Newly released bodycam video shows illegal immigrant truck driver Harjinder Singh being pulled over in a traffic stop with a New Mexico trooper in July. New bodycam footage has been released showing illegal immigrant truck driver Harjinder Singh struggling with limited English after he was pulled over by police for speeding in New Mexico last month - a detail that has since become a major talking point in the case. The footage shows Singh -- the suspect accused of jackknifing his 18-wheeler while making an illegal U-turn in Florida that killed three people -- being stopped by a New Mexico State Police officer on July 3 for allegedly driving 60 mph in a 45-mph zone. During the interaction, Singh appears apologetic as he receives a ticket from the trooper. He initially communicates without issue until after signing paperwork and preparing to leave, when the officer struggles to understand what the trucker is saying.

Florida Attorney General James Uthmeier says the state will'protect citizens at all costs' on'America Reports.' California, Washington and New Mexico may lose millions of dollars in federal funding if they continue to fail to enforce English language requirements for truckers, Transportation Secretary Sean Duffy announced Tuesday. Duffy said the three states have 30 days to comply with federal English Language Proficiency (ELP) requirements after an investigation into a deadly crash in Florida earlier this month revealed the states made significant failures regarding the illegal immigrant truck driver who made an illegal U-turn. "This is about keeping people safe on the road. Your families, your kids, your spouses, your loved ones, your friends," Duffy said. "We all use the roadway, and we need to make sure that those who are driving big rigs -- semis -- can understand the road signs, that they've been well-trained."

Embedding inversion, i.e., reconstructing text given its embedding and black-box access to the embedding encoder, is a fundamental problem in both NLP and security. From the NLP perspective, it helps determine how much semantic information about the input is retained in the embedding. From the security perspective, it measures how much information is leaked by vector databases and embedding-based retrieval systems. State-of-the-art methods for embedding inversion, such as vec2text, have high accuracy but require (a) training a separate model for each embedding, and (b) a large number of queries to the corresponding encoder. We design, implement, and evaluate ZSInvert, a zero-shot inversion method based on the recently proposed adversarial decoding technique. ZSInvert is fast, query-efficient, and can be used for any text embedding without training an embedding-specific inversion model. We measure the effectiveness of ZSInvert on several embeddings and demonstrate that it recovers key semantic information about the corresponding texts.

As the global demand for clean energy intensifies to achieve sustainability and net-zero carbon emission goals, nuclear energy stands out as a reliable solution. However, fully harnessing its potential requires overcoming key challenges, such as the high capital costs associated with nuclear power plants (NPPs). One promising strategy to mitigate these costs involves repurposing sites with existing infrastructure, including coal power plant (CPP) locations, which offer pre-built facilities and utilities. Additionally, brownfield sites - previously developed or underutilized lands often impacted by industrial activity - present another compelling alternative. These sites typically feature valuable infrastructure that can significantly reduce the costs of NPP development. This study introduces a novel multi-objective optimization methodology, leveraging combinatorial search to evaluate over 30,000 potential NPP sites in the United States. Our approach addresses gaps in the current practice of assigning pre-determined weights to each site attribute that could lead to bias in the ranking. Each site is assigned a performance-based score, derived from a detailed combinatorial analysis of its site attributes. The methodology generates a comprehensive database comprising site locations (inputs), attributes (outputs), site score (outputs), and the contribution of each attribute to the site score (outputs). We then use this database to train a machine learning neural network model, enabling rapid predictions of nuclear siting suitability across any location in the contiguous United States.

Drone footage captured in St. Lucie County, Palm Beach Gardens, St. Petersburg, and Siesta Key shows damage to homes and structures after Hurricane Milton and multiple tornadoes tore across the state.

Authorities say they have made an arrest in the arson fire that heavily damaged the Florida mosque Orlando nightclub gunman Omar Mateen occasionally attended. St. Lucie County Sheriff's Office spokesman Bryan Beaty confirmed in an email Wednesday that an arrest had been made but wouldn't disclose any details until a news conference scheduled for later that evening. The Islamic Center of Fort Pierce sustained extensive damage in a fire set late Sunday on the 15th anniversary of the 9/11 terror attacks. The blaze also coincided with the Muslim holiday Eid al-Adha. The fire burned a 10-by-10-foot hole in the roof at the back of the mosque's main building and blackened its eaves with soot.