46,169 research outputs found
Interactive 3D Modeling with a Generative Adversarial Network
This paper proposes the idea of using a generative adversarial network (GAN)
to assist a novice user in designing real-world shapes with a simple interface.
The user edits a voxel grid with a painting interface (like Minecraft). Yet, at
any time, he/she can execute a SNAP command, which projects the current voxel
grid onto a latent shape manifold with a learned projection operator and then
generates a similar, but more realistic, shape using a learned generator
network. Then the user can edit the resulting shape and snap again until he/she
is satisfied with the result. The main advantage of this approach is that the
projection and generation operators assist novice users to create 3D models
characteristic of a background distribution of object shapes, but without
having to specify all the details. The core new research idea is to use a GAN
to support this application. 3D GANs have previously been used for shape
generation, interpolation, and completion, but never for interactive modeling.
The new challenge for this application is to learn a projection operator that
takes an arbitrary 3D voxel model and produces a latent vector on the shape
manifold from which a similar and realistic shape can be generated. We develop
algorithms for this and other steps of the SNAP processing pipeline and
integrate them into a simple modeling tool. Experiments with these algorithms
and tool suggest that GANs provide a promising approach to computer-assisted
interactive modeling.Comment: Published at International Conference on 3D Vision 2017
(http://irc.cs.sdu.edu.cn/3dv/index.html
Reconciling results of LSND, MiniBooNE and other experiments with soft decoherence
We propose an explanation of the LSND signal via quantum-decoherence of the
mass states, which leads to damping of the interference terms in the
oscillation probabilities. The decoherence parameters as well as their energy
dependence are chosen in such a way that the damping affects only oscillations
with the large (atmospheric) and rapidly decreases with the
neutrino energy. This allows us to reconcile the positive LSND signal with
MiniBooNE and other null-result experiments. The standard explanations of
solar, atmospheric, KamLAND and MINOS data are not affected. No new particles,
and in particular, no sterile neutrinos are needed. The LSND signal is
controlled by the 1-3 mixing angle and, depending on the degree
of damping, yields at . The
scenario can be tested at upcoming searches: while the comparison
of near and far detector measurements at reactors should lead to a null-result
a positive signal for is expected in long-baseline accelerator
experiments. The proposed decoherence may partially explain the results of
Gallium detector calibrations and it can strongly affect supernova neutrino
signals.Comment: 20 pages, 3 figure
Non-Gibbs states on a Bose-Hubbard lattice
We study the equilibrium properties of the repulsive quantum Bose-Hubbard
model at high temperatures in arbitrary dimensions, with and without disorder.
In its microcanonical setting the model conserves energy and particle number.
The microcanonical dynamics is characterized by a pair of two densities: energy
density and particle number density . The macrocanonical Gibbs
distribution also depends on two parameters: the inverse nonnegative
temperature and the chemical potential . We prove the existence of
non-Gibbs states, that is, pairs which cannot be mapped onto
. The separation line in the density control parameter space
between Gibbs and non-Gibbs states corresponds to
infinite temperature . The non-Gibbs phase cannot be cured into a
Gibbs one within the standard Gibbs formalism using negative temperatures.Comment: 8 pages, 1 figure, misprints correcte
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