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Women are increasingly visible in politics around the world but there is still a yawning gap in their political representation compared with men. In Myanmar, gender inequality and women’s rights are major challenges across economic, social and political spheres. Myanmar’s historic election in November 2015 saw a big increase in the numbers of women candidates and women MPs elected to parliament: the new government has nearly three times the number of women MPs than the previous one. But with close to 10 percent of elected parliamentary seats held by women, Myanmar is still the worst performer in the region for representation of women in parliament., This paper takes a snapshot of women’s rights and political representation in Myanmar today, looks at the experience of countries around the world in increasing women’s political representation, and examines the potential of a quota system for a country at a true turning point in its history.

Semi-supervised learning (SSL) uses unlabeled data during training to learn better models. Previous studies on SSL for medical image segmentation focused mostly on improving model generalization to unseen data. In some applications, however, our primary interest is not generalization but to obtain optimal predictions on a specific unlabeled database that is fully available during model development. Examples include population studies for extracting imaging phenotypes. This work investigates an often overlooked aspect of SSL, transduction. It focuses on the quality of predictions made on the unlabeled data of interest when they are included for optimization during training, rather than improving generalization. We focus on the self-training framework and explore its potential for transduction. We analyze it through the lens of Information Gain and reveal that learning benefits from the use of calibrated or under-confident models. Our extensive experiments on a large MRI database for multi-class segmentation of traumatic brain lesions shows promising results when comparing transductive with inductive predictions. We believe this study will inspire further research on transductive learning, a well-suited paradigm for medical image analysis., Comment: Published at Domain Adaptation and Representation Transfer (DART) wshop at MICCAI 2021. This version improves methods' names and adds 1 experiment in Tab.3a