Researchers from Seattle Aquarium are studying the sixgill shark to learn more about their elusive lives. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Most sharks have five gill slits on either side. Breakthroughs, discoveries, and DIY tips sent six days a week. Most sharks have five gill slits on either side.

Horrifying next twist in the Alexander brothers case: MAUREEN CALLAHAN exposes an unthinkable perversion that's been hiding in plain sight Alexander brothers' alleged HIGH SCHOOL gang rape video: Classmates speak out on sick'taking turns' footage... as creepy unseen photos are exposed Model Cindy Crawford, 60, mocked for her'out of touch' morning routine: 'Nothing about this is normal' Kentucky mother and daughter turn down $26.5MILLION to sell their farms to secretive tech giant that wants to build data center there Tucker Carlson erupts at Trump adviser as she hurls'SLANDER' claim linking him to synagogue shooting NFL superstar Xavier Worthy spills all on Travis Kelce, the Chiefs' struggles... and having Taylor Swift as his No 1 fan Heartbreaking video shows very elderly DoorDash driver shuffle down customer's driveway with coffee order because he is too poor to retire Amber Valletta, 52, was a '90s Vogue model who made movies with Sandra Bullock and Kate Hudson, see her now Nancy Mace throws herself into Iran warzone as she goes rogue on Middle East rescue mission: 'I AM that person' Hidden toxins in kids' treats EXPOSED: Health guru Jillian Michaels' sit-down with Casey DeSantis reveals dangers lurking in popular foods READ MORE: I communicated with non-human intelligence... and what they told me proves God's existence New research has suggested that UFOs could be clustering around underwater canyons off the US coastline, raising fresh questions about whether mysterious craft could be operating beneath the ocean. An analysis of more than 80,000 reports found concentrated clusters of sightings near steep submarine canyon systems, particularly along the West Coast. The findings stem from an independent study testing the so-called'cryptoterrestrial hypothesis,' which proposes that unidentified aerial phenomena could originate from hidden non-human intelligence on Earth rather than distant planets. Using publicly available UFO sighting data and detailed ocean depth maps, the report identified correlations between reported sightings and deep underwater terrain features. The analysis also uncovered a striking geographical anomaly, with clustering patterns appearing on the West Coast but not on the East or Gulf coasts.

Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, and terrain reconstruction.

Amazon's fall Prime Day sale has come and gone, but a few of the best deals are still available. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Amazon Prime's Latest Prime Day sale has come and gone. If you are a Prime member who missed out, there's some good news--there are some leftover deals still going strong. We're still keeping you updated here with all the best markdowns on our favorite tech gear and gadgets that are still available, from Alexa-enabled speakers to robot vacs to laptops and tablets. The WIRED Reviews team tests products year-round, and at sales events like this, we only recommend deals on stuff we have actually used and approved. We sorted through thousands of deals by hand to make these picks. The Fire HD 10 is Amazon's best tablet for most people . The current model dates from 2023, but the Octa Core processor is plenty fast enough for consuming Amazon Prime content, which is really the primary reason to buy a Fire tablet. The full HD (1080p) screen won't win any awards, but it's good enough for streaming movies. Fire tablets can do double duty as an Echo speaker, too. Turn on Show Mode (swipe down on the notification overlay and check the Show Mode box) and you can query Alexa to your heart's content.

This Philips Norelco is already a champion of versatility matched with low cost--a Swiss army knife of beard, head, burns, and eyebrow guards with a nose trimmer to boot. The trimmer is high-rpm, but still quiet. The guardless blade shaves closer than most, and the shaving foil is even better. The battery lasts more than five hours. Its metal chassis offers comforting durability and heft. And unlike Philips' 9000 series, it can trim while plugged into the wall. The only real drawback is all those guards are difficult to sort and keep track of.

The 2025 Ocean Photographer of the Year announced its winners this week. This photo was taken on April 1, 2024, off Point No Point, WA. In Puget Sound, there's a community of people who prefer watching orcas from the land rather than from boats. Land-based whale watchers in Puget Sound can sometimes get lucky, as these wild apex predators occasionally approach the shore, seemingly curious about their human spectators. My friend is one of those land-based whale enthusiasts, and April 1, 2024, was no ordinary day for her.

