Study on how elections affect democracy and development in Sub-Saharan Africa.
problem Understanding the impact of elections on democracy and development in Sub-Saharan Africa.
method Examined 33 Sub-Saharan African countries post-election, analyzing development indicators over a decade.
result Elections can lead to positive, negative, or no change in development, depending on the pre-election regime and election outcome.
LEMPS predicts malaria prevalence with high accuracy in Ibadan, West Africa.
problem Inadequate malaria prediction systems in highly endemic countries.
method Developed and validated a Locality-specific Elastic-Net based Malaria Prediction System (LEMPS) using 22-years of prospective data.
result LEMPS achieves good generalization performance, predicting monthly prevalence with MAE<=6x10-2 and MSE<=7x10-3.
Mobile apps and machine learning improve malaria prevention and treatment.
problem High malaria cases and deaths in low-income countries.
method Adaptive interventions using mobile health apps and machine learning.
result Increased malaria testing, adherence, and provider skills.
Combines TSP and SC to solve real-world vaccine distribution.
problem Combining TSP and SC for efficient vaccine distribution.
method Mixed Integer Programming (MIP) and machine learning.
result Machine learning approach improves solution efficiency.
Extends diffusion models to handle exponential family distributions for inverse problems.
problem Intractability of likelihood score for non-Gaussian observations.
method Evidence trick to approximate likelihood score for exponential family distributions.
result Effective Bayesian inference on complex Poisson processes and malaria prevalence prediction.
New method combines regional HIV prevention trial data without sharing individual patient info.
problem Regional differences in HIV prevention efficacy, privacy concerns, and data sharing limitations.
method Federated learning approach that combines site-specific estimators via L1-regularization.
result Improved precision in estimating region-specific survival curves.
Maps of infectious disease---charting spatial variations in the force of infection, degree of endemicity, and the burden on human health---provide an essential evidence base to support planning towards global health targets. Contemporary disease mapping efforts have embraced statistical modelling approaches to properly…
Study improves maize yield prediction using BNs with mixed-effects models.
problem Limited causal inference in agronomic data models.
method Integrates random effects into Bayesian networks, leveraging hierarchical data structure.
result Significantly reduces maize yield prediction error from 28% to 17%.
Proposes a stochastic model for South African actuarial use.
problem Long-term forecasting for South African institutions.
method Modeling economic series, estimating parameters, testing stability.
result Validated model for long-term forecasts.
EcoCast predicts biodiversity risks using satellite data and citizen science records.
problem Unprecedented shifts in species distributions due to climate change and habitat loss.
method Spatio-temporal model using sequence-based transformers and continual learning.
result Promising improvements in forecasting bird species distributions compared to Random Forest.
Deep learning predicts infrastructure quality in Africa using satellite imagery.
problem Expensive and limited monitoring of infrastructure quality in developing regions.
method Convolutional neural network trained on Landsat 8 and Sentinel 1 satellite imagery.
result AUROC scores of 0.881 for Electricity, 0.862 for Sewerage, 0.739 for Piped Water, and 0.786 for Roads.
A statistical analysis of financial, economic, and demographic indicators performed by the authors demonstrates (1) that the main countries of East Africa (Uganda, Kenya, and Tanzania) have not escaped the Malthusian Trap yet; (2) that this countries are not likely to follow the "North African path" and to achieve this…
One of the fundamental postulates of the Unified Growth Theory is the claimed existence of three distinctly different regimes of economic growth governed by three distinctly different mechanisms of growth. However, Galor also proposed that the timing of these regimes is different for developed countries and for less-de…
Machine learning predicts seasonal precipitation for East Africa.
problem Predicting seasonal precipitation for East Africa using machine learning.
method Dimension reduction via EOFs, large-scale climate variability indices as features, interpretable ML algorithm.
result The ML approach shows significant positive skill in predicting precipitation for OND season, comparable to ECMWF forecasts.
New method for selecting clusters in residential electricity data.
problem Selecting useful clusters in electricity consumption data.
method Formalizing expert knowledge as external validation measures.
result Successfully reconstructed customer archetypes.
Model predicts unseen climate extremes to inform risk planning.
problem Missing unseen climate extremes in historical records.
method DeepX-GAN model capturing spatial dependence.
result Unseen heat extremes disproportionately threaten vulnerable regions.
