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  • Publication . Conference object . Preprint . Article . 2019 . Embargo End Date: 01 Jan 2019
    Open Access
    Authors: 
    Breton Minnehan; Andreas Savakis;
    Publisher: arXiv

    We propose a data-driven approach for deep convolutional neural network compression that achieves high accuracy with high throughput and low memory requirements. Current network compression methods either find a low-rank factorization of the features that requires more memory, or select only a subset of features by pruning entire filter channels. We propose the Cascaded Projection (CaP) compression method that projects the output and input filter channels of successive layers to a unified low dimensional space based on a low-rank projection. We optimize the projection to minimize classification loss and the difference between the next layer's features in the compressed and uncompressed networks. To solve this non-convex optimization problem we propose a new optimization method of a proxy matrix using backpropagation and Stochastic Gradient Descent (SGD) with geometric constraints. Our cascaded projection approach leads to improvements in all critical areas of network compression: high accuracy, low memory consumption, low parameter count and high processing speed. The proposed CaP method demonstrates state-of-the-art results compressing VGG16 and ResNet networks with over 4x reduction in the number of computations and excellent performance in top-5 accuracy on the ImageNet dataset before and after fine-tuning.

  • Publication . Article . Preprint . 2020 . Embargo End Date: 01 Jan 2020
    Open Access
    Authors: 
    Khaled Ai Thelaya; Marco Agus; Jens Schneider;
    Publisher: arXiv

    In this paper, we present a novel data structure, called the Mixture Graph. This data structure allows us to compress, render, and query segmentation histograms. Such histograms arise when building a mipmap of a volume containing segmentation IDs. Each voxel in the histogram mipmap contains a convex combination (mixture) of segmentation IDs. Each mixture represents the distribution of IDs in the respective voxel's children. Our method factorizes these mixtures into a series of linear interpolations between exactly two segmentation IDs. The result is represented as a directed acyclic graph (DAG) whose nodes are topologically ordered. Pruning replicate nodes in the tree followed by compression allows us to store the resulting data structure efficiently. During rendering, transfer functions are propagated from sources (leafs) through the DAG to allow for efficient, pre-filtered rendering at interactive frame rates. Assembly of histogram contributions across the footprint of a given volume allows us to efficiently query partial histograms, achieving up to 178$\times$ speed-up over na$\mathrm{\"{i}}$ve parallelized range queries. Additionally, we apply the Mixture Graph to compute correctly pre-filtered volume lighting and to interactively explore segments based on shape, geometry, and orientation using multi-dimensional transfer functions. Comment: To appear in IEEE Transacations on Visualization and Computer Graphics (IEEE Vis 2020)

  • Open Access English
    Authors: 
    H. B. Benaoum; S. H. Shaglel;

    We propose a new scaling ansatz in the neutrino Dirac mass matrix to explain the low energy neutrino oscillations data, baryon number asymmetry and neutrinoless double beta decay. In this work, a full reconstruction of the neutrino Dirac mass matrix has been realized from the low energy neutrino oscillations data based on type-I seesaw mechanism. A concrete model based on $A_4$ flavor symmetry has been considered to generate such a neutrino Dirac mass matrix and imposes a relation between the two scaling factors. In this model, the right-handed Heavy Majorana neutrino masses are quasi-degenerate at TeV mass scales. Extensive numerical analysis studies have been carried out to constrain the parameter space of the model from the low energy neutrino oscillations data. It has been found that the parameter space of the Dirac mass matrix elements lies near or below the MeV region and the scaling factor $|\kappa_1|$ has to be less than 10. Furthermore, we have examined the possibility for simultaneous explanation of both neutrino oscillations data and the observed baryon number asymmetry in the Universe. Such an analysis gives further restrictions on the parameter space of the model, thereby explaining the correct neutrino data as well as the baryon number asymmetry via a resonant leptogenesis scenario. Finally, we show that the allowed space for the effective Majorana neutrino mass $m_{ee}$ is also constrained in order to account for the observed baryon asymmetry. Comment: 25 pages, 10 figues, revised version

  • Open Access English
    Authors: 
    Maurizio Capra; Beatrice Bussolino; Alberto Marchisio; Guido Masera; Maurizio Martina; Muhammad Shafique;
    Country: Italy

    Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning (DL) is already present in many applications ranging from computer vision for medicine to autonomous driving of modern cars as well as other sectors in security, healthcare, and finance. However, to achieve impressive performance, these algorithms employ very deep networks, requiring a significant computational power, both during the training and inference time. A single inference of a DL model may require billions of multiply-and-accumulated operations, making the DL extremely compute- and energy-hungry. In a scenario where several sophisticated algorithms need to be executed with limited energy and low latency, the need for cost-effective hardware platforms capable of implementing energy-efficient DL execution arises. This paper first introduces the key properties of two brain-inspired models like Deep Neural Network (DNN), and Spiking Neural Network (SNN), and then analyzes techniques to produce efficient and high-performance designs. This work summarizes and compares the works for four leading platforms for the execution of algorithms such as CPU, GPU, FPGA and ASIC describing the main solutions of the state-of-the-art, giving much prominence to the last two solutions since they offer greater design flexibility and bear the potential of high energy-efficiency, especially for the inference process. In addition to hardware solutions, this paper discusses some of the important security issues that these DNN and SNN models may have during their execution, and offers a comprehensive section on benchmarking, explaining how to assess the quality of different networks and hardware systems designed for them. Accepted for publication in IEEE Access

  • Publication . Preprint . Conference object . Article . 2020
    Open Access
    Authors: 
    Ismail Shahin;
    Publisher: IEEE

    This research aims at identifying the unknown emotion using speaker cues. In this study, we identify the unknown emotion using a two-stage framework. The first stage focuses on identifying the speaker who uttered the unknown emotion, while the next stage focuses on identifying the unknown emotion uttered by the recognized speaker in the prior stage. This proposed framework has been evaluated on an Arabic Emirati-accented speech database uttered by fifteen speakers per gender. Mel-Frequency Cepstral Coefficients (MFCCs) have been used as the extracted features and Hidden Markov Model (HMM) has been utilized as the classifier in this work. Our findings demonstrate that emotion recognition accuracy based on the two-stage framework is greater than that based on the one-stage approach and the state-of-the-art classifiers and models such as Gaussian Mixture Model (GMM), Support Vector Machine (SVM), and Vector Quantization (VQ). The average emotion recognition accuracy based on the two-stage approach is 67.5%, while the accuracy reaches to 61.4%, 63.3%, 64.5%, and 61.5%, based on the one-stage approach, GMM, SVM, and VQ, respectively. The achieved results based on the two-stage framework are very close to those attained in subjective assessment by human listeners. 5 pages

  • Publication . Preprint . Article . 2019 . Embargo End Date: 01 Jan 2019
    Open Access
    Authors: 
    Wheatcroft, Edward; Wynn, Henry; Dent, Chris J.; Smith, Jim Q.; Copeland, Claire L.; Ralph, Daniel; Zachary, Stan;
    Publisher: arXiv

    Scenario Analysis is a risk assessment tool that aims to evaluate the impact of a small number of distinct plausible future scenarios. In this paper, we provide an overview of important aspects of Scenario Analysis including when it is appropriate, the design of scenarios, uncertainty and encouraging creativity. Each of these issues is discussed in the context of climate, energy and legal scenarios.

  • Open Access
    Authors: 
    Liam Connor; J. van Leeuwen; L. C. Oostrum; Emily Petroff; Yogesh Maan; Elizabeth A. K. Adams; Jisk Attema; J. E. Bast; Oliver M. Boersma; H. Dénes; +31 more
    Publisher: Oxford University Press (OUP)
    Country: Netherlands
    Project: NWO | ARTS - the Apertif Radio ... (2300177746), EC | RadioNet (730562), NWO | Microporous membranes fro... (2300159022), EC | ALERT (617199)

