In this paper, we study the problem of quantifying knowledge contained in an LLM regarding a given set of facts.

Jessie Buckley'overwhelmed' to be starring in Oscar-tipped Hamnet The Oscar-tipped Hamnet, starring Jessie Buckley and Paul Mescal, is a film that shows the full range of human emotions, from elation to despair. It begins with a young William Shakespeare falling in love with Agnes (the other name by which the playwright's wife, historically referred to as Anne Hathaway, was known), and goes on to explore their immense grief after tragedy strikes their young family. But while it explores the sad origins of one of Shakespeare's greatest plays, Hamlet, it never portrays Agnes as just the playwright's wife - she is at the heart of the film. She was the full story of what I understand a woman to be, Buckley tells BBC News. And their capacity as women, and as mothers, and as lovers, and as people who have a language unto their own beside gigantic men of literature like Shakespeare.

Flow has shown the promise of per-instance personalization in improving clients' accuracy. We have trained Flow and its baselines on the Stackoverflow dataset for 2000 rounds. The batch size used for each client on each baseline is 16. The default learning rate used is 0.1. All the baselines and Flow variants have been run for 1500 rounds, with 10 clients per round.

We provide theoretical analysis on the convergence of Flow and empirically demonstrate the superiority of Flow in improving clients' accuracy compared

Over the past few years, advances in deep learning have revolutionised the way we interact with everyday devices.

B.4 Comparison against Discrete Laplace Figure 12 compares Skellam and (discrete) Gaussian against the discrete Laplace mechanism under various accounting schemes on privacy compositions.

Is a robot programmed to prank you annoying? Feedback discovers a robot that can mimic Turkish ice cream vendors, who are known for playing tricks on their customers. Researchers concluded that customers, perhaps predictably, don't trust it Feedback is a grumpy sort, so we run a mile when faced with any kind of enforced fun. It is possible, therefore, that we would struggle to buy an ice cream in Turkey, because doing so requires enjoying, or at least tolerating, an extended prank. Turkish ice cream vendors are prone to playing tricks on their customers, like handing them a cone full of ice cream only to whisk it out of their grasp using sleight of hand.

You can't get better at reading until you care about a text. We are English professors who stumbled into a debate about high school pedagogy. We wrote a book to help college instructors teach close reading, the fundamental skill of literary studies. And then, well before it was published, we started hearing from education scholars training high school teachers, and high school teachers themselves, who had caught wind of the book through advance essays and word of mouth. They were interested in how we describe close reading, the tools we provide for teaching it, and the claim we make for its importance.

The clustering and visualisation of high-dimensional data is a ubiquitous task in modern data science. Popular techniques include nonlinear dimensionality reduction methods like t-SNE or UMAP. These methods face the `scale-problem' of clustering: when dealing with the MNIST dataset, do we want to distinguish different digits or do we want to distinguish different ways of writing the digits? The answer is task dependent and depends on scale. We revisit an idea of Robinson & Pierce-Hoffman that exploits an underlying scaling symmetry in t-SNE to replace 2-dimensional with (2+1)-dimensional embeddings where the additional parameter accounts for scale. This gives rise to the t-SNE tree (short: tree-SNE). We prove that the optimal embedding depends continuously on the scaling parameter for all initial conditions outside a set of measure 0: the tree-SNE tree exists. This idea conceivably extends to other attraction-repulsion methods and is illustrated on several examples.