Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. While our framework is generally applicable, we showcase the efficacy of WavePulse in a collaborative project with a team of political scientists focusing on the 2024 Presidential Elections. We use WavePulse to monitor livestreams of 396 news radio stations over a period of three months, processing close to 500,000 hours of audio streams. These streams were converted into time-stamped, diarized transcripts and analyzed to track answer key political science questions at both the national and state levels. Our analysis revealed how local issues interacted with national trends, providing insights into information flow. Our results demonstrate WavePulse's efficacy in capturing and analyzing content from radio livestreams sourced from the Web. Code and dataset can be accessed at \url{https://wave-pulse.io}.

The increasing accessibility of radiometric thermal imaging sensors for unmanned aerial vehicles (UAVs) offers significant potential for advancing AI-driven aerial wildfire management. Radiometric imaging provides per-pixel temperature estimates, a valuable improvement over non-radiometric data that requires irradiance measurements to be converted into visible images using RGB color palettes. Despite its benefits, this technology has been underutilized largely due to a lack of available data for researchers. This study addresses this gap by introducing methods for collecting and processing synchronized visual spectrum and radiometric thermal imagery using UAVs at prescribed fires. The included imagery processing pipeline drastically simplifies and partially automates each step from data collection to neural network input. Further, we present the FLAME 3 dataset, the first comprehensive collection of side-by-side visual spectrum and radiometric thermal imagery of wildland fires. Building on our previous FLAME 1 and FLAME 2 datasets, FLAME 3 includes radiometric thermal Tag Image File Format (TIFFs) and nadir thermal plots, providing a new data type and collection method. This dataset aims to spur a new generation of machine learning models utilizing radiometric thermal imagery, potentially trivializing tasks such as aerial wildfire detection, segmentation, and assessment. A single-burn subset of FLAME 3 for computer vision applications is available on Kaggle with the full 6 burn set available to readers upon request.

This story was originally published by Wired and is reproduced here as part of the Climate Desk collaboration. For the first time in four centuries, it's good to be a beaver. Long persecuted for their pelts and reviled as pests, the dam-building rodents are today hailed by scientists as ecological saviors. Their ponds and wetlands store water in the face of drought, filter out pollutants, furnish habitat for endangered species, and fight wildfires. In California, Castor canadensis is so prized that the state recently committed millions to its restoration.

Urban Building Energy Modeling (UBEM) is an emerging method to investigate urban design and energy systems against the increasing energy demand at urban and neighborhood levels. However, current UBEM methods are mostly physic-based and time-consuming in multiple climate change scenarios. This work proposes CityTFT, a data-driven UBEM framework, to accurately model the energy demands in urban environments. With the empowerment of the underlying TFT framework and an augmented loss function, CityTFT could predict heating and cooling triggers in unseen climate dynamics with an F1 score of 99.98 \% while RMSE of loads of 13.57 kWh.

Artificial intelligence is changing the game for cybersecurity across several industries by providing cutting-edge security technologies that analyze massive quantities of data. AI technology uses its ability to improve network security over time. Today, several organizations are increasingly implementing AI-powered intelligent security solutions & services to understand and reuse threat patterns to identify new coercions. AI technology provides wider security solutions and simplifies complete recognition and acknowledgment procedures related to cyberattacks. Thus, there is a growing demand for AI-based solutions in the end-use industry for cybersecurity.

Deep neural networks have achieved great success in classification tasks during the last years. However, one major problem to the path towards artificial intelligence is the inability of neural networks to accurately detect novel class distributions and therefore, most of the classification algorithms proposed make the assumption that all classes are known prior to the training stage. In this work, we propose a methodology for training a neural network that allows it to efficiently detect novel class distributions without compromising much of its classification accuracy on the test examples of known classes. Experimental results on the CIFAR 100 and MiniImagenet data sets demonstrate the effectiveness of the proposed algorithm. The way this method was constructed also makes it suitable for training any classification algorithm that is based on Maximum Likelihood methods.

A new facial recognition technology caught a man trying to enter the US using a passport belonging to someone else, US officials say. Officials with the US Customs and Border Protection (CBP) and the Office of Field Operations (OFO) intercepted a 26-year-old man, the agencies referred to as an "imposter", who reportedly attempted to use a French passport belonging to someone else, at Washington's Dulles International Airport. The man was travelling to the US from Brazil. "The officer utilised CBP's new facial comparison biometric technology which confirmed the man was not a match to the passport he presented," the CBP press release read. It added: "A search revealed the man's authentic Republic of Congo identification card concealed in his shoe."

Sarah Jeong, a technology journalist hired by the New York Times and vilified online for tweets comparing "dumbass f****** white people" to dogs and saying they would "all go extinct soon", has been targeted for harassment by dishonest trolls, her former employer has claimed. Editors at The Verge, an online tech magazine, denounced what they called "disingenuous" criticism of Ms Jeong by "people acting in bad faith". The senior writer had been the victim of a Gamergate-style campaign designed to "divide and conquer by forcing newsrooms to disavow their colleagues", they suggested. Ms Jeong, 30, posted a string of offensive and apparently racist messages including "#CancelWhitePeople" and "white men are bulls***" up to five years ago. After being uncovered they quickly spread and were picked up by conservative media including the Daily Caller and Gateway Pundit websites.

FILE - In this July 2, 2015, file photo, Peter Koerber, a pilot and air tactical officer with the U.S. Forest Service, talks about the hazards of flying drones over wildfire areas during a news conference in Redding, Calif. The U.S. Department of the Interior says it's working with drone makers and mapping companies to create a system allowing smartphones to quickly update no-fly zones at wildfires.