally loaded single pile or pile group in soft clay and analysis of actions on a jacket structure"

Laterally loaded piles are a typical situation for a large number of cases in which deep foundations are used. Dissertation herein reported, is a focus upon the numerical simulation of laterally loaded piles. In the first chapter the best model settings are largely discussed, so a clear idea about the effects of interface adoption, model dimension, refinement cluster and mesh coarseness is reached. At a second stage, there are three distinct parametric analyses, in which the model response sensibility is studied for variation of interface reduction factor, Eps50 and tensile cut-off. In addition, the adoption of an advanced soil model is analysed (NGI-ADP). This was done in order to use the complex behaviour (different undrained shear strengths are involved) that governs the resisting process of clay under short time static loads. Once set a definitive model, a series of analyses has been carried out with the objective of defining the resistance-deflection (P-y) curves for Plaxis3D (2013) data. Major results of a large number of comparisons made with curves from API (America Petroleum Institute) recommendation are that the empirical curves have almost the same ultimate resistance but a bigger initial stiffness. In the second part of the thesis a simplified structural preliminary design of a jacket structure has been carried out to evaluate the environmental forces that act on it and on its piles foundation. Finally, pile lateral response is studied using the empirical curves.

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.