The rise of Large Language Models (LLMs) offers transfor-mative potential for interpreting complex legal frameworks, such as Title 18 Section 175 of the US Code, which governs biological weapons. These systems hold promise for advancing legal analysis and compliance monitoring in sensitive domains. However, this capability comes with a troubling contradiction: while LLMs can analyze and interpret laws, they also demonstrate alarming vulnerabilities in generating unsafe outputs, such as actionable steps for bioweapon creation, despite their safeguards. To address this challenge, we propose a methodology that integrates knowledge graph construction with Retrieval-Augmented Generation (RAG) to systematically evaluate LLMs' understanding of this law, their capacity to assess legal intent (mens rea), and their potential for unsafe applications. Through structured experiments, we assess their accuracy in identifying legal violations, generating prohibited instructions, and detecting unlawful intent in bioweapons-related scenarios. Our findings reveal significant limitations in LLMs' reasoning and safety mechanisms, but they also point the way forward. By combining enhanced safety protocols with more robust legal reasoning frameworks, this research lays the groundwork for developing LLMs that can ethically and securely assist in sensitive legal domains--ensuring they act as protectors of the law rather than inadvertent enablers of its violation.

Accurate, real-time estimation of barge quantity on inland waterways remains a critical challenge due to the non-self-propelled nature of barges and the limitations of existing monitoring systems. This study introduces a novel method to use Automatic Identification System (AIS) vessel tracking data to predict the number of barges in tow using Machine Learning (ML). To train and test the model, barge instances were manually annotated from satellite scenes across the Lower Mississippi River. Labeled images were matched to AIS vessel tracks using a spatiotemporal matching procedure. A comprehensive set of 30 AIS-derived features capturing vessel geometry, dynamic movement, and trajectory patterns were created and evaluated using Recursive Feature Elimination (RFE) to identify the most predictive variables. Six regression models, including ensemble, kernel-based, and generalized linear approaches, were trained and evaluated. The Poisson Regressor model yielded the best performance, achieving a Mean Absolute Error (MAE) of 1.92 barges using 12 of the 30 features. The feature importance analysis revealed that metrics capturing vessel maneuverability such as course entropy, speed variability and trip length were most predictive of barge count. The proposed approach provides a scalable, readily implementable method for enhancing Maritime Domain Awareness (MDA), with strong potential applications in lock scheduling, port management, and freight planning. Future work will expand the proof of concept presented here to explore model transferability to other inland rivers with differing operational and environmental conditions.

Things to Do in L.A. Tap to enable a layout that focuses on the article. What counts as cheating with AI? Teachers are grappling with how to draw the line This is read by an automated voice. Please report any issues or inconsistencies here . Teachers say AI cheating is "off the charts," but research shows cheating rates remain unchanged since before ChatGPT. Schools favor "AI literacy" and redesigning assignments to encourage ethical technology use.

Small Uncrewed Aerial Systems (sUAS) are increasingly deployed as autonomous swarms in search-and-rescue and other disaster-response scenarios. In these settings, they use computer vision (CV) to detect objects of interest and autonomously adapt their missions. However, traditional CV systems often struggle to recognize unfamiliar objects in open-world environments or to infer their relevance for mission planning. To address this, we incorporate large language models (LLMs) to reason about detected objects and their implications. While LLMs can offer valuable insights, they are also prone to hallucinations and may produce incorrect, misleading, or unsafe recommendations. To ensure safe and sensible decision-making under uncertainty, high-level decisions must be governed by cognitive guardrails. This article presents the design, simulation, and real-world integration of these guardrails for sUAS swarms in search-and-rescue missions.

A little after 6:25 am on November 8, 2018, a 911 dispatcher received the first report of a fire near the Poe Dam in northern California. Nineteen minutes later firefighters caught sight of what would become known as the Camp Fire. Drought had dried out plants in the area, and strong winds were blowing in the direction of Paradise, a town 10 miles to the southeast. "This has got potential for a major incident," fire chief Matt McKenzie reported back to incident command. An hour later hot embers were raining down on the south side of Paradise, sparking spot fires in advance of the main front.

Mrs. Davis is a deeply silly show deeply committed its silliness. And that's precisely what makes it so much fun. The new Peacock series from Tara Hernandez (The Big Bang Theory) and Damon Lindelof (Lost, The Leftovers), pits a nun with a mysterious past against an all-powerful, seemingly omniscient artificial intelligence. Her mission: to find the Holy Grail. You know, another one of those stories.

After a lengthy debate, Los Angeles City Council President Paul Krekorian postponed a contentious vote Tuesday on whether to accept the donation of a nearly $280,000 dog-like robot for the LAPD. "I'd like when it comes back to have the policies that are currently in place before the council as a condition to acceptance of this gift," Krekorian said near the end of Tuesday's discussion. Delaying the vote by 60 days, he said, "would also allow us the opportunity to exhaust every opportunity to have responses to the questions that have been raised about existing deployment capabilities and so forth." Police officials say that the device, nicknamed Spot, would be deployed only in a limited set of circumstances that require a SWAT team response. Its presence, they argue, would allow authorities to avoid unnecessarily putting officers in harm's way and potentially avoid violent encounters.

When it comes to hiring, it can be a challenge for an employer to find the perfect person for the job. As a result, organizations often utilize personality scales as an aid in determining whether a candidate is the right fit. While there are several widely used personality tests on the market, organizations may be looking for traits or skills that are not measured by scales already in existence. Creating a new scale--which takes the work of experts such as personality, organizational, social, or clinical psychologists--can be time-consuming and costly. With this in mind, Ivan Hernandez, an assistant professor in the Virginia Tech Department of Psychology, wanted to find a way to make the creation of personality scales easier and more accessible.

In an effort to streamline the process of diagnosing patients with multiple sclerosis and Parkinson's disease, researchers used digital cameras to capture changes in gait – a symptom of these diseases – and developed a machine-learning algorithm that can differentiate those with MS and PD from people without those neurological conditions. Their findings are reported in the IEEE Journal of Biomedical and Health Informatics. The goal of the research was to make the process of diagnosing these diseases more accessible, said Manuel Hernandez, a University of Illinois Urbana-Champaign professor of kinesiology and community health who led the work with graduate student Rachneet Kaur and industrial and enterprise systems engineering and mathematics professor Richard Sowers. Currently, patients must wait – sometimes for years – to get an appointment with a neurologist to make a diagnosis, Hernandez said. And people in rural communities often must travel long distances to a facility where their condition can be assessed.

RTAA* is probably the best-performing real-time heuristic search algorithm at path-finding tasks in which the environ- ment is not known in advance or in which the environment is known and there is no time for pre-processing. As most real- time search algorithms do, RTAA performs poorly in presence of heuristic depressions, which are bounded areas of the search space in which the heuristic is too low with respect to their border. Recently, it has been shown that LSS-LRTA, a well-known real-time search algorithm, can be improved when search is actively guided away of depressions. In this paper we investigate whether or not RTAA can be improved in the same manner. We propose aRTAA and daRTAA, two algorithms based on RTAA that avoid heuristic depressions. Both algorithms outperform RTAA on standard path-finding tasks, obtaining better-quality solutions when the same time deadline is imposed on the duration of the planning episode.