You probably don't give much thought to the device that you're reading this article on, as long as it looks good and keeps working. But the elements that power and run it are the subject of an escalating struggle between the world's two biggest economies - the US and China - with African countries in the eye of the storm. The African continent is rich in critical minerals and metals - like lithium, rare earths, cobalt and tungsten - which are vital to making and running our personal tech. Such materials are also essential for everything from electric vehicles, to AI data centres, and weapon systems. China has long been the biggest player in the global market for critical minerals and metals.

Both perspectives are useful in practice: parametric inference estimates the tensor of distributional parameters as a whole, while multiway analysis yields its latent factors for interpretation [1]. Both tasks rely fundamentally on tensor decompositions to represent and exploit underlying structure. However, computing tensor decompositions is notoriously difficult. Degeneracy phenomena lead to non-unique or ill-conditioned factorizations [2] and many tensor problems are NP-hard [3], making even approximate computation intractable in general. These issues put into question the reliability of existing tensor-based inference methods. They are particularly pronounced for the canonical polyadic (CP) decomposition [2], which, despite its widespread use, lacks the theoretical guarantees enjoyed by other tensor formats. Computing CP factors, i.e., multiway analysis, with minimal variance across multiple sets of observations would enhance the reliability of multiway analysis and parametric inference, offering practitioners more confidence in their results while reducing the need for extensive data collection. 1

As predictive algorithms grow in popularity, using the same dataset to both train and test a new model has become routine across research, policy, and industry. Sample-splitting attains valid inference on model properties by using separate subsamples to estimate the model and to evaluate it. However, this approach has two drawbacks, since each task uses only part of the data, and different splits can lead to widely different estimates. Averaging across multiple splits, I develop an inference approach that uses more data for training, uses the entire sample for testing, and improves reproducibility. I address the statistical dependence from reusing observations across splits by proving a new central limit theorem for a large class of split-sample estimators under arguably mild and general conditions. Importantly, I make no restrictions on model complexity or convergence rates. I show that confidence intervals based on the normal approximation are valid for many applications, but may undercover in important cases of interest, such as comparing the performance between two models. I develop a new inference approach for such cases, explicitly accounting for the dependence across splits. Moreover, I provide a measure of reproducibility for p-values obtained from split-sample estimators. Finally, I apply my results to two important problems in development and public economics: predicting poverty and learning heterogeneous treatment effects in randomized experiments. I show that my inference approach with repeated cross-fitting achieves better power than previous alternatives, often enough to find statistical significance that would otherwise be missed.

We establish parameter inference for the Poisson canonical polyadic (PCP) tensor model through a latent-variable formulation. Our approach exploits the observation that any random PCP tensor can be derived by marginalizing an unobservable random tensor of one dimension larger. The loglikelihood of this larger dimensional tensor, referred to as the "complete" loglikelihood, is comprised of multiple rank one PCP loglikelihoods. Using this methodology, we first derive non-iterative maximum likelihood estimators for the PCP model and demonstrate that several existing algorithms for fitting non-negative matrix and tensor factorizations are Expectation-Maximization algorithms. Next, we derive the observed and expected Fisher information matrices for the PCP model. The Fisher information provides us crucial insights into the well-posedness of the tensor model, such as the role that tensor rank plays in identifiability and indeterminacy. For the special case of rank one PCP models, we demonstrate that these results are greatly simplified.

Speech technology remains out of reach for most of the over 2300 languages in Africa. We present the first systematic assessment of large-scale synthetic voice corpora for African ASR. We apply a three-step process: LLM-driven text creation, TTS voice synthesis, and ASR fine-tuning. Eight out of ten languages for which we create synthetic text achieved readability scores above 5 out of 7. We evaluated ASR improvement for three (Hausa, Dholuo, Chichewa) and created more than 2,500 hours of synthetic voice data at below 1% of the cost of real data. Fine-tuned Wav2Vec-BERT-2.0 models trained on 250h real and 250h synthetic Hausa matched a 500h real-data-only baseline, while 579h real and 450h to 993h synthetic data created the best performance. We also present gender-disaggregated ASR performance evaluation. For very low-resource languages, gains varied: Chichewa WER improved about 6.5% relative with a 1:2 real-to-synthetic ratio; a 1:1 ratio for Dholuo showed similar improvements on some evaluation data, but not on others. Investigating intercoder reliability, ASR errors and evaluation datasets revealed the need for more robust reviewer protocols and more accurate evaluation data. All data and models are publicly released to invite further work to improve synthetic data for African languages.

Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? Ukraine has hit back at Russia's attempts to disable its energy infrastructure with air strikes that succeeded in disrupting power and heating in two cities across the border. Alexander Gusev, regional governor of Voronezh, said several drones were electronically jammed over the city - home to more than one million people - and sparked a fire at a local utility facility that was quickly extinguished. A Russian Defence Ministry statement made no mention of either the Voronezh or Belgorod areas, reporting 44 Ukrainian drones were destroyed or intercepted by Russian forces during the night.

Taken in Algeria, 'Last Fireworks' is this year's first place category and open competition overall winner. Breakthroughs, discoveries, and DIY tips sent every weekday. The winners of the 2025 Epson International Pano awards have been announced, showcasing photographs of our great, big, beautiful world in ultra-wide glory. Italy's Alex Wides (Alessandro Cantarelli) won the Open Photographer of the Year and the Nature/Landscape category for his fine-art landscapes (seen above and below). Among this year's 3,423 entries, there were more photographs of the Northern Lights than usual, coinciding with the 11-year solar cycle maximum .

Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? The United Kingdom is sending military equipment and personnel to Belgium after a spate of disruptive drone sightings forced the temporary closures of two major airports. Air Chief Marshal Richard Knighton told the BBC network on Sunday that the military had agreed to "deploy our people, our equipment to Belgium to help them" after a request from Belgian authorities. In the past week, both Belgium's main international airport at Brussels and one of Europe's biggest cargo airports at Liege were forced to close temporarily because of drone incursions.

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UK military personnel and equipment are being sent to Belgium to help it bolster its defences after drone incursions on its airspace, suspected of being carried out by Russia. The new head of the UK military, Sir Richard Knighton, told the BBC's Sunday with Laura Kuenssberg that his Belgian counterpart asked for assistance earlier this week and that kit and personnel were on the way. Belgium's main airport Zavantem was forced to close temporarily on Thursday night after drones were spotted nearby . They were also spotted in other locations, including a military base. Sir Richard said it was not known if the incursions were by Russia, but added it was plausible they had been ordered by Moscow.