arXiv:2602.23410v1 Announce Type: new Abstract: Brain foundation models have achieved remarkable advances across a wide range of neuroscience tasks. However, most existing models are limited to a single functional modality, restricting their ability to exploit complementary spatiotemporal dynamics and the collective data scale across imaging techniques. To address this limitation, we propose Brain-OF, the first omnifunctional brain foundation model jointly pretrained on fMRI, EEG and MEG, capable of handling both unimodal and multimodal inputs within a unified framework. […]

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arXiv:2602.10142v1 Announce Type: new Abstract: Smart Voice Assistants (SVAs) are deeply embedded in the lives of youth, yet the mechanisms driving the privacy-protective behaviors among young users remain poorly understood. This study investigates how Canadian youth (aged 16-24) negotiate privacy with SVAs by developing and testing a structural model grounded in five key constructs: perceived privacy risks (PPR), perceived benefits (PPBf), algorithmic transparency and trust (ATT), privacy self-efficacy (PSE), and privacy-protective behaviors (PPB). A cross-sectional survey of N=469 […]

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How a 180-year-old diagnosis explains what AI is doing to work, meaning, and identity in 2026 Part 5 of a six-part series using science fiction as a lens for understanding AI, work, and power in 2026. Work is about a search for daily meaning as well as daily bread. Studs Terkel, Working, 1974 Studs Terkel (an American writer and historian among other things) spent three years interviewing Americans about their jobs. What he found was a story about […]

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Code got faster. The hard part is figuring out what humans should still own. Continue reading on Towards AI »

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Reinforcement learning (RL) has attracted increasing interest for adaptive traffic signal control due to its model-free ability to learn control policies directly from interaction with the traffic environment. However, several challenges remain before RL-based signal control can be considered ready for field deployment. Many existing studies rely on simplified signal timing structures, robustness of trained models under varying traffic demand conditions remains insufficiently evaluated, and runtime efficiency continues to pose challenges when training RL algorithms in traffic microscopic […]

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Something Broke in the WordPress Workflow Here’s a dirty secret about managing WordPress sites in 2026: most of the work is tedious. Not difficult — tedious. You want to change a button color, so you dig through theme settings. You need to add a contact form, so you evaluate six plugins. You want your homepage to look slightly different, so you spend two hours wrestling with a page builder that keeps overriding your CSS. For a platform that […]

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arXiv:2512.08179v2 Announce Type: replace-cross Abstract: We study estimation of the conditional law $P(Y|X=x)$ and continuous functionals $Psi(P(Y|X=x))$ when $Y$ takes values in a locally compact Polish space, $X in mathbb{R}^p$, and the observations arise from a complex survey design. We propose a survey-calibrated distributional random forest (SDRF) that incorporates complex-design features via a pseudo-population bootstrap, PSU-level honesty, and a Maximum Mean Discrepancy (MMD) split criterion computed from kernel mean embeddings of H'{a}jek-type (design-weighted) node distributions. We provide a […]

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We present a geometric framework for Reinforcement Learning (RL) that views policies as maps into the Wasserstein space of action probabilities. First, we define a Riemannian structure induced by stationary distributions, proving its existence in a general context. We then define the tangent space of policies and characterize the geodesics, specifically addressing the measurability of vector fields mapped from the state space to the tangent space of probability measures over the action space. Next, we formulate a general […]

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Guiding collective motion in biological groups is a fundamental challenge in understanding social interaction rules and developing automated systems for animal management. In this study, we propose a deep reinforcement learning (RL) framework for the closed-loop guidance of fish schools using virtual agents. These agents are controlled by policies trained via Proximal Policy Optimization (PPO) in simulation and deployed in physical experiments with rummy-nose tetras (Petitella bleheri), enabling real-time interaction between artificial agents and live individuals. To cope […]

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