68 research outputs found

    A search using GEO600 for gravitational waves coincident with fast radio bursts from SGR 1935+2154

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    The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by the Canadian Hydrogen Intensity Mapping Experiment (CHIME)/FRB and the Survey for Transient Astronomical Radio Emission 2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here, we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts ≤1 s) we derive 50% (90%) upper limits of 1048 (1049) erg for GWs at 300 Hz and 1049 (1050) erg at 2 kHz, and constrain the GW-to-radio energy ratio to ≤1014−1016. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs

    Search for continuous gravitational waves from known pulsars in the first part of the fourth LIGO-Virgo-KAGRA observing run

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    Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of general relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO–Virgo–KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering single-harmonic and dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is 6.4 × 10−27 for the young energetic pulsar J0537−6910, while the lowest constraint on the ellipticity is 8.8 × 10−9 for the bright nearby millisecond pulsar J0437−4715. Additionally, for a subset of 16 targets, we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of nonstandard polarizations as predicted by the Brans–Dicke theory

    Swift-BAT GUANO follow-up of gravitational-wave triggers in the Third LIGO–Virgo–KAGRA Observing Run

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    We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO–Virgo–KAGRA network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received with low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum-likelihood Non-imaging Transient Reconstruction and Temporal Search pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15–350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10−3 Hz, we compute the GW–BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers

    Big Data : Overview

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    Importance and Techniques of Information Hiding : A Review

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    Facial Expression Recognition using Ensemble Learning Technique

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    The most natural, influential and powerful way to communicate or convey a message is face expressions. In the field of computer engineering, facial expression recognition system, is helpful in areas like healthcare system, computer graphics, biometric devices, mobile phones, etc. Technologies such as virtual reality (VR) and augmented reality (AR) make use of facial expression recognition to implement a natural, friendly communication with humans. In this paper an approach for Facial Expression Recognition using Ensemble Learning Technique has been proposed. Ensemble methods use various learning algorithms to obtain good predictive performance that could be obtained from any of the basic learning algorithms alone. In the proposed method, initially the features are extracted from static images using color histograms. This process is done for all images gathered in the training dataset. The ensemble technique is then applied on the featured dataset in order to categorize a given image into one of the six emotions, happy, sad, fear, angry, disgust, and surprise. A satisfactory result has been obtained using static image dataset taken from kaggle and uci machine learning repository.</jats:p

    Deep Learning Techniques for Load Forecasting

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    Online Teaching versus Traditional Teaching – A Survey among Medical Students in Covid 19 Pandemic

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    For decades, scholars have debated which mode of education is superior amongst online versus traditional teaching. Some argue in favour of traditional or face to face teaching and others consider online teaching better. Still others suggested hybrid or blended mode of teaching as the most effective and productive method. This shift in strategy for medical education delivery has been driven by external forces beyond the influence of the institutions as well as by their internal dynamic. However, students’ perception towards online teaching as compared to traditional teaching has largely been overlooked. Aims and Objectives: This study or survey in covid pandemic intends to fill this void in the literature, and explore medical students’ perception and obtain their feedback towards online learning or teaching versus traditional teaching (face to face) mode of education in the medical field. Material and Method: Present survey included 1100 medical students from different medical colleges situated under different universities in North India. Survey was developed, which included a total of 14 questions (5 were demographical). After obtaining their responses, statistical analysis of responses was done. Conclusion: The results of present study concluded that medical students preferred traditional teaching over online mode of teaching. </jats:p
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