Missing values in real-world data pose a significant and unique challenge to algorithmic fairness.

The science of soulmates: Is there someone out there exactly right for you? On Valentine's Day, there's the temptation to believe that somewhere out there is The One: a soulmate, a perfect match, the person you were meant to be with. Across history, humans have always been drawn to the idea that love isn't random. In ancient Greece, Plato imagined that we were once whole beings with four arms, four legs and two faces, so radiant that Zeus split us in two; ever since, each half has roamed the earth searching for its missing other, a myth that gives the modern soulmate its poetic pedigree and the promise that somewhere, someone will finally make us feel complete. In the Middle Ages, troubadours and Arthurian tales recast that longing as courtly love, a fierce, often forbidden devotion like Lancelot's for Guinevere, in which a knight proved his worth through self-sacrifice for a beloved he might never openly declare.

In this episode of, our hosts dive into WIRED's scoop about a secret Trump administration campaign extending right into your backyard. This week, hosts Brian Barrett, Leah Feiger, and Zoë Schiffer discuss WIRED's big scoop on ICE's startling plans to expand to nearly every state in the US. Plus, a WIRED writer lets the viral AI assistant OpenClaw run his life for a week to give listeners a peek of what AI agents can and can't do. ICE Is Expanding Across the US at Breakneck Speed. Write to us at uncannyvalley@wired.com . You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link . I want to continue a conversation that we started yesterday in Slack after work hours for some of us. And this is about the men's short program-- But very specifically want to pick up on the conversation where Zoë had very strong feelings about the results of men's figure skating. I feel like we need to back up because you and Leah authentically care about the Olympics so much and I think just know more about sports than I do. I deeply have never engaged with sports ever, just as a whole rule, as a category. It doesn't exist in my life. Say the lines, say the lines, Zoë, or I'm going to read them verbatim from slack. Wait, I don't even know what you're talking about. I was merely surprised when I watched because the Americans went, I thought, wow, that guy basically fell over and was clumping around the ice, and then Japan went, and they were sailing around like little swans, and then when the gold medal came, it went to the Americans. I couldn't believe what had happened. No one else seemed outraged. For a little backup for our non-ice skating Olympic fans, I was always referring to Ilia Malinin, who a number of publications and sports experts say might actually be one of the greatest figure skaters of all time.

Valve's Steam Machine: Everything we know The company plans to offer rides to the public in Nashville sometime this year. Waymo has gotten a step closer to offering robotaxi rides to the public in Nashville, Tennessee. The company the city and making sure they can operate as fully autonomous rides before launching a paid service in the location. Waymo announced that it was planning to bring its robotaxis to Nashville in September 2025, with the intention opening up rides to the public sometime this year. The company has been testing its technology in Nashville since then, but it has yet say when it'll start accepting bookings for rides.

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The rapid progress of diffusion models highlights the growing need for detecting generated images. Previous research demonstrates that incorporating diffusion-based measurements, such as reconstruction error, can enhance the gener-alizability of detectors. However, ignoring the differing impacts of aleatoric and epistemic uncertainty on reconstruction error can undermine detection performance. Aleatoric uncertainty, arising from inherent data noise, creates ambiguity that impedes accurate detection of generated images. As it reflects random variations within the data (e.g., noise in natural textures), it does not help distinguish generated images. In contrast, epistemic uncertainty, which represents the model's lack of knowledge about unfamiliar patterns, supports detection. In this paper, we propose a novel framework, Diffusion Epistemic Uncertainty with Asymmetric Learning (DEUA), for detecting diffusion-generated images. W e introduce Diffusion Epistemic Uncertainty (DEU) estimation via the Laplace approximation to assess the proximity of data to the manifold of diffusion-generated samples. Additionally, an asymmetric loss function is introduced to train a balanced classifier with larger margins, further enhancing generalizability.

Finite-time central limit theorem (CLT) rates play a central role in modern machine learning. In this paper, we study CLT rates for multivariate dependent data in Wasserstein-$p$ ($W_p$) distance, for general $p \geq 1$. We focus on two fundamental dependence structures that commonly arise in machine learning: locally dependent sequences and geometrically ergodic Markov chains. In both settings, we establish the first optimal $O(n^{-1/2})$ rate in $W_1$, as well as the first $W_p$ ($p\ge 2$) CLT rates under mild moment assumptions, substantially improving the best previously known bounds in these dependent-data regimes. As an application of our optimal $W_1$ rate for locally dependent sequences, we further obtain the first optimal $W_1$-CLT rate for multivariate $U$-statistics. On the technical side, we derive a tractable auxiliary bound for $W_1$ Gaussian approximation errors that is well suited for studying dependent data. For Markov chains, we further prove that the regeneration time of the split chain associated with a geometrically ergodic chain has a geometric tail without assuming strong aperiodicity or other restrictive conditions. These tools may be of independent interests and enable our optimal $W_1$ rates and underpin our $W_p$ ($p\ge 2$) results.