Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Selects Look Up Say More Safety Net Versus Creator Playbook In My Bag Trending Now Back to School Good Connection: Uplifting stories for a digital age Switch Off All Series How'Ray Gunn's Venus Nova, pop star of the (retro) future, evolved Belen Edwards is an Entertainment Reporter at Mashable. She covers movies and TV with a focus on fantasy and science fiction, adaptations, animation, and more nerdy goodness. She is a member of the Critics Choice Association and the Television Critics Association, as well as a Tomatometer-approved critic. All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission.

Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more In My Bag Say More Trending Now Mashable Selects Creator Playbook Back to School Good Connection: Uplifting stories for a digital age Switch Off Mashable Voices Safety Net All Series Elisha Sauers writes about space for Mashable, taking deep dives into NASA's moon and Mars missions, chatting up astronauts and history-making discoverers, and jetting above the clouds . Through 17 years of reporting, she's covered a variety of topics, including health, business, and government, with a penchant for public records requests. She previously worked for in Norfolk, Virginia, and in Annapolis, Maryland. Her work has earned numerous state awards, including the Virginia Press Association's top honor, Best in Show, and national recognition for narrative storytelling. For each year she has covered space, Sauers has won National Headliner Awards, including first place for her Sex in Space series.

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. The Pegasids Meteor shower and Milky Way at Mesquite Flat Sand Dunes of Death Valley in California, on July 9, 2023. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . The summer is generally a low period for stargazing, for the simple reason that there's less darkness to put to use staring at the sky thanks to our longer days and shorter nights.

We present a systematic theoretical framework that interprets masked diffusion models (MDMs) as solutions to energy minimization problems in discrete optimal transport. Specifically, we prove that three distinct energy formulationskinetic, conditional kinetic, and geodesic energyare mathematically equivalent under the structure of MDMs, and that MDMs minimize all three when the mask schedule satisfies a closed-form optimality condition. This unification not only clarifies the theoretical foundations of MDMs, but also motivates practical improvements in sampling. By parameterizing interpolation schedules via Beta distributions, we reduce the schedule design space to a tractable 2D search, enabling efficient post-training tuning without model modification. Experiments on synthetic and real-world benchmarks demonstrate that our energy-inspired schedules outperform hand-crafted baselines, particularly in low-step sampling settings.

ROTC students at Old Dominion subdued and killed ISIS-linked gunman who left one dead, two wounded after shouting'Allahu Akbar' and opened fire Horrifying next twist in the Alexander brothers case: MAUREEN CALLAHAN exposes an unthinkable perversion that's been hiding in plain sight Kentucky mother and daughter turn down $26.5MILLION to sell their farms to secretive tech giant that wants to build data center there Hollywood icon who starred in Psycho after Hitchcock dubbed her'my new Grace Kelly' looks incredible at 95 Kylie Jenner's total humiliation in Hollywood: Derogatory rumor leaves her boyfriend's peers'laughing at her' behind her back Tucker Carlson erupts at Trump adviser as she hurls'SLANDER' claim linking him to synagogue shooting Ben Affleck'scores $600m deal' with Netflix to sell his AI film start-up Long hair over 45 is ageing and try-hard. I've finally cut mine off. Alexander brothers' alleged HIGH SCHOOL rape video: Classmates speak out on sickening footage... as creepy unseen photos are exposed 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 Model Cindy Crawford, 60, mocked for her'out of touch' morning routine: 'Nothing about this is normal' 'Planetary parade' will see SIX planets align in rare spectacle tonight - here's the best time to spot Mercury, Venus, Jupiter, Saturn, Uranus and Neptune in the night sky Keen astronomers are in for a treat tonight, as a rare'planetary parade' of six planets lights up the night sky. Tonight, Mercury, Venus, Jupiter, Saturn, Uranus, and Neptune will all be visible from Earth. Excitingly, four of these planets will be visible with the naked eye, so you won't need any special equipment to enjoy the spectacle.

