Spectral Functions of One-dimensional Models of Correlated Electrons

Using the Ogata-Shiba wave function, the spectral functions of the one-dimensional infinite U Hubbard model are calculated for various concentrations. It is shown that the ``shadow band'' feature due to 2k_F fluctuations becomes more intense close to half-filling. Comparing these results with exact diagonalization data obtained on finite clusters for the finite U Hubbard model and for the t-J model, it is also shown that this feature remains well-defined for physically reasonable values of the parameters (U/t\simeq 10, J/t\simeq 0.4). The ``shadow'' structure in the spectral functions should thus be observable in angle-resolved photoemission experiments for a variety of quasi-one dimensional compounds.Comment: 4 pages, 6 embedded encapsulated PostScript figures, to be published in Phys. Rev. B (RC

Characterization methods dedicated to nanometer-thick hBN layers

Hexagonal boron nitride (hBN) regains interest as a strategic component in graphene engineering and in van der Waals heterostructures built with two dimensional materials. It is crucial then, to handle reliable characterization techniques capable to assess the quality of structural and electronic properties of the hBN material used. We present here characterization procedures based on optical spectroscopies, namely cathodoluminescence and Raman, with the additional support of structural analysis conducted by transmission electron microscopy. We show the capability of optical spectroscopies to investigate and benchmark the optical and structural properties of various hBN thin layers sources

Modeling and segmentation of audio descriptor profiles with segmental models

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AgingMapGAN (AMGAN): High-Resolution Controllable Face Aging with Spatially-Aware Conditional GANs

Existing approaches and datasets for face aging produce results skewed towards the mean, with individual variations and expression wrinkles often invisible or overlooked in favor of global patterns such as the fattening of the face. Moreover, they offer little to no control over the way the faces are aged and can difficultly be scaled to large images, thus preventing their usage in many real-world applications. To address these limitations, we present an approach to change the appearance of a high-resolution image using ethnicity-specific aging information and weak spatial supervision to guide the aging process. We demonstrate the advantage of our proposed method in terms of quality, control, and how it can be used on high-definition images while limiting the computational overhead.Comment: Project page: https://despoisj.github.io/AgingMapGAN

Galactic star formation in parsec-scale resolution simulations

The interstellar medium (ISM) in galaxies is multiphase and cloudy, with stars forming in the very dense, cold gas found in Giant Molecular Clouds (GMCs). Simulating the evolution of an entire galaxy, however, is a computational problem which covers many orders of magnitude, so many simulations cannot reach densities high enough or temperatures low enough to resolve this multiphase nature. Therefore, the formation of GMCs is not captured and the resulting gas distribution is smooth, contrary to observations. We investigate how star formation (SF) proceeds in simulated galaxies when we obtain parsec-scale resolution and more successfully capture the multiphase ISM. Both major mergers and the accretion of cold gas via filaments are dominant contributors to a galaxy's total stellar budget and we examine SF at high resolution in both of these contexts.Comment: 4 pages, 4 figures. To appear in the proceedings for IAU Symposium 270: Computational Star Formation (eds. Alves, Elmegreen, Girart, Trimble

Asymptotic expansion of the optimal control under logarithmic penalty: worked example and open problems

We discuss the problem of expansion of optimal control, state and costate when a logarithmic penalty is applied to constraints. We show that, in a simple case, that the variation of (a regular) junction point, and of the optimal control, state and costate is of order \eps\log \eps, where \eps is the penalty parameter

Nonlinear spectral compression in optical fiber:a new tool for processing degraded signals

We propose two new applications of the spectral focusing by self-phase modulation that occurs in a nonlinear optical fiber. We numerically show the possibility of using nonlinear spectral compression to improve the optical signal to noise ratio and mitigate the amplitude jitter of the signal pulses. We also demonstrate experimentally that use of spectral focusing in a combination with an external sinusoidal phase modulation achieves efficient suppression of coherent spectral background

Detecting Overfitting of Deep Generative Networks via Latent Recovery

State of the art deep generative networks are capable of producing images with such incredible realism that they can be suspected of memorizing training images. It is why it is not uncommon to include visualizations of training set nearest neighbors, to suggest generated images are not simply memorized. We demonstrate this is not sufficient and motivates the need to study memorization/overfitting of deep generators with more scrutiny. This paper addresses this question by i) showing how simple losses are highly effective at reconstructing images for deep generators ii) analyzing the statistics of reconstruction errors when reconstructing training and validation images, which is the standard way to analyze overfitting in machine learning. Using this methodology, this paper shows that overfitting is not detectable in the pure GAN models proposed in the literature, in contrast with those using hybrid adversarial losses, which are amongst the most widely applied generative methods. The paper also shows that standard GAN evaluation metrics fail to capture memorization for some deep generators. Finally, the paper also shows how off-the-shelf GAN generators can be successfully applied to face inpainting and face super-resolution using the proposed reconstruction method, without hybrid adversarial losses