    ABSTRACT We report the detection of a bright fast radio burst, FRB 191108, with Apertif on the Westerbork Synthesis Radio Telescope. The interferometer allows us to localize the FRB to a narrow 5 arcsec × 7 arcmin ellipse by employing both multibeam information within the Apertif phased-array feed beam pattern, and across different tied-array beams. The resulting sightline passes close to Local Group galaxy M33, with an impact parameter of only 18 kpc with respect to the core. It also traverses the much larger circumgalactic medium (CGM) of M31, the Andromeda Galaxy. We find that the shared plasma of the Local Group galaxies could contribute ∼10 per cent of its dispersion measure of 588 pc cm−3. FRB 191108 has a Faraday rotation measure (RM) of +474 $\pm \, 3$ rad m−2, which is too large to be explained by either the Milky Way or the intergalactic medium. Based on the more moderate RMs of other extragalactic sources that traverse the halo of M33, we conclude that the dense magnetized plasma resides in the host galaxy. The FRB exhibits frequency structure on two scales, one that is consistent with quenched Galactic scintillation and broader spectral structure with Δν ≈ 40 MHz. If the latter is due to scattering in the shared M33/M31 CGM, our results constrain the Local Group plasma environment. We found no accompanying persistent radio sources in the Apertif imaging survey data.

  • Open Access English
    Authors: 
    Othman Benomar; M. J. Goupil; Kevin Belkacem; T. Appourchaux; Martin Bo Nielsen; M. Bazot; Laurent Gizon; Shravan M. Hanasoge; Katepalli R. Sreenivasan; B. Marchand;
    Publisher: HAL CCSD
    Country: France

    Oscillation properties are usually measured by fitting symmetric Lorentzian profiles to the power spectra of Sun-like stars. However the line profiles of solar oscillations have been observed to be asymmetrical for the Sun. The physical origin of this line asymmetry is not fully understood, although it should depend on the depth dependence of the source of wave excitation (convective turbulence) and details of the observable (velocity or intensity). For oscillations of the Sun, it has been shown that neglecting the asymmetry leads to systematic errors in the frequency determination. This could subsequently affects the results of seismic inferences of the solar internal structure. Using light curves from the {\it Kepler} spacecraft we have measured mode asymmetries in 43 stars. We confirm that neglecting the asymmetry leads to systematic errors that can exceed the $1\sigma$ confidence intervals for seismic observations longer than one year. Therefore, the application of an asymmetric Lorentzian profile is to be favoured to improve the accuracy of the internal stellar structure and stellar fundamental parameters. We also show that the asymmetry changes sign between cool Sun-like stars and hotter stars. This provides the best constraints to date on the location of the excitation sources across the Hertzsprung-Russel diagram. Comment: 8 pages, 7 Figures, 1 Table, Accepted to ApJ

  • Publication . Conference object . Preprint . Article . 2019
    Open Access English
    Authors: 
    Yichao Yan; Qiang Zhang; Bingbing Ni; Wendong Zhang; Minghao Xu; Xiaokang Yang;

    Person re-identification has achieved great progress with deep convolutional neural networks. However, most previous methods focus on learning individual appearance feature embedding, and it is hard for the models to handle difficult situations with different illumination, large pose variance and occlusion. In this work, we take a step further and consider employing context information for person search. For a probe-gallery pair, we first propose a contextual instance expansion module, which employs a relative attention module to search and filter useful context information in the scene. We also build a graph learning framework to effectively employ context pairs to update target similarity. These two modules are built on top of a joint detection and instance feature learning framework, which improves the discriminativeness of the learned features. The proposed framework achieves state-of-the-art performance on two widely used person search datasets. To appear in CVPR 2019

  • Open Access English
    Authors: 
    S. Antier; S. Agayeva; Mouza Almualla; Supachai Awiphan; A. Baransky; K. Barynova; S. Beradze; M. Blažek; M. Boer; O. A. Burkhonov; +48 more
    Countries: France, Spain
    Project: ARC | ARC Centres of Excellence... (CE170100004), ARC | Discovery Early Career Re... (DE170100891)

