A new wave of theft is rocking the luxury car industry--mixing high-tech with old-school chop-shop techniques to snag vehicles while they're in transport. When Sam Zahr first saw the gray Rolls-Royce Dawn convertible with orange interior and orange roof, he knew he'd found a perfect addition to his fleet. "It was very appealing to our clientele," he told me. As the director of operations at Dream Luxury Rental, he outfits customers in the Detroit area looking to ride in style to a wedding, a graduation, or any other event with high-end vehicles--Rolls-Royces, Lamborghinis, Bentleys, Mercedes G-Wagons, and more. But before he could rent out the Rolls, Zahr needed to get the car to Detroit from Miami, where he bought it from a used-car dealer. His team posted the convertible on Central Dispatch, an online marketplace that's popular among car dealers, manufacturers, and owners who want to arrange vehicle shipments. It's not too complicated, at least in theory: A typical listing includes the type of vehicle, zip codes of the origin and destination, dates for pickup and delivery, and the fee. Anyone with a Central Dispatch account can see the job, and an individual carrier or transport broker who wants it can call the number on the listing. Zahr's team got a call from a transport company that wanted the job. They agreed on the price and scheduled pickup for January 17, 2025.

Retrieval-augmented generation (RAG) often falls short when retrieved context includes confusing semi-relevant passages, or when answering questions require deep contextual understanding and reasoning. We propose an efficient fine-tuning framework, called PrismRAG, that (i) trains the model with distractor-aware QA pairs mixing gold evidence with subtle distractor passages, and (ii) instills reasoning-centric habits that make the LLM plan, rationalize, and synthesize without relying on extensive human engineered instructions. Evaluated across 12 open-book RAG QA benchmarks spanning diverse application domains and scenarios, PrismRAG improves average factuality by 5.4%, outperforming state-of-the-art solutions.

Celebrity nutritionist Daryl Gioffre, from Naples, Florida, tells Fox News Digital about the potential side effects of an ice cream emulsifier called Polysorbate 80, which alters the balance of gut bacteria. The future of gut health monitoring has arrived, thanks to researchers at the California Institute of Technology. Caltech's new invention, PillTrek, is a wireless smart capsule for gut health monitoring that delivers real-time insights from inside your gastrointestinal tract. This swallowable device promises to make invasive procedures a thing of the past, offering convenience and continuous data that traditional methods simply cannot match. Illustration of a woman holding a PillTrek near her mouth, about to swallow it.

Arthroscopy is a minimally invasive surgical procedure used to diagnose and treat joint problems. The clinical workflow of arthroscopy typically involves inserting an arthroscope into the joint through a small incision, during which surgeons navigate and operate largely by relying on their visual assessment through the arthroscope. However, the arthroscope's restricted field of view and lack of depth perception pose challenges in navigating complex articular structures and achieving surgical precision during procedures. Aiming at enhancing intraoperative awareness, we present a robust pipeline that incorporates simultaneous localization and mapping, depth estimation, and 3D Gaussian splatting to realistically reconstruct intra-articular structures solely based on monocular arthroscope video. Extending 3D reconstruction to Augmented Reality (AR) applications, our solution offers AR assistance for articular notch measurement and annotation anchoring in a human-in-the-loop manner. Compared to traditional Structure-from-Motion and Neural Radiance Field-based methods, our pipeline achieves dense 3D reconstruction and competitive rendering fidelity with explicit 3D representation in 7 minutes on average. When evaluated on four phantom datasets, our method achieves RMSE = 2.21mm reconstruction error, PSNR = 32.86 and SSIM = 0.89 on average. Because our pipeline enables AR reconstruction and guidance directly from monocular arthroscopy without any additional data and/or hardware, our solution may hold the potential for enhancing intraoperative awareness and facilitating surgical precision in arthroscopy. Our AR measurement tool achieves accuracy within 1.59 +/- 1.81mm and the AR annotation tool achieves a mIoU of 0.721.

Elon Musk's SpaceX and its competitors are making reliable, and decently-fast satellite internet services a reality thanks to a growing armada of shimmering satellites orbiting overhead. Through its constellation of over 6,000, 500-pound satellites, SpaceX's Starlink internet service already reportedly provides broadband to around three million global users, some in remote locations underserved by traditional internet providers. But what happens when all those aging satellites no longer serve their purpose? A new report from environmentally-focused advocacy group PIRG warns the current approach to decommissioning old satellites, which usually involves having them burn to a crisp when re-entering the atmosphere, lacks meaningful rules and regulation. That absence of oversight, they say, could lead to an increase in dangerous space junk affecting Earth, especially as competing satellite internet companies rush to build out and launch tens of thousands of new satellites into orbit.

