Background Childhood Anemia affects an estimated 40% of children aged 6-59 months globally and arises from heterogeneous nutritional, infectious, and socioeconomic factors that vary substantially across settings. This variability challenges the generalizability of predictive machine learning models, which often degrade under cross-population or temporal shifts. We investigated the utility a modern transformer-based tabular foundation model (TabPFN) as a complementatry framework with respect to supervised classical machine learning methods across diverse country contexts, with particular attention to data-scarce settings where surveillance capacity is most limited. Methods We conducted a multi-country prediction study using Demographic and Health Surveys (DHS) children's recode data from 16 countries spanning Africa, Asia, Latin America, the Caucasus, and the Middle East. The harmonized analytic cohort comprised of (n = 68,856)children aged 6-59 months with valid hemoglobin measurements. Anemia was defined using WHO age and altitude-adjusted thresholds and treated as a binary outcome. We trained Logistic Regression, XGBoost, and LightGBM models using standard supervised learning, and evaluated TabPFN v2.6 in an in-context learning setting. Performance was assessed using Area Under the Receiver Operating Characteristic Curve (AUC-ROC) and other standard classification metrics, with calibration evaluated via Brier score and expected calibration error (ECE). Uncertainty in performance estimates was quantified using bootstrap resampling to derive 95% confidence intervals. Robustness was assessed in a few-shot learning setting. Cross-population generalization was examined using leave-one-country-out (LOCO) validation and reverse-LOCO experiments to assess directional transferability. Subgroup analyses were conducted across five demographic strata: child age group, sex, maternal education, residence type, and household wealth quintile. Feature importance was assessed using standard linear and tree-based explainer SHAP values for the three supervised models and an adapted version of SHAP for TabPFN, aggregated across countries and examined at the country level. TabPFN also yielded the best probabilistic calibration across all 16 countries, achieving the lowest mean Brier score (0.203) and Expected Calibration Error (ECE = 0.042) of all models evaluated; LightGBM and Logistic Regression exhibited the greatest miscalibration, particularly at higher predicted probabilities. Under full-data conditions, within-country discrimination was moderate across all models (AUC-ROC 0.59-0.76) Under LOCO validation, performance declined modestly (AUC-ROC 0.58-0.69) Reverse-LOCO analyses revealed asymmetric and directional transferability, with epidemiologically diverse populations serving as more informative training sources and certain target populations remaining persistently difficult to predict regardless of model or training data.

Popular megachurch in crisis as senior pastor suddenly quits... while bosses furiously DENY sex scandal Missing scientist's shattered car sparks chilling mystery in remote New Mexico mountains Two small airlines join forces to create America's newest budget carrier after Spirit collapse leaves millions scrambling Horrifying final days of killer dad Chris Watts' pregnant wife before she was slaughtered alongside their daughters. Read all the chilling texts and receipts in full for first time: 'My eyes burn from crying' I'm a pastor who attended a secret UFO disclosure meeting. We saw images of'translucent beings' that chilled me to the bone... the files could fulfil a dark biblical prophecy Cheerful Christian mom is pillar of Florida community and loves going on TV... but she has a childhood secret so evil that she stuttered with shock when confronted with it Taxpayers to foot Trump's $1.7 BILLION bill as President sues his own government: 'I'm paying myself' How I lost 3 STONE in 3 WEEKS. I've reversed pre-diabetes and no longer need a knee op: DONAL MACINTYRE's extraordinary investigation Former NFL player Josh Mauro's tragic cause of death revealed after league was left'devastated' by ex-Cardinals and Giants man's sudden passing at 35 Husband of doomed dive group leader says'something must have happened down there' as mystery surrounds why the five attempted to explore'cave so deep even divers with best equipment don't try' Greeks savage Kimberly Guilfoyle as Trump's ambassador opens McDonald's in country celebrated for world-class food I'm godfather to Candace Owens' daughter and Charlie Kirk was my friend... so I know the real reason she's attacking Erika - and I'll never publicly condemn her Death of Alabama woman, 22, 'accidentally' shot in chest by boyfriend's dad is ruled a HOMICIDE Reese Witherspoon and Ryan Phillippe reunite for son's NYU graduation... as Kate Hudson cheers on her boy at same ceremony with Goldie Hawn and Kurt Russell'How do you live with that?' Disgraced Eric Swalwell's'blindsided' wife dresses for revenge... as friends reveal brutal toll sex assault scandal has had on young mom Judge declares another mistrial in disgraced Hollywood mogul Harvey Weinstein's rape case Can't lose weight no matter what you do? These are the 7 surprising reasons why, including'healthy' hacks actually making you put on pounds.

