America's fastest-growing state is selling the perfect lifestyle... and everyone's falling for it I was using my vape 160 times a day, it was costing me a fortune and its toll on my face was truly shocking. Then I discovered a miracle one-day cure... and stopped overnight: MARY KILLEN Lost tomb of the mysterious'cloud people' unearthed after 1,400 years in'discovery of the decade' Devastating truth about Blind Side actor Quinton Aaron: More to this'than everyone is letting on', friends reveal... as co-star Sandra Bullock'monitors' situation Harper Beckham, 14, puts on a stylish display in a fluffy coat and vintage Chanel bag as she heads out in Paris with her family... after Nicola's Peltz's heartbreaking comments about sister-in-law America's earthquake hotspot is more dangerous than feared as scientists make surprising discovery Terrifying animation shows pilot's-eye view of DC mid-air collision between airliner and helicopter that killed 67 Explosive twist in'diva' inmate Bryan Kohberger's life in prison revealed in the FREE The Crime Desk newsletter Marco Rubio'cocoons like a mummy' in bizarre strategy to hide naps from Trump Lawyer, 44, who died on flight to London after falling asleep on her mother's shoulder had undiagnosed cardiac condition, inquest hears Sydney Sweeney shows off her bombshell curves in racy lingerie to promote her new SYRN line - as it's revealed Hollywood Sign bra stunt could leave her facing trespassing and vandalism charges Truth about America's favorite pasture-raised egg brand after tests revealed what its chickens are eating and sparked huge boycotts A NASA jet skidded across a Houston runway Tuesday after a mechanical failure prevented its landing gear from deploying. Footage from Ellington Airport shows the research aircraft touching down before its belly scraped along the runway, sending sparks and flames trailing behind it. Emergency crews rushed in moments later, helping the pilot exit the aircraft as responders secured the scene, KHOU 11 News reported. NASA confirmed that all crew members are safe.

The word dimension for the embedding layer is 300. In Tab. 7 we further evaluate the diversity of COS-CVAE using self-CIDEr We provide additional qualitative results in Tabs. In Tab. 12 we show the divserse captions for novel objects generated by our model and the regions The evaluation server for nocaps accepts only one caption per image and does not support methods modeling one-to-many relationships for images and captions. In Figure 1 (left) we show the average accuracy and diversity scores again averaged across annotators; in Figure 1 (right) we show the accuracy and diversity scores from each annotator. We find that the captions generated by the COS-CV AE are scored to be more accurate compared to COS-CV AE (paired).

Accurate interpretation of Notices to Airmen (NOTAMs) is critical for aviation safety, yet their condensed and cryptic language poses significant challenges to both manual and automated processing. Existing automated systems are typically limited to shallow parsing, failing to extract the actionable intelligence needed for operational decisions. We formalize the complete interpretation task as deep parsing, a dual-reasoning challenge requiring both dynamic knowledge grounding (linking the NOTAM to evolving real-world aeronautical data) and schema-based inference (applying static domain rules to deduce operational status). To tackle this challenge, we propose NOTAM-Evolve, a self-evolving framework that enables a large language model (LLM) to autonomously master complex NOTAM interpretation. Leveraging a knowledge graph-enhanced retrieval module for data grounding, the framework introduces a closed-loop learning process where the LLM progressively improves from its own outputs, minimizing the need for extensive human-annotated reasoning traces. In conjunction with this framework, we introduce a new benchmark dataset of 10,000 expert-annotated NOTAMs. Our experiments demonstrate that NOTAM-Evolve achieves a 30.4% absolute accuracy improvement over the base LLM, establishing a new state of the art on the task of structured NOTAM interpretation.

The filmmaker could not get Tiggy the alien to cooperate. He just needed the glistening brown creature to turn its head. But Tiggy, who was sitting in the passenger's seat of a cop car, kept disobeying. At first Tiggy rotated his gaze only slightly. Then he looked to the wrong side of the camera. Then his skin turned splotchy, like an overripe fruit. The filmmaker was not on a movie set, or Mars. He was sitting at his home computer in Los Angeles using a piece of AI software called FLUX Kontext to generate and regenerate images of the alien, waiting for a workable one to appear. He'd used a different AI tool, Midjourney, to generate the very first image of Tiggy (prompt: "fat blob alien with a tiny mouth and tiny lips"); one called ElevenLabs to create the timbre of Tiggy's voice (the filmmaker's voice overlaid with a synthetic one, then pitch-shifted way up); and yet another called Runway to describe the precise shot he wanted in this scene ("close up on the little alien as they ride in the passenger seat, shallow depth of field").

