723 research outputs found

    The confining trailing string

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    We extend the holographic trailing string picture of a heavy quark to the case of a bulk geometry dual to a confining gauge theory. We compute the classical trailing confining string solution for a static as well as a uniformly moving quark. The trailing string is infinitely extended and approaches a confining horizon, situated at a critical value of the radial coordinate, along one of the space-time directions, breaking boundary rotational invariance. We compute the equations for the fluctuations around the classical solutions, which are used to obtain boundary force correlators controlling the Langevin dynamics of the quark. The imaginary part of the correlators has a non-trivial low-frequency limit, which gives rise to a viscous friction coefficient induced by the confining vacuum. The vacuum correlators are used to define finite-temperature dressed Langevin correlators with an appropriate high-frequency behavior.Comment: 63 pages plus appendices, 19 figures; version accepted for publication in JHE

    Leave-one-out prediction error of systolic arterial pressure time series under paced breathing

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    In this paper we show that different physiological states and pathological conditions may be characterized in terms of predictability of time series signals from the underlying biological system. In particular we consider systolic arterial pressure time series from healthy subjects and Chronic Heart Failure patients, undergoing paced respiration. We model time series by the regularized least squares approach and quantify predictability by the leave-one-out error. We find that the entrainment mechanism connected to paced breath, that renders the arterial blood pressure signal more regular, thus more predictable, is less effective in patients, and this effect correlates with the seriousness of the heart failure. The leave-one-out error separates controls from patients and, when all orders of nonlinearity are taken into account, alive patients from patients for which cardiac death occurred

    Using a distributed Shapley-value based approach to ensure navigability in a social network of smart objects

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    The huge number of nodes that is expected to join the Internet of Things in the short term will add major scalability issues to several procedures. A recent promising approach to these issues is based on social networking solutions to allow objects to autonomously establish social relationships. Every object in the resulting Social IoT (SIoT) exchanges data with its friend objects in a distributed manner to avoid the need for centralized solutions to implement major functionalities, such as: node discovery, information search and trustworthiness management. However, the number and types of established friendship affects network navigability. This paper addresses this issue proposing an efficient, distributed and dynamic strategy for the objects to select the right friends for the benefit of the overall network connectivity. The proposed friendship selection model relies on a Shapley-value based algorithm mapping the friendship selection process in the SIoT onto the coalition formation problem in a corresponding cooperative game. The obtained results show that the proposed solution is able to ensure global navigability, measured in terms of average path length among two nodes in the network, by means of a distributed and wise selection of the number of friend objects a node has to handle

    Redundant variables and Granger causality

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    We discuss the use of multivariate Granger causality in presence of redundant variables: the application of the standard analysis, in this case, leads to under-estimation of causalities. Using the un-normalized version of the causality index, we quantitatively develop the notions of redundancy and synergy in the frame of causality and propose two approaches to group redundant variables: (i) for a given target, the remaining variables are grouped so as to maximize the total causality and (ii) the whole set of variables is partitioned to maximize the sum of the causalities between subsets. We show the application to a real neurological experiment, aiming to a deeper understanding of the physiological basis of abnormal neuronal oscillations in the migraine brain. The outcome by our approach reveals the change in the informational pattern due to repetitive transcranial magnetic stimulations.Comment: 4 pages, 5 figures. Accepted for publication in Physical Review

    Enhancing the navigability in a social network of smart objects: a Shapley-value based approach

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    The Internet of Things (IoT) holds the promise to interconnect any possible object capable of providing useful information about the physical world for the benefit of humans' quality of life. The increasing number of heterogeneous objects that the IoT has to manage introduces crucial scalability issues that still need appropriate solutions. In this respect, one promising proposal is the Social IoT (SIoT) paradigm, whose main principle is to enable objects to autonomously establish social links with each other (adhering to rules set by their owners). "Friend" objects exchange data in a distributed manner and this avoids centralized solutions to implement major functions, such as: node discovery, information search, and trustworthiness management. However, the number and types of established friendships affect network navigability. This issue is the focus of this paper, which proposes an efficient, distributed and dynamic solution for the objects to select the right friends for the benefit of the overall network connectivity. The proposed friendship selection mechanism relies on a game theoretic model and a Shapley-value based algorithm. Two different utility functions are defined and evaluated based on either a group degree centrality and an average local clustering parameter. The comparison in terms of global navigability is measured in terms of average path length for the interconnection of any couple of nodes in the network. Results show that the group degree centrality brings to an enhanced degree of navigability thanks to the ability to create a suitable core of hubs

