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 Election.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 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 https://wave-pulse.io

Through a large-scale study over diverse face images, we show that facial attribute editing using modern generative AI models can severely degrade automated face recognition systems. This degradation persists even with generative models that include additional identity-based loss function. To mitigate this issue, we propose two novel techniques for local and global attribute editing. We empirically ablate twenty-six facial semantic, demographic and expression-based attributes that have been edited using state-of-the-art generative models, and evaluate them using ArcFace and AdaFace matchers on CelebA, CelebAMaskHQ and LFW datasets. Finally, we use LLaVA, an emerging visual question-answering framework for attribute prediction to validate our editing techniques. Our methods outperform the current state-of-the-art at facial editing (BLIP, InstantID) while retaining identity by a significant extent. Our code is available at https://github.com/sudban3089/MIAFR.git.

We consider a wireless jamming game played by a receiver and a malicious interferer that wants to disrupt communication. The outcome of the game depends on the characteristics of the wireless medium with distance-dependent path loss. The players can leverage this by changing their physical location. We consider both a static scenario where a position is kept forever by the players, and a dynamic one where players can change it over subsequent steps. We also include an optional feature for the receiver to catch the jammer when it is too close, which nullifies its jamming. The conclusion is that, in a mobile scenario, losing players see an improvement of their payoffs (or better, they cut their losses). As a result, we characterize the Price of Mobility, i.e., the benefit obtained thanks to the ability of changing position.