Expolring Architectures for CNN-Based Word Spotting

The goal in word spotting is to retrieve parts of document images which are relevant with respect to a certain user-defined query. The recent past has seen attribute-based Convolutional Neural Networks take over this field of research. As is common for other fields of computer vision, the CNNs used for this task are already considerably deep. The question that arises, however, is: How complex does a CNN have to be for word spotting? Are increasingly deeper models giving increasingly bet- ter results or does performance behave asymptotically for these architectures? On the other hand, can similar results be obtained with a much smaller CNN? The goal of this paper is to give an answer to these questions. Therefore, the recently successful TPP- PHOCNet will be compared to a Residual Network, a Densely Connected Convolutional Network and a LeNet architecture empirically. As will be seen in the evaluation, a complex model can be beneficial for word spotting on harder tasks such as the IAM Offline Database but gives no advantage for easier benchmarks such as the George Washington Database

On weak convergence of functionals on smooth random functions

The numbers of level crossings and extremes for random processes and fields play an important role in reliability theory and many engineering applications. In many cases for Gaussian processes the Poisson approximation for their asymptotic distributions is used. This paper extends an approach proposed in Rusakov and Seleznjev (1988) for smooth random processes on a finite interval. It turns out that a number of functionals (including some integervalued ones) become continuous on the space of smooth functions and weak convergence results for the sequences of such continuous functionals are applicable. Examples of such functionals for smooth random processes on infinite intervals and for random fields are studied

Effect of copper content, initial structure, and scheme of treatment on magnetic properties of ultra-thin grain oriented electrical steel

The effect of the copper content, initial structure, and scheme of treatment on the magnetic properties of an ultra-thin grain oriented electrical steel has been investigated. In the material with copper and an initial sharp texture, the nucleation of new grains upon primary recrystallization is connected with deformation twins; in the samples without copper and with copper and diffuse texture, it is connected predominantly with shear bands and transition bands. Upon heating at a rate of ∼0.004 K/s, the temperature of primary recrystallization in the copper-bearing samples is considerably higher than in the copper-free material. Upon heating at a rate of ∼4 K/s the appearance of new grains occurs almost simultaneously for all of the studied samples. In the samples with copper and initial sharp texture after annealing at 1050 C, a significant part of the volume is occupied by grains that had undergone normal grain growth; in the samples without copper and with copper and diffuse texture, anomalous growth is hardly observed at all. To obtain high final magnetic properties of the ultra-thin grain oriented electrical steel produced by the Littmann method, it has been suggested to use an grain oriented electrical steel with 0.5% Cu that exhibits the diffuse orientation of grains as the workpiece. © 2013 Pleiades Publishing, Ltd

A new large N phase transition in YM2

Inspired by the interpretation of two dimensional Yang-Mills theory on a cylinder as a random walk on the gauge group, we point out the existence of a large N transition which is the gauge theory analogue of the cutoff transition in random walks. The transition occurs in the strong coupling region, with the 't Hooft coupling scaling as alpha*log(N), at a critical value of alpha (alpha = 4 on the sphere). The two phases below and above the transition are studied in detail. The effective number of degrees of freedom and the free energy are found to be proportional to N^(2-alpha/2) below the transition and to vanish altogether above it. The expectation value of a Wilson loop is calculated to the leading order and found to coincide in both phases with the strong coupling value.Comment: 23 pages, 3 figure