    Parts of this research were conducted by the Australian Research Council Centre of Excellence for Gravitational Wave Discovery (OzGrav), through project number CE170100004. EJH acknowledges support from an Australian Research Council DECRA Fellowship (DE170100891). AdUP and CCT acknowledge support from Ramon y Cajal fellowships RyC-2012-09975 and RyC-2012-09984 and the Spanish Ministry of Economy and Competitiveness through project AYA2017-89384-P. DAK acknowledges Spanish research project RTI2018-098104-J-I00 (GRBPhot). MB acknowledges funding as 'personal tecnico de apoyo' under fellowship number PTA2016-13192-I. SA is supported by the CNES Postdoctoral Fellowship at Laboratoire AstroParticule et Cosmologie. SA and CL acknowledge the financial support of the Programme National Hautes Energies (PNHE). DT acknowledges the financial support of CNES postdoctoral program. UBAI acknowledges support from the Ministry of Innovative Development through projects FA-Atech-2018-392 and VA-FA-F-2-010. SB acknowledges Shota Rustaveli National Science Foundation (SRNSF) grant no. -PHDF/18-1327. TAROT has been built with the support of the Institut National des Sciences de l'Univers, CNRS, France. TAROT is funded by the CNES and thanks the help of the technical staff of the Observatoire de Haute Provence, OSUPytheas. MP, SK, and MM are supported by European Structural and Investment Fund and the Czech Ministry of Education, Youth and Sports (Projects CZ.02.1.01/0.0/0.0/16 013/0001402, CZ.02.1.01/0.0/0.0/16 013/0001403, and CZ.02.1.01/0.0/0.0/15 003/0000437). NBO, DM, and PG acknowledge financial support from NASA-MUREP-MIRO grant NNX15AP95A, NASA-EPSCoR grant NNX13AD28A, and NSF EiR AST Award 1901296. The GRANDMA collaboration thank the amateur participants to the kilonova-catcher program. The kilonova-catcher program is supported by the IdEx Universite de Paris, ANR-18-IDEX-0001. This research made use of the crossmatch service provided by CDS, Strasbourg. We thank Ulrich Hopp to provide the precise date of observations for AT2019wxt Wendelstein optical observations. GRANDMA (Global Rapid Advanced Network Devoted to the Multi-messenger Addicts) is a network of 25 telescopes of different sizes, including both photometric and spectroscopic facilities. The network aims to coordinate follow-up observations of gravitational-wave (GW) candidate alerts, especially those with large localization uncertainties, to reduce the delay between the initial detection and the optical confirmation. In this paper, we detail GRANDMA's observational performance during Advanced LIGO/Advanced Virgo Observing Run 3 (O3), focusing on the second part of O3; this includes summary statistics pertaining to coverage and possible astrophysical origin of the candidates. To do so, we quantify our observation efficiency in terms of delay between GW candidate trigger time, observations, and the total coverage. Using an optimized and robust coordination system, GRANDMA followed-up about 90 per cent of the GW candidate alerts, that is 49 out of 56 candidates. This led to coverage of over 9000 deg2 during O3. The delay between the GW candidate trigger and the first observation was below 1.5 h for 50 per cent of the alerts. We did not detect any electromagnetic counterparts to the GW candidates during O3, likely due to the very large localization areas (on average thousands of degrees squares) and relatively large distance of the candidates (above 200 Mpc for 60 per cent of binary neutron star, BNS candidates). We derive constraints on potential kilonova properties for two potential BNS coalescences (GW190425 and S200213t), assuming that the events' locations were imaged. © 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. Full list of authors: Antier, S.; Agayeva, S.; Almualla, M.; Awiphan, S.; Baransky, A.; Barynova, K.; Beradze, S.; Blažek, M.; Boër, M.; Burkhonov, O.; Christensen, N.; Coleiro, A.; Corre, D.; Coughlin, M. W.; Crisp, H.; Dietrich, T.; Ducoin, J. -G.; Duverne, P. -A.; Marchal-Duval, G.; Gendre, B.; Gokuldass, P.; Eggenstein, H. B.; Eymar, L.; Hello, P.; Howell, E. J.; Ismailov, N.; Kann, D. A.; Karpov, S.; Klotz, A.; Kochiashvili, N.; Lachaud, C.; Leroy, N.; Lin, W. L.; Li, W. X.; Mašek, M.; Mo, J.; Menard, R.; Morris, D.; Noysena, K.; Orange, N. B.; Prouza, M.; Rattanamala, R.; Sadibekova, T.; Saint-Gelais, D.; Serrau, M.; Simon, A.; Stachie, C.; Thöne, C. C.; Tillayev, Y.; Turpin, D.; de Ugarte Postigo, A.; Vasylenko, V.; Vidadi, Z.; Was, M.; Wang, X. F.; Zhang, J. J.; Zhang, T. M.; Zhang, X. H. Peer reviewed

