The rapid evolution of artificial intelligence (AI), together with the increased availability of social media and news for epidemiological surveillance, are marking a pivotal moment in epidemiology and public health research. Leveraging the power of generative AI, we use an ensemble approach which incorporates multiple Large Language Models (LLMs) to extract valuable actionable epidemiological information from the World Health Organization (WHO) Disease Outbreak News (DONs). DONs is a collection of regular reports on global outbreaks curated by the WHO and the adopted decision-making processes to respond to them. The extracted information is made available in a daily-updated dataset and a knowledge graph, referred to as eKG, derived to provide a nuanced representation of the public health domain knowledge. We provide an overview of this new dataset and describe the structure of eKG, along with the services and tools used to access and utilize the data that we are building on top. These innovative data resources open altogether new opportunities for epidemiological research, and the analysis and surveillance of disease outbreaks.

Whether and how to regulate AI is one of the defining questions of our times - a question that is being debated locally, nationally, and internationally. We argue that much of this debate is proceeding on a false premise. Specifically, our article challenges the prevailing academic consensus that the European Union's AI regulatory framework is fundamentally rights-driven and the correlative presumption that other rights-regarding nations should therefore follow Europe's lead in AI regulation. Rather than taking rights language in EU rules and regulations at face value, we show how EU AI regulation is the logical outgrowth of a particular cultural, political, and historical context. We show that although instruments like the General Data Protection Regulation (GDPR) and the AI Act invoke the language of fundamental rights, these rights are instrumentalized - used as rhetorical cover for governance tools that address systemic risks and maintain institutional stability. As such, we reject claims that the EU's regulatory framework and the substance of its rules should be adopted as universal imperatives and transplanted to other liberal democracies. To add weight to our argument from historical context, we conduct a comparative analysis of AI regulation in five contested domains: data privacy, cybersecurity, healthcare, labor, and misinformation. This EU-US comparison shows that the EU's regulatory architecture is not meaningfully rights-based. Our article's key intervention in AI policy debates is not to suggest that the current American regulatory model is necessarily preferable but that the presumed legitimacy of the EU's AI regulatory approach must be abandoned.

The aviation industry has always been a first mover in adopting technological advancements. This early adoption offers valuable insights because of its stringent regulations and safety - critical procedures. As a result, the aviation industry provides an optimal platform to counter AI vulnerabilities through its tight regulation s, standardization processes, and certification of new technologies . Keywords: AI in aviation; aviation safety; standardization; certifiable AI; regulations 2 Introduction The aviation industry has always been a trailblazer in embracing innovation, constantly driving safer air travel through various technological revolutions from the early days of pioneer flights to the modern era. T he latest frontier lies in the rise of arti ficial intelligence (AI) and it s potential to reshape aviation in extraordinary ways from pre - flight arrangements to in - flight operations and analyze post - flight data . In real - time, AI - powered assistants in cockpits can analyze vast amounts of data to alert pilots of changing weather conditions and determine optimal flight routes . Moreover, AI can vastly improve business intelligence by predicting and mitigating potential delays, reducing congestion, and ensuring smoother operations and safety . As AI continues to develop, the policy landscape on its role and application will evolve. In 1956, computer science researchers across the United States gathered at Dartmouth College in New Hampshire to discuss the formative concepts and ideas on a new branch of computing pegged artificial intelligence. The end goal of this gathering was to advance AI to the point that human assistance and intervention was no longer needed to perform a task. The evolution of AI since this meeting has resulted in decades of research and investment in the AI ecosystem -- a group of AI systems which are linked togethe r to achieve common goals .

Europa's orbit is an ellipse, and the satellite's shape is affected by Jupiter's gravity, becoming deformed when it passes closer to Jupiter. This change in shape creates friction inside Europa, generating enormous amounts of heat in a mechanism known as tidal heating, which melts some of the ice and forms a vast internal ocean beneath the moon's thick ice shell. Europa's internal ocean is salty and is estimated to be about 100 kilometers deep on average, with a total volume of water twice that of all Earth's oceans, despite this moon being considerably smaller than our planet. In addition, it is believed that internal oceans exist on Jupiter's moons Ganymede and Callisto and Saturn's moons Titan and Enceladus. Liquid water is essential for life as we know it, which is why the ocean worlds are at the forefront of the search for extraterrestrial life.

