Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico

Mapping the spatial distribution of poverty in developing countries remains an important and costly challenge. These "poverty maps" are key inputs for poverty targeting, public goods provision, political accountability, and impact evaluation, that are all the more important given the geographic dispersion of the remaining bottom billion severely poor individuals. In this paper we train Convolutional Neural Networks (CNNs) to estimate poverty directly from high and medium resolution satellite images. We use both Planet and Digital Globe imagery with spatial resolutions of 3-5 sq. m. and 50 sq. cm. respectively, covering all 2 million sq. km. of Mexico. Benchmark poverty estimates come from the 2014 MCS-ENIGH combined with the 2015 Intercensus and are used to estimate poverty rates for 2,456 Mexican municipalities. CNNs are trained using the 896 municipalities in the 2014 MCS-ENIGH. We experiment with several architectures (GoogleNet, VGG) and use GoogleNet as a final architecture where weights are fine-tuned from ImageNet. We find that 1) the best models, which incorporate satellite-estimated land use as a predictor, explain approximately 57% of the variation in poverty in a validation sample of 10 percent of MCS-ENIGH municipalities; 2) Across all MCS-ENIGH municipalities explanatory power reduces to 44% in a CNN prediction and landcover model; 3) Predicted poverty from the CNN predictions alone explains 47% of the variation in poverty in the validation sample, and 37% over all MCS-ENIGH municipalities; 4) In urban areas we see slight improvements from using Digital Globe versus Planet imagery, which explain 61% and 54% of poverty variation respectively. We conclude that CNNs can be trained end-to-end on satellite imagery to estimate poverty, although there is much work to be done to understand how the training process influences out of sample validation.Comment: 4 pages, 2 figures, Presented at NIPS 2017 Workshop on Machine Learning for the Developing Worl

Brain-scale Theta Band Functional Connectivity As A Signature of Slow Breathing and Breath-hold Phases

The study reported herein attempts to understand the neural mechanisms engaged in the conscious control of breathing and breath-hold. The variations in the electroencephalogram (EEG) based functional connectivity (FC) of the human brain during consciously controlled breathing at 2 cycles per minute (cpm), and breath-hold have been investigated and reported here. An experimental protocol involving controlled breathing and breath-hold sessions, synchronized to a visual metronome, was designed and administered to 20 healthy subjects (9 females and 11 males). EEG data were collected during these sessions using the 61-channel eego mylab system from ANT Neuro. Further, FC was estimated for all possible pairs of EEG time series data, for 7 EEG bands. Feature selection using a genetic algorithm (GA) was performed to identify a subset of functional connections that would best distinguish the inhale, exhale, inhale-hold, and exhale-hold phases using a random committee classifier. The best accuracy of 93.36 % was obtained when 1161 theta-band functional connections were fed as input to the classifier, highlighting the efficacy of the theta-band functional connectome in distinguishing these phases of the respiratory cycle. This functional network was further characterized using graph measures, and observations illustrated a statistically significant difference in the efficiency of information exchange through the network during different respiratory phases

Mobile Apps Making a Socio-Economic Impact for Managing Power at Underprivileged Homes

The paper presents how connectivity feature integrated into a roof-top solar power system, Inverterless500, designed and developed to electrify off-grid and near off-grid homes in an energy efficient manner, is critical in optimum service delivery, especially for lower income homes. It makes such products not only suitable for different categories of homes, but also economically viable, offering a promising business solution. Monitoring and manageability are unique features that help in maintaining the solution at remote areas of installation where manual intervention is not routinely feasible. The paper the describes the technology and learnings gained from deployment of these systems that helped in improving the product and overall management process

Time-Related Risk of Pulmonary Conduit Re-replacement: A Congenital Heart Surgeons’ Society Study

Background Patients receiving a right ventricle to pulmonary artery conduit (PC) in infancy will require successive procedures or replacements, each with variable longevity. We sought to identify factors associated with time-related risk of a subsequent surgical replacement (PC3) or transcatheter pulmonary valve insertion (TPVI) after a second surgically placed PC (PC2). Methods From 2002 to 2016, 630 patients from 29 Congenital Heart Surgeons’ Society member institutions survived to discharge after initial valved PC insertion (PC1) at age ≤ 2 years. Of those, 355 underwent surgical replacement (PC2) of that initial conduit. Competing risk methodology and multiphase parametric hazard analyses were used to identify factors associated with time-related risk of PC3 or TPVI. Results Of 355 PC2 patients (median follow-up, 5.3 years), 65 underwent PC3 and 41 TPVI. Factors at PC2 associated with increased time-related risk of PC3 were smaller PC2 Z score (hazard ratio [HR] 1.6, P < .001), concomitant aortic valve intervention (HR 7.6, P = .009), aortic allograft (HR 2.2, P = .008), younger age (HR 1.4, P < .001), and larger Z score of PC1 (HR 1.2, P = .04). Factors at PC2 associated with increased time-related risk of TPVI were aortic allograft (HR: 3.3, P = .006), porcine unstented conduit (HR 4.7, P < .001), and older age (HR 2.3, P = .01). Conclusions Aortic allograft as PC2 was associated with increased time-related risk of both PC3 and TPVI. Surgeons may reduce risk of these subsequent procedures by not selecting an aortic homograft at PC2, and by oversizing the conduit when anatomically feasible

Students' participation in collaborative research should be recognised

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