2,239 research outputs found
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
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
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