'Solar Impulse 2' made history by circumnavigating the globe in 2016. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. 'Solar Impulse 2' completed its circumnavigation of the planet, which included a flight over Giza's pyramids, in 2016. Breakthroughs, discoveries, and DIY tips sent six days a week. The groundbreaking experimental aircraft known as has met an untimely end.

Early writing is a tale of two scripts. Egyptian hieroglyphs and Mesopotamian cuneiform both emerged independently about 5300 years ago. The political powers of ancient Egypt and Mesopotamia flourished in the centuries to come, partly because writing helped states control the flow of goods and consolidate power. The pen (or ancient stylus) was mightier than the sword. Or so the conventional story goes. But there is a glaring omission here because, at the dawn of writing, there weren't two scripts. That third, mysterious script, called proto-Elamite, appeared in ancient Iran while cuneiform and hieroglyphs were both in their infancy - and has been shockingly overlooked by all but a handful of scholars since its discovery 125 years ago.

The recent surge in fuel prices due to the war in Iran has spurred demand for electric vehicles around the world, and Chinese car makers are making the most of the opportunity. China is the world's top producer of EVs, and while its manufacturers remain largely shut out of the major car market of the United States, they are benefiting from an uptick in interest and orders via dealerships across Asia and elsewhere. BYD, which overtook Tesla as the world's largest seller of electric vehicles last year and is expanding aggressively overseas, is at the centre of this shift in focus. We survive and are successful without the US market today, BYD executive vice president Stella Li told the BBC at the Beijing Auto Show. Instead of aiming for US customers, the company says its challenge is meeting increased demand in other regions, including Brazil, the UK and Europe.

The over 3,000-year-old statement piece belonged to Ramses the Great. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Ramses is considered the greatest pharaoh of ancient Egypt's New Kingdom. Breakthroughs, discoveries, and DIY tips sent six days a week. Ramses II (1303-1213 BCE), aka Ramses the Great, is easily one of ancient Egyptian history's most recognizable rulers.

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In this paper, we make the case that a scientific theory of deep learning is emerging. By this we mean a theory which characterizes important properties and statistics of the training process, hidden representations, final weights, and performance of neural networks. We pull together major strands of ongoing research in deep learning theory and identify five growing bodies of work that point toward such a theory: (a) solvable idealized settings that provide intuition for learning dynamics in realistic systems; (b) tractable limits that reveal insights into fundamental learning phenomena; (c) simple mathematical laws that capture important macroscopic observables; (d) theories of hyperparameters that disentangle them from the rest of the training process, leaving simpler systems behind; and (e) universal behaviors shared across systems and settings which clarify which phenomena call for explanation. Taken together, these bodies of work share certain broad traits: they are concerned with the dynamics of the training process; they primarily seek to describe coarse aggregate statistics; and they emphasize falsifiable quantitative predictions. We argue that the emerging theory is best thought of as a mechanics of the learning process, and suggest the name learning mechanics. We discuss the relationship between this mechanics perspective and other approaches for building a theory of deep learning, including the statistical and information-theoretic perspectives. In particular, we anticipate a symbiotic relationship between learning mechanics and mechanistic interpretability. We also review and address common arguments that fundamental theory will not be possible or is not important. We conclude with a portrait of important open directions in learning mechanics and advice for beginners. We host further introductory materials, perspectives, and open questions at learningmechanics.pub.

Drone incursions over U.S. military bases reveal critical vulnerabilities in civilian infrastructure, from airports to energy grids, that experts say remain dangerously exposed.

Plus: An unauthorized group has reportedly accessed Anthropic's Mythos. What actually matters in AI right now? It's getting harder to tell amid the constant launches, hype, and warnings. To cut through the noise, reporters and editors have distilled years of analysis into a new essential guide: the 10 Things That Matter in AI Right Now . The list builds on our annual 10 Breakthrough Technologies, but takes a wider view of the ideas, topics, and research shaping AI, spotlighting the trends and breakthroughs shaping the world. We'll be unpacking one item from the list each day here in The Download, explaining what it means and why it matters.