An intuitive risk factors search algorithm: usage of the Bayesian network technique in personalized medicine

The article focuses on the application of the Bayesian networks (BN) technique to problems of personalized medicine. The simple (intuitive) algorithm of BN optimization with respect to the number of nodes using naive network topology is developed. This algorithm allows to increase the BN prediction quality and to identify the most important variables of the network. The parallel program implementing the algorithm has demonstrated good scalability with an increase in the computational cores number, and it can be applied to the large patients database containing thousands of variables. This program is applied for the prediction for the unfavorable outcome of coronary artery disease (CAD) for patients who survived the acute coronary syndrome (ACS). As a result, the quality of the predictions of the investigated networks was significantly improved and the most important risk factors were detected. The significance of the tumor necrosis factor-alpha gene polymorphism for the prediction of the unfavorable outcome of CAD for patients survived after ACS was revealed for the first time.     

Affective machines or the inner self? Drawing the boundaries of the female body in the socialist romantic imagination

This article explores two conflicting aesthetics of the female body in post-Stalinist Soviet science fiction. One represented women of the communist future as explorers of the space frontier in assemblages with machines, testing the cultural border between the female and the technological. Another appealed to the mysterious female nature as the Other of human culture, pushing forward the understanding of socialist progress as a masculine project. This article argues that both aesthetics grew within the cultural phenomenon of socialist Romanticism, which emerged in the mid-1950s as a reaction to Stalin-era quasi-Enlightenment rationality and its dominant style of socialist realism.     

MAP segmentation in Bayesian hidden Markov models: a case study

We consider the problem of estimating the maximum posterior probability (MAP) state sequence for a finite state and finite emission alphabet hidden Markov model (HMM) in the Bayesian setup, where both emission and transition matrices have Dirichlet priors. We study a training set consisting of thousands of protein alignment pairs. The training data is used to set the prior hyperparameters for Bayesian MAP segmentation. Since the Viterbi algorithm is not applicable any more, there is no simple procedure to find the MAP path, and several iterative algorithms are considered and compared. The main goal of the paper is to test the Bayesian setup against the frequentist one, where the parameters of HMM are estimated using the training data.     

Generalized estimating equations for mixtures with varying concentrations

A finite mixture model is considered in which the mixing probabilities vary from observation to observation. A parametric model is assumed for one mixture component distribution, while the others are nonparametric nuisance parameters. Generalized estimating equations (GEE) are proposed for the semi‐parametric estimation. Asymptotic normality of the GEE estimates is demonstrated and the lower bound for their dispersion (asymptotic covariance) matrix is derived. An adaptive technique is developed to derive estimates with nearly optimal small dispersion. An application to the sociological analysis of voting results is discussed. The Canadian Journal of Statistics 41: 217–236; 2013 © 2013 Statistical Society of Canada     

Nonparametric estimation for compound Poisson process via variational analysis on measures

The paper develops new methods of nonparametric estimation of a compound Poisson process. Our key estimator for the compounding (jump) measure is based on series decomposition of functionals of a measure and relies on the steepest descent technique. Our simulation studies for various examples of such measures demonstrate flexibility of our methods. They are particularly suited for discrete jump distributions, not necessarily concentrated on a grid nor on the positive or negative semi-axis. Our estimators also applicable for continuous jump distributions with an additional smoothing step.     

The conditional maximum of Poisson random variables

The conditional maxima of independent Poisson random variables are studied. A triangular array of row-wise independent Poisson random variables is considered. If condition is given for the row-wise sums, then the limiting distribution of the row-wise maxima is concentrated onto two points. The result is in accordance with the classical result of Anderson. The case of general power series distributions is also covered. The model studied in Theorems 2.1 and 2.2 is an analogue of the generalized allocation scheme. It can be considered as a non homogeneous generalized scheme of allocations of at most n balls into N boxes. Then the maximal value of the contents of the boxes is studied.     

Corruption,Policing and Migration: Exploring the Interaction Between Migrant Workers and Police in Russia,Ukraine and the UK

The rights of migrant workers and their experiences in host countries have been high on the political agendas of Europe and beyond. This article uses data from 138 semi‐structured interviews conducted with migrant workers in the UK, Russia and Ukraine to study their relationships with the police in host countries. We aim to contribute to the literature on policing and migration by analysing three different host countries, and the experience of temporary migrant workers regarding policing within each country. We suggest that the interplay between the experiences of policing in home and host countries can provide important insights into the practicalities of policing, but should not be considered apart from the context of the ethnic origin of migrant workers.     

Integration of project management,human resource development,and business teams: a partnership,planning model for organizational training and development initiatives

The changing business environment has challenged organizations to explore unique ways to ensure the success of online training initiatives. Thus, project teams, including training managers, should not only support organizational training and development initiatives; but, they also need to use project-focused, planning models for implementation. This paper proposes a model that is an extension of the model for business and training partnership developed by Price. The proposed model includes the integration of project management, human resource development (HRD), and business teams in order to enhance the success of training implementations through a sequential, planned framework. The model includes phases, best practices, and supporting tools to assist in managing and administering training and development projects. The approach developed in this research includes an account of the activities associated with initiating, planning, executing, monitoring and controlling, and closing processes for an online training initiative conducted in an oil and gas organization. This research also discusses implications of the proposed model for HRD professionals, because they are typically the champion of training and development initiatives within organizations today.     

Computational risk management techniques for fixed charge network flow problems with uncertain arc failures

We consider a formulation for the fixed charge network flow (FCNF) problem subject to multiple uncertain arc failures, which aims to provide a robust optimal flow assignment in the sense of restricting potential losses using Conditional Value-at-Risk (CVaR). We show that a heuristic algorithm referred to as Adaptive Dynamic Cost Updating Procedure (ADCUP) previously developed for the deterministic FCNF problem can be extended to the considered problem under uncertainty and produce high-quality heuristic solutions for large problem instances. The reported computational experiments demonstrate that the described procedure can successfully tackle both the uncertainty considerations and the large size of the networks. High-quality heuristic solutions for problem instances with up to approximately 200,000 arcs have been identified in a reasonable time.     

On the Equivalence of Bayesian and Dominant Strategy Implementation

We consider a standard social choice environment with linear utilities and independent, one‐dimensional, private types. We prove that for any Bayesian incentive compatible mechanism there exists an equivalent dominant strategy incentive compatible mechanism that delivers the same interim expected utilities for all agents and the same ex ante expected social surplus. The short proof is based on an extension of an elegant result due to Gutmann, Kemperman, Reeds, and Shepp (1991). We also show that the equivalence between Bayesian and dominant strategy implementation generally breaks down when the main assumptions underlying the social choice model are relaxed or when the equivalence concept is strengthened to apply to interim expected allocations.