Polling shows that nearly half of Americans now believe aliens have visited this planet--and that the number who aren't sure has dropped by two-thirds. The reasons why, experts say, are complicated. Americans are becoming more open to the idea that aliens have visited Earth, according to a series of polls that show belief in alien visitation has been steadily on the rise since 2012. Almost half--47 percent--of Americans say they think aliens have definitely or probably visited Earth at some point in time, according to a new poll from YouGov conducted in November 2025 that involved 1,114 adult participants. That percentage is up from roughly a third (36 percent) of Americans polled in 2012 by Kelton Research, with the exact same sample size.

Scientists Thought Parkinson's Was in Our Genes. New ideas about chronic illness could revolutionize treatment, if we take the research seriously. Amy Lindberg spent 26 years in the Navy and she still walked like it--with intention, like her chin had someplace to be. But around 2017, her right foot stopped following orders. Lindberg and her husband Brad were five years into their retirement. After moving 10 times for Uncle Sam, they'd bought their dream house near the North Carolina coast. They had a backyard that spilled out onto wetlands. From the kitchen, you could see cranes hunting. They kept bees and played pickleball and watched their children grow. But now Lindberg's right foot was out of rhythm. She worked hard to ignore it, but she couldn't disregard the tremors.

As large language models (LLMs) proliferate in scale, specialization, and latency profiles, the challenge of routing user prompts to the most appropriate model has become increasingly critical for balancing performance and cost. We introduce CARGO (Category-Aware Routing with Gap-based Optimization), a lightweight, confidence-aware framework for dynamic LLM selection. CARGO employs a single embedding-based regressor trained on LLM-judged pairwise comparisons to predict model performance, with an optional binary classifier invoked when predictions are uncertain. This two-stage design enables precise, cost-aware routing without the need for human-annotated supervision. To capture domain-specific behavior, CARGO also supports category-specific regressors trained across five task groups: mathematics, coding, reasoning, summarization, and creative writing. Evaluated on four competitive LLMs (GPT-4o, Claude 3.5 Sonnet, DeepSeek V3, and Perplexity Sonar), CARGO achieves a top-1 routing accuracy of 76.4% and win rates ranging from 72% to 89% against individual experts. These results demonstrate that confidence-guided, lightweight routing can achieve expert-level performance with minimal overhead, offering a practical solution for real-world, multi-model LLM deployments.

Land cover, both present and future, has a significant effect on several important Earth system processes. For example, impervious surfaces heat up and speed up surface water runoff and reduce groundwater infiltration, with concomitant effects on regional hydrology and flood risk. While regional Earth System models have increasing skill at forecasting hydrologic and atmospheric processes at high resolution in future climate scenarios, our ability to forecast land-use and land-cover change (LULC), a critical input to risk and consequences assessment for these scenarios, has lagged behind. In this paper, we propose a new paradigm exploiting Generative AI (GenAI) for land cover change forecasting by framing LULC forecasting as a data synthesis problem conditioned on historical and auxiliary data-sources. We discuss desirable properties of generative models that fundament our research premise, and demonstrate the feasibility of our methodology through experiments on imperviousness forecasting using historical data covering the entire conterminous United States. Specifically, we train a diffusion model for decadal forecasting of imperviousness and compare its performance to a baseline that assumes no change at all. Evaluation across 12 metropolitan areas for a year held-out during training indicate that for average resolutions $\geq 0.7\times0.7km^2$ our model yields MAE lower than such a baseline. This finding corroborates that such a generative model can capture spatiotemporal patterns from historical data that are significant for projecting future change. Finally, we discuss future research to incorporate auxiliary information on physical properties about the Earth, as well as supporting simulation of different scenarios by means of driver variables.

"CHiPs" star Erik Estrada shared a warning about how artificial intelligence can "destroy lives." During an interview with Fox News Digital, the 75-year-old actor and "Divine Renovation" host acknowledged the benefits of AI but cautioned that the new technology is also frequently being used for nefarious purposes. "I think just like the Internet, just like the cell phones, just like everything -- they need to just use the positive side of it," Estrada said. "The side which can help or employ and create goodwill, good things, good jobs, good fortune for people that want to go in that direction and not, of course, use the negative stuff." "CHiPs" star Erik Estrada warned about the dangers posed by AI. (Brian To/FilmMagic) Estrada pointed to how AI can be used to create deepfakes -- deceptive pictures, videos and audio that misrepresent people or events.

