We introduce the novel EAI framework for integrating emotion modeling into LLMs to examine the emotional impact on ethics and LLM-based decision-making in various strategic games, including bargaining and repeated games. Our experimental study with various LLMs demonstrated that emotions can significantly alter the ethical decision-making landscape of LLMs, highlighting the need for robust mechanisms to ensure consistent ethical standards. Our game-theoretic analysis revealed that LLMs are susceptible to emotional biases influenced by model size, alignment strategies, and primary pretraining language. Notably, these biases often diverge from typical human emotional responses, occasionally leading to unexpected drops in cooperation rates, even under positive emotional influence.

Constructing a directed cyclic graph (DCG) is challenged by both algorithmic difficulty and computational burden. Comparing multiple DCGs is even more difficult, compounded by the need to identify dynamic causalities across graphs.

In a move to help alert people to the spread of nearby blazes, Ring is partnering with Watch Duty to let users share their videos on the wildfire tracking app. The nonprofit Watch Duty is partnering with Ring, the Amazon-owned maker of doorbell cameras, to help users share videos of nearby wildfires on Watch Duty's wildfire tracking app. The result is Fire Watch, a new feature being added to Ring's Neighbors app, the stand-alone service that lets users see activity from nearby Ring cameras. If there is a fire in the area, users will be notified and can go into an emergency mode that lets them share videos from their Ring cameras to the feed about that specific fire on Watch Duty's platform . It's not a posting free-for-all; Watch Duty says it will choose which Ring videos to show in Fire Watch, based on relevance.

AB S TRACT This study evaluates the performance of various classifiers in three distinct models: r esponse, r isk, and r esponse - r isk, concerning credit card mail campaigns and default prediction. In the r esponse model, the Extra Trees classifier demonstrates the highest recall level (79.1%), emphasizing its effectiveness in identifying potential responders to targeted credit card offers. Conversely, in the r isk model, the Random Forest classifier exhibits remarkable specificity of 84.1%, crucial for identifying customers least likely to default. Furthermore, in the multi - class r esponse - r isk model, the Random Forest classifier achieve s the highest accuracy (83.2%), indicating its efficacy in discerning both potential responders to credit card mail campaign and low - risk credit card users . In this study, we optimized various performance metrics to solve a specific credit risk and mail responsiveness business problem.

Abstract-- This report presents a heterogeneous robotic system designed for remote primary triage in mass-casualty incidents (MCIs). The system employs a coordinated air-ground team of unmanned aerial vehicles (UA Vs) and unmanned ground vehicles (UGVs) to locate victims, assess their injuries, and prioritize medical assistance without risking the lives of first responders. The UA V identify and provide overhead views of casualties, while UGVs equipped with specialized sensors measure vital signs and detect and localize physical injuries. Unlike previous work that focused on exploration or limited medical evaluation, this system addresses the complete triage process: victim localization, vital sign measurement, injury severity classification, mental status assessment, and data consolidation for first responders. Developed as part of the DARPA Triage Challenge, this approach demonstrates how multi-robot systems can augment human capabilities in disaster response scenarios to maximize lives saved. I. INTRODUCTION Robotics has long sought to augment human capabilities in hazardous scenarios. Mass-casualty incidents (MCIs), such as those resulting from natural disasters, bombings, plane crashes, or industrial chemical spills, present an opportunity for robotic systems to assist first responders. The critical first step of providing medical assistance during MCIs is primary triage: the initial process of locating victims at the site of the MCI and assessing the severity of their injuries to prioritize treatment, which is essential to optimizing survival outcomes. Traditionally, primary triage relies on human responders who may face significant risk and information overload [1], underscoring the potential for automated systems to mitigate these challenges. While prior efforts have explored the use of air-ground robotic teams for search and exploration in disaster zones [2]-[5], few systems have focused specifically on rapid triage. Existing approaches typically solve parts of the problem in isolation without integrating comprehensive triage functions. For example, air-ground teams have also been developed to find and localize objects of interest [3], [6] Authors are with the GRASP Lab, School of Engineering and Applied Sciences, University of Pennsylvania. Authors are with the Perelman School of Medicine, University of Pennsylvania. This work was supported by the DARP A Triage Challenge under grant #HR001123S0011.

This study investigates First Responders' (FRs) attitudes toward the use of semantic information and Situational Awareness (SA) in robotic systems during emergency operations. A structured questionnaire was administered to 22 FRs across eight countries, capturing their demographic profiles, general attitudes toward robots, and experiences with semantics-enhanced SA. Results show that most FRs expressed positive attitudes toward robots, and rated the usefulness of semantic information for building SA at an average of 3.6 out of 5. Semantic information was also valued for its role in predicting unforeseen emergencies (mean 3.9). Participants reported requiring an average of 74.6\% accuracy to trust semantic outputs and 67.8\% for them to be considered useful, revealing a willingness to use imperfect but informative AI support tools. To the best of our knowledge, this study offers novel insights by being one of the first to directly survey FRs on semantic-based SA in a cross-national context. It reveals the types of semantic information most valued in the field, such as object identity, spatial relationships, and risk context-and connects these preferences to the respondents' roles, experience, and education levels. The findings also expose a critical gap between lab-based robotics capabilities and the realities of field deployment, highlighting the need for more meaningful collaboration between FRs and robotics researchers. These insights contribute to the development of more user-aligned and situationally aware robotic systems for emergency response.

A new pilot program aims to help EMS respond quicker, not act as a replacement. Breakthroughs, discoveries, and DIY tips sent every weekday. When they aren't baffling the public or grounding wildfire planes, drones have some pretty solid uses. Apart from unnecessarily fast same-day deliveries, the pilotless aircrafts may soon become a lifesaving emergency response tool . A collaborative team of health experts, community organizations, and universities are in the middle of a pilot program using drones and automated external defibrillators (AEDs).

Emergency Medical Services (EMS) are critical to patient survival in emergencies, but first responders often face intense cognitive demands in high-stakes situations. AI cognitive assistants, acting as virtual partners, have the potential to ease this burden by supporting real-time data collection and decision making. In pursuit of this vision, we introduce EgoEMS, the first end-to-end, high-fidelity, multimodal, multiperson dataset capturing over 20 hours of realistic, procedural EMS activities from an egocentric view in 233 simulated emergency scenarios performed by 62 participants, including 46 EMS professionals. Developed in collaboration with EMS experts and aligned with national standards, EgoEMS is captured using an open-source, low-cost, and replicable data collection system and is annotated with keysteps, timestamped audio transcripts with speaker diarization, action quality metrics, and bounding boxes with segmentation masks. Emphasizing realism, the dataset includes responder-patient interactions reflecting real-world emergency dynamics. We also present a suite of benchmarks for real-time multimodal keystep recognition and action quality estimation, essential for developing AI support tools for EMS. We hope EgoEMS inspires the research community to push the boundaries of intelligent EMS systems and ultimately contribute to improved patient outcomes.