Generating high-quality question-answer pairs for specialized technical domains remains challenging, with existing approaches facing a tradeoff between leveraging expert examples and achieving topical diversity. We present ExpertGenQA, a protocol that combines few-shot learning with structured topic and style categorization to generate comprehensive domain-specific QA pairs. Using U.S. Federal Railroad Administration documents as a test bed, we demonstrate that ExpertGenQA achieves twice the efficiency of baseline few-shot approaches while maintaining $94.4\%$ topic coverage. Through systematic evaluation, we show that current LLM-based judges and reward models exhibit strong bias toward superficial writing styles rather than content quality. Our analysis using Bloom's Taxonomy reveals that ExpertGenQA better preserves the cognitive complexity distribution of expert-written questions compared to template-based approaches. When used to train retrieval models, our generated queries improve top-1 accuracy by $13.02\%$ over baseline performance, demonstrating their effectiveness for downstream applications in technical domains.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. New federal certification rules finalized Monday for train dispatchers and signal repairmen will set minimum standards to counteract the investor pressure on railroads to continually cut costs while making sure those employees have the skills they need to operate all the high-tech systems on today's trains. The new Federal Railroad Administration rules are the latest steps in the agency's broad efforts to improve rail safety since the disastrous East Palestine derailment in Ohio last year although these rules were in the works years before that train crash. FRA Administrator Amit Bose said in an interview with The Associated Press that both these crafts of workers are responsible for some of the advanced technology railroads rely on like the assortment of trackside detectors that help spot mechanical problems before they can cause derailments, so it made sense to set certification standards for them.

We propose DEER (Descriptive Knowledge Graph for Explaining Entity Relationships) - an open and informative form of modeling entity relationships. In DEER, relationships between entities are represented by free-text relation descriptions. For instance, the relationship between entities of machine learning and algorithm can be represented as ``Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.'' To construct DEER, we propose a self-supervised learning method to extract relation descriptions with the analysis of dependency patterns and generate relation descriptions with a transformer-based relation description synthesizing model, where no human labeling is required. Experiments demonstrate that our system can extract and generate high-quality relation descriptions for explaining entity relationships. The results suggest that we can build an open and informative knowledge graph without human annotation.

One of the most widely used buzzwords in the logistics sector in 2022 is "digitalization." The word is a useful umbrella term for the evolution to computer-based processes from manual procedures that relied on pencils and clipboards in the warehouse or printed manifests at the loading dock. But references to the trend nearly always ignore the tactical steps needed to make digitalization happen. Your DC probably doesn't have a magic wand that transforms basic paper checklists into cloud-based software platforms. So how are practitioners driving toward the goal of pulling logistics processes into the 21st century?

Nokia is using machine learning-based artificial intelligence (AI) to identify potential issues at railroad crossings in real time.

We prove that three strategy video games from the Sid Meier's Civilization series: Sid Meier's Civilization: Beyond Earth, Sid Meier's Civilization V, and Sid Meier's Civilization VI, are Turing complete. We achieve this by building three universal Turing machines-one for each game-using only the elements present in the games, and using their internal rules and mechanics as the transition function. The existence of such machines imply that under the assumptions made, the games are undecidable. We show constructions of these machines within a running game session, and we provide a sample execution of an algorithm-the three-state Busy Beaver-with one of our machines.

RAILWAY AGE, SEPTEMBER 2020 ISSUE: Whether it's the track structure or the equipment that operates on it, there are many things that the naked eye cannot readily see. Increasingly, machine vision technology is becoming the best way to identify potential flaws before they lead to failures. "The various machine vision technologies deployed detect thousands of conditions each year that could potentially lead to accidents," says Robert Coakley, Director of Business Development, ENSCO Rail. Compared to manual visual inspections, he says, autonomous machine vision offers advantages of speed, reduced track occupancy, inspection frequency and consistency. The equipment is installed on revenue service trains, can perform inspections at track speed and does not require the additional occupancy of a hi-rail vehicle.

According to compiled statistics, every 90 minutes in the United States a vehicle and a train collide. Train-vehicle crashes are a lot more common than most people assume they are. Plus, sadly, injuries and deaths are the likely result of cars and trains opting to ram into each other. There are about 500 deaths each year in the United States due to failures to safety navigate a railroad crossing, amounting to about 20,000 deaths over the last forty years. Remember this sage advice that was drummed into our heads when first learning to drive: Stop, look, and listen.

Computer vision is a field of study that seeks to develop techniques to help computers see and understand the content of digital images such as photographs and videos. Our work in Computer Vision & Machine Learning powers innovation in areas of various sectors through Accurate & high quality labeled Data from our Professional & well-trained annotators. Computer vision technology is very highly significant and dynamic and it's been selected by many industries in many different ways. The difference is some use cases happen behind the more visible or some are not. Computer vision helps the automotive industry in many ways it offers a platform and We generate accurate and diverse annotations on the datasets to train, validate, and test algorithms related to autonomous vehicles.

Last summer, a 30-car freight train led by three diesel locomotives rumbled down the tracks for 48 miles through the Colorado desert -- with nobody at the controls. But this was no runaway train. In fact, the experiment could be a preview of the rail industry's future. The demonstration at the Transportation Technology Center -- a research and testing facility owned by the Association of American Railroads -- was the debut of driverless train software produced by one of the oldest companies in the industry. Along for the ride were representatives from some of America's largest freight railroads who in recent years have been intrigued by the many ways artificial intelligence (AI) could be applied to one of the nation's oldest industries.