443 research outputs found

    Complex networks in brain electrical activity

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    We analyze the complex networks associated with brain electrical activity. Multichannel EEG measurements are first processed to obtain 3D voxel activations using the tomographic algorithm LORETA. Then, the correlation of the current intensity activation between voxel pairs is computed to produce a voxel cross-correlation coefficient matrix. Using several correlation thresholds, the cross-correlation matrix is then transformed into a network connectivity matrix and analyzed. To study a specific example, we selected data from an earlier experiment focusing on the MMN brain wave. The resulting analysis highlights significant differences between the spatial activations associated with Standard and Deviant tones, with interesting physiological implications. When compared to random data networks, physiological networks are more connected, with longer links and shorter path lengths. Furthermore, as compared to the Deviant case, Standard data networks are more connected, with longer links and shorter path lengths--i.e., with a stronger ``small worlds'' character. The comparison between both networks shows that areas known to be activated in the MMN wave are connected. In particular, the analysis supports the idea that supra-temporal and inferior frontal data work together in the processing of the differences between sounds by highlighting an increased connectivity in the response to a novel sound.Comment: 22 pages, 5 figures. Starlab preprint. This version is an attempt to include better figures (no content change

    Ionospheric tomography using GNSS reflections

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    In this paper, we report a preliminary analysis of the impact of Global Navigation Satellite System Reflections (GNSS-R) data on ionospheric monitoring over the oceans. The focus will be on a single polar Low Earth Orbiter (LEO) mission exploiting GNSS-R as well as Navigation (GNSS-N) and Occultation (GNSS-O) total electron content (TEC) measurements. In order to assess impact of the data, we have simulated GNSS-R/O/N TEC data as would be measured from the LEO and from International Geodesic Service (IGS) ground stations, with an electron density (ED) field generated using a climatic ionospheric model. We have also developed a new tomographic approach inspired by the physics of the hydrogen atom and used it to effectively retrieve the ED field from the simulated TEC data near the orbital plane. The tomographic inversion results demonstrate the significant impact of GNSS-R: three-dimensional ionospheric ED fields are retrieved over the oceans quite accurately, even as, in the spirit of this initial study, the simulation and inversion approaches avoided intensive computation and sophisticated algorithmic elements (such as spatio-temporal smoothing). We conclude that GNSS-R data over the oceans can contribute significantly to a Global/GNSS Ionospheric Observation System (GIOS). Index Terms Global Navigation Satellite System (GNSS), Global Navigation Satellite System Reflections (GNSS-R), ionosphere, Low Earth Orbiter (LEO), tomography

    Stability of naked singularities and algebraically special modes

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    We show that algebraically special modes lead to the instability of naked singularity spacetimes with negative mass. Four-dimensional negative-mass Schwarzschild and Schwarzschild-de Sitter spacetimes are unstable. Stability of the Schwarzschild-anti-de Sitter spacetime depends on boundary conditions. We briefly discuss the generalization of these results to charged and rotating singularities.Comment: 6 pages. ReVTeX4. v2: Minor improvements and extended discussion on boundary conditions. Version to appear in Phys. Rev.

    An Overview on Application of Machine Learning Techniques in Optical Networks

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    Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

    Mosè redivivo. Michelangelo e la Tomba di Giulio II

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    Studi recenti hanno permesso di rivedere la storia della Tomba di Giulio II come un’elaborazione parallela di idee diverse, anche contrastanti tra loro. Questo saggio cerca di dimostrare come la centralità della figura del Mosè, evidente nella soluzione finale di San Pietro in Vincoli, sia un elemento già considerato da Michelangelo, seppur in forma diversa, nel primo progetto dell’opera concepito quarant’anni prima. Si cerca inoltre di ricondurre questa idea mosaica della tomba al contesto di Giulio II e ai rapporti tra Michelangelo e Machiavelli documentati nel 1506. Il saggio propone anche un ritorno alla tradizionale datazione della statua al 1514-1516, in contrasto con le recenti proposte di datare il Mosè molto più tardi, al 1532 o addirittura al 1542. Infine, interpreta questa idea mosaica della tomba, che celebra il papa defunto come un Mosè risorto, come un meta-soggetto, metaforico dello straordinario naturalismo di Michelangelo

    GRB 110709B in the Induced Gravitational Collapse paradigm

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    Context: GRB110709B is the first source for which Swift BAT triggered twice, with a time separation of ~10 min. The first emission (Ep. 1) goes from 40s before the 1{\deg} trigger up to 60s after it. The second (Ep. 2) goes from 35s before the 2{\deg} trigger to 100s after it.[...] Within the Induced Gravitational Collapse (IGC) model, we assume the progenitor to be a close binary system composed of a core of an evolved star and a Neutron Star (NS). The evolved star explodes as a Supernova (SN) and ejects material that is partially accreted by the NS. We identify this process with Ep. 1. The accretion process brings the NS over its critical mass, thus gravitationally collapsing to a BH. This process leads to the GRB emission, Ep. 2.[...] Aims: We analyze the spectra and time variability of Ep. 1 and 2 and compute the relevant parameters of the binary progenitor[...] in the IGC paradigm. Methods: We perform a time-resolved spectral analysis of Ep. 1 with a blackbody (BB) plus a power-law (PL) spectral model. We analyze Ep. 2 within the Fireshell model, identifying the Proper-GRB (P-GRB) and simulating the light curve and spectrum. We establish the redshift to be z=0.75 [...]. Results: We find for Ep. 1 a BB temperature following a broken PL with time, with the PL slopes at early and late times \alpha=0 and \beta=-4+/-2, respectively, and a break at t=41.21s. The total energy of Ep. 1 and 2 are E_{iso}^1=1.42x10^{53}erg and E_{iso}^2=2.43x10^{52}erg, respectively. We find at transparency a Lorentz factor \Gamma~173, laboratory radius of 6.04x10^{13}cm, P-GRB observed temperature kT_{P-GRB}=12.36keV, baryon load B=0.0057 and P-GRB energy E_{P-GRB}=3.44x10^{50}erg. [...] Conclusions: We interpret GRB110709B as a member of the IGC sources, together with GRB970828, GRB090618 and GRB101023. The XRT data during Ep. 2 offers an unprecedented tool for improving the diagnostic of GRBs emission.Comment: 12 pages, 17 figures, to appear on A&
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