Knowledge, Attitudes, and Practices about Malaria and Its Control in Rural Northwest Tanzania

Background. We assessed community knowledge, attitudes, and practices on malaria as well as acceptability to indoor residual spraying. Material and Methods. A cross-sectional survey was done in a community in Geita district (northwest Tanzania). Household heads (n = 366) were interviewed Results. Knowledge on malaria transmission, prevention, and treatment was reasonable; 56% of respondents associated the disease with mosquito bites, with a significant difference between education level and knowledge on transmission (P < .001). Knowledge of mosquito breeding areas was also associated with education (illiterate: 22%; literate: 59% (P < .001). Bed nets were used by 236 (64.5%), and usage was significantly associated with education level (P < .01). The level of bed net ownership was 77.3%. Most respondents (86.3%) agreed with indoor residual spraying of insecticides. Health facilities were the first option for malaria treatment by 47.3%. Artemether-lumefantrine was the most common antimalarial therapy used. Conclusions. Despite reasonable knowledge on malaria and its preventive measures, there is a need to improve availability of information through proper community channels. Special attention should be given to illiterate community members. High acceptance of indoor residual spraying and high level of bed net ownership should be taken as an advantage to improve malaria control

MuProp: Unbiased Backpropagation for Stochastic Neural Networks

This is the final version of the article. It first appeared from International Conference on Learning Representations via http://arxiv.org/abs/1511.05176v3Deep neural networks are powerful parametric models that can be trained efficiently using the backpropagation algorithm. Stochastic neural networks combine the power of large parametric functions with that of graphical models, which makes it possible to learn very complex distributions. However, as backpropagation is not directly applicable to stochastic networks that include discrete sampling operations within their computational graph, training such networks remains difficult. We present MuProp, an unbiased gradient estimator for stochastic networks, designed to make this task easier. MuProp improves on the likelihood-ratio estimator by reducing its variance using a control variate based on the first-order Taylor expansion of a mean-field network. Crucially, unlike prior attempts at using backpropagation for training stochastic networks, the resulting estimator is unbiased and well behaved. Our experiments on structured output prediction and discrete latent variable modeling demonstrate that MuProp yields consistently good performance across a range of difficult tasks.ALTA; Jesus College Cambridge; Cambridge-Tubingen PhD Fellowshi