Gear Home The Home Depot's early Black Friday Ryobi sale: Get two batteries and a power tool for just $99 The free tools include popular options like impact drivers, reciprocating saws, yard tools, and more. We may earn revenue from the products available on this page and participate in affiliate programs. Right now, if you buy a pair of Ryobi batteries (with a charger) from The Home Depot, you can get a free tool worth up to $99 to go with them. You can choose from essential power tools, including a reciprocating saw, a 1/2-inch impact, or even a string trimmer . You can never have too many batteries.

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.

Whether you're shopping for your closest friend who has everything or a grumpy family member who criticizes every gift you've ever given, we have the best suggestions for you. We may earn revenue from the products available on this page and participate in affiliate programs. Your friends and family deserve the best possible gifts. But, shopping can be tricky. You don't want to give them something impersonal, like a gift card, but you also can't resort to drawing them a card with a Christmas tree on it again. It's our job to find the best products and deals, so we've spent way too much time digging up a ton of products that pretty much anyone would like.

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.

Video Anomaly Detection (VAD), which aims to detect anomalies that deviate from expectation, has attracted increasing attention in recent years. Existing advancements in VAD primarily focus on model architectures and training strategies, while devoting insufficient attention to evaluation metrics and benchmarks. In this paper, we rethink VAD evaluation methods through comprehensive analyses, revealing three critical limitations in current practices: 1) existing metrics are significantly influenced by single annotation bias; 2) current metrics fail to reward early detection of anomalies; 3) available benchmarks lack the capability to evaluate scene overfitting of fully/weakly-supervised algorithms. To address these limitations, we propose three novel evaluation methods: first, we establish probabilistic AUC/AP (Prob-AUC/AP) metrics utlizing multi-round annotations to mitigate single annotation bias; second, we develop a Latency-aware Average Precision (LaAP) metric that rewards early and accurate anomaly detection; and finally, we introduce two hard normal benchmarks (UCF-HN, MSAD-HN) with videos specifically designed to evaluate scene overfitting. We report performance comparisons of ten state-of-the-art VAD approaches using our proposed evaluation methods, providing novel perspectives for future VAD model development. We release our data and code in https://github.com/Kamino666/RethinkingVAD.

We quantify the impact of Generative Artificial Intelligence (GenAI) on firm productivity through a series of large-scale randomized field experiments involving millions of users and products at a leading cross-border online retail platform. Over six months in 2023-2024, GenAI-based enhancements were integrated into seven consumer-facing business workflows. We find that GenAI adoption significantly increases sales, with treatment effects ranging from $0\%$ to $16.3\%$, depending on GenAI's marginal contribution relative to existing firm practices. Because inputs and prices were held constant across experimental arms, these gains map directly into total factor productivity improvements. Across the four GenAI applications with positive effects, the implied annual incremental value is approximately $\$ 5$ per consumer-an economically meaningful impact given the retailer's scale and the early stage of GenAI adoption. The primary mechanism operates through higher conversion rates, consistent with GenAI reducing frictions in the marketplace and improving consumer experience. We also document substantial heterogeneity: smaller and newer sellers, as well as less experienced consumers, exhibit disproportionately larger gains. Our findings provide novel, large-scale causal evidence on the productivity effects of GenAI in online retail, highlighting both its immediate value and broader potential.