New POV video shows a mutually beneficial relationship between remoras and the gentle giants. Breakthroughs, discoveries, and DIY tips sent every weekday. In addition to its pod, the sizable cetacean generally hosts dozens of remoras. Also known as a suckerfish, these evolutionary wonders in the family hitch rides on whales in order to make a meal of the sea lice and other crustaceans that also make a home on the marine mammal's skin. To accomplish this, the remora possesses a distinctive, oval dorsal fin that functions like an adapted suction cup.

Breakthroughs, discoveries, and DIY tips sent every weekday. Japan is calling in its army to wrestle its ongoing bear problem . Last month, the country's Ministry of the Environment reported that Asian black bear or moon bear () and brown bear () populations have attacked over 100 people since March. With at least 10 fatalities among the tally, the government announced on November 5 that it is stepping up control efforts by deploying soldiers to Akita prefecture on the island of Honshu in northern Japan. In a statement to reporters, Akita's Governor Kenta Suzuki called the situation "desperate," noting that sightings and attacks are now occurring daily.

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Breakthroughs, discoveries, and DIY tips sent every weekday. Humans don't need to blast off into space to find some truly alien-looking wonders. The deepest depths of our ocean are like another planet, teeming with the charismatic "Casper" octopus, the carnivorous (aka the flying spaghetti monster), and even some sharks . A team from Kumamoto University in Japan recently uncovered a deep-sea anemone that has a tight bond with hermit crabs. These wispy pink invertebrates build shell-like "homes" for the crabs.

Plus loud rats and other weird things we learned this week. Breakthroughs, discoveries, and DIY tips sent every weekday. What's the weirdest thing you learned this week? Well, whatever it is, we promise you'll have an even weirder answer if you listen to's hit podcast . It's your new favorite source for the strangest science-adjacent facts, figures, and Wikipedia spirals the editors of can muster.

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Dynamic pricing in retail requires policies that adapt to shifting demand while coordinating decisions across related products. We present a systematic empirical study of multi-agent reinforcement learning for retail price optimization, comparing a strong MAPPO baseline with a graph-attention-augmented variant (MAPPO+GAT) that leverages learned interactions among products. Using a simulated pricing environment derived from real transaction data, we evaluate profit, stability across random seeds, fairness across products, and training efficiency under a standardized evaluation protocol. The results indicate that MAPPO provides a robust and reproducible foundation for portfolio-level price control, and that MAPPO+GAT further enhances performance by sharing information over the product graph without inducing excessive price volatility. These results indicate that graph-integrated MARL provides a more scalable and stable solution than independent learners for dynamic retail pricing, offering practical advantages in multi-product decision-making.

-- Accurate multi - horizon retail forecasts are critical for inventory and promotions. We present a novel study of weekly Walmart sales (45 stores, 2010 - 2012) using a Temporal Fusion Transformer (TFT) that fuses static store identifiers with time - varying exoge nous signals (holidays, CPI, fuel price, temperature). The pipeline produces 1 - 5 - week - ahead probabilistic forecasts via QuantileLoss, yielding calibrated 90% prediction intervals and interpretability through variable - selection networks, static enr ichment, and temporal attention. On a fixed 2012 hold - out dataset, TFT achieves an RMSE of $ 57.9k USD per store - week and an R of 0.9875. Across 5 - fold chronological cross - validation, the averages are RMSE = $ 64.6k USD and R = 0.9844, outperforming XGB, CNN, LSTM, and CNN - LSTM baseline models .

Language model finetuning often results in learning undesirable traits in combination with desired ones. To address this, we propose inoculation prompting: modifying finetuning data by prepending a short system-prompt instruction that deliberately elicits the undesirable trait. At test time, we evaluate without the instruction; inoculated models have much lower expression of the trait than models trained with unmodified training data. Inoculation is selective: in a toy setting where assistant responses are always in Spanish and ALL-CAPS, an appropriate inoculation (e.g., ``You always speak in Spanish.'') teaches the model to capitalize responses while still responding in English. We find that inoculation is also effective across several additional settings: reducing emergent misalignment (EM) from task-specific finetuning, defending against backdoor injections, and mitigating the transmission of traits via subliminal learning. Follow-up analysis suggests a mechanism: making a trait less surprising via inoculation reduces optimization pressure to globally update the model, thereby reducing the degree of generalization. Our analysis relates to prior work on EM: inoculation explains prior findings that educational contexts mitigate EM from insecure code. Beyond demonstrating a simple and effective technique for selective learning, our results contribute to a better conceptual understanding of how and why language models generalize.

Things to Do in L.A. Tap to enable a layout that focuses on the article. KitKat was friendly with many customers of Randa's Market in San Francisco's Mission District. This is read by an automated voice. Please report any issues or inconsistencies here . San Francisco has been mourning the death of KitKat, a beloved corner-store cat who died after being struck by a Waymo robotaxi last week.