Large-scale Network Analysis on Distributed Architectures

Questa dissertazione esamina le sfide e i limiti che gli algoritmi di analisi di grafi incontrano in architetture distribuite costituite da personal computer. In particolare, analizza il comportamento dell'algoritmo del PageRank così come implementato in una popolare libreria C++ di analisi di grafi distribuiti, la Parallel Boost Graph Library (Parallel BGL). I risultati qui presentati mostrano che il modello di programmazione parallela Bulk Synchronous Parallel è inadatto all'implementazione efficiente del PageRank su cluster costituiti da personal computer. L'implementazione analizzata ha infatti evidenziato una scalabilità negativa, il tempo di esecuzione dell'algoritmo aumenta linearmente in funzione del numero di processori. Questi risultati sono stati ottenuti lanciando l'algoritmo del PageRank della Parallel BGL su un cluster di 43 PC dual-core con 2GB di RAM l'uno, usando diversi grafi scelti in modo da facilitare l'identificazione delle variabili che influenzano la scalabilità. Grafi rappresentanti modelli diversi hanno dato risultati differenti, mostrando che c'è una relazione tra il coefficiente di clustering e l'inclinazione della retta che rappresenta il tempo in funzione del numero di processori. Ad esempio, i grafi Erdős–Rényi, aventi un basso coefficiente di clustering, hanno rappresentato il caso peggiore nei test del PageRank, mentre i grafi Small-World, aventi un alto coefficiente di clustering, hanno rappresentato il caso migliore. Anche le dimensioni del grafo hanno mostrato un'influenza sul tempo di esecuzione particolarmente interessante. Infatti, si è mostrato che la relazione tra il numero di nodi e il numero di archi determina il tempo totale.

High precision radial velocities with giano spectra

Radial velocities measured from near-infrared (NIR) spectra are a potential tool to search for extrasolar planets around cool stars. High resolution infrared spectrographs now available reach the high precision of visible instruments, with a constant improvement over time. GIANO is an infrared echelle spectrograph and it is a powerful tool to provide high resolution spectra for accurate radial velocity measurements of exo-planets and for chemical and dynamical studies of stellar or extragalactic objects. No other IR instruments have the GIANO's capability to cover the entire NIR wavelength range. In this work we develop an ensemble of IDL procedures to measure high precision radial velocities on a few GIANO spectra acquired during the commissioning run, using the telluric lines as wevelength reference. In Section 1.1 various exoplanet search methods are described. They exploit different properties of the planetary system. In Section 1.2 we describe the exoplanet population discovered trough the different methods. In Section 1.3 we explain motivations for NIR radial velocities and the challenges related the main issue that has limited the pursuit of high-precision NIR radial velocity, that is, the lack of a suitable calibration method. We briefly describe calibration methods in the visible and the solutions for IR calibration, for instance, the use of telluric lines. The latter has advantages and problems, described in detail. In this work we use telluric lines as wavelength reference. In Section 1.4 the Cross Correlation Function (CCF) method is described. This method is widely used to measure the radial velocities.In Section 1.5 we describe GIANO and its main science targets. In Chapter 2 observational data obtained with GIANO spectrograph are presented and the choice criteria are reported. In Chapter 3 we describe the detail of the analysis and examine in depth the flow chart reported in Section 3.1. In Chapter 4 we give the radial velocities measured with our IDL procedure for all available targets. We obtain an rms scatter in radial velocities of about 7 m/s. Finally, we conclude that GIANO can be used to measure radial velocities of late type stars with an accuracy close to or better than 10 m/s, using telluric lines as wevelength reference. In 2014 September GIANO is being operative at TNG for Science Verification and more observational data will allow to further refine this analysis.

Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.

Convolutional Neural Network Architectures for Template Matching

The Neural Networks customized and tested in this thesis (WaldoNet, FlowNet and PatchNet) are a first exploration and approach to the Template Matching task. The possibilities of extension are therefore many and some are proposed below. During my thesis, I have analyzed the functioning of the classical algorithms and adapted with deep learning algorithms. The features extracted from both the template and the query images resemble the keypoints of the SIFT algorithm. Then, instead of similarity function or keypoints matching, WaldoNet and PatchNet use the convolutional layer to compare the features, while FlowNet uses the correlational layer. In addition, I have identified the major challenges of the Template Matching task (affine/non-affine transformations, intensity changes...) and solved them with a careful design of the dataset.