Accurately predicting travel mode choice is essential for effective transportation planning, yet traditional statistical and machine learning models are constrained by rigid assumptions, limited contextual reasoning, and reduced generalizability. This study explores the potential of Large Language Models (LLMs) as a more flexible and context-aware approach to travel mode choice prediction, enhanced by Retrieval-Augmented Generation (RAG) to ground predictions in empirical data. We develop a modular framework for integrating RAG into LLM-based travel mode choice prediction and evaluate four retrieval strategies: basic RAG, RAG with balanced retrieval, RAG with a cross-encoder for re-ranking, and RAG with balanced retrieval and cross-encoder for re-ranking. These strategies are tested across three LLM architectures (OpenAI GPT-4o, o4-mini, and o3) to examine the interaction between model reasoning capabilities and retrieval methods. Using the 2023 Puget Sound Regional Household Travel Survey data, we conduct a series of experiments to evaluate model performance. The results demonstrate that RAG substantially enhances predictive accuracy across a range of models. Notably, the GPT-4o model combined with balanced retrieval and cross-encoder re-ranking achieves the highest accuracy of 80.8%, exceeding that of conventional statistical and machine learning baselines. Furthermore, LLM-based models exhibit superior generalization abilities relative to these baselines. Findings highlight the critical interplay between LLM reasoning capabilities and retrieval strategies, demonstrating the importance of aligning retrieval strategies with model capabilities to maximize the potential of LLM-based travel behavior modeling.

Big trajectory data hold great promise for human mobility analysis, but their utility is often constrained by the absence of critical traveler attributes, particularly sociodemographic information. While prior studies have explored predicting such attributes from mobility patterns, they often overlooked underlying cognitive mechanisms and exhibited low predictive accuracy. This study introduces SILIC, short for Sociodemographic Inference with LLM-guided Inverse Reinforcement Learning (IRL) and Cognitive Chain Reasoning (CCR), a theoretically grounded framework that leverages LLMs to infer sociodemographic attributes from observed mobility patterns by capturing latent behavioral intentions and reasoning through psychological constructs. Particularly, our approach explicitly follows the Theory of Planned Behavior (TPB), a foundational behavioral framework in transportation research, to model individuals' latent cognitive processes underlying travel decision-making. The LLMs further provide heuristic guidance to improve IRL reward function initialization and update by addressing its ill-posedness and optimization challenges arising from the vast and unstructured reward space. Evaluated in the 2017 Puget Sound Regional Council Household Travel Survey, our method substantially outperforms state-of-the-art baselines and shows great promise for enriching big trajectory data to support more behaviorally grounded applications in transportation planning and beyond.

Large Language Models (LLMs) have been widely used to generate responses on social topics due to their world knowledge and generative capabilities. Beyond reasoning and generation performance, political bias is an essential issue that warrants attention. Political bias, as a universal phenomenon in human society, may be transferred to LLMs and distort LLMs' behaviors of information acquisition and dissemination with humans, leading to unequal access among different groups of people. To prevent LLMs from reproducing and reinforcing political biases, and to encourage fairer LLM-human interactions, comprehensively examining political bias in popular LLMs becomes urgent and crucial. In this study, we systematically measure the political biases in a wide range of LLMs, using a curated set of questions addressing political bias in various contexts. Our findings reveal distinct patterns in how LLMs respond to political topics. For highly polarized topics, most LLMs exhibit a pronounced left-leaning bias. Conversely, less polarized topics elicit greater consensus, with similar response patterns across different LLMs. Additionally, we analyze how LLM characteristics, including release date, model scale, and region of origin affect political bias. The results indicate political biases evolve with model scale and release date, and are also influenced by regional factors of LLMs.

This systematic review explores the use of machine learning (ML) in predicting diabetes, focusing on datasets, algorithms, training methods, and evaluation metrics. It examines datasets like the Singapore National Diabetic Retinopathy Screening program, REPLACE-BG, National Health and Nutrition Examination Survey, and Pima Indians Diabetes Database. The review assesses the performance of ML algorithms like CNN, SVM, Logistic Regression, and XGBoost in predicting diabetes outcomes. The study emphasizes the importance of interdisciplinary collaboration and ethical considerations in ML-based diabetes prediction models.