    Improved Holographic Yang-Mills at Finite Temperature: Comparison with Data

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    The semi-phenomenological improved holographic model for QCD is confronted with data of the pure glue, large-Nc gauge theory. After fitting two phenomenological parameters in the potential, the model can reproduce in detail all thermodynamic functions at finite temperature. It also reproduces in detail all known spin-0 and spin-2 glueball observables at zero temperature and predicts the rest of the 0++ and 2++ towers. A similar two parameter fit in the CP-odd sector postdicts the correct second 0+- glueball mass, and predicts the rest of the 0+- tower.Comment: 36 pages, 10 figures; references added, minor typos corrected; v3: corrected numerical mistake in section 5.4

    How to exploit the Social Internet of Things: Query Generation Model and Device Profiles’ Dataset

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    The future Internet of Things (IoT) will be characterized by an increasing number of object-to-object interactions for the implementation of distributed applications running in smart environments. The Social IoT (SIoT) is one of the possible paradigms that is proposed to make the objects’ interactions easier by facilitating the search of services and the management of objects’ trustworthiness. In this scenario, we address the issue of modeling the queries that are generated by the objects when fulfilling applications’ requests that could be provided by any of the peers in the SIoT. To this, the defined model takes into account the objects’ major features in terms of typology and associated functionalities, and the characteristics of the applications. We have then generated a dataset, by extracting objects’ information and positions from the city of Santander in Spain. We have classified all the available devices according to the FIWARE Data Models, so as to enable the portability of the dataset among different platforms. The dataset and the proposed query generation model are made available to the research community to study the navigability of the SIoT network, with an application also to other IoT networks. Experimental analyses have also been conducted, which give some key insights on the impact of the query model parameters on the average number of hops needed for each search

    Measuring randomness by leave-one-out prediction error. Analysis of EEG after painful stimulation

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    Abstract A parametric approach, to measure randomness in time series, is presented. Time series are modelled by a kernel machine performing regularized least squares and the leave-one-out (LOO) error is used to quantify unpredictability. On analyzing simulated data sets, we find that structure in data leads to a minimum of the LOO error as the regularizing parameter is varied. We consider electroencephalographic signals from migraineurs and healthy humans, after painful stimulation and use the proposed approach to detect changes of physiological state and to find differences between the response from patients and healthy subjects. As painful stimulus causes organization of the local activity in the cortex, EEG series become more predictable after stimulation. This phenomenon is less evident in patients: the inadequate cortical response to pain in migraineurs separates patients from controls with a probability close to 0.005

    Visually evoked phase synchronization changes of alpha rhythm in migraine: Correlations with clinical features

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    Objective: This study aimed to compute phase synchronization of the alpha band from a multichannel electroencephalogram (EEG) recorded under repetitive flash stimulation from migraine patients without aura. This allowed examination of ongoing EEG activity during visual stimulation in the pain-free phase of migraine. Methods: Flash stimuli at frequencies of 3, 6, 9, 12, 15, 18, 21, 24, and 27 Hz were delivered to 15 migraine patients without aura and 15 controls, with the EEG recorded from 18 scalp electrodes, referred to the linked earlobes. The EEG signals were filtered in the alpha (7.5ďż˝ 13 Hz) band. For all stimulus frequencies that we evaluated, the phase synchronization index was based on the Hilbert transformation. Results: Phase synchronization separated the patients and controls for the 9, 24 and 27 Hz stimulus frequencies; hyper phase synchronization was observed in patients, whereas healthy subjects were characterized by a reduced phase synchronization. These differences were found in all regions of the scalp. Conclusions: During migraine, the brain synchronizes to the idling rhythm of the visual areas under certain photic stimulations; in normal subjects however, brain regions involved in the processing of sensory information demonstrate desynchronized activity. Hypersynchronization of the alpha rhythm may suggest a state of cortical hypoexcitability during the interictal phase of migraine. Significance: The employment of non-linear EEG analysis may identify subtle functional changes in the migraine brain. D 2005 Elsevier B.V. All rights reserved
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