Fifty years after her death in New York, Hannah Arendt has become the most popular philosopher of our time. For good reason: Her views are just as timely as ever. It must be so nice to play Hannah Arendt. No fewer than five actresses are on stage this evening at the Deutsches Theater Berlin to portray the philosopher. The piece is an adaptation of the graphic novel by American illustrator Ken Krimstein about the philosopher's life, called The Three Escapes of Hannah Arendt," combined with scenes from the famous interview that journalist Günter Gaus conducted with Arendt in 1964 for German public broadcaster ZDF. The article you are reading originally appeared in German in issue 49/2025 (November 28th, 2025) of DER SPIEGEL. They play Arendt and a few of her contemporaries, the philosopher Martin Heidegger, the writer Walter Benjamin, her husband Heinrich Blücher. There is a great deal of speech in the play, especially from Arendt herself. The places of her life are ticked off, her ...

Children take part in an extracurricular club about coding and AI in Cambridge. Children take part in an extracurricular club about coding and AI in Cambridge. Generation AI: fears of'social divide' unless all children learn computing skills In a Cambridge classroom, Joseph, 10, trained his AI model to discern between drawings of apples and drawings of smiles. "AI gets lots of things wrong," he said, as it mistakenly identified a fruit as a face. He set about retraining it and, in a flash, he had it back on track - instinctively understanding the inner nature of artificial intelligence and machine learning in a way few adults do.

Identification and Estimation under Multiple Versions of Treatment: Mixture-of-Experts Approach Kohei Y oshikawa Shuichi Kawano January 5, 2026 Abstract The Stable Unit Treatment Value Assumption (SUTV A) includes the condition that there are no multiple versions of treatment in causal inference. Though we could not control the implementation of treatment in observational studies, multiple versions may exist in the treatment. It has been pointed out that ignoring such multiple versions of treatment can lead to biased estimates of causal effects, but a causal inference framework that explicitly deals with the unbiased identification and estimation of version-specific causal effects has not been fully developed yet. Thus, obtaining a deeper understanding for mechanisms of the complex treatments is difficult. In this paper, we introduce the Mixture-of-Experts framework into causal inference and develop a methodology for estimating the causal effects of latent versions. This approach enables explicit estimation of version-specific causal effects even if the versions are not observed. Numerical experiments demonstrate the effectiveness of the proposed method. Keywords causal inference multiple versions of treatment compound treatments mixture-of-experts EM algorithm 1 Introduction In the theory of causal inference, a fundamental starting point is the potential outcomes framework since Rubin (1980), whose core assumption is the Stable Unit Treatment Value Assumption (SUTV A).

Numerical simulation of complex physical phenomena is a core enabling technology for digital twins, which are comprised of physical and virtual assets with a two-way flow of information: data from the physical asset is used to construct and/or calibrate the virtual asset (a numerical model), while numerical predictions from the virtual asset are used for control or decision-making for the physical asset [42]. To be viable for practical application, the virtual asset must be able to produce predictions rapidly and reliably; however, the underlying physics that are of interest for digital twin applications can typically only be accurately simulated using a large number of degrees of freedom, leading to computationally expensive numerical simulations. The explainability and computational efficiency of decisions made by the digital twin play a key role in safety-critical applications, making explainable artificial intelligence an essential ingredient [24]. Model reduction techniques are one such explainable scientific machine learning technique that construct low-dimensional systems, called reduced-order models (ROMs), to serve as computationally inexpensive surrogates for a high-dimensional physics simulation [4, 20]. This paper introduces a technique for adaptively constructing ROMs to emulate systems with parametric dependence, that is, systems whose behavior varies with some set of parameters, usually representing physical properties. We focus on systems where the parametric dependence manifests in the operators defining the model, not merely in initial conditions or external inputs.

Microwave Vacuum Drying, or MVD, may be a real MVP for snack foods. Breakthroughs, discoveries, and DIY tips sent every weekday. It's hard to stop after eating a single potato chip --and that's kind of their whole problem. The deep-fried, popular salty snack is loaded with unhealthy fats, oils, and other unwanted ingredients that are linked with numerous health problems. Unfortunately, those are also the flavor profiles humans are evolutionarily wired to crave.

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In applications with significant class imbalance or asymmetric costs, metrics such as the $F_β$-measure, AM measure, Jaccard similarity coefficient, and weighted accuracy offer more suitable evaluation criteria than standard binary classification loss. However, optimizing these metrics present significant computational and statistical challenges. Existing approaches often rely on the characterization of the Bayes-optimal classifier, and use threshold-based methods that first estimate class probabilities and then seek an optimal threshold. This leads to algorithms that are not tailored to restricted hypothesis sets and lack finite-sample performance guarantees. In this work, we introduce principled algorithms for optimizing generalized metrics, supported by $H$-consistency and finite-sample generalization bounds. Our approach reformulates metric optimization as a generalized cost-sensitive learning problem, enabling the design of novel surrogate loss functions with provable $H$-consistency guarantees. Leveraging this framework, we develop new algorithms, METRO (Metric Optimization), with strong theoretical performance guarantees. We report the results of experiments demonstrating the effectiveness of our methods compared to prior baselines.

These plant-based meal kits and delivery services bring healthy preprepared meals and meal kits to your door. Plant-Based meal kit services are a modern miracle for vegetarians and vegans, who usually aren't afforded the same conveniences as meat eaters or those without dietary restrictions. We at WIRED love meal kits, because they're all about modern convenience--you can eat what you want, even if you're on a specialty diet or have strong food preferences, without ever leaving your house. Gone are the days of grocery shopping and scouring online for recipes; these contemporary plant-based meal kit services do the heavy lifting for you using curated menus and algorithms, with choices for both premade microwavable meals and kits where you do the cooking yourself. Some plant-based meal kit services, like Hungryroot, use AI customization to curate menus based on your specific tastes. Others, like Daily Harvest, have a set selection of choices so you can always keep your freezer stocked with plant-based, gluten-free meals to have on hand. I'm vegan, so I know how difficult it can be to find new recipes that will actually taste good without breaking the bank. Plus, plant-based meal kits are a great way to try out new foods and recipes, especially if you're looking to switch to a healthier diet in the new year.

Due to its low storage cost and fast search speed, cross-modal retrieval based on hashing has attracted widespread attention and is widely used in real-world applications of social media search.