How frequent are close supermassive binary black holes in powerful jet sources?

24 pages, 36 figures. © 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)Supermassive black hole binariesmay be detectable by an upcoming suite of gravitationalwave experiments. Their binary nature can also be revealed by radio jets via a short-period precession driven by the orbital motion as well as the geodetic precession at typically longer periods. We have investigated Karl G. Jansky Very Large Array and Multi-Element Radio Linked Interferometer Network (MERLIN) radio maps of powerful jet sources for morphological evidence of geodetic precession. For perhaps the best-studied source, Cygnus A, we find strong evidence for geodetic precession. Projection effects can enhance precession features, for which we find indications in strongly projected sources. For a complete sample of 33 3CR radio sources, we find strong evidence for jet precession in 24 cases (73 per cent). The morphology of the radio maps suggests that the precession periods are of the order of 10 6- 10 7 yr. We consider different explanations for the morphological features and conclude that geodetic precession is the best explanation. The frequently observed gradual jet angle changes in samples of powerful blazars can be explained by orbital motion. Both observations can be explained simultaneously by postulating that a high fraction of powerful radio sources have subparsec supermassive black hole binaries.We consider complementary evidence and discuss if any jetted supermassive black hole with some indication of precession could be detected as individual gravitational wave source in the near future. This appears unlikely, with the possible exception of M87.Peer reviewedFinal Published versio

RDD-Eclat: Approaches to Parallelize Eclat Algorithm on Spark RDD Framework

Initially, a number of frequent itemset mining (FIM) algorithms have been designed on the Hadoop MapReduce, a distributed big data processing framework. But, due to heavy disk I/O, MapReduce is found to be inefficient for such highly iterative algorithms. Therefore, Spark, a more efficient distributed data processing framework, has been developed with in-memory computation and resilient distributed dataset (RDD) features to support the iterative algorithms. On the Spark RDD framework, Apriori and FP-Growth based FIM algorithms have been designed, but Eclat-based algorithm has not been explored yet. In this paper, RDD-Eclat, a parallel Eclat algorithm on the Spark RDD framework is proposed with its five variants. The proposed algorithms are evaluated on the various benchmark datasets, which shows that RDD-Eclat outperforms the Spark-based Apriori by many times. Also, the experimental results show the scalability of the proposed algorithms on increasing the number of cores and size of the dataset.Comment: 16 pages, 6 figures, ICCNCT 201

On-line Context Aware Physical Activity Recognition from the Accelerometer and Audio Sensors of Smartphones

International audienceActivity Recognition (AR) from smartphone sensors has be-come a hot topic in the mobile computing domain since it can provide ser-vices directly to the user (health monitoring, fitness, context-awareness) as well as for third party applications and social network (performance sharing, profiling). Most of the research effort has been focused on direct recognition from accelerometer sensors and few studies have integrated the audio channel in their model despite the fact that it is a sensor that is always available on all kinds of smartphones. In this study, we show that audio features bring an important performance improvement over an accelerometer based approach. Moreover, the study demonstrates the interest of considering the smartphone location for on-line context-aware AR and the prediction power of audio features for this task. Finally, an-other contribution of the study is the collected corpus that is made avail-able to the community for AR recognition from audio and accelerometer sensors

The OPERA experiment Target Tracker

The main task of the Target Tracker detector of the long baseline neutrino oscillation OPERA experiment is to locate in which of the target elementary constituents, the lead/emulsion bricks, the neutrino interactions have occurred and also to give calorimetric information about each event. The technology used consists in walls of two planes of plastic scintillator strips, one per transverse direction. Wavelength shifting fibres collect the light signal emitted by the scintillator strips and guide it to both ends where it is read by multi-anode photomultiplier tubes. All the elements used in the construction of this detector and its main characteristics are described.Comment: 25 pages, submitted to Nuclear Instrument and Method

Rhythmic dynamics and synchronization via dimensionality reduction : application to human gait

Reliable characterization of locomotor dynamics of human walking is vital to understanding the neuromuscular control of human locomotion and disease diagnosis. However, the inherent oscillation and ubiquity of noise in such non-strictly periodic signals pose great challenges to current methodologies. To this end, we exploit the state-of-the-art technology in pattern recognition and, specifically, dimensionality reduction techniques, and propose to reconstruct and characterize the dynamics accurately on the cycle scale of the signal. This is achieved by deriving a low-dimensional representation of the cycles through global optimization, which effectively preserves the topology of the cycles that are embedded in a high-dimensional Euclidian space. Our approach demonstrates a clear advantage in capturing the intrinsic dynamics and probing the subtle synchronization patterns from uni/bivariate oscillatory signals over traditional methods. Application to human gait data for healthy subjects and diabetics reveals a significant difference in the dynamics of ankle movements and ankle-knee coordination, but not in knee movements. These results indicate that the impaired sensory feedback from the feet due to diabetes does not influence the knee movement in general, and that normal human walking is not critically dependent on the feedback from the peripheral nervous system

Emulsion sheet doublets as interface trackers for the OPERA experiment

New methods for efficient and unambiguous interconnection between electronic counters and target units based on nuclear photographic emulsion films have been developed. The application to the OPERA experiment, that aims at detecting oscillations between mu neutrino and tau neutrino in the CNGS neutrino beam, is reported in this paper. In order to reduce background due to latent tracks collected before installation in the detector, on-site large-scale treatments of the emulsions ("refreshing") have been applied. Changeable Sheet (CSd) packages, each made of a doublet of emulsion films, have been designed, assembled and coupled to the OPERA target units ("ECC bricks"). A device has been built to print X-ray spots for accurate interconnection both within the CSd and between the CSd and the related ECC brick. Sample emulsion films have been extensively scanned with state-of-the-art automated optical microscopes. Efficient track-matching and powerful background rejection have been achieved in tests with electronically tagged penetrating muons. Further improvement of in-doublet film alignment was obtained by matching the pattern of low-energy electron tracks. The commissioning of the overall OPERA alignment procedure is in progress.Comment: 19 pages, 19 figure