Structured Sparse Modelling with Hierarchical GP

In this paper a new Bayesian model for sparse linear regression with a spatio-temporal structure is proposed. It incorporates the structural assumptions based on a hierarchical Gaussian process prior for spike and slab coefficients. We design an inference algorithm based on Expectation Propagation and evaluate the model over the real data.Comment: SPARS 201

Anatomy of a stalled revolution: processes of reproduction and change in Russian women's gender ideologies

Russia’s gender revolution notoriously produced women’s economic empowerment without domestic equality. Although the Soviet state vastly expanded women’s employment, this had little impact on a starkly unequal gender division of domestic labor. Such “stalling” is common, but in Russia its extent and persistence presents a puzzle, requiring us to investigate linkages between macro-level factors and micro-level interactions regarding the gender division of domestic labor. We do this by focusing on gender ideology, an important variable explaining the gender division of domestic labor that bridges the macro-level of the gender order and the micro-interactional level. We use longitudinal qualitative data to examine continuity and change in young Russian women’s gender ideologies between 1999 and 2010. Based on analysis of 115 in-depth interviews from 23 respondents, we identify traditional and egalitarian trajectories and the processes underlying them, showing how the male breadwinner schema and an ideology of women’s independence support traditionalism, while non-traditional breadwinning and interactional support from men facilitate egalitarianism. Our analysis enables us to explain the Soviet gender paradox and distinguish sources of change in the post-Soviet era. Our theoretical contribution is to situate gender ideology in a multi-level framework, the efficacy of which we demonstrate in our empirical analysis

“Behind every great man…”: the male marriage wage premium examined qualitatively

Studies across diverse national contexts reliably show that married men earn more than unmarried men, but the mechanisms responsible for this are still disputed. This article explores the male marriage wage premium from a new perspective, using longitudinal qualitative data from Russia (N = 94). Qualitative research is particularly suited to identifying underlying processes and, by analyzing men's accounts of the influence of their marital trajectories on their work, the authors were able to reexamine existing hypotheses and develop new ones. They propose 4 mechanisms that they hypothesize can influence men's work motivation and performance: premarital planning, 2 distinct “breadwinner” effects using expectancy and self determination theory, and monitoring by wives. They integrate these mechanisms within gender theory, arguing that the treatment aspect of the male marriage wage premium is an outcome of the “coproduction” of masculinity within marriage. Their recontextualization of existing theory also enables them to reveal weaknesses in the specialization hypothesis

Influence of the age of the first insemination on the milk production of cows

The article presents the results of milk productivity and quality indicators of milk, depending on the age of the first insemination. The results obtained showed that under the conditions of the Izhagroplem Research and Production Complex of the Izhevsk State Agricultural Academy, it is necessary to inseminate replacement heifers at the age of 14-15 months upon reaching a live weight of 545 kg, which will allow you to get the first calving at the age of 23-24 months

BCCNet: Bayesian classifier combination neural network

Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (GOs) and NGOs in response to their critical information requirements. We co-create products with UK and in-country commercial, GO and NGO partners to ensure the machine learning algorithms address appropriate user needs whether for tactical decision making or evidence-based policy decisions. In one particular case, we developed and deployed a novel algorithm, BCCNet, to quickly process large quantities of unstructured data to prevent and respond to natural disasters. Crowdsourcing provides an efficient mechanism to generate labels from unstructured data to prime machine learning algorithms for large scale data analysis. However, these labels are often imperfect with qualities varying among different citizen scientists, which prohibits their direct use with many state-of-the-art machine learning techniques. We describe BCCNet, a framework that simultaneously aggregates biased and contradictory labels from the crowd and trains an automatic classifier to process new data. Our case studies, mosquito sound detection for malaria prevention and damage detection for disaster response, show the efficacy of our method in the challenging context of developing world applications.Comment: Presented at NeurIPS 2018 Workshop on Machine Learning for the Developing Worl

Disaster mapping from satellites:damage detection with crowdsourced point labels

High-resolution satellite imagery available immediately after disaster events is crucial for response planning as it facilitates broad situational awareness of critical infrastructure status such as building damage, flooding, and obstructions to access routes. Damage mapping at this scale would require hundreds of expert person-hours. However, a combination of crowdsourcing and recent advances in deep learning reduces the effort needed to just a few hours in real time. Asking volunteers to place point marks, as opposed to shapes of actual damaged areas, significantly decreases the required analysis time for response during the disaster. However, different volunteers may be inconsistent in their marking. This work presents methods for aggregating potentially inconsistent damage marks to train a neural network damage detector