The application of large deep learning models in weather forecasting has led to significant advancements in the field, including higher-resolution forecasting and extended prediction periods exemplified by models such as Pangu and Fuxi. Despite these successes, previous research has largely been characterized by the neglect of extreme weather events, and the availability of datasets specifically curated for such events remains limited. Given the critical importance of accurately forecasting extreme weather, this study introduces a comprehensive dataset that incorporates high-resolution extreme weather cases derived from the High-Resolution Rapid Refresh (HRRR) data, a 3-km real-time dataset provided by NOAA. We also evaluate the current state-of-the-art deep learning models and Numerical Weather Prediction (NWP) systems on HR-Extreme, and provide a improved baseline deep learning model called HR-Heim which has superior performance on both general loss and HR-Extreme compared to others. Our results reveal that the errors of extreme weather cases are significantly larger than overall forecast error, highlighting them as an crucial source of loss in weather prediction. These findings underscore the necessity for future research to focus on improving the accuracy of extreme weather forecasts to enhance their practical utility.

Inspired by the legal doctrine of stare decisis, which leverages precedents (prior cases) for informed decision-making, we explore methods to integrate them into LJP models. To facilitate precedent retrieval, we train a retriever with a fine-grained relevance signal based on the overlap ratio of alleged articles between cases. We investigate two strategies to integrate precedents: direct incorporation at inference via label interpolation based on case proximity and during training via a precedent fusion module using a stacked-cross attention model. We employ joint training of the retriever and LJP models to address latent space divergence between them. Our experiments on LJP tasks from the ECHR jurisdiction reveal that integrating precedents during training coupled with joint training of the retriever and LJP model, outperforms models without precedents or with precedents incorporated only at inference, particularly benefiting sparser articles.

The precision of contouring target structures and organs-at-risk (OAR) in radiotherapy planning is crucial for ensuring treatment efficacy and patient safety. Recent advancements in deep learning (DL) have significantly improved OAR contouring performance, yet the reliability of these models, especially in the presence of out-of-distribution (OOD) scenarios, remains a concern in clinical settings. This application study explores the integration of epistemic uncertainty estimation within the OAR contouring workflow to enable OOD detection in clinically relevant scenarios, using specifically compiled data. Furthermore, we introduce an advanced statistical method for OOD detection to enhance the methodological framework of uncertainty estimation. Our empirical evaluation demonstrates that epistemic uncertainty estimation is effective in identifying instances where model predictions are unreliable and may require an expert review. Notably, our approach achieves an AUC-ROC of 0.95 for OOD detection, with a specificity of 0.95 and a sensitivity of 0.92 for implant cases, underscoring its efficacy. This study addresses significant gaps in the current research landscape, such as the lack of ground truth for uncertainty estimation and limited empirical evaluations. Additionally, it provides a clinically relevant application of epistemic uncertainty estimation in an FDA-approved and widely used clinical solution for OAR segmentation from Varian, a Siemens Healthineers company, highlighting its practical benefits.

Data-driven machine learning models for weather forecasting have made transformational progress in the last 1-2 years, with state-of-the-art ones now outperforming the best physics-based models for a wide range of skill scores. Given the strong links between weather and climate modelling, this raises the question whether machine learning models could also revolutionize climate science, for example by informing mitigation and adaptation to climate change or to generate larger ensembles for more robust uncertainty estimates. Here, we show that current state-of-the-art machine learning models trained for weather forecasting in present-day climate produce skillful forecasts across different climate states corresponding to pre-industrial, present-day, and future 2.9K warmer climates. This indicates that the dynamics shaping the weather on short timescales may not differ fundamentally in a changing climate. It also demonstrates out-of-distribution generalization capabilities of the machine learning models that are a critical prerequisite for climate applications. Nonetheless, two of the models show a global-mean cold bias in the forecasts for the future warmer climate state, i.e. they drift towards the colder present-day climate they have been trained for. A similar result is obtained for the pre-industrial case where two out of three models show a warming. We discuss possible remedies for these biases and analyze their spatial distribution, revealing complex warming and cooling patterns that are partly related to missing ocean-sea ice and land surface information in the training data. Despite these current limitations, our results suggest that data-driven machine learning models will provide powerful tools for climate science and transform established approaches by complementing conventional physics-based models.

