When you purchase through links in our articles, we may earn a small commission. Make the holidays last all year round with these eye-catching permanent outdoor lights, all steeply discounted for Cyber Monday. Dread the prospect of climbing a ladder each winter to hang string lights from your eaves? With a set of permanent outdoor lights, you only must risk life and limb once to keep your home lit with holiday cheer. These twinkling smart lights are designed to stay put all year round.

An aerial view of an Amazon Web Services Data Center known as U.S. East 1 in Ashburn, Virginia, on Oct. 20 | REUTERS STOCKHOLM/LONDON - The global boom in data centers as companies increasingly outsource information storage and ramp up use of energy-intensive artificial intelligence is creating a key challenge for the industry -- how to keep cool. An outage at the world's biggest exchange operator CME Group from late Thursday that halted trade on its popular currency platform and in futures spanning foreign exchange, commodities, Treasuries and stocks has put a spotlight on data centers overheating. The problem was a cooling issue at data centers operated by Dallas-headquartered CyrusOne, which operates more than 55 centers in the U.S., Europe and Japan. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Amazon's Cyber Monday sale drops prices on tons of its most popular and best-reviewed products. Get all your holiday shopping done now. We may earn revenue from the products available on this page and participate in affiliate programs. The Sunday scaries don't exist on the day before Cyber Monday . In fact, the only fear you should have right now is that you'll miss out on the best deals on the biggest online shopping day of the year.

Here's where things stand on Monday, December 1. The number of casualties from a Russian attack on Ukraine's Kyiv on Sunday rose to one person killed and 18 wounded, according to regional Governor Mykola Kalashnyk. In southern Kherson, at least two people were killed, and seven others were wounded in more Russian attacks, Governor Oleksandr Prokudin said on Telegram. In the Donetsk region, at least two people were killed, and five were injured in Russian attacks on Saturday, according to Governor Vadym Filashkin. In Russia, a Ukrainian drone attack killed two men in the Belgorod region, the region's operational headquarters said in a post on Telegram.

NTT Docomo Business has begun deploying drones to locate and drive away wild bears, in partnership with the village of Showa in Fukushima Prefecture, informed sources have said. The high-performance drones, featuring a camera equipped with a thermal imaging function, can be effective in the early morning and at night, when bears tend to be active. The aircraft support the long-term evolution, or LTE, wireless communication standard, which is used for mobile phones. NTT Docomo Business, under the aegis of the group led by telecommunications giant NTT, plans to start offering the service to other municipalities as a new measure to address bear attacks at a time when reports on damage caused by the animals are increasing around the country. The drones search areas where bears were spotted to help hunters cull them. The thermal camera is used to locate bears if they flee into bushes.

Gear Amazon's Cyber Monday deals on Celestron Telescopes make this a perfect time to start stargazing Whether you want a basic scope or a more advanced system, Amazon has tons of Celestron models for the lowest prices of the year during Cyber Monday. We may earn revenue from the products available on this page and participate in affiliate programs. Whether you're finally ready to point a telescope at the Moon or you want to upgrade to a more powerful rig for deep-sky objects, these Celestron telescope and accessory deals cover just about every level of backyard astronomer. This is a great option for users at any level. If you want your first "real" telescope but don't want to spend your nights guessing which fuzzy blob is which, this is the one to grab.

AI agents are increasingly deployed in ecosystems where they repeatedly interact not only with each other but also with humans. In this work, we study these human-AI ecosystems from a theoretical perspective, focusing on the classical framework of repeated pricing games. In our stylized model, the AI agents play equilibrium strategies, and one or more humans manually perform the pricing task instead of adopting an AI agent, thereby defecting to a no-regret strategy. Motivated by how populations of AI agents can sustain supracompetitive prices, we investigate whether high prices persist under such defections. Our main finding is that even a single human defection can destabilize collusion and drive down prices, and multiple defections push prices even closer to competitive levels. We further show how the nature of collusion changes under defection-aware AI agents. Taken together, our results characterize when algorithmic collusion is fragile--and when it persists--in mixed ecosystems of AI agents and humans.

Intensive care unit (ICU) data are highly irregular, heterogeneous, and temporally fragmented, posing challenges for generalizable clinical prediction. We present PULSE-ICU, a self-supervised foundation model that learns event-level ICU representations from large-scale EHR sequences without resampling or manual feature engineering. A unified embedding module encodes event identity, continuous values, units, and temporal attributes, while a Longformer-based encoder enables efficient modeling of long trajectories. PULSE-ICU was fine-tuned across 18 prediction tasks, including mortality, intervention forecasting, and phenotype identification, achieving strong performance across task types. External validation on eICU, HiRID, and P12 showed substantial improvements with minimal fine-tuning, demonstrating robustness to domain shift and variable constraints. These findings suggest that foundation-style modeling can improve data efficiency and adaptability, providing a scalable framework for ICU decision support across diverse clinical environments.

Machine learning models perform well across domains such as diagnostics, weather forecasting, NLP, and autonomous driving, but their limited uncertainty handling restricts use in safety-critical settings. Traditional neural networks often fail to detect out-of-domain (OOD) data and may output confident yet incorrect predictions. Bayesian neural networks (BNNs) address this by providing probabilistic estimates, but incur high computational cost because predictions require sampling weight distributions and multiple forward passes. The Probabilistic Forward Pass (PFP) offers a highly efficient approximation to Stochastic Variational Inference (SVI) by assuming Gaussian-distributed weights and activations, enabling fully analytic uncertainty propagation and replacing sampling with a single deterministic forward pass. We present an end-to-end pipeline for training, compiling, optimizing, and deploying PFP-based BNNs on embedded ARM CPUs. Using the TVM deep learning compiler, we implement a dedicated library of Gaussian-propagating operators for multilayer perceptrons and convolutional neural networks, combined with manual and automated tuning strategies. Ablation studies show that PFP consistently outperforms SVI in computational efficiency, achieving speedups of up to 4200x for small mini-batches. PFP-BNNs match SVI-BNNs on Dirty-MNIST in accuracy, uncertainty estimation, and OOD detection while greatly reducing compute cost. These results highlight the potential of combining Bayesian approximations with code generation to enable efficient BNN deployment on resource-constrained systems.

Split learning is well known as a method for resolving data privacy concerns by training a model on distributed devices, thereby avoiding data sharing that raises privacy issues. However, high network communication costs are always an impediment to split learning, especially for large foundation models that require transmitting large amounts of high-dimensional data. To resolve this issue, we present a new multimodal model structure that incorporates a learning-based data compression method, which compresses model embeddings into low-bit integers while preserving the model's performance, greatly reducing the transmission costs between partitions. We then determine the optimal number of discrete representation levels based on a solid theoretical foundation from entropy coding.