Human health is increasingly threatened by exposure to hazardous substances, particularly persistent and toxic chemicals. The link between these substances, often encountered in complex mixtures, and various diseases are demonstrated in scientific studies. However, this information is scattered across several sources and hardly accessible by humans and machines. This paper evaluates current practices for publishing/accessing information on hazardous chemicals and proposes a novel platform designed to facilitate retrieval of critical chemical data in urgent situations. The platform aggregates information from multiple sources and organizes it into a structured knowledge graph. Users can access this information through a visual interface such as Neo4J Bloom and dashboards, or via natural language queries using a Chatbot. Our findings demonstrate a significant reduction in the time and effort required to access vital chemical information when datasets follow FAIR principles. Furthermore, we discuss the lessons learned from the development and implementation of this platform and provide recommendations for data owners and publishers to enhance data reuse and interoperability. This work aims to improve the accessibility and usability of chemical information by healthcare professionals, thereby supporting better health outcomes and informed decision-making in the face of patients exposed to chemical intoxication risks.

Adapting models to a language that was only partially present in the pre-training data requires fine-tuning, which is expensive in terms of both data and computational resources. As an alternative to fine-tuning, we explore the potential of activation steering-based techniques to enhance model performance on Italian tasks. Through our experiments we show that Italian steering (i) can be successfully applied to different models, (ii) achieves performances comparable to, or even better than, fine-tuned models for Italian, and (iii) yields higher quality and consistency in Italian generations. We also discuss the utility of steering and fine-tuning in the contemporary LLM landscape where models are anyway getting high Italian performances even if not explicitly trained in this language.

This would likely have three parts: a lander, an autonomous ice-thawing robot, and some sort of self-navigating submersible. Indeed, several groups from multiple countries already have working prototypes of ice-diving robots and smart submersibles that they are set to test in Earth's own frigid landscapes, from Alaska to Antarctica, in the next few years But Earth's oceans are pale simulacra of Europa's extreme environment. To plumb the ocean of this Jovian moon, engineers must work out a way to get missions to survive a never-ending rain of radiation that fries electronic circuits. They must also plow through an ice shell that's at least twice as thick as Mount Everest is tall. "There are a lot of hard problems that push up right against the limits of what's possible," says Richard Camilli, an expert on autonomous robotic systems at the Woods Hole Oceanographic Institution's Deep Submergence Laboratory.

We created a fine-grained AI system for the detection of antisemitism. This Explainable AI will identify English and German anti-Semitic expressions of dehumanization, verbal aggression and conspiracies in online social media messages across platforms, to support high-level decision making.

This study presents a new approach for estimating confidence in machine learning model predictions, specifically in regression tasks utilizing Earth Observation (EO) data, with a particular focus on mosquito abundance (MA) estimation. We take advantage of a Variational AutoEncoder architecture, to derive a confidence metric by the latent space representations of EO datasets. This methodology is pivotal in establishing a correlation between the Euclidean distance in latent representations and the Absolute Error (AE) in individual MA predictions. Our research focuses on EO datasets from the Veneto region in Italy and the Upper Rhine Valley in Germany, targeting areas significantly affected by mosquito populations. A key finding is a notable correlation of 0.46 between the AE of MA predictions and the proposed confidence metric. This correlation signifies a robust, new metric for quantifying the reliability and enhancing the trustworthiness of the AI model's predictions in the context of both EO data analysis and mosquito abundance studies.

Sampling autonomy for icy moon lander missions requires understanding of topographic and photometric properties of the sampling terrain. Unavailability of high resolution visual datasets (either bird-eye view or point-of-view from a lander) is an obstacle for selection, verification or development of perception systems. We attempt to alleviate this problem by: 1) proposing Graphical Utility for Icy moon Surface Simulations (GUISS) framework, for versatile stereo dataset generation that spans the spectrum of bulk photometric properties, and 2) focusing on a stereo-based visual perception system and evaluating both traditional and deep learning-based algorithms for depth estimation from stereo matching. The surface reflectance properties of icy moon terrains (Enceladus and Europa) are inferred from multispectral datasets of previous missions. With procedural terrain generation and physically valid illumination sources, our framework can fit a wide range of hypotheses with respect to visual representations of icy moon terrains. This is followed by a study over the performance of stereo matching algorithms under different visual hypotheses. Finally, we emphasize the standing challenges to be addressed for simulating perception data assets for icy moons such as Enceladus and Europa. Our code can be found here: https://github.com/nasa-jpl/guiss.