The word dimension for the embedding layer is 300. In Tab. 7 we further evaluate the diversity of COS-CVAE using self-CIDEr We provide additional qualitative results in Tabs. In Tab. 12 we show the divserse captions for novel objects generated by our model and the regions The evaluation server for nocaps accepts only one caption per image and does not support methods modeling one-to-many relationships for images and captions. In Figure 1 (left) we show the average accuracy and diversity scores again averaged across annotators; in Figure 1 (right) we show the accuracy and diversity scores from each annotator. We find that the captions generated by the COS-CV AE are scored to be more accurate compared to COS-CV AE (paired).

At least 15 million videos have been snatched by tech companies. Listen to more stories on the Noa app. W hen Jon Peters uploaded his first video to YouTube in 2010, he had no idea where it would lead. He was a professional woodworker running a small business who decided to film himself making a dining table with some old legs he had found in a barn. It turned out that people liked his candid style, and as he posted more videos, a fan base began to grow.

Last year, filmmaker Paul Schrader--the director of Blue Collar, American Gigolo, and First Reformed, and writer of Martin Scorsese's Taxi Driver--issued what seemed like the last word on artificial intelligence in Hollywood filmmaking. A few days after the release of Denis Villeneuve's sci-fi blockbuster Dune: Part Two, Schrader asked his Facebook followers: "Will Dune 3 be made by AI? And, if it is, how will we know?" Schrader is well regarded not only as a director, but one of cinema's top-shelf curmudgeons, quick with a wry burn or baiting shit-post. But his Dune tweet seemed like more than another provocation. It spoke to a mounting feeling among many filmgoers, myself included: that Hollywood had stooped to producing sleek, antiseptic images so devoid of personality that they might as well have been made not by a living, breathing, thinking, feeling artist, but by a computer.

Deepfake Technology Unveiled: The Commoditization of AI and Its Impact on Digital Trust. With the increasing accessibility of generative AI, tools for voice cloning, face-swapping, and synthetic media creation have advanced significantly, lowering both financial and technical barriers for their use. While these technologies present innovative opportunities, their rapid growth raises concerns about trust, privacy, and security. This white paper explores the implications of deepfake technology, analyzing its role in enabling fraud, misinformation, and the erosion of authenticity in multimedia. Using cost-effective, easy to use tools such as Runway, Rope, and ElevenLabs, we explore how realistic deepfakes can be created with limited resources, demonstrating the risks posed to individuals and organizations alike. By analyzing the technical and ethical challenges of deepfake mitigation and detection, we emphasize the urgent need for regulatory frameworks, public awareness, and collaborative efforts to maintain trust in digital media.

Elon Musk and Donald Trump are no longer friends. Tension between the two exploded into public view in the middle of last week, with each leveling sharp barbs at the other. Four days into the public feud between the world's most powerful person and the world's richest person, though, I declare Musk the loser. An unstoppable force has lost its battle with an immovable object. From my colleagues Hugo Lowell and Andrew Roth: On Thursday, Elon Musk called for Donald Trump's impeachment and mocked his connections to the convicted sex offender Jeffrey Epstein, as the US president threatened to cancel federal contracts and tax subsidies for Musk's companies, in an extraordinary social media feud that erupted between the former allies.

The increasing complexity of autonomous systems has amplified the need for accurate and reliable labeling of runway and taxiway markings to ensure operational safety. Precise detection and labeling of these markings are critical for tasks such as navigation, landing assistance, and ground control automation. Existing labeling algorithms, like the Automated Line Identification and Notation Algorithm (ALINA), have demonstrated success in identifying taxiway markings but encounter significant challenges when applied to runway markings. This limitation arises due to notable differences in line characteristics, environmental context, and interference from elements such as shadows, tire marks, and varying surface conditions. To address these challenges, we modified ALINA by adjusting color thresholds and refining region of interest (ROI) selection to better suit runway-specific contexts. While these modifications yielded limited improvements, the algorithm still struggled with consistent runway identification, often mislabeling elements such as the horizon or non-relevant background features. This highlighted the need for a more robust solution capable of adapting to diverse visual interferences. In this paper, we propose integrating a classification step using a Convolutional Neural Network (CNN) named AssistNet. By incorporating this classification step, the detection pipeline becomes more resilient to environmental variations and misclassifications. This work not only identifies the challenges but also outlines solutions, paving the way for improved automated labeling techniques essential for autonomous aviation systems.