Two agents involved in the shooting deaths of US citizens in Minneapolis are reportedly part of highly militarized DHS units whose extreme tactics are generally reserved for war zones. An officer with a Department of Homeland Security Special Response Team stands against a protester in Portland, Oregon. As Minneapolis continues to reel from the fatal shooting of 37-year-old intensive care nurse Alex Pretti by federal agents on the morning of January 24, the international spotlight is firmly fixed on the heavily armed and masked operatives who have spearheaded the Trump administration's violent immigration sweeps. At the heart of the deployment in Minnesota, as well as the chaotic clashes with communities in Southern California and Illinois, are hundreds of agents that operate within Immigration and Customs Enforcement (ICE) and Customs and Border Protection: ICE's two Special Response Teams (SRT), CBP's one SRT, and the Border Patrol Tactical Unit (BORTAC). These paramilitary tactical units behave not like local police, but instead like special forces in Iraq, Afghanistan, or other far-flung battlefields from the Forever Wars of the past quarter century.

We advance the understanding of robotic intervention in high-risk scenarios by examining their potential to distract and impede a school shooter. To evaluate this concept, we conducted a virtual reality study with 150 university participants role-playing as a school shooter. Within the simulation, an autonomous robot predicted the shooter's movements and positioned itself strategically to interfere and distract. The strategy the robot used to approach the shooter was manipulated -- either moving directly in front of the shooter (aggressive) or maintaining distance (passive) -- and the distraction method, ranging from no additional cues (low), to siren and lights (medium), to siren, lights, and smoke to impair visibility (high). An aggressive, high-distraction robot reduced the number of victims by 46.6% relative to a no-robot control. This outcome underscores both the potential of robotic intervention to enhance safety and the pressing ethical questions surrounding their use in school environments.

Two summers ago, the Santa Monica-based company behind the popular video game "Call of Duty" sent a letter to a 24-year-old man in Antioch, Tenn., who went by the online handle "Lerggy." Known in real life as Ryan Rothholz, court filings say, he is the creator of "Lergware," hacking software that enabled Call of Duty players to cheat by kicking opponents offline. A lawsuit filed in May against Rothholz and others allegedly involved in the hacking scheme is the latest salvo in years-long campaign by Activision-Blizzard and other companies to rid their games of cheating. The war is being waged in the Central District of California civil courts, but the defendants are scattered across the country and as far away as Australia. An immersive "first-person shooter" game, Call of Duty takes players into simulated, realistic military combat.

Flooding can damage pavement infrastructure significantly, causing both immediate and long-term structural and functional issues. This research investigates how flooding events affect pavement deterioration, specifically focusing on measuring pavement roughness by the International Roughness Index (IRI). To quantify these effects, we utilized 20 years of pavement condition data from TxDOT's PMIS database, which is integrated with flood event data, including duration and spatial extent. Statistical analyses were performed to compare IRI values before and after flooding and to calculate the deterioration rates influenced by flood exposure. Moreover, we applied Explainable Artificial Intelligence (XAI) techniques, such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to assess the impact of flooding on pavement performance. The results demonstrate that flood-affected pavements experience a more rapid increase in roughness compared to non-flooded sections. These findings emphasize the need for proactive flood mitigation strategies, including improved drainage systems, flood-resistant materials, and preventative maintenance, to enhance pavement resilience in vulnerable regions.

This research presents a novel approach to computational framing analysis, called Semantic Relations-based Unsupervised Framing Analysis (SUFA). SUFA leverages semantic relations and dependency parsing algorithms to identify and assess entity-centric emphasis frames in news media reports. This innovative method is derived from two studies -- qualitative and computational -- using a dataset related to gun violence, demonstrating its potential for analyzing entity-centric emphasis frames. This article discusses SUFA's strengths, limitations, and application procedures. Overall, the SUFA approach offers a significant methodological advancement in computational framing analysis, with its broad applicability across both the social sciences and computational domains.