This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguistics, and social sciences. Through rigorous testing, o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance in areas ranging from coding challenges to scientific reasoning and from language processing to creative problem-solving. Key findings include: -83.3% success rate in solving complex competitive programming problems, surpassing many human experts. -Superior ability in generating coherent and accurate radiology reports, outperforming other evaluated models. -100% accuracy in high school-level mathematical reasoning tasks, providing detailed step-by-step solutions. -Advanced natural language inference capabilities across general and specialized domains like medicine. -Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis. -Remarkable proficiency in anthropology and geology, demonstrating deep understanding and reasoning in these specialized fields. -Strong capabilities in quantitative investing. O1 has comprehensive financial knowledge and statistical modeling skills. -Effective performance in social media analysis, including sentiment analysis and emotion recognition. The model excelled particularly in tasks requiring intricate reasoning and knowledge integration across various fields. While some limitations were observed, including occasional errors on simpler problems and challenges with certain highly specialized concepts, the overall results indicate significant progress towards artificial general intelligence.

With the increase of data in day-to-day life, businesses and different stakeholders need to analyze the data for better predictions. Traditionally, relational data has been a source of various insights, but with the increase in computational power and the need to understand deeper relationships between entities, the need to design new techniques has arisen. For this graph data analysis has become an extraordinary tool for understanding the data, which reveals more realistic and flexible modelling of complex relationships. Recently, Graph Neural Networks (GNNs) have shown great promise in various applications, such as social network analysis, recommendation systems, drug discovery, and more. However, many adversarial attacks can happen over the data, whether during training (poisoning attack) or during testing (evasion attack), which can adversely manipulate the desired outcome from the GNN model. Therefore, it is crucial to make the GNNs robust to such attacks. The existing robustness methods are computationally demanding and perform poorly when the intensity of attack increases. This paper presents a computationally efficient framework, namely, pLapGNN, based on weighted p-Laplacian for making GNNs robust. Empirical evaluation on real datasets establishes the efficacy and efficiency of the proposed method.

The Federal Communications Commission (FCC) has officially issued its full recommended fine against political consultant Steve Kramer for a series of illegal robocalls using deepfake AI technology and caller ID spoofing during the New Hampshire primaries. Kramer must pay 6 million in fines in the next 30 days or the Department of Justice will handle collection, according to a FCC statement. Kramer violated the Truth in Caller ID Act passed in 2009 that prohibits anyone from "knowingly transmit misleading or inaccurate caller identification information with the intent to defraud, cause harm or wrongfully obtain anything of value," according to legislative records. The law preceded the widespread usage of AI, but the FCC voted unanimously to have it apply to such deepfakes this past February. The phony robocalls delivered pre-recorded audio of President Biden's voice using deepfake AI technology to New Hampshire residents leading up to the 2024 presidential primary election.

Elon Musk has hit back at the UK government after he was not invited to an international investment summit following his controversial social media posts during last month's riots. Musk said on X on Thursday: "I don't think anyone should go to the UK when they're releasing convicted pedophiles in order to imprison people for social media posts." He seemed to be referring to the prison early release scheme, initiated by the Labour government to ease pressure on a system it has said is "on the point of collapse" due to a lack of capacity. The billionaire owner of X has used the platform to suggest civil war in Britain is "inevitable", and to criticise Keir Starmer as rioting broke out after disinformation spread about the killing of three children in Southport. Ministers initially said the early release scheme would not apply to the most serious offenders, but later confirmed that prisoners who had completed a sentence for a serious crime and were serving a consecutive sentence for a lesser one would qualify.

A Pennsylvania police officer on Thursday told lawmakers that a local operator's bullet ultimately stopped failed assassination attempt shooter Thomas Crooks before the U.S. Secret Service fatally shot him. Edward Lenz's testimony came Thursday morning during a hearing before the House Trump Assassination Attempt Task Force, which has been tasked with investigating the July 13 shooting of former President Donald Trump, the first of two recent assassination attempts against him. "Across the two counter assault teams, the quick reaction force, three sniper teams and support personnel, we provided total manpower of 44 persons, exceeding the number requested by the Secret Service," Lenz, a commander with the Butler County Emergency Services Unit (ESU), said in his opening remarks. "At no point during the planning process was Butler County ESU asked to secure the AGR complex, nor the perimeter surrounding that area. At no point during the planning process was Butler ESU asked to deploy a sniper team to the roof of the AGR complex."

An Ohio-based company sells robotic dogs being used by the Ukrainian military against Russia, which have the ability to be outfitted with flamethrowers. Authorities are investigating a mysterious "deep fake" video call that successfully impersonated a Ukrainian high official. Democratic Sen. Benjamin Cardin announced Wednesday that he had turned over materials to law enforcement after an unknown suspect had tricked him onto a video call via impersonating a foreign official. "In recent days, a malign actor engaged in a deceptive attempt to have a conversation with me by posing as a known individual. After immediately becoming clear that the individual I was engaging with was not who they claimed to be, I ended the call and my office took swift action, alerting the relevant authorities."