2,650 research outputs found
A survey of comics research in computer science
Graphical novels such as comics and mangas are well known all over the world.
The digital transition started to change the way people are reading comics,
more and more on smartphones and tablets and less and less on paper. In the
recent years, a wide variety of research about comics has been proposed and
might change the way comics are created, distributed and read in future years.
Early work focuses on low level document image analysis: indeed comic books are
complex, they contains text, drawings, balloon, panels, onomatopoeia, etc.
Different fields of computer science covered research about user interaction
and content generation such as multimedia, artificial intelligence,
human-computer interaction, etc. with different sets of values. We propose in
this paper to review the previous research about comics in computer science, to
state what have been done and to give some insights about the main outlooks
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
Convolutional neural networks have recently demonstrated high-quality
reconstruction for single-image super-resolution. In this paper, we propose the
Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively
reconstruct the sub-band residuals of high-resolution images. At each pyramid
level, our model takes coarse-resolution feature maps as input, predicts the
high-frequency residuals, and uses transposed convolutions for upsampling to
the finer level. Our method does not require the bicubic interpolation as the
pre-processing step and thus dramatically reduces the computational complexity.
We train the proposed LapSRN with deep supervision using a robust Charbonnier
loss function and achieve high-quality reconstruction. Furthermore, our network
generates multi-scale predictions in one feed-forward pass through the
progressive reconstruction, thereby facilitates resource-aware applications.
Extensive quantitative and qualitative evaluations on benchmark datasets show
that the proposed algorithm performs favorably against the state-of-the-art
methods in terms of speed and accuracy.Comment: This work is accepted in CVPR 2017. The code and datasets are
available on http://vllab.ucmerced.edu/wlai24/LapSRN
MaNGA galaxy properties -- II. A detailed comparison of observed and simulated spiral galaxy scaling relations
We present a catalogue of dynamical properties for 2368 late-type galaxies
from the MaNGA survey. The latter complements the catalogue of photometric
properties for the same sample based on deep optical DESI photometry processed
with AutoProf. Rotation curves (RCs), extracted by model fitting H
velocity maps from the MaNGA Data Analysis Pipeline, extend out to 1.4 (1.9)
R for the primary (secondary) MaNGA samples. The RCs and ancillary MaNGA
Pipe3D data products were used to construct various fundamental galaxy scaling
relations that are also compared uniformly with similar relations from NIHAO
zoom-in simulations. Simulated NIHAO galaxies were found to broadly reproduce
the observed MaNGA galaxy population for .
Some discrepancies remain, such as those pertaining to central stellar
densities and the diversity of RCs due to strong feedback schemes. Also
presented are spatially-resolved scatters for the velocity-size-stellar mass
(VRM) structural relations using MaNGA and NIHAO samples. The scatter for
these relations in the galaxian interiors is a consequence of the diversity of
inner RC shapes, while scatter in the outskirts is dictated by the large range
of stellar surface densities which itself is driven by sporadic star formation.
The detailed spatially-resolved scatter analysis highlights the complex
interplay between local and global astrophysical processes and provides a
strong constraint to numerical simulations.Comment: 21 pages, 13 Figures, Accepted for publication in MNRA
Viscous Withdrawal of Miscible Liquid Layers
In viscous withdrawal, a converging flow imposed in an upper layer of viscous
liquid entrains liquid from a lower, stably stratified layer. Using the idea
that a thin tendril is entrained by a local straining flow, we propose a
scaling law for the volume flux of liquid entrained from miscible liquid
layers. A long-wavelength model including only local information about the
withdrawal flow is degenerate, with multiple tendril solutions for one
withdrawal condition. Including information about the global geometry of the
withdrawal flow removes the degeneracy while introducing only a logarithmic
dependence on the global flow parameters into the scaling law.Comment: 4 pages, 4 figure
The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment
The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in
operation since July 2014. This paper describes the second data release from
this phase, and the fourteenth from SDSS overall (making this, Data Release
Fourteen or DR14). This release makes public data taken by SDSS-IV in its first
two years of operation (July 2014-2016). Like all previous SDSS releases, DR14
is cumulative, including the most recent reductions and calibrations of all
data taken by SDSS since the first phase began operations in 2000. New in DR14
is the first public release of data from the extended Baryon Oscillation
Spectroscopic Survey (eBOSS); the first data from the second phase of the
Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2),
including stellar parameter estimates from an innovative data driven machine
learning algorithm known as "The Cannon"; and almost twice as many data cubes
from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous
release (N = 2812 in total). This paper describes the location and format of
the publicly available data from SDSS-IV surveys. We provide references to the
important technical papers describing how these data have been taken (both
targeting and observation details) and processed for scientific use. The SDSS
website (www.sdss.org) has been updated for this release, and provides links to
data downloads, as well as tutorials and examples of data use. SDSS-IV is
planning to continue to collect astronomical data until 2020, and will be
followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14
happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov
2017 (this is the "post-print" and "post-proofs" version; minor corrections
only from v1, and most of errors found in proofs corrected
Unsupervised Diverse Colorization via Generative Adversarial Networks
Colorization of grayscale images has been a hot topic in computer vision.
Previous research mainly focuses on producing a colored image to match the
original one. However, since many colors share the same gray value, an input
grayscale image could be diversely colored while maintaining its reality. In
this paper, we design a novel solution for unsupervised diverse colorization.
Specifically, we leverage conditional generative adversarial networks to model
the distribution of real-world item colors, in which we develop a fully
convolutional generator with multi-layer noise to enhance diversity, with
multi-layer condition concatenation to maintain reality, and with stride 1 to
keep spatial information. With such a novel network architecture, the model
yields highly competitive performance on the open LSUN bedroom dataset. The
Turing test of 80 humans further indicates our generated color schemes are
highly convincible
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