Advanced search in
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arrow_drop_down
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Include:
1,960 Research products, page 1 of 196
  • Publication . Conference object . Preprint . Article . 2019 . Embargo End Date: 01 Jan 2019
    Open Access
    Authors: 
    Breton Minnehan; Andreas Savakis;
    Publisher: arXiv

    We propose a data-driven approach for deep convolutional neural network compression that achieves high accuracy with high throughput and low memory requirements. Current network compression methods either find a low-rank factorization of the features that requires more memory, or select only a subset of features by pruning entire filter channels. We propose the Cascaded Projection (CaP) compression method that projects the output and input filter channels of successive layers to a unified low dimensional space based on a low-rank projection. We optimize the projection to minimize classification loss and the difference between the next layer's features in the compressed and uncompressed networks. To solve this non-convex optimization problem we propose a new optimization method of a proxy matrix using backpropagation and Stochastic Gradient Descent (SGD) with geometric constraints. Our cascaded projection approach leads to improvements in all critical areas of network compression: high accuracy, low memory consumption, low parameter count and high processing speed. The proposed CaP method demonstrates state-of-the-art results compressing VGG16 and ResNet networks with over 4x reduction in the number of computations and excellent performance in top-5 accuracy on the ImageNet dataset before and after fine-tuning.

  • Publication . Article . Preprint . 2020 . Embargo End Date: 01 Jan 2020
    Open Access
    Authors: 
    Khaled Ai Thelaya; Marco Agus; Jens Schneider;
    Publisher: arXiv

    In this paper, we present a novel data structure, called the Mixture Graph. This data structure allows us to compress, render, and query segmentation histograms. Such histograms arise when building a mipmap of a volume containing segmentation IDs. Each voxel in the histogram mipmap contains a convex combination (mixture) of segmentation IDs. Each mixture represents the distribution of IDs in the respective voxel's children. Our method factorizes these mixtures into a series of linear interpolations between exactly two segmentation IDs. The result is represented as a directed acyclic graph (DAG) whose nodes are topologically ordered. Pruning replicate nodes in the tree followed by compression allows us to store the resulting data structure efficiently. During rendering, transfer functions are propagated from sources (leafs) through the DAG to allow for efficient, pre-filtered rendering at interactive frame rates. Assembly of histogram contributions across the footprint of a given volume allows us to efficiently query partial histograms, achieving up to 178$\times$ speed-up over na$\mathrm{\"{i}}$ve parallelized range queries. Additionally, we apply the Mixture Graph to compute correctly pre-filtered volume lighting and to interactively explore segments based on shape, geometry, and orientation using multi-dimensional transfer functions. Comment: To appear in IEEE Transacations on Visualization and Computer Graphics (IEEE Vis 2020)

  • Open Access English
    Authors: 
    H. B. Benaoum; S. H. Shaglel;

    We propose a new scaling ansatz in the neutrino Dirac mass matrix to explain the low energy neutrino oscillations data, baryon number asymmetry and neutrinoless double beta decay. In this work, a full reconstruction of the neutrino Dirac mass matrix has been realized from the low energy neutrino oscillations data based on type-I seesaw mechanism. A concrete model based on $A_4$ flavor symmetry has been considered to generate such a neutrino Dirac mass matrix and imposes a relation between the two scaling factors. In this model, the right-handed Heavy Majorana neutrino masses are quasi-degenerate at TeV mass scales. Extensive numerical analysis studies have been carried out to constrain the parameter space of the model from the low energy neutrino oscillations data. It has been found that the parameter space of the Dirac mass matrix elements lies near or below the MeV region and the scaling factor $|\kappa_1|$ has to be less than 10. Furthermore, we have examined the possibility for simultaneous explanation of both neutrino oscillations data and the observed baryon number asymmetry in the Universe. Such an analysis gives further restrictions on the parameter space of the model, thereby explaining the correct neutrino data as well as the baryon number asymmetry via a resonant leptogenesis scenario. Finally, we show that the allowed space for the effective Majorana neutrino mass $m_{ee}$ is also constrained in order to account for the observed baryon asymmetry. Comment: 25 pages, 10 figues, revised version

  • Open Access English
    Authors: 
    Maurizio Capra; Beatrice Bussolino; Alberto Marchisio; Guido Masera; Maurizio Martina; Muhammad Shafique;
    Country: Italy

    Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning (DL) is already present in many applications ranging from computer vision for medicine to autonomous driving of modern cars as well as other sectors in security, healthcare, and finance. However, to achieve impressive performance, these algorithms employ very deep networks, requiring a significant computational power, both during the training and inference time. A single inference of a DL model may require billions of multiply-and-accumulated operations, making the DL extremely compute- and energy-hungry. In a scenario where several sophisticated algorithms need to be executed with limited energy and low latency, the need for cost-effective hardware platforms capable of implementing energy-efficient DL execution arises. This paper first introduces the key properties of two brain-inspired models like Deep Neural Network (DNN), and Spiking Neural Network (SNN), and then analyzes techniques to produce efficient and high-performance designs. This work summarizes and compares the works for four leading platforms for the execution of algorithms such as CPU, GPU, FPGA and ASIC describing the main solutions of the state-of-the-art, giving much prominence to the last two solutions since they offer greater design flexibility and bear the potential of high energy-efficiency, especially for the inference process. In addition to hardware solutions, this paper discusses some of the important security issues that these DNN and SNN models may have during their execution, and offers a comprehensive section on benchmarking, explaining how to assess the quality of different networks and hardware systems designed for them. Accepted for publication in IEEE Access

  • Publication . Preprint . Conference object . Article . 2020
    Open Access
    Authors: 
    Ismail Shahin;
    Publisher: IEEE

    This research aims at identifying the unknown emotion using speaker cues. In this study, we identify the unknown emotion using a two-stage framework. The first stage focuses on identifying the speaker who uttered the unknown emotion, while the next stage focuses on identifying the unknown emotion uttered by the recognized speaker in the prior stage. This proposed framework has been evaluated on an Arabic Emirati-accented speech database uttered by fifteen speakers per gender. Mel-Frequency Cepstral Coefficients (MFCCs) have been used as the extracted features and Hidden Markov Model (HMM) has been utilized as the classifier in this work. Our findings demonstrate that emotion recognition accuracy based on the two-stage framework is greater than that based on the one-stage approach and the state-of-the-art classifiers and models such as Gaussian Mixture Model (GMM), Support Vector Machine (SVM), and Vector Quantization (VQ). The average emotion recognition accuracy based on the two-stage approach is 67.5%, while the accuracy reaches to 61.4%, 63.3%, 64.5%, and 61.5%, based on the one-stage approach, GMM, SVM, and VQ, respectively. The achieved results based on the two-stage framework are very close to those attained in subjective assessment by human listeners. 5 pages

  • Publication . Preprint . Article . 2019 . Embargo End Date: 01 Jan 2019
    Open Access
    Authors: 
    Wheatcroft, Edward; Wynn, Henry; Dent, Chris J.; Smith, Jim Q.; Copeland, Claire L.; Ralph, Daniel; Zachary, Stan;
    Publisher: arXiv

    Scenario Analysis is a risk assessment tool that aims to evaluate the impact of a small number of distinct plausible future scenarios. In this paper, we provide an overview of important aspects of Scenario Analysis including when it is appropriate, the design of scenarios, uncertainty and encouraging creativity. Each of these issues is discussed in the context of climate, energy and legal scenarios.

  • Open Access
    Authors: 
    Liam Connor; J. van Leeuwen; L. C. Oostrum; Emily Petroff; Yogesh Maan; Elizabeth A. K. Adams; Jisk Attema; J. E. Bast; Oliver M. Boersma; H. Dénes; +31 more
    Publisher: Oxford University Press (OUP)
    Country: Netherlands
    Project: NWO | ARTS - the Apertif Radio ... (2300177746), EC | RadioNet (730562), NWO | Microporous membranes fro... (2300159022), EC | ALERT (617199)

