Search results for " data analysis"

Explicit Granger causality in kernel Hilbert spaces

2020

Granger causality (GC) is undoubtedly the most widely used method to infer cause-effect relations from observational time series. Several nonlinear alternatives to GC have been proposed based on kernel methods. We generalize kernel Granger causality by considering the variables cross-relations explicitly in Hilbert spaces. The framework is shown to generalize the linear and kernel GC methods, and comes with tighter bounds of performance based on Rademacher complexity. We successfully evaluate its performance in standard dynamical systems, as well as to identify the arrow of time in coupled R\"ossler systems, and is exploited to disclose the El Ni\~no-Southern Oscillation (ENSO) phenomenon f…

Universal freezing of quantum correlations within the geometric approach

2015

Quantum correlations in a composite system can be measured by resorting to a geometric approach, according to which the distance from the state of the system to a suitable set of classically correlated states is considered. Here we show that all distance functions, which respect natural assumptions of invariance under transposition, convexity, and contractivity under quantum channels, give rise to geometric quantifiers of quantum correlations which exhibit the peculiar freezing phenomenon, i.e., remain constant during the evolution of a paradigmatic class of states of two qubits each independently interacting with a non-dissipative decohering environment. Our results demonstrate from first …

Modelling and Simulation in Science, Proceedings of the 6th International Workshop on Data Analysis in Astronomy >

2007

Probabilistic Anomaly Detection for Wireless Sensor Networks

2011

Wireless Sensor Networks (WSN) are increasingly gaining popularity as a tool for environmental monitoring, however ensuring the reliability of their operation is not trivial, and faulty sensors are not uncommon; moreover, the deployment environment may influence the correct functioning of a sensor node, which might thus be mistakenly classified as damaged. In this paper we propose a probabilistic algorithm to detect a faulty node considering its sensed data, and the surrounding environmental conditions. The algorithm was tested with a real dataset acquired in a work environment, characterized by the presence of actuators that also affect the actual trend of the monitored physical quantities.

Twitter spam account detection by effective labeling

2019

In the last years, the widespread diffusion of Online Social Networks (OSNs) has enabled new forms of communications that make it easier for people to interact remotely. Unfortunately, one of the first consequences of such a popularity is the increasing number of malicious users who sign-up and use OSNs for non-legit activities. In this paper we focus on spam detection, and present some preliminary results of a system that aims at speeding up the creation of a large-scale annotated dataset for spam account detection on Twitter. To this aim, two different algorithms capable of capturing the spammer behaviors, i.e., to share malicious urls and recurrent contents, are exploited. Experimental r…

Recensione a Ferruccio Biolcati-Rinaldi, Cristiano Vezzoni, L’analisi secondaria nella ricerca sociale, STUDI DI SOCIOLOGIA, Il Mulino, Itinerari, Bo…

2014

XMM-Newton large programme on SN1006 - II. Thermal emission

2016

Based on the XMM-Newton large program on SN1006 and our newly developed spatially resolved spectroscopy tools (Paper~I), we study the thermal emission from ISM and ejecta of SN1006 by analyzing the spectra extracted from 583 tessellated regions dominated by thermal emission. With some key improvements in spectral analysis as compared to Paper~I, we obtain much better spectral fitting results with less residuals. The spatial distributions of the thermal and ionization states of the ISM and ejecta show different features, which are consistent with a scenario that the ISM (ejecta) is heated and ionized by the forward (reverse) shock propagating outward (inward). Different elements have differe…

Rapid parameter estimation of discrete decaying signals using autoencoder networks

2021

Machine learning: science and technology 2(4), 045024 (2021). doi:10.1088/2632-2153/ac1eea

Synergistic integration of optical and microwave satellite data for crop yield estimation

2019

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation (EO) remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index (EVI) derived from MODIS and Vegetation Optical Depth (VOD) derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or des…

unitas: the universal tool for annotation of small RNAs

2017

AbstractBackgroundNext generation sequencing is a key technique in small RNA biology research that has led to the discovery of functionally different classes of small non-coding RNAs in the past years. However, reliable annotation of the extensive amounts of small non-coding RNA data produced by high-throughput sequencing is time-consuming and requires robust bioinformatics expertise. Moreover, existing tools have a number of shortcomings including a lack of sensitivity under certain conditions, limited number of supported species or detectable sub-classes of small RNAs.ResultsHere we introduce unitas, an out-of-the-box ready software for complete annotation of small RNA sequence datasets, …