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

    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
    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 ... (10066), EC | RadioNet (730562), EC | ALERT (617199), NWO | Microporous membranes fro... (5831), NWO | ARGOThe Apertif Radio – G... (31150)

    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.

  • Publication . Preprint . Article . 2019
    Open Access English
    Authors: 
    Wheatcroft, Edward; Wynn, Henry; Dent, Chris J.; Smith, Jim Q.; Copeland, Claire L.; Ralph, Daniel; Zachary, Stan;

    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 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

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

    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

  • Publication . Article . Preprint . Other literature type . 2013
    Open Access
    Authors: 
    Cornelius A. Rietveld; Sarah E. Medland; Jaime Derringer; Jian Yang; Tõnu Esko; Nicolas W. Martin; Harm-Jan Westra; Konstantin Shakhbazov; Abdel Abdellaoui; Arpana Agrawal; +173 more
    Countries: United Kingdom, Croatia, Netherlands, Australia, United States
    Project: NIH | NBER Center for Aging and... (5P30AG012810-15), EC | DEVHEALTH (269874), NSF | EAGER Proposal: Workshop ... (1064089), WT , NIH | FINANCIAL STATUS--RETIREM... (2P01AG005842-04), NIH | ECONOMICS OF AGING TRAINI... (5T32AG000186-10), EC | GMI (230374)

    A genome-wide association study (GWAS) of educational attainment was conducted in a discovery sample of 101,069 individuals and a replication sample of 25,490. Three independent single-nucleotide polymorphisms (SNPs) are genome-wide significant (rs9320913, rs11584700, rs4851266), and all three replicate. Estimated effects sizes are small (coefficient of determination R2 ≈ 0.02%), approximately 1 month of schooling per allele. A linear polygenic score from all measured SNPs accounts for ≈2% of the variance in both educational attainment and cognitive function. Genes in the region of the loci have previously been associated with health, cognitive, and central nervous system phenotypes, and bioinformatics analyses suggest the involvement of the anterior caudate nucleus. These findings provide promising candidate SNPs for follow-up work, and our effect size estimates can anchor power analyses in social-science genetics. Economics

  • Open Access English
    Authors: 
    Khaled Ai Thelaya; Marco Agus; Jens Schneider;

    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
    Authors: 
    Maurizio Capra; Beatrice Bussolino; Alberto Marchisio; Guido Masera; Maurizio Martina; Muhammad Shafique;
    Publisher: arXiv
    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. Comment: Accepted for publication in IEEE Access

  • Open Access
    Authors: 
    Aaron A. Dutton; Andrea V. Macciò; Jonas Frings; Liang Wang; G. S. Stinson; Camilla Penzo; Xi Kang;
    Publisher: arXiv
    Project: EC | MW-DISK (321035)

    We compare the half-light circular velocities, V_{1/2}, of dwarf galaxies in the Local Group to the predicted circular velocity curves of galaxies in the NIHAO suite of LCDM simulations. We use a subset of 34 simulations in which the central galaxy has a stellar luminosity in the range 0.5 x 10^5 < L_V < 2 x 10^8 L_{sun}. The NIHAO galaxy simulations reproduce the relation between stellar mass and halo mass from abundance matching, as well as the observed half-light size vs luminosity relation. The corresponding dissipationless simulations over-predict the V_{1/2}, recovering the problem known as too big to fail (TBTF). By contrast, the NIHAO simulations have expanded dark matter haloes, and provide an excellent match to the distribution of V_{1/2} for galaxies with L_V > 2 x 10^6 L_{sun}. For lower luminosities our simulations predict very little halo response, and tend to over predict the observed circular velocities. In the context of LCDM, this could signal the increased stochasticity of star formation in haloes below M_{halo} \sim 10^{10} M_{sun}, or the role of environmental effects. Thus, haloes that are "too big to fail", do not fail LCDM, but haloes that are "too small to pass" (the galaxy formation threshold) provide a future test of LCDM. 6 pages, 3 figures, accepted to MNRAS letters

  • Publication . Article . Preprint . 2016 . Embargo End Date: 01 Jan 2016
    Open Access
    Authors: 
    Fumiki Yoshihara; Tomoko Fuse; Sahel Ashhab; Kosuke Kakuyanagi; Shiro Saito; Kouichi Semba;
    Publisher: arXiv

    The interaction between an atom and the electromagnetic field inside a cavity has played a crucial role in the historical development of our understanding of light-matter interaction and is a central part of various quantum technologies, such as lasers and many quantum computing architectures. The emergence of superconducting qubits has allowed the realization of strong and ultrastrong coupling between artificial atoms and cavities. If the coupling strength $g$ becomes as large as the atomic and cavity frequencies ($\Delta$ and $\omega_{\rm o}$ respectively), the energy eigenstates including the ground state are predicted to be highly entangled. This qualitatively new regime can be called the deep strong-coupling regime, and there has been an ongoing debate over whether it is fundamentally possible to realize this regime in realistic physical systems. By inductively coupling a flux qubit and an LC oscillator via Josephson junctions, we have realized circuits with $g/\omega_{\rm o}$ ranging from 0.72 to 1.34 and $g/\Delta\gg 1$. Using spectroscopy measurements, we have observed unconventional transition spectra, with patterns resembling masquerade masks, that are characteristic of this new regime. Our results provide a basis for ground-state-based entangled-pair generation and open a new direction of research on strongly correlated light-matter states in circuit-quantum electrodynamics. Comment: 3 figures, Methods, and Supplementary Information

Advanced search in
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
3,061 Research products, page 1 of 307
  • Publication . Preprint . Conference object . Article . 2019
    Open Access
    Authors: 
    Yichao Yan; Qiang Zhang; Bingbing Ni; Wendong Zhang; Minghao Xu; Xiaokang Yang;
    Publisher: IEEE

    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
    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 ... (10066), EC | RadioNet (730562), EC | ALERT (617199), NWO | Microporous membranes fro... (5831), NWO | ARGOThe Apertif Radio – G... (31150)

    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.