Optimizes QoS in FSO links over South Africa using ensemble learning.
problem Impact of weather on QoS in FSO links.
method Ensemble learning models (Random Forest, ADaBoost Regression, Stacking Regression, Gradient Boost Regression, Multilayer Neural Network) applied to meteorological data.
result Significant enhancement in QoS with RMSE and R-squared values of 0.0032 and 0.9906 respectively at George.
SAGDA generates synthetic agricultural datasets to improve ML in African farming.
problem Data scarcity in African agriculture limits machine learning innovations.
method SAGDA is an open-source Python toolkit that generates, augments, and validates synthetic agricultural datasets.
result SAGDA enhances ML applications in agriculture, such as yield prediction and fertilizer recommendation.
East African tech experts identify machine learning challenges.
problem Machine learning research needs in East African tech industry.
method 46 interviews with tech professionals.
result Concrete machine learning problems identified.
There certainly is little or no doubt that politicians, sometimes consciously and sometimes not, exert a significant impact on stock markets. The evolving volatility over the Republican Donald Trump's surprise victory in the US presidential election is a perfect example when politicians, through announced policies, sen…
Satellite imagery helps adjust for unobserved confounders in observational studies.
problem Adjusting for confounding factors in observational studies with non-tabular data like satellite imagery.
method Formalizing conditions for causal effect identification, estimation, and sensitivity analysis.
result Demonstrated the use of satellite imagery as a proxy for unobserved confounders in anti-poverty aid programs.
What are East Africa's industrial opportunities? In this article we explore this question by using the Product Space to study the productive structure of five south-east African countries: Kenya, Mozambique, Rwanda, Tanzania and Zambia. The Product Space is a network connecting products that tend to be exported by the …
Paper aims to improve education online in South Africa using NMT for Setswana.
problem Inclusion of machine translation for low-resourced local languages in online education.
method Demonstrates state-of-the-art performance on English-to-Setswana translation using the Transformer architecture.
result Current NMT techniques show promise for African languages, beating previous methods by 5.33 BLEU points.
Neural machine translation models trained for 5 South African languages.
problem Lack of resources and research for machine translation in African languages.
method Training neural machine translation models for 5 South African languages using modern techniques.
result Promises of neural machine translation for African languages.
Framework simulates systemic risk in South African banking sector.
problem Monitoring systemic risk in banking systems.
method Network-based approach considering shock propagation and systemic market risks.
result Simulated systemic risk spikes align with subjective assessments.
The paper improves marine buoy placement to detect ships robustly against disruptions.
problem Detecting fishing vessels in the presence of natural and man-made disruptions.
method Formulated as a clustering problem, used dropout k-means and k-median to improve buoy placement robustness.
result Improved ship detection probability with dropout k-means compared to classic methods.
System builds Somali ASR for UN humanitarian efforts.
problem Developing ASR for under-resourced Somali language.
method Acoustic model training with annotated speech, neural architectures, language model data augmentation, acoustic data perturbation.
result Best system achieved 53.75% word error rate.
The paper debiases machine learning predictions to correct bias in regression coefficients.
problem Bias in regression coefficients from machine learning predictions.
method Proposes an adversarial machine learning algorithm to de-bias predictions.
result Adversarial predictions recover true coefficients, while naive predictions are biased.
We use bank-level balance sheet data from 2005 to 2010 to study interactions within the banking system of five emerging countries: Argentina, Brazil, Mexico, South Africa, and Taiwan. For each country we construct a financial network based on the leverage ratio dependence between each pair of banks, and find results th…
Neural networks improve ocean temperature forecasting and data interpolation.
problem Forecasting and reconstructing sea surface temperature from satellite data.
method Patch-level neural network representations that mimic numerical integration schemes.
result Neural networks outperform other data-driven models in forecasting and missing data interpolation.
CNN-DTW system improves keyword spotting in under-resourced languages.
problem Keyword spotting in nearly zero-resource languages.
method Multilingual bottleneck features, CNN-DTW, DTW template matching, convolutional neural network.
result Multilingual BNFs improve CNN-DTW by 10.9%.
New method estimates active subspaces for jump-discontinuous functions.
problem Estimating active subspaces for discontinuous functions like ABMs.
method Extending active subspaces to discontinuous functions, using Gaussian process.
result Identifies important parameters in ABM simulations of refugee movement.