Nonverbal communication is integral to human interaction, with gestures, facial expressions, and body language conveying critical aspects of intent and emotion. However, existing large language models (LLMs) fail to effectively incorporate these nonverbal elements, limiting their capacity to create fully immersive conversational experiences. We introduce MARS, a multimodal language model designed to understand and generate nonverbal cues alongside text, bridging this gap in conversational AI. Our key innovation is VENUS, a large-scale dataset comprising annotated videos with time-aligned text, facial expressions, and body language. Leveraging VENUS, we train MARS with a next-token prediction objective, combining text with vector-quantized nonverbal representations to achieve multimodal understanding and generation within a unified framework. Based on various analyses of the VENUS datasets, we validate its substantial scale and high effectiveness. Our quantitative and qualitative results demonstrate that MARS successfully generates text and nonverbal languages, corresponding to conversational input.

It seems like it should be pretty easy to get to Mercury. The little rocky planet is so much closer to Earth than distant destinations like Jupiter, where we've successfully sent multiple spacecraft. Plus, it doesn't have a crushing atmosphere like our nearest neighbor Venus. But, in fact, it's actually really difficult to reach the innermost planet of our solar system--which makes it that much more impressive that the ESA and JAXA's BepiColombo mission has almost reached Mercury, recently completing its final flyby of the planet before entering orbit next year. Reaching Mercury is such a challenge because "the gravitational pull of the Sun is very strong near Mercury, which makes it difficult for spacecraft to slow down enough to enter orbit around the planet," explains Lina Hadid, staff scientist at CNRS in France and principal investigator of one of BepiColombo's instruments.

From NASA's mission to study Jupiter's icy moon Europa to Elon Musk's SpaceX catching its Starship rocket mid-air, there's no doubt 2024 saw some incredible space feats. 'In 2024, NASA made leap after giant leap to explore, discover, and inspire – all while bringing real, tangible, and substantial benefits to the American people and to all of humanity,' said NASA Administrator Bill Nelson. And 2025 is set to be an even more remarkable year for space agencies and companies around the world, who have an assortment of exciting missions lined up. Among them are NASA, which is sending two twin spacecraft to Mars – although its upcoming return to the moon has been delayed yet again. There's also the European Space Agency, which is set to launch its futuristic'Space Rider' spaceplane – described as a'robotic laboratory the size of two minivans'.

This paper details a significant milestone towards maturing a buoyant aerial robotic platform, or aerobot, for flight in the Venus clouds. We describe two flights of our subscale altitude-controlled aerobot, fabricated from the materials necessary to survive Venus conditions. During these flights over the Nevada Black Rock desert, the prototype flew at the identical atmospheric densities as 54 to 55 km cloud layer altitudes on Venus. We further describe a first-principle aerobot dynamics model which we validate against the Nevada flight data and subsequently employ to predict the performance of future aerobots on Venus. The aerobot discussed in this paper is under JPL development for an in-situ mission flying multiple circumnavigations of Venus, sampling the chemical and physical properties of the planet's atmosphere and also remotely sensing surface properties.

Knowledge Graph Question Answering (KGQA) methods seek to answer Natural Language questions using the relational information stored in Knowledge Graphs (KGs). With the recent advancements of Large Language Models (LLMs) and their remarkable reasoning abilities, there is a growing trend to leverage them for KGQA. However, existing methodologies have only focused on answering factual questions, e.g., "In which city was Silvio Berlusconi's first wife born?", leaving questions involving commonsense reasoning that real-world users may pose more often, e.g., "Do I need separate visas to see the Venus of Willendorf and attend the Olympics this summer?" unaddressed. In this work, we first observe that existing LLM-based methods for KGQA struggle with hallucination on such questions, especially on queries targeting long-tail entities (e.g., non-mainstream and recent entities), thus hindering their applicability in real-world applications especially since their reasoning processes are not easily verifiable. In response, we propose Right for Right Reasons (R3), a commonsense KGQA methodology that allows for a verifiable reasoning procedure by axiomatically surfacing intrinsic commonsense knowledge of LLMs and grounding every factual reasoning step on KG triples. Through experimental evaluations across three different tasks--question answering, claim verification, and preference matching--our findings showcase R3 as a superior approach, outperforming existing methodologies and notably reducing instances of hallucination and reasoning errors.