  • Publication . Preprint . Article . 2019
    Open Access English
    Authors: 
    Wheatcroft, Edward; Wynn, Henry; Dent, Chris J.; Smith, Jim Q.; Copeland, Claire L.; Ralph, Daniel; Zachary, Stan;

    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 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

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

    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

  • Publication . Article . Preprint . Other literature type . 2013
    Open Access
    Authors: 
    Cornelius A. Rietveld; Sarah E. Medland; Jaime Derringer; Jian Yang; Tõnu Esko; Nicolas W. Martin; Harm-Jan Westra; Konstantin Shakhbazov; Abdel Abdellaoui; Arpana Agrawal; +173 more
    Countries: United Kingdom, Croatia, Netherlands, Australia, United States
    Project: NIH | NBER Center for Aging and... (5P30AG012810-15), EC | DEVHEALTH (269874), NSF | EAGER Proposal: Workshop ... (1064089), WT , NIH | FINANCIAL STATUS--RETIREM... (2P01AG005842-04), NIH | ECONOMICS OF AGING TRAINI... (5T32AG000186-10), EC | GMI (230374)

    A genome-wide association study (GWAS) of educational attainment was conducted in a discovery sample of 101,069 individuals and a replication sample of 25,490. Three independent single-nucleotide polymorphisms (SNPs) are genome-wide significant (rs9320913, rs11584700, rs4851266), and all three replicate. Estimated effects sizes are small (coefficient of determination R2 ≈ 0.02%), approximately 1 month of schooling per allele. A linear polygenic score from all measured SNPs accounts for ≈2% of the variance in both educational attainment and cognitive function. Genes in the region of the loci have previously been associated with health, cognitive, and central nervous system phenotypes, and bioinformatics analyses suggest the involvement of the anterior caudate nucleus. These findings provide promising candidate SNPs for follow-up work, and our effect size estimates can anchor power analyses in social-science genetics. Economics

  • Open Access English
    Authors: 
    Khaled Ai Thelaya; Marco Agus; Jens Schneider;

    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
    Authors: 
    Maurizio Capra; Beatrice Bussolino; Alberto Marchisio; Guido Masera; Maurizio Martina; Muhammad Shafique;
    Publisher: arXiv
    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. Comment: Accepted for publication in IEEE Access

  • Open Access
    Authors: 
    Aaron A. Dutton; Andrea V. Macciò; Jonas Frings; Liang Wang; G. S. Stinson; Camilla Penzo; Xi Kang;
    Publisher: arXiv
    Project: EC | MW-DISK (321035)

    We compare the half-light circular velocities, V_{1/2}, of dwarf galaxies in the Local Group to the predicted circular velocity curves of galaxies in the NIHAO suite of LCDM simulations. We use a subset of 34 simulations in which the central galaxy has a stellar luminosity in the range 0.5 x 10^5 < L_V < 2 x 10^8 L_{sun}. The NIHAO galaxy simulations reproduce the relation between stellar mass and halo mass from abundance matching, as well as the observed half-light size vs luminosity relation. The corresponding dissipationless simulations over-predict the V_{1/2}, recovering the problem known as too big to fail (TBTF). By contrast, the NIHAO simulations have expanded dark matter haloes, and provide an excellent match to the distribution of V_{1/2} for galaxies with L_V > 2 x 10^6 L_{sun}. For lower luminosities our simulations predict very little halo response, and tend to over predict the observed circular velocities. In the context of LCDM, this could signal the increased stochasticity of star formation in haloes below M_{halo} \sim 10^{10} M_{sun}, or the role of environmental effects. Thus, haloes that are "too big to fail", do not fail LCDM, but haloes that are "too small to pass" (the galaxy formation threshold) provide a future test of LCDM. 6 pages, 3 figures, accepted to MNRAS letters

  • Publication . Article . Preprint . 2016 . Embargo End Date: 01 Jan 2016
    Open Access
    Authors: 
    Fumiki Yoshihara; Tomoko Fuse; Sahel Ashhab; Kosuke Kakuyanagi; Shiro Saito; Kouichi Semba;
    Publisher: arXiv

    The interaction between an atom and the electromagnetic field inside a cavity has played a crucial role in the historical development of our understanding of light-matter interaction and is a central part of various quantum technologies, such as lasers and many quantum computing architectures. The emergence of superconducting qubits has allowed the realization of strong and ultrastrong coupling between artificial atoms and cavities. If the coupling strength $g$ becomes as large as the atomic and cavity frequencies ($\Delta$ and $\omega_{\rm o}$ respectively), the energy eigenstates including the ground state are predicted to be highly entangled. This qualitatively new regime can be called the deep strong-coupling regime, and there has been an ongoing debate over whether it is fundamentally possible to realize this regime in realistic physical systems. By inductively coupling a flux qubit and an LC oscillator via Josephson junctions, we have realized circuits with $g/\omega_{\rm o}$ ranging from 0.72 to 1.34 and $g/\Delta\gg 1$. Using spectroscopy measurements, we have observed unconventional transition spectra, with patterns resembling masquerade masks, that are characteristic of this new regime. Our results provide a basis for ground-state-based entangled-pair generation and open a new direction of research on strongly correlated light-matter states in circuit-quantum electrodynamics. Comment: 3 figures, Methods, and Supplementary Information

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