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University of Milano-Bicocca

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  1. arXiv

    Kernel solvers stop choking on preconditioner memory: sketched blocks push model size to ten million parameters with better accuracy

    The preconditioner memory of kernel ridge regression no longer grows with model size, letting Bloks scale to ten million parameters and match or beat state-of-the-art accuracy on large-scale benchmarks such as Higgs.

  2. arXiv

    BDA treats council move types as annotator-model observations, achieving best calibration at zero extra calls and improved adversarial robustness

    The work introduces Bayesian Dialectical Argumentation (BDA), which treats a multi-LLM council's typed moves—who proposed, challenged, or conceded which answer—as observations of a classical annotator model with per-agent reliabilities, recasting multi-agent deliberation as a reliability estimation problem; across binary and multi-class benchmarks, BDA achieves the best calibration among zero-cost council aggregation methods, requires no additional LLM calls, improves robustness under persistent adversarial coalitions, and remains competitive in clean settings.

  3. After reviewing 922 citations, 41 international experts extended the acute exacerbation definition from IPF to all fibrotic interstitial lung diseases and proposed a new acute respiratory worsening framework

    An international multidisciplinary working group of 41 experts systematically reviewed 922 citations up to March 17, 2025, revised the 2016 acute exacerbation (AE) definition—previously limited to idiopathic pulmonary fibrosis (IPF)—into an AE-fILD definition applicable across all fibrotic interstitial lung diseases (anchored in diffuse alveolar damage with or without organizing pneumonia), proposed the broader concept of acute respiratory worsening (ARW) to capture acute deteriorations not attributable to DAD, and summarized evidence on epidemiology, risk factors, prognosis, and management while discussing AE as a clinical trial endpoint.

  4. Critical assessment of theoretical modelling of single-atom catalysts

    Using the hydrogen evolution reaction (HER) as a prototypical case, this study analyses the limitations of current first-principles approaches, particularly those based on the computational hydrogen electrode (CHE), for predicting single-atom catalyst (SAC) activity, identifying factors such as the sensitivity of reaction thermodynamics to the local atomic environment, the often-unknown experimental structure of SACs, neglected SAC-specific reaction intermediates, solvent effects, catalyst evolution under operating conditions, material instability, and intrinsic density functional theory approximations as sources of discrepancy between theory and experiment, and proposing that integrating these chemical complexities and uncertainties, potentially through artificial intelligence and data-dr

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