The ocean’s bad boys don’t just cruise alone—they have preferred swimming partners.

The complete list may be seen in Table 8. Here are a few general notes about these strings: 1. Based on their recommendations, we did the following: 1. zh, zh_Latn: This resulted in the special filters described below. URLs) the corpora were in languages different from the LangID predictions. This is mainly mis-rendered PDFs and may have practical applications for denoising, or for decoding such garbled PDFs.

Theoptimalbaseline, however, israrelyusedinpractice (Sutton & Barto (2018); foran exception, see (Peters & Schaal, 2008)). Equation (1) thentakesthefollowingform: r E R(x)= E (R(x) B)r log (x).

As we look back on 2025, the world experienced a year of both remarkable achievement and profound challenge in global health. Multilateralism, science and solidarity were tested as never before, underscoring a fundamental truth: International cooperation is not optional. It is essential if we are to protect and promote health for everyone, everywhere in 2026 and beyond. Perhaps the most significant milestone was the adoption by WHO Member States of the Pandemic Agreement, a landmark step towards making the world safer from future pandemics. Alongside this, amendments to the International Health Regulations came into force, including a new "pandemic emergency" alert level designed to trigger stronger global cooperation.

Synthetic data generation is an important tool for privacy-preserving data sharing. While diffusion models have set recent benchmarks, flow matching (FM) offers a promising alternative. This paper presents different ways to implement flow matching for tabular data synthesis. We provide a comprehensive empirical study that compares flow matching (FM and variational FM) with a state-of-the-art diffusion method (TabDDPM and TabSyn) in tabular data synthesis. We evaluate both the standard Optimal Transport (OT) and the Variance Preserving (VP) probability paths, and also compare deterministic and stochastic samplers -- something possible when learning to generate using \textit{variational} flow matching -- characterising the empirical relationship between data utility and privacy risk. Our key findings reveal that flow matching, particularly TabbyFlow, outperforms diffusion baselines. Flow matching methods also achieves better performance with remarkably low function evaluations ($\leq$ 100 steps), offering a substantial computational advantage. The choice of probability path is also crucial, as using the OT path demonstrates superior performance, while VP has potential for producing synthetic data with lower disclosure risk. Lastly, our results show that making flows stochastic not only preserves marginal distributions but, in some instances, enables the generation of high utility synthetic data with reduced disclosure risk.

Do You Know What I Know? Steven Pinker argues that common knowledge makes the world go round--and off the rails. Take your young kid with you as you commute through Penn Station and you'll find that you have a lot to explain. Walking through the Long Island Railroad concourse, my son was perplexed by the close proximity of three chicken-themed restaurants--Chick-fil-A, Raising Cane's, and Pollo Campero--and by the fact that a shop called Gotham News mainly seemed to sell candy and bottled water. He also wanted to know why some people, as they strolled or waited, drank out of cans in brown paper bags.

For example, in the Pandemic Response Challenge experiment, the context consisted of data about the geographic region for which the predictions were made, e.g., historical data of COVID-19 cases and intervention policies; actions were future schedules of intervention policies for the region; and outcomes were predicted future cases of COVID-19 along with the stringency