3,313 research outputs found
Wall-thickness changes predicted in hollow-drawn tubing
Hollow-tube drawing or tube sinking theory is based on the concept of continuous distribution of dislocations. Material composition, parameter influence, and die-angle are determining factors in derivation of the theoretical model
Correlated Equilibria of Classical Strategic Games with Quantum Signals
Correlated equilibria are sometimes more efficient than the Nash equilibria
of a game without signals. We investigate whether the availability of quantum
signals in the context of a classical strategic game may allow the players to
achieve even better efficiency than in any correlated equilibrium with
classical signals, and find the answer to be positive.Comment: 8 pages, LaTe
Mesoscale theory of grains and cells: crystal plasticity and coarsening
Solids with spatial variations in the crystalline axes naturally evolve into
cells or grains separated by sharp walls. Such variations are mathematically
described using the Nye dislocation density tensor. At high temperatures,
polycrystalline grains form from the melt and coarsen with time: the
dislocations can both climb and glide. At low temperatures under shear the
dislocations (which allow only glide) form into cell structures. While both the
microscopic laws of dislocation motion and the macroscopic laws of coarsening
and plastic deformation are well studied, we hitherto have had no simple,
continuum explanation for the evolution of dislocations into sharp walls. We
present here a mesoscale theory of dislocation motion. It provides a
quantitative description of deformation and rotation, grounded in a microscopic
order parameter field exhibiting the topologically conserved quantities. The
topological current of the Nye dislocation density tensor is derived from a
microscopic theory of glide driven by Peach-Koehler forces between dislocations
using a simple closure approximation. The resulting theory is shown to form
sharp dislocation walls in finite time, both with and without dislocation
climb.Comment: 5 pages, 3 figure
Affine actions on non-archimedean trees
We initiate the study of affine actions of groups on -trees for a
general ordered abelian group ; these are actions by dilations rather
than isometries. This gives a common generalisation of isometric action on a
-tree, and affine action on an -tree as studied by I. Liousse. The
duality between based length functions and actions on -trees is
generalised to this setting. We are led to consider a new class of groups:
those that admit a free affine action on a -tree for some .
Examples of such groups are presented, including soluble Baumslag-Solitar
groups and the discrete Heisenberg group.Comment: 27 pages. Section 1.4 expanded, typos corrected from previous versio
Blazar Flaring Patterns (B-FlaP): Classifying Blazar Candidates of Uncertain type in the third Fermi-LAT catalog by Artificial Neural Networks
The Fermi Large Area Telescope (LAT) is currently the most important facility
for investigating the GeV -ray sky. With Fermi LAT more than three
thousand -ray sources have been discovered so far. 1144 () of
the sources are active galaxies of the blazar class, and 573 () are
listed as Blazar Candidate of Uncertain type (BCU), or sources without a
conclusive classification. We use the Empirical Cumulative Distribution
Functions (ECDF) and the Artificial Neural Networks (ANN) for a fast method of
screening and classification for BCUs based on data collected at -ray
energies only, when rigorous multiwavelength analysis is not available. Based
on our method, we classify 342 BCUs as BL Lacs and 154 as FSRQs, while 77
objects remain uncertain. Moreover, radio analysis and direct observations in
ground-based optical observatories are used as counterparts to the statistical
classifications to validate the method. This approach is of interest because of
the increasing number of unclassified sources in Fermi catalogs and because
blazars and in particular their subclass High Synchrotron Peak (HSP) objects
are the main targets of atmospheric Cherenkov telescopes.Comment: 18 pages, 17 figures, accepted for publication on MNRA
Dictionary Learning-based Inpainting on Triangular Meshes
The problem of inpainting consists of filling missing or damaged regions in
images and videos in such a way that the filling pattern does not produce
artifacts that deviate from the original data. In addition to restoring the
missing data, the inpainting technique can also be used to remove undesired
objects. In this work, we address the problem of inpainting on surfaces through
a new method based on dictionary learning and sparse coding. Our method learns
the dictionary through the subdivision of the mesh into patches and rebuilds
the mesh via a method of reconstruction inspired by the Non-local Means method
on the computed sparse codes. One of the advantages of our method is that it is
capable of filling the missing regions and simultaneously removes noise and
enhances important features of the mesh. Moreover, the inpainting result is
globally coherent as the representation based on the dictionaries captures all
the geometric information in the transformed domain. We present two variations
of the method: a direct one, in which the model is reconstructed and restored
directly from the representation in the transformed domain and a second one,
adaptive, in which the missing regions are recreated iteratively through the
successive propagation of the sparse code computed in the hole boundaries,
which guides the local reconstructions. The second method produces better
results for large regions because the sparse codes of the patches are adapted
according to the sparse codes of the boundary patches. Finally, we present and
analyze experimental results that demonstrate the performance of our method
compared to the literature
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