One icy morning in February, nearly 200 people gathered in a church in downtown Richmond, Va. Most had awakened before dawn and driven in from across the state. There were Republicans and Democrats from rural farms and D.C. exurbs. They shared one goal: to fight back against AI development in a region with the largest concentration of data centers in the world. "Aren't you tired of being ignored by both parties, and having your quality of life and your environment absolutely destroyed by corporate greed?" state senator Danica Roem said, to a standing ovation. The activists--wearing homemade shirts with slogans like Boondoggle: Data Center in Botetourt County--marched to the state capitol and spent the day testifying to lawmakers about their fears over data centers' impacts on electricity, water, noise pollution, and more. Some lawmakers pledged to help: "You're getting a sh-t deal," state delegate John McAuliff told activists. The phrase captured many people's feelings toward the AI industry as a whole. Not much unites Americans these days.

This paper concerns the structure of learned representations in text-guided generative models, focusing on score-based models.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Your morning catch-up: Mayor Lurie has SF feeling better, California's job market is taking a hit and more big stories Why outrage is erupting over Trump plan to exclude nursing from'professional' designation This is read by an automated voice. Please report any issues or inconsistencies here . Trump administration proposes excluding nursing and other fields from "professional" designation, capping graduate student loans. Nursing leaders warn the policy will worsen California's severe nurse shortage by discouraging graduate degrees required for teaching and specialized patient care.

VLMs are being quickly adopted for a variety of tasks ranging from few-shot classification to text-guided image generation, making debiasing VLM embeddings crucial.

Many interpretation methods for neural models in natural language processing investigate how information is encoded inside hidden representations.

Our healthcare systems are struggling with the ageing population resulting in an increasing demand and rising expenditures while facing a shortage of healthcare professionals at the same time [7, 12]. When a system is under stress and demand exceeds supply, among other strategies, scheduling resources efficiently and planning becomes important [8]. Hospitals are a critical component of the healthcare system, playing a vital role in care coordination, system development, and supporting population health needs [11]. Efficient planning in hospitals is important to utilized the limited resources in the best possible manner. Here approaches from Operations Research can be of benefit to optimize planning problems such as admission planning, bed allocation, nurse scheduling and surgery scheduling [6, 10]. It has been recognized in the past that resources should be planned in an integrated manner to improve the overall outcomes instead of focusing on individual departments or resources [10].

The BBC has upheld 20 complaints over impartiality after presenter Martine Croxall altered a script she was reading live on the BBC News Channel which referred to pregnant people earlier this year. Croxall was introducing an interview about research on groups most at risk during UK heatwaves, which quoted a release from the London School of Hygiene and Tropical Medicine. The presenter changed her script to instead say women, and the BBC's Executive Complaints Unit said it considered her facial expression to express a controverial view about trans people. The presenter said: Malcolm Mistry, who was involved in the research, says that the aged, pregnant people women and those with pre-existing health conditions need to take precautions. The ECU said it considered Croxall's facial expression laid it open to the interpretation that it indicated a particular viewpoint in the controversies currently surrounding trans ideology.

Background/Objectives: Efficient task allocation in hospital emergency departments (EDs) is critical for operational efficiency and patient care quality, yet the complexity of staff coordination poses significant challenges. This study proposes a simulation-based framework for modeling doctors and nurses as intelligent agents guided by computational trust mechanisms. The objective is to explore how trust-informed coordination can support decision making in ED management. Methods: The framework was implemented in Unity, a 3D graphics platform, where agents assess their competence before undertaking tasks and adaptively coordinate with colleagues. The simulation environment enables real-time observation of workflow dynamics, resource utilization, and patient outcomes. We examined three scenarios - Baseline, Replacement, and Training - reflecting alternative staff management strategies. Results: Trust-informed task allocation balanced patient safety and efficiency by adapting to nurse performance levels. In the Baseline scenario, prioritizing safety reduced errors but increased patient delays compared to a FIFO policy. The Replacement scenario improved throughput and reduced delays, though at additional staffing cost. The training scenario forstered long-term skill development among low-performing nurses, despite short-term delays and risks. These results highlight the trade-off between immediate efficiency gains and sustainable capacity building in ED staffing. Conclusions: The proposed framework demonstrates the potential of computational trust for evidence-based decision support in emergency medicine. By linking staff coordination with adaptive decision making, it provides hospital managers with a tool to evaluate alternative policies under controlled and repeatable conditions, while also laying a foundation for future AI-driven personalized decision support.