Data describing historical growth of income per capita [Gross Domestic Product per capita (GDP/cap)] for the world economic growth and for the growth in Western Europe, Eastern Europe, Asia, former USSR, Africa and Latin America are analysed. They follow closely the linearly-modulated hyperbolic distributions represent…
Historical economic growth in Latin America is analysed using the data of Maddison. Unified Growth Theory is found to be contradicted by these data in the same way as it is contradicted by the economic growth in Africa, Asia, former USSR, Western Europe, Eastern Europe and by the world economic growth. Paradoxically, U…
Study finds internal view preferred over external view in defining startups.
problem Insufficient understanding of how to start a new business.
method Empirical study with 701 entrepreneurs reflecting on nine resource categories and three business mission elements.
result Tendency to favor internal view over external view, stronger in stable economies.
Improves spatial statistics with nonstationary kernels.
problem Limited expressiveness of stationary kernels in spatial statistics.
method Exploits Fourier feature representations and Gaussian processes to learn nonstationary kernels.
result Nonstationary kernels improve generalization and interpretability.
Improved ASR-free keyword spotting in under-resourced languages.
problem Dynamic time warping for keyword spotting in languages with limited resources.
method Multilingual bottleneck extractor and correspondence autoencoder integration.
result More than 11% absolute improvement in ROC AUC over MFCCs.
The paper develops methods for causal function estimation and inference with multiway clustered data.
problem Estimation and inference for causal functions under multiway clustering.
method Two-step procedure using machine learning for nuisance parameters and projection onto basis functions.
result Rejects the null hypothesis of uniformly zero effects and reveals heterogeneous treatment effects.
A control-theoretic model tackles microfinance sustainability issues.
problem High default rates in microfinance due to lack of data and information.
method A novel control-theoretic model that learns microfinance decision policies.
result The model achieves financial inclusion, fairness, social welfare, and sustainability.
ELECTS model predicts crop type early from satellite data.
problem Early decision-making in crop type mapping.
method End-to-end deep learning model with modular design.
result ELECTS reduces data requirements for accurate early predictions.
Mobile phones detect mosquitoes for malaria research.
problem Manual mosquito detection is inefficient and costly.
method Low-cost smartphone app for automated mosquito detection.
result Mobile system achieves excellent multi-species detection.
Study compares market microstructure between two South African exchanges.
problem Understanding price response dynamics and market microstructure differences between two South African exchanges.
method Comparative analysis of returns distributions, auto-correlations, price impact, and trading costs on different time scales.
result Similar stylized facts emerge as measurement time scale increases, but price responses vary significantly.
Machine learning speeds up LSM parameter optimization and uncertainty quantification.
problem Optimizing and assessing uncertainty of LSM parameters.
method Combining MCMC with Gaussian process regression.
result 50,000 times faster than direct MCMC application.
The study revises GDPpc trends and redistributes economic power among countries.
problem Analyzing global economic power shifts over time.
method Modeling GDPpc as a trend and fluctuations, analyzing historical data.
result Revised GDPpc trends show significant shifts in global economic power.
Transformer model outperforms classical methods in childhood anemia prediction across diverse countries.
problem Generalizing childhood anemia prediction models across different countries and data scarcity.
method Transformer-based tabular foundation model compared to classical supervised methods using DHS data.
result Transformer model achieves lower Brier score and ECE in low-data settings, outperforming classical models.
A new risk measure (FRM) for EM FI returns helps investors protect against volatility and policy instability.
problem Systemic risk in EM FI returns due to external shocks and domestic policy instability.
method Daily FRM-EM measure applied to 25 largest EM FI returns, incorporating Macro factors.
result FRM-EM captures systemic risk behavior in EM FI returns, reaching maximum during crises.
We tested 45 indices and common stocks traded in the South African stock market for the possible existence of a bubble over the period from Jan. 2003 to May 2006. A bubble is defined by a faster-than-exponential acceleration with significant log-periodic oscillations. The faster-than-exponential acceleration characteri…
Study predicts droughts 1 month ahead using neural networks and reduces model complexity.
problem Increasing frequency of droughts affecting livelihoods and economies.
method Mixed model approach using artificial neural networks (ANN) with GAM space search.
result 1-month lagged variables are most predictive, with ANN model achieving R2=0.78.