At first, in mid-November, the mysterious lights were seen blinking across the night skies of New Jersey. Reports of incandescent flying objects were logged in New York, Pennsylvania and Maryland. Bystanders in Virginia Beach said they saw an aircraft "unlike any other they've seen". Sightings have now come from as far afield as Louisiana, Florida and Arizona. People across the US are looking up.

A former NASA astronaut has come forward to reveal that he personally witnessed'two metallic spherical orbs' whizz by his plane this August while flying above Texas. Leroy Chiao, who served as the commander of Expedition 10 to the International Space Station (ISS) in 2004 and 2005, was 9,000 feet in the air when objects'zipped' on the left side of his airplane. He said one flew on top of the other and each was about three feet in diameter. 'It's just kinda dumb luck that they didn't hit me,' said Chiao. The former NASA astronaut estimates that the orbs were only'about 20 feet away.' 'It could've been a bad result, if they had actually hit me,' Chiao said.

If Google's generative AI Gemini Assistant chatbot is to surpass OpenAI's ChatGPT in popularity in the coming years, it may have to do so without some of the promotional partnerships that helped thrust Google search front and center into Americans' lives. In a US federal court filing on Friday, Google proposed a series of restrictions that for three years would bar the company from requiring its device manufacturer, browser, and wireless carrier licensees to distribute Gemini to their US users. Google also would give those partners more flexibility in how they set their default search provider for their users. Google's proposal counters last month's call from the US Justice Department for Google to not only loosen its grip over partners, but also share more data with competitors and divest its Chrome browser business. The company on Friday formally rejected the idea of selling off any piece of its business or turning over more information to rivals. And its proposed restrictions could be construed as narrower than those sought by the government.

FIRST ON FOX: A pro-tech advocacy group has released a new report warning of the growing threat posed by China's artificial intelligence technology and its open-source approach that could threaten the national and economic security of the United States. The report, published by American Edge Project, states that "China is rapidly advancing its own open-source ecosystem as an alternative to American technology and using it as a Trojan horse to implant its CCP values into global infrastructure." "Their progress is both significant and concerning: Chinese-developed open-source AI tools are already outperforming Western models on key benchmarks, while operating at dramatically lower costs, accelerating global adoption. Through its Belt and Road Initiative (BRI), which spans more than 155 countries on four continents, and its Digital Silk Road (DSR), China is exporting its technology worldwide, fostering increased global dependence, undermining democratic norms, and threatening U.S. leadership and global security." The report outlines how Chinese AI models censor historical events that could paint China in a bad light, deny or minimize human rights abuses, and filter criticism of Chinese political leaders.

It's been a busy year in cybersecurity, but it's not over yet. This week, we revealed how hackers figured out how to "jailbreak" digital license plates--which are legally issued in at least a couple of states and are valid across the US--allowing them to change the license plate number to basically anything. That means someone with this capability can avoid tolls and tickets, or even change their plate to be the same as their enemy. While the company that makes the plates, Reviver, makes clear that doing this would be both illegal and a terms-of-service violation, we're guessing that the people who want to hide their car's credentials so they can speed all over town aren't too concerned about that. Staff at the Cybersecurity and Infrastructure Security Agency are preparing for an uncertain future.

More than 20 years later, the system is getting a makeover. After 9/11, Washington, D.C.'s airspace got a significant security boost. Now, over two decades later, this system is getting a cutting-edge makeover. The National Capital Region (NCR) is rolling out an advanced artificial intelligence-based visual recognition system that's taking air defense to a whole new level. GET SECURITY ALERTS, EXPERT TIPS -- SIGN UP FOR KURT'S NEWSLETTER -- THE CYBERGUY REPORT HERE The Enhanced Regional Situational Awareness (ERSA) system represents a dramatic upgrade from previous security technologies.

Ukrainian drones have hit residential buildings in the Russian city of Kazan, more than 1,000 kilometres (600 miles) from the front line, bringing the war deep into the heart of Russia. The Russian Ministry of Defence said the city, located some 800km (500 miles) east of Moscow, was attacked by three waves of drones between 7:40am and 9:20am (04:40 and 06:20 GMT) on Saturday. Eight drones were used in the attack, according to the press service of the Tatarstan regional government. Six hit residential buildings, one hit an industrial facility and another was shot down over a river, it said in a statement. Local authorities said there were no casualties.

This work introduces a novel graph neural networks (GNNs)-based method to predict stream water temperature and reduce model bias across locations of different income and education levels. Traditional physics-based models often have limited accuracy because they are necessarily approximations of reality. Recently, there has been an increasing interest of using GNNs in modeling complex water dynamics in stream networks. Despite their promise in improving the accuracy, GNNs can bring additional model bias through the aggregation process, where node features are updated by aggregating neighboring nodes. The bias can be especially pronounced when nodes with similar sensitive attributes are frequently connected. We introduce a new method that leverages physical knowledge to represent the node influence in GNNs, and then utilizes physics-based influence to refine the selection and weights over the neighbors. The objective is to facilitate equitable treatment over different sensitive groups in the graph aggregation, which helps reduce spatial bias over locations, especially for those in underprivileged groups. The results on the Delaware River Basin demonstrate the effectiveness of the proposed method in preserving equitable performance across locations in different sensitive groups.

We introduce AgriBench, the first agriculture benchmark designed to evaluate MultiModal Large Language Models (MM-LLMs) for agriculture applications. To further address the agriculture knowledge-based dataset limitation problem, we propose MM-LUCAS, a multimodal agriculture dataset, that includes 1,784 landscape images, segmentation masks, depth maps, and detailed annotations (geographical location, country, date, land cover and land use taxonomic details, quality scores, aesthetic scores, etc), based on the Land Use/Cover Area Frame Survey (LUCAS) dataset, which contains comparable statistics on land use and land cover for the European Union (EU) territory. This work presents a groundbreaking perspective in advancing agriculture MM-LLMs and is still in progress, offering valuable insights for future developments and innovations in specific expert knowledge-based MM-LLMs.

It is further motivated by the fact that both C and Rust can target similar applications (low-level, performance-critical libraries) and are supported by Clang-based compiler toolchains. Though desirable, C-Rust translation is challenging: C and (safe) Rust employ different typing systems (strongly typed variables and no raw pointers in Rust) and different variable access rules (arbitrary accesses in C, while strict borrowing rules in Rust), amongst other differences. Manual migration of even moderately sized codebases requires multiple person-weeks of effort, motivating the need for automatic translation techniques. There are two main approaches for code translation: rule-based/symbolic and LLM (Large Language Model)-based. Rule-based translation approaches often operate on a terse intermediate representation (for achieving full coverage with a limited rule set) and thus often produce uninterpretable target code. Symbolic program synthesis approaches (e.g., [2, 34]), on the other hand, often do not scale to multi-function codebases. LLMs shine in both these respects: they produce natural/interpretable code and have superior scaling capabilities.