    ABSTRACT We report the detection of a bright fast radio burst, FRB 191108, with Apertif on the Westerbork Synthesis Radio Telescope. The interferometer allows us to localize the FRB to a narrow 5 arcsec × 7 arcmin ellipse by employing both multibeam information within the Apertif phased-array feed beam pattern, and across different tied-array beams. The resulting sightline passes close to Local Group galaxy M33, with an impact parameter of only 18 kpc with respect to the core. It also traverses the much larger circumgalactic medium (CGM) of M31, the Andromeda Galaxy. We find that the shared plasma of the Local Group galaxies could contribute ∼10 per cent of its dispersion measure of 588 pc cm−3. FRB 191108 has a Faraday rotation measure (RM) of +474 $\pm \, 3$ rad m−2, which is too large to be explained by either the Milky Way or the intergalactic medium. Based on the more moderate RMs of other extragalactic sources that traverse the halo of M33, we conclude that the dense magnetized plasma resides in the host galaxy. The FRB exhibits frequency structure on two scales, one that is consistent with quenched Galactic scintillation and broader spectral structure with Δν ≈ 40 MHz. If the latter is due to scattering in the shared M33/M31 CGM, our results constrain the Local Group plasma environment. We found no accompanying persistent radio sources in the Apertif imaging survey data.

  • Open Access English
    Authors: 
    Othman Benomar; M. J. Goupil; Kevin Belkacem; T. Appourchaux; Martin Bo Nielsen; M. Bazot; Laurent Gizon; Shravan M. Hanasoge; Katepalli R. Sreenivasan; B. Marchand;
    Publisher: HAL CCSD
    Country: France

    Oscillation properties are usually measured by fitting symmetric Lorentzian profiles to the power spectra of Sun-like stars. However the line profiles of solar oscillations have been observed to be asymmetrical for the Sun. The physical origin of this line asymmetry is not fully understood, although it should depend on the depth dependence of the source of wave excitation (convective turbulence) and details of the observable (velocity or intensity). For oscillations of the Sun, it has been shown that neglecting the asymmetry leads to systematic errors in the frequency determination. This could subsequently affects the results of seismic inferences of the solar internal structure. Using light curves from the {\it Kepler} spacecraft we have measured mode asymmetries in 43 stars. We confirm that neglecting the asymmetry leads to systematic errors that can exceed the $1\sigma$ confidence intervals for seismic observations longer than one year. Therefore, the application of an asymmetric Lorentzian profile is to be favoured to improve the accuracy of the internal stellar structure and stellar fundamental parameters. We also show that the asymmetry changes sign between cool Sun-like stars and hotter stars. This provides the best constraints to date on the location of the excitation sources across the Hertzsprung-Russel diagram. Comment: 8 pages, 7 Figures, 1 Table, Accepted to ApJ

  • Publication . Conference object . Preprint . Article . 2019
    Open Access English
    Authors: 
    Yichao Yan; Qiang Zhang; Bingbing Ni; Wendong Zhang; Minghao Xu; Xiaokang Yang;

    Person re-identification has achieved great progress with deep convolutional neural networks. However, most previous methods focus on learning individual appearance feature embedding, and it is hard for the models to handle difficult situations with different illumination, large pose variance and occlusion. In this work, we take a step further and consider employing context information for person search. For a probe-gallery pair, we first propose a contextual instance expansion module, which employs a relative attention module to search and filter useful context information in the scene. We also build a graph learning framework to effectively employ context pairs to update target similarity. These two modules are built on top of a joint detection and instance feature learning framework, which improves the discriminativeness of the learned features. The proposed framework achieves state-of-the-art performance on two widely used person search datasets. To appear in CVPR 2019

  • Open Access English
    Authors: 
    S. Antier; S. Agayeva; Mouza Almualla; Supachai Awiphan; A. Baransky; K. Barynova; S. Beradze; M. Blažek; M. Boer; O. A. Burkhonov; +48 more
    Countries: France, Spain
    Project: ARC | ARC Centres of Excellence... (CE170100004), ARC | Discovery Early Career Re... (DE170100891)