The Final Exam, a recently released video game in which you play as a student caught amid a school shooting, lasts for around ten minutes, about the length of a real shooting event in a U.S. school. The game opens in an empty locker room. You hear distant gunfire, screams, harried footsteps, and the thudding of heavy furniture being overturned. The sense of disharmony is immediate: a familiar scene of youth and learning is grimly debased into one of peril. As the lockers surround you, their doors gaping, you feel caged: get me out of here. Moments later, as you enter the gymnasium, a two-minute countdown flashes on screen.

In this paper, we reported our experiments with various strategies to improve code-mixed humour and sarcasm detection. We did all of our experiments for Hindi-English code-mixed scenario, as we have the linguistic expertise for the same. We experimented with three approaches, namely (i) native sample mixing, (ii) multi-task learning (MTL), and (iii) prompting very large multilingual language models (VMLMs). In native sample mixing, we added monolingual task samples in code-mixed training sets. In MTL learning, we relied on native and code-mixed samples of a semantically related task (hate detection in our case). Finally, in our third approach, we evaluated the efficacy of VMLMs via few-shot context prompting. Some interesting findings we got are (i) adding native samples improved humor (raising the F1-score up to 6.76%) and sarcasm (raising the F1-score up to 8.64%) detection, (ii) training MLMs in an MTL framework boosted performance for both humour (raising the F1-score up to 10.67%) and sarcasm (increment up to 12.35% in F1-score) detection, and (iii) prompting VMLMs couldn't outperform the other approaches. Finally, our ablation studies and error analysis discovered the cases where our model is yet to improve. We provided our code for reproducibility.

The high volume and rapid evolution of content on social media present major challenges for studying the stance of social media users. In this work, we develop a two stage stance labeling method that utilizes the user-hashtag bipartite graph and the user-user interaction graph. In the first stage, a simple and efficient heuristic for stance labeling uses the user-hashtag bipartite graph to iteratively update the stance association of user and hashtag nodes via a label propagation mechanism. This set of soft labels is then integrated with the user-user interaction graph to train a graph neural network (GNN) model using semi-supervised learning. We evaluate this method on two large-scale datasets containing tweets related to climate change from June 2021 to June 2022 and gun control from January 2022 to January 2023. Our experiments demonstrate that enriching text-based embeddings of users with network information from the user interaction graph using our semi-supervised GNN method outperforms both classifiers trained on user textual embeddings and zero-shot classification using LLMs such as GPT4. We discuss the need for integrating nuanced understanding from social science with the scalability of computational methods to better understand how polarization on social media occurs for divisive issues such as climate change and gun control.

At a Fresno County elementary school, a masked man with a fake gun carried out an active-shooter drill without most of the teachers and parents being informed ahead of time. At San Marino High School, police officers planned to fire blanks to mimic the sound of gunfire, but the drill was ultimately canceled over concerns of traumatizing students. More recently, a principal at a San Gabriel elementary school was placed on a leave of absence after allegedly using her fingers to mime holding a gun and pretending to shoot kids, telling them, "Boom. The rise in active-shooter drills at American schools has coincided with the growing phenomenon of mass shootings in the U.S., as well as media coverage focused on school massacres including Columbine, Sandy Hook and Uvalde. These drills have taken place at 95% of U.S. public schools as of the 2015-16 school year, according to the Education Department's National Center for Education statistics.

Families of gun violence victims are using artificial intelligence to recreate their loved ones' voices and taunt lawmakers who oppose gun control on the sixth anniversary of the Parkland massacre. The robocall messages are being sent to senators and House members who support the National Rifle Association and Second Amendment rights in a campaign that launched on Valentine's Day, Wednesday, according to the Associated Press. Manuel and Patricia Oliver, whose son Joaquin "Guac" Oliver died in the 2018 high school shooting in Parkland, Florida, said the campaign run through The Shotline website is intended to spur Congress to ban the sale of guns like the AR-15 rifle. "We come from a place where gun violence is a problem, but you will never see a 19-year-old with an AR-15 getting into a school and shooting people," Manuel Oliver told the Associated Press in an interview. The Olivers, immigrants from Venezuela, became activists after Joaquin and 13 other students at Marjory Stoneman Douglas High School were murdered by a 19-year-old killer with a rifle.