Fader Networks: Manipulating Images by Sliding Attributes

This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space. As a result, after training, our model can generate different realistic versions of an input image by varying the attribute values. By using continuous attribute values, we can choose how much a specific attribute is perceivable in the generated image. This property could allow for applications where users can modify an image using sliding knobs, like faders on a mixing console, to change the facial expression of a portrait, or to update the color of some objects. Compared to the state-of-the-art which mostly relies on training adversarial networks in pixel space by altering attribute values at train time, our approach results in much simpler training schemes and nicely scales to multiple attributes. We present evidence that our model can significantly change the perceived value of the attributes while preserving the naturalness of images.Comment: NIPS 201

Learning advanced mathematical computations from examples

Using transformers over large generated datasets, we train models to learn mathematical properties of differential systems, such as local stability, behavior at infinity and controllability. We achieve near perfect prediction of qualitative characteristics, and good approximations of numerical features of the system. This demonstrates that neural networks can learn to perform complex computations, grounded in advanced theory, from examples, without built-in mathematical knowledge

Factors affecting utilization of health facilities for labour and childbirth: a case study from rural Uganda

BACKGROUND: Since 2000 considerable attention has been placed on maternal health outcomes as the 5th Millennium Goal. In Uganda, only 65% of births are delivered by a skilled birth attendant, contributing to the 435 women that die in every 100,000 births from unattended complications. Factors that impact a women's decision on where to deliver include cost and household barriers, poor health services and lack of education. METHODS: Insight into factors impacting maternal health decision-making in two villages in South Eastern Uganda, were explored through a cross-sectional study using focus group discussions (FDGs) with men and women and administering a simple questionnaire. RESULTS: For men and women in the villages, cultural and community patterns of behavior have the strongest impact on delivery options. While women with no complications could often find options to deliver safely, lack of emergency obstetric care remains a strong factor in maternal deaths. CONCLUSIONS: This article proposes that communities be engaged in identifying and leveraging their strengths to find solutions for challenges facing women in achieving safe deliveries