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

Multiple Component Learning for Object Detection

Object detection is one of the key problems in computer vision. In the last decade, discriminative learning approaches have proven effective in detecting rigid objects, achieving very low false positives rates. The field has also seen a resurgence of part-based recognition methods, with impressive results on highly articulated, diverse object categories. In this paper we propose a discriminative learning approach for detection that is inspired by part-based recognition approaches. Our method, Multiple Component Learning (MCL), automatically learns individual component classifiers and combines these into an overall classifier. Unlike previous methods, which rely on either fairly restricted part models or labeled part data, MCL learns powerful component classifiers in a weakly supervised manner, where object labels are provided but part labels are not. The basis Of MCL lies in learning a set classifier; we achieve this by combining boosting with weakly supervised learning, specifically the Multiple Instance Learning framework (MIL). MCL is general, and we demonstrate results on a range of data from computer audition and computer vision. In particular, MCL outperforms all existing methods on the challenging INRIA pedestrian detection dataset, and unlike methods that are not part-based, MCL is quite robust to occlusions

Discovering novel systemic biomarkers in photos of the external eye

External eye photos were recently shown to reveal signs of diabetic retinal disease and elevated HbA1c. In this paper, we evaluate if external eye photos contain information about additional systemic medical conditions. We developed a deep learning system (DLS) that takes external eye photos as input and predicts multiple systemic parameters, such as those related to the liver (albumin, AST); kidney (eGFR estimated using the race-free 2021 CKD-EPI creatinine equation, the urine ACR); bone & mineral (calcium); thyroid (TSH); and blood count (Hgb, WBC, platelets). Development leveraged 151,237 images from 49,015 patients with diabetes undergoing diabetic eye screening in 11 sites across Los Angeles county, CA. Evaluation focused on 9 pre-specified systemic parameters and leveraged 3 validation sets (A, B, C) spanning 28,869 patients with and without diabetes undergoing eye screening in 3 independent sites in Los Angeles County, CA, and the greater Atlanta area, GA. We compared against baseline models incorporating available clinicodemographic variables (e.g. age, sex, race/ethnicity, years with diabetes). Relative to the baseline, the DLS achieved statistically significant superior performance at detecting AST>36, calcium=300, and WBC<4 on validation set A (a patient population similar to the development sets), where the AUC of DLS exceeded that of the baseline by 5.2-19.4%. On validation sets B and C, with substantial patient population differences compared to the development sets, the DLS outperformed the baseline for ACR>=300 and Hgb<11 by 7.3-13.2%. Our findings provide further evidence that external eye photos contain important biomarkers of systemic health spanning multiple organ systems. Further work is needed to investigate whether and how these biomarkers can be translated into clinical impact

Formality theorems for Hochschild complexes and their applications

We give a popular introduction to formality theorems for Hochschild complexes and their applications. We review some of the recent results and prove that the truncated Hochschild cochain complex of a polynomial algebra is non-formal.Comment: Submitted to proceedings of Poisson 200

Single- and coupled-channel radial inverse scattering with supersymmetric transformations

The present status of the coupled-channel inverse-scattering method with supersymmetric transformations is reviewed. We first revisit in a pedagogical way the single-channel case, where the supersymmetric approach is shown to provide a complete solution to the inverse-scattering problem. A special emphasis is put on the differences between conservative and non-conservative transformations. In particular, we show that for the zero initial potential, a non-conservative transformation is always equivalent to a pair of conservative transformations. These single-channel results are illustrated on the inversion of the neutron-proton triplet eigenphase shifts for the S and D waves. We then summarize and extend our previous works on the coupled-channel case and stress remaining difficulties and open questions. We mostly concentrate on two-channel examples to illustrate general principles while keeping mathematics as simple as possible. In particular, we discuss the difference between the equal-threshold and different-threshold problems. For equal thresholds, conservative transformations can provide non-diagonal Jost and scattering matrices. Iterations of such transformations are shown to lead to practical algorithms for inversion. A convenient technique where the mixing parameter is fitted independently of the eigenphases is developed with iterations of pairs of conjugate transformations and applied to the neutron-proton triplet S-D scattering matrix, for which exactly-solvable matrix potential models are constructed. For different thresholds, conservative transformations do not seem to be able to provide a non-trivial coupling between channels. In contrast, a single non-conservative transformation can generate coupled-channel potentials starting from the zero potential and is a promising first step towards a full solution to the coupled-channel inverse problem with threshold differences.Comment: Topical review, 84 pages, 7 figures, 93 reference

Training discriminative computer vision models with weak supervision

Statistical machine learning techniques have transformed computer vision research in the last two decades, and have led to many breakthroughs in object detection, recognition and tracking. Such data-driven methods extrapolate rules from a set of labeled examples, freeing us from designing and tuning a system by hand for a particular application or domain. Discriminative learning methods, which directly learn to differentiate categories of data rather than modeling the data itself, have been shown to be particularly effective. However, the requirement of a large set of labeled examples becomes prohibitively expensive, especially if we consider scaling to a wide range of domains and applications. In this dissertation we explore weakly supervised methods of training discriminative models for a number of computer vision applications. These methods require weaker forms of annotation that are easier and/or cheaper to obtain, and can learn in situations where the ground truth is inherently ambiguous. Many of the algorithms in this dissertation are based on a particular form of weakly supervised learning called Multiple Instance Learning (MIL). Our final contribution is a theoretical analysis of MIL that takes into account the characteristics of applications in computer vision and related area