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CORTICAL PLASTICITY INVESTIGATED WITH TRANSCRANIAL MAGNETIC STIMULATION TECHNIQUES IN PATIENTS WITH MOVEMENT DISORDERS”
No full textThis thesis have been designed, performed or completed during the period between 2010 to 2013 in the Department of Neurology and Psychiatry, Sapienza University of Rome, Italy, (supervised by Prof. Alfredo Berardelli).
The main focus of this thesis is the investigation of mechanisms of Long-Term potentiation and Long-Term depression (LTP)/(LTD)-like plasticity in primary motor cortex (M1) with repetitive transcranial magnetic stimulation (rTMS) techniques such as Theta Bursts Stimulation (TBS) in patients with different types of movement disorders including Focal Hand Dystonia (FHD) and Multiple System Atrophy (MSA).
Section 1 will introduce the general knowledge and biophysical principles of Transcranial Magnetic Stimulation (TMS). Section 2 will report two studies investigating abnormalities of LTP/LTD-like plasticity in M1 in patients with FHD and MSA. The first study (Suppa A, Marsili L, Di Stasio F, Latorre A, Parvez AK, Colosimo C, Berardelli A.) is mainly focused on cortical plasticity and motor learning in patients with FHD. This study showed that patients with FHD are characterized by altered cortical plasticity in M1. In addition, in FHD patients, motor execution of simple finger movements is abnormal and characterized by reducing movement speed and altered early motor learning. The second study (Belvisi D, Suppa A, Marsili L, Di Stasio F, Parvez AK, Agostino R, Fabbrini G, Berardelli A.) focused on cortical plasticity in patients with MSA. This study showed reduced LTP/LTD-like plasticity in MSA patients reflecting abnormalities in the cortico-basal ganglia-thalamo-cortical motor circuit
New criteria and methods for cyanobacteria risk assessment and risk management in water for human consumption
No full textMeasurement of the properties of the new particle observed within the search for the Standard Model Higgs Boson in the H->ZZ(*)->4l decay channel at ATLAS
No full textThe discovery of a new particle within the search for the Standard Model Higgs boson in the H->ZZ->4l channel at ATLAS, with about 25/fb of data collected in pp collisions at the LHC, is discussed. Different hypotheses on the quantum numbers of the new boson are tested, by means of spin-parity studies based on a matrix element description of the H->ZZ decay amplitude. Prospects for the measurement of the tensor structure of the HZZ vertex in the spin zero hypothesis at a high--luminosity LHC are also presented
Use of an integrated methodological approach to assess contaminant fate and transport in a coastal aquifer
No full textTimely Processing of Big Data in Collaborative Large-Scale Distributed Systems
No full textToday’s Big Data phenomenon, characterized by huge volumes of data produced at very high rates by heterogeneous and geographically dispersed sources, is fostering the employment of large-scale distributed
systems in order to leverage parallelism, fault tolerance and locality awareness with the aim of delivering suitable performances. Among the several areas where Big Data is gaining increasing significance, the protection of Critical Infrastructure is one of the most strategic since it impacts on the stability and safety of entire countries. Intrusion detection mechanisms can benefit a lot from novel Big Data technologies because these allow to exploit much more information in order to sharpen the accuracy of threats discovery.
A key aspect for increasing even more the amount of data at disposal for detection purposes is the collaboration (meant as information sharing) among distinct actors that share the common goal of maximizing the chances to recognize malicious activities earlier. Indeed, if an agreement can be found to share their data, they all have the possibility to definitely improve their cyber defenses. The abstraction of Semantic Room (SR) allows interested parties to form trusted and contractually regulated federations, the Semantic Rooms, for the sake of secure information sharing and processing. Another crucial point for the effectiveness of cyber protection mechanisms is the timeliness of the detection, because the sooner a threat is identified, the faster proper countermeasures can be put in place so as to confine any damage.
Within this context, the contributions reported in this thesis are threefold
* As a case study to show how collaboration can enhance the efficacy of security tools, we developed a novel algorithm for the detection of stealthy port scans, named R-SYN (Ranked SYN port scan detection). We implemented it in three distinct technologies, all of them integrated within an SR-compliant architecture that allows for collaboration through information sharing: (i) in a centralized Complex Event Processing (CEP) engine (Esper), (ii) in a framework for distributed event processing (Storm) and (iii) in Agilis, a novel platform for batch-oriented processing which leverages the Hadoop framework and a RAM-based storage for fast data access. Regardless of the employed technology, all the evaluations have shown that increasing the number of participants (that is, increasing the amount of input data at disposal), allows to improve the detection accuracy. The experiments made clear that a distributed approach allows for lower detection latency and for keeping up with higher input throughput, compared with a centralized one.
* Distributing the computation over a set of physical nodes introduces the issue of improving the way available resources are assigned to the elaboration tasks to execute, with the aim of minimizing the
time the computation takes to complete. We investigated this aspect in Storm by developing two distinct scheduling algorithms, both aimed at decreasing the average elaboration time of the single
input event by decreasing the inter-node traffic. Experimental evaluations showed that these two algorithms can improve the performance up to 30%.
* Computations in online processing platforms (like Esper and Storm) are run continuously, and the need of refining running computations or adding new computations, together with the need to cope with the variability of the input, requires the possibility to adapt the resource allocation at runtime, which entails a set of additional problems. Among them, the most relevant concern how to cope with incoming data and processing state while the topology is being reconfigured, and the issue of temporary reduced performance. At this aim, we also explored the alternative approach of running the computation periodically on batches of input data: although it involves a performance penalty on the elaboration latency, it allows to eliminate the great complexity of dynamic reconfigurations. We chose Hadoop as batch-oriented processing framework and we developed some strategies specific for dealing with computations based on time windows, which are very likely to be used for pattern recognition purposes, like in the case of intrusion detection. Our evaluations provided a comparison of these strategies and made evident the kind of performance that this approach can provide
SPATIAL REGRESSION IN LARGE DATASETS: PROBLEM SET SOLUTION
No full textIn this dissertation we investigate a possible attempt to combine the Data Mining methods and traditional Spatial Autoregressive models, in the context of large spatial datasets.
We start to considere the numerical difficulties to handle massive datasets by the usual approach based on Maximum Likelihood estimation for spatial models and Spatial Two-Stage
Least Squares.
So, we conduct an experiment by Monte Carlo simulations to compare the accuracy and computational complexity for decomposition and approximation techniques to solve the problem of computing the Jacobian in spatial models, for various regular lattice structures. In particular,
we consider one of the most common spatial econometric models: spatial lag (or SAR,
spatial autoregressive model).
Also, we provide new evidences in the literature, by examining the double effect on computational
complexity of these methods: the influence of "size effect" and "sparsity effect".
To overcome this computational problem, we propose a data mining methodology as CART
(Classification and Regression Tree) that explicitly considers the phenomenon of spatial autocorrelation
on pseudo-residuals, in order to remove this effect and to improve the accuracy,
with significant saving in computational complexity in wide range of spatial datasets: realand simulated data
