Aarhus University

AlphaProof Nexus pairs LLM proof generation with step-by-step Lean compiler verification in an agent loop, autonomously resolving 9 of 353 open Erdős problems, two of them open for 56 years, at a few hundred dollars of inference cost per problem.

AlphaProof Nexus pairs LLM proof generation with step-by-step Lean compiler verification in an agent loop, autonomously resolving 9 of 353 open Erdős problems, two of them open for 56 years, at a few hundred dollars of inference cost per problem.

This review spans seven areas—community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials—to assess the advances, constraints and future directions of big data in psychiatry, concluding that big data has fostered transdisciplinarity and illuminated the intricacy, heterogeneity and variability of mechanisms underlying psychiatric disorders, yet sample size alone does not guarantee more precise estimates and a gap remains between big data and clinical application, so future work must attend equally to data quality and methodological rigor, conceptual models, and clinical questions.

This review spans seven areas—community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials—to assess the advances, constraints and future directions of big data in psychiatry, concluding that big data has fostered transdisciplinarity and illuminated the intricacy, heterogeneity and variability of mechanisms underlying psychiatric disorders, yet sample size alone does not guarantee more precise estimates and a gap remains between big data and clinical application, so future work must attend equally to data quality and methodological rigor, conceptual models, and clinical questions.

This study proposes HARU-Net, a hybrid attention residual U-Net trained on a cadaver dataset of human hemimandibles acquired with a high-resolution cone-beam computed tomography (CBCT) protocol for low-dose CBCT denoising; the architecture embeds a hybrid attention transformer block within each skip connection, a residual hybrid attention transformer group at the bottleneck, and residual learning convolutional blocks, and reports the highest peak signal-to-noise ratio of 37.52 dB, the second-highest SSIM of 0.9557, and the lowest GMSD of 0.1084, while maintaining substantially lower computational complexity than transformer-based methods.

This study proposes HARU-Net, a hybrid attention residual U-Net trained on a cadaver dataset of human hemimandibles acquired with a high-resolution cone-beam computed tomography (CBCT) protocol for low-dose CBCT denoising; the architecture embeds a hybrid attention transformer block within each skip connection, a residual hybrid attention transformer group at the bottleneck, and residual learning convolutional blocks, and reports the highest peak signal-to-noise ratio of 37.52 dB, the second-highest SSIM of 0.9557, and the lowest GMSD of 0.1084, while maintaining substantially lower computational complexity than transformer-based methods.