Historical Document Image Segmentation with LDA-Initialized Deep Neural Networks

In this paper, we present a novel approach to perform deep neural networks layer-wise weight initialization using Linear Discriminant Analysis (LDA). Typically, the weights of a deep neural network are initialized with: random values, greedy layer-wise pre-training (usually as Deep Belief Network or as auto-encoder) or by re-using the layers from another network (transfer learning). Hence, many training epochs are needed before meaningful weights are learned, or a rather similar dataset is required for seeding a fine-tuning of transfer learning. In this paper, we describe how to turn an LDA into either a neural layer or a classification layer. We analyze the initialization technique on historical documents. First, we show that an LDA-based initialization is quick and leads to a very stable initialization. Furthermore, for the task of layout analysis at pixel level, we investigate the effectiveness of LDA-based initialization and show that it outperforms state-of-the-art random weight initialization methods.Comment: 5 page

N-light-N: Read The Friendly Manual

This documentation wants to be a "user manual" for the N-light-N framework. The goal is not only to introduce the framework but also to provide enough information such that one can start modifying and upgrading it after reading this document. This document is divided into five chapters. The main purpose of Chapter 1 is to introduce into our notation and formulation. It refers to further literature for deeper introductions into the theory. Chapter 2 gives quick-start information allowing to start using the framework in an extremely short time. Chapter 3 provides an overview of the framework's architecture. Interactions among different entities are explained and the main work flow is provided. Chapter 4 explains how to write a custom XML script for the framework. Proper usage of all implemented commands is described. Finally, Chapter 6 explains how to extend the framework by creating your own script commands, layers (encoder/decoder), and autoencoders. Having read both Chapters 3 and 6 before starting to extend the framework is extremely recommended. As the framework will evolve, this documentation should be kept up-to-date

A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis

Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of human-annotated learning samples. With the advent of deep neural networks, a promising way to cope with the lack of training data is to pre-train models on images from a different domain and then fine-tune them on historical documents. In the current research, a typical example of such cross-domain transfer learning is the use of neural networks that have been pre-trained on the ImageNet database for object recognition. It remains a mostly open question whether or not this pre-training helps to analyse historical documents, which have fundamentally different image properties when compared with ImageNet. In this paper, we present a comprehensive empirical survey on the effect of ImageNet pre-training for diverse historical document analysis tasks, including character recognition, style classification, manuscript dating, semantic segmentation, and content-based retrieval. While we obtain mixed results for semantic segmentation at pixel-level, we observe a clear trend across different network architectures that ImageNet pre-training has a positive effect on classification as well as content-based retrieval

Relationship between Ground Reaction Force Characteristics and Bone Mineral Density of the Hip and Spine in Male Runners

International Journal of Exercise Science 15(1): 655-666, 2022. The purpose of this study was to determine the relationship between running ground reaction force (GRF) characteristics and hip and lumbar spine bone mineral density (BMD) values in male runners. Individuals who ran at least 48.3 km per week and were injury-free were recruited. Kistler force plates collected running vertical and anteroposterior GRF data. A Hologic Discovery W bone densitometer measured lumbar spine and five regional hip BMD values. Only runners who consistently used a rear foot strike pattern were included (n = 32). Pearson correlation coefficients were calculated between BMD values and various GRF values and step-wise multiple regression was run to predict BMD values from the various GRF values. The vertical impact force was significantly correlated with the lumbar spine and four of the five hip BMD values (r \u3e 0.374, p \u3c 0.035). Both the peak early loading rate (ELR) and average ELR were significantly correlated with the lumbar spine and Ward’s triangle BMD (r \u3e 0.430, p \u3c 0.014), while the average active loading rate was correlated only with the Ward’s triangle BMD (r = 0.438, p = 0.012). Multiple regression revealed the peak impact force was the predictor for every hip region BMD other than the trochanter and the average ELR as a predictor for the lumbar spine BMD. The peak braking force was negatively correlated with the Ward’s triangle BMD (r = - 0.414, p = 0.019). It appears that the large forces and loading rates associated with rear foot striking may be advantageous and predictive for BMD at the hip and spine

On the mechanism of photoinduced dimer dissociation in the plant UVR8 photoreceptor

UV-B absorption by the photoreceptor UV resistance locus 8 (UVR8) consisting of two identical protein units triggers a signal chain used by plants in connection with protection and repair of UV-B induced damage. X-ray structural analysis of the purified protein [Christie JM, et al. (2012) Science 335(6075):1492–1496] [Wu D, et al. (2012) Nature 484(7393): 214–220] has revealed that the dimer is held together by arginine–aspartate salt bridges. In this paper we address the initial processes in the signal chain. On the basis of high-level quantum-chemical calculations, we propose a mechanism for the photodissociation of UVR8 that consists of three steps: (i) In each monomer, multiple tryptophans form an extended light-harvesting system in which the L_a excited state of Trp233 experiences strong electrostatic stabilization by the protein environment. The strong stabilization singles out this tryptophan to be an efficient exciton acceptor that accumulates the excitation energy from the entire protein subunit. (ii) A fast decay of the locally excited state by charge separation generates the radical ion pair Trp285(+)-Trp233(−) with a dipole moment of ∼18 D. (iii) Key to the proposed mechanism is that this large dipole moment drives the breaking of the salt bridges between the two monomer subunits. The suggested mechanism for the UV-B–driven dissociation of the dimer that rests on the prominent players Trp233 and Trp285 explains the experimental results obtained from mutagenesis of UVR8

Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass

In the last decades we have witnessed the success of applications of Artificial Intelligence to playing games. In this work we address the challenging field of games with hidden information and card games in particular. Jass is a very popular card game in Switzerland and is closely connected with Swiss culture. To the best of our knowledge, performances of Artificial Intelligence agents in the game of Jass do not outperform top players yet. Our contribution to the community is two-fold. First, we provide an overview of the current state-of-the-art of Artificial Intelligence methods for card games in general. Second, we discuss their application to the use-case of the Swiss card game Jass. This paper aims to be an entry point for both seasoned researchers and new practitioners who want to join in the Jass challenge