30 research outputs found
A new proof for the decidability of D0L ultimate periodicity
We give a new proof for the decidability of the D0L ultimate periodicity
problem based on the decidability of p-periodicity of morphic words adapted to
the approach of Harju and Linna.Comment: In Proceedings WORDS 2011, arXiv:1108.341
The Critical Exponent is Computable for Automatic Sequences
The critical exponent of an infinite word is defined to be the supremum of
the exponent of each of its factors. For k-automatic sequences, we show that
this critical exponent is always either a rational number or infinite, and its
value is computable. Our results also apply to variants of the critical
exponent, such as the initial critical exponent of Berthe, Holton, and Zamboni
and the Diophantine exponent of Adamczewski and Bugeaud. Our work generalizes
or recovers previous results of Krieger and others, and is applicable to other
situations; e.g., the computation of the optimal recurrence constant for a
linearly recurrent k-automatic sequence.Comment: In Proceedings WORDS 2011, arXiv:1108.341
The complexity of tangent words
In a previous paper, we described the set of words that appear in the coding
of smooth (resp. analytic) curves at arbitrary small scale. The aim of this
paper is to compute the complexity of those languages.Comment: In Proceedings WORDS 2011, arXiv:1108.341
Infinite permutations vs. infinite words
I am going to compare well-known properties of infinite words with those of
infinite permutations, a new object studied since middle 2000s. Basically, it
was Sergey Avgustinovich who invented this notion, although in an early study
by Davis et al. permutations appear in a very similar framework as early as in
1977. I am going to tell about periodicity of permutations, their complexity
according to several definitions and their automatic properties, that is, about
usual parameters of words, now extended to permutations and behaving sometimes
similarly to those for words, sometimes not. Another series of results concerns
permutations generated by infinite words and their properties. Although this
direction of research is young, many people, including two other speakers of
this meeting, have participated in it, and I believe that several more topics
for further study are really promising.Comment: In Proceedings WORDS 2011, arXiv:1108.341
Physics of Solar Prominences: II - Magnetic Structure and Dynamics
Observations and models of solar prominences are reviewed. We focus on
non-eruptive prominences, and describe recent progress in four areas of
prominence research: (1) magnetic structure deduced from observations and
models, (2) the dynamics of prominence plasmas (formation and flows), (3)
Magneto-hydrodynamic (MHD) waves in prominences and (4) the formation and
large-scale patterns of the filament channels in which prominences are located.
Finally, several outstanding issues in prominence research are discussed, along
with observations and models required to resolve them.Comment: 75 pages, 31 pictures, review pape
Placental growth factor may predict increased left ventricular mass index in patients with mild to moderate chronic kidney disease – a prospective observational study
Einfluss von Beimengungen auf die durch das System Titan(III)-chlorid-Triäthylaluminium katalysierte Polymerisation des Propylens
High-resolution characterization of deformation induced martensite in large areas of fatigued austenitic stainless steel using deep learning
Abstract
This paper aims to demonstrate a novel technique enabling the accurate visualization and fast mapping of deformation-induced α′-martensite produced during cyclic straining of a metastable austenitic stainless steel, refined by reversion annealing to different grain sizes. The technique is based on energy and angular separation of the signal electrons in a scanning electron microscope (SEM). Collection of the inelastic backscattered electrons emitted under high take-off angles from a sample surface results in the acquisition of micrographs with high sensitivity to structural defects, such as dislocations, grain boundaries, and other imperfections. The areas with a high density of lattice imperfections reduce the penetration depth of the primary electrons, and simultaneously affect the signal electrons leaving the specimen. This results in an increase in the inelastic backscattered electrons yielded from the vicinity of α′-martensite, and a bright halo surrounds this phase. The α′-martensite phase can thus be separated from the austenitic matrix in SEM micrographs. In this work, we propose a deep learning method for a precise α′-martensite mapping within a large area. Various deep learning-based methods have been tested, and the best result measured by both Dice loss and IoU scores has been achieved using the U-Net architecture extended by the ResNet encoder