ELPA: A parallel solver for the generalized eigenvalue problem

For symmetric (hermitian) (dense or banded) matrices the computation of eigenvalues and eigenvectors Ax = λBx is an important task, e.g. in electronic structure calculations. If a larger number of eigenvectors are needed, often direct solvers are applied. On parallel architectures the ELPA implementation has proven to be very efficient, also compared to other parallel solvers like EigenExa or MAGMA. The main improvement that allows better parallel efficiency in ELPA is the two-step transformation of dense to band to tridiagonal form. This was the achievement of the ELPA project. The continuation of this project has been targeting at additional improvements like allowing monitoring and autotuning of the ELPA code, optimizing the code for different architectures, developing curtailed algorithms for banded A and B, and applying the improved code to solve typical examples in electronic structure calculations. In this paper we will present the outcome of this project

FEBUKO and MODMEP: Field measurements and modelling of aerosol and cloud multiphase processes

An overview of the two FEBUKO aerosol–cloud interaction field experiments in the Thüringer Wald (Germany) in October 2001 and 2002 and the corresponding modelling project MODMEP is given. Experimentally, a variety of measurement methods were deployed to probe the gas phase, particles and cloud droplets at three sites upwind, downwind and within an orographic cloud with special emphasis on the budgets and interconversions of organic gas and particle phase constituents. Out of a total of 14 sampling periods within 30 cloud events three events (EI, EII and EIII) are selected for detailed analysis. At various occasions an impact of the cloud process on particle chemical composition such as on the organic compounds content, sulphate and nitrate and also on particle size distributions and particle mass is observed. Moreover, direct phase transfer of polar organic compound from the gas phase is found to be very important for the understanding of cloudwater composition. For the modelling side, a main result of the MODMEP project is the development of a cloud model, which combines a complex multiphase chemistry with detailed microphysics. Both components are described in a fine-resolved particle/drop spectrum. New numerical methods are developed for an efficient solution of the entire complex model. A further development of the CAPRAM mechanism has lead to a more detailed description of tropospheric aqueous phase organic chemistry. In parallel, effective tools for the reduction of highly complex reaction schemes are provided. Techniques are provided and tested which allow the description of complex multiphase chemistry and of detailed microphysics in multidimensional chemistry-transport models

Humanity's Last Exam

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai

Humanity's Last Exam

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai

Three Airway Management Techniques for Airway Decontamination in Massive Emesis: A Manikin Study

Introduction: Emesis occurs during airway management and results in pulmonary aspiration at rates of 0.01% – 0.11% in fasted patients undergoing general anesthesia and 0% - 22% in non-fasted emergency department patients. Suction-assisted laryngoscopy and airway decontamination (SALAD) involves maneuvering a suction catheter into the hypopharynx, while performing laryngoscopy and endotracheal intubation. Intentional esophageal intubation (IEI) involves blindly intubating the esophagus to control emesis before endotracheal intubation.  Both are previously described techniques for endotracheal intubation in the setting of massive emesis. This study compares the SALAD and IEI techniques with the traditional approach of ad hoc, rigid suction catheter airway decontamination and endotracheal intubation in the setting of massive simulated emesis.Methods: Senior anesthesiology and emergency medicine (EM) residents were randomized into three trial arms: the traditional, IEI, or SALAD. Each resident watched an instructional video on the assigned technique, performed the technique on a manikin, and completed the trial simulation with the SALAD simulation manikin. The primary trial outcome was aspirate volume collected in the manikin’s lower airway.  Secondary outcomes included successful intubation, intubation attempts, and time to successful intubation. We also collected pre- and post-simulation demographics and confidence questionnaire data.Results: Thirty-one residents (21 anesthesiology and 10 EM residents) were randomized. Baseline group characteristics were similar. The mean aspirate volumes collected in the lower airway (standard deviation [SD]) in the traditional, IEI, and SALAD arms were 72 (45) milliliters per liter (mL), 100 (45) mL, and 83 (42) mL, respectively (p = 0.392). Intubation success was 100% in all groups. Times (SD) to successful intubation in the traditional, IEI, and SALAD groups were 1.69 (1.31) minutes, 1.74 (1.09) minutes, and 1.74 (0.93) minutes, respectively (p = 0.805).  Overall, residents reported increased confidence (1.0 [0.0-1.0]; P = 0.002) and skill (1.0 [0.0-1.0]; P < 0.001) in airway management after completion of the study.Conclusion: The intubation techniques provided similar performance results in our study, suggesting any one of the three can be employed in the setting of massive emesis; although this conclusion deserves further study. Residents reported increased confidence and skill in airway management following the experience, suggesting use of the manikin provides a learning impact.