Knowledge, Atitude and Perception regarding National Health Programmes among villagers of Chauras, Tehri-Garhwal, Uttarakhand

Background and Objective: Since India became independent, several measures have been undertaken by the national government to improve the health of the people. Prominent among these measures are the national health programmes. The main objective of these National Health programmes are protection and promotion of national and individual health. The main objective of this study was to assess the knowledge, attitude and perception regarding various national health programmes among the villagers. Methods: It is a descriptive and observational study. The study subjects comprised 273 respondents belonging to 15 to 64 years age group. The collection tool used was a pre designed questionnaire, which was pre-tested. Results: 60% of respondents were adults, about 16 percent were educated up to primary level and more than 40% belonged to scheduled castes. Nearly 20% were aware about National AIDS Control Programme and 6.59% had clear knowledge about HIV/AIDS. Only 4.02% knew about the national vector borne disease control programme and 24% women clearly knew about exclusive breast feeding. Peripheral health workers were the most common source of information regarding these programmes. 64% of respondents opined that these national health programmes are good. Conclusion: Low level of knowledge was observed among the respondents regarding National Health Programmes

Labeled Memory Networks for Online Model Adaptation

Augmenting a neural network with memory that can grow without growing the number of trained parameters is a recent powerful concept with many exciting applications. We propose a design of memory augmented neural networks (MANNs) called Labeled Memory Networks (LMNs) suited for tasks requiring online adaptation in classification models. LMNs organize the memory with classes as the primary key.The memory acts as a second boosted stage following a regular neural network thereby allowing the memory and the primary network to play complementary roles. Unlike existing MANNs that write to memory for every instance and use LRU based memory replacement, LMNs write only for instances with non-zero loss and use label-based memory replacement. We demonstrate significant accuracy gains on various tasks including word-modelling and few-shot learning. In this paper, we establish their potential in online adapting a batch trained neural network to domain-relevant labeled data at deployment time. We show that LMNs are better than other MANNs designed for meta-learning. We also found them to be more accurate and faster than state-of-the-art methods of retuning model parameters for adapting to domain-specific labeled data.Comment: Accepted at AAAI 2018, 8 page

Coherent Probabilistic Aggregate Queries on Long-horizon Forecasts

Long range forecasts are the starting point of many decision support systems that need to draw inference from high-level aggregate patterns on forecasted values. State of the art time-series forecasting methods are either subject to concept drift on long-horizon forecasts, or fail to accurately predict coherent and accurate high-level aggregates. In this work, we present a novel probabilistic forecasting method that produces forecasts that are coherent in terms of base level and predicted aggregate statistics. We achieve the coherency between predicted base-level and aggregate statistics using a novel inference method based on KL-divergence that can be solved efficiently in closed form. We show that our method improves forecast performance across both base level and unseen aggregates post inference on real datasets ranging three diverse domains. (\href{https://github.com/pratham16cse/AggForecaster}{Project URL})Comment: 7 pages, 1 figure, 1 table, 1 algorith