    Parts of this research were conducted by the Australian Research Council Centre of Excellence for Gravitational Wave Discovery (OzGrav), through project number CE170100004. EJH acknowledges support from an Australian Research Council DECRA Fellowship (DE170100891). AdUP and CCT acknowledge support from Ramon y Cajal fellowships RyC-2012-09975 and RyC-2012-09984 and the Spanish Ministry of Economy and Competitiveness through project AYA2017-89384-P. DAK acknowledges Spanish research project RTI2018-098104-J-I00 (GRBPhot). MB acknowledges funding as 'personal tecnico de apoyo' under fellowship number PTA2016-13192-I. SA is supported by the CNES Postdoctoral Fellowship at Laboratoire AstroParticule et Cosmologie. SA and CL acknowledge the financial support of the Programme National Hautes Energies (PNHE). DT acknowledges the financial support of CNES postdoctoral program. UBAI acknowledges support from the Ministry of Innovative Development through projects FA-Atech-2018-392 and VA-FA-F-2-010. SB acknowledges Shota Rustaveli National Science Foundation (SRNSF) grant no. -PHDF/18-1327. TAROT has been built with the support of the Institut National des Sciences de l'Univers, CNRS, France. TAROT is funded by the CNES and thanks the help of the technical staff of the Observatoire de Haute Provence, OSUPytheas. MP, SK, and MM are supported by European Structural and Investment Fund and the Czech Ministry of Education, Youth and Sports (Projects CZ.02.1.01/0.0/0.0/16 013/0001402, CZ.02.1.01/0.0/0.0/16 013/0001403, and CZ.02.1.01/0.0/0.0/15 003/0000437). NBO, DM, and PG acknowledge financial support from NASA-MUREP-MIRO grant NNX15AP95A, NASA-EPSCoR grant NNX13AD28A, and NSF EiR AST Award 1901296. The GRANDMA collaboration thank the amateur participants to the kilonova-catcher program. The kilonova-catcher program is supported by the IdEx Universite de Paris, ANR-18-IDEX-0001. This research made use of the crossmatch service provided by CDS, Strasbourg. We thank Ulrich Hopp to provide the precise date of observations for AT2019wxt Wendelstein optical observations. GRANDMA (Global Rapid Advanced Network Devoted to the Multi-messenger Addicts) is a network of 25 telescopes of different sizes, including both photometric and spectroscopic facilities. The network aims to coordinate follow-up observations of gravitational-wave (GW) candidate alerts, especially those with large localization uncertainties, to reduce the delay between the initial detection and the optical confirmation. In this paper, we detail GRANDMA's observational performance during Advanced LIGO/Advanced Virgo Observing Run 3 (O3), focusing on the second part of O3; this includes summary statistics pertaining to coverage and possible astrophysical origin of the candidates. To do so, we quantify our observation efficiency in terms of delay between GW candidate trigger time, observations, and the total coverage. Using an optimized and robust coordination system, GRANDMA followed-up about 90 per cent of the GW candidate alerts, that is 49 out of 56 candidates. This led to coverage of over 9000 deg2 during O3. The delay between the GW candidate trigger and the first observation was below 1.5 h for 50 per cent of the alerts. We did not detect any electromagnetic counterparts to the GW candidates during O3, likely due to the very large localization areas (on average thousands of degrees squares) and relatively large distance of the candidates (above 200 Mpc for 60 per cent of binary neutron star, BNS candidates). We derive constraints on potential kilonova properties for two potential BNS coalescences (GW190425 and S200213t), assuming that the events' locations were imaged. © 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. Full list of authors: Antier, S.; Agayeva, S.; Almualla, M.; Awiphan, S.; Baransky, A.; Barynova, K.; Beradze, S.; Blažek, M.; Boër, M.; Burkhonov, O.; Christensen, N.; Coleiro, A.; Corre, D.; Coughlin, M. W.; Crisp, H.; Dietrich, T.; Ducoin, J. -G.; Duverne, P. -A.; Marchal-Duval, G.; Gendre, B.; Gokuldass, P.; Eggenstein, H. B.; Eymar, L.; Hello, P.; Howell, E. J.; Ismailov, N.; Kann, D. A.; Karpov, S.; Klotz, A.; Kochiashvili, N.; Lachaud, C.; Leroy, N.; Lin, W. L.; Li, W. X.; Mašek, M.; Mo, J.; Menard, R.; Morris, D.; Noysena, K.; Orange, N. B.; Prouza, M.; Rattanamala, R.; Sadibekova, T.; Saint-Gelais, D.; Serrau, M.; Simon, A.; Stachie, C.; Thöne, C. C.; Tillayev, Y.; Turpin, D.; de Ugarte Postigo, A.; Vasylenko, V.; Vidadi, Z.; Was, M.; Wang, X. F.; Zhang, J. J.; Zhang, T. M.; Zhang, X. H. Peer reviewed

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