A few months ago, I flew to Houston to visit a small startup called Lunar Resources, which aspires to build the largest telescope in the solar system--not on Earth but on the far side of the moon. Houston is nicknamed Space City; on the ride from the airport, I passed the ballpark where the Astros play, and, outside a McDonald's on East NASA Parkway, I saw a giant sculpture of an astronaut holding French fries. I found Lunar Resources in a boxy building where the company leases square footage from the aerospace contractor Lockheed Martin. Elliot Carol, the C.E.O. and co-founder of Lunar Resources, is thirty-three, with a cherubic face and curly hair speckled with gray. Although he grew up in Connecticut and previously worked as a hedge-fund manager, he was wearing black cowboy boots.

Ensemble forecasting is crucial for improving weather predictions, especially for forecasts of extreme events. Constructing an ensemble prediction system (EPS) based on conventional NWP models is highly computationally expensive. ML models have emerged as valuable tools for deterministic weather forecasts, providing forecasts with significantly reduced computational requirements and even surpassing the forecast performance of traditional NWP models. However, challenges arise when applying ML models to ensemble forecasting. Recent ML models, such as GenCast and SEEDS model, rely on the ERA5 EDA or operational NWP ensemble members for forecast generation. Their spatial resolution is also considered too coarse for many applications. To overcome these limitations, we introduce FuXi-ENS, an advanced ML model designed to deliver 6-hourly global ensemble weather forecasts up to 15 days. This model runs at a significantly increased spatial resolution of 0.25\textdegree, incorporating 5 atmospheric variables at 13 pressure levels, along with 13 surface variables. By leveraging the inherent probabilistic nature of Variational AutoEncoder (VAE), FuXi-ENS optimizes a loss function that combines the CRPS and the KL divergence between the predicted and target distribution, facilitating the incorporation of flow-dependent perturbations in both initial conditions and forecast. This innovative approach makes FuXi-ENS an advancement over the traditional ones that use L1 loss combined with the KL loss in standard VAE models for ensemble weather forecasting. Results demonstrate that FuXi-ENS outperforms ensemble forecasts from the ECMWF, a world leading NWP model, in the CRPS of 98.1% of 360 variable and forecast lead time combinations. This achievement underscores the potential of the FuXi-ENS model to enhance ensemble weather forecasts, offering a promising direction for further development in this field.

The volume of remote sensing data is experiencing rapid growth, primarily due to the plethora of space and air platforms equipped with an array of sensors. Due to limited hardware and battery constraints the data is transmitted back to Earth for processing. The large amounts of data along with security concerns call for new compression and encryption techniques capable of preserving reconstruction quality while minimizing the transmission cost of this data back to Earth. This study investigates image compression based on convolutional variational autoencoders (CVAE), which are capable of substantially reducing the volume of transmitted data while guaranteeing secure lossy image reconstruction. CVAEs have been demonstrated to outperform conventional compression methods such as JPEG2000 by a substantial margin on compression benchmark datasets. The proposed model draws on the strength of the CVAE's capability to abstract data into highly insightful latent spaces, and combining it with the utilization of an entropy bottleneck is capable of finding an optimal balance between compressibility and reconstruction quality. The balance is reached by optimizing over a composite loss function that represents the rate-distortion curve.

We introduce a new Bayesian admixture model intended for exploratory analysis of communication networks--specifically, the discovery and visualization of topic-specific subnetworks in email data sets. Our model produces principled visualizations of email networks, i.e., visualizations that have precise mathematical interpretations in terms of our model and its relationship to the observed data. We validate our modeling assumptions by demonstrating that our model achieves better link prediction performance than three state-of-the-art network models and exhibits topic coherence comparable to that of latent Dirichlet allocation. We showcase our model's ability to discover and visualize topic-specific communication patterns using a new email data set: the New Hanover County email network. We provide an extensive analysis of these communication patterns, leading us to recommend our model for any exploratory analysis of email networks or other similarly-structured communication data. Finally, we advocate for principled visualization as a primary objective in the development of new network models.

There's something fun and nostalgic about playing board games, which offer a welcome break from screens of every kind. Not only are board games a great way to spend some time with friends and family, they keep our minds sharp, can promote relaxation and may create social opportunities, experts say. Board games promote positive mental health in many ways. "From a cognitive perspective, many board games challenge our executive functioning or more advanced brain skills," said Carol Lambdin-Pattavina, OTD, a member of the American Occupational Therapy Association. "These skills include working memory, mental flexibility, emotional regulation, organization and more," she said.