Bidets are extremely common in northern Italy, where the Milano Cortina Games are being played. One of the first bidets in Italy was installed at the Palace of Caserta for Queen Maria Carolina in the late 1700s. Bidets are now, once again, having a moment. As international athletes and journalists descend on northern Italy for the 2026 Winter Olympics, certain participants have wondered about the additional piece of equipment in their bathrooms. Europeans, quite familiar with the oval basins, have found themselves similarly perplexed by their confusion.

These second-generation smart glasses give you superpowers--if the software behaves. Optional R1 smart ring makes control simpler. Software stability remains an issue. Smart ring is an extra $249. Navigation needs a little finessing.

The maker of Ozempic and Wegovy is suing a rival firm for selling what it says are unsafe, knock-off versions of its weight-loss drugs in the US. Danish company Novo Nordisk asked US courts on Monday to ban Hims & Hers' range of weight-loss pills and injections, which it says are not approved by US authorities and infringe on its patent. The legal drama began on Friday after Hims & Hers launched a new weight-loss pill, leading to an initial threat from Novo Nordisk. Over the weekend, Hims & Hers said it would stop selling the pill. On Monday, its share price slumped as it called Novo Nordisk's decision to press ahead with the lawsuit a blatant attack.

How to ensure fairness in algorithmic decision making models is an important task in machine learning [12,15]. Over the past years, many researchers have been devoted to the design of fair classification algorithms withrespecttoapre-defined protected attribute,suchasraceorsex,anda decision task/model, such as hiring [1,11,24]. In particular,one line of the work istoincorporate fairness constraints into classic learning algorithms tobuild fair classifiers from potentially biased data [4,13,29,31-33]. Most of previous research generally focuses on a single decision model.

Stein discrepancies (SDs) monitor convergence andnon-convergence inapprox-imate inference when exact integration and sampling are intractable. However,the computation of a Stein discrepancy can be prohibitive if the Stein operator - often a sum over likelihood terms or potentials - is expensive to evaluate.

To address this, we propose Concept Embedding Models, a novel family of concept bottleneck models which goes beyond the current accuracy-vs-interpretability trade-off by learning interpretable highdimensional conceptrepresentations.

Standard concentration inequalities are well suited for learning problems where the goal is to minimize the expected riskE[`(h,X)]. However, the expected risk--the mean performance of an algorithm--might fail to capture the underlying phenomenon of interest.