Retrieval-Augmented Generation (RAG) has recently demonstrated the performance of Large Language Models (LLMs) in the knowledge-intensive tasks such as Question-Answering (QA). RAG expands the query context by incorporating external knowledge bases to enhance the response accuracy. However, it would be inefficient to access LLMs multiple times for each query and unreliable to retrieve all the relevant documents by a single query. We have found that even though there is low relevance between some critical documents and query, it is possible to retrieve the remaining documents by combining parts of the documents with the query. To mine the relevance, a two-stage retrieval framework called Dynamic-Relevant Retrieval-Augmented Generation (DR-RAG) is proposed to improve document retrieval recall and the accuracy of answers while maintaining efficiency. Additionally, a compact classifier is applied to two different selection strategies to determine the contribution of the retrieved documents to answering the query and retrieve the relatively relevant documents. Meanwhile, DR-RAG call the LLMs only once, which significantly improves the efficiency of the experiment. The experimental results on multi-hop QA datasets show that DR-RAG can significantly improve the accuracy of the answers and achieve new progress in QA systems.

While there are a plethora of methods for link prediction in knowledge graphs, state-of-the-art approaches are often black box, obfuscating model reasoning and thereby limiting the ability of users to make informed decisions about model predictions. Recently, methods have emerged to generate prediction explanations for Knowledge Graph Embedding models, a widely-used class of methods for link prediction. The question then becomes, how well do these explanation systems work? To date this has generally been addressed anecdotally, or through time-consuming user research. In this work, we present an in-depth exploration of a simple link prediction explanation method we call LinkLogic, that surfaces and ranks explanatory information used for the prediction. Importantly, we construct the first-ever link prediction explanation benchmark, based on family structures present in the FB13 dataset. We demonstrate the use of this benchmark as a rich evaluation sandbox, probing LinkLogic quantitatively and qualitatively to assess the fidelity, selectivity and relevance of the generated explanations. We hope our work paves the way for more holistic and empirical assessment of knowledge graph prediction explanation methods in the future.

Almost 11 years after Boston Dynamics revealed the Atlas humanoid robot, it's finally being retired. The DARPA-funded robot was designed for search-and-rescue missions, but it rose to fame thanks to videos showing off its dance moves and--let's be honest--rudimentary parkour skills. Atlas is trotting off into the sunset with one final YouTube video, thankfully including plenty of bloopers -- which are the best parts. Boston Dynamics, of course, has more commercially successful robots in its lineup, including Spot. Meta's Oversight Board will rule on AI-generated sexual images Motorola's Edge 50 phone series includes a wood option You can get these reports delivered daily direct to your inbox.

Ten U.S. and 2 United Arab Emirates astronauts have just completed 2 years of training NASA confirmed on Monday that an object that crashed into a Naples, Florida, home last month was a piece of hardware from the International Space Station that was supposed to burn up on re-entry before reaching the surface of Earth. Alejandro Otero said a piece of equipment from the International Space Station hit his Naples home, posting photos of the object on X in response to an astronomer who was tracking where and when the equipment would enter the Earth's atmosphere. Otero told the astronomer it looked like one of the pieces had missed Fort Myers, and landed inside his home. "Tore through the roof and went thru 2 floors," he posted on X, adding that it almost hit his son. FLORIDA MAN SAYS SPACE OBJECT CRASHED INTO HIS HOUSE.

On March 8, a piece of space debris plunged through a roof in Naples, FL, ripped through two floors and (fortunately) missed the son of homeowner Alejandro Otero. On Tuesday, NASA confirmed the results of its analysis of the incident. As suspected, it's a piece of equipment dumped from the International Space Station (ISS) three years ago. NASA's investigation of the object at Kennedy Space Center in Cape Canaveral confirmed it was a piece of the EP-9 support equipment used to mount batteries onto a cargo pallet, which the ISS' robotic arm dropped on March 11, 2021. The haul, made up of discarded nickel-hydrogen batteries, was expected to orbit Earth between two to four years (it split the difference, lasting almost exactly three) "before burning up harmlessly in the atmosphere," as NASA predicted at the time.