Soviet/Russian Military Capabilities: Assessing Tech, Manpower, & Loyalty

Since the Imperialist times of Peter the Great, Russia’s military ideology has been largely predicated on the goal of creating a large and powerful army. In an attempt to gain territory and prestige, a nation’s military strength was often reduced to a mere game of numbers in order to overpower the opposing side. Of course, weapons and tactics were also involved, but they meant nothing without the men who were needed to utilize them and perform accordingly. Overtime, as new threats began to emerge and a different international dynamic began to form with improved technological systems and weaponry, large conventional armies became significantly less effective. For a long time, however, Soviet Russia was unyielding to change. A Peter the Great mentality rang supreme in the minds of military elites who fostered a strong opposition to any means of reform despite repeated attempts by Soviet and Russian leaders. This force against change resonated in the attitudes and loyalty towards the Soviet and Russian military establishment, and further set Russia back in terms of its outdated technology and overall decreasing military capacity. Although some may say that Russia was a bit late in the game to display noticeable trends in military improvements, this study seeks to answer the question of where Russia lies now in terms of its military capabilities and citizens’ attitudes towards the military itself and their duty to serve. In other words, this study tests the question of how an improvement in military technology, coupled with a more streamlined personnel base, reflects a change in Russia’s military capabilities and in associated attitudes overtime. Background on the history and progress of military reform in Russia is provided and analyzed in light on capability measurements, followed by an evaluation of the 2008 Russo-Georgia War. Additionally, a case comparison of the 1979 Afghanistan crisis and the current intervention in Syria is conducted to demonstrate a change in capabilities and attitudes towards the military establishment. Finally, an analysis of loyalty towards military duty from a psychological perspective is preformed and further coupled with a discussion of how a shift in attitudes has occurred in parallel with military reform in both Soviet and present day Russia. The assessment of loyalty further adds to the analysis of military capabilities due to the connection between increased loyalty and compliance on the one hand, and enhanced military capabilities on the other. The study ends with implications associated with the findings

The number and probability of canalizing functions

Canalizing functions have important applications in physics and biology. For example, they represent a mechanism capable of stabilizing chaotic behavior in Boolean network models of discrete dynamical systems. When comparing the class of canalizing functions to other classes of functions with respect to their evolutionary plausibility as emergent control rules in genetic regulatory systems, it is informative to know the number of canalizing functions with a given number of input variables. This is also important in the context of using the class of canalizing functions as a constraint during the inference of genetic networks from gene expression data. To this end, we derive an exact formula for the number of canalizing Boolean functions of n variables. We also derive a formula for the probability that a random Boolean function is canalizing for any given bias p of taking the value 1. In addition, we consider the number and probability of Boolean functions that are canalizing for exactly k variables. Finally, we provide an algorithm for randomly generating canalizing functions with a given bias p and any number of variables, which is needed for Monte Carlo simulations of Boolean networks

Temporal patterns of gene expression via nonmetric multidimensional scaling analysis

Motivation: Microarray experiments result in large scale data sets that require extensive mining and refining to extract useful information. We have been developing an efficient novel algorithm for nonmetric multidimensional scaling (nMDS) analysis for very large data sets as a maximally unsupervised data mining device. We wish to demonstrate its usefulness in the context of bioinformatics. In our motivation is also an aim to demonstrate that intrinsically nonlinear methods are generally advantageous in data mining. Results: The Pearson correlation distance measure is used to indicate the dissimilarity of the gene activities in transcriptional response of cell cycle-synchronized human fibroblasts to serum [Iyer et al., Science vol. 283, p83 (1999)]. These dissimilarity data have been analyzed with our nMDS algorithm to produce an almost circular arrangement of the genes. The temporal expression patterns of the genes rotate along this circular arrangement. If an appropriate preparation procedure may be applied to the original data set, linear methods such as the principal component analysis (PCA) could achieve reasonable results, but without data preprocessing linear methods such as PCA cannot achieve a useful picture. Furthermore, even with an appropriate data preprocessing, the outcomes of linear procedures are not as clearcut as those by nMDS without preprocessing.Comment: 11 pages, 6 figures + online only 2 color figures, submitted to Bioinformatic

Computing the output distribution and selection probabilities of a stack filter from the DNF of its positive Boolean function

Many nonlinear filters used in practise are stack filters. An algorithm is presented which calculates the output distribution of an arbitrary stack filter S from the disjunctive normal form (DNF) of its underlying positive Boolean function. The so called selection probabilities can be computed along the way.Comment: This is the version published in Journal of Mathematical Imaging and Vision, online first, 1 august 201